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<button class="button is-small 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=Ghosh%2C+S+K&start=50" class="pagination-next" >Next </a> <ul class="pagination-list"> <li> <a href="/search/?searchtype=author&query=Ghosh%2C+S+K&start=0" class="pagination-link is-current" aria-label="Goto page 1">1 </a> </li> <li> <a href="/search/?searchtype=author&query=Ghosh%2C+S+K&start=50" class="pagination-link " aria-label="Page 2" aria-current="page">2 </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/2502.06924">arXiv:2502.06924</a> <span> [<a href="https://arxiv.org/pdf/2502.06924">pdf</a>, <a href="https://arxiv.org/format/2502.06924">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="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> XAMBA: Enabling Efficient State Space Models on Resource-Constrained Neural Processing Units </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Das%2C+A">Arghadip Das</a>, <a href="/search/cs?searchtype=author&query=Raha%2C+A">Arnab Raha</a>, <a href="/search/cs?searchtype=author&query=Kundu%2C+S">Shamik Kundu</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Soumendu Kumar Ghosh</a>, <a href="/search/cs?searchtype=author&query=Mathaikutty%2C+D">Deepak Mathaikutty</a>, <a href="/search/cs?searchtype=author&query=Raghunathan%2C+V">Vijay Raghunathan</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="2502.06924v3-abstract-short" style="display: inline;"> State-Space Models (SSMs) have emerged as efficient alternatives to transformers for sequential data tasks, offering linear or near-linear scalability with sequence length, making them ideal for long-sequence applications in NLP, vision, and edge AI, including real-time transcription, translation, and contextual search. These applications require lightweight, high-performance models for deployment… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.06924v3-abstract-full').style.display = 'inline'; document.getElementById('2502.06924v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.06924v3-abstract-full" style="display: none;"> State-Space Models (SSMs) have emerged as efficient alternatives to transformers for sequential data tasks, offering linear or near-linear scalability with sequence length, making them ideal for long-sequence applications in NLP, vision, and edge AI, including real-time transcription, translation, and contextual search. These applications require lightweight, high-performance models for deployment on resource-constrained devices like laptops and PCs. Designing specialized accelerators for every emerging neural network is costly and impractical; instead, optimizing models for existing NPUs in AI PCs provides a scalable solution. To this end, we propose XAMBA, the first framework to enable and optimize SSMs on commercial off-the-shelf (COTS) state-of-the-art (SOTA) NPUs. XAMBA follows a three-step methodology: (1) enabling SSMs on NPUs, (2) optimizing performance to meet KPI requirements, and (3) trading accuracy for additional performance gains. After enabling SSMs on NPUs, XAMBA mitigates key bottlenecks using CumBA and ReduBA, replacing sequential CumSum and ReduceSum operations with matrix-based computations, significantly improving execution speed and memory efficiency. Additionally, ActiBA enhances performance by approximating expensive activation functions (e.g., Swish, Softplus) using piecewise linear mappings, reducing latency with minimal accuracy loss. Evaluations on an Intel Core Ultra Series 2 AI PC show that XAMBA achieves up to 2.6X speed-up over the baseline. Our implementation is available at https://github.com/arghadippurdue/XAMBA. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.06924v3-abstract-full').style.display = 'none'; document.getElementById('2502.06924v3-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 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 10 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.10942">arXiv:2409.10942</a> <span> [<a href="https://arxiv.org/pdf/2409.10942">pdf</a>, <a href="https://arxiv.org/format/2409.10942">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"> Optimizing TinyML: The Impact of Reduced Data Acquisition Rates for Time Series Classification on Microcontrollers </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Samanta%2C+R">Riya Samanta</a>, <a href="/search/cs?searchtype=author&query=Saha%2C+B">Bidyut Saha</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Soumya K. Ghosh</a>, <a href="/search/cs?searchtype=author&query=Roy%2C+R+B">Ram Babu Roy</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="2409.10942v1-abstract-short" style="display: inline;"> Tiny Machine Learning (TinyML) enables efficient, lowcost, and privacy preserving machine learning inference directly on microcontroller units (MCUs) connected to sensors. Optimizing models for these constrained environments is crucial. This paper investigates how reducing data acquisition rates affects TinyML models for time series classification, focusing on resource-constrained, battery operate… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.10942v1-abstract-full').style.display = 'inline'; document.getElementById('2409.10942v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.10942v1-abstract-full" style="display: none;"> Tiny Machine Learning (TinyML) enables efficient, lowcost, and privacy preserving machine learning inference directly on microcontroller units (MCUs) connected to sensors. Optimizing models for these constrained environments is crucial. This paper investigates how reducing data acquisition rates affects TinyML models for time series classification, focusing on resource-constrained, battery operated IoT devices. By lowering data sampling frequency, we aim to reduce computational demands RAM usage, energy consumption, latency, and MAC operations by approximately fourfold while maintaining similar classification accuracies. Our experiments with six benchmark datasets (UCIHAR, WISDM, PAMAP2, MHEALTH, MITBIH, and PTB) showed that reducing data acquisition rates significantly cut energy consumption and computational load, with minimal accuracy loss. For example, a 75\% reduction in acquisition rate for MITBIH and PTB datasets led to a 60\% decrease in RAM usage, 75\% reduction in MAC operations, 74\% decrease in latency, and 70\% reduction in energy consumption, without accuracy loss. These results offer valuable insights for deploying efficient TinyML models in constrained environments. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.10942v1-abstract-full').style.display = 'none'; document.getElementById('2409.10942v1-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 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/2409.01628">arXiv:2409.01628</a> <span> [<a href="https://arxiv.org/pdf/2409.01628">pdf</a>, <a href="https://arxiv.org/format/2409.01628">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="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> CTG-KrEW: Generating Synthetic Structured Contextually Correlated Content by Conditional Tabular GAN with K-Means Clustering and Efficient Word Embedding </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Samanta%2C+R">Riya Samanta</a>, <a href="/search/cs?searchtype=author&query=Saha%2C+B">Bidyut Saha</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Soumya K. Ghosh</a>, <a href="/search/cs?searchtype=author&query=Das%2C+S+K">Sajal K. 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="2409.01628v1-abstract-short" style="display: inline;"> Conditional Tabular Generative Adversarial Networks (CTGAN) and their various derivatives are attractive for their ability to efficiently and flexibly create synthetic tabular data, showcasing strong performance and adaptability. However, there are certain critical limitations to such models. The first is their inability to preserve the semantic integrity of contextually correlated words or phrase… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.01628v1-abstract-full').style.display = 'inline'; document.getElementById('2409.01628v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.01628v1-abstract-full" style="display: none;"> Conditional Tabular Generative Adversarial Networks (CTGAN) and their various derivatives are attractive for their ability to efficiently and flexibly create synthetic tabular data, showcasing strong performance and adaptability. However, there are certain critical limitations to such models. The first is their inability to preserve the semantic integrity of contextually correlated words or phrases. For instance, skillset in freelancer profiles is one such attribute where individual skills are semantically interconnected and indicative of specific domain interests or qualifications. The second challenge of traditional approaches is that, when applied to generate contextually correlated tabular content, besides generating semantically shallow content, they consume huge memory resources and CPU time during the training stage. To address these problems, we introduce a novel framework, CTGKrEW (Conditional Tabular GAN with KMeans Clustering and Word Embedding), which is adept at generating realistic synthetic tabular data where attributes are collections of semantically and contextually coherent words. CTGKrEW is trained and evaluated using a dataset from Upwork, a realworld freelancing platform. Comprehensive experiments were conducted to analyze the variability, contextual similarity, frequency distribution, and associativity of the generated data, along with testing the framework's system feasibility. CTGKrEW also takes around 99\% less CPU time and 33\% less memory footprints than the conventional approach. Furthermore, we developed KrEW, a web application to facilitate the generation of realistic data containing skill-related information. This application, available at https://riyasamanta.github.io/krew.html, is freely accessible to both the general public and the research community. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.01628v1-abstract-full').style.display = 'none'; document.getElementById('2409.01628v1-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 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/2409.00093">arXiv:2409.00093</a> <span> [<a href="https://arxiv.org/pdf/2409.00093">pdf</a>, <a href="https://arxiv.org/format/2409.00093">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"> Towards Sustainable Personalized On-Device Human Activity Recognition with TinyML and Cloud-Enabled Auto Deployment </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Saha%2C+B">Bidyut Saha</a>, <a href="/search/cs?searchtype=author&query=Samanta%2C+R">Riya Samanta</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Soumya K Ghosh</a>, <a href="/search/cs?searchtype=author&query=Roy%2C+R+B">Ram Babu Roy</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="2409.00093v1-abstract-short" style="display: inline;"> Human activity recognition (HAR) holds immense potential for transforming health and fitness monitoring, yet challenges persist in achieving personalized outcomes and sustainability for on-device continuous inferences. This work introduces a wrist-worn smart band designed to address these challenges through a novel combination of on-device TinyML-driven computing and cloud-enabled auto-deployment.… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.00093v1-abstract-full').style.display = 'inline'; document.getElementById('2409.00093v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.00093v1-abstract-full" style="display: none;"> Human activity recognition (HAR) holds immense potential for transforming health and fitness monitoring, yet challenges persist in achieving personalized outcomes and sustainability for on-device continuous inferences. This work introduces a wrist-worn smart band designed to address these challenges through a novel combination of on-device TinyML-driven computing and cloud-enabled auto-deployment. Leveraging inertial measurement unit (IMU) sensors and a customized 1D Convolutional Neural Network (CNN) for personalized HAR, users can tailor activity classes to their unique movement styles with minimal calibration. By utilising TinyML for local computations, the smart band reduces the necessity for constant data transmission and radio communication, which in turn lowers power consumption and reduces carbon footprint. This method also enhances the privacy and security of user data by limiting its transmission. Through transfer learning and fine-tuning on user-specific data, the system achieves a 37\% increase in accuracy over generalized models in personalized settings. Evaluation using three benchmark datasets, WISDM, PAMAP2, and the BandX demonstrates its effectiveness across various activity domains. Additionally, this work presents a cloud-supported framework for the automatic deployment of TinyML models to remote wearables, enabling seamless customization and on-device inference, even with limited target data. By combining personalized HAR with sustainable strategies for on-device continuous inferences, this system represents a promising step towards fostering healthier and more sustainable societies worldwide. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.00093v1-abstract-full').style.display = 'none'; document.getElementById('2409.00093v1-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 August, 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/2408.16535">arXiv:2408.16535</a> <span> [<a href="https://arxiv.org/pdf/2408.16535">pdf</a>, <a href="https://arxiv.org/format/2408.16535">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"> TinyTNAS: GPU-Free, Time-Bound, Hardware-Aware Neural Architecture Search for TinyML Time Series Classification </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Saha%2C+B">Bidyut Saha</a>, <a href="/search/cs?searchtype=author&query=Samanta%2C+R">Riya Samanta</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Soumya K. Ghosh</a>, <a href="/search/cs?searchtype=author&query=Roy%2C+R+B">Ram Babu Roy</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="2408.16535v1-abstract-short" style="display: inline;"> In this work, we present TinyTNAS, a novel hardware-aware multi-objective Neural Architecture Search (NAS) tool specifically designed for TinyML time series classification. Unlike traditional NAS methods that rely on GPU capabilities, TinyTNAS operates efficiently on CPUs, making it accessible for a broader range of applications. Users can define constraints on RAM, FLASH, and MAC operations to di… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.16535v1-abstract-full').style.display = 'inline'; document.getElementById('2408.16535v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.16535v1-abstract-full" style="display: none;"> In this work, we present TinyTNAS, a novel hardware-aware multi-objective Neural Architecture Search (NAS) tool specifically designed for TinyML time series classification. Unlike traditional NAS methods that rely on GPU capabilities, TinyTNAS operates efficiently on CPUs, making it accessible for a broader range of applications. Users can define constraints on RAM, FLASH, and MAC operations to discover optimal neural network architectures within these parameters. Additionally, the tool allows for time-bound searches, ensuring the best possible model is found within a user-specified duration. By experimenting with benchmark dataset UCI HAR, PAMAP2, WISDM, MIT BIH, and PTB Diagnostic ECG Databas TinyTNAS demonstrates state-of-the-art accuracy with significant reductions in RAM, FLASH, MAC usage, and latency. For example, on the UCI HAR dataset, TinyTNAS achieves a 12x reduction in RAM usage, a 144x reduction in MAC operations, and a 78x reduction in FLASH memory while maintaining superior accuracy and reducing latency by 149x. Similarly, on the PAMAP2 and WISDM datasets, it achieves a 6x reduction in RAM usage, a 40x reduction in MAC operations, an 83x reduction in FLASH, and a 67x reduction in latency, all while maintaining superior accuracy. Notably, the search process completes within 10 minutes in a CPU environment. These results highlight TinyTNAS's capability to optimize neural network architectures effectively for resource-constrained TinyML applications, ensuring both efficiency and high performance. The code for TinyTNAS is available at the GitHub repository and can be accessed at https://github.com/BidyutSaha/TinyTNAS.git. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.16535v1-abstract-full').style.display = 'none'; document.getElementById('2408.16535v1-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 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.11510">arXiv:2408.11510</a> <span> [<a href="https://arxiv.org/pdf/2408.11510">pdf</a>, <a href="https://arxiv.org/format/2408.11510">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Emerging Technologies">cs.ET</span> </div> </div> <p class="title is-5 mathjax"> Empowering Volunteer Crowdsourcing Services: A Serverless-assisted, Skill and Willingness Aware Task Assignment Approach for Amicable Volunteer Involvement </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Samanta%2C+R">Riya Samanta</a>, <a href="/search/cs?searchtype=author&query=Sethi%2C+B">Biswajeet Sethi</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Soumya K Ghosh</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="2408.11510v1-abstract-short" style="display: inline;"> Volunteer crowdsourcing (VCS) leverages citizen interaction to address challenges by utilizing individuals' knowledge and skills. Complex social tasks often require collaboration among volunteers with diverse skill sets, and their willingness to engage is crucial. Matching tasks with the most suitable volunteers remains a significant challenge. VCS platforms face unpredictable demands in terms of… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.11510v1-abstract-full').style.display = 'inline'; document.getElementById('2408.11510v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.11510v1-abstract-full" style="display: none;"> Volunteer crowdsourcing (VCS) leverages citizen interaction to address challenges by utilizing individuals' knowledge and skills. Complex social tasks often require collaboration among volunteers with diverse skill sets, and their willingness to engage is crucial. Matching tasks with the most suitable volunteers remains a significant challenge. VCS platforms face unpredictable demands in terms of tasks and volunteer requests, complicating the prediction of resource requirements for the volunteer-to-task assignment process. To address these challenges, we introduce the Skill and Willingness-Aware Volunteer Matching (SWAM) algorithm, which allocates volunteers to tasks based on skills, willingness, and task requirements. We also developed a serverless framework to deploy SWAM. Our method outperforms conventional solutions, achieving a 71% improvement in end-to-end latency efficiency. We achieved a 92% task completion ratio and reduced task waiting time by 56%, with an overall utility gain 30% higher than state-of-the-art baseline methods. This framework contributes to generating effective volunteer and task matches, supporting grassroots community coordination and fostering citizen involvement, ultimately contributing to social good. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.11510v1-abstract-full').style.display = 'none'; document.getElementById('2408.11510v1-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 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.11498">arXiv:2408.11498</a> <span> [<a href="https://arxiv.org/pdf/2408.11498">pdf</a>, <a href="https://arxiv.org/format/2408.11498">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Emerging Technologies">cs.ET</span> </div> </div> <p class="title is-5 mathjax"> Sustainable Volunteer Engagement: Ensuring Potential Retention and Skill Diversity for Balanced Workforce Composition in Crowdsourcing Paradigm </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Samanta%2C+R">Riya Samanta</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Soumya K Ghosh</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="2408.11498v2-abstract-short" style="display: inline;"> Crowdsourcing (CS) faces the challenge of managing complex, skill-demanding tasks, which requires effective task assignment and retention strategies to sustain a balanced workforce. This challenge has become more significant in Volunteer Crowdsourcing Services (VCS). This study introduces Workforce Composition Balance (WCB), a novel framework designed to maintain workforce diversity in VCS by dyna… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.11498v2-abstract-full').style.display = 'inline'; document.getElementById('2408.11498v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.11498v2-abstract-full" style="display: none;"> Crowdsourcing (CS) faces the challenge of managing complex, skill-demanding tasks, which requires effective task assignment and retention strategies to sustain a balanced workforce. This challenge has become more significant in Volunteer Crowdsourcing Services (VCS). This study introduces Workforce Composition Balance (WCB), a novel framework designed to maintain workforce diversity in VCS by dynamically adjusting retention decisions. The WCB framework integrates the Volunteer Retention and Value Enhancement (VRAVE) algorithm with advanced skill-based task assignment methods. It ensures efficient remuneration policy for both assigned and unassigned potential volunteers by incorporating their potential levels, participation dividends, and satisfaction scores. Comparative analysis with three state-of-the-art baselines on real dataset shows that our WCB framework achieves 1.4 times better volunteer satisfaction and a 20% higher task retention rate, with only a 12% increase in remuneration. The effectiveness of the proposed WCB approach is to enhance the volunteer engagement and their long-term retention, thus making it suitable for functioning of social good applications where a potential and skilled volunteer workforce is crucial for sustainable community services. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.11498v2-abstract-full').style.display = 'none'; document.getElementById('2408.11498v2-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> 31 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 21 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.09026">arXiv:2403.09026</a> <span> [<a href="https://arxiv.org/pdf/2403.09026">pdf</a>, <a href="https://arxiv.org/format/2403.09026">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Hardware Architecture">cs.AR</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"> FlexNN: A Dataflow-aware Flexible Deep Learning Accelerator for Energy-Efficient Edge Devices </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Raha%2C+A">Arnab Raha</a>, <a href="/search/cs?searchtype=author&query=Mathaikutty%2C+D+A">Deepak A. Mathaikutty</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Soumendu K. Ghosh</a>, <a href="/search/cs?searchtype=author&query=Kundu%2C+S">Shamik Kundu</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="2403.09026v2-abstract-short" style="display: inline;"> This paper introduces FlexNN, a Flexible Neural Network accelerator, which adopts agile design principles to enable versatile dataflows, enhancing energy efficiency. Unlike conventional convolutional neural network accelerator architectures that adhere to fixed dataflows (such as input, weight, output, or row stationary) for transferring activations and weights between storage and compute units, o… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.09026v2-abstract-full').style.display = 'inline'; document.getElementById('2403.09026v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.09026v2-abstract-full" style="display: none;"> This paper introduces FlexNN, a Flexible Neural Network accelerator, which adopts agile design principles to enable versatile dataflows, enhancing energy efficiency. Unlike conventional convolutional neural network accelerator architectures that adhere to fixed dataflows (such as input, weight, output, or row stationary) for transferring activations and weights between storage and compute units, our design revolutionizes by enabling adaptable dataflows of any type through software configurable descriptors. Considering that data movement costs considerably outweigh compute costs from an energy perspective, the flexibility in dataflow allows us to optimize the movement per layer for minimal data transfer and energy consumption, a capability unattainable in fixed dataflow architectures. To further enhance throughput and reduce energy consumption in the FlexNN architecture, we propose a novel sparsity-based acceleration logic that utilizes fine-grained sparsity in both the activation and weight tensors to bypass redundant computations, thus optimizing the convolution engine within the hardware accelerator. Extensive experimental results underscore a significant enhancement in the performance and energy efficiency of FlexNN relative to existing DNN accelerators. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.09026v2-abstract-full').style.display = 'none'; document.getElementById('2403.09026v2-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> 11 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 13 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">Version 1. Work started in 2019</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.04379">arXiv:2403.04379</a> <span> [<a href="https://arxiv.org/pdf/2403.04379">pdf</a>, <a href="https://arxiv.org/format/2403.04379">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> </div> </div> <p class="title is-5 mathjax"> Performance evaluation of conditional handover in 5G systems under fading scenario </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Deb%2C+S">Souvik Deb</a>, <a href="/search/cs?searchtype=author&query=Rathod%2C+M">Megh Rathod</a>, <a href="/search/cs?searchtype=author&query=Balamurugan%2C+R">Rishi Balamurugan</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Shankar K. Ghosh</a>, <a href="/search/cs?searchtype=author&query=Singh%2C+R+K">Rajeev K. Singh</a>, <a href="/search/cs?searchtype=author&query=Sanyal%2C+S">Samriddha Sanyal</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="2403.04379v1-abstract-short" style="display: inline;"> To enhance the handover performance in fifth generation (5G) cellular systems, conditional handover (CHO) has been evolved as a promising solution. Unlike A3 based handover where handover execution is certain after receiving handover command from the serving access network, in CHO, handover execution is conditional on the RSRP measurements from both current and target access networks, as well as o… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.04379v1-abstract-full').style.display = 'inline'; document.getElementById('2403.04379v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.04379v1-abstract-full" style="display: none;"> To enhance the handover performance in fifth generation (5G) cellular systems, conditional handover (CHO) has been evolved as a promising solution. Unlike A3 based handover where handover execution is certain after receiving handover command from the serving access network, in CHO, handover execution is conditional on the RSRP measurements from both current and target access networks, as well as on mobility parameters such as preparation and execution offsets. Analytic evaluation of conditional handover performance is unprecedented in literature. In this work, handover performance of CHO has been carried out in terms of handover latency, handover packet loss and handover failure probability. A Markov model accounting the effect of different mobility parameters (e.g., execution offset, preparation offset, time-to-preparation and time-to-execution), UE velocity and channel fading characteristics; has been proposed to characterize handover failure. Results obtained from the analytic model has been validated against extensive simulation results. Our study reveal that optimal configuration of $O_{exec}$, $O_{prep}$, $T_{exec}$ and $T_{prep}$ is actually conditional on underlying UE velocity and fading characteristics. This study will be helpful for the mobile operators to choose appropriate thresholds of the mobility parameters under different channel condition and UE velocities. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.04379v1-abstract-full').style.display = 'none'; document.getElementById('2403.04379v1-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 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/2401.02469">arXiv:2401.02469</a> <span> [<a href="https://arxiv.org/pdf/2401.02469">pdf</a>, <a href="https://arxiv.org/format/2401.02469">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</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.teler.2024.100116">10.1016/j.teler.2024.100116 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Modern Computing: Vision and Challenges </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Gill%2C+S+S">Sukhpal Singh Gill</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+H">Huaming Wu</a>, <a href="/search/cs?searchtype=author&query=Patros%2C+P">Panos Patros</a>, <a href="/search/cs?searchtype=author&query=Ottaviani%2C+C">Carlo Ottaviani</a>, <a href="/search/cs?searchtype=author&query=Arora%2C+P">Priyansh Arora</a>, <a href="/search/cs?searchtype=author&query=Pujol%2C+V+C">Victor Casamayor Pujol</a>, <a href="/search/cs?searchtype=author&query=Haunschild%2C+D">David Haunschild</a>, <a href="/search/cs?searchtype=author&query=Parlikad%2C+A+K">Ajith Kumar Parlikad</a>, <a href="/search/cs?searchtype=author&query=Cetinkaya%2C+O">Oktay Cetinkaya</a>, <a href="/search/cs?searchtype=author&query=Lutfiyya%2C+H">Hanan Lutfiyya</a>, <a href="/search/cs?searchtype=author&query=Stankovski%2C+V">Vlado Stankovski</a>, <a href="/search/cs?searchtype=author&query=Li%2C+R">Ruidong Li</a>, <a href="/search/cs?searchtype=author&query=Ding%2C+Y">Yuemin Ding</a>, <a href="/search/cs?searchtype=author&query=Qadir%2C+J">Junaid Qadir</a>, <a href="/search/cs?searchtype=author&query=Abraham%2C+A">Ajith Abraham</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Soumya K. Ghosh</a>, <a href="/search/cs?searchtype=author&query=Song%2C+H+H">Houbing Herbert Song</a>, <a href="/search/cs?searchtype=author&query=Sakellariou%2C+R">Rizos Sakellariou</a>, <a href="/search/cs?searchtype=author&query=Rana%2C+O">Omer Rana</a>, <a href="/search/cs?searchtype=author&query=Rodrigues%2C+J+J+P+C">Joel J. P. C. Rodrigues</a>, <a href="/search/cs?searchtype=author&query=Kanhere%2C+S+S">Salil S. Kanhere</a>, <a href="/search/cs?searchtype=author&query=Dustdar%2C+S">Schahram Dustdar</a>, <a href="/search/cs?searchtype=author&query=Uhlig%2C+S">Steve Uhlig</a>, <a href="/search/cs?searchtype=author&query=Ramamohanarao%2C+K">Kotagiri Ramamohanarao</a>, <a href="/search/cs?searchtype=author&query=Buyya%2C+R">Rajkumar Buyya</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="2401.02469v1-abstract-short" style="display: inline;"> Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.02469v1-abstract-full').style.display = 'inline'; document.getElementById('2401.02469v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.02469v1-abstract-full" style="display: none;"> Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.02469v1-abstract-full').style.display = 'none'; document.getElementById('2401.02469v1-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 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">Preprint submitted to Telematics and Informatics Reports, Elsevier (2024)</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Elsevier Telematics and Informatics Reports, Volume 13, 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/2312.09321">arXiv:2312.09321</a> <span> [<a href="https://arxiv.org/pdf/2312.09321">pdf</a>, <a href="https://arxiv.org/format/2312.09321">other</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> </div> </div> <p class="title is-5 mathjax"> OSTINATO: Cross-host Attack Correlation Through Attack Activity Similarity Detection </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Sutanu Kumar Ghosh</a>, <a href="/search/cs?searchtype=author&query=Satvat%2C+K">Kiavash Satvat</a>, <a href="/search/cs?searchtype=author&query=Gjomemo%2C+R">Rigel Gjomemo</a>, <a href="/search/cs?searchtype=author&query=Venkatakrishnan%2C+V+N">V. N. Venkatakrishnan</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="2312.09321v1-abstract-short" style="display: inline;"> Modern attacks against enterprises often have multiple targets inside the enterprise network. Due to the large size of these networks and increasingly stealthy attacks, attacker activities spanning multiple hosts are extremely difficult to correlate during a threat-hunting effort. In this paper, we present a method for an efficient cross-host attack correlation across multiple hosts. Unlike previo… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.09321v1-abstract-full').style.display = 'inline'; document.getElementById('2312.09321v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.09321v1-abstract-full" style="display: none;"> Modern attacks against enterprises often have multiple targets inside the enterprise network. Due to the large size of these networks and increasingly stealthy attacks, attacker activities spanning multiple hosts are extremely difficult to correlate during a threat-hunting effort. In this paper, we present a method for an efficient cross-host attack correlation across multiple hosts. Unlike previous works, our approach does not require lateral movement detection techniques or host-level modifications. Instead, our approach relies on an observation that attackers have a few strategic mission objectives on every host that they infiltrate, and there exist only a handful of techniques for achieving those objectives. The central idea behind our approach involves comparing (OS agnostic) activities on different hosts and correlating the hosts that display the use of similar tactics, techniques, and procedures. We implement our approach in a tool called Ostinato and successfully evaluate it in threat hunting scenarios involving DARPA-led red team engagements spanning 500 hosts and in another multi-host attack scenario. Ostinato successfully detected 21 additional compromised hosts, which the underlying host-based detection system overlooked in activities spanning multiple days of the attack campaign. Additionally, Ostinato successfully reduced alarms generated from the underlying detection system by more than 90%, thus helping to mitigate the threat alert fatigue problem <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.09321v1-abstract-full').style.display = 'none'; document.getElementById('2312.09321v1-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 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">21 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/2306.03823">arXiv:2306.03823</a> <span> [<a href="https://arxiv.org/pdf/2306.03823">pdf</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="Computation and Language">cs.CL</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.iotcps.2023.06.002">10.1016/j.iotcps.2023.06.002 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Transformative Effects of ChatGPT on Modern Education: Emerging Era of AI Chatbots </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Gill%2C+S+S">Sukhpal Singh Gill</a>, <a href="/search/cs?searchtype=author&query=Xu%2C+M">Minxian Xu</a>, <a href="/search/cs?searchtype=author&query=Patros%2C+P">Panos Patros</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+H">Huaming Wu</a>, <a href="/search/cs?searchtype=author&query=Kaur%2C+R">Rupinder Kaur</a>, <a href="/search/cs?searchtype=author&query=Kaur%2C+K">Kamalpreet Kaur</a>, <a href="/search/cs?searchtype=author&query=Fuller%2C+S">Stephanie Fuller</a>, <a href="/search/cs?searchtype=author&query=Singh%2C+M">Manmeet Singh</a>, <a href="/search/cs?searchtype=author&query=Arora%2C+P">Priyansh Arora</a>, <a href="/search/cs?searchtype=author&query=Parlikad%2C+A+K">Ajith Kumar Parlikad</a>, <a href="/search/cs?searchtype=author&query=Stankovski%2C+V">Vlado Stankovski</a>, <a href="/search/cs?searchtype=author&query=Abraham%2C+A">Ajith Abraham</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Soumya K. Ghosh</a>, <a href="/search/cs?searchtype=author&query=Lutfiyya%2C+H">Hanan Lutfiyya</a>, <a href="/search/cs?searchtype=author&query=Kanhere%2C+S+S">Salil S. Kanhere</a>, <a href="/search/cs?searchtype=author&query=Bahsoon%2C+R">Rami Bahsoon</a>, <a href="/search/cs?searchtype=author&query=Rana%2C+O">Omer Rana</a>, <a href="/search/cs?searchtype=author&query=Dustdar%2C+S">Schahram Dustdar</a>, <a href="/search/cs?searchtype=author&query=Sakellariou%2C+R">Rizos Sakellariou</a>, <a href="/search/cs?searchtype=author&query=Uhlig%2C+S">Steve Uhlig</a>, <a href="/search/cs?searchtype=author&query=Buyya%2C+R">Rajkumar Buyya</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="2306.03823v1-abstract-short" style="display: inline;"> ChatGPT, an AI-based chatbot, was released to provide coherent and useful replies based on analysis of large volumes of data. In this article, leading scientists, researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research seeks to improve our knowledge of ChatGPT capabilities and its use in the education sector, identifying potential concerns and cha… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.03823v1-abstract-full').style.display = 'inline'; document.getElementById('2306.03823v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.03823v1-abstract-full" style="display: none;"> ChatGPT, an AI-based chatbot, was released to provide coherent and useful replies based on analysis of large volumes of data. In this article, leading scientists, researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research seeks to improve our knowledge of ChatGPT capabilities and its use in the education sector, identifying potential concerns and challenges. Our preliminary evaluation concludes that ChatGPT performed differently in each subject area including finance, coding and maths. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported hallucinations within Generative AI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.03823v1-abstract-full').style.display = 'none'; document.getElementById('2306.03823v1-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 May, 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">Preprint submitted to IoTCPS Elsevier (2023)</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Internet of Things and Cyber-Physical Systems (Elsevier), Volume 4, 2024, Pages 19-23 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2212.13897">arXiv:2212.13897</a> <span> [<a href="https://arxiv.org/pdf/2212.13897">pdf</a>, <a href="https://arxiv.org/format/2212.13897">other</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> </div> </div> <p class="title is-5 mathjax"> What You Like: Generating Explainable Topical Recommendations for Twitter Using Social Annotations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Bhattacharya%2C+P">Parantapa Bhattacharya</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S">Saptarshi Ghosh</a>, <a href="/search/cs?searchtype=author&query=Zafar%2C+M+B">Muhammad Bilal Zafar</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Soumya K. Ghosh</a>, <a href="/search/cs?searchtype=author&query=Ganguly%2C+N">Niloy Ganguly</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="2212.13897v1-abstract-short" style="display: inline;"> With over 500 million tweets posted per day, in Twitter, it is difficult for Twitter users to discover interesting content from the deluge of uninteresting posts. In this work, we present a novel, explainable, topical recommendation system, that utilizes social annotations, to help Twitter users discover tweets, on topics of their interest. A major challenge in using traditional rating dependent r… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.13897v1-abstract-full').style.display = 'inline'; document.getElementById('2212.13897v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2212.13897v1-abstract-full" style="display: none;"> With over 500 million tweets posted per day, in Twitter, it is difficult for Twitter users to discover interesting content from the deluge of uninteresting posts. In this work, we present a novel, explainable, topical recommendation system, that utilizes social annotations, to help Twitter users discover tweets, on topics of their interest. A major challenge in using traditional rating dependent recommendation systems, like collaborative filtering and content based systems, in high volume social networks is that, due to attention scarcity most items do not get any ratings. Additionally, the fact that most Twitter users are passive consumers, with 44% users never tweeting, makes it very difficult to use user ratings for generating recommendations. Further, a key challenge in developing recommendation systems is that in many cases users reject relevant recommendations if they are totally unfamiliar with the recommended item. Providing a suitable explanation, for why the item is recommended, significantly improves the acceptability of recommendation. By virtue of being a topical recommendation system our method is able to present simple topical explanations for the generated recommendations. Comparisons with state-of-the-art matrix factorization based collaborative filtering, content based and social recommendations demonstrate the efficacy of the proposed approach. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.13897v1-abstract-full').style.display = 'none'; document.getElementById('2212.13897v1-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 December, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2211.11362">arXiv:2211.11362</a> <span> [<a href="https://arxiv.org/pdf/2211.11362">pdf</a>, <a href="https://arxiv.org/format/2211.11362">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> </div> </div> <p class="title is-5 mathjax"> Crowdsensing-based Road Damage Detection Challenge (CRDDC-2022) </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Arya%2C+D">Deeksha Arya</a>, <a href="/search/cs?searchtype=author&query=Maeda%2C+H">Hiroya Maeda</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Sanjay Kumar Ghosh</a>, <a href="/search/cs?searchtype=author&query=Toshniwal%2C+D">Durga Toshniwal</a>, <a href="/search/cs?searchtype=author&query=Omata%2C+H">Hiroshi Omata</a>, <a href="/search/cs?searchtype=author&query=Kashiyama%2C+T">Takehiro Kashiyama</a>, <a href="/search/cs?searchtype=author&query=Sekimoto%2C+Y">Yoshihide Sekimoto</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="2211.11362v1-abstract-short" style="display: inline;"> This paper summarizes the Crowdsensing-based Road Damage Detection Challenge (CRDDC), a Big Data Cup organized as a part of the IEEE International Conference on Big Data'2022. The Big Data Cup challenges involve a released dataset and a well-defined problem with clear evaluation metrics. The challenges run on a data competition platform that maintains a real-time online evaluation system for the p… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.11362v1-abstract-full').style.display = 'inline'; document.getElementById('2211.11362v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2211.11362v1-abstract-full" style="display: none;"> This paper summarizes the Crowdsensing-based Road Damage Detection Challenge (CRDDC), a Big Data Cup organized as a part of the IEEE International Conference on Big Data'2022. The Big Data Cup challenges involve a released dataset and a well-defined problem with clear evaluation metrics. The challenges run on a data competition platform that maintains a real-time online evaluation system for the participants. In the presented case, the data constitute 47,420 road images collected from India, Japan, the Czech Republic, Norway, the United States, and China to propose methods for automatically detecting road damages in these countries. More than 60 teams from 19 countries registered for this competition. The submitted solutions were evaluated using five leaderboards based on performance for unseen test images from the aforementioned six countries. This paper encapsulates the top 11 solutions proposed by these teams. The best-performing model utilizes ensemble learning based on YOLO and Faster-RCNN series models to yield an F1 score of 76% for test data combined from all 6 countries. The paper concludes with a comparison of current and past challenges and provides direction for the future. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.11362v1-abstract-full').style.display = 'none'; document.getElementById('2211.11362v1-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 November, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 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">9 pages 2 figures 5 tables. arXiv admin note: text overlap with arXiv:2011.08740</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> E.0; J.0 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2209.08538">arXiv:2209.08538</a> <span> [<a href="https://arxiv.org/pdf/2209.08538">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> <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"> RDD2022: A multi-national image dataset for automatic Road Damage Detection </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Arya%2C+D">Deeksha Arya</a>, <a href="/search/cs?searchtype=author&query=Maeda%2C+H">Hiroya Maeda</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Sanjay Kumar Ghosh</a>, <a href="/search/cs?searchtype=author&query=Toshniwal%2C+D">Durga Toshniwal</a>, <a href="/search/cs?searchtype=author&query=Sekimoto%2C+Y">Yoshihide Sekimoto</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="2209.08538v1-abstract-short" style="display: inline;"> The data article describes the Road Damage Dataset, RDD2022, which comprises 47,420 road images from six countries, Japan, India, the Czech Republic, Norway, the United States, and China. The images have been annotated with more than 55,000 instances of road damage. Four types of road damage, namely longitudinal cracks, transverse cracks, alligator cracks, and potholes, are captured in the dataset… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2209.08538v1-abstract-full').style.display = 'inline'; document.getElementById('2209.08538v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2209.08538v1-abstract-full" style="display: none;"> The data article describes the Road Damage Dataset, RDD2022, which comprises 47,420 road images from six countries, Japan, India, the Czech Republic, Norway, the United States, and China. The images have been annotated with more than 55,000 instances of road damage. Four types of road damage, namely longitudinal cracks, transverse cracks, alligator cracks, and potholes, are captured in the dataset. The annotated dataset is envisioned for developing deep learning-based methods to detect and classify road damage automatically. The dataset has been released as a part of the Crowd sensing-based Road Damage Detection Challenge (CRDDC2022). The challenge CRDDC2022 invites researchers from across the globe to propose solutions for automatic road damage detection in multiple countries. The municipalities and road agencies may utilize the RDD2022 dataset, and the models trained using RDD2022 for low-cost automatic monitoring of road conditions. Further, computer vision and machine learning researchers may use the dataset to benchmark the performance of different algorithms for other image-based applications of the same type (classification, object detection, etc.). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2209.08538v1-abstract-full').style.display = 'none'; document.getElementById('2209.08538v1-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, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 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">16 pages, 20 figures, IEEE BigData Cup - Crowdsensing-based Road damage detection challenge (CRDDC'2022)</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> E.0; J.0 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2209.01045">arXiv:2209.01045</a> <span> [<a href="https://arxiv.org/pdf/2209.01045">pdf</a>, <a href="https://arxiv.org/format/2209.01045">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Databases">cs.DB</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/10.1007/978-3-319-76348-4_96">10.1007/978-3-319-76348-4_96 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> eDWaaS: A Scalable Educational Data Warehouse as a Service </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Khan%2C+A">Anupam Khan</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S">Sourav Ghosh</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Soumya K. Ghosh</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="2209.01045v1-abstract-short" style="display: inline;"> The university management is perpetually in the process of innovating policies to improve the quality of service. Intellectual growth of the students, the popularity of university are some of the major areas that management strives to improve upon. Relevant historical data is needed in support of taking any decision. Furthermore, providing data to various university ranking frameworks is a frequen… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2209.01045v1-abstract-full').style.display = 'inline'; document.getElementById('2209.01045v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2209.01045v1-abstract-full" style="display: none;"> The university management is perpetually in the process of innovating policies to improve the quality of service. Intellectual growth of the students, the popularity of university are some of the major areas that management strives to improve upon. Relevant historical data is needed in support of taking any decision. Furthermore, providing data to various university ranking frameworks is a frequent activity in recent years. The format of such requirement changes frequently which requires efficient manual effort. Maintaining a data warehouse can be a solution to this problem. However, both in-house and outsourced implementation of a dedicated data warehouse may not be a cost-effective and smart solution. This work proposes an educational data warehouse as a service (eDWaaS) model to store historical data for multiple universities. The proposed multi-tenant schema facilitates the universities to maintain their data warehouse in a cost-effective solution. It also addresses the scalability issues in implementing such data warehouse as a service model. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2209.01045v1-abstract-full').style.display = 'none'; document.getElementById('2209.01045v1-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 September, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 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">17th International Conference on Intelligent Systems Design and Applications (ISDA 2017). Advances in Intelligent Systems and Computing, vol 736. Springer, Cham. 7th World Congress on Information and Communication Technologies (WICT 2017). December 14-16, 2017. 漏2018 Springer International Publishing AG, part of Springer Nature</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Intelligent Systems Design and Applications. ISDA 2017. Advances in Intelligent Systems and Computing, vol 736. Springer, Cham. pp. 998-1007 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2203.04159">arXiv:2203.04159</a> <span> [<a href="https://arxiv.org/pdf/2203.04159">pdf</a>, <a href="https://arxiv.org/format/2203.04159">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</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.iot.2022.100514">10.1016/j.iot.2022.100514 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> AI for Next Generation Computing: Emerging Trends and Future Directions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Gill%2C+S+S">Sukhpal Singh Gill</a>, <a href="/search/cs?searchtype=author&query=Xu%2C+M">Minxian Xu</a>, <a href="/search/cs?searchtype=author&query=Ottaviani%2C+C">Carlo Ottaviani</a>, <a href="/search/cs?searchtype=author&query=Patros%2C+P">Panos Patros</a>, <a href="/search/cs?searchtype=author&query=Bahsoon%2C+R">Rami Bahsoon</a>, <a href="/search/cs?searchtype=author&query=Shaghaghi%2C+A">Arash Shaghaghi</a>, <a href="/search/cs?searchtype=author&query=Golec%2C+M">Muhammed Golec</a>, <a href="/search/cs?searchtype=author&query=Stankovski%2C+V">Vlado Stankovski</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+H">Huaming Wu</a>, <a href="/search/cs?searchtype=author&query=Abraham%2C+A">Ajith Abraham</a>, <a href="/search/cs?searchtype=author&query=Singh%2C+M">Manmeet Singh</a>, <a href="/search/cs?searchtype=author&query=Mehta%2C+H">Harshit Mehta</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Soumya K. Ghosh</a>, <a href="/search/cs?searchtype=author&query=Baker%2C+T">Thar Baker</a>, <a href="/search/cs?searchtype=author&query=Parlikad%2C+A+K">Ajith Kumar Parlikad</a>, <a href="/search/cs?searchtype=author&query=Lutfiyya%2C+H">Hanan Lutfiyya</a>, <a href="/search/cs?searchtype=author&query=Kanhere%2C+S+S">Salil S. Kanhere</a>, <a href="/search/cs?searchtype=author&query=Sakellariou%2C+R">Rizos Sakellariou</a>, <a href="/search/cs?searchtype=author&query=Dustdar%2C+S">Schahram Dustdar</a>, <a href="/search/cs?searchtype=author&query=Rana%2C+O">Omer Rana</a>, <a href="/search/cs?searchtype=author&query=Brandic%2C+I">Ivona Brandic</a>, <a href="/search/cs?searchtype=author&query=Uhlig%2C+S">Steve Uhlig</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="2203.04159v1-abstract-short" style="display: inline;"> Autonomic computing investigates how systems can achieve (user) specified control outcomes on their own, without the intervention of a human operator. Autonomic computing fundamentals have been substantially influenced by those of control theory for closed and open-loop systems. In practice, complex systems may exhibit a number of concurrent and inter-dependent control loops. Despite research into… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.04159v1-abstract-full').style.display = 'inline'; document.getElementById('2203.04159v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2203.04159v1-abstract-full" style="display: none;"> Autonomic computing investigates how systems can achieve (user) specified control outcomes on their own, without the intervention of a human operator. Autonomic computing fundamentals have been substantially influenced by those of control theory for closed and open-loop systems. In practice, complex systems may exhibit a number of concurrent and inter-dependent control loops. Despite research into autonomic models for managing computer resources, ranging from individual resources (e.g., web servers) to a resource ensemble (e.g., multiple resources within a data center), research into integrating Artificial Intelligence (AI) and Machine Learning (ML) to improve resource autonomy and performance at scale continues to be a fundamental challenge. The integration of AI/ML to achieve such autonomic and self-management of systems can be achieved at different levels of granularity, from full to human-in-the-loop automation. In this article, leading academics, researchers, practitioners, engineers, and scientists in the fields of cloud computing, AI/ML, and quantum computing join to discuss current research and potential future directions for these fields. Further, we discuss challenges and opportunities for leveraging AI and ML in next generation computing for emerging computing paradigms, including cloud, fog, edge, serverless and quantum computing environments. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.04159v1-abstract-full').style.display = 'none'; document.getElementById('2203.04159v1-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> 5 March, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 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">Accepted for Publication in Elsevier IoT Journal, 2022</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2202.04882">arXiv:2202.04882</a> <span> [<a href="https://arxiv.org/pdf/2202.04882">pdf</a>, <a href="https://arxiv.org/format/2202.04882">other</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="Audio and Speech Processing">eess.AS</span> </div> </div> <p class="title is-5 mathjax"> Auditory Model based Phase-Aware Bayesian Spectral Amplitude Estimator for Single-Channel Speech Enhancement </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Samui%2C+S">Suman Samui</a>, <a href="/search/cs?searchtype=author&query=Chakrabarti%2C+I">Indrajit Chakrabarti</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Soumya K. Ghosh</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="2202.04882v1-abstract-short" style="display: inline;"> Bayesian estimation of short-time spectral amplitude is one of the most predominant approaches for the enhancement of the noise corrupted speech. The performance of these estimators are usually significantly improved when any perceptually relevant cost function is considered. On the other hand, the recent progress in the phase-based speech signal processing have shown that the phase-only enhanceme… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.04882v1-abstract-full').style.display = 'inline'; document.getElementById('2202.04882v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2202.04882v1-abstract-full" style="display: none;"> Bayesian estimation of short-time spectral amplitude is one of the most predominant approaches for the enhancement of the noise corrupted speech. The performance of these estimators are usually significantly improved when any perceptually relevant cost function is considered. On the other hand, the recent progress in the phase-based speech signal processing have shown that the phase-only enhancement based on spectral phase estimation methods can also provide joint improvement in the perceived speech quality and intelligibility, even in low SNR conditions. In this paper, to take advantage of both the perceptually motivated cost function involving STSAs of estimated and true clean speech and utilizing the prior spectral phase information, we have derived a phase-aware Bayesian STSA estimator. The parameters of the cost function are chosen based on the characteristics of the human auditory system, namely, the dynamic compressive nonlinearity of the cochlea, the perceived loudness theory and the simultaneous masking properties of the ear. This type of parameter selection scheme results in more noise reduction while limiting the speech distortion. The derived STSA estimator is optimal in the MMSE sense if the prior phase information is available. In practice, however, typically only an estimate of the clean speech phase can be obtained via employing different types of spectral phase estimation techniques which have been developed throughout the last few years. In a blind setup, we have evaluated the proposed Bayesian STSA estimator with different types of standard phase estimation methods available in the literature. Experimental results have shown that the proposed estimator can achieve substantial improvement in performance than the traditional phase-blind approaches. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.04882v1-abstract-full').style.display = 'none'; document.getElementById('2202.04882v1-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 February, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 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">Submitted to IEEE</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2107.14047">arXiv:2107.14047</a> <span> [<a href="https://arxiv.org/pdf/2107.14047">pdf</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 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/10.1109/TALE48869.2020.9368439">10.1109/TALE48869.2020.9368439 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Measuring Domain Knowledge for Early Prediction of Student Performance: A Semantic Approach </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Khan%2C+A">Anupam Khan</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S">Sourav Ghosh</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Soumya K. Ghosh</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="2107.14047v1-abstract-short" style="display: inline;"> The growing popularity of data mining catalyses the researchers to explore various exciting aspects of education. Early prediction of student performance is an emerging area among them. The researchers have used various predictors in performance modelling studies. Although prior cognition can affect student performance, establishing their relationship is still an open research challenge. Quantifyi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2107.14047v1-abstract-full').style.display = 'inline'; document.getElementById('2107.14047v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2107.14047v1-abstract-full" style="display: none;"> The growing popularity of data mining catalyses the researchers to explore various exciting aspects of education. Early prediction of student performance is an emerging area among them. The researchers have used various predictors in performance modelling studies. Although prior cognition can affect student performance, establishing their relationship is still an open research challenge. Quantifying the knowledge from readily available data is the major challenge here. We have proposed a semantic approach for this purpose. Association mining on nearly 0.35 million observations establishes that prior cognition impacts the student performance. The proposed approach of measuring domain knowledge can help the early performance modelling studies to use it as a predictor. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2107.14047v1-abstract-full').style.display = 'none'; document.getElementById('2107.14047v1-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 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">Published in 2020 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE). 8 pages, 5 figures (includes 16 plots). Accessible at https://ieeexplore.ieee.org/document/9368439</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> 2020 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE), Takamatsu, Japan, 2020, pp. 444-451 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2106.04804">arXiv:2106.04804</a> <span> [<a href="https://arxiv.org/pdf/2106.04804">pdf</a>, <a href="https://arxiv.org/format/2106.04804">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"> EMFlow: Data Imputation in Latent Space via EM and Deep Flow Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ma%2C+Q">Qi Ma</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Sujit K. Ghosh</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="2106.04804v2-abstract-short" style="display: inline;"> The presence of missing values within high-dimensional data is an ubiquitous problem for many applied sciences. A serious limitation of many available data mining and machine learning methods is their inability to handle partially missing values and so an integrated approach that combines imputation and model estimation is vital for down-stream analysis. A computationally fast algorithm, called EM… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2106.04804v2-abstract-full').style.display = 'inline'; document.getElementById('2106.04804v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2106.04804v2-abstract-full" style="display: none;"> The presence of missing values within high-dimensional data is an ubiquitous problem for many applied sciences. A serious limitation of many available data mining and machine learning methods is their inability to handle partially missing values and so an integrated approach that combines imputation and model estimation is vital for down-stream analysis. A computationally fast algorithm, called EMFlow, is introduced that performs imputation in a latent space via an online version of Expectation-Maximization (EM) algorithm by using a normalizing flow (NF) model which maps the data space to a latent space. The proposed EMFlow algorithm is iterative, involving updating the parameters of online EM and NF alternatively. Extensive experimental results for high-dimensional multivariate and image datasets are presented to illustrate the superior performance of the EMFlow compared to a couple of recently available methods in terms of both predictive accuracy and speed of algorithmic convergence. We provide code for all our experiments. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2106.04804v2-abstract-full').style.display = 'none'; document.getElementById('2106.04804v2-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> 31 July, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 June, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2105.06255">arXiv:2105.06255</a> <span> [<a href="https://arxiv.org/pdf/2105.06255">pdf</a>, <a href="https://arxiv.org/format/2105.06255">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="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Machine Assistance for Credit Card Approval? Random Wheel can Recommend and Explain </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Khan%2C+A">Anupam Khan</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Soumya K. Ghosh</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="2105.06255v1-abstract-short" style="display: inline;"> Approval of credit card application is one of the censorious business decision the bankers are usually taking regularly. The growing number of new card applications and the enormous outstanding amount of credit card bills during the recent pandemic make this even more challenging nowadays. Some of the previous studies suggest the usage of machine intelligence for automating the approval process to… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2105.06255v1-abstract-full').style.display = 'inline'; document.getElementById('2105.06255v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2105.06255v1-abstract-full" style="display: none;"> Approval of credit card application is one of the censorious business decision the bankers are usually taking regularly. The growing number of new card applications and the enormous outstanding amount of credit card bills during the recent pandemic make this even more challenging nowadays. Some of the previous studies suggest the usage of machine intelligence for automating the approval process to mitigate this challenge. However, the effectiveness of such automation may depend on the richness of the training dataset and model efficiency. We have recently developed a novel classifier named random wheel which provides a more interpretable output. In this work, we have used an enhanced version of random wheel to facilitate a trustworthy recommendation for credit card approval process. It not only produces more accurate and precise recommendation but also provides an interpretable confidence measure. Besides, it explains the machine recommendation for each credit card application as well. The availability of recommendation confidence and explanation could bring more trust in the machine provided intelligence which in turn can enhance the efficiency of the credit card approval process. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2105.06255v1-abstract-full').style.display = 'none'; document.getElementById('2105.06255v1-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> 11 May, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 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">14 pages, 8 figures, 2 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/2104.01600">arXiv:2104.01600</a> <span> [<a href="https://arxiv.org/pdf/2104.01600">pdf</a>, <a href="https://arxiv.org/format/2104.01600">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"> STOPPAGE: Spatio-temporal Data Driven Cloud-Fog-Edge Computing Framework for Pandemic Monitoring and Management </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ghosh%2C+S">Shreya Ghosh</a>, <a href="/search/cs?searchtype=author&query=Mukherjee%2C+A">Anwesha Mukherjee</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Soumya K Ghosh</a>, <a href="/search/cs?searchtype=author&query=Buyya%2C+R">Rajkumar Buyya</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="2104.01600v1-abstract-short" style="display: inline;"> Several researches and evidence show the increasing likelihood of pandemics (large-scale outbreaks of infectious disease) which has far reaching sequels in all aspects of human lives ranging from rapid mortality rates to economic and social disruption across the world. In the recent time, COVID-19 (Coronavirus Disease 2019) pandemic disrupted normal human lives, and motivated by the urgent need of… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2104.01600v1-abstract-full').style.display = 'inline'; document.getElementById('2104.01600v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2104.01600v1-abstract-full" style="display: none;"> Several researches and evidence show the increasing likelihood of pandemics (large-scale outbreaks of infectious disease) which has far reaching sequels in all aspects of human lives ranging from rapid mortality rates to economic and social disruption across the world. In the recent time, COVID-19 (Coronavirus Disease 2019) pandemic disrupted normal human lives, and motivated by the urgent need of combating COVID-19, researchers have put significant efforts in modelling and analysing the disease spread patterns for effective preventive measures (in addition to developing pharmaceutical solutions, like vaccine). In this regards, it is absolutely necessary to develop an analytics framework by extracting and incorporating the knowledge of heterogeneous datasources to deliver insights in improving administrative policy and enhance the preparedness to combat the pandemic. Specifically, human mobility, travel history and other transport statistics have significant impacts on the spread of any infectious disease. In this direction, this paper proposes a spatio-temporal knowledge mining framework, named STOPPAGE to model the impact of human mobility and other contextual information over large geographic area in different temporal scales. The framework has two major modules: (i) Spatio-temporal data and computing infrastructure using fog/edge based architecture; and (ii) Spatio-temporal data analytics module to efficiently extract knowledge from heterogeneous data sources. Typically, we develop a Pandemic-knowledge graph to discover correlations among mobility information and disease spread, a deep learning architecture to predict the next hot-spot zones; and provide necessary support in home-health monitoring utilizing Femtolet and fog/edge based solutions. The experimental evaluations on real-life datasets related to COVID-19 in India illustrate the efficacy of the proposed methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2104.01600v1-abstract-full').style.display = 'none'; document.getElementById('2104.01600v1-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 April, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 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">16 pages, 11 figures, pre-print submitted to journal</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2011.08740">arXiv:2011.08740</a> <span> [<a href="https://arxiv.org/pdf/2011.08740">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> <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/10.1109/BigData50022.2020.9377790">10.1109/BigData50022.2020.9377790 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Global Road Damage Detection: State-of-the-art Solutions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Arya%2C+D">Deeksha Arya</a>, <a href="/search/cs?searchtype=author&query=Maeda%2C+H">Hiroya Maeda</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Sanjay Kumar Ghosh</a>, <a href="/search/cs?searchtype=author&query=Toshniwal%2C+D">Durga Toshniwal</a>, <a href="/search/cs?searchtype=author&query=Omata%2C+H">Hiroshi Omata</a>, <a href="/search/cs?searchtype=author&query=Kashiyama%2C+T">Takehiro Kashiyama</a>, <a href="/search/cs?searchtype=author&query=Sekimoto%2C+Y">Yoshihide Sekimoto</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="2011.08740v1-abstract-short" style="display: inline;"> This paper summarizes the Global Road Damage Detection Challenge (GRDDC), a Big Data Cup organized as a part of the IEEE International Conference on Big Data'2020. The Big Data Cup challenges involve a released dataset and a well-defined problem with clear evaluation metrics. The challenges run on a data competition platform that maintains a leaderboard for the participants. In the presented case,… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2011.08740v1-abstract-full').style.display = 'inline'; document.getElementById('2011.08740v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2011.08740v1-abstract-full" style="display: none;"> This paper summarizes the Global Road Damage Detection Challenge (GRDDC), a Big Data Cup organized as a part of the IEEE International Conference on Big Data'2020. The Big Data Cup challenges involve a released dataset and a well-defined problem with clear evaluation metrics. The challenges run on a data competition platform that maintains a leaderboard for the participants. In the presented case, the data constitute 26336 road images collected from India, Japan, and the Czech Republic to propose methods for automatically detecting road damages in these countries. In total, 121 teams from several countries registered for this competition. The submitted solutions were evaluated using two datasets test1 and test2, comprising 2,631 and 2,664 images. This paper encapsulates the top 12 solutions proposed by these teams. The best performing model utilizes YOLO-based ensemble learning to yield an F1 score of 0.67 on test1 and 0.66 on test2. The paper concludes with a review of the facets that worked well for the presented challenge and those that could be improved in future challenges. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2011.08740v1-abstract-full').style.display = 'none'; document.getElementById('2011.08740v1-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, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 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">11 Pages, 2 Figures, 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/2008.13101">arXiv:2008.13101</a> <span> [<a href="https://arxiv.org/pdf/2008.13101">pdf</a>, <a href="https://arxiv.org/format/2008.13101">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="Computers and Society">cs.CY</span> </div> </div> <p class="title is-5 mathjax"> Transfer Learning-based Road Damage Detection for Multiple Countries </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Arya%2C+D">Deeksha Arya</a>, <a href="/search/cs?searchtype=author&query=Maeda%2C+H">Hiroya Maeda</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Sanjay Kumar Ghosh</a>, <a href="/search/cs?searchtype=author&query=Toshniwal%2C+D">Durga Toshniwal</a>, <a href="/search/cs?searchtype=author&query=Mraz%2C+A">Alexander Mraz</a>, <a href="/search/cs?searchtype=author&query=Kashiyama%2C+T">Takehiro Kashiyama</a>, <a href="/search/cs?searchtype=author&query=Sekimoto%2C+Y">Yoshihide Sekimoto</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="2008.13101v1-abstract-short" style="display: inline;"> Many municipalities and road authorities seek to implement automated evaluation of road damage. However, they often lack technology, know-how, and funds to afford state-of-the-art equipment for data collection and analysis of road damages. Although some countries, like Japan, have developed less expensive and readily available Smartphone-based methods for automatic road condition monitoring, other… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2008.13101v1-abstract-full').style.display = 'inline'; document.getElementById('2008.13101v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2008.13101v1-abstract-full" style="display: none;"> Many municipalities and road authorities seek to implement automated evaluation of road damage. However, they often lack technology, know-how, and funds to afford state-of-the-art equipment for data collection and analysis of road damages. Although some countries, like Japan, have developed less expensive and readily available Smartphone-based methods for automatic road condition monitoring, other countries still struggle to find efficient solutions. This work makes the following contributions in this context. Firstly, it assesses the usability of the Japanese model for other countries. Secondly, it proposes a large-scale heterogeneous road damage dataset comprising 26620 images collected from multiple countries using smartphones. Thirdly, we propose generalized models capable of detecting and classifying road damages in more than one country. Lastly, we provide recommendations for readers, local agencies, and municipalities of other countries when one other country publishes its data and model for automatic road damage detection and classification. Our dataset is available at (https://github.com/sekilab/RoadDamageDetector/). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2008.13101v1-abstract-full').style.display = 'none'; document.getElementById('2008.13101v1-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 August, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 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">16 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/2007.08121">arXiv:2007.08121</a> <span> [<a href="https://arxiv.org/pdf/2007.08121">pdf</a>, <a href="https://arxiv.org/format/2007.08121">other</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="Systems and Control">eess.SY</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/10.1145/3411498.3419966">10.1145/3411498.3419966 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Skip to Secure: Securing Cyber-physical Control Loops with Intentionally Skipped Executions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Adhikary%2C+S">Sunandan Adhikary</a>, <a href="/search/cs?searchtype=author&query=Koley%2C+I">Ipsita Koley</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S">Sumana Ghosh</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Saurav Kumar Ghosh</a>, <a href="/search/cs?searchtype=author&query=Dey%2C+S">Soumyajit Dey</a>, <a href="/search/cs?searchtype=author&query=Mukhopadhyay%2C+D">Debdeep Mukhopadhyay</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="2007.08121v1-abstract-short" style="display: inline;"> We consider the problem of provably securing a given control loop implementation in the presence of adversarial interventions on data exchange between plant and controller. Such interventions can be thwarted using continuously operating monitoring systems and also cryptographic techniques, both of which consume network and computational resources. We provide a principled approach for intentional s… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.08121v1-abstract-full').style.display = 'inline'; document.getElementById('2007.08121v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2007.08121v1-abstract-full" style="display: none;"> We consider the problem of provably securing a given control loop implementation in the presence of adversarial interventions on data exchange between plant and controller. Such interventions can be thwarted using continuously operating monitoring systems and also cryptographic techniques, both of which consume network and computational resources. We provide a principled approach for intentional skipping of control loop executions which may qualify as a useful control theoretic countermeasure against stealthy attacks which violate message integrity and authenticity. As is evident from our experiments, such a control theoretic counter-measure helps in lowering the cryptographic security measure overhead and resulting resource consumption in Control Area Network (CAN) based automotive CPS without compromising performance and safety. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.08121v1-abstract-full').style.display = 'none'; document.getElementById('2007.08121v1-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 July, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 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">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/2006.11383">arXiv:2006.11383</a> <span> [<a href="https://arxiv.org/pdf/2006.11383">pdf</a>, <a href="https://arxiv.org/format/2006.11383">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">stat.ML</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 Non-Iterative Quantile Change Detection Method in Mixture Model with Heavy-Tailed Components </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Li%2C+Y">Yuantong Li</a>, <a href="/search/cs?searchtype=author&query=Ma%2C+Q">Qi Ma</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Sujit K. Ghosh</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="2006.11383v1-abstract-short" style="display: inline;"> Estimating parameters of mixture model has wide applications ranging from classification problems to estimating of complex distributions. Most of the current literature on estimating the parameters of the mixture densities are based on iterative Expectation Maximization (EM) type algorithms which require the use of either taking expectations over the latent label variables or generating samples fr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.11383v1-abstract-full').style.display = 'inline'; document.getElementById('2006.11383v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2006.11383v1-abstract-full" style="display: none;"> Estimating parameters of mixture model has wide applications ranging from classification problems to estimating of complex distributions. Most of the current literature on estimating the parameters of the mixture densities are based on iterative Expectation Maximization (EM) type algorithms which require the use of either taking expectations over the latent label variables or generating samples from the conditional distribution of such latent labels using the Bayes rule. Moreover, when the number of components is unknown, the problem becomes computationally more demanding due to well-known label switching issues \cite{richardson1997bayesian}. In this paper, we propose a robust and quick approach based on change-point methods to determine the number of mixture components that works for almost any location-scale families even when the components are heavy tailed (e.g., Cauchy). We present several numerical illustrations by comparing our method with some of popular methods available in the literature using simulated data and real case studies. The proposed method is shown be as much as 500 times faster than some of the competing methods and are also shown to be more accurate in estimating the mixture distributions by goodness-of-fit tests. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.11383v1-abstract-full').style.display = 'none'; document.getElementById('2006.11383v1-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 June, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2002.12412">arXiv:2002.12412</a> <span> [<a href="https://arxiv.org/pdf/2002.12412">pdf</a>, <a href="https://arxiv.org/ps/2002.12412">ps</a>, <a href="https://arxiv.org/format/2002.12412">other</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="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Formal Synthesis of Monitoring and Detection Systems for Secure CPS Implementations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Koley%2C+I">Ipsita Koley</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Saurav Kumar Ghosh</a>, <a href="/search/cs?searchtype=author&query=Dey%2C+S">Soumyajit Dey</a>, <a href="/search/cs?searchtype=author&query=Mukhopadhyay%2C+D">Debdeep Mukhopadhyay</a>, <a href="/search/cs?searchtype=author&query=N%2C+A+K+K">Amogh Kashyap K N</a>, <a href="/search/cs?searchtype=author&query=Singh%2C+S+K">Sachin Kumar Singh</a>, <a href="/search/cs?searchtype=author&query=Lokesh%2C+L">Lavanya Lokesh</a>, <a href="/search/cs?searchtype=author&query=Purakkal%2C+J+N">Jithin Nalu Purakkal</a>, <a href="/search/cs?searchtype=author&query=Sinha%2C+N">Nishant Sinha</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="2002.12412v1-abstract-short" style="display: inline;"> We consider the problem of securing a given control loop implementation of a cyber-physical system (CPS) in the presence of Man-in-the-Middle attacks on data exchange between plant and controller over a compromised network. To this end, there exist various detection schemes that provide mathematical guarantees against such attacks for the theoretical control model. However, such guarantees may not… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.12412v1-abstract-full').style.display = 'inline'; document.getElementById('2002.12412v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2002.12412v1-abstract-full" style="display: none;"> We consider the problem of securing a given control loop implementation of a cyber-physical system (CPS) in the presence of Man-in-the-Middle attacks on data exchange between plant and controller over a compromised network. To this end, there exist various detection schemes that provide mathematical guarantees against such attacks for the theoretical control model. However, such guarantees may not hold for the actual control software implementation. In this article, we propose a formal approach towards synthesizing attack detectors with varying thresholds which can prevent performance degrading stealthy attacks while minimizing false alarms. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.12412v1-abstract-full').style.display = 'none'; document.getElementById('2002.12412v1-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> 27 February, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 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">4 Pages, Date 2019 Poster Presentation</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1911.07351">arXiv:1911.07351</a> <span> [<a href="https://arxiv.org/pdf/1911.07351">pdf</a>, <a href="https://arxiv.org/ps/1911.07351">ps</a>, <a href="https://arxiv.org/format/1911.07351">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="Distributed, Parallel, and Cluster Computing">cs.DC</span> </div> </div> <p class="title is-5 mathjax"> Caching Techniques to Improve Latency in Serverless Architectures </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ghosh%2C+B+C">Bishakh Chandra Ghosh</a>, <a href="/search/cs?searchtype=author&query=Addya%2C+S+K">Sourav Kanti Addya</a>, <a href="/search/cs?searchtype=author&query=Somy%2C+N+B">Nishant Baranwal Somy</a>, <a href="/search/cs?searchtype=author&query=Nath%2C+S+B">Shubha Brata Nath</a>, <a href="/search/cs?searchtype=author&query=Chakraborty%2C+S">Sandip Chakraborty</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Soumya K Ghosh</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="1911.07351v1-abstract-short" style="display: inline;"> Serverless computing has gained a significant traction in recent times because of its simplicity of development, deployment and fine-grained billing. However, while implementing complex services comprising databases, file stores, or more than one serverless function, the performance in terms of latency of serving requests often degrades severely. In this work, we analyze different serverless archi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.07351v1-abstract-full').style.display = 'inline'; document.getElementById('1911.07351v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1911.07351v1-abstract-full" style="display: none;"> Serverless computing has gained a significant traction in recent times because of its simplicity of development, deployment and fine-grained billing. However, while implementing complex services comprising databases, file stores, or more than one serverless function, the performance in terms of latency of serving requests often degrades severely. In this work, we analyze different serverless architectures with AWS Lambda services and compare their performance in terms of latency with a traditional virtual machine (VM) based approach. We observe that database access latency in serverless architecture is almost 14 times than that in VM based setup. Further, we introduce some caching strategies which can improve the response time significantly, and compare their performance. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.07351v1-abstract-full').style.display = 'none'; document.getElementById('1911.07351v1-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, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2019. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1909.08795">arXiv:1909.08795</a> <span> [<a href="https://arxiv.org/pdf/1909.08795">pdf</a>, <a href="https://arxiv.org/format/1909.08795">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Discrete Mathematics">cs.DM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Data Structures and Algorithms">cs.DS</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/10.1007/978-3-030-39219-2_9">10.1007/978-3-030-39219-2_9 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Hardness and approximation for the geodetic set problem in some graph classes </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Chakraborty%2C+D">Dibyayan Chakraborty</a>, <a href="/search/cs?searchtype=author&query=Foucaud%2C+F">Florent Foucaud</a>, <a href="/search/cs?searchtype=author&query=Gahlawat%2C+H">Harmender Gahlawat</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Subir Kumar Ghosh</a>, <a href="/search/cs?searchtype=author&query=Roy%2C+B">Bodhayan Roy</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="1909.08795v1-abstract-short" style="display: inline;"> In this paper, we study the computational complexity of finding the \emph{geodetic number} of graphs. A set of vertices $S$ of a graph $G$ is a \emph{geodetic set} if any vertex of $G$ lies in some shortest path between some pair of vertices from $S$. The \textsc{Minimum Geodetic Set (MGS)} problem is to find a geodetic set with minimum cardinality. In this paper, we prove that solving the \textsc… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1909.08795v1-abstract-full').style.display = 'inline'; document.getElementById('1909.08795v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1909.08795v1-abstract-full" style="display: none;"> In this paper, we study the computational complexity of finding the \emph{geodetic number} of graphs. A set of vertices $S$ of a graph $G$ is a \emph{geodetic set} if any vertex of $G$ lies in some shortest path between some pair of vertices from $S$. The \textsc{Minimum Geodetic Set (MGS)} problem is to find a geodetic set with minimum cardinality. In this paper, we prove that solving the \textsc{MGS} problem is NP-hard on planar graphs with a maximum degree six and line graphs. We also show that unless $P=NP$, there is no polynomial time algorithm to solve the \textsc{MGS} problem with sublogarithmic approximation factor (in terms of the number of vertices) even on graphs with diameter $2$. On the positive side, we give an $O\left(\sqrt[3]{n}\log n\right)$-approximation algorithm for the \textsc{MGS} problem on general graphs of order $n$. We also give a $3$-approximation algorithm for the \textsc{MGS} problem on the family of solid grid graphs which is a subclass of planar graphs. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1909.08795v1-abstract-full').style.display = 'none'; document.getElementById('1909.08795v1-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 September, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Proceedings CALDAM 2020. Lecture Notes in Computer Science 12016:102-115, 2020 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1905.10622">arXiv:1905.10622</a> <span> [<a href="https://arxiv.org/pdf/1905.10622">pdf</a>, <a href="https://arxiv.org/format/1905.10622">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 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.patrec.2021.06.011">10.1016/j.patrec.2021.06.011 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Beyond Visual Semantics: Exploring the Role of Scene Text in Image Understanding </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Dey%2C+A+U">Arka Ujjal Dey</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Suman Kumar Ghosh</a>, <a href="/search/cs?searchtype=author&query=Valveny%2C+E">Ernest Valveny</a>, <a href="/search/cs?searchtype=author&query=Harit%2C+G">Gaurav Harit</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="1905.10622v3-abstract-short" style="display: inline;"> Images with visual and scene text content are ubiquitous in everyday life. However, current image interpretation systems are mostly limited to using only the visual features, neglecting to leverage the scene text content. In this paper, we propose to jointly use scene text and visual channels for robust semantic interpretation of images. We do not only extract and encode visual and scene text cues… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1905.10622v3-abstract-full').style.display = 'inline'; document.getElementById('1905.10622v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1905.10622v3-abstract-full" style="display: none;"> Images with visual and scene text content are ubiquitous in everyday life. However, current image interpretation systems are mostly limited to using only the visual features, neglecting to leverage the scene text content. In this paper, we propose to jointly use scene text and visual channels for robust semantic interpretation of images. We do not only extract and encode visual and scene text cues, but also model their interplay to generate a contextual joint embedding with richer semantics. The contextual embedding thus generated is applied to retrieval and classification tasks on multimedia images, with scene text content, to demonstrate its effectiveness. In the retrieval framework, we augment our learned text-visual semantic representation with scene text cues, to mitigate vocabulary misses that may have occurred during the semantic embedding. To deal with irrelevant or erroneous recognition of scene text, we also apply query-based attention to our text channel. We show how the multi-channel approach, involving visual semantics and scene text, improves upon state of the art. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1905.10622v3-abstract-full').style.display = 'none'; document.getElementById('1905.10622v3-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 December, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 25 May, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2019. </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">The paper is under consideration at Pattern Recognition Letters</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1904.08624">arXiv:1904.08624</a> <span> [<a href="https://arxiv.org/pdf/1904.08624">pdf</a>, <a href="https://arxiv.org/ps/1904.08624">ps</a>, <a href="https://arxiv.org/format/1904.08624">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Geometry">cs.CG</span> </div> </div> <p class="title is-5 mathjax"> On conflict-free chromatic guarding of simple polygons </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=%C3%87a%C4%9F%C4%B1r%C4%B1c%C4%B1%2C+O">Onur 脟a臒谋r谋c谋</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Subir Kumar Ghosh</a>, <a href="/search/cs?searchtype=author&query=Hlin%C4%9Bn%C3%BD%2C+P">Petr Hlin臎n媒</a>, <a href="/search/cs?searchtype=author&query=Roy%2C+B">Bodhayan Roy</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="1904.08624v3-abstract-short" style="display: inline;"> We study the problem of colouring the vertices of a polygon, such that every viewer in it can see a unique colour. The goal is to minimise the number of colours used. This is also known as the conflict-free chromatic guarding problem with vertex guards, and is motivated, e.g., by the problem of radio frequency assignment to sensors placed at the polygon vertices. We first study the scenario in whi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1904.08624v3-abstract-full').style.display = 'inline'; document.getElementById('1904.08624v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1904.08624v3-abstract-full" style="display: none;"> We study the problem of colouring the vertices of a polygon, such that every viewer in it can see a unique colour. The goal is to minimise the number of colours used. This is also known as the conflict-free chromatic guarding problem with vertex guards, and is motivated, e.g., by the problem of radio frequency assignment to sensors placed at the polygon vertices. We first study the scenario in which viewers can be all points of the polygon (such as a mobile robot which moves in the interior of the polygon). We efficiently solve the related problem of minimising the number of guards and approximate (up to only an additive error) the number of colours required in the special case of polygons called funnels. Then we give an upper bound of O(log^2 n) colours on n-vertex weak visibility polygons, by decomposing the problem into sub-funnels. This bound generalises to all simple polygons. We briefly look also at the second scenario, in which the viewers are only the vertices of the polygon. We show a lower bound of 3 colours in the general case of simple polygons and conjecture that this is tight. We also prove that already deciding whether 1 or 2 colours are enough is NP-complete. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1904.08624v3-abstract-full').style.display = 'none'; document.getElementById('1904.08624v3-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> 5 April, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 18 April, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2019. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1812.10095">arXiv:1812.10095</a> <span> [<a href="https://arxiv.org/pdf/1812.10095">pdf</a>, <a href="https://arxiv.org/format/1812.10095">other</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"> Tensor-Train Long Short-Term Memory for Monaural Speech Enhancement </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Samui%2C+S">Suman Samui</a>, <a href="/search/cs?searchtype=author&query=Chakrabarti%2C+I">Indrajit Chakrabarti</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Soumya K. Ghosh</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="1812.10095v1-abstract-short" style="display: inline;"> In recent years, Long Short-Term Memory (LSTM) has become a popular choice for speech separation and speech enhancement task. The capability of LSTM network can be enhanced by widening and adding more layers. However, this would introduce millions of parameters in the network and also increase the requirement of computational resources. These limitations hinders the efficient implementation of RNN… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1812.10095v1-abstract-full').style.display = 'inline'; document.getElementById('1812.10095v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1812.10095v1-abstract-full" style="display: none;"> In recent years, Long Short-Term Memory (LSTM) has become a popular choice for speech separation and speech enhancement task. The capability of LSTM network can be enhanced by widening and adding more layers. However, this would introduce millions of parameters in the network and also increase the requirement of computational resources. These limitations hinders the efficient implementation of RNN models in low-end devices such as mobile phones and embedded systems with limited memory. To overcome these issues, we proposed to use an efficient alternative approach of reducing parameters by representing the weight matrix parameters of LSTM based on Tensor-Train (TT) format. We called this Tensor-Train factorized LSTM as TT-LSTM model. Based on this TT-LSTM units, we proposed a deep TensorNet model for single-channel speech enhancement task. Experimental results in various test conditions and in terms of standard speech quality and intelligibility metrics, demonstrated that the proposed deep TT-LSTM based speech enhancement framework can achieve competitive performances with the state-of-the-art uncompressed RNN model, even though the proposed model architecture is orders of magnitude less complex. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1812.10095v1-abstract-full').style.display = 'none'; document.getElementById('1812.10095v1-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 December, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2018. </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 IEEE Signal Processing Letters</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1811.07325">arXiv:1811.07325</a> <span> [<a href="https://arxiv.org/pdf/1811.07325">pdf</a>, <a href="https://arxiv.org/ps/1811.07325">ps</a>, <a href="https://arxiv.org/format/1811.07325">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> </div> </div> <p class="title is-5 mathjax"> Stark: Fast and Scalable Strassen's Matrix Multiplication using Apache Spark </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Misra%2C+C">Chandan Misra</a>, <a href="/search/cs?searchtype=author&query=Bhattacharya%2C+S">Sourangshu Bhattacharya</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Soumya K. Ghosh</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="1811.07325v2-abstract-short" style="display: inline;"> This paper presents a new fast, highly scalable distributed matrix multiplication algorithm on Apache Spark, called Stark, based on Strassen's matrix multiplication algorithm. Stark preserves Strassen's 7 multiplications scheme in a distributed environment and thus achieves faster execution. It is based on two new ideas; it creates a recursion tree of computation where each level of such tree corr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1811.07325v2-abstract-full').style.display = 'inline'; document.getElementById('1811.07325v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1811.07325v2-abstract-full" style="display: none;"> This paper presents a new fast, highly scalable distributed matrix multiplication algorithm on Apache Spark, called Stark, based on Strassen's matrix multiplication algorithm. Stark preserves Strassen's 7 multiplications scheme in a distributed environment and thus achieves faster execution. It is based on two new ideas; it creates a recursion tree of computation where each level of such tree corresponds to division and combination of distributed matrix blocks in the form of Resilient Distributed Datasets(RDDs); It processes each divide and combine step in parallel and memorize the sub-matrices by intelligently tagging matrix blocks in it. To the best of our knowledge, Stark is the first Strassen's implementation in Spark platform. We show experimentally that Stark has a strong scalability with increasing matrix size enabling us to multiply two (16384 x 16384) matrices with 28% and 36% less wall clock time than Marlin and MLLib respectively, state-of-the-art matrix multiplication approaches based on Spark. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1811.07325v2-abstract-full').style.display = 'none'; document.getElementById('1811.07325v2-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> 22 November, 2018; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 18 November, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2018. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1808.09801">arXiv:1808.09801</a> <span> [<a href="https://arxiv.org/pdf/1808.09801">pdf</a>, <a href="https://arxiv.org/format/1808.09801">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="Social and Information Networks">cs.SI</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/10.1109/SMARTCOMP.2018.00091">10.1109/SMARTCOMP.2018.00091 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> PS-Sim: A Framework for Scalable Simulation of Participatory Sensing Data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Barnwal%2C+R+P">Rajesh P Barnwal</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+N">Nirnay Ghosh</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Soumya K Ghosh</a>, <a href="/search/cs?searchtype=author&query=Das%2C+S+K">Sajal K 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="1808.09801v1-abstract-short" style="display: inline;"> Emergence of smartphone and the participatory sensing (PS) paradigm have paved the way for a new variant of pervasive computing. In PS, human user performs sensing tasks and generates notifications, typically in lieu of incentives. These notifications are real-time, large-volume, and multi-modal, which are eventually fused by the PS platform to generate a summary. One major limitation with PS is t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1808.09801v1-abstract-full').style.display = 'inline'; document.getElementById('1808.09801v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1808.09801v1-abstract-full" style="display: none;"> Emergence of smartphone and the participatory sensing (PS) paradigm have paved the way for a new variant of pervasive computing. In PS, human user performs sensing tasks and generates notifications, typically in lieu of incentives. These notifications are real-time, large-volume, and multi-modal, which are eventually fused by the PS platform to generate a summary. One major limitation with PS is the sparsity of notifications owing to lack of active participation, thus inhibiting large scale real-life experiments for the research community. On the flip side, research community always needs ground truth to validate the efficacy of the proposed models and algorithms. Most of the PS applications involve human mobility and report generation following sensing of any event of interest in the adjacent environment. This work is an attempt to study and empirically model human participation behavior and event occurrence distributions through development of a location-sensitive data simulation framework, called PS-Sim. From extensive experiments it has been observed that the synthetic data generated by PS-Sim replicates real participation and event occurrence behaviors in PS applications, which may be considered for validation purpose in absence of the groundtruth. As a proof-of-concept, we have used real-life dataset from a vehicular traffic management application to train the models in PS-Sim and cross-validated the simulated data with other parts of the same dataset. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1808.09801v1-abstract-full').style.display = 'none'; document.getElementById('1808.09801v1-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 August, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2018. </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 and Appeared in Proceedings of IEEE International Conference on Smart Computing (SMARTCOMP-2018)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1806.08279">arXiv:1806.08279</a> <span> [<a href="https://arxiv.org/pdf/1806.08279">pdf</a>, <a href="https://arxiv.org/format/1806.08279">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"> Don't only Feel Read: Using Scene text to understand advertisements </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Dey%2C+A+U">Arka Ujjal Dey</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Suman K. Ghosh</a>, <a href="/search/cs?searchtype=author&query=Valveny%2C+E">Ernest Valveny</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="1806.08279v3-abstract-short" style="display: inline;"> We propose a framework for automated classification of Advertisement Images, using not just Visual features but also Textual cues extracted from embedded text. Our approach takes inspiration from the assumption that Ad images contain meaningful textual content, that can provide discriminative semantic interpretetion, and can thus aid in classifcation tasks. To this end, we develop a framework usin… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1806.08279v3-abstract-full').style.display = 'inline'; document.getElementById('1806.08279v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1806.08279v3-abstract-full" style="display: none;"> We propose a framework for automated classification of Advertisement Images, using not just Visual features but also Textual cues extracted from embedded text. Our approach takes inspiration from the assumption that Ad images contain meaningful textual content, that can provide discriminative semantic interpretetion, and can thus aid in classifcation tasks. To this end, we develop a framework using off-the-shelf components, and demonstrate the effectiveness of Textual cues in semantic Classfication tasks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1806.08279v3-abstract-full').style.display = 'none'; document.getElementById('1806.08279v3-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 November, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 21 June, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2018. </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 CVPR 2018 Workshop: Towards Automatic Understanding of Visual Advertisements (ADS)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1804.10819">arXiv:1804.10819</a> <span> [<a href="https://arxiv.org/pdf/1804.10819">pdf</a>, <a href="https://arxiv.org/format/1804.10819">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"> Learning Cross-Modal Deep Embeddings for Multi-Object Image Retrieval using Text and Sketch </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Dey%2C+S">Sounak Dey</a>, <a href="/search/cs?searchtype=author&query=Dutta%2C+A">Anjan Dutta</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Suman K. Ghosh</a>, <a href="/search/cs?searchtype=author&query=Valveny%2C+E">Ernest Valveny</a>, <a href="/search/cs?searchtype=author&query=Llad%C3%B3s%2C+J">Josep Llad贸s</a>, <a href="/search/cs?searchtype=author&query=Pal%2C+U">Umapada Pal</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="1804.10819v1-abstract-short" style="display: inline;"> In this work we introduce a cross modal image retrieval system that allows both text and sketch as input modalities for the query. A cross-modal deep network architecture is formulated to jointly model the sketch and text input modalities as well as the the image output modality, learning a common embedding between text and images and between sketches and images. In addition, an attention model is… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1804.10819v1-abstract-full').style.display = 'inline'; document.getElementById('1804.10819v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1804.10819v1-abstract-full" style="display: none;"> In this work we introduce a cross modal image retrieval system that allows both text and sketch as input modalities for the query. A cross-modal deep network architecture is formulated to jointly model the sketch and text input modalities as well as the the image output modality, learning a common embedding between text and images and between sketches and images. In addition, an attention model is used to selectively focus the attention on the different objects of the image, allowing for retrieval with multiple objects in the query. Experiments show that the proposed method performs the best in both single and multiple object image retrieval in standard datasets. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1804.10819v1-abstract-full').style.display = 'none'; document.getElementById('1804.10819v1-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> 28 April, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2018. </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 ICPR 2018</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1804.04365">arXiv:1804.04365</a> <span> [<a href="https://arxiv.org/pdf/1804.04365">pdf</a>, <a href="https://arxiv.org/format/1804.04365">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> </div> </div> <p class="title is-5 mathjax"> A Survey of Fog Computing and Communication: Current Researches and Future Directions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Nath%2C+S+B">Shubha Brata Nath</a>, <a href="/search/cs?searchtype=author&query=Gupta%2C+H">Harshit Gupta</a>, <a href="/search/cs?searchtype=author&query=Chakraborty%2C+S">Sandip Chakraborty</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Soumya K Ghosh</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="1804.04365v1-abstract-short" style="display: inline;"> In this survey, we discuss the evolution of distributed computing from the utility computing to the fog computing, various research challenges for the development of fog computing environments, the current status on fog computing research along with a taxonomy of various existing works in this direction. Then, we focus on the architectures of fog computing systems, technologies for enabling fog, f… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1804.04365v1-abstract-full').style.display = 'inline'; document.getElementById('1804.04365v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1804.04365v1-abstract-full" style="display: none;"> In this survey, we discuss the evolution of distributed computing from the utility computing to the fog computing, various research challenges for the development of fog computing environments, the current status on fog computing research along with a taxonomy of various existing works in this direction. Then, we focus on the architectures of fog computing systems, technologies for enabling fog, fog computing features, security and privacy of fog, the QoS parameters, applications of fog, and give critical insights of various works done on this domain. Lastly, we briefly discuss about different fog computing associations that closely work on the development of fog based platforms and services, and give a summary of various types of overheads associated with fog computing platforms. Finally, we provide a thorough discussion on the future scopes and open research areas in fog computing as an enabler for the next generation computing paradigm. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1804.04365v1-abstract-full').style.display = 'none'; document.getElementById('1804.04365v1-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 April, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2018. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1801.04723">arXiv:1801.04723</a> <span> [<a href="https://arxiv.org/pdf/1801.04723">pdf</a>, <a href="https://arxiv.org/ps/1801.04723">ps</a>, <a href="https://arxiv.org/format/1801.04723">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> </div> </div> <p class="title is-5 mathjax"> SPIN: A Fast and Scalable Matrix Inversion Method in Apache Spark </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Misra%2C+C">Chandan Misra</a>, <a href="/search/cs?searchtype=author&query=Bhattacharya%2C+S">Sourangshu Bhattacharya</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Soumya K. Ghosh</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="1801.04723v1-abstract-short" style="display: inline;"> The growth of big data in domains such as Earth Sciences, Social Networks, Physical Sciences, etc. has lead to an immense need for efficient and scalable linear algebra operations, e.g. Matrix inversion. Existing methods for efficient and distributed matrix inversion using big data platforms rely on LU decomposition based block-recursive algorithms. However, these algorithms are complex and requir… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1801.04723v1-abstract-full').style.display = 'inline'; document.getElementById('1801.04723v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1801.04723v1-abstract-full" style="display: none;"> The growth of big data in domains such as Earth Sciences, Social Networks, Physical Sciences, etc. has lead to an immense need for efficient and scalable linear algebra operations, e.g. Matrix inversion. Existing methods for efficient and distributed matrix inversion using big data platforms rely on LU decomposition based block-recursive algorithms. However, these algorithms are complex and require a lot of side calculations, e.g. matrix multiplication, at various levels of recursion. In this paper, we propose a different scheme based on Strassen's matrix inversion algorithm (mentioned in Strassen's original paper in 1969), which uses far fewer operations at each level of recursion. We implement the proposed algorithm, and through extensive experimentation, show that it is more efficient than the state of the art methods. Furthermore, we provide a detailed theoretical analysis of the proposed algorithm, and derive theoretical running times which match closely with the empirically observed wall clock running times, thus explaining the U-shaped behaviour w.r.t. block-sizes. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1801.04723v1-abstract-full').style.display = 'none'; document.getElementById('1801.04723v1-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 January, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2018. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1712.06778">arXiv:1712.06778</a> <span> [<a href="https://arxiv.org/pdf/1712.06778">pdf</a>, <a href="https://arxiv.org/format/1712.06778">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Learning Representations from Road Network for End-to-End Urban Growth Simulation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Pal%2C+S">Saptarshi Pal</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Soumya K Ghosh</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="1712.06778v3-abstract-short" style="display: inline;"> From our experiences in the past, we have seen that the growth of cities is very much dependent on the transportation networks. In mega cities, transportation networks determine to a significant extent as to where the people will move and houses will be built. Hence, transportation network data is crucial to an urban growth prediction system. Existing works have used manually derived distance base… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1712.06778v3-abstract-full').style.display = 'inline'; document.getElementById('1712.06778v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1712.06778v3-abstract-full" style="display: none;"> From our experiences in the past, we have seen that the growth of cities is very much dependent on the transportation networks. In mega cities, transportation networks determine to a significant extent as to where the people will move and houses will be built. Hence, transportation network data is crucial to an urban growth prediction system. Existing works have used manually derived distance based features based on the road networks to build models on urban growth. But due to the non-generic and laborious nature of the manual feature engineering process, we can shift to End-to-End systems which do not rely on manual feature engineering. In this paper, we propose a method to integrate road network data to an existing Rule based End-to-End framework without manual feature engineering. Our method employs recurrent neural networks to represent road networks in a structured way such that it can be plugged into the previously proposed End-to-End framework. The proposed approach enhances the performance in terms of Figure of Merit, Producer's accuracy, User's accuracy and Overall accuracy of the existing Rule based End-to-End framework. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1712.06778v3-abstract-full').style.display = 'none'; document.getElementById('1712.06778v3-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 February, 2018; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 18 December, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2017. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1712.05492">arXiv:1712.05492</a> <span> [<a href="https://arxiv.org/pdf/1712.05492">pdf</a>, <a href="https://arxiv.org/format/1712.05492">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Geometry">cs.CG</span> </div> </div> <p class="title is-5 mathjax"> Constant Approximation Algorithms for Guarding Simple Polygons using Vertex Guards </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Bhattacharya%2C+P">Pritam Bhattacharya</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Subir Kumar Ghosh</a>, <a href="/search/cs?searchtype=author&query=Pal%2C+S">Sudebkumar Pal</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="1712.05492v2-abstract-short" style="display: inline;"> The art gallery problem enquires about the least number of guards sufficient to ensure that an art gallery, represented by a simple polygon $P$, is fully guarded. Most standard versions of this problem are known to be NP-hard. In 1987, Ghosh provided a deterministic $\mathcal{O}(\log n)$-approximation algorithm for the case of vertex guards and edge guards in simple polygons. In the same paper, Gh… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1712.05492v2-abstract-full').style.display = 'inline'; document.getElementById('1712.05492v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1712.05492v2-abstract-full" style="display: none;"> The art gallery problem enquires about the least number of guards sufficient to ensure that an art gallery, represented by a simple polygon $P$, is fully guarded. Most standard versions of this problem are known to be NP-hard. In 1987, Ghosh provided a deterministic $\mathcal{O}(\log n)$-approximation algorithm for the case of vertex guards and edge guards in simple polygons. In the same paper, Ghosh also conjectured the existence of constant ratio approximation algorithms for these problems. We present here three polynomial-time algorithms with a constant approximation ratio for guarding an $n$-sided simple polygon $P$ using vertex guards. (i) The first algorithm, that has an approximation ratio of 18, guards all vertices of $P$ in $\mathcal{O}(n^4)$ time. (ii) The second algorithm, that has the same approximation ratio of 18, guards the entire boundary of $P$ in $\mathcal{O}(n^5)$ time. (iii) The third algorithm, that has an approximation ratio of 27, guards all interior and boundary points of $P$ in $\mathcal{O}(n^5)$ time. Further, these algorithms can be modified to obtain similar approximation ratios while using edge guards. The significance of our results lies in the fact that these results settle the conjecture by Ghosh regarding the existence of constant-factor approximation algorithms for this problem, which has been open since 1987 despite several attempts by researchers. Our approximation algorithms exploit several deep visibility structures of simple polygons which are interesting in their own right. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1712.05492v2-abstract-full').style.display = 'none'; document.getElementById('1712.05492v2-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> 11 April, 2018; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 14 December, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2017. </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">39 pages, 31 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/1711.10801">arXiv:1711.10801</a> <span> [<a href="https://arxiv.org/pdf/1711.10801">pdf</a>, <a href="https://arxiv.org/format/1711.10801">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Other Computer Science">cs.OH</span> </div> </div> <p class="title is-5 mathjax"> Rule based End-to-End Learning Framework for Urban Growth Prediction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Pal%2C+S">Saptarshi Pal</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Soumya K Ghosh</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="1711.10801v4-abstract-short" style="display: inline;"> Due to the rapid growth of urban areas in the past decades, it has become increasingly important to model and monitor urban growth in mega cities. Although several researchers have proposed models for simulating urban growth, they have been primarily dependent on various manually selected spatial and nonspatial explanatory features for building models. A practical difficulty with this approach is… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1711.10801v4-abstract-full').style.display = 'inline'; document.getElementById('1711.10801v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1711.10801v4-abstract-full" style="display: none;"> Due to the rapid growth of urban areas in the past decades, it has become increasingly important to model and monitor urban growth in mega cities. Although several researchers have proposed models for simulating urban growth, they have been primarily dependent on various manually selected spatial and nonspatial explanatory features for building models. A practical difficulty with this approach is manual selection procedure, which tends to make model design process laborious and non-generic. Despite the fact that explanatory features provide us with the understanding of a complex process, there has been no standard set of features in urban growth prediction over which scholars have consensus. Hence, design and deploying of systems for urban growth prediction have remained challenging tasks. In order to reduce the dependency on human devised features, we have proposed a novel End-to-End prediction framework to represent remotely sensed satellite data in terms of rules of a cellular automata model in order to improve the performance of urban growth prediction. Using our End-to-End framework, we have achieved superior performance in Figure of Merit, Producer's accuracy, User's accuracy, and Overall accuracy metrics respectively over existing learning based methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1711.10801v4-abstract-full').style.display = 'none'; document.getElementById('1711.10801v4-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 May, 2018; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 29 November, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2017. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1707.02131">arXiv:1707.02131</a> <span> [<a href="https://arxiv.org/pdf/1707.02131">pdf</a>, <a href="https://arxiv.org/format/1707.02131">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"> SigNet: Convolutional Siamese Network for Writer Independent Offline Signature Verification </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Dey%2C+S">Sounak Dey</a>, <a href="/search/cs?searchtype=author&query=Dutta%2C+A">Anjan Dutta</a>, <a href="/search/cs?searchtype=author&query=Toledo%2C+J+I">J. Ignacio Toledo</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Suman K. Ghosh</a>, <a href="/search/cs?searchtype=author&query=Llados%2C+J">Josep Llados</a>, <a href="/search/cs?searchtype=author&query=Pal%2C+U">Umapada Pal</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="1707.02131v2-abstract-short" style="display: inline;"> Offline signature verification is one of the most challenging tasks in biometrics and document forensics. Unlike other verification problems, it needs to model minute but critical details between genuine and forged signatures, because a skilled falsification might often resembles the real signature with small deformation. This verification task is even harder in writer independent scenarios which… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1707.02131v2-abstract-full').style.display = 'inline'; document.getElementById('1707.02131v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1707.02131v2-abstract-full" style="display: none;"> Offline signature verification is one of the most challenging tasks in biometrics and document forensics. Unlike other verification problems, it needs to model minute but critical details between genuine and forged signatures, because a skilled falsification might often resembles the real signature with small deformation. This verification task is even harder in writer independent scenarios which is undeniably fiscal for realistic cases. In this paper, we model an offline writer independent signature verification task with a convolutional Siamese network. Siamese networks are twin networks with shared weights, which can be trained to learn a feature space where similar observations are placed in proximity. This is achieved by exposing the network to a pair of similar and dissimilar observations and minimizing the Euclidean distance between similar pairs while simultaneously maximizing it between dissimilar pairs. Experiments conducted on cross-domain datasets emphasize the capability of our network to model forgery in different languages (scripts) and handwriting styles. Moreover, our designed Siamese network, named SigNet, exceeds the state-of-the-art results on most of the benchmark signature datasets, which paves the way for further research in this direction. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1707.02131v2-abstract-full').style.display = 'none'; document.getElementById('1707.02131v2-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 September, 2017; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 7 July, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2017. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1706.01487">arXiv:1706.01487</a> <span> [<a href="https://arxiv.org/pdf/1706.01487">pdf</a>, <a href="https://arxiv.org/format/1706.01487">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"> Visual attention models for scene text recognition </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Suman K. Ghosh</a>, <a href="/search/cs?searchtype=author&query=Valveny%2C+E">Ernest Valveny</a>, <a href="/search/cs?searchtype=author&query=Bagdanov%2C+A+D">Andrew D. Bagdanov</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="1706.01487v1-abstract-short" style="display: inline;"> In this paper we propose an approach to lexicon-free recognition of text in scene images. Our approach relies on a LSTM-based soft visual attention model learned from convolutional features. A set of feature vectors are derived from an intermediate convolutional layer corresponding to different areas of the image. This permits encoding of spatial information into the image representation. In this… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1706.01487v1-abstract-full').style.display = 'inline'; document.getElementById('1706.01487v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1706.01487v1-abstract-full" style="display: none;"> In this paper we propose an approach to lexicon-free recognition of text in scene images. Our approach relies on a LSTM-based soft visual attention model learned from convolutional features. A set of feature vectors are derived from an intermediate convolutional layer corresponding to different areas of the image. This permits encoding of spatial information into the image representation. In this way, the framework is able to learn how to selectively focus on different parts of the image. At every time step the recognizer emits one character using a weighted combination of the convolutional feature vectors according to the learned attention model. Training can be done end-to-end using only word level annotations. In addition, we show that modifying the beam search algorithm by integrating an explicit language model leads to significantly better recognition results. We validate the performance of our approach on standard SVT and ICDAR'03 scene text datasets, showing state-of-the-art performance in unconstrained text recognition. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1706.01487v1-abstract-full').style.display = 'none'; document.getElementById('1706.01487v1-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> 5 June, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2017. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1609.01190">arXiv:1609.01190</a> <span> [<a href="https://arxiv.org/pdf/1609.01190">pdf</a>, <a href="https://arxiv.org/format/1609.01190">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> </div> </div> <p class="title is-5 mathjax"> SDFog: A Software Defined Computing Architecture for QoS Aware Service Orchestration over Edge Devices </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Gupta%2C+H">Harshit Gupta</a>, <a href="/search/cs?searchtype=author&query=Nath%2C+S+B">Shubha Brata Nath</a>, <a href="/search/cs?searchtype=author&query=Chakraborty%2C+S">Sandip Chakraborty</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Soumya K. Ghosh</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="1609.01190v1-abstract-short" style="display: inline;"> Cloud computing revolutionized the information technology (IT) industry by offering dynamic and infinite scaling, on-demand resources and utility-oriented usage. However, recent changes in user traffic and requirements have exposed the shortcomings of cloud computing, particularly the inability to deliver real-time responses and handle massive surge in data volumes. Fog computing, that brings back… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1609.01190v1-abstract-full').style.display = 'inline'; document.getElementById('1609.01190v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1609.01190v1-abstract-full" style="display: none;"> Cloud computing revolutionized the information technology (IT) industry by offering dynamic and infinite scaling, on-demand resources and utility-oriented usage. However, recent changes in user traffic and requirements have exposed the shortcomings of cloud computing, particularly the inability to deliver real-time responses and handle massive surge in data volumes. Fog computing, that brings back partial computation load from the cloud to the edge devices, is envisioned to be the next big change in computing, and has the potential to address these challenges. Being a highly distributed, loosely coupled and still in the emerging phase, standardization, quality-of-service management and dynamic adaptability are the key challenges faced by fog computing research fraternity today. This article aims to address these issues by proposing a service-oriented middleware that leverages the convergence of cloud and fog computing along with software defined networking (SDN) and network function virtualization (NFV) to achieve the aforementioned goals. The proposed system, called "Software Defined Fog" (SDFog), abstracts connected entities as services and allows applications to orchestrate these services with end-to-end QoS requirements. A use-case showing the necessity of such a middleware has been presented to show the efficacy of the SDN-based QoS control over the Fog. This article aims at developing an integrated system to realize the software-defined control over fog infrastructure. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1609.01190v1-abstract-full').style.display = 'none'; document.getElementById('1609.01190v1-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> 5 September, 2016; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2016. </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">This work has been submitted to the IEEE for possible publication</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1606.02007">arXiv:1606.02007</a> <span> [<a href="https://arxiv.org/pdf/1606.02007">pdf</a>, <a href="https://arxiv.org/format/1606.02007">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> </div> </div> <p class="title is-5 mathjax"> iFogSim: A Toolkit for Modeling and Simulation of Resource Management Techniques in Internet of Things, Edge and Fog Computing Environments </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Gupta%2C+H">Harshit Gupta</a>, <a href="/search/cs?searchtype=author&query=Dastjerdi%2C+A+V">Amir Vahid Dastjerdi</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Soumya K. Ghosh</a>, <a href="/search/cs?searchtype=author&query=Buyya%2C+R">Rajkumar Buyya</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="1606.02007v1-abstract-short" style="display: inline;"> Internet of Things (IoT) aims to bring every object (e.g. smart cameras, wearable, environmental sensors, home appliances, and vehicles) online, hence generating massive amounts of data that can overwhelm storage systems and data analytics applications. Cloud computing offers services at the infrastructure level that can scale to IoT storage and processing requirements. However, there are applicat… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1606.02007v1-abstract-full').style.display = 'inline'; document.getElementById('1606.02007v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1606.02007v1-abstract-full" style="display: none;"> Internet of Things (IoT) aims to bring every object (e.g. smart cameras, wearable, environmental sensors, home appliances, and vehicles) online, hence generating massive amounts of data that can overwhelm storage systems and data analytics applications. Cloud computing offers services at the infrastructure level that can scale to IoT storage and processing requirements. However, there are applications such as health monitoring and emergency response that require low latency, and delay caused by transferring data to the cloud and then back to the application can seriously impact their performances. To overcome this limitation, Fog computing paradigm has been proposed, where cloud services are extended to the edge of the network to decrease the latency and network congestion. To realize the full potential of Fog and IoT paradigms for real-time analytics, several challenges need to be addressed. The first and most critical problem is designing resource management techniques that determine which modules of analytics applications are pushed to each edge device to minimize the latency and maximize the throughput. To this end, we need a evaluation platform that enables the quantification of performance of resource management policies on an IoT or Fog computing infrastructure in a repeatable manner. In this paper we propose a simulator, called iFogSim, to model IoT and Fog environments and measure the impact of resource management techniques in terms of latency, network congestion, energy consumption, and cost. We describe two case studies to demonstrate modeling of an IoT environment and comparison of resource management policies. Moreover, scalability of the simulation toolkit in terms of RAM consumption and execution time is verified under different circumstances. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1606.02007v1-abstract-full').style.display = 'none'; document.getElementById('1606.02007v1-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 June, 2016; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2016. </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">Cloud Computing and Distributed Systems Laboratory, The University of Melbourne, June 6, 2016</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> CLOUDS-TR-2016-2 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1601.02752">arXiv:1601.02752</a> <span> [<a href="https://arxiv.org/pdf/1601.02752">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> </div> </div> <p class="title is-5 mathjax"> Fog Computing: Principles, Architectures, and Applications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Dastjerdi%2C+A+V">Amir Vahid Dastjerdi</a>, <a href="/search/cs?searchtype=author&query=Gupta%2C+H">Harshit Gupta</a>, <a href="/search/cs?searchtype=author&query=Calheiros%2C+R+N">Rodrigo N. Calheiros</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Soumya K. Ghosh</a>, <a href="/search/cs?searchtype=author&query=Buyya%2C+R">Rajkumar Buyya</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="1601.02752v2-abstract-short" style="display: inline;"> The Internet of Everything (IoE) solutions gradually bring every object online, and processing data in centralized cloud does not scale to requirements of such environment. This is because, there are applications such as health monitoring and emergency response that require low latency and delay caused by transferring data to the cloud and then back to the application can seriously impact the perf… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1601.02752v2-abstract-full').style.display = 'inline'; document.getElementById('1601.02752v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1601.02752v2-abstract-full" style="display: none;"> The Internet of Everything (IoE) solutions gradually bring every object online, and processing data in centralized cloud does not scale to requirements of such environment. This is because, there are applications such as health monitoring and emergency response that require low latency and delay caused by transferring data to the cloud and then back to the application can seriously impact the performance. To this end, Fog computing has emerged, where cloud computing is extended to the edge of the network to decrease the latency and network congestion. Fog computing is a paradigm for managing a highly distributed and possibly virtualized environment that provides compute and network services between sensors and cloud data centers. This chapter provides background and motivations on emergence of Fog computing and defines its key characteristics. In addition, a reference architecture for Fog computing is presented and recent related development and applications are discussed. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1601.02752v2-abstract-full').style.display = 'none'; document.getElementById('1601.02752v2-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> 27 January, 2016; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 12 January, 2016; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2016. </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">26 pages, 6 figures, a Book Chapter in Internet of Things: Principles and Paradigms, R. Buyya and A. Dastjerdi (eds), Morgan Kaufmann, Burlington, Massachusetts, USA, 2016</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1509.06457">arXiv:1509.06457</a> <span> [<a href="https://arxiv.org/pdf/1509.06457">pdf</a>, <a href="https://arxiv.org/format/1509.06457">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Trading and Market Microstructure">q-fin.TR</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">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Identifying collusion groups using spectral clustering </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sarswat%2C+S">Suneel Sarswat</a>, <a href="/search/cs?searchtype=author&query=Abraham%2C+K+M">Kandathil Mathew Abraham</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Subir Kumar Ghosh</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="1509.06457v2-abstract-short" style="display: inline;"> In an illiquid stock, traders can collude and place orders on a predetermined price and quantity at a fixed schedule. This is usually done to manipulate the price of the stock or to create artificial liquidity in the stock, which may mislead genuine investors. Here, the problem is to identify such group of colluding traders. We modeled the problem instance as a graph, where each trader corresponds… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1509.06457v2-abstract-full').style.display = 'inline'; document.getElementById('1509.06457v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1509.06457v2-abstract-full" style="display: none;"> In an illiquid stock, traders can collude and place orders on a predetermined price and quantity at a fixed schedule. This is usually done to manipulate the price of the stock or to create artificial liquidity in the stock, which may mislead genuine investors. Here, the problem is to identify such group of colluding traders. We modeled the problem instance as a graph, where each trader corresponds to a vertex of the graph and trade corresponds to edges of the graph. Further, we assign weights on edges depending on total volume, total number of trades, maximum change in the price and commonality between two vertices. Spectral clustering algorithms are used on the constructed graph to identify colluding group(s). We have compared our results with simulated data to show the effectiveness of spectral clustering to detecting colluding groups. Moreover, we also have used parameters of real data to test the effectiveness of our algorithm. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1509.06457v2-abstract-full').style.display = 'none'; document.getElementById('1509.06457v2-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, 2016; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 22 September, 2015; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2015. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1507.06056">arXiv:1507.06056</a> <span> [<a href="https://arxiv.org/pdf/1507.06056">pdf</a>, <a href="https://arxiv.org/format/1507.06056">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="Discrete Mathematics">cs.DM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Data Structures and Algorithms">cs.DS</span> </div> </div> <p class="title is-5 mathjax"> A National Effort for Motivating Indian Students and Teachers towards Algorithmic Research </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Subir Kumar Ghosh</a>, <a href="/search/cs?searchtype=author&query=Pal%2C+S+P">Sudebkumar Prasant Pal</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="1507.06056v3-abstract-short" style="display: inline;"> During 2008-2015, twenty-two introductory workshops on graph and geometric algorithms were organized for teachers and students (undergraduate, post-graduate and doctoral) of engineering colleges and universities at different states and union territories of India. The lectures were meant to provide exposure to the field of graph and geometric algorithms and to motivate the participants towards rese… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1507.06056v3-abstract-full').style.display = 'inline'; document.getElementById('1507.06056v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1507.06056v3-abstract-full" style="display: none;"> During 2008-2015, twenty-two introductory workshops on graph and geometric algorithms were organized for teachers and students (undergraduate, post-graduate and doctoral) of engineering colleges and universities at different states and union territories of India. The lectures were meant to provide exposure to the field of graph and geometric algorithms and to motivate the participants towards research. Fifty-eight professors from TIFR, IITs, IISc, IMSc, CMI, ISI Kolkata, and other institutes and universities delivered invited lectures on different topics in the design and analysis of algorithms, discrete applied mathematics, computer graphics, computer vision, and robotics. The first four workshops were funded by TIFR, BRNS and IIT Kharagpur, and the remaining workshops were funded by the NBHM. In this paper, we present the salient features of these workshops, and state our observations on the national impact of these workshops. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1507.06056v3-abstract-full').style.display = 'none'; document.getElementById('1507.06056v3-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, 2016; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 22 July, 2015; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2015. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> F.2.0; G.2.1 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1505.07778">arXiv:1505.07778</a> <span> [<a href="https://arxiv.org/pdf/1505.07778">pdf</a>, <a href="https://arxiv.org/format/1505.07778">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 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/10.1109/ICDAR.2015.7333888">10.1109/ICDAR.2015.7333888 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Query by String word spotting based on character bi-gram indexing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Suman K. Ghosh</a>, <a href="/search/cs?searchtype=author&query=Valveny%2C+E">Ernest Valveny</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="1505.07778v1-abstract-short" style="display: inline;"> In this paper we propose a segmentation-free query by string word spotting method. Both the documents and query strings are encoded using a recently proposed word representa- tion that projects images and strings into a common atribute space based on a pyramidal histogram of characters(PHOC). These attribute models are learned using linear SVMs over the Fisher Vector representation of the images a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1505.07778v1-abstract-full').style.display = 'inline'; document.getElementById('1505.07778v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1505.07778v1-abstract-full" style="display: none;"> In this paper we propose a segmentation-free query by string word spotting method. Both the documents and query strings are encoded using a recently proposed word representa- tion that projects images and strings into a common atribute space based on a pyramidal histogram of characters(PHOC). These attribute models are learned using linear SVMs over the Fisher Vector representation of the images along with the PHOC labels of the corresponding strings. In order to search through the whole page, document regions are indexed per character bi- gram using a similar attribute representation. On top of that, we propose an integral image representation of the document using a simplified version of the attribute model for efficient computation. Finally we introduce a re-ranking step in order to boost retrieval performance. We show state-of-the-art results for segmentation-free query by string word spotting in single-writer and multi-writer standard datasets <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1505.07778v1-abstract-full').style.display = 'none'; document.getElementById('1505.07778v1-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> 28 May, 2015; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2015. </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">To be published in ICDAR2015</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1409.4621">arXiv:1409.4621</a> <span> [<a href="https://arxiv.org/pdf/1409.4621">pdf</a>, <a href="https://arxiv.org/format/1409.4621">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Geometry">cs.CG</span> </div> </div> <p class="title is-5 mathjax"> Approximability of Guarding Weak Visibility Polygons </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Bhattacharya%2C+P">Pritam Bhattacharya</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+K">Subir Kumar Ghosh</a>, <a href="/search/cs?searchtype=author&query=Roy%2C+B">Bodhayan Roy</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="1409.4621v5-abstract-short" style="display: inline;"> The art gallery problem enquires about the least number of guards that are sufficient to ensure that an art gallery, represented by a polygon $P$, is fully guarded. In 1998, the problems of finding the minimum number of point guards, vertex guards, and edge guards required to guard $P$ were shown to be APX-hard by Eidenbenz, Widmayer and Stamm. In 1987, Ghosh presented approximation algorithms for… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1409.4621v5-abstract-full').style.display = 'inline'; document.getElementById('1409.4621v5-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1409.4621v5-abstract-full" style="display: none;"> The art gallery problem enquires about the least number of guards that are sufficient to ensure that an art gallery, represented by a polygon $P$, is fully guarded. In 1998, the problems of finding the minimum number of point guards, vertex guards, and edge guards required to guard $P$ were shown to be APX-hard by Eidenbenz, Widmayer and Stamm. In 1987, Ghosh presented approximation algorithms for vertex guards and edge guards that achieved a ratio of $\mathcal{O}(\log n)$, which was improved upto $\mathcal{O}(\log\log OPT)$ by King and Kirkpatrick in 2011. It has been conjectured that constant-factor approximation algorithms exist for these problems. We settle the conjecture for the special class of polygons that are weakly visible from an edge and contain no holes by presenting a 6-approximation algorithm for finding the minimum number of vertex guards that runs in $\mathcal{O}(n^2)$ time. On the other hand, for weak visibility polygons with holes, we present a reduction from the Set Cover problem to show that there cannot exist a polynomial time algorithm for the vertex guard problem with an approximation ratio better than $((1 - 蔚)/12)\ln n$ for any $蔚>0$, unless NP=P. We also show that, for the special class of polygons without holes that are orthogonal as well as weakly visible from an edge, the approximation ratio can be improved to 3. Finally, we consider the Point Guard problem and show that it is NP-hard in the case of polygons weakly visible from an edge. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1409.4621v5-abstract-full').style.display = 'none'; document.getElementById('1409.4621v5-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 April, 2016; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 16 September, 2014; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2014. </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">23 pages, 21 figures, 30 citations</span> </p> </li> </ol> <nav class="pagination is-small is-centered breathe-horizontal" role="navigation" aria-label="pagination"> <a 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