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is-small is-link">Go</button> </div> </div> </form> </div> </div> <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/2411.10879">arXiv:2411.10879</a> <span> [<a href="https://arxiv.org/pdf/2411.10879">pdf</a>, <a href="https://arxiv.org/format/2411.10879">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="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</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"> BanglaDialecto: An End-to-End AI-Powered Regional Speech Standardization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Samin%2C+M+N+S">Md. Nazmus Sadat Samin</a>, <a href="/search/cs?searchtype=author&query=Ahad%2C+J+I">Jawad Ibn Ahad</a>, <a href="/search/cs?searchtype=author&query=Medha%2C+T+A">Tanjila Ahmed Medha</a>, <a href="/search/cs?searchtype=author&query=Rahman%2C+F">Fuad Rahman</a>, <a href="/search/cs?searchtype=author&query=Amin%2C+M+R">Mohammad Ruhul Amin</a>, <a href="/search/cs?searchtype=author&query=Mohammed%2C+N">Nabeel Mohammed</a>, <a href="/search/cs?searchtype=author&query=Rahman%2C+S">Shafin Rahman</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="2411.10879v1-abstract-short" style="display: inline;"> This study focuses on recognizing Bangladeshi dialects and converting diverse Bengali accents into standardized formal Bengali speech. Dialects, often referred to as regional languages, are distinctive variations of a language spoken in a particular location and are identified by their phonetics, pronunciations, and lexicon. Subtle changes in pronunciation and intonation are also influenced by geo… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10879v1-abstract-full').style.display = 'inline'; document.getElementById('2411.10879v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.10879v1-abstract-full" style="display: none;"> This study focuses on recognizing Bangladeshi dialects and converting diverse Bengali accents into standardized formal Bengali speech. Dialects, often referred to as regional languages, are distinctive variations of a language spoken in a particular location and are identified by their phonetics, pronunciations, and lexicon. Subtle changes in pronunciation and intonation are also influenced by geographic location, educational attainment, and socioeconomic status. Dialect standardization is needed to ensure effective communication, educational consistency, access to technology, economic opportunities, and the preservation of linguistic resources while respecting cultural diversity. Being the fifth most spoken language with around 55 distinct dialects spoken by 160 million people, addressing Bangla dialects is crucial for developing inclusive communication tools. However, limited research exists due to a lack of comprehensive datasets and the challenges of handling diverse dialects. With the advancement in multilingual Large Language Models (mLLMs), emerging possibilities have been created to address the challenges of dialectal Automated Speech Recognition (ASR) and Machine Translation (MT). This study presents an end-to-end pipeline for converting dialectal Noakhali speech to standard Bangla speech. This investigation includes constructing a large-scale diverse dataset with dialectal speech signals that tailored the fine-tuning process in ASR and LLM for transcribing the dialect speech to dialect text and translating the dialect text to standard Bangla text. Our experiments demonstrated that fine-tuning the Whisper ASR model achieved a CER of 0.8% and WER of 1.5%, while the BanglaT5 model attained a BLEU score of 41.6% for dialect-to-standard text translation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10879v1-abstract-full').style.display = 'none'; document.getElementById('2411.10879v1-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> <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 2024 IEEE International Conference on Big Data (IEEE BigData)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.10878">arXiv:2411.10878</a> <span> [<a href="https://arxiv.org/pdf/2411.10878">pdf</a>, <a href="https://arxiv.org/format/2411.10878">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="Information Retrieval">cs.IR</span> </div> </div> <p class="title is-5 mathjax"> Empowering Meta-Analysis: Leveraging Large Language Models for Scientific Synthesis </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ahad%2C+J+I">Jawad Ibn Ahad</a>, <a href="/search/cs?searchtype=author&query=Sultan%2C+R+M">Rafeed Mohammad Sultan</a>, <a href="/search/cs?searchtype=author&query=Kaikobad%2C+A">Abraham Kaikobad</a>, <a href="/search/cs?searchtype=author&query=Rahman%2C+F">Fuad Rahman</a>, <a href="/search/cs?searchtype=author&query=Amin%2C+M+R">Mohammad Ruhul Amin</a>, <a href="/search/cs?searchtype=author&query=Mohammed%2C+N">Nabeel Mohammed</a>, <a href="/search/cs?searchtype=author&query=Rahman%2C+S">Shafin Rahman</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="2411.10878v1-abstract-short" style="display: inline;"> This study investigates the automation of meta-analysis in scientific documents using large language models (LLMs). Meta-analysis is a robust statistical method that synthesizes the findings of multiple studies support articles to provide a comprehensive understanding. We know that a meta-article provides a structured analysis of several articles. However, conducting meta-analysis by hand is labor… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10878v1-abstract-full').style.display = 'inline'; document.getElementById('2411.10878v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.10878v1-abstract-full" style="display: none;"> This study investigates the automation of meta-analysis in scientific documents using large language models (LLMs). Meta-analysis is a robust statistical method that synthesizes the findings of multiple studies support articles to provide a comprehensive understanding. We know that a meta-article provides a structured analysis of several articles. However, conducting meta-analysis by hand is labor-intensive, time-consuming, and susceptible to human error, highlighting the need for automated pipelines to streamline the process. Our research introduces a novel approach that fine-tunes the LLM on extensive scientific datasets to address challenges in big data handling and structured data extraction. We automate and optimize the meta-analysis process by integrating Retrieval Augmented Generation (RAG). Tailored through prompt engineering and a new loss metric, Inverse Cosine Distance (ICD), designed for fine-tuning on large contextual datasets, LLMs efficiently generate structured meta-analysis content. Human evaluation then assesses relevance and provides information on model performance in key metrics. This research demonstrates that fine-tuned models outperform non-fine-tuned models, with fine-tuned LLMs generating 87.6% relevant meta-analysis abstracts. The relevance of the context, based on human evaluation, shows a reduction in irrelevancy from 4.56% to 1.9%. These experiments were conducted in a low-resource environment, highlighting the study's contribution to enhancing the efficiency and reliability of meta-analysis automation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10878v1-abstract-full').style.display = 'none'; document.getElementById('2411.10878v1-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> <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 2024 IEEE International Conference on Big Data (IEEE BigData)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.13173">arXiv:2408.13173</a> <span> [<a href="https://arxiv.org/pdf/2408.13173">pdf</a>, <a href="https://arxiv.org/format/2408.13173">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 