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23rd IEEE International Conference on Software Quality, Reliability, and Security Companion, QRS-C 2023

作者： Liu, Jinrun Tang, Xinyu Li, Linlin Chen, Panpan Liu, Yepang Southern University of Science and Technology Shenzhen Guangdong China Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering China

ISBN: (纸本)9798350359398

Programmers often seek help from Q&A websites to resolve issues they encounter during programming. Stack Overflow has been a widely used platform for this purpose for over a decade. Recently, revolutionary AI-powered platforms like ChatGPT have quickly gained popularity among programmers for their efficient and personalized programming assistance via natural language interactions. Both platforms can offer valuable assistance to programmers, but it's unclear which is more effective at enhancing programmer productivity. In our paper, we conducted an exploratory user study to compare the performance of Stack Overflow and ChatGPT in enhancing programmer productivity. Two groups of students with similar programming abilities were instructed to use the two platforms to solve three different types of programming tasks: Algorithmic challenges, library usage, and debugging. During the experiments, we measured and compared the quality of code produced and the time taken to complete tasks for the two groups. The results show that, concerning code quality, ChatGPT outperforms Stack Overflow significantly in helping complete algorithmic and library-related tasks, while Stack Overflow is better for debugging tasks. Regarding task completion speed, the ChatGPT group is obviously faster than the Stack Overflow group in the algorithmic challenge, but the two groups have a similar performance in the other two tasks. Additionally, we conducted a post-experiment survey with the participants to understand how the platforms have helped them complete the programming tasks. We analyzed the questionnaires to summarize ChatGPT and Stack Overflow's strengths and weaknesses pointed out by the participants. By comparing these, we identified the reasons behind the two platforms' divergent performances in programming assistance. © 2023 IEEE.

关键词： ChatGPT programming Stack Overflow user study

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Toolkit for Satellite Visualization using Auxiliary Data 15

Toolkit for Satellite Visualization using Auxiliary Data

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Veena, O.S. Suhas, G.K. Department of Computer Science & Engineering Akshaya Institute of Technology Affiliated to Visvesvaraya Technological University Tumkur572106 India Research center for Computer & Information Sciences Akshaya Institute of Technology Affiliated to Visvesvaraya Technological University Tumkur572106 India

ISBN: (纸本)9798331300579

This paper provides a complete model for satellite tracking that uses Two Line Elements (TLE) data to accurately predict and track satellite locations over *** three-phase graphical user interface of the model allows for real-time viewing of satellite positions and critical tracking data: input, prediction, and *** work offers a comprehensive method for tracking and visualizing satellites in low Earth orbit (LEO), geostationary orbit, and remote sensing orbit utilizing TLE *** latitude and longitude calculations allow for ongoing trajectory mapping, which makes it easier to track satellite movement *** and clarity are further enhanced by visualizing satellite *** their courses, satellite names are added to make identification *** integrated method has significant benefits for applications including space exploration, telecommunication, and environmental ***, it makes constellation dynamics and satellite interactions more understandable, developing satellite-based technologies for a variety of useful uses. © Grenze Scientific Society, 2024.

关键词： Graphical user interfaces

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COIN: Cost-Efficient Traffic engineering with Various Pricing Schemes in Clouds 42

COIN: Cost-Efficient Traffic Engineering with Various Pricin...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Zhao, Gongming Wang, Jingzhou Xu, Hongli Yu, Zhuolong Qiao, Chunming University of Science and Technology of China School of Computer Science and Technology China University of Science and Technology of China Suzhou Institute for Advanced Research China Johns Hopkins University Department of Computer Science United States University at Buffalo Department of Computer Science and Engineering United States

