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name="searchtype"><option value="all">All fields</option><option value="title">Title</option><option selected value="author">Author(s)</option><option value="abstract">Abstract</option><option value="comments">Comments</option><option value="journal_ref">Journal reference</option><option value="acm_class">ACM classification</option><option value="msc_class">MSC classification</option><option value="report_num">Report number</option><option value="paper_id">arXiv identifier</option><option value="doi">DOI</option><option value="orcid">ORCID</option><option value="license">License (URI)</option><option value="author_id">arXiv author ID</option><option value="help">Help pages</option><option value="full_text">Full text</option></select> <input id="query" name="query" type="text" value="Jamil, H"> <ul id="abstracts"><li><input checked id="abstracts-0" name="abstracts" type="radio" value="show"> <label for="abstracts-0">Show abstracts</label></li><li><input id="abstracts-1" name="abstracts" type="radio" value="hide"> <label for="abstracts-1">Hide abstracts</label></li></ul> </div> <div class="box field is-grouped is-grouped-multiline level-item"> <div class="control"> <span class="select is-small"> <select id="size" name="size"><option value="25">25</option><option selected value="50">50</option><option value="100">100</option><option value="200">200</option></select> </span> <label for="size">results per page</label>. </div> <div class="control"> <label for="order">Sort results by</label> <span class="select is-small"> <select id="order" name="order"><option selected value="-announced_date_first">Announcement date (newest first)</option><option value="announced_date_first">Announcement date (oldest first)</option><option value="-submitted_date">Submission date (newest first)</option><option value="submitted_date">Submission date (oldest first)</option><option value="">Relevance</option></select> </span> </div> <div class="control"> <button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2503.13662">arXiv:2503.13662</a> <span> [<a href="https://arxiv.org/pdf/2503.13662">pdf</a>, <a href="https://arxiv.org/format/2503.13662">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> <span class="tag is-small is-grey 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="Performance">cs.PF</span> </div> </div> <p class="title is-5 mathjax"> Optimizing Data Transfer Performance and Energy Efficiency with Deep Reinforcement Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jamil%2C+H">Hasubil Jamil</a>, <a href="/search/cs?searchtype=author&query=Goldverg%2C+J">Jacob Goldverg</a>, <a href="/search/cs?searchtype=author&query=Rodrigues%2C+E">Elvis Rodrigues</a>, <a href="/search/cs?searchtype=author&query=Nine%2C+M+S+Q+Z">MD S Q Zulkar Nine</a>, <a href="/search/cs?searchtype=author&query=Kosar%2C+T">Tevfik Kosar</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="2503.13662v1-abstract-short" style="display: inline;"> The rapid growth of data across fields of science and industry has increased the need to improve the performance of end-to-end data transfers while using the resources more efficiently. In this paper, we present a dynamic, multiparameter reinforcement learning (RL) framework that adjusts application-layer transfer settings during data transfers on shared networks. Our method strikes a balance betw… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.13662v1-abstract-full').style.display = 'inline'; document.getElementById('2503.13662v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2503.13662v1-abstract-full" style="display: none;"> The rapid growth of data across fields of science and industry has increased the need to improve the performance of end-to-end data transfers while using the resources more efficiently. In this paper, we present a dynamic, multiparameter reinforcement learning (RL) framework that adjusts application-layer transfer settings during data transfers on shared networks. Our method strikes a balance between high throughput and low energy utilization by employing reward signals that focus on both energy efficiency and fairness. The RL agents can pause and resume transfer threads as needed, pausing during heavy network use and resuming when resources are available, to prevent overload and save energy. We evaluate several RL techniques and compare our solution with state-of-the-art methods by measuring computational overhead, adaptability, throughput, and energy consumption. Our experiments show up to 25% increase in throughput and up to 40% reduction in energy usage at the end systems compared to baseline methods, highlighting a fair and energy-efficient way to optimize data transfers in shared network environments. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.13662v1-abstract-full').style.display = 'none'; document.getElementById('2503.13662v1-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 March, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2025. </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">Will be submitted to TPDS</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.17078">arXiv:2410.17078</a> <span> [<a href="https://arxiv.org/pdf/2410.17078">pdf</a>, <a href="https://arxiv.org/format/2410.17078">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"> FlowTracer: A Tool for Uncovering Network Path Usage Imbalance in AI Training Clusters </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jamil%2C+H">Hasibul Jamil</a>, <a href="/search/cs?searchtype=author&query=Alim%2C+A">Abdul Alim</a>, <a href="/search/cs?searchtype=author&query=Schares%2C+L">Laurent Schares</a>, <a href="/search/cs?searchtype=author&query=Maniotis%2C+P">Pavlos Maniotis</a>, <a href="/search/cs?searchtype=author&query=Schour%2C+L">Liran Schour</a>, <a href="/search/cs?searchtype=author&query=Sydney%2C+A">Ali Sydney</a>, <a href="/search/cs?searchtype=author&query=Kayi%2C+A">Abdullah Kayi</a>, <a href="/search/cs?searchtype=author&query=Kosar%2C+T">Tevfik Kosar</a>, <a href="/search/cs?searchtype=author&query=Karacali%2C+B">Bengi Karacali</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.17078v2-abstract-short" style="display: inline;"> The increasing complexity of AI workloads, especially distributed Large Language Model (LLM) training, places significant strain on the networking infrastructure of parallel data centers and supercomputing systems. While Equal-Cost Multi- Path (ECMP) routing distributes traffic over parallel paths, hash collisions often lead to imbalanced network resource utilization and performance bottlenecks. T… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.17078v2-abstract-full').style.display = 'inline'; document.getElementById('2410.17078v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.17078v2-abstract-full" style="display: none;"> The increasing complexity of AI workloads, especially distributed Large Language Model (LLM) training, places significant strain on the networking infrastructure of parallel data centers and supercomputing systems. While Equal-Cost Multi- Path (ECMP) routing distributes traffic over parallel paths, hash collisions often lead to imbalanced network resource utilization and performance bottlenecks. This paper presents FlowTracer, a tool designed to analyze network path utilization and evaluate different routing strategies. FlowTracer aids in debugging network inefficiencies by providing detailed visibility into traffic distribution and helping to identify the root causes of performance degradation, such as issues caused by hash collisions. By offering flow-level insights, FlowTracer enables system operators to optimize routing, reduce congestion, and improve the performance of distributed AI workloads. We use a RoCEv2-enabled cluster with a leaf-spine network and 16 400-Gbps nodes to demonstrate how FlowTracer can be used to compare the flow imbalances of ECMP routing against a statically configured network. The example showcases a 30% reduction in imbalance, as measured by a new metric we introduce. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.17078v2-abstract-full').style.display = 'none'; document.getElementById('2410.17078v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 24 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 22 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Submitted for peer reviewing in IEEE ICC 2025</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.11890">arXiv:2410.11890</a> <span> [<a href="https://arxiv.org/pdf/2410.11890">pdf</a>, <a href="https://arxiv.org/format/2410.11890">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Retrieval">cs.IR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> </div> </div> <p class="title is-5 mathjax"> Online Digital Investigative Journalism using SociaLens </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jamil%2C+H+M">Hasan M. Jamil</a>, <a href="/search/cs?searchtype=author&query=Rubaiat%2C+S+Y">Sajratul Y. Rubaiat</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.11890v1-abstract-short" style="display: inline;"> Media companies witnessed a significant transformation with the rise of the internet, bigdata, machine learning (ML) and AI. Recent emergence of large language models (LLM) have added another aspect to this transformation. Researchers believe that with the help of these technologies, investigative digital journalism will enter a new era. Using a smart set of data gathering and analysis tools, jour… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.11890v1-abstract-full').style.display = 'inline'; document.getElementById('2410.11890v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.11890v1-abstract-full" style="display: none;"> Media companies