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International Joint Conference on computer science and Software Engineering (JCSSE)

作者： Tanapat Samakit Chutiporn Anutariya Marut Buranarach ICT Department Computer Science & Information Management School of Engineering and Technology Asian Institute of Technology Thailand Data Science and Analytics Research Group National Electronics and Computer Technology Center Thailand

Open Government data (OGD) refers to the provision of data produced by the government to the general public, in a format that is readily readable and can be used by machines with ease. It can also promote transparency, improve decision-making, enhance accountability, create economic opportunities, and encourage civic engagement. The OGD can help citizens understand the government and its legitimacy and transparency. Thus, when the government shares its data with people, it helps to create trust by being transparent, accountable, and promoting innovative solutions that benefit everyone. However, each published dataset has no indication of its quality assessment at all; thus, making it difficult for citizens to assess the reliability of the data from the OGD. Therefore, a data quality assessment for OGD should be developed. This will help create effective datasets which in turn help users understand the data. This study proposes QUALYST, a system that assesses Thailand's OGD dataset and validates it for analytic and visualization purposes. The study focuses on designing the data storage and implementing the assessment system. Furthermore, the proposed data quality dimensions, the developed data pipeline, and the assessment process are elaborated. Finally, the prototype system is demonstrated using Thailand's OGD datasets with examples in a visualized format.

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ADAPTIVE Q-NETWORK: ON-THE-FLY TARGET SELECTION FOR DEEP REINFORCEMENT LEARNING

arXiv

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arXiv 2024年

作者： Vincent, Théo Wahren, Fabian Peters, Jan Belousov, Boris D’Eramo, Carlo DFKI GmbH SAIROL Germany Department of Computer Science TU Darmstadt Germany Hessian.ai TU Darmstadt Germany Center for AI and Data Science University of Würzburg Germany

Deep Reinforcement Learning (RL) is well known for being highly sensitive to hyperparameters, requiring practitioners substantial efforts to optimize them for the problem at hand. This also limits the applicability of RL in real-world scenarios. In recent years, the field of automated Reinforcement Learning (AutoRL) has grown in popularity by trying to address this issue. However, these approaches typically hinge on additional samples to select well-performing hyperparameters, hindering sample-efficiency and practicality. Furthermore, most AutoRL methods are heavily based on already existing AutoML methods, which were originally developed neglecting the additional challenges inherent to RL due to its non-stationarities. In this work, we propose a new approach for AutoRL, called Adaptive Q-Network (AdaQN), that is tailored to RL to take into account the non-stationarity of the optimization procedure without requiring additional samples. AdaQN learns several Q-functions, each one trained with different hyperparameters, which are updated online using the Q-function with the smallest approximation error as a shared target. Our selection scheme simultaneously handles different hyperparameters while coping with the non-stationarity induced by the RL optimization procedure and being orthogonal to any critic-based RL algorithm. We demonstrate that AdaQN is theoretically sound and empirically validate it in MuJoCo control problems and Atari 2600 games, showing benefits in sample-efficiency, overall performance, robustness to stochasticity and training stability. Our code is available at https://***/theovincent/AdaDQN. © 2024, CC BY-NC-ND.

关键词： Deep reinforcement learning

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Delay Tolerant and Energy Reduced Task Allocation in Internet of Things with Cloud Systems 1

Delay Tolerant and Energy Reduced Task Allocation in Interne...

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1st IEEE International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022

作者： Raghunath Patil, Dipak Borkar, Bharat Markad, Ashok Kadlag, Sunil Kumbhkar, Makhan Jamal, Ahmad Amrutvahini College of Engineering Dept. of Computer Engineering Pune Sangamner India Amrutvahini College of Engineering Dept. of Information Technology Pune Sangamner India Amrutvahini College of Engineering Department of Electrical Engineering Pune Sangamner India Christian Eminent College Indore & Visiting Faculty School of Data Science and Forecasting Devi Ahilya University Department of Computer Science & Electronics Indore India Tulas Institute Department of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9781665456876