ISBN: (纸本)9798350334142

The rapid growth of cloud services has brought a significant increase in inter-datacenter traffic. To transfer data among geographically distributed datacenters, cloud providers need to purchase bandwidth from ISPs. The data transferring cost has become one of the major expenses for cloud providers. Therefore, it is essential for a cloud provider to carefully allocate inter-datacenter traffic among the ISPs' links to minimize the costs. Exiting solutions mainly focus on the situations where all links adopt the same pricing scheme. However, in practice, ISPs usually provide multiple pricing schemes for their links due to market competition, which makes the existing solutions nonoptimal. Thus, a new traffic engineering approach that considers various pricing schemes is needed. This paper presents COIN, a new framework for cost-efficient traffic engineering with various pricing schemes. We propose a partition rounding traffic engineering algorithm based on linear independence analysis. The approximation factors and time complexity are formally analyzed. We further conduct large-scale simulations with real- world topologies and datasets. Extensive simulation results show that COIN can save the data transferring cost by up to 54.54% compared with the state-of-the-art solutions. © 2023 IEEE.

关键词： Cost engineering

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Demand Prediction Based Slice Reconfiguration Using Dueling Deep Q-Network

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China Communications 2022年第5期19卷 267-285页

作者： Wanqing Guan Haijun Zhang School of Computer&Communication Engineering University of Science and Technology BeijingBeijing 100083China Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services Institute of Artificial IntelligenceUniversity of Science and Technology BeijingBeijing 100083China

To satisfy diversified service demands of vertical industries,network slicing enables efficient resource allocation of a common infrastructure by creating isolated logical ***,uncertainty and dynamics of service demands will cause performance *** to operation costs and resource constraints,it is challenging to maintain high quality of user experience while obtaining high revenue for service providers(SPs).This paper develops an optimal and fast slice reconfiguration(OFSR)framework based on reinforcement learning,where a novel scheme is proposed to offer optimal decisions for reconfiguring diverse slices.A demand prediction model is proposed to capture changes in resource requirements,based on which the OFSR scheme is triggered to determine whether to perform slice *** the large state and action spaces generated from uncertain service time and resource requirements,deep dueling architecture is adopted to improve the convergence *** simulations validate the effectiveness of the proposed framework in achieving higher long-term revenue for SPs.

关键词： network slicing slice reconfiguration reinforcement learning resource allocation

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Effect of Vanadium Nitride(VN)Particles on Microstructure and Mechanical Properties of Extruded AZ31 Mg Alloy

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Acta Metallurgica Sinica(English Letters) 2023年第2期36卷 237-250页

作者： Wei Qiu Wen Xie Qi-Feng Li Wei-Ying Huang Li-Bo Zhou Wei Chen Jian Chen Yan-Jie Ren Mao-Hai Yao Ai-Hu Xiong Zhuo-Ran Zeng School of Energy and Power Engineering Changsha University of Science and TechnologyChangsha410114China Key Laboratory of Energy Efficiency and Clean Utilization The Education Department of Hunan ProvinceChangsha University of Science and TechnologyChangsha410114China Hunan Rare Earth Metal Material Research Institute Institute of Rare Earth Light AlloysChangsha410126China College of Engineering and Computer Science Australian National UniversityActonACT2601Australia

The effect of vanadium nitride(VN)particles additives on microstructure and mechanical properties of the extruded AZ31 Mg alloy was systematically *** experimental results revealed that the addition of 0.5 wt%VN decreased the average grain size of AZ31 Mg alloy from 6.4 to 4.9µ*** the increase in VN content,the refining effect would weaken because excessive VN particles would negatively affect the dynamic recrystallization process of the *** scanning electron microscopy and energy-dispersive spectroscopy indicated that AlN,VN and Al-V-N particles with different morphologies were distributed in the streamline along the extrusion direction during the extrusion *** mechanical properties of AZ31 Mg alloy vary with the addition of *** extruded AZ31+0.5 wt%VN Mg alloy possesses an excellent combination of high strength and *** yield strength and ultimate tensile strength of the extruded AZ31+0.5 wt%VN Mg alloy were increased without sacrificing *** is mainly due to the grain refinement caused by double-heterogeneous nucleation *** a further increase in VN content,the presence of excessive VN particles increases the stress concentration,and the initiation source of microcracks in the alloy during alloy deformation makes the cracks more easily propagated and results in a decrease in the ductility of the extruded alloy.