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/10.1145/3672539.3686749">10.1145/3672539.3686749 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Demonstration of Wheeler: A Three-Wheeled Input Device for Usable, Efficient, and Versatile Non-Visual Interaction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Islam%2C+M+T">Md Touhidul Islam</a>, <a href="/search/cs?searchtype=author&query=Sojib%2C+N">Noushad Sojib</a>, <a href="/search/cs?searchtype=author&query=Kabir%2C+I">Imran Kabir</a>, <a href="/search/cs?searchtype=author&query=Amit%2C+A+R">Ashiqur Rahman Amit</a>, <a href="/search/cs?searchtype=author&query=Amin%2C+M+R">Mohammad Ruhul Amin</a>, <a href="/search/cs?searchtype=author&query=Billah%2C+S+M">Syed Masum Billah</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="2408.13173v1-abstract-short" style="display: inline;"> Navigating multi-level menus with complex hierarchies remains a big challenge for blind and low-vision users, who predominantly use screen readers to interact with computers. To that end, we demonstrate Wheeler, a three-wheeled input device with two side buttons that can speed up complex multi-level hierarchy navigation in common applications. When in operation, the three wheels of Wheeler are eac… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.13173v1-abstract-full').style.display = 'inline'; document.getElementById('2408.13173v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.13173v1-abstract-full" style="display: none;"> Navigating multi-level menus with complex hierarchies remains a big challenge for blind and low-vision users, who predominantly use screen readers to interact with computers. To that end, we demonstrate Wheeler, a three-wheeled input device with two side buttons that can speed up complex multi-level hierarchy navigation in common applications. When in operation, the three wheels of Wheeler are each mapped to a different level in the application hierarchy. Each level can be independently traversed using its designated wheel, allowing users to navigate through multiple levels efficiently. Wheeler's three wheels can also be repurposed for other tasks such as 2D cursor manipulation. In this demonstration, we describe the different operation modes and usage of Wheeler. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.13173v1-abstract-full').style.display = 'none'; document.getElementById('2408.13173v1-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 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 at UIST'24 Adjunct (Oct 13-16, 2024, Pittsburgh, PA, USA)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.13166">arXiv:2408.13166</a> <span> [<a href="https://arxiv.org/pdf/2408.13166">pdf</a>, <a href="https://arxiv.org/format/2408.13166">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 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/10.1145/3654777.3676396">10.1145/3654777.3676396 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Wheeler: A Three-Wheeled Input Device for Usable, Efficient, and Versatile Non-Visual Interaction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Islam%2C+M+T">Md Touhidul Islam</a>, <a href="/search/cs?searchtype=author&query=Sojib%2C+N">Noushad Sojib</a>, <a href="/search/cs?searchtype=author&query=Kabir%2C+I">Imran Kabir</a>, <a href="/search/cs?searchtype=author&query=Amit%2C+A+R">Ashiqur Rahman Amit</a>, <a href="/search/cs?searchtype=author&query=Amin%2C+M+R">Mohammad Ruhul Amin</a>, <a href="/search/cs?searchtype=author&query=Billah%2C+S+M">Syed Masum Billah</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="2408.13166v1-abstract-short" style="display: inline;"> Blind users rely on keyboards and assistive technologies like screen readers to interact with user interface (UI) elements. In modern applications with complex UI hierarchies, navigating to different UI elements poses a significant accessibility challenge. Users must listen to screen reader audio descriptions and press relevant keyboard keys one at a time. This paper introduces Wheeler, a novel th… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.13166v1-abstract-full').style.display = 'inline'; document.getElementById('2408.13166v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.13166v1-abstract-full" style="display: none;"> Blind users rely on keyboards and assistive technologies like screen readers to interact with user interface (UI) elements. In modern applications with complex UI hierarchies, navigating to different UI elements poses a significant accessibility challenge. Users must listen to screen reader audio descriptions and press relevant keyboard keys one at a time. This paper introduces Wheeler, a novel three-wheeled, mouse-shaped stationary input device, to address this issue. Informed by participatory sessions, Wheeler enables blind users to navigate up to three hierarchical levels in an app independently using three wheels instead of navigating just one level at a time using a keyboard. The three wheels also offer versatility, allowing users to repurpose them for other tasks, such as 2D cursor manipulation. A study with 12 blind users indicates a significant reduction (40%) in navigation time compared to using a keyboard. Further, a diary study with our blind co-author highlights Wheeler's additional benefits, such as accessing UI elements with partial metadata and facilitating mixed-ability collaboration. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.13166v1-abstract-full').style.display = 'none'; document.getElementById('2408.13166v1-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 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">Paper accepted at UIST'24 (Oct 13-16, Pittsburgh, PA, USA)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.11879">arXiv:2408.11879</a> <span> [<a href="https://arxiv.org/pdf/2408.11879">pdf</a>, <a href="https://arxiv.org/format/2408.11879">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="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Beyond Labels: Aligning Large Language Models with Human-like Reasoning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kabir%2C+M+R">Muhammad Rafsan Kabir</a>, <a href="/search/cs?searchtype=author&query=Sultan%2C+R+M">Rafeed Mohammad Sultan</a>, <a href="/search/cs?searchtype=author&query=Asif%2C+I+H">Ihsanul Haque Asif</a>, <a href="/search/cs?searchtype=author&query=Ahad%2C+J+I">Jawad Ibn Ahad</a>, <a href="/search/cs?searchtype=author&query=Rahman%2C+F">Fuad Rahman</a>, <a href="/search/cs?searchtype=author&query=Amin%2C+M+R">Mohammad Ruhul Amin</a>, <a href="/search/cs?searchtype=author&query=Mohammed%2C+N">Nabeel Mohammed</a>, <a href="/search/cs?searchtype=author&query=Rahman%2C+S">Shafin Rahman</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="2408.11879v1-abstract-short" style="display: inline;"> Aligning large language models (LLMs) with a human reasoning approach ensures that LLMs produce morally correct and human-like decisions. Ethical concerns are raised because current models are prone to generating false positives and providing malicious responses. To contribute to this issue, we have curated an ethics dataset named Dataset for Aligning Reasons (DFAR), designed to aid in aligning la… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.11879v1-abstract-full').style.display = 'inline'; document.getElementById('2408.11879v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.11879v1-abstract-full" style="display: none;"> Aligning large language models (LLMs) with a human reasoning approach ensures that LLMs produce morally correct and human-like decisions. Ethical concerns are raised because current models are prone to generating false positives and