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<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/2411.14006">arXiv:2411.14006</a> <span> [<a href="https://arxiv.org/pdf/2411.14006">pdf</a>, <a href="https://arxiv.org/format/2411.14006">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Data Structures and Algorithms">cs.DS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Hardware Architecture">cs.AR</span> <span class="tag is-small is-grey 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="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"> Experimental comparison of graph-based approximate nearest neighbor search algorithms on edge devices </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ganbarov%2C+A">Ali Ganbarov</a>, <a href="/search/cs?searchtype=author&query=Yuan%2C+J">Jicheng Yuan</a>, <a href="/search/cs?searchtype=author&query=Le-Tuan%2C+A">Anh Le-Tuan</a>, <a href="/search/cs?searchtype=author&query=Hauswirth%2C+M">Manfred Hauswirth</a>, <a href="/search/cs?searchtype=author&query=Le-Phuoc%2C+D">Danh Le-Phuoc</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.14006v1-abstract-short" style="display: inline;"> In this paper, we present an experimental comparison of various graph-based approximate nearest neighbor (ANN) search algorithms deployed on edge devices for real-time nearest neighbor search applications, such as smart city infrastructure and autonomous vehicles. To the best of our knowledge, this specific comparative analysis has not been previously conducted. While existing research has explore… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.14006v1-abstract-full').style.display = 'inline'; document.getElementById('2411.14006v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.14006v1-abstract-full" style="display: none;"> In this paper, we present an experimental comparison of various graph-based approximate nearest neighbor (ANN) search algorithms deployed on edge devices for real-time nearest neighbor search applications, such as smart city infrastructure and autonomous vehicles. To the best of our knowledge, this specific comparative analysis has not been previously conducted. While existing research has explored graph-based ANN algorithms, it has often been limited to single-threaded implementations on standard commodity hardware. Our study leverages the full computational and storage capabilities of edge devices, incorporating additional metrics such as insertion and deletion latency of new vectors and power consumption. This comprehensive evaluation aims to provide valuable insights into the performance and suitability of these algorithms for edge-based real-time tracking systems enhanced by nearest-neighbor search algorithms. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.14006v1-abstract-full').style.display = 'none'; document.getElementById('2411.14006v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.11078">arXiv:2407.11078</a> <span> [<a href="https://arxiv.org/pdf/2407.11078">pdf</a>, <a href="https://arxiv.org/format/2407.11078">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Overcoming Catastrophic Forgetting in Federated Class-Incremental Learning via Federated Global Twin Generator </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Nguyen%2C+T">Thinh Nguyen</a>, <a href="/search/cs?searchtype=author&query=Doan%2C+K+D">Khoa D Doan</a>, <a href="/search/cs?searchtype=author&query=Nguyen%2C+B+T">Binh T. Nguyen</a>, <a href="/search/cs?searchtype=author&query=Le-Phuoc%2C+D">Danh Le-Phuoc</a>, <a href="/search/cs?searchtype=author&query=Wong%2C+K">Kok-Seng Wong</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="2407.11078v1-abstract-short" style="display: inline;"> Federated Class-Incremental Learning (FCIL) increasingly becomes important in the decentralized setting, where it enables multiple participants to collaboratively train a global model to perform well on a sequence of tasks without sharing their private data. In FCIL, conventional Federated Learning algorithms such as FedAVG often suffer from catastrophic forgetting, resulting in significant perfor… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.11078v1-abstract-full').style.display = 'inline'; document.getElementById('2407.11078v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.11078v1-abstract-full" style="display: none;"> Federated Class-Incremental Learning (FCIL) increasingly becomes important in the decentralized setting, where it enables multiple participants to collaboratively train a global model to perform well on a sequence of tasks without sharing their private data. In FCIL, conventional Federated Learning algorithms such as FedAVG often suffer from catastrophic forgetting, resulting in significant performance declines on earlier tasks. Recent works, based on generative models, produce synthetic images to help mitigate this issue across all classes, but these approaches' testing accuracy on previous classes is still much lower than recent classes, i.e., having better plasticity than stability. To overcome these issues, this paper presents Federated Global Twin Generator (FedGTG), an FCIL framework that exploits privacy-preserving generative-model training on the global side without accessing client data. Specifically, the server trains a data generator