Through the utilization of the Internet, cloud computing supplies storage and computation resources to deliver services for many sectors. The speed of such systems suffers, though, since delay-sensitive systems, such as smart city and health applications, increasingly need for processing over massive volumes of data transmitted to centralised datacenters. When compared to cloud services, the paradigms of fog as well as edge computing provide innovative solutions by affecting the financial closer to the customer and by having low energy consumption and latency effectiveness. To optimise expense and resource efficiency, enhance QoS, and increase security and privacy, it is crucial to discover the best locations for services and assets inside the 3 IoT. The bipartite graph task scheduling method we provide in this research enhances cost effectiveness in real world applications with strict deadlines. The proposed method's performance in terms of delay, network congestion, as well as cost is deployed and tested to use the iFogSim simulator, an expanded version of CloudSim. The evaluation results demonstrate that the suggested algorithm outperforms Round-Robin and Minimum Response Time methods in terms of both cost as well as throughput. © 2022 IEEE.

关键词： Internet of things

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An Improved Bayesian Learner based on Weighted Beta Kernel Density Estimation 14

An Improved Bayesian Learner based on Weighted Beta Kernel D...

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14th International Conference on Advanced computer Information Technologies, ACIT 2024

作者： Liu, Yuanhu Ye, Zhiwei Bai, Wanfang Kochan, Orest Zhang, Yuquan Xu, Donglei Hubei University of Technology School of Computer and Science Wuhan China Xining Big Data Service Administration Consultants Co. Ltd Wuhan China Lviv Polytechnic National University Department of Measuring-Information Technologies Lviv Ukraine Wuhan Fiberhome Technical Consultants Co. Ltd. Wuhan China

ISBN: (纸本)9798350350036

There are problems in classification tasks where noisy data situations can affect the probability density fitting, as well as adaptive problems with current Bayesian classifiers constructed with symmetric kernel density estimation. In this paper, we will propose a plain Bayesian classifier improved based on weighted beta kernel density estimation. The bandwidth selection is also derived for beta kernel estimation. Compared with the classifier based on symmetric kernel density estimator, the beta kernel can be more adaptive to data changes and can solve certain boundary influence problems. Meanwhile, for the presence of noisy data in the data, a weighted kernel density estimation is combined with the beta kernel to weaken the influence of some of noisy data in fitting the data distribution. The experimental results show that our proposed weighted beta kernel density estimation plain Bayesian classifier method has obvious effect and improvement in fitting probability density distribution and data classification for tightly supported data. © 2024 IEEE.

关键词： Bayesian classifier Beta kernel Weight Kernel density estimation

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A Smart Air Pollutants Monitoring System Using IOT Technologies

A Smart Air Pollutants Monitoring System Using IOT Technolog...

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Intelligent Systems for Communication, IoT and Security (ICISCoIS), International Conference on

作者： M Pyingkodi N R Wilfred Blessing K Thenmozhi K Kanimozhi L Pavithira P K Sandhya Balakrishnan Department of Computer Applications Kongu Engineering College Erode India IT Department College of Computing and Information Sciences University of Technology and Applied Sciences-Ibri(Government) Oman Department of Computer Science Kristu Jayanti College Bengaluru India Department of Information Technology Sri Krishna Adithya College of Arts and Science Coimbatore Department of Computer Science with Data Analytics PSG college of Arts & Science College Coimbatore Department of Computer Science CMS College of Science and Commerce Coimbatore

On international basis, one in all our century's essential problems is air pollutants. Every year, the range of individuals killed via way of means of pollutants climbs, with air pollutants being the main cause. The Internet of Things (IoT) is used worldwide community of “inventive gadgets” which could sense, and join to, and have interaction with different humans and systems. The solution is furnished via way of means of a sensor-primarily based totally hardware module that can be deployed anywhere. These gadgets, which can be hooked up on mild poles, homes, and different structures, wirelessly speak air pleasant facts to a far flung server. The microprocessor collects facts from IoT sensing gadgets and sends it to a cloud server for processing. These facts can be used to expecting while the vicinity is maximum polluted. The machine additionally gives air pleasant statistics through a cell application, permitting clients to choose alternatives wherein the air pleasant is great. It is likewise a totally less expensive device.