witnessed a significant transformation with the rise of the internet, bigdata, machine learning (ML) and AI. Recent emergence of large language models (LLM) have added another aspect to this transformation. Researchers believe that with the help of these technologies, investigative digital journalism will enter a new era. Using a smart set of data gathering and analysis tools, journalists will be able to create data driven contents and insights in unprecedented ways. In this paper, we introduce a versatile and autonomous investigative journalism tool, called {\em SociaLens}, for identifying and extracting query specific data from online sources, responding to probing queries and drawing conclusions entailed by large volumes of data using ML analytics fully autonomously. We envision its use in investigative journalism, law enforcement and social policy planning. The proposed system capitalizes on the integration of ML technology with LLMs and advanced bigdata search techniques. We illustrate the functionality of SociaLens using a focused case study on rape incidents in a developing country and demonstrate that journalists can gain nuanced insights without requiring coding expertise they might lack. SociaLens is designed as a ChatBot that is capable of contextual conversation, find and collect data relevant to queries, initiate ML tasks to respond to queries, generate textual and visual reports, all fully autonomously within the ChatBot environment. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.11890v1-abstract-full').style.display = 'none'; document.getElementById('2410.11890v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.09650">arXiv:2406.09650</a> <span> [<a href="https://arxiv.org/pdf/2406.09650">pdf</a>, <a href="https://arxiv.org/format/2406.09650">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"> Carbon-Aware End-to-End Data Movement </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Goldverg%2C+J">Jacob Goldverg</a>, <a href="/search/cs?searchtype=author&query=Jamil%2C+H">Hasibul Jamil</a>, <a href="/search/cs?searchtype=author&query=Rodriguez%2C+E">Elvis Rodriguez</a>, <a href="/search/cs?searchtype=author&query=Kosar%2C+T">Tevfik Kosar</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.09650v1-abstract-short" style="display: inline;"> The latest trends in the adoption of cloud, edge, and distributed computing, as well as a rise in applying AI/ML workloads, have created a need to measure, monitor, and reduce the carbon emissions of these compute-intensive workloads and the associated communication costs. The data movement over networks has considerable carbon emission that has been neglected due to the difficulty in measuring th… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.09650v1-abstract-full').style.display = 'inline'; document.getElementById('2406.09650v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.09650v1-abstract-full" style="display: none;"> The latest trends in the adoption of cloud, edge, and distributed computing, as well as a rise in applying AI/ML workloads, have created a need to measure, monitor, and reduce the carbon emissions of these compute-intensive workloads and the associated communication costs. The data movement over networks has considerable carbon emission that has been neglected due to the difficulty in measuring the carbon footprint of a given end-to-end network path. We present a novel network carbon footprint measuring mechanism and propose three ways in which users can optimize scheduling network-intensive tasks to enable carbon savings through shifting tasks in time, space, and overlay networks based on the geographic carbon intensity. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.09650v1-abstract-full').style.display = 'none'; document.getElementById('2406.09650v1-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 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.16159">arXiv:2405.16159</a> <span> [<a href="https://arxiv.org/pdf/2405.16159">pdf</a>, <a href="https://arxiv.org/format/2405.16159">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="Databases">cs.DB</span> </div> </div> <p class="title is-5 mathjax"> A Declarative Query Language for Scientific Machine Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jamil%2C+H+M">Hasan M Jamil</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.16159v1-abstract-short" style="display: inline;"> The popularity of data science as a discipline and its importance in the emerging economy and industrial progress dictate that machine learning be democratized for the masses. This also means that the current practice of workforce training using machine learning tools, which requires low-level statistical and algorithmic details, is a barrier that needs to be addressed. Similar to data management… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.16159v1-abstract-full').style.display = 'inline'; document.getElementById('2405.16159v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.16159v1-abstract-full" style="display: none;"> The popularity of data science as a discipline and its importance in the emerging economy and industrial progress dictate that machine learning be democratized for the masses. This also means that the current practice of workforce training using machine learning tools, which requires low-level statistical and algorithmic details, is a barrier that needs to be addressed. Similar to data management languages such as SQL, machine learning needs to be practiced at a conceptual level to help make it a staple tool for general users. In particular, the technical sophistication demanded by existing machine learning frameworks is prohibitive for many scientists who are not computationally savvy or well versed in machine learning techniques. The learning curve to use the needed machine learning tools is also too high for them to take advantage of these powerful platforms to rapidly advance science. In this paper, we introduce a new declarative machine learning query language, called {\em MQL}, for naive users. We discuss its merit and possible ways of implementing it over a traditional relational database system. We discuss two materials science experiments implemented using MQL on a materials science workflow system called MatFlow. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.16159v1-abstract-full').style.display = 'none'; document.getElementById('2405.16159v1-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, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.08949">arXiv:2404.08949</a> <span> [<a href="https://arxiv.org/pdf/2404.08949">pdf</a>, <a href="https://arxiv.org/format/2404.08949">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Multimodal Cross-Document Event Coreference Resolution Using Linear Semantic Transfer and Mixed-Modality Ensembles </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Nath%2C+A">Abhijnan Nath</a>, <a href="/search/cs?searchtype=author&query=Jamil%2C+H">Huma Jamil</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+S+R">Shafiuddin Rehan Ahmed</a>, <a href="/search/cs?searchtype=author&query=Baker%2C+G">George Baker</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+R">Rahul Ghosh</a>, <a href="/search/cs?searchtype=author&query=Martin%2C+J+H">James H. Martin</a>, <a href="/search/cs?searchtype=author&query=Blanchard%2C+N">Nathaniel Blanchard</a>, <a href="/search/cs?searchtype=author&query=Krishnaswamy%2C+N">Nikhil Krishnaswamy</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2404.08949v1-abstract-short" style="display: inline;"> Event coreference resolution (ECR) is the task of determining whether distinct mentions of events within a multi-document corpus are actually linked to the same underlying occurrence. Images of the events can help facilitate resolution when language is ambiguous. Here, we propose a multimodal cross-document event coreference resolution method that integrates visual and textual cues with a simple l… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.08949v1-abstract-full').style.display = 'inline'; document.getElementById('2404.08949v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.08949v1-abstract-full" style="display: none;"> Event coreference resolution (ECR) is the task of determining whether distinct mentions of events within a multi-document corpus are actually linked to the same underlying occurrence. Images of the events can help facilitate resolution when language is ambiguous. Here, we propose a multimodal cross-document event coreference resolution method that integrates visual and textual cues with a simple linear map between vision and language models. As existing ECR benchmark datasets rarely provide images for all event mentions, we augment the popular ECB+ dataset with event-centric images scraped from the internet and generated using image diffusion models. We establish three methods that incorporate images and text for coreference: 1) a standard fused model with finetuning, 2) a novel linear mapping method without finetuning and 3) an ensembling approach based on splitting mention pairs by semantic and discourse-level difficulty. We evaluate on 2 datasets: the augmented ECB+, and AIDA Phase 1. Our ensemble systems using cross-modal linear mapping establish an upper limit (91.9 CoNLL F1) on ECB+ ECR performance given the preprocessing assumptions used, and establish a novel baseline on AIDA Phase 1. Our results demonstrate the utility of multimodal information in ECR for certain challenging coreference problems, and highlight a need for more multimodal resources in the coreference resolution space. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.08949v1-abstract-full').style.display = 'none'; document.getElementById('2404.08949v1-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 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">To appear at LREC-COLING 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.06636">arXiv:2402.06636</a> <span> [<a href="https://arxiv.org/pdf/2402.06636">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> </div> </div> <p class="title is-5 mathjax"> A Multichain based marketplace Architecture </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Farooq%2C+M+S">Muhammad Shoaib Farooq</a>, <a href="/search/cs?searchtype=author&query=Jamil%2C+H">Hamza Jamil</a>, <a href="/search/cs?searchtype=author&query=Riaz%2C+H+S">Hafiz Sohail Riaz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2402.06636v1-abstract-short" style="display: inline;"> ]A multichain non-fungible tokens (NFTs) marketplace is a decentralized platform where users can buy, sell, and trade NFTs across multiple blockchain networks by using cross communication bridge. In past most of NFT marketplace was based on singlechain in which NFTs have been bought, sold, and traded on a same blockchain network without the need for any external platform. The singlechain based mar… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.06636v1-abstract-full').style.display = 'inline'; document.getElementById('2402.06636v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.06636v1-abstract-full" style="display: none;"> ]A multichain non-fungible tokens (NFTs) marketplace is a decentralized platform where users can buy, sell, and trade NFTs across multiple blockchain networks by using cross communication bridge. In past most of NFT marketplace was based on singlechain in which NFTs have been bought, sold, and traded on a same blockchain network without the need for any external platform. The singlechain based marketplace have faced number of issues such as performance, scalability, flexibility and limited transaction throughput consequently long confirmation times and high transaction fees during high network usage. Firstly, this paper provides the comprehensive overview about NFT Multichain architecture and explore the challenges and opportunities of designing and implementation phase of multichain NFT marketplace to overcome the issue of single chain-based architecture. NFT multichain marketplace architecture includes different blockchain networks that communicate with each other. Secondly, this paper discusses the concept of mainchain interacting with sidechains which refers to multi blockchain architecture where multiple blockchain networks are connected to each other in a hierarchical structure and identifies key challenges related to interoperability, security, scalability, and user adoption. Finally, we proposed a novel architecture for a multichain NFT marketplace, which leverages the benefits of multiple blockchain networks and marketplaces to overcome these key challenges. Moreover, proposed architecture is evaluated through a case study, demonstrating its ability to support efficient and secure transactions across multiple blockchain networks and highlighting the future trends NFTs and marketplaces and comprehensive discussion about the technology. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.06636v1-abstract-full').style.display = 'none'; document.getElementById('2402.06636v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 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">15</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.09177">arXiv:2310.09177</a> <span> [<a href="https://arxiv.org/pdf/2310.09177">pdf</a>, <a href="https://arxiv.org/ps/2310.09177">ps</a>, <a href="https://arxiv.org/format/2310.09177">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 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/10.3390/s24082509">10.3390/s24082509 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Future Industrial Applications: Exploring LPWAN-Driven IoT Protocols </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Islam%2C+M">Mahbubul Islam</a>, <a href="/search/cs?searchtype=author&query=Jamil%2C+H+M+M">Hossain Md. Mubashshir Jamil</a>, <a href="/search/cs?searchtype=author&query=Pranto%2C+S+A">Samiul Ahsan Pranto</a>, <a href="/search/cs?searchtype=author&query=Das%2C+R+K">Rupak Kumar Das</a>, <a href="/search/cs?searchtype=author&query=Amin%2C+A">Al Amin</a>, <a href="/search/cs?searchtype=author&query=Khan%2C+A">Arshia Khan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2310.09177v2-abstract-short" style="display: inline;"> The Internet of Things (IoT) will bring about the next industrial revolution in Industry 4.0. The communication aspect of IoT devices is one of the most critical factors in choosing the suitable device for the suitable usage. So far, the IoT physical layer communication challenges have been met with various communications protocols that provide varying strengths and weaknesses. Moreover, most of t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.09177v2-abstract-full').style.display = 'inline'; document.getElementById('2310.09177v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.09177v2-abstract-full" style="display: none;"> The Internet of Things (IoT) will bring about the next industrial revolution in Industry 4.0. The communication aspect of IoT devices is one of the most critical factors in choosing the suitable device for the suitable usage. So far, the IoT physical layer communication challenges have been met with various communications protocols that provide varying strengths and weaknesses. Moreover, most of them are wireless protocols due to the sheer number of device requirements for IoT. This paper summarizes the network architectures of some of the most popular IoT wireless communications protocols. It also presents a comparative analysis of critical features, including power consumption, coverage, data rate, security, cost, and Quality of Service (QoS). This comparative study shows that Low Power Wide Area Network (LPWAN) based IoT protocols (LoRa, Sigfox, NB-IoT, LTE-M ) are more suitable for future industrial applications because of their energy efficiency, high coverage, and cost efficiency. In addition, the study also presents an industrial Internet of Things (IIoT) application perspective on the suitability of LPWAN protocols in a particular scenario and addresses some open issues that need to be researched. Thus, this study can assist in deciding the most suitable protocol for an industrial and production field. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.09177v2-abstract-full').style.display = 'none'; document.getElementById('2310.09177v2-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 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 13 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> s24082509 </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Sensors 2024, 24, 2509 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2305.01808">arXiv:2305.01808</a> <span> [<a href="https://arxiv.org/pdf/2305.01808">pdf</a>, <a href="https://arxiv.org/format/2305.01808">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="Optimization and Control">math.OC</span> </div> </div> <p class="title is-5 mathjax"> Hamming Similarity and Graph Laplacians for Class Partitioning and Adversarial Image Detection </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jamil%2C+H">Huma Jamil</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+Y">Yajing Liu</a>, <a href="/search/cs?searchtype=author&query=Caglar%2C+T">Turgay Caglar</a>, <a href="/search/cs?searchtype=author&query=Cole%2C+C+M">Christina M. Cole</a>, <a href="/search/cs?searchtype=author&query=Blanchard%2C+N">Nathaniel Blanchard</a>, <a href="/search/cs?searchtype=author&query=Peterson%2C+C">Christopher Peterson</a>, <a href="/search/cs?searchtype=author&query=Kirby%2C+M">Michael Kirby</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2305.01808v2-abstract-short" style="display: inline;"> Researchers typically investigate neural network representations by examining activation outputs for one or more layers of a network. Here, we investigate the potential for ReLU activation patterns (encoded as bit vectors) to aid in understanding and interpreting the behavior of neural networks. We utilize Representational Dissimilarity Matrices (RDMs) to investigate the coherence of data within t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.01808v2-abstract-full').style.display = 'inline'; document.getElementById('2305.01808v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.01808v2-abstract-full" style="display: none;"> Researchers typically investigate neural network representations by examining activation outputs for one or more layers of a network. Here, we investigate the potential for ReLU activation patterns (encoded as bit vectors) to aid in understanding and interpreting the behavior of neural networks. We utilize Representational Dissimilarity Matrices (RDMs) to investigate the coherence of data within the embedding spaces of a deep neural network. From each layer of a network, we extract and utilize bit vectors to construct similarity scores between images. From these similarity scores, we build a similarity matrix for a collection of images drawn from 2 classes. We then apply Fiedler partitioning to the associated Laplacian matrix to separate the classes. Our results indicate, through bit vector representations, that the network continues to refine class detectability with the last ReLU layer achieving better than 95\% separation accuracy. Additionally, we demonstrate that bit vectors aid in adversarial image detection, again achieving over 95\% accuracy in separating adversarial and non-adversarial images using a simple classifier. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.01808v2-abstract-full').style.display = 'none'; document.getElementById('2305.01808v2-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 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 2 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">accepted by the Workshop TAG in Pattern Recognition with Applications at the Computer Vision and Pattern Recognition (CVPR) 2023</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2211.13305">arXiv:2211.13305</a> <span> [<a href="https://arxiv.org/pdf/2211.13305">pdf</a>, <a href="https://arxiv.org/format/2211.13305">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="Cryptography and Security">cs.CR</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"> Dual Graphs of Polyhedral Decompositions for the Detection of Adversarial Attacks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jamil%2C+H">Huma Jamil</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+Y">Yajing Liu</a>, <a href="/search/cs?searchtype=author&query=Cole%2C+C+M">Christina M. Cole</a>, <a href="/search/cs?searchtype=author&query=Blanchard%2C+N">Nathaniel Blanchard</a>, <a href="/search/cs?searchtype=author&query=King%2C+E+J">Emily J. King</a>, <a href="/search/cs?searchtype=author&query=Kirby%2C+M">Michael Kirby</a>, <a href="/search/cs?searchtype=author&query=Peterson%2C+C">Christopher Peterson</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="2211.13305v2-abstract-short" style="display: inline;"> Previous work has shown that a neural network with the rectified linear unit (ReLU) activation function leads to a convex polyhedral decomposition of the input space. These decompositions can be represented by a dual graph with vertices corresponding to polyhedra and edges corresponding to polyhedra sharing a facet, which is a subgraph of a Hamming graph. This paper illustrates how one can utilize… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.13305v2-abstract-full').style.display = 'inline'; document.getElementById('2211.13305v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2211.13305v2-abstract-full" style="display: none;"> Previous work has shown that a neural network with the rectified linear unit (ReLU) activation function leads to a convex polyhedral decomposition of the input space. These decompositions can be represented by a dual graph with vertices corresponding to polyhedra and edges corresponding to polyhedra sharing a facet, which is a subgraph of a Hamming graph. This paper illustrates how one can utilize the dual graph to detect and analyze adversarial attacks in the context of digital images. When an image passes through a network containing ReLU nodes, the firing or non-firing at a node can be encoded as a bit ($1$ for ReLU activation, $0$ for ReLU non-activation). The sequence of all bit activations identifies the image with a bit vector, which identifies it with a polyhedron in the decomposition and, in turn, identifies it with a vertex in the dual graph. We identify ReLU bits that are discriminators between non-adversarial and adversarial images and examine how well collections of these discriminators can ensemble vote to build an adversarial image detector. Specifically, we examine the similarities and differences of ReLU bit vectors for adversarial images, and their non-adversarial counterparts, using a pre-trained ResNet-50 architecture. While this paper focuses on adversarial digital images, ResNet-50 architecture, and the ReLU activation function, our methods extend to other network architectures, activation functions, and types of datasets. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.13305v2-abstract-full').style.display = 'none'; document.getElementById('2211.13305v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 December, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 23 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">978-1-6654-8045-1/22/\$31.00 漏2022 IEEE The 6th Workshop on Graph Techniques for Adversarial Activity Analytics (GTA 2022)</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 68T01; 51M20; 68R10 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2211.11949">arXiv:2211.11949</a> <span> [<a href="https://arxiv.org/pdf/2211.11949">pdf</a>, <a href="https://arxiv.org/format/2211.11949">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="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Performance">cs.PF</span> </div> </div> <p class="title is-5 mathjax"> A Reinforcement Learning Approach to Optimize Available Network Bandwidth Utilization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jamil%2C+H">Hasibul Jamil</a>, <a href="/search/cs?searchtype=author&query=Rodrigues%2C+E">Elvis Rodrigues</a>, <a href="/search/cs?searchtype=author&query=Goldverg%2C+J">Jacob Goldverg</a>, <a href="/search/cs?searchtype=author&query=Kosar%2C+T">Tevfik Kosar</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="2211.11949v2-abstract-short" style="display: inline;"> Efficient data transfers over high-speed, long-distance shared networks require proper utilization of available network bandwidth. Using parallel TCP streams enables an application to utilize network parallelism and can improve transfer throughput; however, finding the optimum number of parallel TCP streams is challenging due to nondeterministic background traffic sharing the same network. Additio… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.11949v2-abstract-full').style.display = 'inline'; document.getElementById('2211.11949v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2211.11949v2-abstract-full" style="display: none;"> Efficient data transfers over high-speed, long-distance shared networks require proper utilization of available network bandwidth. Using parallel TCP streams enables an application to utilize network parallelism and can improve transfer throughput; however, finding the optimum number of parallel TCP streams is challenging due to nondeterministic background traffic sharing the same network. Additionally, the non-stationary, multi-objectiveness, and partially-observable nature of network signals in the host systems add extra complexity in finding the current network condition. In this work, we present a novel approach to finding the optimum number of parallel TCP streams using deep reinforcement learning (RL). We devise a learning-based algorithm capable of generalizing different network conditions and utilizing the available network bandwidth intelligently. Contrary to rule-based heuristics that do not generalize well in unknown network scenarios, our RL-based solution can dynamically discover and adapt the parallel TCP stream numbers to maximize the network bandwidth utilization without congesting the network and ensure fairness among contending transfers. We extensively evaluated our RL-based algorithm's performance, comparing it with several state-of-the-art online optimization algorithms. The results show that our RL-based algorithm can find near-optimal solutions 40% faster while achieving up to 15% higher throughput. We also show that, unlike a greedy algorithm, our devised RL-based algorithm can avoid network congestion and fairly share the available network resources among contending transfers. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.11949v2-abstract-full').style.display = 'none'; document.getElementById('2211.11949v2-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 November, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 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">Submitted to ICC 2023, converted to 12 pages , conference submission was for 7 pages</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> C.4; C.2.3; I.2.6 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2207.11504">arXiv:2207.11504</a> <span> [<a href="https://arxiv.org/pdf/2207.11504">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> </div> </div> <p class="title is-5 mathjax"> Intelligent 3D Network Protocol for Multimedia Data Classification using Deep Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Syed%2C+A">Arslan Syed</a>, <a href="/search/cs?searchtype=author&query=Aldhahri%2C+E+A">Eman A. Aldhahri</a>, <a href="/search/cs?searchtype=author&query=Iqbal%2C+M+M">Muhammad Munawar Iqbal</a>, <a href="/search/cs?searchtype=author&query=Ali%2C+A">Abid Ali</a>, <a href="/search/cs?searchtype=author&query=Muthanna%2C+A">Ammar Muthanna</a>, <a href="/search/cs?searchtype=author&query=Jamil%2C+H">Harun Jamil</a>, <a href="/search/cs?searchtype=author&query=Jamil%2C+F">Faisal Jamil</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="2207.11504v1-abstract-short" style="display: inline;"> In videos, the human's actions are of three-dimensional (3D) signals. These videos investigate the spatiotemporal knowledge of human behavior. The promising ability is investigated using 3D convolution neural networks (CNNs). The 3D CNNs have not yet achieved high output for their well-established two-dimensional (2D) equivalents in still photographs. Board 3D Convolutional Memory and Spatiotempor… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.11504v1-abstract-full').style.display = 'inline'; document.getElementById('2207.11504v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2207.11504v1-abstract-full" style="display: none;"> In videos, the human's actions are of three-dimensional (3D) signals. These videos investigate the spatiotemporal knowledge of human behavior. The promising ability is investigated using 3D convolution neural networks (CNNs). The 3D CNNs have not yet achieved high output for their well-established two-dimensional (2D) equivalents in still photographs. Board 3D Convolutional Memory and Spatiotemporal fusion face training difficulty preventing 3D CNN from accomplishing remarkable evaluation. In this paper, we implement Hybrid Deep Learning Architecture that combines STIP and 3D CNN features to enhance the performance of 3D videos effectively. After implementation, the more detailed and deeper charting for training in each circle of space-time fusion. The training model further enhances the results after handling complicated evaluations of models. The video classification model is used in this implemented model. Intelligent 3D Network Protocol for Multimedia Data Classification using Deep Learning is introduced to further understand spacetime association in human endeavors. In the implementation of the result, the well-known dataset, i.e., UCF101 to, evaluates the performance of the proposed hybrid technique. The results beat the proposed hybrid technique that substantially beats the initial 3D CNNs. The results are compared with state-of-the-art frameworks from literature for action recognition on UCF101 with an accuracy of 95%. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.11504v1-abstract-full').style.display = 'none'; document.getElementById('2207.11504v1-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 July, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 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">21 pages, 10 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2.11; H.4; C.2.2 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2205.08606">arXiv:2205.08606</a> <span> [<a href="https://arxiv.org/pdf/2205.08606">pdf</a>, <a href="https://arxiv.org/format/2205.08606">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="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Performance">cs.PF</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/HPSR48589.2020.9098974">10.1109/HPSR48589.2020.9098974 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Multibit Tries Packet Classification with Deep Reinforcement Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jamil%2C+H">Hasibul Jamil</a>, <a href="/search/cs?searchtype=author&query=Weng%2C+N">Ning Weng</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="2205.08606v1-abstract-short" style="display: inline;"> High performance packet classification is a key component to support scalable network applications like firewalls, intrusion detection, and differentiated services. With ever increasing in the line-rate in core networks, it becomes a great challenge to design a scalable and high performance packet classification solution using hand-tuned heuristics approaches. In this paper, we present a scalable… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.08606v1-abstract-full').style.display = 'inline'; document.getElementById('2205.08606v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2205.08606v1-abstract-full" style="display: none;"> High performance packet classification is a key component to support scalable network applications like firewalls, intrusion detection, and differentiated services. With ever increasing in the line-rate in core networks, it becomes a great challenge to design a scalable and high performance packet classification solution using hand-tuned heuristics approaches. In this paper, we present a scalable learning-based packet classification engine and its performance evaluation. By exploiting the sparsity of ruleset, our algorithm uses a few effective bits (EBs) to extract a large number of candidate rules with just a few of memory access. These effective bits are learned with deep reinforcement learning and they are used to create a bitmap to filter out the majority of rules which do not need to be full-matched to improve the online system performance. Moreover, our EBs learning-based selection method is independent of the ruleset, which can be applied to varying rulesets. Our multibit tries classification engine outperforms lookup time both in worst and average case by 55% and reduce memory footprint, compared to traditional decision tree without EBs. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.08606v1-abstract-full').style.display = 'none'; document.getElementById('2205.08606v1-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 May, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 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">6 pages. arXiv admin note: text overlap with arXiv:2205.07973</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> C.2 </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> 2020 IEEE 21st International Conference on High Performance Switching and Routing (HPSR), 2020, pp. 1-6 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2205.07973">arXiv:2205.07973</a> <span> [<a href="https://arxiv.org/pdf/2205.07973">pdf</a>, <a href="https://arxiv.org/format/2205.07973">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="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Performance">cs.PF</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/10.1049/ntw2.12038">10.1049/ntw2.12038 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Many Field Packet Classification with Decomposition and Reinforcement Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jamil%2C+H">Hasibul Jamil</a>, <a href="/search/cs?searchtype=author&query=Yang%2C+N">Ning Yang</a>, <a href="/search/cs?searchtype=author&query=Weng%2C+N">Ning Weng</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="2205.07973v1-abstract-short" style="display: inline;"> Scalable packet classification is a key requirement to support scalable network applications like firewalls, intrusion detection, and differentiated services. With ever increasing in the line-rate in core networks, it becomes a great challenge to design a scalable packet classification solution using hand-tuned heuristics approaches. In this paper, we present a scalable learning-based packet class… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.07973v1-abstract-full').style.display = 'inline'; document.getElementById('2205.07973v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2205.07973v1-abstract-full" style="display: none;"> Scalable packet classification is a key requirement to support scalable network applications like firewalls, intrusion detection, and differentiated services. With ever increasing in the line-rate in core networks, it becomes a great challenge to design a scalable packet classification solution using hand-tuned heuristics approaches. In this paper, we present a scalable learning-based packet classification engine by building an efficient data structure for different ruleset with many fields. Our method consists of the decomposition of fields into subsets and building separate decision trees on those subsets using a deep reinforcement learning procedure. To decompose given fields of a ruleset, we consider different grouping metrics like standard deviation of individual fields and introduce a novel metric called diversity index (DI). We examine different decomposition schemes and construct decision trees for each scheme using deep reinforcement learning and compare the results. The results show that the SD decomposition metrics results in 11.5% faster than DI metrics, 25% faster than random 2 and 40% faster than random 1. Furthermore, our learning-based selection method can be applied to varying rulesets due to its ruleset independence. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.07973v1-abstract-full').style.display = 'none'; document.getElementById('2205.07973v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 May, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 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">13 pages, published in IET Netw. arXiv admin note: substantial text overlap with arXiv:1902.10319 by other authors</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> C.2 </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> IET Netw 2022 1-16 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2204.07601">arXiv:2204.07601</a> <span> [<a href="https://arxiv.org/pdf/2204.07601">pdf</a>, <a href="https://arxiv.org/ps/2204.07601">ps</a>, <a href="https://arxiv.org/format/2204.07601">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Performance">cs.PF</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/ICCCN54977.2022.9868866">10.1109/ICCCN54977.2022.9868866 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Energy-Efficient Data Transfer Optimization via Decision-Tree Based Uncertainty Reduction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jamil%2C+H">Hasibul Jamil</a>, <a href="/search/cs?searchtype=author&query=Rodolph%2C+L">Lavone Rodolph</a>, <a href="/search/cs?searchtype=author&query=Goldverg%2C+J">Jacob Goldverg</a>, <a href="/search/cs?searchtype=author&query=Kosar%2C+T">Tevfik Kosar</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="2204.07601v2-abstract-short" style="display: inline;"> The increase and rapid growth of data produced by scientific instruments, the Internet of Things (IoT), and social media is causing data transfer performance and resource consumption to garner much attention in the research community. The network infrastructure and end systems that enable this extensive data movement use a substantial amount of electricity, measured in terawatt-hours per year. Man… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2204.07601v2-abstract-full').style.display = 'inline'; document.getElementById('2204.07601v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2204.07601v2-abstract-full" style="display: none;"> The increase and rapid growth of data produced by scientific instruments, the Internet of Things (IoT), and social media is causing data transfer performance and resource consumption to garner much attention in the research community. The network infrastructure and end systems that enable this extensive data movement use a substantial amount of electricity, measured in terawatt-hours per year. Managing energy consumption within the core networking infrastructure is an active research area, but there is a limited amount of work on reducing power consumption at the end systems during active data transfers. This paper presents a novel two-phase dynamic throughput and energy optimization model that utilizes an offline decision-search-tree based clustering technique to encapsulate and categorize historical data transfer log information and an online search optimization algorithm to find the best application and kernel layer parameter combination to maximize the achieved data transfer throughput while minimizing the energy consumption. Our model also incorporates an ensemble method to reduce aleatoric uncertainty in finding optimal application and kernel layer parameters during the offline analysis phase. The experimental evaluation results show that our decision-tree based model outperforms the state-of-the-art solutions in this area by achieving 117% higher throughput on average and also consuming 19% less energy at the end systems during active data transfers. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2204.07601v2-abstract-full').style.display = 'none'; document.getElementById('2204.07601v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 24 April, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 15 April, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 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">10 pages accepted to be published in IEEE ICCCN2022</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> 2022 International Conference on Computer Communications and Networks (ICCCN) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1811.04162">arXiv:1811.04162</a> <span> [<a href="https://arxiv.org/pdf/1811.04162">pdf</a>, <a href="https://arxiv.org/format/1811.04162">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="Databases">cs.DB</span> </div> </div> <p class="title is-5 mathjax"> Computational Thinking with the Web Crowd using CodeMapper </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Vanvorce%2C+P">Patrick Vanvorce</a>, <a href="/search/cs?searchtype=author&query=Jamil%2C+H+M">Hasan M. Jamil</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="1811.04162v1-abstract-short" style="display: inline;"> It has been argued that computational thinking should precede computer programming in the course of a career in computing. This argument is the basis for the slogan "logic first, syntax later" and the development of many cryptic syntax removed programming languages such as Scratch!, Blockly and Visual Logic. The goal is to focus on the structuring of the semantic relationships among the logical bu… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1811.04162v1-abstract-full').style.display = 'inline'; document.getElementById('1811.04162v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1811.04162v1-abstract-full" style="display: none;"> It has been argued that computational thinking should precede computer programming in the course of a career in computing. This argument is the basis for the slogan "logic first, syntax later" and the development of many cryptic syntax removed programming languages such as Scratch!, Blockly and Visual Logic. The goal is to focus on the structuring of the semantic relationships among the logical building blocks to yield solutions to computational problems. While this approach is helping novice programmers and early learners, the gap between computational thinking and professional programming using high level languages such as C++, Python and Java is quite wide. It is wide enough for about one third students in first college computer science classes to drop out or fail. In this paper, we introduce a new programming platform, called the CodeMapper, in which learners are able to build computational logic in independent modules and aggregate them to create complex modules. Code{\em Mapper} is an abstract development environment in which rapid visual prototyping of small to substantially large systems is possible by combining already developed independent modules in logical steps. The challenge we address involves supporting a visual development environment in which "annotated code snippets" authored by the masses in social computing sites such as SourceForge, StackOverflow or GitHub can also be used as is into prototypes and mapped to real executable programs. CodeMapper thus facilitates soft transition from visual programming to syntax driven programming without having to practice syntax too heavily. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1811.04162v1-abstract-full').style.display = 'none'; document.getElementById('1811.04162v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 November, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 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">8 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/1811.04160">arXiv:1811.04160</a> <span> [<a href="https://arxiv.org/pdf/1811.04160">pdf</a>, <a href="https://arxiv.org/format/1811.04160">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> <p class="title is-5 mathjax"> Meet Cyrus - The Query by Voice Mobile Assistant for the Tutoring and Formative Assessment of SQL Learners </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Godinez%2C+J+E">Josue Espinosa Godinez</a>, <a href="/search/cs?searchtype=author&query=Jamil%2C+H+M">Hasan M. Jamil</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="1811.04160v1-abstract-short" style="display: inline;"> Being declarative, SQL stands a better chance at being the programming language for conceptual computing next to natural language programming. We examine the possibility of using SQL as a back-end for natural language database programming. Distinctly from keyword based SQL querying, keyword dependence and SQL's table structure constraints are significantly less pronounced in our approach. We prese… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1811.04160v1-abstract-full').style.display = 'inline'; document.getElementById('1811.04160v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1811.04160v1-abstract-full" style="display: none;"> Being declarative, SQL stands a better chance at being the programming language for conceptual computing next to natural language programming. We examine the possibility of using SQL as a back-end for natural language database programming. Distinctly from keyword based SQL querying, keyword dependence and SQL's table structure constraints are significantly less pronounced in our approach. We present a mobile device voice query interface, called Cyrus, to arbitrary relational databases. Cyrus supports a large type of query classes, sufficient for an entry level database class. Cyrus is also application independent, allows test database adaptation, and not limited to specific sets of keywords or natural language sentence structures. It's cooperative error reporting is more intuitive, and iOS based mobile platform is also more accessible compared to most contemporary mobile and voice enabled systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1811.04160v1-abstract-full').style.display = 'none'; document.getElementById('1811.04160v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 November, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 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">6 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/1706.03272">arXiv:1706.03272</a> <span> [<a href="https://arxiv.org/pdf/1706.03272">pdf</a>, <a href="https://arxiv.org/ps/1706.03272">ps</a>, <a href="https://arxiv.org/format/1706.03272">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Programming Languages">cs.PL</span> </div> </div> <p class="title is-5 mathjax"> Computational Thinking in Patch </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jamil%2C+H+M">Hasan M. Jamil</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="1706.03272v1-abstract-short" style="display: inline;"> With the future likely to see even more pervasive computation, computational thinking (problem-solving skills incorporating computing knowledge) is now being recognized as a fundamental skill needed by all students. Computational thinking is conceptualizing as opposed to programming, promotes natural human thinking style than algorithmic reasoning, complements and combines mathematical and enginee… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1706.03272v1-abstract-full').style.display = 'inline'; document.getElementById('1706.03272v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1706.03272v1-abstract-full" style="display: none;"> With the future likely to see even more pervasive computation, computational thinking (problem-solving skills incorporating computing knowledge) is now being recognized as a fundamental skill needed by all students. Computational thinking is conceptualizing as opposed to programming, promotes natural human thinking style than algorithmic reasoning, complements and combines mathematical and engineering thinking, and it emphasizes ideas, not artifacts. In this paper, we outline a new visual language, called Patch, using which students are able to express their solutions to eScience computational problems in abstract visual tools. Patch is closer to high level procedural languages such as C++ or Java than Scratch or Snap! but similar to them in ease of use and combines simplicity and expressive power in one single platform. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1706.03272v1-abstract-full').style.display = 'none'; document.getElementById('1706.03272v1-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 June, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 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">11 pages, 3 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1705.00959">arXiv:1705.00959</a> <span> [<a href="https://arxiv.org/pdf/1705.00959">pdf</a>, <a href="https://arxiv.org/ps/1705.00959">ps</a>, <a href="https://arxiv.org/format/1705.00959">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Programming Languages">cs.PL</span> </div> </div> <p class="title is-5 mathjax"> Smart Assessment of and Tutoring for Computational Thinking MOOC Assignments using MindReader </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jamil%2C+H+M">Hasan M. Jamil</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="1705.00959v1-abstract-short" style="display: inline;"> One of the major hurdles toward automatic semantic understanding of computer programs is the lack of knowledge about what constitutes functional equivalence of code segments. We postulate that a sound knowledgebase can be used to deductively understand code segments in a hierarchical fashion by first de-constructing a code and then reconstructing it from elementary knowledge and equivalence rules… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1705.00959v1-abstract-full').style.display = 'inline'; document.getElementById('1705.00959v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1705.00959v1-abstract-full" style="display: none;"> One of the major hurdles toward automatic semantic understanding of computer programs is the lack of knowledge about what constitutes functional equivalence of code segments. We postulate that a sound knowledgebase can be used to deductively understand code segments in a hierarchical fashion by first de-constructing a code and then reconstructing it from elementary knowledge and equivalence rules of elementary code segments. The approach can also be engineered to produce computable programs from conceptual and abstract algorithms as an inverse function. In this paper, we introduce the core idea behind the MindReader online assessment system that is able to understand a wide variety of elementary algorithms students learn in their entry level programming classes such as Java, C++ and Python. The MindReader system is able to assess student assignments and guide them how to develop correct and better code in real time without human assistance. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1705.00959v1-abstract-full').style.display = 'none'; document.getElementById('1705.00959v1-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 April, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2017. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1703.10692">arXiv:1703.10692</a> <span> [<a href="https://arxiv.org/pdf/1703.10692">pdf</a>, <a href="https://arxiv.org/ps/1703.10692">ps</a>, <a href="https://arxiv.org/format/1703.10692">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> <p class="title is-5 mathjax"> Knowledge Rich Natural Language Queries over Structured Biological Databases </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jamil%2C+H+M">Hasan M. Jamil</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="1703.10692v1-abstract-short" style="display: inline;"> Increasingly, keyword, natural language and NoSQL queries are being used for information retrieval from traditional as well as non-traditional databases such as web, document, image, GIS, legal, and health databases. While their popularity are undeniable for obvious reasons, their engineering is far from simple. In most part, semantics and intent preserving mapping of a well understood natural lan… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1703.10692v1-abstract-full').style.display = 'inline'; document.getElementById('1703.10692v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1703.10692v1-abstract-full" style="display: none;"> Increasingly, keyword, natural language and NoSQL queries are being used for information retrieval from traditional as well as non-traditional databases such as web, document, image, GIS, legal, and health databases. While their popularity are undeniable for obvious reasons, their engineering is far from simple. In most part, semantics and intent preserving mapping of a well understood natural language query expressed over a structured database schema to a structured query language is still a difficult task, and research to tame the complexity is intense. In this paper, we propose a multi-level knowledge-based middleware to facilitate such mappings that separate the conceptual level from the physical level. We augment these multi-level abstractions with a concept reasoner and a query strategy engine to dynamically link arbitrary natural language querying to well defined structured queries. We demonstrate the feasibility of our approach by presenting a Datalog based prototype system, called BioSmart, that can compute responses to arbitrary natural language queries over arbitrary databases once a syntactic classification of the natural language query is made. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1703.10692v1-abstract-full').style.display = 'none'; document.getElementById('1703.10692v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 March, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2017. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1607.02669">arXiv:1607.02669</a> <span> [<a href="https://arxiv.org/pdf/1607.02669">pdf</a>, <a href="https://arxiv.org/ps/1607.02669">ps</a>, <a href="https://arxiv.org/format/1607.02669">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> <p class="title is-5 mathjax"> A Novel Model for Distributed Big Data Service Composition using Stratified Functional Graph Matching </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Rivero%2C+C+R">Carlos R. Rivero</a>, <a href="/search/cs?searchtype=author&query=Jamil%2C+H+M">Hasan M. Jamil</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="1607.02669v1-abstract-short" style="display: inline;"> A significant number of current industrial applications rely on web services. A cornerstone task in these applications is discovering a suitable service that meets the threshold of some user needs. Then, those services can be composed to perform specific functionalities. We argue that the prevailing approach to compose services based on the "all or nothing" paradigm is limiting and leads to exceed… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1607.02669v1-abstract-full').style.display = 'inline'; document.getElementById('1607.02669v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1607.02669v1-abstract-full" style="display: none;"> A significant number of current industrial applications rely on web services. A cornerstone task in these applications is discovering a suitable service that meets the threshold of some user needs. Then, those services can be composed to perform specific functionalities. We argue that the prevailing approach to compose services based on the "all or nothing" paradigm is limiting and leads to exceedingly high rejection of potentially suitable services. Furthermore, contemporary models do not allow "mix and match" composition from atomic services of different composite services when binary matching is not possible or desired. In this paper, we propose a new model for service composition based on "stratified graph summarization" and "service stitching". We discuss the limitations of existing approaches with a motivating example, present our approach to overcome these limitations, and outline a possible architecture for service composition from atomic services. Our thesis is that, with the advent of Big Data, our approach will reduce latency in service discovery, and will improve efficiency and accuracy of matchmaking and composition of services. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1607.02669v1-abstract-full').style.display = 'none'; document.getElementById('1607.02669v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 July, 2016; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 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">15 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/1606.01957">arXiv:1606.01957</a> <span> [<a href="https://arxiv.org/pdf/1606.01957">pdf</a>, <a href="https://arxiv.org/format/1606.01957">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> <p class="title is-5 mathjax"> Reliable Querying of Very Large, Fast Moving and Noisy Predicted Interaction Data using Hierarchical Crowd Curation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jamil%2C+H+M">Hasan M. Jamil</a>, <a href="/search/cs?searchtype=author&query=Sadri%2C+F">Fereidoon Sadri</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="1606.01957v1-abstract-short" style="display: inline;"> The abundance of predicted and mined but uncertain biological data show huge needs for massive, efficient and scalable curation efforts. The human expertise warranted by any successful curation enterprize is often economically prohibitive especially for speculative end user queries that may not ultimately bear fruit. So the challenge remains in devising a low cost engine capable of delivering fast… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1606.01957v1-abstract-full').style.display = 'inline'; document.getElementById('1606.01957v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1606.01957v1-abstract-full" style="display: none;"> The abundance of predicted and mined but uncertain biological data show huge needs for massive, efficient and scalable curation efforts. The human expertise warranted by any successful curation enterprize is often economically prohibitive especially for speculative end user queries that may not ultimately bear fruit. So the challenge