and a feature generator to create two types of information from all seen classes, and then it sends the synthetic data to the client side. The clients then use feature-direction-controlling losses to make the local models retain knowledge and learn new tasks well. We extensively analyze the robustness of FedGTG on natural images, as well as its ability to converge to flat local minima and achieve better-predicting confidence (calibration). Experimental results on CIFAR-10, CIFAR-100, and tiny-ImageNet demonstrate the improvements in accuracy and forgetting measures of FedGTG compared to previous frameworks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.11078v1-abstract-full').style.display = 'none'; document.getElementById('2407.11078v1-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 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 68T07 (Primary); 68T45 (Secondary) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.20431">arXiv:2405.20431</a> <span> [<a href="https://arxiv.org/pdf/2405.20431">pdf</a>, <a href="https://arxiv.org/format/2405.20431">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="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Exploring the Practicality of Federated Learning: A Survey Towards the Communication Perspective </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Le%2C+K">Khiem Le</a>, <a href="/search/cs?searchtype=author&query=Luong-Ha%2C+N">Nhan Luong-Ha</a>, <a href="/search/cs?searchtype=author&query=Nguyen-Duc%2C+M">Manh Nguyen-Duc</a>, <a href="/search/cs?searchtype=author&query=Le-Phuoc%2C+D">Danh Le-Phuoc</a>, <a href="/search/cs?searchtype=author&query=Do%2C+C">Cuong Do</a>, <a href="/search/cs?searchtype=author&query=Wong%2C+K">Kok-Seng Wong</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="2405.20431v1-abstract-short" style="display: inline;"> Federated Learning (FL) is a promising paradigm that offers significant advancements in privacy-preserving, decentralized machine learning by enabling collaborative training of models across distributed devices without centralizing data. However, the practical deployment of FL systems faces a significant bottleneck: the communication overhead caused by frequently exchanging large model updates bet… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.20431v1-abstract-full').style.display = 'inline'; document.getElementById('2405.20431v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.20431v1-abstract-full" style="display: none;"> Federated Learning (FL) is a promising paradigm that offers significant advancements in privacy-preserving, decentralized machine learning by enabling collaborative training of models across distributed devices without centralizing data. However, the practical deployment of FL systems faces a significant bottleneck: the communication overhead caused by frequently exchanging large model updates between numerous devices and a central server. This communication inefficiency can hinder training speed, model performance, and the overall feasibility of real-world FL applications. In this survey, we investigate various strategies and advancements made in communication-efficient FL, highlighting their impact and potential to overcome the communication challenges inherent in FL systems. Specifically, we define measures for communication efficiency, analyze sources of communication inefficiency in FL systems, and provide a taxonomy and comprehensive review of state-of-the-art communication-efficient FL methods. Additionally, we discuss promising future research directions for enhancing the communication efficiency of FL systems. By addressing the communication bottleneck, FL can be effectively applied and enable scalable and practical deployment across diverse applications that require privacy-preserving, decentralized machine learning, such as IoT, healthcare, or finance. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.20431v1-abstract-full').style.display = 'none'; document.getElementById('2405.20431v1-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 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/2404.19492">arXiv:2404.19492</a> <span> [<a href="https://arxiv.org/pdf/2404.19492">pdf</a>, <a href="https://arxiv.org/format/2404.19492">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"> Reducing Communication Overhead in the IoT-Edge-Cloud Continuum: A Survey on Protocols and Data Reduction Strategies </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Krekovi%C4%87%2C+D">Dora Krekovi膰</a>, <a href="/search/cs?searchtype=author&query=Krivi%C4%87%2C+P">Petar Krivi膰</a>, <a href="/search/cs?searchtype=author&query=%C5%BDarko%2C+I+P">Ivana Podnar 沤arko</a>, <a href="/search/cs?searchtype=author&query=Ku%C5%A1ek%2C+M">Mario Ku拧ek</a>, <a href="/search/cs?searchtype=author&query=Le-Phuoc%2C+D">Danh Le-Phuoc</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="2404.19492v2-abstract-short" style="display: inline;"> The adoption of the Internet of Things (IoT) deployments has led to a sharp increase in network traffic as a vast number of IoT devices communicate with each other and IoT services through the IoT-edge-cloud continuum. This network traffic increase poses a major challenge to the global communications infrastructure since it hinders communication performance and