关键词： Cloud computing Microprocessors Air pollution Hardware Sensors Servers Internet of Things

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Linear optimal transport subspaces for point set classification

arXiv

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arXiv 2024年

作者： Shifat-E-Rabbi, Mohammad Pathan, Naqib Sad Li, Shiying Zhuang, Yan Rubaiyat, Abu Hasnat Mohammad Rohde, Gustavo K. The Department of Electrical and Computer Engineering North South University Dhaka Bangladesh The Imaging and Data Science Laboratory The Department of Electrical and Computer Engineering University of Virginia CharlottesvilleVA United States The Department of Mathematics University of North Carolina Chapel HillNC United States The Department of Radiology and Imaging Sciences National Institutes Health Clinical Center MD United States The Imaging and Data Science Laboratory The Department of Biomedical Engineering The Department of Electrical and Computer Engineering University of Virginia CharlottesvilleVA United States

Learning from point sets is an essential component in many computer vision and machine learning applications. Native, unordered, and permutation invariant set structure space is challenging to model, particularly for point set classification under spatial deformations. Here we propose a framework for classifying point sets experiencing certain types of spatial deformations, with a particular emphasis on datasets featuring affine deformations. Our approach employs the Linear Optimal Transport (LOT) transform to obtain a linear embedding of set-structured data. Utilizing the mathematical properties of the LOT transform, we demonstrate its capacity to accommodate variations in point sets by constructing a convex data space, effectively simplifying point set classification problems. Our method, which employs a nearest-subspace algorithm in the LOT space, demonstrates label efficiency, non-iterative behavior, and requires no hyper-parameter tuning. It achieves competitive accuracies compared to state-of-the-art methods across various point set classification tasks. Furthermore, our approach exhibits robustness in out-of-distribution scenarios where training and test distributions vary in terms of deformation magnitudes. Copyright © 2024, The Authors. All rights reserved.

关键词： Classification (of information)

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Deep Residual and Classified Neural Networks for Inverse Halftoning

Deep Residual and Classified Neural Networks for Inverse Hal...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Jing-Ming Guo S. Sankarasrinivasan Let Viet Hung Wei Liu Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan School of Data and Computer Science Sun Yat-Sen University China

Inverse Halftoning is an ill-posed problem which restores a continuous-tone image from a halftone image. Many conventional inverse halftoning methods have tried to solve this problem, yet the recovered images still suffer several unwanted artifacts and fine details losses. In addition, recent deep neural network-based approaches have shown their advantages on restoration of the high-quality images with rich textures and detailed information. However, it is truly challenging for these deep learning methods to reconstruct a variety of different halftone patterns. For instance, the model trained with the halftone patterns of homogenous distribution cannot perform ideally for high structural information patterns. To solve this problem, an inverse halftoning based on deep residual neural network (DRNN) and variance classification is proposed. The proposed method utilizes benefits of progressive learning concept involving two main stages: First, the DRNN extracts numerous intrinsic features of an image, and significantly removes the halftone patterns. Subsequently, consecutive deep residual blocks are integrated to network restoring the fine details with good accuracy. Consequently, the proposed model comprises the integration of various DRNNs which are trained over various statistical ranges with respect to the statistics of halftone patches. Comprehensive experimental results demonstrate that the proposed deep learning-based technique significantly outperforms not only the conventional methods but also deep learning approaches.

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Revolutionizing Machine Learning Security: The Role of Quantum-Enhanced Federated Learning

Revolutionizing Machine Learning Security: The Role of Quant...