providing malicious responses. To contribute to this issue, we have curated an ethics dataset named Dataset for Aligning Reasons (DFAR), designed to aid in aligning language models to generate human-like reasons. The dataset comprises statements with ethical-unethical labels and their corresponding reasons. In this study, we employed a unique and novel fine-tuning approach that utilizes ethics labels and their corresponding reasons (L+R), in contrast to the existing fine-tuning approach that only uses labels (L). The original pre-trained versions, the existing fine-tuned versions, and our proposed fine-tuned versions of LLMs were then evaluated on an ethical-unethical classification task and a reason-generation task. Our proposed fine-tuning strategy notably outperforms the others in both tasks, achieving significantly higher accuracy scores in the classification task and lower misalignment rates in the reason-generation task. The increase in classification accuracies and decrease in misalignment rates indicate that the L+R fine-tuned models align more with human ethics. Hence, this study illustrates that injecting reasons has substantially improved the alignment of LLMs, resulting in more human-like responses. We have made the DFAR dataset and corresponding codes publicly available at https://github.com/apurba-nsu-rnd-lab/DFAR. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.11879v1-abstract-full').style.display = 'none'; document.getElementById('2408.11879v1-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 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 ICPR 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/2401.16638">arXiv:2401.16638</a> <span> [<a href="https://arxiv.org/pdf/2401.16638">pdf</a>, <a href="https://arxiv.org/format/2401.16638">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> </div> </div> <p class="title is-5 mathjax"> Breaking Free Transformer Models: Task-specific Context Attribution Promises Improved Generalizability Without Fine-tuning Pre-trained LLMs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Tytarenko%2C+S">Stepan Tytarenko</a>, <a href="/search/cs?searchtype=author&query=Amin%2C+M+R">Mohammad Ruhul Amin</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="2401.16638v1-abstract-short" style="display: inline;"> Fine-tuning large pre-trained language models (LLMs) on particular datasets is a commonly employed strategy in Natural Language Processing (NLP) classification tasks. However, this approach usually results in a loss of models generalizability. In this paper, we present a framework that allows for maintaining generalizability, and enhances the performance on the downstream task by utilizing task-sp… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.16638v1-abstract-full').style.display = 'inline'; document.getElementById('2401.16638v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.16638v1-abstract-full" style="display: none;"> Fine-tuning large pre-trained language models (LLMs) on particular datasets is a commonly employed strategy in Natural Language Processing (NLP) classification tasks. However, this approach usually results in a loss of models generalizability. In this paper, we present a framework that allows for maintaining generalizability, and enhances the performance on the downstream task by utilizing task-specific context attribution. We show that a linear transformation of the text representation from any transformer model using the task-specific concept operator results in a projection onto the latent concept space, referred to as context attribution in this paper. The specific concept operator is optimized during the supervised learning stage via novel loss functions. The proposed framework demonstrates that context attribution of the text representation for each task objective can improve the capacity of the discriminator function and thus achieve better performance for the classification task. Experimental results on three datasets, namely HateXplain, IMDB reviews, and Social Media Attributions, illustrate that the proposed model attains superior accuracy and generalizability. Specifically, for the non-fine-tuned BERT on the HateXplain dataset, we observe 8% improvement in accuracy and 10% improvement in F1-score. Whereas for the IMDB dataset, fine-tuned state-of-the-art XLNet is outperformed by 1% for both accuracy and F1-score. Furthermore, in an out-of-domain cross-dataset test, DistilBERT fine-tuned on the IMDB dataset in conjunction with the proposed model improves the F1-score on the HateXplain dataset by 7%. For the Social Media Attributions dataset of YouTube comments, we observe 5.2% increase in F1-metric. The proposed framework is implemented with PyTorch and provided open-source on GitHub. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.16638v1-abstract-full').style.display = 'none'; document.getElementById('2401.16638v1-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 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">8 pages, 3 figures, 5 tables, To be published in 2024 AAAI workshop on Responsible Language Models (ReLM)</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2.7; I.2.4 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.03078">arXiv:2311.03078</a> <span> [<a href="https://arxiv.org/pdf/2311.03078">pdf</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> </div> </div> <p class="title is-5 mathjax"> BanLemma: A Word Formation Dependent Rule and Dictionary Based Bangla Lemmatizer </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Afrin%2C+S">Sadia Afrin</a>, <a href="/search/cs?searchtype=author&query=Chowdhury%2C+M+S+M">Md. Shahad Mahmud Chowdhury</a>, <a href="/search/cs?searchtype=author&query=Islam%2C+M+E">Md. Ekramul Islam</a>, <a href="/search/cs?searchtype=author&query=Khan%2C+F+A">Faisal Ahamed Khan</a>, <a href="/search/cs?searchtype=author&query=Chowdhury%2C+L+I">Labib Imam Chowdhury</a>, <a href="/search/cs?searchtype=author&query=Mahtab%2C+M+M">MD. Motahar Mahtab</a>, <a href="/search/cs?searchtype=author&query=Chowdhury%2C+N+N">Nazifa Nuha Chowdhury</a>, <a href="/search/cs?searchtype=author&query=Forkan%2C+M">Massud Forkan</a>, <a href="/search/cs?searchtype=author&query=Kundu%2C+N">Neelima Kundu</a>, <a href="/search/cs?searchtype=author&query=Arif%2C+H">Hakim Arif</a>, <a href="/search/cs?searchtype=author&query=Rashid%2C+M+M+O">Mohammad Mamun Or Rashid</a>, <a href="/search/cs?searchtype=author&query=Amin%2C+M+R">Mohammad Ruhul Amin</a>, <a href="/search/cs?searchtype=author&query=Mohammed%2C+N">Nabeel Mohammed</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="2311.03078v1-abstract-short" style="display: inline;"> Lemmatization holds significance in both natural language processing (NLP) and linguistics, as it effectively decreases data density and aids in comprehending contextual meaning. However, due to the highly inflected nature and morphological richness, lemmatization in Bangla text poses a complex challenge. In this study, we propose linguistic rules for lemmatization and utilize a dictionary along w… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.03078v1-abstract-full').style.display = 'inline'; document.getElementById('2311.03078v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.03078v1-abstract-full" style="display: none;"> Lemmatization holds significance in both natural language processing (NLP) and linguistics, as it effectively decreases data density and aids in comprehending contextual meaning. However, due to the highly inflected nature and morphological richness, lemmatization in Bangla text poses a complex challenge. In this study, we propose linguistic rules for lemmatization and utilize a dictionary along with the rules to design a lemmatizer specifically for Bangla. Our system aims to lemmatize words based on their parts of speech class within a given sentence. Unlike previous rule-based approaches, we analyzed the suffix marker occurrence according to the morpho-syntactic values and then utilized sequences of suffix markers instead of entire suffixes. To develop our rules, we analyze a large corpus of Bangla text from various domains, sources, and time periods to observe the word formation of inflected words. The lemmatizer achieves an accuracy of 96.36% when tested against a manually annotated test dataset by trained linguists and demonstrates competitive performance on three previously published Bangla lemmatization datasets. We are making the code and datasets publicly available at https://github.com/eblict-gigatech/BanLemma in order to contribute to the further advancement of Bangla NLP. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.03078v1-abstract-full').style.display = 'none'; document.getElementById('2311.03078v1-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, 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/2306.06147">arXiv:2306.06147</a> <span> [<a href="https://arxiv.org/pdf/2306.06147">pdf</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> </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/10.1145/3580305.3599904">10.1145/3580305.3599904 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> SentiGOLD: A Large Bangla Gold Standard Multi-Domain Sentiment Analysis Dataset and its Evaluation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Islam%2C+M+E">Md. Ekramul Islam</a>, <a href="/search/cs?searchtype=author&query=Chowdhury%2C+L">Labib Chowdhury</a>, <a href="/search/cs?searchtype=author&query=Khan%2C+F+A">Faisal Ahamed Khan</a>, <a href="/search/cs?searchtype=author&query=Hossain%2C+S">Shazzad Hossain</a>, <a href="/search/cs?searchtype=author&query=Hossain%2C+S">Sourave Hossain</a>, <a href="/search/cs?searchtype=author&query=Rashid%2C+M+M+O">Mohammad Mamun Or Rashid</a>, <a href="/search/cs?searchtype=author&query=Mohammed%2C+N">Nabeel Mohammed</a>, <a href="/search/cs?searchtype=author&query=Amin%2C+M+R">Mohammad Ruhul Amin</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="2306.06147v1-abstract-short" style="display: inline;"> This study introduces SentiGOLD, a Bangla multi-domain sentiment analysis dataset. Comprising 70,000 samples, it was created from diverse sources and annotated by a gender-balanced team of linguists. SentiGOLD adheres to established linguistic conventions agreed upon by the Government of Bangladesh and a Bangla linguistics committee. Unlike English and other languages, Bangla lacks standard sentim… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.06147v1-abstract-full').style.display = 'inline'; document.getElementById('2306.06147v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.06147v1-abstract-full" style="display: none;"> This study introduces SentiGOLD, a Bangla multi-domain sentiment analysis dataset. Comprising 70,000 samples, it was created from diverse sources and annotated by a gender-balanced team of linguists. SentiGOLD adheres to established linguistic conventions agreed upon by the Government of Bangladesh and a Bangla linguistics committee. Unlike English and other languages, Bangla lacks standard sentiment analysis datasets due to the absence of a national linguistics framework. The dataset incorporates data from online video comments, social media posts, blogs, news, and other sources while maintaining domain and class distribution rigorously. It spans 30 domains (e.g., politics, entertainment, sports) and includes 5 sentiment classes (strongly negative, weakly negative, neutral, and strongly positive). The annotation scheme, approved by the national linguistics committee, ensures a robust Inter Annotator Agreement (IAA) with a Fleiss' kappa score of 0.88. Intra- and cross-dataset evaluation protocols are applied to establish a standard classification system. Cross-dataset evaluation on the noisy SentNoB dataset presents a challenging test scenario. Additionally, zero-shot experiments demonstrate the generalizability of SentiGOLD. The top model achieves a macro f1 score of 0.62 (intra-dataset) across 5 classes, setting a benchmark, and 0.61 (cross-dataset from SentNoB) across 3 classes, comparable to the state-of-the-art. Fine-tuned sentiment analysis model can be accessed at https://sentiment.bangla.gov.bd. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.06147v1-abstract-full').style.display = 'none'; document.getElementById('2306.06147v1-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 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 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 in KDD 2023 Applied Data Science Track; 12 pages, 14 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/2305.10698">arXiv:2305.10698</a> <span> [<a href="https://arxiv.org/pdf/2305.10698">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Retrieval">cs.IR</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="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Ranking the locations and predicting future crime occurrence by retrieving news from different Bangla online newspapers </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Hossain%2C+J">Jumman Hossain</a>, <a href="/search/cs?searchtype=author&query=Das%2C+R+C">Rajib Chandra Das</a>, <a href="/search/cs?searchtype=author&query=Amin%2C+M+R">Md. Ruhul Amin</a>, <a href="/search/cs?searchtype=author&query=Islam%2C+M+S">Md. Saiful 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="2305.10698v1-abstract-short" style="display: inline;"> There have thousands of crimes are happening daily all around. But people keep statistics only few of them, therefore crime rates are increasing day by day. The reason behind can be less concern or less statistics of previous crimes. It is much more important to observe the previous crime statistics for general people to make their outing decision and police for catching the criminals are taking s… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.10698v1-abstract-full').style.display = 'inline'; document.getElementById('2305.10698v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.10698v1-abstract-full" style="display: none;"> There have thousands of crimes are happening daily all around. But people keep statistics only few of them, therefore crime rates are increasing day by day. The reason behind can be less concern or less statistics of previous crimes. It is much more important to observe the previous crime statistics for general people to make their outing decision and police for catching the criminals are taking steps to restrain the crimes and tourists to make their travelling decision. National institute of justice releases crime survey data for the country, but does not offer crime statistics up to Union or Thana level. Considering all of these cases we have come up with an approach which can give an approximation to people about the safety of a specific location with crime ranking of different areas locating the crimes on a map including a future crime occurrence prediction mechanism. Our approach relies on different online Bangla newspapers for crawling the crime data, stemming and keyword extraction, location finding algorithm, cosine similarity, naive Bayes classifier, and a custom crime prediction model <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.10698v1-abstract-full').style.display = 'none'; document.getElementById('2305.10698v1-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 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">9 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/2210.07286">arXiv:2210.07286</a> <span> [<a