关键词： AZ31 Mg alloy Extrusion VN particles Microstructure Mechanical properties

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On Which Nodes Does GCN Fail? Enhancing GCN From the Node Perspective 41

On Which Nodes Does GCN Fail? Enhancing GCN From the Node Pe...

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41st International Conference on Machine Learning, ICML 2024

作者： Huang, Jincheng Shen, Jialie Shi, Xiaoshuang Zhu, Xiaofeng School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Computer Science City University of London London United Kingdom Sichuan Artificial Intelligence Research Institute Yibin China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China

The label smoothness assumption is at the core of Graph Convolutional Networks (GCNs): nodes in a local region have similar labels. Thus, GCN performs local feature smoothing operation to adhere to this assumption. However, there exist some nodes whose labels obtained by feature smoothing conflict with the label smoothness assumption. We find that the label smoothness assumption and the process of feature smoothing are both problematic on these nodes, and call these nodes out of GCN's control (OOC nodes). In this paper, first, we design the corresponding algorithm to locate the OOC nodes, then we summarize the characteristics of OOC nodes that affect their representation learning, and based on their characteristics, we present DaGCN, an efficient framework that can facilitate the OOC nodes. Extensive experiments verify the superiority of the proposed method and demonstrate that current advanced GCNs are improvements specifically on OOC nodes;the remaining nodes under GCN's control (UC nodes) are already optimally represented by vanilla GCN on most datasets. Copyright 2024 by the author(s)

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Epileptic seizure detection in EEG signals via an enhanced hybrid CNN with an integrated attention mechanism

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Mathematical Biosciences and engineering 2025年第1期22卷 73-105页

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao56000 Thailand Department of Applied Statistics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research Institute King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand

Epileptic seizures, a prevalent neurological condition, necessitate precise and prompt identification for optimal care. Nevertheless, the intricate characteristics of electroencephalography (EEG) signals, noise, and the want for real-time analysis require enhancement in the creation of dependable detection approaches. Despite advances in machine learning and deep learning, capturing the intricate spatial and temporal patterns in EEG data remains challenging. This study introduced a novel deep learning framework combining a convolutional neural network (CNN), bidirectional gated recurrent unit (BiGRU), and convolutional block attention module (CBAM). The CNN extracts spatial features, the BiGRU captures long-term temporal dependencies, and the CBAM emphasizes critical spatial and temporal regions, creating a hybrid architecture optimized for EEG pattern recognition. Evaluation of a public EEG dataset revealed superior performance compared to existing methods. The model achieved 99.00% accuracy in binary classification, 96.20% in three-class tasks, 92.00% in four-class scenarios, and 89.00% in five-class classification. High sensitivity (89.00–99.00%) and specificity (89.63–99.00%) across all tasks highlighted the model’s robust ability to identify diverse EEG patterns. This approach supports healthcare professionals in diagnosing epileptic seizures accurately and promptly, improving patient outcomes and quality of life. © 2025 American institute of Mathematical sciences. All rights reserved.

关键词： Electroencephalography

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Enhanced Health Monitoring System for Internet of Medical Things using Machine Learning 15

Enhanced Health Monitoring System for Internet of Medical Th...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Swedheetha, C. Manikandan, P. Rajalakshmi, J. Muthuraja, T. Kavin Kumar, K. Chandrasekaran, Gokul Sriram Srivathsan, N. Velammal College of Engineering and Technology Department of Computer Science and Engineering India Kalasalingam Academy of Research and Education Krishnankoil Department of Electronics and Communication Engineering Virudhunagar India Sethu Institute of Technology Department of Electronics and Communication Engineering Virudhnagar India Kongu Engineering College Perundurai Department of Electronics and Communication Engineering Erode638060 India Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350370249