also puts significant strain on the… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.19492v2-abstract-full').style.display = 'inline'; document.getElementById('2404.19492v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.19492v2-abstract-full" style="display: none;"> The adoption of the Internet of Things (IoT) deployments has led to a sharp increase in network traffic as a vast number of IoT devices communicate with each other and IoT services through the IoT-edge-cloud continuum. This network traffic increase poses a major challenge to the global communications infrastructure since it hinders communication performance and also puts significant strain on the energy consumption of IoT devices. To address these issues, efficient and collaborative IoT solutions which enable information exchange while reducing the transmitted data and associated network traffic are crucial. This survey provides a comprehensive overview of the communication technologies and protocols as well as data reduction strategies that contribute to this goal. First, we present a comparative analysis of prevalent communication technologies in the IoT domain, highlighting their unique characteristics and exploring the potential for protocol composition and joint usage to enhance overall communication efficiency within the IoT-edge-cloud continuum. Next, we investigate various data traffic reduction techniques tailored to the IoT-edge-cloud context and evaluate their applicability and effectiveness on resource-constrained and devices. Finally, we investigate the emerging concepts that have the potential to further reduce the communication overhead in the IoT-edge-cloud continuum, including cross-layer optimization strategies and Edge AI techniques for IoT data reduction. The paper offers a comprehensive roadmap for developing efficient and scalable solutions across the layers of the IoT-edge-cloud continuum that are beneficial for real-time processing to alleviate network congestion in complex IoT environments. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.19492v2-abstract-full').style.display = 'none'; document.getElementById('2404.19492v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 30 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.01988">arXiv:2404.01988</a> <span> [<a href="https://arxiv.org/pdf/2404.01988">pdf</a>, <a href="https://arxiv.org/format/2404.01988">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Cooperative Students: Navigating Unsupervised Domain Adaptation in Nighttime Object Detection </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Yuan%2C+J">Jicheng Yuan</a>, <a href="/search/cs?searchtype=author&query=Le-Tuan%2C+A">Anh Le-Tuan</a>, <a href="/search/cs?searchtype=author&query=Hauswirth%2C+M">Manfred Hauswirth</a>, <a href="/search/cs?searchtype=author&query=Le-Phuoc%2C+D">Danh Le-Phuoc</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="2404.01988v3-abstract-short" style="display: inline;"> Unsupervised Domain Adaptation (UDA) has shown significant advancements in object detection under well-lit conditions; however, its performance degrades notably in low-visibility scenarios, especially at night, posing challenges not only for its adaptability in low signal-to-noise ratio (SNR) conditions but also for the reliability and efficiency of automated vehicles. To address this problem, we… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.01988v3-abstract-full').style.display = 'inline'; document.getElementById('2404.01988v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.01988v3-abstract-full" style="display: none;"> Unsupervised Domain Adaptation (UDA) has shown significant advancements in object detection under well-lit conditions; however, its performance degrades notably in low-visibility scenarios, especially at night, posing challenges not only for its adaptability in low signal-to-noise ratio (SNR) conditions but also for the reliability and efficiency of automated vehicles. To address this problem, we propose a \textbf{Co}operative \textbf{S}tudents (\textbf{CoS}) framework that innovatively employs global-local transformations (GLT) and a proxy-based target consistency (PTC) mechanism to capture the spatial consistency in day- and night-time scenarios effectively, and thus bridge the significant domain shift across contexts. Building upon this, we further devise an adaptive IoU-informed thresholding (AIT) module to gradually avoid overlooking potential true positives and enrich the latent information in the target domain. Comprehensive experiments show that CoS essentially enhanced UDA performance in low-visibility conditions and surpasses current state-of-the-art techniques, achieving an increase in mAP of 3.0\%, 1.9\%, and 2.5\% on BDD100K, SHIFT, and ACDC datasets, respectively. Code is available at https://github.com/jichengyuan/Cooperitive_Students. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.01988v3-abstract-full').style.display = 'none'; document.getElementById('2404.01988v3-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> 8 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 2 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">Code is available at https://github.com/jichengyuan/Cooperitive_Students</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.15605">arXiv:2403.15605</a> <span> [<a href="https://arxiv.org/pdf/2403.15605">pdf</a>, <a href="https://arxiv.org/format/2403.15605">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Efficiently Assemble Normalization Layers and Regularization for Federated Domain Generalization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Le%2C+K">Khiem Le</a>, <a href="/search/cs?searchtype=author&query=Ho%2C+L">Long Ho</a>, <a href="/search/cs?searchtype=author&query=Do%2C+C">Cuong Do</a>, <a href="/search/cs?searchtype=author&query=Le-Phuoc%2C+D">Danh Le-Phuoc</a>, <a href="/search/cs?searchtype=author&query=Wong%2C+K">Kok-Seng Wong</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.15605v1-abstract-short" style="display: inline;"> Domain shift is a formidable issue in Machine Learning that causes a model to suffer from performance degradation when tested on unseen domains. Federated Domain Generalization (FedDG) attempts to train a global model using collaborative clients in a privacy-preserving manner that can generalize well to unseen clients possibly with domain shift. However, most existing FedDG methods either cause ad… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.15605v1-abstract-full').style.display = 'inline'; document.getElementById('2403.15605v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.15605v1-abstract-full" style="display: none;"> Domain shift is a formidable issue in Machine Learning that causes a model to suffer from performance degradation when tested on unseen domains. Federated Domain Generalization (FedDG) attempts to train a global model using collaborative clients in a privacy-preserving manner that can generalize well to unseen clients possibly with domain shift. However, most existing FedDG methods either cause additional privacy risks of data leakage or induce significant costs in client communication and computation, which are major concerns in the Federated Learning paradigm. To circumvent these challenges, here we introduce a novel architectural method for FedDG, namely gPerXAN, which relies on a normalization scheme working with a guiding regularizer. In particular, we carefully design Personalized eXplicitly Assembled Normalization to enforce client models selectively filtering domain-specific features that are biased towards local data while retaining discrimination of those features. Then, we incorporate a simple yet effective regularizer to guide these models in directly capturing domain-invariant representations that the global model's classifier can leverage. Extensive experimental results on two benchmark datasets, i.e., PACS and Office-Home, and a real-world medical dataset, Camelyon17, indicate that our proposed method outperforms other existing methods in addressing this particular problem. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.15605v1-abstract-full').style.display = 'none'; document.getElementById('2403.15605v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2309.13610">arXiv:2309.13610</a> <span> [<a href="https://arxiv.org/pdf/2309.13610">pdf</a>, <a href="https://arxiv.org/format/2309.13610">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> VisionKG: Unleashing the Power of Visual Datasets via Knowledge Graph </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Yuan%2C+J">Jicheng Yuan</a>, <a href="/search/cs?searchtype=author&query=Le-Tuan%2C+A">Anh Le-Tuan</a>, <a href="/search/cs?searchtype=author&query=Nguyen-Duc%2C+M">Manh Nguyen-Duc</a>, <a href="/search/cs?searchtype=author&query=Tran%2C+T">Trung-Kien Tran</a>, <a href="/search/cs?searchtype=author&query=Hauswirth%2C+M">Manfred Hauswirth</a>, <a href="/search/cs?searchtype=author&query=Le-Phuoc%2C+D">Danh Le-Phuoc</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2309.13610v2-abstract-short" style="display: inline;"> The availability of vast amounts of visual data with heterogeneous features is a key factor for developing, testing, and benchmarking of new computer vision (CV) algorithms and architectures. Most visual datasets are created and curated for specific tasks or with limited image data distribution for very specific situations, and there is no unified approach to manage and access them across diverse… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.13610v2-abstract-full').style.display = 'inline'; document.getElementById('2309.13610v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.13610v2-abstract-full" style="display: none;"> The availability of vast amounts of visual data with heterogeneous features is a key factor for developing, testing, and benchmarking of new computer vision (CV) algorithms and architectures. Most visual datasets are created and curated for specific tasks or with limited image data distribution for very specific situations, and there is no unified approach to manage and access them across diverse sources, tasks, and taxonomies. This not only creates unnecessary overheads when building robust visual recognition systems, but also introduces biases into learning systems and limits the capabilities of data-centric AI. To address these problems, we propose the Vision Knowledge Graph (VisionKG), a novel resource that interlinks, organizes and manages visual datasets via knowledge graphs and Semantic Web technologies. It can serve as a unified framework facilitating simple access and querying of state-of-the-art visual datasets, regardless of their heterogeneous formats and taxonomies. One of the key differences between our approach and existing methods is that ours is knowledge-based rather than metadatabased. It enhances the enrichment of the semantics at both image and instance levels and offers various data retrieval and exploratory services via SPARQL. VisionKG currently contains 519 million RDF triples that describe approximately 40 million entities, and are accessible at https://vision.semkg.org and through APIs. With the integration of 30 datasets and four popular CV tasks, we demonstrate its usefulness across various scenarios when working with CV pipelines. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.13610v2-abstract-full').style.display = 'none'; document.getElementById('2309.13610v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 24 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted at ESWC 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/2309.09364">arXiv:2309.09364</a> <span> [<a href="https://arxiv.org/pdf/2309.09364">pdf</a>, <a href="https://arxiv.org/format/2309.09364">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="Distributed, Parallel, and Cluster Computing">cs.DC</span> </div> </div> <p class="title is-5 mathjax"> Building a P2P RDF Store for Edge Devices </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Guo%2C+X">Xuanchi Guo</a>, <a href="/search/cs?searchtype=author&query=Le-Tuan%2C+A">Anh Le-Tuan</a>, <a href="/search/cs?searchtype=author&query=Le-Phuoc%2C+D">Danh Le-Phuoc</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2309.09364v1-abstract-short" style="display: inline;"> The Semantic Web technologies have been used in the Internet of Things (IoT) to facilitate data interoperability and address data heterogeneity issues. The Resource Description Framework (RDF) model is employed in the integration of IoT data, with RDF engines serving as gateways for semantic integration. However, storing and querying RDF data obtained from distributed sources across a dynamic netw… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.09364v1-abstract-full').style.display = 'inline'; document.getElementById('2309.09364v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.09364v1-abstract-full" style="display: none;"> The Semantic Web technologies have been used in the Internet of Things (IoT) to facilitate data interoperability and address data heterogeneity issues. The Resource Description Framework (RDF) model is employed in the integration of IoT data, with RDF engines serving as gateways for semantic integration. However, storing and querying RDF data obtained from distributed sources across a dynamic network of edge devices presents a challenging task. The distributed nature of the edge shares similarities with Peer-to-Peer (P2P) systems. These similarities include attributes like node heterogeneity, limited availability, and resources. The nodes primarily undertake tasks related to data storage and processing. Therefore, the P2P models appear to present an attractive approach for constructing distributed RDF stores. Based on P-Grid, a data indexing mechanism for load balancing and range query processing in P2P systems, this paper proposes a design for storing and sharing RDF data on P2P networks of low-cost edge devices. Our design aims to integrate both P-Grid and an edge-based RDF storage solution, RDF4Led for building an P2P RDF engine. This integration can maintain RDF data access and query processing while scaling with increasing data and network size. We demonstrated the scaling behavior of our implementation on a P2P network, involving up to 16 nodes of Raspberry Pi 4 devices. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.09364v1-abstract-full').style.display = 'none'; document.getElementById('2309.09364v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted to IoT Conference 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/2308.10555">arXiv:2308.10555</a> <span> [<a href="https://arxiv.org/pdf/2308.10555">pdf</a>, <a href="https://arxiv.org/format/2308.10555">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> </div> </div> <p class="title is-5 mathjax"> Semantic Programming for Device-Edge-Cloud Continuum </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Le-Tuan%2C+A">Anh Le-Tuan</a>, <a href="/search/cs?searchtype=author&query=Bowden%2C+D">David Bowden</a>, <a href="/search/cs?searchtype=author&query=Le-Phuoc%2C+D">Danh Le-Phuoc</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="2308.10555v3-abstract-short" style="display: inline;"> This position paper presents ThothSP, a Semantic Programming framework with the aim of lowering the coding effort in building smart applications on the Device-Edge-Cloud continuum by leveraging semantic knowledge. It introduces a novel neural-symbolic stream fusion mechanism, which enables the specification of data fusion pipelines via declarative rules, with degrees of learnable probabilistic wei… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.10555v3-abstract-full').style.display = 'inline'; document.getElementById('2308.10555v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2308.10555v3-abstract-full" style="display: none;"> This position