href="https://arxiv.org/pdf/2210.07286">pdf</a>, <a href="https://arxiv.org/format/2210.07286">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"> Augmenting Online Classes with an Attention Tracking Tool May Improve Student Engagement </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sharma%2C+A+S">Arnab Sen Sharma</a>, <a href="/search/cs?searchtype=author&query=Amin%2C+M+R">Mohammad Ruhul Amin</a>, <a href="/search/cs?searchtype=author&query=Fuad%2C+M">Muztaba Fuad</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="2210.07286v1-abstract-short" style="display: inline;"> Online remote learning has certain advantages, such as higher flexibility and greater inclusiveness. However, a caveat is the teachers' limited ability to monitor student interaction during an online class, especially while teachers are sharing their screens. We have taken feedback from 12 teachers experienced in teaching undergraduate-level online classes on the necessity of an attention tracking… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.07286v1-abstract-full').style.display = 'inline'; document.getElementById('2210.07286v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2210.07286v1-abstract-full" style="display: none;"> Online remote learning has certain advantages, such as higher flexibility and greater inclusiveness. However, a caveat is the teachers' limited ability to monitor student interaction during an online class, especially while teachers are sharing their screens. We have taken feedback from 12 teachers experienced in teaching undergraduate-level online classes on the necessity of an attention tracking tool to understand student engagement during an online class. This paper outlines the design of such a monitoring tool that automatically tracks the attentiveness of the whole class by tracking students' gazes on the screen and alerts the teacher when the attention score goes below a certain threshold. We assume the benefits are twofold; 1) teachers will be able to ascertain if the students are attentive or being engaged with the lecture contents and 2) the students will become more attentive in online classes because of this passive monitoring system. In this paper, we present the preliminary design and feasibility of using the proposed tool and discuss its applicability in augmenting online classes. Finally, we surveyed 31 students asking their opinion on the usability as well as the ethical and privacy concerns of using such a monitoring tool. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.07286v1-abstract-full').style.display = 'none'; document.getElementById('2210.07286v1-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> 13 October, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2022. </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, 10 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/2206.00372">arXiv:2206.00372</a> <span> [<a href="https://arxiv.org/pdf/2206.00372">pdf</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> </div> </div> <p class="title is-5 mathjax"> BD-SHS: A Benchmark Dataset for Learning to Detect Online Bangla Hate Speech in Different Social Contexts </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Romim%2C+N">Nauros Romim</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+M">Mosahed Ahmed</a>, <a href="/search/cs?searchtype=author&query=Islam%2C+M+S">Md. Saiful Islam</a>, <a href="/search/cs?searchtype=author&query=Sharma%2C+A+S">Arnab Sen Sharma</a>, <a href="/search/cs?searchtype=author&query=Talukder%2C+H">Hriteshwar Talukder</a>, <a href="/search/cs?searchtype=author&query=Amin%2C+M+R">Mohammad Ruhul Amin</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="2206.00372v1-abstract-short" style="display: inline;"> Social media platforms and online streaming services have spawned a new breed of Hate Speech (HS). Due to the massive amount of user-generated content on these sites, modern machine learning techniques are found to be feasible and cost-effective to tackle this problem. However, linguistically diverse datasets covering different social contexts in which offensive language is typically used are requ… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.00372v1-abstract-full').style.display = 'inline'; document.getElementById('2206.00372v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2206.00372v1-abstract-full" style="display: none;"> Social media platforms and online streaming services have spawned a new breed of Hate Speech (HS). Due to the massive amount of user-generated content on these sites, modern machine learning techniques are found to be feasible and cost-effective to tackle this problem. However, linguistically diverse datasets covering different social contexts in which offensive language is typically used are required to train generalizable models. In this paper, we identify the shortcomings of existing Bangla HS datasets and introduce a large manually labeled dataset BD-SHS that includes HS in different social contexts. The labeling criteria were prepared following a hierarchical annotation process, which is the first of its kind in Bangla HS to the best of our knowledge. The dataset includes more than 50,200 offensive comments crawled from online social networking sites and is at least 60% larger than any existing Bangla HS datasets. We present the benchmark result of our dataset by training different NLP models resulting in the best one achieving an F1-score of 91.0%. In our experiments, we found that a word embedding trained exclusively using 1.47 million comments from social media and streaming sites consistently resulted in better modeling of HS detection in comparison to other pre-trained embeddings. Our dataset and all accompanying codes is publicly available at github.com/naurosromim/hate-speech-dataset-for-Bengali-social-media <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.00372v1-abstract-full').style.display = 'none'; document.getElementById('2206.00372v1-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 June, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2112.04298">arXiv:2112.04298</a> <span> [<a href="https://arxiv.org/pdf/2112.04298">pdf</a>, <a href="https://arxiv.org/format/2112.04298">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> <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"> GCA-Net : Utilizing Gated Context Attention for Improving Image Forgery Localization and Detection </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Das%2C+S">Sowmen Das</a>, <a href="/search/cs?searchtype=author&query=Islam%2C+M+S">Md. Saiful Islam</a>, <a href="/search/cs?searchtype=author&query=Amin%2C+M+R">Md. Ruhul Amin</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="2112.04298v3-abstract-short" style="display: inline;"> Forensic analysis of manipulated pixels requires the identification of various hidden and subtle features from images. Conventional image recognition models generally fail at this task because they are biased and more attentive toward the dominant local and spatial features. In this paper, we propose a novel Gated Context Attention Network (GCA-Net) that utilizes non-local attention in conjunction… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.04298v3-abstract-full').style.display = 'inline'; document.getElementById('2112.04298v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2112.04298v3-abstract-full" style="display: none;"> Forensic analysis of manipulated pixels requires the identification of various hidden and subtle features from images. Conventional image recognition models generally fail at this task because they are biased and more attentive toward the dominant local and spatial features. In this paper, we propose