This brief looks at mobile operation, speech recognition, and voice robotization. Patients with physical limitations, such as Parkinson's disease and cerebral palsy, as well as those with speech impairments, can express their requirements with this application. The initiative enhances the general well-being and independence of people with impairments. Increasing the automation of current technologies is the main objective of this design. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Blockchain-Based Cognitive Computing Model for Data Security on a Cloud Platform

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computers, Materials & Continua 2023年第12期77卷 3305-3323页

作者： Xiangmin Guo Guangjun Liang Jiayin Liu Xianyi Chen Department of Computer Information and Cyber Security Jiangsu Police InstituteNanjing210031China Jiangsu Electronic Data Forensics and Analysis Engineering Research Center Jiangsu Police InstituteNanjing210031China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China

Cloud storage is widely used by large companies to store vast amounts of data and files,offering flexibility,financial savings,and ***,information shoplifting poses significant threats,potentially leading to poor performance and privacy ***-based cognitive computing can help protect and maintain information security and privacy in cloud platforms,ensuring businesses can focus on business *** ensure data security in cloud platforms,this research proposed a blockchain-based Hybridized Data Driven Cognitive Computing(HD2C)***,the proposed HD2C framework addresses breaches of the privacy information of mixed participants of the Internet of Things(IoT)in the ***2C is developed by combining Federated Learning(FL)with a Blockchain consensus algorithm to connect smart contracts with Proof of ***“Data Island”problem can be solved by FL’s emphasis on privacy and lightning-fast processing,while Blockchain provides a decentralized incentive structure that is impervious to *** with Blockchain allows quick consensus through smart member selection and *** HD2C paradigm significantly improves the computational processing efficiency of intelligent *** analysis results derived from IIoT datasets confirm HD2C *** compared to other consensus algorithms,the Blockchain PoA’s foundational cost is *** accuracy and memory utilization evaluation results predict the total benefits of the *** comparison to the values 0.004 and 0.04,the value of 0.4 achieves good *** to the experiment results,the number of transactions per second has minimal impact on memory *** findings of this study resulted in the development of a brand-new IIoT framework based on blockchain technology.

关键词： Blockchain Internet of Things(IoT) blockchain based cognitive computing Hybridized Data Driven Cognitive Computing(HD2C) Federated Learning(FL) Proof of Authority(PoA)

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NEGATIVELY CORRELATED ENSEMBLE REINFORCEMENT LEARNING FOR ONLINE DIVERSE GAME LEVEL GENERATION 12

NEGATIVELY CORRELATED ENSEMBLE REINFORCEMENT LEARNING FOR ON...

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12th International Conference on Learning Representations, ICLR 2024

作者： Wang, Ziqi Hu, Chengpeng Liu, Jialin Yao, Xin Department of Computer Science and Engineering Southern University of Science and Technology China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology China Department of Computing and Decision Sciences Lingnan University Hong Kong

Deep reinforcement learning has recently been successfully applied to online procedural content generation in which a policy determines promising game-level segments. However, existing methods can hardly discover diverse level patterns, while the lack of diversity makes the gameplay boring. This paper proposes an ensemble reinforcement learning approach that uses multiple negatively correlated sub-policies to generate different alternative level segments, and stochastically selects one of them following a dynamic selector policy. A novel policy regularisation technique is integrated into the approach to diversify the generated alternatives. In addition, we develop theorems to provide general methodologies for optimising policy regularisation in a Markov decision process. The proposed approach is compared with several state-of-the-art policy ensemble methods and classic methods on a well-known level generation benchmark, with two different reward functions expressing game-design goals from different perspectives. Results show that our approach boosts level diversity notably with competitive performance in terms of the reward. Furthermore, by varying the regularisation coefficient values, the trained generators form a well-spread Pareto front, allowing explicit trade-offs between diversity and rewards of generated levels. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Reinforcement learning

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