paper presents ThothSP, a Semantic Programming framework with the aim of lowering the coding effort in building smart applications on the Device-Edge-Cloud continuum by leveraging semantic knowledge. It introduces a novel neural-symbolic stream fusion mechanism, which enables the specification of data fusion pipelines via declarative rules, with degrees of learnable probabilistic weights. Moreover, it includes an adaptive federator that allows the Thoth>runtime to be distributed across multiple compute nodes in a network, and to coordinate their resources to collaboratively process tasks by delegating partial workloads to their peers. To demonstrate ThothSP's capability, we report a case study on a distributed camera network to show ThothSP's behaviour against a traditional edge-cloud setup. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.10555v3-abstract-full').style.display = 'none'; document.getElementById('2308.10555v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 21 August, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 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 to CEC Workshop 2023. arXiv admin note: text overlap with arXiv:2202.13958</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2305.19831">arXiv:2305.19831</a> <span> [<a href="https://arxiv.org/pdf/2305.19831">pdf</a>, <a href="https://arxiv.org/format/2305.19831">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="Distributed, Parallel, and Cluster Computing">cs.DC</span> </div> </div> <p class="title is-5 mathjax"> An Empirical Study of Federated Learning on IoT-Edge Devices: Resource Allocation and Heterogeneity </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Wong%2C+K">Kok-Seng Wong</a>, <a href="/search/cs?searchtype=author&query=Nguyen-Duc%2C+M">Manh Nguyen-Duc</a>, <a href="/search/cs?searchtype=author&query=Le-Huy%2C+K">Khiem Le-Huy</a>, <a href="/search/cs?searchtype=author&query=Ho-Tuan%2C+L">Long Ho-Tuan</a>, <a href="/search/cs?searchtype=author&query=Do-Danh%2C+C">Cuong Do-Danh</a>, <a href="/search/cs?searchtype=author&query=Le-Phuoc%2C+D">Danh Le-Phuoc</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="2305.19831v1-abstract-short" style="display: inline;"> Nowadays, billions of phones, IoT and edge devices around the world generate data continuously, enabling many Machine Learning (ML)-based products and applications. However, due to increasing privacy concerns and regulations, these data tend to reside on devices (clients) instead of being centralized for performing traditional ML model training. Federated Learning (FL) is a distributed approach in… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.19831v1-abstract-full').style.display = 'inline'; document.getElementById('2305.19831v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.19831v1-abstract-full" style="display: none;"> Nowadays, billions of phones, IoT and edge devices around the world generate data continuously, enabling many Machine Learning (ML)-based products and applications. However, due to increasing privacy concerns and regulations, these data tend to reside on devices (clients) instead of being centralized for performing traditional ML model training. Federated Learning (FL) is a distributed approach in which a single server and multiple clients collaboratively build an ML model without moving data away from clients. Whereas existing studies on FL have their own experimental evaluations, most experiments were conducted using a simulation setting or a small-scale testbed. This might limit the understanding of FL implementation in realistic environments. In this empirical study, we systematically conduct extensive experiments on a large network of IoT and edge devices (called IoT-Edge devices) to present FL real-world characteristics, including learning performance and operation (computation and communication) costs. Moreover, we mainly concentrate on heterogeneous scenarios, which is the most challenging issue of FL. By investigating the feasibility of on-device implementation, our study provides valuable insights for researchers and practitioners, promoting the practicality of FL and assisting in improving the current design of real FL systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.19831v1-abstract-full').style.display = 'none'; document.getElementById('2305.19831v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 31 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2212.07771">arXiv:2212.07771</a> <span> [<a href="https://arxiv.org/pdf/2212.07771">pdf</a>, <a href="https://arxiv.org/format/2212.07771">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Temporal Saliency Detection Towards Explainable Transformer-based Timeseries Forecasting </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Duong-Trung%2C+N">Nghia Duong-Trung</a>, <a href="/search/cs?searchtype=author&query=Nguyen%2C+D">Duc-Manh Nguyen</a>, <a href="/search/cs?searchtype=author&query=Le-Phuoc%2C+D">Danh Le-Phuoc</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2212.07771v3-abstract-short" style="display: inline;"> Despite the notable advancements in numerous Transformer-based models, the task of long multi-horizon time series forecasting remains a persistent challenge, especially towards explainability. Focusing on commonly used