a novel Gated Context Attention Network (GCA-Net) that utilizes non-local attention in conjunction with a gating mechanism in order to capture the finer image discrepancies and better identify forged regions. The proposed framework uses high dimensional embeddings to filter and aggregate the relevant context from coarse feature maps at various stages of the decoding process. This improves the network's understanding of global differences and reduces false-positive localizations. Our evaluation on standard image forensic benchmarks shows that GCA-Net can both compete against and improve over state-of-the-art networks by an average of 4.7% AUC. Additional ablation studies also demonstrate the method's robustness against attributions and resilience to false-positive predictions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.04298v3-abstract-full').style.display = 'none'; document.getElementById('2112.04298v3-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 April, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 8 December, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2021. </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 at the CVPR 2022 Media Forensics Workshop</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2112.01902">arXiv:2112.01902</a> <span> [<a href="https://arxiv.org/pdf/2112.01902">pdf</a>, <a href="https://arxiv.org/format/2112.01902">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> </div> </div> <p class="title is-5 mathjax"> HS-BAN: A Benchmark Dataset of Social Media Comments for Hate Speech Detection in Bangla </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Romim%2C+N">Nauros Romim</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+M">Mosahed Ahmed</a>, <a href="/search/cs?searchtype=author&query=Islam%2C+M+S">Md Saiful Islam</a>, <a href="/search/cs?searchtype=author&query=Sharma%2C+A+S">Arnab Sen Sharma</a>, <a href="/search/cs?searchtype=author&query=Talukder%2C+H">Hriteshwar Talukder</a>, <a href="/search/cs?searchtype=author&query=Amin%2C+M+R">Mohammad Ruhul Amin</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="2112.01902v1-abstract-short" style="display: inline;"> In this paper, we present HS-BAN, a binary class hate speech (HS) dataset in Bangla language consisting of more than 50,000 labeled comments, including 40.17% hate and rest are non hate speech. While preparing the dataset a strict and detailed annotation guideline was followed to reduce human annotation bias. The HS dataset was also preprocessed linguistically to extract different types of slang c… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.01902v1-abstract-full').style.display = 'inline'; document.getElementById('2112.01902v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2112.01902v1-abstract-full" style="display: none;"> In this paper, we present HS-BAN, a binary class hate speech (HS) dataset in Bangla language consisting of more than 50,000 labeled comments, including 40.17% hate and rest are non hate speech. While preparing the dataset a strict and detailed annotation guideline was followed to reduce human annotation bias. The HS dataset was also preprocessed linguistically to extract different types of slang currently people write using symbols, acronyms, or alternative spellings. These slang words were further categorized into traditional and non-traditional slang lists and included in the results of this paper. We explored traditional linguistic features and neural network-based methods to develop a benchmark system for hate speech detection for the Bangla language. Our experimental results show that existing word embedding models trained with informal texts perform better than those trained with formal text. Our benchmark shows that a Bi-LSTM model on top of the FastText informal word embedding achieved 86.78% F1-score. We will make the dataset available for public use. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.01902v1-abstract-full').style.display = 'none'; document.getElementById('2112.01902v1-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 December, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2021. </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 ICON 21 (Rejected)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2110.05906">arXiv:2110.05906</a> <span> [<a href="https://arxiv.org/pdf/2110.05906">pdf</a>, <a href="https://arxiv.org/format/2110.05906">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</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"> Energy-cost aware off-grid base stations with IoT devices for developing a green heterogeneous network </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Islam%2C+K+Z">Khondoker Ziaul Islam</a>, <a href="/search/cs?searchtype=author&query=Hossain%2C+M+S">MD. Sanwar Hossain</a>, <a href="/search/cs?searchtype=author&query=Amin%2C+B+M+R">B. M. Ruhul Amin</a>, <a href="/search/cs?searchtype=author&query=Sohel%2C+F">Ferdous Sohel</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="2110.05906v1-abstract-short" style="display: inline;"> Heterogeneous network (HetNet) is a specified cellular platform to tackle the rapidly growing anticipated data traffic. From communications perspective, data loads can be mapped to energy loads that are generally placed on the operator networks. Meanwhile, renewable energy aided networks offer to curtail fossil fuel consumption, so to reduce environmental pollution. This paper proposes a renewable… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.05906v1-abstract-full').style.display = 'inline'; document.getElementById('2110.05906v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2110.05906v1-abstract-full" style="display: none;"> Heterogeneous network (HetNet) is a specified cellular platform to tackle the rapidly growing anticipated data traffic. From communications perspective, data loads can be mapped to energy loads that are generally placed on the operator networks. Meanwhile, renewable energy aided networks offer to curtail fossil fuel consumption, so to reduce environmental pollution. This paper proposes a renewable energy based power supply architecture for off-grid HetNet using a novel energy sharing model. Solar photovoltaic (PV) along with sufficient energy storage devices are used for each macro, micro, pico, or femto base station (BS). Additionally, biomass generator (BG) is used for macro and micro BSs. The collocated macro and micro BSs are connected through end-to-end resistive lines. A novel weighted proportional-fair resource-scheduling algorithm with sleep mechanisms is proposed for non-real time (NRT) applications by trading-off the power consumption and communication delays. Furthermore, the proposed algorithm with extended discontinuous reception (eDRX) and power saving mode (PSM) for narrowband internet of things (IoT) applications extends battery lifetime for IoT devices. HOMER optimization software is used to perform optimal system architecture, economic, and carbon footprint analyses while Monte-Carlo simulation tool is used for evaluating the throughput and energy efficiency performances. The proposed algorithms are valid for the practical data of the rural areas. We demonstrate the proposed power supply architecture is energy-efficient, cost-effective, reliable, and eco-friendly. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.05906v1-abstract-full').style.display = 'none'; document.getElementById('2110.05906v1-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 October, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2107.14095">arXiv:2107.14095</a> <span> [<a href="https://arxiv.org/pdf/2107.14095">pdf</a>, <a href="https://arxiv.org/format/2107.14095">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> </div> </div> <p class="title is-5 mathjax"> Exploring the Scope and Potential of Local Newspaper-based Dengue Surveillance in Bangladesh </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Tasnim%2C+N">Nazia Tasnim</a>, <a href="/search/cs?searchtype=author&query=Shihab%2C+M+I+H">Md. Istiak Hossain Shihab</a>, <a href="/search/cs?searchtype=author&query=Rahman%2C+M">Moqsadur Rahman</a>, <a href="/search/cs?searchtype=author&query=Islam%2C+S+R">Sheikh Rabiul Islam</a>, <a href="/search/cs?searchtype=author&query=Amin%2C+M+R">Mohammad Ruhul Amin</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="2107.14095v1-abstract-short" style="display: inline;"> Dengue fever has been considered to be one of the global public health problems of the twenty-first century, especially in tropical and subtropical countries of the global south. The high morbidity and mortality rates of Dengue fever impose a huge economic and health burden for middle and low-income countries. It is so prevalent in such regions that enforcing a granular level of surveillance is qu… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2107.14095v1-abstract-full').style.display = 'inline'; document.getElementById('2107.14095v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2107.14095v1-abstract-full" style="display: none;"> Dengue fever has been considered to be one of the global public health problems of the twenty-first century, especially in tropical and subtropical countries of the global south. The high morbidity and mortality rates of Dengue fever impose a huge economic and health burden for middle and low-income countries. It is so prevalent in such regions that enforcing a granular level of surveillance is quite impossible. Therefore, it is crucial to explore an alternative cost-effective solution that can provide updates of the ongoing situation in a timely manner. In this paper, we explore the scope and potential of a local newspaper-based dengue surveillance system, using well-known data-mining techniques, in Bangladesh from the analysis of the news contents written in the native language. In addition, we explain the working procedure of developing a novel database, using human-in-the-loop technique, for further analysis, and classification of dengue and its intervention-related news. Our classification method has an f-score of 91.45%, and matches the ground truth of reported cases quite closely. Based on the dengue and intervention-related news, we identified the regions where more intervention efforts are needed to reduce the rate of dengue infection. A demo of this project can be accessed at: http://erdos.dsm.fordham.edu:3009/ <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2107.14095v1-abstract-full').style.display = 'none'; document.getElementById('2107.14095v1-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 July, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2021. </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, Joint KDD 2021 Health Day and 2021 KDD Workshop on Applied Data Science for Healthcare</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2102.09603">arXiv:2102.09603</a> <span> [<a href="https://arxiv.org/pdf/2102.09603">pdf</a>, <a href="https://arxiv.org/format/2102.09603">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> <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"> Towards Solving the DeepFake Problem : An Analysis on Improving DeepFake Detection using Dynamic Face Augmentation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Das%2C+S">Sowmen Das</a>, <a href="/search/cs?searchtype=author&query=Seferbekov%2C+S">Selim Seferbekov</a>, <a href="/search/cs?searchtype=author&query=Datta%2C+A">Arup Datta</a>, <a href="/search/cs?searchtype=author&query=Islam%2C+M+S">Md. Saiful Islam</a>, <a href="/search/cs?searchtype=author&query=Amin%2C+M+R">Md. Ruhul Amin</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="2102.09603v3-abstract-short" style="display: inline;"> The creation of altered and manipulated faces has become more common due to the improvement of DeepFake generation methods. Simultaneously, we have seen detection models' development for differentiating between a manipulated and original face from image or video content. In this paper, we focus on identifying the limitations and shortcomings of existing deepfake detection frameworks. We identified… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2102.09603v3-abstract-full').style.display = 'inline'; document.getElementById('2102.09603v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2102.09603v3-abstract-full" style="display: none;"> The creation of altered and manipulated faces has become more common due to the improvement of DeepFake generation methods. Simultaneously, we have seen detection models' development for differentiating between a manipulated and original face from image or video content. In this paper, we focus on identifying the limitations and shortcomings of existing deepfake detection frameworks. We identified some key problems surrounding deepfake detection through quantitative and qualitative analysis of existing methods and datasets. We found that deepfake datasets are highly oversampled, causing models to become easily overfitted. The datasets are created using a small set of real faces to generate multiple fake samples. When trained on these datasets, models tend to memorize the actors' faces and labels instead of learning fake features. To mitigate this problem, we propose a simple data augmentation method termed Face-Cutout. Our method dynamically cuts out regions of an image using the face landmark information. It helps the model selectively attend to only the relevant regions of the input. Our evaluation experiments show that Face-Cutout can successfully improve the data variation and alleviate the problem of overfitting. Our method achieves a reduction in LogLoss of 15.2% to 35.3% on different datasets, compared to other occlusion-based techniques. Moreover, we also propose a general-purpose data pre-processing guideline to train and evaluate existing architectures allowing us to improve the generalizability of these models for deepfake detection. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2102.09603v3-abstract-full').style.display = 'none'; document.getElementById('2102.09603v3-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 August, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 18 February, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2012.07538">arXiv:2012.07538</a> <span> [<a href="https://arxiv.org/pdf/2012.07538">pdf</a>, <a href="https://arxiv.org/format/2012.07538">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> </div> </div> <p class="title is-5 mathjax"> Sentiment analysis in Bengali via transfer learning using multi-lingual BERT </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Islam%2C+K+I">Khondoker Ittehadul Islam</a>, <a href="/search/cs?searchtype=author&query=Islam%2C+M+S">Md. Saiful Islam</a>, <a href="/search/cs?searchtype=author&query=Amin%2C+M+R">Md Ruhul Amin</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="2012.07538v1-abstract-short" style="display: inline;"> Sentiment analysis (SA) in Bengali is challenging due to this Indo-Aryan language's highly inflected properties with more than 160 different inflected forms for verbs and 36 different forms for noun and 24 different forms for pronouns. The lack of standard labeled datasets in the Bengali domain makes the task of SA even harder. In this paper, we present manually tagged 2-class and 3-class SA datas… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2012.07538v1-abstract-full').style.display = 