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is-link">Go</button> </div> </div> </form> </div> </div> <nav class="pagination is-small is-centered breathe-horizontal" role="navigation" aria-label="pagination"> <a href="" class="pagination-previous is-invisible">Previous </a> <a href="/search/?searchtype=author&query=Ahmed%2C+I&start=50" class="pagination-next" >Next </a> <ul class="pagination-list"> <li> <a href="/search/?searchtype=author&query=Ahmed%2C+I&start=0" class="pagination-link is-current" aria-label="Goto page 1">1 </a> </li> <li> <a href="/search/?searchtype=author&query=Ahmed%2C+I&start=50" class="pagination-link " aria-label="Page 2" aria-current="page">2 </a> </li> <li> <a href="/search/?searchtype=author&query=Ahmed%2C+I&start=100" class="pagination-link " aria-label="Page 3" aria-current="page">3 </a> </li> </ul> </nav> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.12028">arXiv:2411.12028</a> <span> [<a href="https://arxiv.org/pdf/2411.12028">pdf</a>, <a href="https://arxiv.org/format/2411.12028">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> In-Situ Melt Pool Characterization via Thermal Imaging for Defect Detection in Directed Energy Deposition Using Vision Transformers </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Era%2C+I+Z">Israt Zarin Era</a>, <a href="/search/cs?searchtype=author&query=Zhou%2C+F">Fan Zhou</a>, <a href="/search/cs?searchtype=author&query=Raihan%2C+A+S">Ahmed Shoyeb Raihan</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+I">Imtiaz Ahmed</a>, <a href="/search/cs?searchtype=author&query=Abul-Haj%2C+A">Alan Abul-Haj</a>, <a href="/search/cs?searchtype=author&query=Craig%2C+J">James Craig</a>, <a href="/search/cs?searchtype=author&query=Das%2C+S">Srinjoy Das</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+Z">Zhichao Liu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.12028v1-abstract-short" style="display: inline;"> Directed Energy Deposition (DED) offers significant potential for manufacturing complex and multi-material parts. However, internal defects such as porosity and cracks can compromise mechanical properties and overall performance. This study focuses on in-situ monitoring and characterization of melt pools associated with porosity, aiming to improve defect detection and quality control in DED-printe… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12028v1-abstract-full').style.display = 'inline'; document.getElementById('2411.12028v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.12028v1-abstract-full" style="display: none;"> Directed Energy Deposition (DED) offers significant potential for manufacturing complex and multi-material parts. However, internal defects such as porosity and cracks can compromise mechanical properties and overall performance. This study focuses on in-situ monitoring and characterization of melt pools associated with porosity, aiming to improve defect detection and quality control in DED-printed parts. Traditional machine learning approaches for defect identification rely on extensive labeled datasets, often scarce and expensive to generate in real-world manufacturing. To address this, our framework employs self-supervised learning on unlabeled melt pool data using a Vision Transformer-based Masked Autoencoder (MAE) to produce highly representative embeddings. These fine-tuned embeddings are leveraged via transfer learning to train classifiers on a limited labeled dataset, enabling the effective identification of melt pool anomalies. We evaluate two classifiers: (1) a Vision Transformer (ViT) classifier utilizing the fine-tuned MAE Encoder's parameters and (2) the fine-tuned MAE Encoder combined with an MLP classifier head. Our framework achieves overall accuracy ranging from 95.44% to 99.17% and an average F1 score exceeding 80%, with the ViT Classifier slightly outperforming the MAE Encoder Classifier. This demonstrates the scalability and cost-effectiveness of our approach for automated quality control in DED, effectively detecting defects with minimal labeled data. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12028v1-abstract-full').style.display = 'none'; document.getElementById('2411.12028v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.10822">arXiv:2411.10822</a> <span> [<a href="https://arxiv.org/pdf/2411.10822">pdf</a>, <a href="https://arxiv.org/format/2411.10822">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Materials Science">cond-mat.mtrl-sci</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Engineering, Finance, and Science">cs.CE</span> </div> </div> <p class="title is-5 mathjax"> A Data-Efficient Sequential Learning Framework for Melt Pool Defect Classification in Laser Powder Bed Fusion </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Raihan%2C+A+S">Ahmed Shoyeb Raihan</a>, <a href="/search/cs?searchtype=author&query=Harper%2C+A">Austin Harper</a>, <a href="/search/cs?searchtype=author&query=Era%2C+I+Z">Israt Zarin Era</a>, <a href="/search/cs?searchtype=author&query=Al-Shebeeb%2C+O">Omar Al-Shebeeb</a>, <a href="/search/cs?searchtype=author&query=Wuest%2C+T">Thorsten Wuest</a>, <a href="/search/cs?searchtype=author&query=Das%2C+S">Srinjoy Das</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+I">Imtiaz Ahmed</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.10822v1-abstract-short" style="display: inline;"> Ensuring the quality and reliability of Metal Additive Manufacturing (MAM) components is crucial, especially in the Laser Powder Bed Fusion (L-PBF) process, where melt pool defects such as keyhole, balling, and lack of fusion can significantly compromise structural integrity. This study presents SL-RF+ (Sequentially Learned Random Forest with Enhanced Sampling), a novel Sequential Learning (SL) fr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10822v1-abstract-full').style.display = 'inline'; document.getElementById('2411.10822v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.10822v1-abstract-full" style="display: none;"> Ensuring the quality and reliability of Metal Additive Manufacturing (MAM) components is crucial, especially in the Laser Powder Bed Fusion (L-PBF) process, where melt pool defects such as keyhole, balling, and lack of fusion can significantly compromise structural integrity. This study presents SL-RF+ (Sequentially Learned Random Forest with Enhanced Sampling), a novel Sequential Learning (SL) framework for melt pool defect classification designed to maximize data efficiency and model accuracy in data-scarce environments. SL-RF+ utilizes RF classifier combined with Least Confidence Sampling (LCS) and Sobol sequence-based synthetic sampling to iteratively select the most informative samples to learn from, thereby refining the model's decision boundaries with minimal labeled data. Results show that SL-RF+ outperformed traditional machine learning models across key performance metrics, including accuracy, precision, recall, and F1 score, demonstrating significant robustness in identifying melt pool defects with limited data. This framework efficiently captures complex defect patterns by focusing on high-uncertainty regions in the process parameter space, ultimately achieving superior classification performance without the need for extensive labeled datasets. While this study utilizes pre-existing experimental data, SL-RF+ shows strong potential for real-world applications in pure sequential learning settings, where data is acquired and labeled incrementally, mitigating the high costs and time constraints of sample acquisition. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10822v1-abstract-full').style.display = 'none'; document.getElementById('2411.10822v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.09916">arXiv:2411.09916</a> <span> [<a href="https://arxiv.org/pdf/2411.09916">pdf</a>, <a href="https://arxiv.org/format/2411.09916">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Software Engineering">cs.SE</span> </div> </div> <p class="title is-5 mathjax"> LLMs are Imperfect, Then What? An Empirical Study on LLM Failures in Software Engineering </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Tie%2C+J">Jiessie Tie</a>, <a href="/search/cs?searchtype=author&query=Yao%2C+B">Bingsheng Yao</a>, <a href="/search/cs?searchtype=author&query=Li%2C+T">Tianshi Li</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+S+I">Syed Ishtiaque Ahmed</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+D">Dakuo Wang</a>, <a href="/search/cs?searchtype=author&query=Zhou%2C+S">Shurui Zhou</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.09916v1-abstract-short" style="display: inline;"> Software engineers are integrating AI assistants into their workflows to enhance productivity and reduce cognitive strain. However, experiences vary significantly, with some engineers finding large language models (LLMs), like ChatGPT, beneficial, while others consider them counterproductive. Researchers also found that ChatGPT's answers included incorrect information. Given the fact that LLMs are… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.09916v1-abstract-full').style.display = 'inline'; document.getElementById('2411.09916v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.09916v1-abstract-full" style="display: none;"> Software engineers are integrating AI assistants into their workflows to enhance productivity and reduce cognitive strain. However, experiences vary significantly, with some engineers finding large language models (LLMs), like ChatGPT, beneficial, while others consider them counterproductive. Researchers also found that ChatGPT's answers included incorrect information. Given the fact that LLMs are still imperfect, it is important to understand how to best incorporate LLMs into the workflow for software engineering (SE) task completion. Therefore, we conducted an observational study with 22 participants using ChatGPT as a coding assistant in a non-trivial SE task to understand the practices, challenges, and opportunities for using LLMs for SE tasks. We identified the cases where ChatGPT failed, their root causes, and the corresponding mitigation solutions used by users. These findings contribute to the overall understanding and strategies for human-AI interaction on SE tasks. Our study also highlights future research and tooling support directions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.09916v1-abstract-full').style.display = 'none'; document.getElementById('2411.09916v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.05856">arXiv:2411.05856</a> <span> [<a href="https://arxiv.org/pdf/2411.05856">pdf</a>, <a href="https://arxiv.org/ps/2411.05856">ps</a>, <a href="https://arxiv.org/format/2411.05856">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Engineering, Finance, and Science">cs.CE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Evaluating the Economic Implications of Using Machine Learning in Clinical Psychiatry </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Hossain%2C+S">Soaad Hossain</a>, <a href="/search/cs?searchtype=author&query=Rasalingam%2C+J">James Rasalingam</a>, <a href="/search/cs?searchtype=author&query=Waheed%2C+A">Arhum Waheed</a>, <a href="/search/cs?searchtype=author&query=Awil%2C+F">Fatah Awil</a>, <a href="/search/cs?searchtype=author&query=Kandiah%2C+R">Rachel Kandiah</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+S+I">Syed Ishtiaque Ahmed</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.05856v1-abstract-short" style="display: inline;"> With the growing interest in using AI and machine learning (ML) in medicine, there is an increasing number of literature covering the application and ethics of using AI and ML in areas of medicine such as clinical psychiatry. The problem is that there is little literature covering the economic aspects associated with using ML in clinical psychiatry. This study addresses this gap by specifically st… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.05856v1-abstract-full').style.display = 'inline'; document.getElementById('2411.05856v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.05856v1-abstract-full" style="display: none;"> With the growing interest in using AI and machine learning (ML) in medicine, there is an increasing number of literature covering the application and ethics of using AI and ML in areas of medicine such as clinical psychiatry. The problem is that there is little literature covering the economic aspects associated with using ML in clinical psychiatry. This study addresses this gap by specifically studying the economic implications of using ML in clinical psychiatry. In this paper, we evaluate the economic implications of using ML in clinical psychiatry through using three problem-oriented case studies, literature on economics, socioeconomic and medical AI, and two types of health economic evaluations. In addition, we provide details on fairness, legal, ethics and other considerations for ML in clinical psychiatry. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.05856v1-abstract-full').style.display = 'none'; document.getElementById('2411.05856v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">11 pages, submitted to Machine Learning for Health (ML4H) 2024</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 68T01 <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2; J.4; I.2.1; K.4.3 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.04299">arXiv:2411.04299</a> <span> [<a href="https://arxiv.org/pdf/2411.04299">pdf</a>, <a href="https://arxiv.org/format/2411.04299">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Software Engineering">cs.SE</span> </div> </div> <p class="title is-5 mathjax"> An Empirical Study on Automatically Detecting AI-Generated Source Code: How Far Are We? </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Suh%2C+H">Hyunjae Suh</a>, <a href="/search/cs?searchtype=author&query=Tafreshipour%2C+M">Mahan Tafreshipour</a>, <a href="/search/cs?searchtype=author&query=Li%2C+J">Jiawei Li</a>, <a href="/search/cs?searchtype=author&query=Bhattiprolu%2C+A">Adithya Bhattiprolu</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+I">Iftekhar Ahmed</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.04299v1-abstract-short" style="display: inline;"> Artificial Intelligence (AI) techniques, especially Large Language Models (LLMs), have started gaining popularity among researchers and software developers for generating source code. However, LLMs have been shown to generate code with quality issues and also incurred copyright/licensing infringements. Therefore, detecting whether a piece of source code is written by humans or AI has become necess… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.04299v1-abstract-full').style.display = 'inline'; document.getElementById('2411.04299v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.04299v1-abstract-full" style="display: none;"> Artificial Intelligence (AI) techniques, especially Large Language Models (LLMs), have started gaining popularity among researchers and software developers for generating source code. However, LLMs have been shown to generate code with quality issues and also incurred copyright/licensing infringements. Therefore, detecting whether a piece of source code is written by humans or AI has become necessary. This study first presents an empirical analysis to investigate the effectiveness of the existing AI detection tools in detecting AI-generated code. The results show that they all perform poorly and lack sufficient generalizability to be practically deployed. Then, to improve the performance of AI-generated code detection, we propose a range of approaches, including fine-tuning the LLMs and machine learning-based classification with static code metrics or code embedding generated from Abstract Syntax Tree (AST). Our best model outperforms state-of-the-art AI-generated code detector (GPTSniffer) and achieves an F1 score of 82.55. We also conduct an ablation study on our best-performing model to investigate the impact of different source code features on its performance. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.04299v1-abstract-full').style.display = 'none'; document.getElementById('2411.04299v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted at The 47th IEEE/ACM International Conference on Software Engineering (ICSE 2025)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.01414">arXiv:2411.01414</a> <span> [<a href="https://arxiv.org/pdf/2411.01414">pdf</a>, <a href="https://arxiv.org/format/2411.01414">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Software Engineering">cs.SE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> A Deep Dive Into Large Language Model Code Generation Mistakes: What and Why? </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Chen%2C+Q">QiHong Chen</a>, <a href="/search/cs?searchtype=author&query=Li%2C+J">Jiawei Li</a>, <a href="/search/cs?searchtype=author&query=Deng%2C+J">Jiecheng Deng</a>, <a href="/search/cs?searchtype=author&query=Yu%2C+J">Jiachen Yu</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+J+T+J">Justin Tian Jin Chen</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+I">Iftekhar Ahmed</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.01414v1-abstract-short" style="display: inline;"> Recent advancements in Large Language Models (LLMs) have led to their widespread application in automated code generation. However, these models can still generate defective code that deviates from the specification. Previous research has mainly focused on the mistakes in LLM-generated standalone functions, overlooking real-world software development situations where the successful generation of t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.01414v1-abstract-full').style.display = 'inline'; document.getElementById('2411.01414v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.01414v1-abstract-full" style="display: none;"> Recent advancements in Large Language Models (LLMs) have led to their widespread application in automated code generation. However, these models can still generate defective code that deviates from the specification. Previous research has mainly focused on the mistakes in LLM-generated standalone functions, overlooking real-world software development situations where the successful generation of the code requires software contexts such as external dependencies. In this paper, we considered both of these code generation situations and identified a range of \textit{non-syntactic mistakes} arising from LLMs' misunderstandings of coding question specifications. Seven categories of non-syntactic mistakes were identified through extensive manual analyses, four of which were missed by previous works. To better understand these mistakes, we proposed six reasons behind these mistakes from various perspectives. Moreover, we explored the effectiveness of LLMs in detecting mistakes and their reasons. Our evaluation demonstrated that GPT-4 with the ReAct prompting technique can achieve an F1 score of up to 0.65 when identifying reasons for LLM's mistakes, such as misleading function signatures. We believe that these findings offer valuable insights into enhancing the quality of LLM-generated code. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.01414v1-abstract-full').style.display = 'none'; document.getElementById('2411.01414v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.15207">arXiv:2410.15207</a> <span> [<a href="https://arxiv.org/pdf/2410.15207">pdf</a>, <a href="https://arxiv.org/ps/2410.15207">ps</a>, <a href="https://arxiv.org/format/2410.15207">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1145/3686926">10.1145/3686926 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> The Politics of Fear and the Experience of Bangladeshi Religious Minority Communities Using Social Media Platforms </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Rifat%2C+M+R">Mohammad Rashidujjaman Rifat</a>, <a href="/search/cs?searchtype=author&query=Das%2C+D">Dipto Das</a>, <a href="/search/cs?searchtype=author&query=Podder%2C+A">Arpon Podder</a>, <a href="/search/cs?searchtype=author&query=Jannat%2C+M">Mahiratul Jannat</a>, <a href="/search/cs?searchtype=author&query=Soden%2C+R">Robert Soden</a>, <a href="/search/cs?searchtype=author&query=Semaan%2C+B">Bryan Semaan</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+S+I">Syed Ishtiaque Ahmed</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.15207v1-abstract-short" style="display: inline;"> Despite significant research on online harm, polarization, public deliberation, and justice, CSCW still lacks a comprehensive understanding of the experiences of religious minorities, particularly in relation to fear, as prominently evident in our study. Gaining faith-sensitive insights into the expression, participation, and inter-religious interactions on social media can contribute to CSCW's li… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.15207v1-abstract-full').style.display = 'inline'; document.getElementById('2410.15207v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.15207v1-abstract-full" style="display: none;"> Despite significant research on online harm, polarization, public deliberation, and justice, CSCW still lacks a comprehensive understanding of the experiences of religious minorities, particularly in relation to fear, as prominently evident in our study. Gaining faith-sensitive insights into the expression, participation, and inter-religious interactions on social media can contribute to CSCW's literature on online safety and interfaith communication. In pursuit of this goal, we conducted a six-month-long, interview-based study with the Hindu, Buddhist, and Indigenous communities in Bangladesh. Our study draws on an extensive body of research encompassing the spiral of silence, the cultural politics of fear, and communication accommodation to examine how social media use by religious minorities is influenced by fear, which is associated with social conformity, misinformation, stigma, stereotypes, and South Asian postcolonial memory. Moreover, we engage with scholarly perspectives from religious studies, justice, and South Asian violence and offer important critical insights and design lessons for the CSCW literature on public deliberation, justice, and interfaith communication. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.15207v1-abstract-full').style.display = 'none'; document.getElementById('2410.15207v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">accepted at CSCW24</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> published by PACMHCI (CSCW2) 2024 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.14950">arXiv:2410.14950</a> <span> [<a href="https://arxiv.org/pdf/2410.14950">pdf</a>, <a href="https://arxiv.org/format/2410.14950">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1145/3700794.3700802">10.1145/3700794.3700802 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> A Civics-oriented Approach to Understanding Intersectionally Marginalized Users' Experience with Hate Speech Online </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sultana%2C+A">Achhiya Sultana</a>, <a href="/search/cs?searchtype=author&query=Das%2C+D">Dipto Das</a>, <a href="/search/cs?searchtype=author&query=Alam%2C+S+B">Saadia Binte Alam</a>, <a href="/search/cs?searchtype=author&query=Shidujaman%2C+M">Mohammad Shidujaman</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+S+I">Syed Ishtiaque Ahmed</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.14950v1-abstract-short" style="display: inline;"> While content moderation in online platforms marginalizes users in the Global South at large, users of certain identities are further marginalized. Such users often come from Indigenous ethnic minority groups or identify as women. Through a qualitative study based on 18 semi-structured interviews, this paper explores how such users' experiences with hate speech online in Bangladesh are shaped by t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.14950v1-abstract-full').style.display = 'inline'; document.getElementById('2410.14950v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.14950v1-abstract-full" style="display: none;"> While content moderation in online platforms marginalizes users in the Global South at large, users of certain identities are further marginalized. Such users often come from Indigenous ethnic minority groups or identify as women. Through a qualitative study based on 18 semi-structured interviews, this paper explores how such users' experiences with hate speech online in Bangladesh are shaped by their intersectional identities. Through a civics-oriented approach, we examined the spectrum of their legal status, membership, rights, and participation as users of online platforms. Drawing analogies with the concept of citizenship, we develop the concept of usership that offers a user-centered metaphor in studying moderation and platform governance. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.14950v1-abstract-full').style.display = 'none'; document.getElementById('2410.14950v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">accepted to ICTD 24</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.12245">arXiv:2410.12245</a> <span> [<a href="https://arxiv.org/pdf/2410.12245">pdf</a>, <a href="https://arxiv.org/format/2410.12245">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Advancing Healthcare: Innovative ML Approaches for Improved Medical Imaging in Data-Constrained Environments </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Amin%2C+A">Al Amin</a>, <a href="/search/cs?searchtype=author&query=Hasan%2C+K">Kamrul Hasan</a>, <a href="/search/cs?searchtype=author&query=Zein-Sabatto%2C+S">Saleh Zein-Sabatto</a>, <a href="/search/cs?searchtype=author&query=Hong%2C+L">Liang Hong</a>, <a href="/search/cs?searchtype=author&query=Shetty%2C+S">Sachin Shetty</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+I">Imtiaz Ahmed</a>, <a href="/search/cs?searchtype=author&query=Islam%2C+T">Tariqul Islam</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.12245v1-abstract-short" style="display: inline;"> Healthcare industries face challenges when experiencing rare diseases due to limited samples. Artificial Intelligence (AI) communities overcome this situation to create synthetic data which is an ethical and privacy issue in the medical domain. This research introduces the CAT-U-Net framework as a new approach to overcome these limitations, which enhances feature extraction from medical images wit… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.12245v1-abstract-full').style.display = 'inline'; document.getElementById('2410.12245v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.12245v1-abstract-full" style="display: none;"> Healthcare industries face challenges when experiencing rare diseases due to limited samples. Artificial Intelligence (AI) communities overcome this situation to create synthetic data which is an ethical and privacy issue in the medical domain. This research introduces the CAT-U-Net framework as a new approach to overcome these limitations, which enhances feature extraction from medical images without the need for large datasets. The proposed framework adds an extra concatenation layer with downsampling parts, thereby improving its ability to learn from limited data while maintaining patient privacy. To validate, the proposed framework's robustness, different medical conditioning datasets were utilized including COVID-19, brain tumors, and wrist fractures. The framework achieved nearly 98% reconstruction accuracy, with a Dice coefficient close to 0.946. The proposed CAT-U-Net has the potential to make a big difference in medical image diagnostics in settings with limited data. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.12245v1-abstract-full').style.display = 'none'; document.getElementById('2410.12245v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">7 pages, 7 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.10850">arXiv:2410.10850</a> <span> [<a href="https://arxiv.org/pdf/2410.10850">pdf</a>, <a href="https://arxiv.org/format/2410.10850">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> </div> </div> <p class="title is-5 mathjax"> On the Reliability of Large Language Models to Misinformed and Demographically-Informed Prompts </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Aremu%2C+T">Toluwani Aremu</a>, <a href="/search/cs?searchtype=author&query=Akinwehinmi%2C+O">Oluwakemi Akinwehinmi</a>, <a href="/search/cs?searchtype=author&query=Nwagu%2C+C">Chukwuemeka Nwagu</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+S+I">Syed Ishtiaque Ahmed</a>, <a href="/search/cs?searchtype=author&query=Orji%2C+R">Rita Orji</a>, <a href="/search/cs?searchtype=author&query=Del+Amo%2C+P+A">Pedro Arnau Del Amo</a>, <a href="/search/cs?searchtype=author&query=Saddik%2C+A+E">Abdulmotaleb El Saddik</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.10850v2-abstract-short" style="display: inline;"> We investigate and observe the behaviour and performance of Large Language Model (LLM)-backed chatbots in addressing misinformed prompts and questions with demographic information within the domains of Climate Change and Mental Health. Through a combination of quantitative and qualitative methods, we assess the chatbots' ability to discern the veracity of statements, their adherence to facts, and… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.10850v2-abstract-full').style.display = 'inline'; document.getElementById('2410.10850v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.10850v2-abstract-full" style="display: none;"> We investigate and observe the behaviour and performance of Large Language Model (LLM)-backed chatbots in addressing misinformed prompts and questions with demographic information within the domains of Climate Change and Mental Health. Through a combination of quantitative and qualitative methods, we assess the chatbots' ability to discern the veracity of statements, their adherence to facts, and the presence of bias or misinformation in their responses. Our quantitative analysis using True/False questions reveals that these chatbots can be relied on to give the right answers to these close-ended questions. However, the qualitative insights, gathered from domain experts, shows that there are still concerns regarding privacy, ethical implications, and the necessity for chatbots to direct users to professional services. We conclude that while these chatbots hold significant promise, their deployment in sensitive areas necessitates careful consideration, ethical oversight, and rigorous refinement to ensure they serve as a beneficial augmentation to human expertise rather than an autonomous solution. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.10850v2-abstract-full').style.display = 'none'; document.getElementById('2410.10850v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 6 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Study conducted between August and December 2023. Under review at AAAI-AI Magazine. Submitted for archival purposes only</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.08308">arXiv:2410.08308</a> <span> [<a href="https://arxiv.org/pdf/2410.08308">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Machine Learning for Missing Value Imputation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ahmad%2C+A+F">Abu Fuad Ahmad</a>, <a href="/search/cs?searchtype=author&query=Alshammari%2C+K">Khaznah Alshammari</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+I">Istiaque Ahmed</a>, <a href="/search/cs?searchtype=author&query=Sayed%2C+M+S">MD Shohel Sayed</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.08308v1-abstract-short" style="display: inline;"> In recent times, a considerable number of research studies have been carried out to address the issue of Missing Value Imputation (MVI). MVI aims to provide a primary solution for datasets that have one or more missing attribute values. The advancements in Artificial Intelligence (AI) drive the development of new and improved machine learning (ML) algorithms and methods. The advancements in ML hav… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.08308v1-abstract-full').style.display = 'inline'; document.getElementById('2410.08308v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.08308v1-abstract-full" style="display: none;"> In recent times, a considerable number of research studies have been carried out to address the issue of Missing Value Imputation (MVI). MVI aims to provide a primary solution for datasets that have one or more missing attribute values. The advancements in Artificial Intelligence (AI) drive the development of new and improved machine learning (ML) algorithms and methods. The advancements in ML have opened up significant opportunities for effectively imputing these missing values. The main objective of this article is to conduct a comprehensive and rigorous review, as well as analysis, of the state-of-the-art ML applications in MVI methods. This analysis seeks to enhance researchers' understanding of the subject and facilitate the development of robust and impactful interventions in data preprocessing for Data Analytics. The review is performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) technique. More than 100 articles published between 2014 and 2023 are critically reviewed, considering the methods and findings. Furthermore, the latest literature is examined to scrutinize the trends in MVI methods and their evaluation. The accomplishments and limitations of the existing literature are discussed in detail. The survey concludes by identifying the current gaps in research and providing suggestions for future research directions and emerging trends in related fields of interest. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.08308v1-abstract-full').style.display = 'none'; document.getElementById('2410.08308v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.08295">arXiv:2410.08295</a> <span> [<a href="https://arxiv.org/pdf/2410.08295">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Impact of Missing Values in Machine Learning: A Comprehensive Analysis </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ahmad%2C+A+F">Abu Fuad Ahmad</a>, <a href="/search/cs?searchtype=author&query=Sayeed%2C+M+S">Md Shohel Sayeed</a>, <a href="/search/cs?searchtype=author&query=Alshammari%2C+K">Khaznah Alshammari</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+I">Istiaque Ahmed</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.08295v1-abstract-short" style="display: inline;"> Machine learning (ML) has become a ubiquitous tool across various domains of data mining and big data analysis. The efficacy of ML models depends heavily on high-quality datasets, which are often complicated by the presence of missing values. Consequently, the performance and generalization of ML models are at risk in the face of such datasets. This paper aims to examine the nuanced impact of miss… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.08295v1-abstract-full').style.display = 'inline'; document.getElementById('2410.08295v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.08295v1-abstract-full" style="display: none;"> Machine learning (ML) has become a ubiquitous tool across various domains of data mining and big data analysis. The efficacy of ML models depends heavily on high-quality datasets, which are often complicated by the presence of missing values. Consequently, the performance and generalization of ML models are at risk in the face of such datasets. This paper aims to examine the nuanced impact of missing values on ML workflows, including their types, causes, and consequences. Our analysis focuses on the challenges posed by missing values, including biased inferences, reduced predictive power, and increased computational burdens. The paper further explores strategies for handling missing values, including imputation techniques and removal strategies, and investigates how missing values affect model evaluation metrics and introduces complexities in cross-validation and model selection. The study employs case studies and real-world examples to illustrate the practical implications of addressing missing values. Finally, the discussion extends to future research directions, emphasizing the need for handling missing values ethically and transparently. The primary goal of this paper is to provide insights into the pervasive impact of missing values on ML models and guide practitioners toward effective strategies for achieving robust and reliable model outcomes. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.08295v1-abstract-full').style.display = 'none'; document.getElementById('2410.08295v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.02915">arXiv:2410.02915</a> <span> [<a href="https://arxiv.org/pdf/2410.02915">pdf</a>, <a href="https://arxiv.org/format/2410.02915">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Software Engineering">cs.SE</span> </div> </div> <p class="title is-5 mathjax"> Does the Order of Fine-tuning Matter and Why? </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Chen%2C+Q">Qihong Chen</a>, <a href="/search/cs?searchtype=author&query=Li%2C+J">Jiawei Li</a>, <a href="/search/cs?searchtype=author&query=Suh%2C+H">Hyunjae Suh</a>, <a href="/search/cs?searchtype=author&query=Jiang%2C+L">Lianghao Jiang</a>, <a href="/search/cs?searchtype=author&query=Zhou%2C+Z">Zheng Zhou</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+J">Jingze Chen</a>, <a href="/search/cs?searchtype=author&query=Gesi%2C+J">Jiri Gesi</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+I">Iftekhar Ahmed</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.02915v1-abstract-short" style="display: inline;"> To improve the performance on a target task, researchers have fine-tuned language models with an intermediate task before the target task of interest. However, previous works have focused on the pre-trained language models and downstream tasks in Natural Language Processing (NLP) and considered only one intermediate task. The effect of fine-tuning multiple intermediate tasks and their ordering on… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.02915v1-abstract-full').style.display = 'inline'; document.getElementById('2410.02915v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.02915v1-abstract-full" style="display: none;"> To improve the performance on a target task, researchers have fine-tuned language models with an intermediate task before the target task of interest. However, previous works have focused on the pre-trained language models and downstream tasks in Natural Language Processing (NLP) and considered only one intermediate task. The effect of fine-tuning multiple intermediate tasks and their ordering on target task performance has not been fully explored in Software Engineering. In this study, we perform the first empirical study on analyzing the impact of task ordering on target task performance. Experimental results show that there is an impact of task ordering on target task performance by up to 6% of performance gain and up to 4% of performance loss. To explain such an impact, we consider a variety of potential factors, including the characteristics of dataset (syntactic similarity and semantic similarity analysis, dataset size), model (probing task and attention analysis), and task (task affinity analysis). Our study provides Software Engineering researchers and practitioners with insights into the effect of task orderings and how to select the one that is cost-effective while achieving the best performance gain. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.02915v1-abstract-full').style.display = 'none'; document.getElementById('2410.02915v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 3 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.16732">arXiv:2409.16732</a> <span> [<a href="https://arxiv.org/pdf/2409.16732">pdf</a>, <a href="https://arxiv.org/format/2409.16732">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> </div> </div> <p class="title is-5 mathjax"> "It Explains What I am Currently Going Through Perfectly to a Tee": Understanding User Perceptions on LLM-Enhanced Narrative Interventions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Bhattacharjee%2C+A">Ananya Bhattacharjee</a>, <a href="/search/cs?searchtype=author&query=Xu%2C+S+Y">Sarah Yi Xu</a>, <a href="/search/cs?searchtype=author&query=Rao%2C+P">Pranav Rao</a>, <a href="/search/cs?searchtype=author&query=Zeng%2C+Y">Yuchen Zeng</a>, <a href="/search/cs?searchtype=author&query=Meyerhoff%2C+J">Jonah Meyerhoff</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+S+I">Syed Ishtiaque Ahmed</a>, <a href="/search/cs?searchtype=author&query=Mohr%2C+D+C">David C Mohr</a>, <a href="/search/cs?searchtype=author&query=Liut%2C+M">Michael Liut</a>, <a href="/search/cs?searchtype=author&query=Mariakakis%2C+A">Alex Mariakakis</a>, <a href="/search/cs?searchtype=author&query=Kornfield%2C+R">Rachel Kornfield</a>, <a href="/search/cs?searchtype=author&query=Williams%2C+J+J">Joseph Jay Williams</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.16732v2-abstract-short" style="display: inline;"> Stories about overcoming personal struggles can effectively illustrate the application of psychological theories in real life, yet they may fail to resonate with individuals' experiences. In this work, we employ large language models (LLMs) to create tailored narratives that acknowledge and address unique challenging thoughts and situations faced by individuals. Our study, involving 346 young adul… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.16732v2-abstract-full').style.display = 'inline'; document.getElementById('2409.16732v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.16732v2-abstract-full" style="display: none;"> Stories about overcoming personal struggles can effectively illustrate the application of psychological theories in real life, yet they may fail to resonate with individuals' experiences. In this work, we employ large language models (LLMs) to create tailored narratives that acknowledge and address unique challenging thoughts and situations faced by individuals. Our study, involving 346 young adults across two settings, demonstrates that LLM-enhanced stories were perceived to be better than human-written ones in conveying key takeaways, promoting reflection, and reducing belief in negative thoughts. These stories were not only seen as more relatable but also similarly authentic to human-written ones, highlighting the potential of LLMs in helping young adults manage their struggles. The findings of this work provide crucial design considerations for future narrative-based digital mental health interventions, such as the need to maintain relatability without veering into implausibility and refining the wording and tone of AI-enhanced content. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.16732v2-abstract-full').style.display = 'none'; document.getElementById('2409.16732v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 25 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.16420">arXiv:2409.16420</a> <span> [<a href="https://arxiv.org/pdf/2409.16420">pdf</a>, <a href="https://arxiv.org/format/2409.16420">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Deep Learning Model-Based Channel Estimation for THz Band Massive MIMO with RF Impairments </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Tarafder%2C+P">Pulok Tarafder</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+I">Imtiaz Ahmed</a>, <a href="/search/cs?searchtype=author&query=Rawat%2C+D+B">Danda B. Rawat</a>, <a href="/search/cs?searchtype=author&query=Annavajjala%2C+R">Ramesh Annavajjala</a>, <a href="/search/cs?searchtype=author&query=Mishra%2C+K+V">Kumar Vijay Mishra</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.16420v1-abstract-short" style="display: inline;"> THz band enabled large scale massive MIMO (M-MIMO) is considered as a key enabler for the 6G technology, given its enormous bandwidth and for its low latency connectivity. In the large-scale M-MIMO configuration, enlarged array aperture and small wavelengths of THz results in an amalgamation of both far field and near field paths, which makes tasks such as channel estimation for THz M-MIMO highly… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.16420v1-abstract-full').style.display = 'inline'; document.getElementById('2409.16420v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.16420v1-abstract-full" style="display: none;"> THz band enabled large scale massive MIMO (M-MIMO) is considered as a key enabler for the 6G technology, given its enormous bandwidth and for its low latency connectivity. In the large-scale M-MIMO configuration, enlarged array aperture and small wavelengths of THz results in an amalgamation of both far field and near field paths, which makes tasks such as channel estimation for THz M-MIMO highly challenging. Moreover, at the THz transceiver, radio frequency (RF) impairments such as phase noise (PN) of the analog devices also leads to degradation in channel estimation performance. Classical estimators as well as traditional deep learning (DL) based algorithms struggle to maintain their robustness when performing for large scale antenna arrays i.e., M-MIMO, and when RF impairments are considered for practical usage. To effectively address this issue, it is crucial to utilize a neural network (NN) that has the ability to study the behaviors of the channel and RF impairment correlations, such as a recurrent neural network (RNN). The RF impairments act as sequential noise data which is subsequently incorporated with the channel data, leading to choose a specific type of RNN known as bidirectional long short-term memory (BiLSTM) which is followed by gated recurrent units (GRU) to process the sequential data. Simulation results demonstrate that our proposed model outperforms other benchmark approaches at various signal-to-noise ratio (SNR) levels. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.16420v1-abstract-full').style.display = 'none'; document.getElementById('2409.16420v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 24 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted to the MILCOM Workshop 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.10781">arXiv:2409.10781</a> <span> [<a href="https://arxiv.org/pdf/2409.10781">pdf</a>, <a href="https://arxiv.org/format/2409.10781">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Software Engineering">cs.SE</span> </div> </div> <p class="title is-5 mathjax"> Investigating the Impact of Code Comment Inconsistency on Bug Introducing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Radmanesh%2C+S">Shiva Radmanesh</a>, <a href="/search/cs?searchtype=author&query=Imani%2C+A">Aaron Imani</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+I">Iftekhar Ahmed</a>, <a href="/search/cs?searchtype=author&query=Moshirpour%2C+M">Mohammad Moshirpour</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.10781v1-abstract-short" style="display: inline;"> Code comments are essential for clarifying code functionality, improving readability, and facilitating collaboration among developers. Despite their importance, comments often become outdated, leading to inconsistencies with the corresponding code. This can mislead developers and potentially introduce bugs. Our research investigates the impact of code-comment inconsistency on bug introduction usin… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.10781v1-abstract-full').style.display = 'inline'; document.getElementById('2409.10781v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.10781v1-abstract-full" style="display: none;"> Code comments are essential for clarifying code functionality, improving readability, and facilitating collaboration among developers. Despite their importance, comments often become outdated, leading to inconsistencies with the corresponding code. This can mislead developers and potentially introduce bugs. Our research investigates the impact of code-comment inconsistency on bug introduction using large language models, specifically GPT-3.5. We first compare the performance of the GPT-3.5 model with other state-of-the-art methods in detecting these inconsistencies, demonstrating the superiority of GPT-3.5 in this domain. Additionally, we analyze the temporal evolution of code-comment inconsistencies and their effect on bug proneness over various timeframes using GPT-3.5 and Odds ratio analysis. Our findings reveal that inconsistent changes are around 1.5 times more likely to lead to a bug-introducing commit than consistent changes, highlighting the necessity of maintaining consistent and up-to-date comments in software development. This study provides new insights into the relationship between code-comment inconsistency and software quality, offering a comprehensive analysis of its impact over time, demonstrating that the impact of code-comment inconsistency on bug introduction is highest immediately after the inconsistency is introduced and diminishes over time. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.10781v1-abstract-full').style.display = 'none'; document.getElementById('2409.10781v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.06018">arXiv:2409.06018</a> <span> [<a href="https://arxiv.org/pdf/2409.06018">pdf</a>, <a href="https://arxiv.org/format/2409.06018">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Pioneering Precision in Lumbar Spine MRI Segmentation with Advanced Deep Learning and Data Enhancement </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ahmed%2C+I">Istiak Ahmed</a>, <a href="/search/cs?searchtype=author&query=Hossain%2C+M+T">Md. Tanzim Hossain</a>, <a href="/search/cs?searchtype=author&query=Nahid%2C+M+Z+I">Md. Zahirul Islam Nahid</a>, <a href="/search/cs?searchtype=author&query=Sanjid%2C+K+S">Kazi Shahriar Sanjid</a>, <a href="/search/cs?searchtype=author&query=Junayed%2C+M+S+S">Md. Shakib Shahariar Junayed</a>, <a href="/search/cs?searchtype=author&query=Uddin%2C+M+M">M. Monir Uddin</a>, <a href="/search/cs?searchtype=author&query=Khan%2C+M+M">Mohammad Monirujjaman Khan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.06018v1-abstract-short" style="display: inline;"> This study presents an advanced approach to lumbar spine segmentation using deep learning techniques, focusing on addressing key challenges such as class imbalance and data preprocessing. Magnetic resonance imaging (MRI) scans of patients with low back pain are meticulously preprocessed to accurately represent three critical classes: vertebrae, spinal canal, and intervertebral discs (IVDs). By rec… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.06018v1-abstract-full').style.display = 'inline'; document.getElementById('2409.06018v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.06018v1-abstract-full" style="display: none;"> This study presents an advanced approach to lumbar spine segmentation using deep learning techniques, focusing on addressing key challenges such as class imbalance and data preprocessing. Magnetic resonance imaging (MRI) scans of patients with low back pain are meticulously preprocessed to accurately represent three critical classes: vertebrae, spinal canal, and intervertebral discs (IVDs). By rectifying class inconsistencies in the data preprocessing stage, the fidelity of the training data is ensured. The modified U-Net model incorporates innovative architectural enhancements, including an upsample block with leaky Rectified Linear Units (ReLU) and Glorot uniform initializer, to mitigate common issues such as the dying ReLU problem and improve stability during training. Introducing a custom combined loss function effectively tackles class imbalance, significantly improving segmentation accuracy. Evaluation using a comprehensive suite of metrics showcases the superior performance of this approach, outperforming existing methods and advancing the current techniques in lumbar spine segmentation. These findings hold significant advancements for enhanced lumbar spine MRI and segmentation diagnostic accuracy. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.06018v1-abstract-full').style.display = 'none'; document.getElementById('2409.06018v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.02502">arXiv:2408.02502</a> <span> [<a href="https://arxiv.org/pdf/2408.02502">pdf</a>, <a href="https://arxiv.org/format/2408.02502">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Software Engineering">cs.SE</span> </div> </div> <p class="title is-5 mathjax"> Context Conquers Parameters: Outperforming Proprietary LLM in Commit Message Generation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Imani%2C+A">Aaron Imani</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+I">Iftekhar Ahmed</a>, <a href="/search/cs?searchtype=author&query=Moshirpour%2C+M">Mohammad Moshirpour</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.02502v2-abstract-short" style="display: inline;"> Commit messages provide descriptions of the modifications made in a commit using natural language, making them crucial for software maintenance and evolution. Recent developments in Large Language Models (LLMs) have led to their use in generating high-quality commit messages, such as the Omniscient Message Generator (OMG). This method employs GPT-4 to produce state-of-the-art commit messages. Howe… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.02502v2-abstract-full').style.display = 'inline'; document.getElementById('2408.02502v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.02502v2-abstract-full" style="display: none;"> Commit messages provide descriptions of the modifications made in a commit using natural language, making them crucial for software maintenance and evolution. Recent developments in Large Language Models (LLMs) have led to their use in generating high-quality commit messages, such as the Omniscient Message Generator (OMG). This method employs GPT-4 to produce state-of-the-art commit messages. However, the use of proprietary LLMs like GPT-4 in coding tasks raises privacy and sustainability concerns, which may hinder their industrial adoption. Considering that open-source LLMs have achieved competitive performance in developer tasks such as compiler validation, this study investigates whether they can be used to generate commit messages that are comparable with OMG. Our experiments show that an open-source LLM can generate commit messages that are comparable to those produced by OMG. In addition, through a series of contextual refinements, we propose lOcal MessagE GenerAtor (OMEGA) , a CMG approach that uses a 4-bit quantized 8B open-source LLM. OMEGA produces state-of-the-art commit messages, surpassing the performance of GPT-4 in practitioners' preference. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.02502v2-abstract-full').style.display = 'none'; document.getElementById('2408.02502v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 5 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted in ICSE 2025</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.14652">arXiv:2406.14652</a> <span> [<a href="https://arxiv.org/pdf/2406.14652">pdf</a>, <a href="https://arxiv.org/format/2406.14652">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Multiagent Systems">cs.MA</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cellular Automata and Lattice Gases">nlin.CG</span> </div> </div> <p class="title is-5 mathjax"> Singular knee identification to support emergence recognition in physical swarm and cellular automata trajectories </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Faruque%2C+I+A">Imraan A. Faruque</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+I">Ishriak Ahmed</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.14652v1-abstract-short" style="display: inline;"> After decades of attention, emergence continues to lack a centralized mathematical definition that leads to a rigorous emergence test applicable to physical flocks and swarms, particularly those containing both deterministic elements (eg, interactions) and stochastic perturbations like measurement noise. This study develops a heuristic test based on singular value curve analysis of data matrices c… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.14652v1-abstract-full').style.display = 'inline'; document.getElementById('2406.14652v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.14652v1-abstract-full" style="display: none;"> After decades of attention, emergence continues to lack a centralized mathematical definition that leads to a rigorous emergence test applicable to physical flocks and swarms, particularly those containing both deterministic elements (eg, interactions) and stochastic perturbations like measurement noise. This study develops a heuristic test based on singular value curve analysis of data matrices containing deterministic and Gaussian noise signals. The minimum detection criteria are identified, and statistical and matrix space analysis developed to determine upper and lower bounds. This study applies the analysis to representative examples by using recorded trajectories of mixed deterministic and stochastic trajectories for multi-agent, cellular automata, and biological video. Examples include Cucker Smale and Vicsek flocking, Gaussian noise and its integration, recorded observations of bird flocking, and 1D cellular automata. Ensemble simulations including measurement noise are performed to compute statistical variation and discussed relative to random matrix theory noise bounds. The results indicate singular knee analysis of recorded trajectories can detect gradated levels on a continuum of structure and noise. Across the eight singular value decay metrics considered, the angle subtended at the singular value knee emerges with the most potential for supporting cross-embodiment emergence detection, the size of noise bounds is used as an indication of required sample size, and the presence of a large fraction of singular values inside noise bounds as an indication of noise. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.14652v1-abstract-full').style.display = 'none'; document.getElementById('2406.14652v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">14 pages, 11 figures; includes 2 supplementary pages</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.07765">arXiv:2406.07765</a> <span> [<a href="https://arxiv.org/pdf/2406.07765">pdf</a>, <a href="https://arxiv.org/format/2406.07765">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Software Engineering">cs.SE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1016/j.infsof.2024.107610">10.1016/j.infsof.2024.107610 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Using AI-Based Coding Assistants in Practice: State of Affairs, Perceptions, and Ways Forward </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sergeyuk%2C+A">Agnia Sergeyuk</a>, <a href="/search/cs?searchtype=author&query=Golubev%2C+Y">Yaroslav Golubev</a>, <a href="/search/cs?searchtype=author&query=Bryksin%2C+T">Timofey Bryksin</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+I">Iftekhar Ahmed</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.07765v2-abstract-short" style="display: inline;"> Context. The last several years saw the emergence of AI assistants for code - multi-purpose AI-based helpers in software engineering. As they become omnipresent in all aspects of software development, it becomes critical to understand their usage patterns. Objective. We aim to better understand how specifically developers are using AI assistants, why they are not using them in certain parts of t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.07765v2-abstract-full').style.display = 'inline'; document.getElementById('2406.07765v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.07765v2-abstract-full" style="display: none;"> Context. The last several years saw the emergence of AI assistants for code - multi-purpose AI-based helpers in software engineering. As they become omnipresent in all aspects of software development, it becomes critical to understand their usage patterns. Objective. We aim to better understand how specifically developers are using AI assistants, why they are not using them in certain parts of their development workflow, and what needs to be improved in the future. Methods. In this work, we carried out a large-scale survey aimed at how AI assistants are used, focusing on specific software development activities and stages. We collected opinions of 481 programmers on five broad activities: (a) implementing new features, (b) writing tests, (c) bug triaging, (d) refactoring, and (e) writing natural-language artifacts, as well as their individual stages. Results. Our results provide a novel comparison of different stages where AI assistants are used that is both comprehensive and detailed. It highlights specific activities that developers find less enjoyable and want to delegate to an AI assistant, e.g., writing tests and natural-language artifacts. We also determine more granular stages where AI assistants are used, such as generating tests and generating docstrings, as well as less studied parts of the workflow, such as generating test data. Among the reasons for not using assistants, there are general aspects like trust and company policies, as well as more concrete issues like the lack of project-size context, which can be the focus of the future research. Conclusion. The provided analysis highlights stages of software development that developers want to delegate and that are already popular for using AI assistants, which can be a good focus for features aimed to help developers right now. The main reasons for not using AI assistants can serve as a guideline for future work. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.07765v2-abstract-full').style.display = 'none'; document.getElementById('2406.07765v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 11 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Published in Information and Software Technology. 32 pages, 4 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.09960">arXiv:2405.09960</a> <span> [<a href="https://arxiv.org/pdf/2405.09960">pdf</a>, <a href="https://arxiv.org/format/2405.09960">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> A Unified Deep Transfer Learning Model for Accurate IoT Localization in Diverse Environments </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ahmed%2C+A+I">Abdullahi Isa Ahmed</a>, <a href="/search/cs?searchtype=author&query=Etiabi%2C+Y">Yaya Etiabi</a>, <a href="/search/cs?searchtype=author&query=Azim%2C+A+W">Ali Waqar Azim</a>, <a href="/search/cs?searchtype=author&query=Amhoud%2C+E+M">El Mehdi Amhoud</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.09960v1-abstract-short" style="display: inline;"> Internet of Things (IoT) is an ever-evolving technological paradigm that is reshaping industries and societies globally. Real-time data collection, analysis, and decision-making facilitated by localization solutions form the foundation for location-based services, enabling them to support critical functions within diverse IoT ecosystems. However, most existing works on localization focus on single… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.09960v1-abstract-full').style.display = 'inline'; document.getElementById('2405.09960v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.09960v1-abstract-full" style="display: none;"> Internet of Things (IoT) is an ever-evolving technological paradigm that is reshaping industries and societies globally. Real-time data collection, analysis, and decision-making facilitated by localization solutions form the foundation for location-based services, enabling them to support critical functions within diverse IoT ecosystems. However, most existing works on localization focus on single environment, resulting in the development of multiple models to support multiple environments. In the context of smart cities, these raise costs and complexity due to the dynamicity of such environments. To address these challenges, this paper presents a unified indoor-outdoor localization solution that leverages transfer learning (TL) schemes to build a single deep learning model. The model accurately predicts the localization of IoT devices in diverse environments. The performance evaluation shows that by adopting an encoder-based TL scheme, we can improve the baseline model by about 17.18% in indoor environments and 9.79% in outdoor environments. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.09960v1-abstract-full').style.display = 'none'; document.getElementById('2405.09960v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">6 pages, 8 figures, IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 2024)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.02340">arXiv:2405.02340</a> <span> [<a href="https://arxiv.org/pdf/2405.02340">pdf</a>, <a href="https://arxiv.org/format/2405.02340">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Applications">stat.AP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> A Comprehensive Approach to Carbon Dioxide Emission Analysis in High Human Development Index Countries using Statistical and Machine Learning Techniques </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Khosravi%2C+H">Hamed Khosravi</a>, <a href="/search/cs?searchtype=author&query=Raihan%2C+A+S">Ahmed Shoyeb Raihan</a>, <a href="/search/cs?searchtype=author&query=Islam%2C+F">Farzana Islam</a>, <a href="/search/cs?searchtype=author&query=Nimbarte%2C+A">Ashish Nimbarte</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+I">Imtiaz Ahmed</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.02340v1-abstract-short" style="display: inline;"> Reducing Carbon dioxide (CO2) emission is vital at both global and national levels, given their significant role in exacerbating climate change. CO2 emission, stemming from a variety of industrial and economic activities, are major contributors to the greenhouse effect and global warming, posing substantial obstacles in addressing climate issues. It's imperative to forecast CO2 emission trends and… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.02340v1-abstract-full').style.display = 'inline'; document.getElementById('2405.02340v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.02340v1-abstract-full" style="display: none;"> Reducing Carbon dioxide (CO2) emission is vital at both global and national levels, given their significant role in exacerbating climate change. CO2 emission, stemming from a variety of industrial and economic activities, are major contributors to the greenhouse effect and global warming, posing substantial obstacles in addressing climate issues. It's imperative to forecast CO2 emission trends and classify countries based on their emission patterns to effectively mitigate worldwide carbon emission. This paper presents an in-depth comparative study on the determinants of CO2 emission in twenty countries with high Human Development Index (HDI), exploring factors related to economy, environment, energy use, and renewable resources over a span of 25 years. The study unfolds in two distinct phases: initially, statistical techniques such as Ordinary Least Squares (OLS), fixed effects, and random effects models are applied to pinpoint significant determinants of CO2 emission. Following this, the study leverages supervised and unsupervised machine learning (ML) methods to further scrutinize and understand the factors influencing CO2 emission. Seasonal AutoRegressive Integrated Moving Average with eXogenous variables (SARIMAX), a supervised ML model, is first used to predict emission trends from historical data, offering practical insights for policy formulation. Subsequently, Dynamic Time Warping (DTW), an unsupervised learning approach, is used to group countries by similar emission patterns. The dual-phase approach utilized in this study significantly improves the accuracy of CO2 emission predictions while also providing a deeper insight into global emission trends. By adopting this thorough analytical framework, nations can develop more focused and effective carbon reduction policies, playing a vital role in the global initiative to combat climate change. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.02340v1-abstract-full').style.display = 'none'; document.getElementById('2405.02340v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.00946">arXiv:2404.00946</a> <span> [<a href="https://arxiv.org/pdf/2404.00946">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Exploring the Efficacy of Group-Normalization in Deep Learning Models for Alzheimer's Disease Classification </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Habib%2C+G">Gousia Habib</a>, <a href="/search/cs?searchtype=author&query=Malik%2C+I+A">Ishfaq Ahmed Malik</a>, <a href="/search/cs?searchtype=author&query=Ahmad%2C+J">Jameel Ahmad</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+I">Imtiaz Ahmed</a>, <a href="/search/cs?searchtype=author&query=Qureshi%2C+S">Shaima Qureshi</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2404.00946v1-abstract-short" style="display: inline;"> Batch Normalization is an important approach to advancing deep learning since it allows multiple networks to train simultaneously. A problem arises when normalizing along the batch dimension because B.N.'s error increases significantly as batch size shrinks because batch statistics estimates are inaccurate. As a result, computer vision tasks like detection, segmentation, and video, which require t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.00946v1-abstract-full').style.display = 'inline'; document.getElementById('2404.00946v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.00946v1-abstract-full" style="display: none;"> Batch Normalization is an important approach to advancing deep learning since it allows multiple networks to train simultaneously. A problem arises when normalizing along the batch dimension because B.N.'s error increases significantly as batch size shrinks because batch statistics estimates are inaccurate. As a result, computer vision tasks like detection, segmentation, and video, which require tiny batches based on memory consumption, aren't suitable for using Batch Normalization for larger model training and feature transfer. Here, we explore Group Normalization as an easy alternative to using Batch Normalization A Group Normalization is a channel normalization method in which each group is divided into different channels, and the corresponding mean and variance are calculated for each group. Group Normalization computations are accurate across a wide range of batch sizes and are independent of batch size. When trained using a large ImageNet database on ResNet-50, GN achieves a very low error rate of 10.6% compared to Batch Normalization. when a smaller batch size of only 2 is used. For usual batch sizes, the performance of G.N. is comparable to that of Batch Normalization, but at the same time, it outperforms other normalization techniques. Implementing Group Normalization as a direct alternative to B.N to combat the serious challenges faced by the Batch Normalization in deep learning models with comparable or improved classification accuracy. Additionally, Group Normalization can be naturally transferred from the pre-training to the fine-tuning phase. . <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.00946v1-abstract-full').style.display = 'none'; document.getElementById('2404.00946v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">19 pages, 3 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.00470">arXiv:2404.00470</a> <span> [<a href="https://arxiv.org/pdf/2404.00470">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> </div> </div> <p class="title is-5 mathjax"> Classification of Short Segment Pediatric Heart Sounds Based on a Transformer-Based Convolutional Neural Network </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Hassanuzzaman%2C+M">Md Hassanuzzaman</a>, <a href="/search/cs?searchtype=author&query=Hasan%2C+N+A">Nurul Akhtar Hasan</a>, <a href="/search/cs?searchtype=author&query=Mamun%2C+M+A+A">Mohammad Abdullah Al Mamun</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+K+I">Khawza I Ahmed</a>, <a href="/search/cs?searchtype=author&query=Khandoker%2C+A+H">Ahsan H Khandoker</a>, <a href="/search/cs?searchtype=author&query=Mostafa%2C+R">Raqibul Mostafa</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2404.00470v1-abstract-short" style="display: inline;"> Congenital anomalies arising as a result of a defect in the structure of the heart and great vessels are known as congenital heart diseases or CHDs. A PCG can provide essential details about the mechanical conduction system of the heart and point out specific patterns linked to different kinds of CHD. This study aims to investigate the minimum signal duration required for the automatic classificat… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.00470v1-abstract-full').style.display = 'inline'; document.getElementById('2404.00470v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.00470v1-abstract-full" style="display: none;"> Congenital anomalies arising as a result of a defect in the structure of the heart and great vessels are known as congenital heart diseases or CHDs. A PCG can provide essential details about the mechanical conduction system of the heart and point out specific patterns linked to different kinds of CHD. This study aims to investigate the minimum signal duration required for the automatic classification of heart sounds. This study also investigated the optimum signal quality assessment indicator (Root Mean Square of Successive Differences) RMSSD and (Zero Crossings Rate) ZCR value. Mel-frequency cepstral coefficients (MFCCs) based feature is used as an input to build a Transformer-Based residual one-dimensional convolutional neural network, which is then used for classifying the heart sound. The study showed that 0.4 is the ideal threshold for getting suitable signals for the RMSSD and ZCR indicators. Moreover, a minimum signal length of 5s is required for effective heart sound classification. It also shows that a shorter signal (3 s heart sound) does not have enough information to categorize heart sounds accurately, and the longer signal (15 s heart sound) may contain more noise. The best accuracy, 93.69%, is obtained for the 5s signal to distinguish the heart sound. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.00470v1-abstract-full').style.display = 'none'; document.getElementById('2404.00470v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">16 pages,11 Figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.17093">arXiv:2403.17093</a> <span> [<a href="https://arxiv.org/pdf/2403.17093">pdf</a>, <a href="https://arxiv.org/format/2403.17093">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Enhancing UAV Security Through Zero Trust Architecture: An Advanced Deep Learning and Explainable AI Analysis </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Haque%2C+E">Ekramul Haque</a>, <a href="/search/cs?searchtype=author&query=Hasan%2C+K">Kamrul Hasan</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+I">Imtiaz Ahmed</a>, <a href="/search/cs?searchtype=author&query=Alam%2C+M+S">Md. Sahabul Alam</a>, <a href="/search/cs?searchtype=author&query=Islam%2C+T">Tariqul Islam</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.17093v1-abstract-short" style="display: inline;"> In the dynamic and ever-changing domain of Unmanned Aerial Vehicles (UAVs), the utmost importance lies in guaranteeing resilient and lucid security measures. This study highlights the necessity of implementing a Zero Trust Architecture (ZTA) to enhance the security of unmanned aerial vehicles (UAVs), hence departing from conventional perimeter defences that may expose vulnerabilities. The Zero Tru… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.17093v1-abstract-full').style.display = 'inline'; document.getElementById('2403.17093v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.17093v1-abstract-full" style="display: none;"> In the dynamic and ever-changing domain of Unmanned Aerial Vehicles (UAVs), the utmost importance lies in guaranteeing resilient and lucid security measures. This study highlights the necessity of implementing a Zero Trust Architecture (ZTA) to enhance the security of unmanned aerial vehicles (UAVs), hence departing from conventional perimeter defences that may expose vulnerabilities. The Zero Trust Architecture (ZTA) paradigm requires a rigorous and continuous process of authenticating all network entities and communications. The accuracy of our methodology in detecting and identifying unmanned aerial vehicles (UAVs) is 84.59\%. This is achieved by utilizing Radio Frequency (RF) signals within a Deep Learning framework, a unique method. Precise identification is crucial in Zero Trust Architecture (ZTA), as it determines network access. In addition, the use of eXplainable Artificial Intelligence (XAI) tools such as SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) contributes to the improvement of the model's transparency and interpretability. Adherence to Zero Trust Architecture (ZTA) standards guarantees that the classifications of unmanned aerial vehicles (UAVs) are verifiable and comprehensible, enhancing security within the UAV field. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.17093v1-abstract-full').style.display = 'none'; document.getElementById('2403.17093v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">6 pages, 5 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.09836">arXiv:2403.09836</a> <span> [<a href="https://arxiv.org/pdf/2403.09836">pdf</a>, <a href="https://arxiv.org/format/2403.09836">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Empowering Healthcare through Privacy-Preserving MRI Analysis </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Amin%2C+A">Al Amin</a>, <a href="/search/cs?searchtype=author&query=Hasan%2C+K">Kamrul Hasan</a>, <a href="/search/cs?searchtype=author&query=Zein-Sabatto%2C+S">Saleh Zein-Sabatto</a>, <a href="/search/cs?searchtype=author&query=Chimba%2C+D">Deo Chimba</a>, <a href="/search/cs?searchtype=author&query=Hong%2C+L">Liang Hong</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+I">Imtiaz Ahmed</a>, <a href="/search/cs?searchtype=author&query=Islam%2C+T">Tariqul Islam</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.09836v1-abstract-short" style="display: inline;"> In the healthcare domain, Magnetic Resonance Imaging (MRI) assumes a pivotal role, as it employs Artificial Intelligence (AI) and Machine Learning (ML) methodologies to extract invaluable insights from imaging data. Nonetheless, the imperative need for patient privacy poses significant challenges when collecting data from diverse healthcare sources. Consequently, the Deep Learning (DL) communities… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.09836v1-abstract-full').style.display = 'inline'; document.getElementById('2403.09836v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.09836v1-abstract-full" style="display: none;"> In the healthcare domain, Magnetic Resonance Imaging (MRI) assumes a pivotal role, as it employs Artificial Intelligence (AI) and Machine Learning (ML) methodologies to extract invaluable insights from imaging data. Nonetheless, the imperative need for patient privacy poses significant challenges when collecting data from diverse healthcare sources. Consequently, the Deep Learning (DL) communities occasionally face difficulties detecting rare features. In this research endeavor, we introduce the Ensemble-Based Federated Learning (EBFL) Framework, an innovative solution tailored to address this challenge. The EBFL framework deviates from the conventional approach by emphasizing model features over sharing sensitive patient data. This unique methodology fosters a collaborative and privacy-conscious environment for healthcare institutions, empowering them to harness the capabilities of a centralized server for model refinement while upholding the utmost data privacy standards.Conversely, a robust ensemble architecture boasts potent feature extraction capabilities, distinguishing itself from a single DL model. This quality makes it remarkably dependable for MRI analysis. By harnessing our groundbreaking EBFL methodology, we have achieved remarkable precision in the classification of brain tumors, including glioma, meningioma, pituitary, and non-tumor instances, attaining a precision rate of 94% for the Global model and an impressive 96% for the Ensemble model. Our models underwent rigorous evaluation using conventional performance metrics such as Accuracy, Precision, Recall, and F1 Score. Integrating DL within the Federated Learning (FL) framework has yielded a methodology that offers precise and dependable diagnostics for detecting brain tumors. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.09836v1-abstract-full').style.display = 'none'; document.getElementById('2403.09836v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">6</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.04130">arXiv:2403.04130</a> <span> [<a href="https://arxiv.org/pdf/2403.04130">pdf</a>, <a href="https://arxiv.org/format/2403.04130">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> An Explainable AI Framework for Artificial Intelligence of Medical Things </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Amin%2C+A">Al Amin</a>, <a href="/search/cs?searchtype=author&query=Hasan%2C+K">Kamrul Hasan</a>, <a href="/search/cs?searchtype=author&query=Zein-Sabatto%2C+S">Saleh Zein-Sabatto</a>, <a href="/search/cs?searchtype=author&query=Chimba%2C+D">Deo Chimba</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+I">Imtiaz Ahmed</a>, <a href="/search/cs?searchtype=author&query=Islam%2C+T">Tariqul Islam</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.04130v1-abstract-short" style="display: inline;"> The healthcare industry has been revolutionized by the convergence of Artificial Intelligence of Medical Things (AIoMT), allowing advanced data-driven solutions to improve healthcare systems. With the increasing complexity of Artificial Intelligence (AI) models, the need for Explainable Artificial Intelligence (XAI) techniques become paramount, particularly in the medical domain, where transparent… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.04130v1-abstract-full').style.display = 'inline'; document.getElementById('2403.04130v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.04130v1-abstract-full" style="display: none;"> The healthcare industry has been revolutionized by the convergence of Artificial Intelligence of Medical Things (AIoMT), allowing advanced data-driven solutions to improve healthcare systems. With the increasing complexity of Artificial Intelligence (AI) models, the need for Explainable Artificial Intelligence (XAI) techniques become paramount, particularly in the medical domain, where transparent and interpretable decision-making becomes crucial. Therefore, in this work, we leverage a custom XAI framework, incorporating techniques such as Local Interpretable Model-Agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), and Gradient-weighted Class Activation Mapping (Grad-Cam), explicitly designed for the domain of AIoMT. The proposed framework enhances the effectiveness of strategic healthcare methods and aims to instill trust and promote understanding in AI-driven medical applications. Moreover, we utilize a majority voting technique that aggregates predictions from multiple convolutional neural networks (CNNs) and leverages their collective intelligence to make robust and accurate decisions in the healthcare system. Building upon this decision-making process, we apply the XAI framework to brain tumor detection as a use case demonstrating accurate and transparent diagnosis. Evaluation results underscore the exceptional performance of the XAI framework, achieving high precision, recall, and F1 scores with a training accuracy of 99% and a validation accuracy of 98%. Combining advanced XAI techniques with ensemble-based deep-learning (DL) methodologies allows for precise and reliable brain tumor diagnoses as an application of AIoMT. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.04130v1-abstract-full').style.display = 'none'; document.getElementById('2403.04130v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">7 pages, 8 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.03819">arXiv:2403.03819</a> <span> [<a href="https://arxiv.org/pdf/2403.03819">pdf</a>, <a href="https://arxiv.org/format/2403.03819">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Software Engineering">cs.SE</span> </div> </div> <p class="title is-5 mathjax"> Does Documentation Matter? An Empirical Study of Practitioners' Perspective on Open-Source Software Adoption </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Imani%2C+A">Aaron Imani</a>, <a href="/search/cs?searchtype=author&query=Radmanesh%2C+S">Shiva Radmanesh</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+I">Iftekhar Ahmed</a>, <a href="/search/cs?searchtype=author&query=Moshirpour%2C+M">Mohammad Moshirpour</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.03819v1-abstract-short" style="display: inline;"> In recent years, open-source software (OSS) has become increasingly prevalent in developing software products. While OSS documentation is the primary source of information provided by the developers' community about a product, its role in the industry's adoption process has yet to be examined. We conducted semi-structured interviews and an online survey to provide insight into this area. Based on… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.03819v1-abstract-full').style.display = 'inline'; document.getElementById('2403.03819v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.03819v1-abstract-full" style="display: none;"> In recent years, open-source software (OSS) has become increasingly prevalent in developing software products. While OSS documentation is the primary source of information provided by the developers' community about a product, its role in the industry's adoption process has yet to be examined. We conducted semi-structured interviews and an online survey to provide insight into this area. Based on interviews and survey insights, we developed a topic model to collect relevant information from OSS documentation automatically. Additionally, according to our survey responses regarding challenges associated with OSS documentation, we propose a novel information augmentation approach, DocMentor, by combining OSS documentation corpus TF-IDF scores and ChatGPT. Through explaining technical terms and providing examples and references, our approach enhances the documentation context and improves practitioners' understanding. Our tool's effectiveness is assessed by surveying practitioners. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.03819v1-abstract-full').style.display = 'none'; document.getElementById('2403.03819v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.00965">arXiv:2403.00965</a> <span> [<a href="https://arxiv.org/pdf/2403.00965">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Applications">stat.AP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Binary Gaussian Copula Synthesis: A Novel Data Augmentation Technique to Advance ML-based Clinical Decision Support Systems for Early Prediction of Dialysis Among CKD Patients </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Khosravi%2C+H">Hamed Khosravi</a>, <a href="/search/cs?searchtype=author&query=Das%2C+S">Srinjoy Das</a>, <a href="/search/cs?searchtype=author&query=Al-Mamun%2C+A">Abdullah Al-Mamun</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+I">Imtiaz Ahmed</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.00965v1-abstract-short" style="display: inline;"> The Center for Disease Control estimates that over 37 million US adults suffer from chronic kidney disease (CKD), yet 9 out of 10 of these individuals are unaware of their condition due to the absence of symptoms in the early stages. It has a significant impact on patients' quality of life, particularly when it progresses to the need for dialysis. Early prediction of dialysis is crucial as it can… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.00965v1-abstract-full').style.display = 'inline'; document.getElementById('2403.00965v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.00965v1-abstract-full" style="display: none;"> The Center for Disease Control estimates that over 37 million US adults suffer from chronic kidney disease (CKD), yet 9 out of 10 of these individuals are unaware of their condition due to the absence of symptoms in the early stages. It has a significant impact on patients' quality of life, particularly when it progresses to the need for dialysis. Early prediction of dialysis is crucial as it can significantly improve patient outcomes and assist healthcare providers in making timely and informed decisions. However, developing an effective machine learning (ML)-based Clinical Decision Support System (CDSS) for early dialysis prediction poses a key challenge due to the imbalanced nature of data. To address this challenge, this study evaluates various data augmentation techniques to understand their effectiveness on real-world datasets. We propose a new approach named Binary Gaussian Copula Synthesis (BGCS). BGCS is tailored for binary medical datasets and excels in generating synthetic minority data that mirrors the distribution of the original data. BGCS enhances early dialysis prediction by outperforming traditional methods in detecting dialysis patients. For the best ML model, Random Forest, BCGS achieved a 72% improvement, surpassing the state-of-the-art augmentation approaches. Also, we present a ML-based CDSS, designed to aid clinicians in making informed decisions. CDSS, which utilizes decision tree models, is developed to improve patient outcomes, identify critical variables, and thereby enable clinicians to make proactive decisions, and strategize treatment plans effectively for CKD patients who are more likely to require dialysis in the near future. Through comprehensive feature analysis and meticulous data preparation, we ensure that the CDSS's dialysis predictions are not only accurate but also actionable, providing a valuable tool in the management and treatment of CKD. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.00965v1-abstract-full').style.display = 'none'; document.getElementById('2403.00965v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.16790">arXiv:2402.16790</a> <span> [<a href="https://arxiv.org/pdf/2402.16790">pdf</a>, <a href="https://arxiv.org/format/2402.16790">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Software Engineering">cs.SE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Beyond Self-learned Attention: Mitigating Attention Bias in Transformer-based Models Using Attention Guidance </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Gesi%2C+J">Jiri Gesi</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+I">Iftekhar Ahmed</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2402.16790v1-abstract-short" style="display: inline;"> Transformer-based models have demonstrated considerable potential for source code modeling tasks in software engineering. However, they are limited by their dependence solely on automatic self-attention weight learning mechanisms. Previous studies have shown that these models overemphasize delimiters added by tokenizers (e.g., [CLS], [SEP]), which may lead to overlooking essential information in t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.16790v1-abstract-full').style.display = 'inline'; document.getElementById('2402.16790v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.16790v1-abstract-full" style="display: none;"> Transformer-based models have demonstrated considerable potential for source code modeling tasks in software engineering. However, they are limited by their dependence solely on automatic self-attention weight learning mechanisms. Previous studies have shown that these models overemphasize delimiters added by tokenizers (e.g., [CLS], [SEP]), which may lead to overlooking essential information in the original input source code. To address this challenge, we introduce SyntaGuid, a novel approach that utilizes the observation that attention weights tend to be biased towards specific source code syntax tokens and abstract syntax tree (AST) elements in fine-tuned language models when they make correct predictions. SyntaGuid facilitates the guidance of attention-weight learning, leading to improved model performance on various software engineering tasks. We evaluate the effectiveness of SyntaGuid on multiple tasks and demonstrate that it outperforms existing state-of-the-art models in overall performance without requiring additional data. Experimental result shows that SyntaGuid can improve overall performance up to 3.25% and fix up to 28.3% wrong predictions. Our work represents the first attempt to guide the attention of Transformer-based models towards critical source code tokens during fine-tuning, highlighting the potential for enhancing Transformer-based models in software engineering. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.16790v1-abstract-full').style.display = 'none'; document.getElementById('2402.16790v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2401.17622">arXiv:2401.17622</a> <span> [<a href="https://arxiv.org/pdf/2401.17622">pdf</a>, <a href="https://arxiv.org/format/2401.17622">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Software Engineering">cs.SE</span> </div> </div> <p class="title is-5 mathjax"> Commit Messages in the Age of Large Language Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Lopes%2C+C+V">Cristina V. Lopes</a>, <a href="/search/cs?searchtype=author&query=Klotzman%2C+V+I">Vanessa I. Klotzman</a>, <a href="/search/cs?searchtype=author&query=Ma%2C+I">Iris Ma</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+I">Iftekar Ahmed</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2401.17622v2-abstract-short" style="display: inline;"> Commit messages are explanations of changes made to a codebase that are stored in version control systems. They help developers understand the codebase as it evolves. However, writing commit messages can be tedious and inconsistent among developers. To address this issue, researchers have tried using different methods to automatically generate commit messages, including rule-based, retrieval-based… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.17622v2-abstract-full').style.display = 'inline'; document.getElementById('2401.17622v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.17622v2-abstract-full" style="display: none;"> Commit messages are explanations of changes made to a codebase that are stored in version control systems. They help developers understand the codebase as it evolves. However, writing commit messages can be tedious and inconsistent among developers. To address this issue, researchers have tried using different methods to automatically generate commit messages, including rule-based, retrieval-based, and learning-based approaches. Advances in large language models offer new possibilities for generating commit messages. In this study, we evaluate the performance of OpenAI's ChatGPT for generating commit messages based on code changes. We compare the results obtained with ChatGPT to previous automatic commit message generation methods that have been trained specifically on commit data. Our goal is to assess the extent to which large pre-trained language models can generate commit messages that are both quantitatively and qualitatively acceptable. We found that ChatGPT was able to outperform previous Automatic Commit Message Generation (ACMG) methods by orders of magnitude, and that, generally, the messages it generates are both accurate and of high-quality. We also provide insights, and a categorization, for the cases where it fails. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.17622v2-abstract-full').style.display = 'none'; document.getElementById('2401.17622v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 31 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Submitted to FSE 23 on Feb 6 2023</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2401.13623">arXiv:2401.13623</a> <span> [<a href="https://arxiv.org/pdf/2401.13623">pdf</a>, <a href="https://arxiv.org/format/2401.13623">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Software Engineering">cs.SE</span> </div> </div> <p class="title is-5 mathjax"> What Makes a Great Software Quality Assurance Engineer? </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Farias%2C+R+S">Roselane Silva Farias</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+I">Iftekhar Ahmed</a>, <a href="/search/cs?searchtype=author&query=de+Almeida%2C+E+S">Eduardo Santana de Almeida</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2401.13623v1-abstract-short" style="display: inline;"> Software Quality Assurance (SQA) Engineers are responsible for assessing a product during every phase of the software development process to ensure that the outcomes of each phase and the final product possess the desired qualities. In general, a great SQA engineer needs to have a different set of abilities from development engineers to effectively oversee the entire product development process fr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.13623v1-abstract-full').style.display = 'inline'; document.getElementById('2401.13623v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.13623v1-abstract-full" style="display: none;"> Software Quality Assurance (SQA) Engineers are responsible for assessing a product during every phase of the software development process to ensure that the outcomes of each phase and the final product possess the desired qualities. In general, a great SQA engineer needs to have a different set of abilities from development engineers to effectively oversee the entire product development process from beginning to end. Recent empirical studies identified important attributes of software engineers and managers, but the quality assurance role is overlooked. As software quality aspects have become more of a priority in the life cycle of software development, employers seek professionals that best suit the company's objectives and new graduates desire to make a valuable contribution through their job as an SQA engineer, but what makes them great? We addressed this knowledge gap by conducting 25 semi-structured interviews and 363 survey respondents with software quality assurance engineers from different companies around the world. We use the data collected from these activities to derive a comprehensive set of attributes that are considered important. As a result of the interviews, twenty-five attributes were identified and grouped into five main categories: personal, social, technical, management, and decision-making attributes. Through a rating survey, we confirmed that the distinguishing characteristics of great SQA engineers are curiosity, the ability to communicate effectively, and critical thinking skills. This work will guide further studies with SQA practitioners, by considering contextual factors and providing some implications for research and practice. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.13623v1-abstract-full').style.display = 'none'; document.getElementById('2401.13623v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 24 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">17 pages, 6 figures, 12 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2401.11131">arXiv:2401.11131</a> <span> [<a href="https://arxiv.org/pdf/2401.11131">pdf</a>, <a href="https://arxiv.org/format/2401.11131">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> </div> </div> <p class="title is-5 mathjax"> Towards a Non-Ideal Methodological Framework for Responsible ML </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Mothilal%2C+R+K">Ramaravind Kommiya Mothilal</a>, <a href="/search/cs?searchtype=author&query=Guha%2C+S">Shion Guha</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+S+I">Syed Ishtiaque Ahmed</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2401.11131v1-abstract-short" style="display: inline;"> Though ML practitioners increasingly employ various Responsible ML (RML) strategies, their methodological approach in practice is still unclear. In particular, the constraints, assumptions, and choices of practitioners with technical duties -- such as developers, engineers, and data scientists -- are often implicit, subtle, and under-scrutinized in HCI and related fields. We interviewed 22 technic… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.11131v1-abstract-full').style.display = 'inline'; document.getElementById('2401.11131v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.11131v1-abstract-full" style="display: none;"> Though ML practitioners increasingly employ various Responsible ML (RML) strategies, their methodological approach in practice is still unclear. In particular, the constraints, assumptions, and choices of practitioners with technical duties -- such as developers, engineers, and data scientists -- are often implicit, subtle, and under-scrutinized in HCI and related fields. We interviewed 22 technically oriented ML practitioners across seven domains to understand the characteristics of their methodological approaches to RML through the lens of ideal and non-ideal theorizing of fairness. We find that practitioners' methodological approaches fall along a spectrum of idealization. While they structured their approaches through ideal theorizing, such as by abstracting RML workflow from the inquiry of applicability of ML, they did not pay deliberate attention and systematically documented their non-ideal approaches, such as diagnosing imperfect conditions. We end our paper with a discussion of a new methodological approach, inspired by elements of non-ideal theory, to structure technical practitioners' RML process and facilitate collaboration with other stakeholders. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.11131v1-abstract-full').style.display = 'none'; document.getElementById('2401.11131v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">20 pages, single-column, preprint for conference</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2401.10745">arXiv:2401.10745</a> <span> [<a href="https://arxiv.org/pdf/2401.10745">pdf</a>, <a href="https://arxiv.org/ps/2401.10745">ps</a>, <a href="https://arxiv.org/format/2401.10745">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Engineering, Finance, and Science">cs.CE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Ethical Artificial Intelligence Principles and Guidelines for the Governance and Utilization of Highly Advanced Large Language Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Hossain%2C+S">Soaad Hossain</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+S+I">Syed Ishtiaque Ahmed</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2401.10745v2-abstract-short" style="display: inline;"> Given the success of ChatGPT, LaMDA and other large language models (LLMs), there has been an increase in development and usage of LLMs within the technology sector and other sectors. While the level in which LLMs has not reached a level where it has surpassed human intelligence, there will be a time when it will. Such LLMs can be referred to as advanced LLMs. Currently, there are limited usage of… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.10745v2-abstract-full').style.display = 'inline'; document.getElementById('2401.10745v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.10745v2-abstract-full" style="display: none;"> Given the success of ChatGPT, LaMDA and other large language models (LLMs), there has been an increase in development and usage of LLMs within the technology sector and other sectors. While the level in which LLMs has not reached a level where it has surpassed human intelligence, there will be a time when it will. Such LLMs can be referred to as advanced LLMs. Currently, there are limited usage of ethical artificial intelligence (AI) principles and guidelines addressing advanced LLMs due to the fact that we have not reached that point yet. However, this is a problem as once we do reach that point, we will not be adequately prepared to deal with the aftermath of it in an ethical and optimal way, which will lead to undesired and unexpected consequences. This paper addresses this issue by discussing what ethical AI principles and guidelines can be used to address highly advanced LLMs. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.10745v2-abstract-full').style.display = 'none'; document.getElementById('2401.10745v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 19 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">5 pages, accepted to workshop on Responsible Language Models (ReLM) at Association of the Advancement of Artificial Intelligence Conference (AAAI 2024)</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 68Txx <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2; K.4.1; K.5.2; K.6.5; K.4.2 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2401.05579">arXiv:2401.05579</a> <span> [<a href="https://arxiv.org/pdf/2401.05579">pdf</a>, <a href="https://arxiv.org/format/2401.05579">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> An Augmented Surprise-guided Sequential Learning Framework for Predicting the Melt Pool Geometry </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Raihan%2C+A+S">Ahmed Shoyeb Raihan</a>, <a href="/search/cs?searchtype=author&query=Khosravi%2C+H">Hamed Khosravi</a>, <a href="/search/cs?searchtype=author&query=Bhuiyan%2C+T+H">Tanveer Hossain Bhuiyan</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+I">Imtiaz Ahmed</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2401.05579v1-abstract-short" style="display: inline;"> Metal Additive Manufacturing (MAM) has reshaped the manufacturing industry, offering benefits like intricate design, minimal waste, rapid prototyping, material versatility, and customized solutions. However, its full industry adoption faces hurdles, particularly in achieving consistent product quality. A crucial aspect for MAM's success is understanding the relationship between process parameters… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.05579v1-abstract-full').style.display = 'inline'; document.getElementById('2401.05579v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.05579v1-abstract-full" style="display: none;"> Metal Additive Manufacturing (MAM) has reshaped the manufacturing industry, offering benefits like intricate design, minimal waste, rapid prototyping, material versatility, and customized solutions. However, its full industry adoption faces hurdles, particularly in achieving consistent product quality. A crucial aspect for MAM's success is understanding the relationship between process parameters and melt pool characteristics. Integrating Artificial Intelligence (AI) into MAM is essential. Traditional machine learning (ML) methods, while effective, depend on large datasets to capture complex relationships, a significant challenge in MAM due to the extensive time and resources required for dataset creation. Our study introduces a novel surprise-guided sequential learning framework, SurpriseAF-BO, signaling a significant shift in MAM. This framework uses an iterative, adaptive learning process, modeling the dynamics between process parameters and melt pool characteristics with limited data, a key benefit in MAM's cyber manufacturing context. Compared to traditional ML models, our sequential learning method shows enhanced predictive accuracy for melt pool dimensions. Further improving our approach, we integrated a Conditional Tabular Generative Adversarial Network (CTGAN) into our framework, forming the CT-SurpriseAF-BO. This produces synthetic data resembling real experimental data, improving learning effectiveness. This enhancement boosts predictive precision without requiring additional physical experiments. Our study demonstrates the power of advanced data-driven techniques in cyber manufacturing and the substantial impact of sequential AI and ML, particularly in overcoming MAM's traditional challenges. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.05579v1-abstract-full').style.display = 'none'; document.getElementById('2401.05579v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2401.01265">arXiv:2401.01265</a> <span> [<a href="https://arxiv.org/pdf/2401.01265">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Neural and Evolutionary Computing">cs.NE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Optimal Synthesis of Finite State Machines with Universal Gates using Evolutionary Algorithm </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ullah%2C+N">Noor Ullah</a>, <a href="/search/cs?searchtype=author&query=Yahya%2C+K+M">Khawaja M. Yahya</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+I">Irfan Ahmed</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2401.01265v1-abstract-short" style="display: inline;"> This work presents an optimization method for the synthesis of finite state machines. The focus is on the reduction in the on-chip area and the cost of the circuit. A list of finite state machines from MCNC91 benchmark circuits have been evolved using Cartesian Genetic Programming. On the average, almost 30% of reduction in the total number of gates has been achieved. The effects of some parameter… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.01265v1-abstract-full').style.display = 'inline'; document.getElementById('2401.01265v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.01265v1-abstract-full" style="display: none;"> This work presents an optimization method for the synthesis of finite state machines. The focus is on the reduction in the on-chip area and the cost of the circuit. A list of finite state machines from MCNC91 benchmark circuits have been evolved using Cartesian Genetic Programming. On the average, almost 30% of reduction in the total number of gates has been achieved. The effects of some parameters on the evolutionary process have also been discussed in the paper. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.01265v1-abstract-full').style.display = 'none'; document.getElementById('2401.01265v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.13581">arXiv:2312.13581</a> <span> [<a href="https://arxiv.org/pdf/2312.13581">pdf</a>, <a href="https://arxiv.org/format/2312.13581">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> </div> </div> <p class="title is-5 mathjax"> Understanding the Role of Large Language Models in Personalizing and Scaffolding Strategies to Combat Academic Procrastination </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Bhattacharjee%2C+A">Ananya Bhattacharjee</a>, <a href="/search/cs?searchtype=author&query=Zeng%2C+Y">Yuchen Zeng</a>, <a href="/search/cs?searchtype=author&query=Xu%2C+S+Y">Sarah Yi Xu</a>, <a href="/search/cs?searchtype=author&query=Kulzhabayeva%2C+D">Dana Kulzhabayeva</a>, <a href="/search/cs?searchtype=author&query=Ma%2C+M">Minyi Ma</a>, <a href="/search/cs?searchtype=author&query=Kornfield%2C+R">Rachel Kornfield</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+S+I">Syed Ishtiaque Ahmed</a>, <a href="/search/cs?searchtype=author&query=Mariakakis%2C+A">Alex Mariakakis</a>, <a href="/search/cs?searchtype=author&query=Czerwinski%2C+M+P">Mary P Czerwinski</a>, <a href="/search/cs?searchtype=author&query=Kuzminykh%2C+A">Anastasia Kuzminykh</a>, <a href="/search/cs?searchtype=author&query=Liut%2C+M">Michael Liut</a>, <a href="/search/cs?searchtype=author&query=Williams%2C+J+J">Joseph Jay Williams</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2312.13581v1-abstract-short" style="display: inline;"> Traditional interventions for academic procrastination often fail to capture the nuanced, individual-specific factors that underlie them. Large language models (LLMs) hold immense potential for addressing this gap by permitting open-ended inputs, including the ability to customize interventions to individuals' unique needs. However, user expectations and potential limitations of LLMs in this conte… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.13581v1-abstract-full').style.display = 'inline'; document.getElementById('2312.13581v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.13581v1-abstract-full" style="display: none;"> Traditional interventions for academic procrastination often fail to capture the nuanced, individual-specific factors that underlie them. Large language models (LLMs) hold immense potential for addressing this gap by permitting open-ended inputs, including the ability to customize interventions to individuals' unique needs. However, user expectations and potential limitations of LLMs in this context remain underexplored. To address this, we conducted interviews and focus group discussions with 15 university students and 6 experts, during which a technology probe for generating personalized advice for managing procrastination was presented. Our results highlight the necessity for LLMs to provide structured, deadline-oriented steps and enhanced user support mechanisms. Additionally, our results surface the need for an adaptive approach to questioning based on factors like busyness. These findings offer crucial design implications for the development of LLM-based tools for managing procrastination while cautioning the use of LLMs for therapeutic guidance. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.13581v1-abstract-full').style.display = 'none'; document.getElementById('2312.13581v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.04063">arXiv:2312.04063</a> <span> [<a href="https://arxiv.org/pdf/2312.04063">pdf</a>, <a href="https://arxiv.org/format/2312.04063">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> An unsupervised approach towards promptable defect segmentation in laser-based additive manufacturing by Segment Anything </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Era%2C+I+Z">Israt Zarin Era</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+I">Imtiaz Ahmed</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+Z">Zhichao Liu</a>, <a href="/search/cs?searchtype=author&query=Das%2C+S">Srinjoy Das</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2312.04063v3-abstract-short" style="display: inline;"> Foundation models are currently driving a paradigm shift in computer vision tasks for various fields including biology, astronomy, and robotics among others, leveraging user-generated prompts to enhance their performance. In the Laser Additive Manufacturing (LAM) domain, accurate image-based defect segmentation is imperative to ensure product quality and facilitate real-time process control. Howev… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.04063v3-abstract-full').style.display = 'inline'; document.getElementById('2312.04063v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.04063v3-abstract-full" style="display: none;"> Foundation models are currently driving a paradigm shift in computer vision tasks for various fields including biology, astronomy, and robotics among others, leveraging user-generated prompts to enhance their performance. In the Laser Additive Manufacturing (LAM) domain, accurate image-based defect segmentation is imperative to ensure product quality and facilitate real-time process control. However, such tasks are often characterized by multiple challenges including the absence of labels and the requirement for low latency inference among others. Porosity is a very common defect in LAM due to lack of fusion, entrapped gas, and keyholes, directly affecting mechanical properties like tensile strength, stiffness, and hardness, thereby compromising the quality of the final product. To address these issues, we construct a framework for image segmentation using a state-of-the-art Vision Transformer (ViT) based Foundation model (Segment Anything Model) with a novel multi-point prompt generation scheme using unsupervised clustering. Utilizing our framework we perform porosity segmentation in a case study of laser-based powder bed fusion (L-PBF) and obtain high accuracy without using any labeled data to guide the prompt tuning process. By capitalizing on lightweight foundation model inference combined with unsupervised prompt generation, we envision constructing a real-time anomaly detection pipeline that could revolutionize current laser additive manufacturing processes, thereby facilitating the shift towards Industry 4.0 and promoting defect-free production along with operational efficiency. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.04063v3-abstract-full').style.display = 'none'; document.getElementById('2312.04063v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 7 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">18 pages, 9 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.10926">arXiv:2311.10926</a> <span> [<a href="https://arxiv.org/pdf/2311.10926">pdf</a>, <a href="https://arxiv.org/format/2311.10926">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Software Engineering">cs.SE</span> </div> </div> <p class="title is-5 mathjax"> Finding the Needle in a Haystack: Detecting Bug Occurrences in Gameplay Videos </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Truelove%2C+A">Andrew Truelove</a>, <a href="/search/cs?searchtype=author&query=Rong%2C+S">Shiyue Rong</a>, <a href="/search/cs?searchtype=author&query=de+Almeida%2C+E+S">Eduardo Santana de Almeida</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+I">Iftekhar Ahmed</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2311.10926v1-abstract-short" style="display: inline;"> The presence of bugs in video games can bring significant consequences for developers. To avoid these consequences, developers can leverage gameplay videos to identify and fix these bugs. Video hosting websites such as YouTube provide access to millions of game videos, including videos that depict bug occurrences, but the large amount of content can make finding bug instances challenging. We prese… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.10926v1-abstract-full').style.display = 'inline'; document.getElementById('2311.10926v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.10926v1-abstract-full" style="display: none;"> The presence of bugs in video games can bring significant consequences for developers. To avoid these consequences, developers can leverage gameplay videos to identify and fix these bugs. Video hosting websites such as YouTube provide access to millions of game videos, including videos that depict bug occurrences, but the large amount of content can make finding bug instances challenging. We present an automated approach that uses machine learning to predict whether a segment of a gameplay video contains the depiction of a bug. We analyzed 4,412 segments of 198 gameplay videos to predict whether a segment contains an instance of a bug. Additionally, we investigated how our approach performs when applied across different specific genres of video games and on videos from the same game. We also analyzed the videos in the dataset to investigate what characteristics of the visual features might explain the classifier's prediction. Finally, we conducted a user study to examine the benefits of our automated approach against a manual analysis. Our findings indicate that our approach is effective at detecting segments of a video that contain bugs, achieving a high F1 score of 0.88, outperforming the current state-of-the-art technique for bug classification of gameplay video segments. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.10926v1-abstract-full').style.display = 'none'; document.getElementById('2311.10926v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.09591">arXiv:2311.09591</a> <span> [<a href="https://arxiv.org/pdf/2311.09591">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Materials Science">cond-mat.mtrl-sci</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Accelerating material discovery with a threshold-driven hybrid acquisition policy-based Bayesian optimization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Raihan%2C+A+S">Ahmed Shoyeb Raihan</a>, <a href="/search/cs?searchtype=author&query=Khosravi%2C+H">Hamed Khosravi</a>, <a href="/search/cs?searchtype=author&query=Das%2C+S">Srinjoy Das</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+I">Imtiaz Ahmed</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2311.09591v1-abstract-short" style="display: inline;"> Advancements in materials play a crucial role in technological progress. However, the process of discovering and developing materials with desired properties is often impeded by substantial experimental costs, extensive resource utilization, and lengthy development periods. To address these challenges, modern approaches often employ machine learning (ML) techniques such as Bayesian Optimization (B… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.09591v1-abstract-full').style.display = 'inline'; document.getElementById('2311.09591v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.09591v1-abstract-full" style="display: none;"> Advancements in materials play a crucial role in technological progress. However, the process of discovering and developing materials with desired properties is often impeded by substantial experimental costs, extensive resource utilization, and lengthy development periods. To address these challenges, modern approaches often employ machine learning (ML) techniques such as Bayesian Optimization (BO), which streamline the search for optimal materials by iteratively selecting experiments that are most likely to yield beneficial results. However, traditional BO methods, while beneficial, often struggle with balancing the trade-off between exploration and exploitation, leading to sub-optimal performance in material discovery processes. This paper introduces a novel Threshold-Driven UCB-EI Bayesian Optimization (TDUE-BO) method, which dynamically integrates the strengths of Upper Confidence Bound (UCB) and Expected Improvement (EI) acquisition functions to optimize the material discovery process. Unlike the classical BO, our method focuses on efficiently navigating the high-dimensional material design space (MDS). TDUE-BO begins with an exploration-focused UCB approach, ensuring a comprehensive initial sweep of the MDS. As the model gains confidence, indicated by reduced uncertainty, it transitions to the more exploitative EI method, focusing on promising areas identified earlier. The UCB-to-EI switching policy dictated guided through continuous monitoring of the model uncertainty during each step of sequential sampling results in navigating through the MDS more efficiently while ensuring rapid convergence. The effectiveness of TDUE-BO is demonstrated through its application on three different material datasets, showing significantly better approximation and optimization performance over the EI and UCB-based BO methods in terms of the RMSE scores and convergence efficiency, respectively. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.09591v1-abstract-full').style.display = 'none'; document.getElementById('2311.09591v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.09333">arXiv:2311.09333</a> <span> [<a href="https://arxiv.org/pdf/2311.09333">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Strategic Data Augmentation with CTGAN for Smart Manufacturing: Enhancing Machine Learning Predictions of Paper Breaks in Pulp-and-Paper Production </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Khosravi%2C+H">Hamed Khosravi</a>, <a href="/search/cs?searchtype=author&query=Farhadpour%2C+S">Sarah Farhadpour</a>, <a href="/search/cs?searchtype=author&query=Grandhi%2C+M">Manikanta Grandhi</a>, <a href="/search/cs?searchtype=author&query=Raihan%2C+A+S">Ahmed Shoyeb Raihan</a>, <a href="/search/cs?searchtype=author&query=Das%2C+S">Srinjoy Das</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+I">Imtiaz Ahmed</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2311.09333v1-abstract-short" style="display: inline;"> A significant challenge for predictive maintenance in the pulp-and-paper industry is the infrequency of paper breaks during the production process. In this article, operational data is analyzed from a paper manufacturing machine in which paper breaks are relatively rare but have a high economic impact. Utilizing a dataset comprising 18,398 instances derived from a quality assurance protocol, we ad… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.09333v1-abstract-full').style.display = 'inline'; document.getElementById('2311.09333v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.09333v1-abstract-full" style="display: none;"> A significant challenge for predictive maintenance in the pulp-and-paper industry is the infrequency of paper breaks during the production process. In this article, operational data is analyzed from a paper manufacturing machine in which paper breaks are relatively rare but have a high economic impact. Utilizing a dataset comprising 18,398 instances derived from a quality assurance protocol, we address the scarcity of break events (124 cases) that pose a challenge for machine learning predictive models. With the help of Conditional Generative Adversarial Networks (CTGAN) and Synthetic Minority Oversampling Technique (SMOTE), we implement a novel data augmentation framework. This method ensures that the synthetic data mirrors the distribution of the real operational data but also seeks to enhance the performance metrics of predictive modeling. Before and after the data augmentation, we evaluate three different machine learning algorithms-Decision Trees (DT), Random Forest (RF), and Logistic Regression (LR). Utilizing the CTGAN-enhanced dataset, our study achieved significant improvements in predictive maintenance performance metrics. The efficacy of CTGAN in addressing data scarcity was evident, with the models' detection of machine breaks (Class 1) improving by over 30% for Decision Trees, 20% for Random Forest, and nearly 90% for Logistic Regression. With this methodological advancement, this study contributes to industrial quality control and maintenance scheduling by addressing rare event prediction in manufacturing processes. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.09333v1-abstract-full').style.display = 'none'; document.getElementById('2311.09333v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.14548">arXiv:2310.14548</a> <span> [<a href="https://arxiv.org/pdf/2310.14548">pdf</a>, <a href="https://arxiv.org/format/2310.14548">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Software Engineering">cs.SE</span> </div> </div> <p class="title is-5 mathjax"> Test Smell: A Parasitic Energy Consumer in Software Testing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Misu%2C+M+R+H">Md Rakib Hossain Misu</a>, <a href="/search/cs?searchtype=author&query=Li%2C+J">Jiawei Li</a>, <a href="/search/cs?searchtype=author&query=Bhattiprolu%2C+A">Adithya Bhattiprolu</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+Y">Yang Liu</a>, <a href="/search/cs?searchtype=author&query=Almeida%2C+E">Eduardo Almeida</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+I">Iftekhar Ahmed</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2310.14548v1-abstract-short" style="display: inline;"> Traditionally, energy efficiency research has focused on reducing energy consumption at the hardware level and, more recently, in the design and coding phases of the software development life cycle. However, software testing's impact on energy consumption did not receive attention from the research community. Specifically, how test code design quality and test smell (e.g., sub-optimal design and b… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.14548v1-abstract-full').style.display = 'inline'; document.getElementById('2310.14548v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.14548v1-abstract-full" style="display: none;"> Traditionally, energy efficiency research has focused on reducing energy consumption at the hardware level and, more recently, in the design and coding phases of the software development life cycle. However, software testing's impact on energy consumption did not receive attention from the research community. Specifically, how test code design quality and test smell (e.g., sub-optimal design and bad practices in test code) impact energy consumption has not been investigated yet. This study examined 12 Apache projects to analyze the association between test smell and its effects on energy consumption in software testing. We conducted a mixed-method empirical analysis from two dimensions; software (data mining in Apache projects) and developers' views (a survey of 62 software practitioners). Our findings show that: 1) test smell is associated with energy consumption in software testing. Specifically smelly part of a test case consumes 10.92\% more energy compared to the non-smelly part. 2) certain test smells are more energy-hungry than others, 3) refactored test cases tend to consume less energy than their smelly counterparts, and 4) most developers lack knowledge about test smells' impact on energy consumption. We conclude the paper with several observations that can direct future research and developments. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.14548v1-abstract-full').style.display = 'none'; document.getElementById('2310.14548v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.12425">arXiv:2310.12425</a> <span> [<a href="https://arxiv.org/pdf/2310.12425">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Software Engineering">cs.SE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Automated Repair of Declarative Software Specifications in the Era of Large Language Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Hasan%2C+M+R">Md Rashedul Hasan</a>, <a href="/search/cs?searchtype=author&query=Li%2C+J">Jiawei Li</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+I">Iftekhar Ahmed</a>, <a href="/search/cs?searchtype=author&query=Bagheri%2C+H">Hamid Bagheri</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2310.12425v2-abstract-short" style="display: inline;"> The growing adoption of declarative software specification languages, coupled with their inherent difficulty in debugging, has underscored the need for effective and automated repair techniques applicable to such languages. Researchers have recently explored various methods to automatically repair declarative software specifications, such as template-based repair, feedback-driven iterative repair,… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.12425v2-abstract-full').style.display = 'inline'; document.getElementById('2310.12425v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.12425v2-abstract-full" style="display: none;"> The growing adoption of declarative software specification languages, coupled with their inherent difficulty in debugging, has underscored the need for effective and automated repair techniques applicable to such languages. Researchers have recently explored various methods to automatically repair declarative software specifications, such as template-based repair, feedback-driven iterative repair, and bounded exhaustive approaches. The latest developments in large language models provide new opportunities for the automatic repair of declarative specifications. In this study, we assess the effectiveness of utilizing OpenAI's ChatGPT to repair software specifications written in the Alloy declarative language. Unlike imperative languages, specifications in Alloy are not executed but rather translated into logical formulas and evaluated using backend constraint solvers to identify specification instances and counterexamples to assertions. Our evaluation focuses on ChatGPT's ability to improve the correctness and completeness of Alloy declarative specifications through automatic repairs. We analyze the results produced by ChatGPT and compare them with those of leading automatic Alloy repair methods. Our study revealed that while ChatGPT falls short in comparison to existing techniques, it was able to successfully repair bugs that no other technique could address. Our analysis also identified errors in ChatGPT's generated repairs, including improper operator usage, type errors, higher-order logic misuse, and relational arity mismatches. Additionally, we observed instances of hallucinations in ChatGPT-generated repairs and inconsistency in its results. Our study provides valuable insights for software practitioners, researchers, and tool builders considering ChatGPT for declarative specification repairs. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.12425v2-abstract-full').style.display = 'none'; document.getElementById('2310.12425v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 18 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">13 Pages with reference, 4 Tables, 2 Figures, 2 Listings</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2309.17140">arXiv:2309.17140</a> <span> [<a href="https://arxiv.org/pdf/2309.17140">pdf</a>, <a href="https://arxiv.org/format/2309.17140">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Software Engineering">cs.SE</span> </div> </div> <p class="title is-5 mathjax"> A Snapshot of the Mental Health of Software Professionals </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=de+Almeida%2C+E+S">Eduardo Santana de Almeida</a>, <a href="/search/cs?searchtype=author&query=de+Nunes%2C+I+O">Ingrid Oliveira de Nunes</a>, <a href="/search/cs?searchtype=author&query=de+Oliveira%2C+R+P">Raphael Pereira de Oliveira</a>, <a href="/search/cs?searchtype=author&query=Carvalho%2C+M+L+L">Michelle Larissa Luciano Carvalho</a>, <a href="/search/cs?searchtype=author&query=Brunoni%2C+A+R">Andre Russowsky Brunoni</a>, <a href="/search/cs?searchtype=author&query=Rong%2C+S">Shiyue Rong</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+I">Iftekhar Ahmed</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2309.17140v1-abstract-short" style="display: inline;"> Mental health disorders affect a large number of people, leading to many lives being lost every year. These disorders affect struggling individuals and businesses whose productivity decreases due to days of lost work or lower employee performance. Recent studies provide alarming numbers of individuals who suffer from mental health disorders, e.g., depression and anxiety, in particular contexts, su… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.17140v1-abstract-full').style.display = 'inline'; document.getElementById('2309.17140v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.17140v1-abstract-full" style="display: none;"> Mental health disorders affect a large number of people, leading to many lives being lost every year. These disorders affect struggling individuals and businesses whose productivity decreases due to days of lost work or lower employee performance. Recent studies provide alarming numbers of individuals who suffer from mental health disorders, e.g., depression and anxiety, in particular contexts, such as academia. In the context of the software industry, there are limited studies that aim to understand the presence of mental health disorders and the characteristics of jobs in this context that can be triggers for the deterioration of the mental health of software professionals. In this paper, we present the results of a survey with 500 software professionals. We investigate different aspects of their mental health and the characteristics of their work to identify possible triggers of mental health deterioration. Our results provide the first evidence that mental health is a critical issue to be addressed in the software industry, as well as raise the direction of changes that can be done in this context to improve the mental health of software professionals. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.17140v1-abstract-full').style.display = 'none'; document.getElementById('2309.17140v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">12 pages, 3 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2309.13402">arXiv:2309.13402</a> <span> [<a href="https://arxiv.org/pdf/2309.13402">pdf</a>, <a href="https://arxiv.org/format/2309.13402">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> ML Algorithm Synthesizing Domain Knowledge for Fungal Spores Concentration Prediction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Syed%2C+M+A+B">Md Asif Bin Syed</a>, <a href="/search/cs?searchtype=author&query=Wasi%2C+A+T">Azmine Toushik Wasi</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+I">Imtiaz Ahmed</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2309.13402v1-abstract-short" style="display: inline;"> The pulp and paper manufacturing industry requires precise quality control to ensure pure, contaminant-free end products suitable for various applications. Fungal spore concentration is a crucial metric that affects paper usability, and current testing methods are labor-intensive with delayed results, hindering real-time control strategies. To address this, a machine learning algorithm utilizing t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.13402v1-abstract-full').style.display = 'inline'; document.getElementById('2309.13402v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.13402v1-abstract-full" style="display: none;"> The pulp and paper manufacturing industry requires precise quality control to ensure pure, contaminant-free end products suitable for various applications. Fungal spore concentration is a crucial metric that affects paper usability, and current testing methods are labor-intensive with delayed results, hindering real-time control strategies. To address this, a machine learning algorithm utilizing time-series data and domain knowledge was proposed. The optimal model employed Ridge Regression achieving an MSE of 2.90 on training and validation data. This approach could lead to significant improvements in efficiency and sustainability by providing real-time predictions for fungal spore concentrations. This paper showcases a promising method for real-time fungal spore concentration prediction, enabling stringent quality control measures in the pulp-and-paper industry. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.13402v1-abstract-full').style.display = 'none'; document.getElementById('2309.13402v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2309.01319">arXiv:2309.01319</a> <span> [<a href="https://arxiv.org/pdf/2309.01319">pdf</a>, <a href="https://arxiv.org/format/2309.01319">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> An ML-assisted OTFS vs. OFDM adaptable modem </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ahmed%2C+I+Z">I. Zakir Ahmed</a>, <a href="/search/cs?searchtype=author&query=Sadjadpour%2C+H+R">Hamid R. Sadjadpour</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2309.01319v2-abstract-short" style="display: inline;"> The Orthogonal-Time-Frequency-Space (OTFS) signaling is known to be resilient to doubly-dispersive channels, which impacts high mobility scenarios. On the other hand, the Orthogonal-Frequency-Division-Multiplexing (OFDM) waveforms enjoy the benefits of the reuse of legacy architectures, simplicity of receiver design, and low-complexity detection. Several studies that compare the performance of OFD… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.01319v2-abstract-full').style.display = 'inline'; document.getElementById('2309.01319v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.01319v2-abstract-full" style="display: none;"> The Orthogonal-Time-Frequency-Space (OTFS) signaling is known to be resilient to doubly-dispersive channels, which impacts high mobility scenarios. On the other hand, the Orthogonal-Frequency-Division-Multiplexing (OFDM) waveforms enjoy the benefits of the reuse of legacy architectures, simplicity of receiver design, and low-complexity detection. Several studies that compare the performance of OFDM and OTFS have indicated mixed outcomes due to the plethora of system parameters at play beyond high-mobility conditions. In this work, we exemplify this observation using simulations and propose a deep neural network (DNN)-based adaptation scheme to switch between using either an OTFS or OFDM signal processing chain at the transmitter and receiver for optimal mean-squared-error (MSE) performance. The DNN classifier is trained to switch between the two schemes by observing the channel condition, received SNR, and modulation format. We compare the performance of the OTFS, OFDM, and the proposed switched-waveform scheme. The simulations indicate superior performance with the proposed scheme with a well-trained DNN, thus improving the MSE performance of the communication significantly. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.01319v2-abstract-full').style.display = 'none'; document.getElementById('2309.01319v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 3 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted for publication in IEEE Future Networks World Forum 2023</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2307.06410">arXiv:2307.06410</a> <span> [<a href="https://arxiv.org/pdf/2307.06410">pdf</a>, <a href="https://arxiv.org/format/2307.06410">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Software Engineering">cs.SE</span> </div> </div> <p class="title is-5 mathjax"> Test case quality: an empirical study on belief and evidence </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Lucr%C3%A9dio%2C+D">Daniel Lucr茅dio</a>, <a href="/search/cs?searchtype=author&query=Vincenzi%2C+A+M+R">Auri Marcelo Rizzo Vincenzi</a>, <a href="/search/cs?searchtype=author&query=de+Almeida%2C+E+S">Eduardo Santana de Almeida</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+I">Iftekhar Ahmed</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2307.06410v1-abstract-short" style="display: inline;"> Software testing is a mandatory activity in any serious software development process, as bugs are a reality in software development. This raises the question of quality: good tests are effective in finding bugs, but until a test case actually finds a bug, its effectiveness remains unknown. Therefore, determining what constitutes a good or bad test is necessary. This is not a simple task, and there… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.06410v1-abstract-full').style.display = 'inline'; document.getElementById('2307.06410v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2307.06410v1-abstract-full" style="display: none;"> Software testing is a mandatory activity in any serious software development process, as bugs are a reality in software development. This raises the question of quality: good tests are effective in finding bugs, but until a test case actually finds a bug, its effectiveness remains unknown. Therefore, determining what constitutes a good or bad test is necessary. This is not a simple task, and there are a number of studies that identify different characteristics of a good test case. A previous study evaluated 29 hypotheses regarding what constitutes a good test case, but the findings are based on developers' beliefs, which are subjective and biased. In this paper we investigate eight of these hypotheses, through an extensive empirical study based on open software repositories. Despite our best efforts, we were unable to find evidence that supports these beliefs. This indicates that, although these hypotheses represent good software engineering advice, they do not necessarily mean that they are enough to provide the desired outcome of good testing code. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.06410v1-abstract-full').style.display = 'none'; document.getElementById('2307.06410v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 July, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">12 pages, 1 figure, 3 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2307.02412">arXiv:2307.02412</a> <span> [<a href="https://arxiv.org/pdf/2307.02412">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Android Malware Detection using Machine learning: A Review </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Chowdhury%2C+M+N">Md Naseef-Ur-Rahman Chowdhury</a>, <a href="/search/cs?searchtype=author&query=Haque%2C+A">Ahshanul Haque</a>, <a href="/search/cs?searchtype=author&query=Soliman%2C+H">Hamdy Soliman</a>, <a href="/search/cs?searchtype=author&query=Hossen%2C+M+S">Mohammad Sahinur Hossen</a>, <a href="/search/cs?searchtype=author&query=Fatima%2C+T">Tanjim Fatima</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+I">Imtiaz Ahmed</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2307.02412v1-abstract-short" style="display: inline;"> Malware for Android is becoming increasingly dangerous to the safety of mobile devices and the data they hold. Although machine learning(ML) techniques have been shown to be effective at detecting malware for Android, a comprehensive analysis of the methods used is required. We review the current state of Android malware detection us ing machine learning in this paper. We begin by providing an ove… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.02412v1-abstract-full').style.display = 'inline'; document.getElementById('2307.02412v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2307.02412v1-abstract-full" style="display: none;"> Malware for Android is becoming increasingly dangerous to the safety of mobile devices and the data they hold. Although machine learning(ML) techniques have been shown to be effective at detecting malware for Android, a comprehensive analysis of the methods used is required. We review the current state of Android malware detection us ing machine learning in this paper. We begin by providing an overview of Android malware and the security issues it causes. Then, we look at the various supervised, unsupervised, and deep learning machine learning approaches that have been utilized for Android malware detection. Addi tionally, we present a comparison of the performance of various Android malware detection methods and talk about the performance evaluation metrics that are utilized to evaluate their efficacy. Finally, we draw atten tion to the drawbacks and difficulties of the methods that are currently in use and suggest possible future directions for research in this area. In addition to providing insights into the current state of Android malware detection using machine learning, our review provides a comprehensive overview of the subject. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.02412v1-abstract-full').style.display = 'none'; document.getElementById('2307.02412v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">22 pages,2 figures, IntelliSys 2023</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2305.12543">arXiv:2305.12543</a> <span> [<a href="https://arxiv.org/pdf/2305.12543">pdf</a>, <a href="https://arxiv.org/format/2305.12543">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> A Reinforcement Learning Approach for Robust Supervisory Control of UAVs Under Disturbances </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ahmed%2C+I">Ibrahim Ahmed</a>, <a href="/search/cs?searchtype=author&query=Quinones-Grueiro%2C+M">Marcos Quinones-Grueiro</a>, <a href="/search/cs?searchtype=author&query=Biswas%2C+G">Gautam Biswas</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2305.12543v1-abstract-short" style="display: inline;"> In this work, we present an approach to supervisory reinforcement learning control for unmanned aerial vehicles (UAVs). UAVs are dynamic systems where control decisions in response to disturbances in the environment have to be made in the order of milliseconds. We formulate a supervisory control architecture that interleaves with extant embedded control and demonstrates robustness to environmental… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.12543v1-abstract-full').style.display = 'inline'; document.getElementById('2305.12543v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.12543v1-abstract-full" style="display: none;"> In this work, we present an approach to supervisory reinforcement learning control for unmanned aerial vehicles (UAVs). UAVs are dynamic systems where control decisions in response to disturbances in the environment have to be made in the order of milliseconds. We formulate a supervisory control architecture that interleaves with extant embedded control and demonstrates robustness to environmental disturbances in the form of adverse wind conditions. We run case studies with a Tarot T-18 Octorotor to demonstrate the effectiveness of our approach and compare it against a classic cascade control architecture used in most vehicles. While the results show the performance difference is marginal for nominal operations, substantial performance improvement is obtained with the supervisory RL approach under unseen wind conditions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.12543v1-abstract-full').style.display = 'none'; document.getElementById('2305.12543v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">In review (2023-05-16)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2305.12158">arXiv:2305.12158</a> <span> [<a href="https://arxiv.org/pdf/2305.12158">pdf</a>, <a href="https://arxiv.org/format/2305.12158">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Model-based adaptation for sample efficient transfer in reinforcement learning control of parameter-varying systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ahmed%2C+I">Ibrahim Ahmed</a>, <a href="/search/cs?searchtype=author&query=Quinones-Grueiro%2C+M">Marcos Quinones-Grueiro</a>, <a href="/search/cs?searchtype=author&query=Biswas%2C+G">Gautam Biswas</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2305.12158v1-abstract-short" style="display: inline;"> In this paper, we leverage ideas from model-based control to address the sample efficiency problem of reinforcement learning (RL) algorithms. Accelerating learning is an active field of RL highly relevant in the context of time-varying systems. Traditional transfer learning methods propose to use prior knowledge of the system behavior to devise a gradual or immediate data-driven transformation of… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.12158v1-abstract-full').style.display = 'inline'; document.getElementById('2305.12158v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.12158v1-abstract-full" style="display: none;"> In this paper, we leverage ideas from model-based control to address the sample efficiency problem of reinforcement learning (RL) algorithms. Accelerating learning is an active field of RL highly relevant in the context of time-varying systems. Traditional transfer learning methods propose to use prior knowledge of the system behavior to devise a gradual or immediate data-driven transformation of the control policy obtained through RL. Such transformation is usually computed by estimating the performance of previous control policies based on measurements recently collected from the system. However, such retrospective measures have debatable utility with no guarantees of positive transfer in most cases. Instead, we propose a model-based transformation, such that when actions from a control policy are applied to the target system, a positive transfer is achieved. The transformation can be used as an initialization for the reinforcement learning process to converge to a new optimum. We validate the performance of our approach through four benchmark examples. We demonstrate that our approach is more sample-efficient than fine-tuning with reinforcement learning alone and achieves comparable performance to linear-quadratic-regulators and model-predictive control when an accurate linear model is known in the three cases. If an accurate model is not known, we empirically show that the proposed approach still guarantees positive transfer with jump-start improvement. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.12158v1-abstract-full').style.display = 'none'; document.getElementById('2305.12158v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Published to IEEE CoDiT 2023</span> </p> </li> </ol> <nav class="pagination is-small is-centered breathe-horizontal" role="navigation" aria-label="pagination"> <a href="" class="pagination-previous is-invisible">Previous </a> <a 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