saliency maps in explaining DNN in general, our quest is to build attention-based architecture that can automatically encode saliency-related temporal patterns by establishing conne… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.07771v3-abstract-full').style.display = 'inline'; document.getElementById('2212.07771v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2212.07771v3-abstract-full" style="display: none;"> Despite the notable advancements in numerous Transformer-based models, the task of long multi-horizon time series forecasting remains a persistent challenge, especially towards explainability. Focusing on commonly used saliency maps in explaining DNN in general, our quest is to build attention-based architecture that can automatically encode saliency-related temporal patterns by establishing connections with appropriate attention heads. Hence, this paper introduces Temporal Saliency Detection (TSD), an effective approach that builds upon the attention mechanism and applies it to multi-horizon time series prediction. While our proposed architecture adheres to the general encoder-decoder structure, it undergoes a significant renovation in the encoder component, wherein we incorporate a series of information contracting and expanding blocks inspired by the U-Net style architecture. The TSD approach facilitates the multiresolution analysis of saliency patterns by condensing multi-heads, thereby progressively enhancing the forecasting of complex time series data. Empirical evaluations illustrate the superiority of our proposed approach compared to other models across multiple standard benchmark datasets in diverse far-horizon forecasting settings. The initial TSD achieves substantial relative improvements of 31% and 46% over several models in the context of multivariate and univariate prediction. We believe the comprehensive investigations presented in this study will offer valuable insights and benefits to future research endeavors. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.07771v3-abstract-full').style.display = 'none'; document.getElementById('2212.07771v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 15 December, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 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">Published at the International Workshop on Explainable and Interpretable Machine Learning (XI-ML), 26th European Conference on Artificial Intelligence (ECAI 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/2202.13958">arXiv:2202.13958</a> <span> [<a href="https://arxiv.org/pdf/2202.13958">pdf</a>, <a href="https://arxiv.org/format/2202.13958">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="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> CQELS 2.0: Towards A Unified Framework for Semantic Stream Fusion </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Le-Tuan%2C+A">Anh Le-Tuan</a>, <a href="/search/cs?searchtype=author&query=Nguyen-Duc%2C+M">Manh Nguyen-Duc</a>, <a href="/search/cs?searchtype=author&query=Le%2C+C">Chien-Quang Le</a>, <a href="/search/cs?searchtype=author&query=Tran%2C+T">Trung-Kien Tran</a>, <a href="/search/cs?searchtype=author&query=Hauswirth%2C+M">Manfred Hauswirth</a>, <a href="/search/cs?searchtype=author&query=Eiter%2C+T">Thomas Eiter</a>, <a href="/search/cs?searchtype=author&query=Le-Phuoc%2C+D">Danh Le-Phuoc</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2202.13958v1-abstract-short" style="display: inline;"> We present CQELS 2.0, the second version of Continuous Query Evaluation over Linked Streams. CQELS 2.0 is a platform-agnostic federated execution framework towards semantic stream fusion. In this version, we introduce a novel neural-symbolic stream reasoning component that enables specifying deep neural network (DNN) based data fusion pipelines via logic rules with learnable probabilistic degrees… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.13958v1-abstract-full').style.display = 'inline'; document.getElementById('2202.13958v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2202.13958v1-abstract-full" style="display: none;"> We present CQELS 2.0, the second version of Continuous Query Evaluation over Linked Streams. CQELS 2.0 is a platform-agnostic federated execution framework towards semantic stream fusion. In this version, we introduce a novel neural-symbolic stream reasoning component that enables specifying deep neural network (DNN) based data fusion pipelines via logic rules with learnable probabilistic degrees as weights. As a platform-agnostic framework, CQELS 2.0 can be implemented for devices with different hardware architectures (from embedded devices to cloud infrastructures). Moreover, this version also includes an adaptive federator that allows CQELS instances on different nodes in a network to coordinate their resources to distribute processing pipelines by delegating partial workloads to their peers via subscribing continuous queries <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.13958v1-abstract-full').style.display = 'none'; document.getElementById('2202.13958v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 February, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Stream Reasoning Workshop 2021 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2201.11625">arXiv:2201.11625</a> <span> [<a