'inline'; document.getElementById('2012.07538v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2012.07538v1-abstract-full" style="display: none;"> Sentiment analysis (SA) in Bengali is challenging due to this Indo-Aryan language's highly inflected properties with more than 160 different inflected forms for verbs and 36 different forms for noun and 24 different forms for pronouns. The lack of standard labeled datasets in the Bengali domain makes the task of SA even harder. In this paper, we present manually tagged 2-class and 3-class SA datasets in Bengali. We also demonstrate that the multi-lingual BERT model with relevant extensions can be trained via the approach of transfer learning over those novel datasets to improve the state-of-the-art performance in sentiment classification tasks. This deep learning model achieves an accuracy of 71\% for 2-class sentiment classification compared to the current state-of-the-art accuracy of 68\%. We also present the very first Bengali SA classifier for the 3-class manually tagged dataset, and our proposed model achieves an accuracy of 60\%. We further use this model to analyze the sentiment of public comments in the online daily newspaper. Our analysis shows that people post negative comments for political or sports news more often, while the religious article comments represent positive sentiment. The dataset and code is publicly available at https://github.com/KhondokerIslam/Bengali\_Sentiment. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2012.07538v1-abstract-full').style.display = 'none'; document.getElementById('2012.07538v1-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 December, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2020. </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</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1610.00369">arXiv:1610.00369</a> <span> [<a href="https://arxiv.org/pdf/1610.00369">pdf</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="Information Retrieval">cs.IR</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="Neural and Evolutionary Computing">cs.NE</span> </div> </div> <p class="title is-5 mathjax"> Sentiment Analysis on Bangla and Romanized Bangla Text (BRBT) using Deep Recurrent models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Hassan%2C+A">A. Hassan</a>, <a href="/search/cs?searchtype=author&query=Amin%2C+M+R">M. R. Amin</a>, <a href="/search/cs?searchtype=author&query=Mohammed%2C+N">N. Mohammed</a>, <a href="/search/cs?searchtype=author&query=Azad%2C+A+K+A">A. K. A. Azad</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="1610.00369v2-abstract-short" style="display: inline;"> Sentiment Analysis (SA) is an action research area in the digital age. With rapid and constant growth of online social media sites and services, and the increasing amount of textual data such as - statuses, comments, reviews etc. available in them, application of automatic SA is on the rise. However, most of the research works on SA in natural language processing (NLP) are based on English languag… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1610.00369v2-abstract-full').style.display = 'inline'; document.getElementById('1610.00369v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1610.00369v2-abstract-full" style="display: none;"> Sentiment Analysis (SA) is an action research area in the digital age. With rapid and constant growth of online social media sites and services, and the increasing amount of textual data such as - statuses, comments, reviews etc. available in them, application of automatic SA is on the rise. However, most of the research works on SA in natural language processing (NLP) are based on English language. Despite being the sixth most widely spoken language in the world, Bangla still does not have a large and standard dataset. Because of this, recent research works in Bangla have failed to produce results that can be both comparable to works done by others and reusable as stepping stones for future researchers to progress in this field. Therefore, we first tried to provide a textual dataset - that includes not just Bangla, but Romanized Bangla texts as well, is substantial, post-processed and multiple validated, ready to be used in SA experiments. We tested this dataset in Deep Recurrent model, specifically, Long Short Term Memory (LSTM), using two types of loss functions - binary crossentropy and categorical crossentropy, and also did some experimental pre-training by using data from one validation to pre-train the other and vice versa. Lastly, we documented the results along with some analysis on them, which were promising. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1610.00369v2-abstract-full').style.display = 'none'; document.getElementById('1610.00369v2-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 November, 2016; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 2 October, 2016; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2016. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1401.6082">arXiv:1401.6082</a> <span> [<a href="https://arxiv.org/pdf/1401.6082">pdf</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> </div> </div> <p class="title is-5 mathjax"> Performance Evaluation of Two-Hop Wireless Link under Nakagami-m Fading </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Nadia%2C+A">Afsana Nadia</a>, <a href="/search/cs?searchtype=author&query=Chowdhury%2C+A+R">Arifur Rahim Chowdhury</a>, <a href="/search/cs?searchtype=author&query=Hossain%2C+M+S">Md. Shoayeb Hossain</a>, <a href="/search/cs?searchtype=author&query=Islam%2C+M+I">Md. Imdadul Islam</a>, <a href="/search/cs?searchtype=author&query=Amin%2C+M+R">M. R. Amin</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="1401.6082v1-abstract-short" style="display: inline;"> Now-a-days, intense research is going on two-hop wireless link under different fading conditions with its remedial measures. In this paper work, a two-hop link under three different conditions is considered: (i) MIMO on both hops, (ii) MISO in first hop and SIMO in second hop and finally (iii) SIMO in first hop and MISO in second hop. The three models used here give the flexibility of using STBC (… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1401.6082v1-abstract-full').style.display = 'inline'; document.getElementById('1401.6082v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1401.6082v1-abstract-full" style="display: none;"> Now-a-days, intense research is going on two-hop wireless link under different fading conditions with its remedial measures. In this paper work, a two-hop link under three different conditions is considered: (i) MIMO on both hops, (ii) MISO in first hop and SIMO in second hop and finally (iii) SIMO in first hop and MISO in second hop. The three models used here give the flexibility of using STBC (Space Time Block Coding) and combining scheme on any of the source to relay (S- R) and relay to destination (R-D) link. Even incorporation of Transmitting Antenna Selection (TAS) is possible on any link. Here, the variation of SER (Symbol Error Rate) is determined against mean SNR (Signal-to-Noise Ratio) of R-D link for three different modulation schemes: BPSK, 8-PSK and 16-PSK, taking the number of antennas and SNR of S-R link as parameters under Nakagami -m fading condition. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1401.6082v1-abstract-full').style.display = 'none'; document.getElementById('1401.6082v1-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, 2013; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2014. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> IJACSA,Vol. 4,No. 7,July 2013 </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a 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