remains in devising a low cost engine capable of delivering fast but tentative annotation and curation of a set of data items that can be authoritatively validated by experts later demanding significantly small investment. The aim thus is to make a large volume of predicted data available for use as early as possible with an acceptable degree of confidence in their accuracy while the curation continues. In this paper, we present a novel approach to annotation and curation of biological database contents using crowd computing. The technical contribution is in the identification and management of trust of mechanical turks, and support for ad hoc declarative queries, both of which are leveraged to support reliable analytics using noisy predicted interactions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1606.01957v1-abstract-full').style.display = 'none'; document.getElementById('1606.01957v1-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">15 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/1312.0189">arXiv:1312.0189</a> <span> [<a href="https://arxiv.org/pdf/1312.0189">pdf</a>, <a href="https://arxiv.org/ps/1312.0189">ps</a>, <a href="https://arxiv.org/format/1312.0189">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> <span class="tag is-small is-grey 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="Social and Information Networks">cs.SI</span> </div> </div> <p class="title is-5 mathjax"> Empowering Evolving Social Network Users with Privacy Rights </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jamil%2C+H+M">Hasan M. Jamil</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="1312.0189v1-abstract-short" style="display: inline;"> Considerable concerns exist over privacy on social networks, and huge debates persist about how to extend the artifacts users need to effectively protect their rights to privacy. While many interesting ideas have been proposed, no single approach appears to be comprehensive enough to be the front runner. In this paper, we propose a comprehensive and novel reference conceptual model for privacy in… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1312.0189v1-abstract-full').style.display = 'inline'; document.getElementById('1312.0189v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1312.0189v1-abstract-full" style="display: none;"> Considerable concerns exist over privacy on social networks, and huge debates persist about how to extend the artifacts users need to effectively protect their rights to privacy. While many interesting ideas have been proposed, no single approach appears to be comprehensive enough to be the front runner. In this paper, we propose a comprehensive and novel reference conceptual model for privacy in constantly evolving social networks and establish its novelty by briefly contrasting it with contemporary research. We also present the contours of a possible query language that we can develop with desirable features in light of the reference model, and refer to a new query language, {\em PiQL}, developed on the basis of this model that aims to support user driven privacy policy authoring and enforcement. The strength of our model is that such extensions are now possible by developing appropriate linguistic constructs as part of query languages such as SQL, as demonstrated in PiQL. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1312.0189v1-abstract-full').style.display = 'none'; document.getElementById('1312.0189v1-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 December, 2013; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2013. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">8 pages, 1 figure</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1311.2342">arXiv:1311.2342</a> <span> [<a href="https://arxiv.org/pdf/1311.2342">pdf</a>, <a href="https://arxiv.org/ps/1311.2342">ps</a>, <a href="https://arxiv.org/format/1311.2342">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> <p class="title is-5 mathjax"> Anatomy of Graph Matching based on an XQuery and RDF Implementation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Rivero%2C+C+R">Carlos R. Rivero</a>, <a href="/search/cs?searchtype=author&query=Jamil%2C+H+M">Hasan M. Jamil</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="1311.2342v1-abstract-short" style="display: inline;"> Graphs are becoming one of the most popular data modeling paradigms since they are able to model complex relationships that cannot be easily captured using traditional data models. One of the major tasks of graph management is graph matching, which aims to find all of the subgraphs in a data graph that match a query graph. In the literature, proposals in this context are classified into two differ… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1311.2342v1-abstract-full').style.display = 'inline'; document.getElementById('1311.2342v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1311.2342v1-abstract-full" style="display: none;"> Graphs are becoming one of the most popular data modeling paradigms since they are able to model complex relationships that cannot be easily captured using traditional data models. One of the major tasks of graph management is graph matching, which aims to find all of the subgraphs in a data graph that match a query graph. In the literature, proposals in this context are classified into two different categories: graph-at-a-time, which process the whole query graph at the same time, and vertex-at-a-time, which process a single vertex of the query graph at the same time. In this paper, we propose a new vertex-at-a-time proposal that is based on graphlets, each of which comprises a vertex of a graph, all of the immediate neighbors of that vertex, and all of the edges that relate those neighbors. Furthermore, we also use the concept of minimum hub covers, each of which comprises a subset of vertices in the query graph that account for all of the edges in that graph. We present the algorithms of our proposal and describe an implementation based on XQuery and RDF. Our evaluation results show that our proposal is appealing to perform graph matching. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1311.2342v1-abstract-full').style.display = 'none'; document.getElementById('1311.2342v1-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 November, 2013; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2013. </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">Technical report</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1311.1626">arXiv:1311.1626</a> <span> [<a href="https://arxiv.org/pdf/1311.1626">pdf</a>, <a href="https://arxiv.org/ps/1311.1626">ps</a>, <a href="https://arxiv.org/format/1311.1626">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> <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"> Trade-offs Computing Minimum Hub Cover toward Optimized Graph Query Processing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Yelbay%2C+B">Belma Yelbay</a>, <a href="/search/cs?searchtype=author&query=Birbil%2C+S+I">S. Ilker Birbil</a>, <a href="/search/cs?searchtype=author&query=Bulbul%2C+K">Kerem Bulbul</a>, <a href="/search/cs?searchtype=author&query=Jamil%2C+H+M">Hasan M. Jamil</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="1311.1626v2-abstract-short" style="display: inline;"> As techniques for graph query processing mature, the need for optimization is increasingly becoming an imperative. Indices are one of the key ingredients toward efficient query processing strategies via cost-based optimization. Due to the apparent absence of a common representation model, it is difficult to make a focused effort toward developing access structures, metrics to evaluate query costs,… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1311.1626v2-abstract-full').style.display = 'inline'; document.getElementById('1311.1626v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1311.1626v2-abstract-full" style="display: none;"> As techniques for graph query processing mature, the need for optimization is increasingly becoming an imperative. Indices are one of the key ingredients toward efficient query processing strategies via cost-based optimization. Due to the apparent absence of a common representation model, it is difficult to make a focused effort toward developing access structures, metrics to evaluate query costs, and choose alternatives. In this context, recent interests in covering-based graph matching appears to be a promising direction of research. In this paper, our goal is to formally introduce a new graph representation model, called Minimum Hub Cover, and demonstrate that this representation offers interesting strategic advantages, facilitates construction of candidate graphs from graph fragments, and helps leverage indices in novel ways for query optimization. However, similar to other covering problems, minimum hub cover is NP-hard, and thus is a natural candidate for optimization. We claim that computing the minimum hub cover leads to substantial cost reduction for graph query processing. We present a computational characterization of minimum hub cover based on integer programming to substantiate our claim and investigate its computational cost on various graph types. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1311.1626v2-abstract-full').style.display = 'none'; document.getElementById('1311.1626v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 November, 2013; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 7 November, 2013; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2013. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">12 pages, 6 figures and 2 algorithms</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" 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