href="https://arxiv.org/pdf/2201.11625">pdf</a>, <a href="https://arxiv.org/format/2201.11625">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> SemRob: Towards Semantic Stream Reasoning for Robotic Operating Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Nguyen-Duc%2C+M">Manh Nguyen-Duc</a>, <a href="/search/cs?searchtype=author&query=Le-Tuan%2C+A">Anh Le-Tuan</a>, <a href="/search/cs?searchtype=author&query=Hauswirth%2C+M">Manfred Hauswirth</a>, <a href="/search/cs?searchtype=author&query=Bowden%2C+D">David Bowden</a>, <a href="/search/cs?searchtype=author&query=Le-Phuoc%2C+D">Danh Le-Phuoc</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="2201.11625v1-abstract-short" style="display: inline;"> Stream processing and reasoning is getting considerable attention in various application domains such as IoT, Industry IoT and Smart Cities. In parallel, reasoning and knowledge-based features have attracted research into many areas of robotics, such as robotic mapping, perception and interaction. To this end, the Semantic Stream Reasoning (SSR) framework can unify the representations of symbolic/… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2201.11625v1-abstract-full').style.display = 'inline'; document.getElementById('2201.11625v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2201.11625v1-abstract-full" style="display: none;"> Stream processing and reasoning is getting considerable attention in various application domains such as IoT, Industry IoT and Smart Cities. In parallel, reasoning and knowledge-based features have attracted research into many areas of robotics, such as robotic mapping, perception and interaction. To this end, the Semantic Stream Reasoning (SSR) framework can unify the representations of symbolic/semantic streams with deep neural networks, to integrate high-dimensional data streams, such as video streams and LiDAR point clouds, with traditional graph or relational stream data. As such, this positioning and system paper will outline our approach to build a platform to facilitate semantic stream reasoning capabilities on a robotic operating system called SemRob. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2201.11625v1-abstract-full').style.display = 'none'; document.getElementById('2201.11625v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 January, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Stream Reasoning Workshop, Milano 2021 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2201.05026">arXiv:2201.05026</a> <span> [<a href="https://arxiv.org/pdf/2201.05026">pdf</a>, <a href="https://arxiv.org/format/2201.05026">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey 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="Databases">cs.DB</span> </div> </div> <p class="title is-5 mathjax"> Fantastic Data and How to Query Them </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Tran%2C+T">Trung-Kien Tran</a>, <a href="/search/cs?searchtype=author&query=Le-Tuan%2C+A">Anh Le-Tuan</a>, <a href="/search/cs?searchtype=author&query=Nguyen-Duc%2C+M">Manh Nguyen-Duc</a>, <a href="/search/cs?searchtype=author&query=Yuan%2C+J">Jicheng Yuan</a>, <a href="/search/cs?searchtype=author&query=Le-Phuoc%2C+D">Danh Le-Phuoc</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="2201.05026v1-abstract-short" style="display: inline;"> It is commonly acknowledged that the availability of the huge amount of (training) data is one of the most important factors for many recent advances in Artificial Intelligence (AI). However, datasets are often designed for specific tasks in narrow AI sub areas and there is no unified way to manage and access them. This not only creates unnecessary overheads when training or deploying Machine Lear… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2201.05026v1-abstract-full').style.display = 'inline'; document.getElementById('2201.05026v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2201.05026v1-abstract-full" style="display: none;"> It is commonly acknowledged that the availability of the huge amount of (training) data is one of the most important factors for many recent advances in Artificial Intelligence (AI). However, datasets are often designed for specific tasks in narrow AI sub areas and there is no unified way to manage and access them. This not only creates unnecessary overheads when training or deploying Machine Learning models but also limits the understanding of the data, which is very important for data-centric AI. In this paper, we present our vision about a unified framework for different datasets so that they can be integrated and queried easily, e.g., using standard query languages. We demonstrate this in our ongoing work to create a framework for datasets in Computer Vision and show its advantages in different scenarios. Our demonstration is available at https://vision.semkg.org. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2201.05026v1-abstract-full').style.display = 'none'; document.getElementById('2201.05026v1-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 January, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> NeurIPS Data-Centric AI Workshop 2021 </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary">  <div class="column"> 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