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Emerging Research in Computational science (ICERCS), International Conference on

作者： Kannan E Ravikumar S Carmel Mary Belinda M J Vijay K Anstey Vathani Sriram Kannan Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala RD Institute of Science and Technology Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences Chennai India Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India Department of Civil Engineering Vel Tech Rangarajan Dr Sagunthala RD Institute of Science and Technology Data Engineering Fidelity Investments Chennai India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534974

In today's age of collaborative machine learning where data sharing happens across organizations and privacy becomes a real issue. Old-school methods of securing systems work, but tend to lack against popular new attacks. The research uses a novel approach that uses quantum computing to reimagine what privacy means. An article which deals with the combination of quantum technologies and federated learning systems, specifically discussing how it may set a new standard for data privacy. The plan is to use quantum key distribution and entanglement-based protocols in order to fortify the defences of collaborative machine learning systems against adversarial attacks or unauthorized access, according a consortium statement. The approach to quantum -enhanced federated learning presented provides a solution not only with secure improvements but also implementing more extreme circumstances in decentralised learning scenarios, such as challenges due to different data available and regulatory demands. This paper provides a theoretical benchmark for quantum federated learning and focuses on the underlying concepts leading to excellent security guarantees. Applications and simulations show that this framework can be used in practice with effectiveness. Furthermore, this work benchmarks the scalability of quantum federated learning for different classical and common machine-learning models. The results are a major step towards the goal of making more sophisticated privacy solutions available and allow to create secure, private-first workspace for collaborative machine learning. By leveraging the latest in quantum computing along with specialized tactics designed to combat rapidly evolving data protection threats, this approach will provide a strong foundation for future applications of shared machine learning within distributed quantum environments.

关键词： Privacy Quantum computing Protocols Federated learning Quantum entanglement Scalability Computational modeling Collaboration Quantum key distribution Security

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An Efficient data Storage Technique for User Files in Cloud

An Efficient Data Storage Technique for User Files in Cloud

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Quantum Technologies, Communications, Computing, Hardware and Embedded Systems Security (iQ-CCHESS), International Conference on

作者： R. Eswari A. Vamshi M. Sumaiya Sultan Department of Computer Applications National Institute of Technology Tiruchirappalli India Department of Data Science St.Joseph’s College Tiruchirappalli India

A remote data storage service provided by cloud providers is known as cloud storage. It substitutes an online-accessible central storage repository for the need for nearby physical storage devices. Cloud storage is efficient due to its wide range of accessibility. An efficient approach for managing file storage in cloud computing environments is presented in this paper. The proposed method utilizes the concept of dividing files into blocks and combining them to generate file hashes using Merkle hash tree (MHT). These file hashes facilitate quick and reliable data integrity verification. Upon transmission of multiple files to the Cloud Service Provider (CSP), a secure storage process is implemented within the cloud environment. The data packets are organized and managed through a $B+$ tree structure. The proposed scheme also supports dynamic file operations. Where users can modify files, and for each modification, a new file hash is generated by merging the block hashes through MHT. By combining the efficiency of Merkle hash tree and B+ tree structures, this proposed system offers a dependable solution for effectively managing files in cloud storage. This method makes a significant contribution to the fields of cloud computing and file management by improving data security, integrity, and accessibility.

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Fair yet Asymptotically Equal Collaborative Learning

arXiv

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arXiv 2023年

作者： Lin, Xiaoqiang Xu, Xinyi Ng, See-Kiong Foo, Chuan-Sheng Low, Bryan Kian Hsiang Department of Computer Science National University of Singapore Singapore Institute for Infocomm Research A*STAR Singapore Institute of Data Science National University of Singapore Singapore

In collaborative learning with streaming data, nodes (e.g., organizations) jointly and continuously learn a machine learning (ML) model by sharing the latest model updates computed from their latest streaming data. For the more resourceful nodes to be willing to share their model updates, they need to be fairly incentivized. This paper explores an incentive design that guarantees fairness so that nodes receive rewards commensurate to their contributions. Our approach leverages an explore-then-exploit formulation to estimate the nodes' contributions (i.e., exploration) for realizing our theoretically guaranteed fair incentives (i.e., exploitation). However, we observe a "rich get richer" phenomenon arising from the existing approaches to guarantee fairness and it discourages the participation of the less resourceful nodes. To remedy this, we additionally preserve asymptotic equality, i.e., less resourceful nodes achieve equal performance eventually to the more resourceful/"rich" nodes. We empirically demonstrate in two settings with real-world streaming data: federated online incremental learning and federated reinforcement learning, that our proposed approach outperforms existing baselines in fairness and learning performance while remaining competitive in preserving equality. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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