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3rd International Conference on Emergent Converging Technologies and Biomedical Systems, ETBS 2023

作者： Sharma, Annu Devasenapathy, Deepa Raja, M. Shadrach, Finney Daniel Shirgire, Anil Arun, R. Yau, Thomas Moh Shan Department of Computer Science and Applications Rajarajeshwari College of Engineering Karnataka Bengaluru560074 India Instructor-II/Computing and Software Engineering U.A. Whitaker College of Engineering Florida Gulf Coast University Fort Myers United States Department of Computer Science and Engineering CMR Institute of Technology Karnataka Bengaluru560037 India Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Tamil Nadu Coimbatore641407 India Department of Civil Engineering Dr. D Y Patil Institute of Technology Maharashtra Pimpri Pune411018 India Department of Computer Science and Engineering Hindusthan Institute of Technology Coimbatore India Director of Centre of University Courses and Innovative Learning University of Technology Sarawak Jalan Universiti Sibu Sarawak96000 Malaysia

ISBN: (纸本)9789819986453

Video surveillance is mostly used for monitoring distant videos and the cloud memory in the system provides more secure to the data. But, in the existing methodologies poor intrusion detection accuracy as well as poor performance rate is considered as the main demerits. So, to overcome the above-mentioned demerits, a novel video surveillance-based Intrusion Detection System (IDS) (VS-IDS) in the edge cloud environment via deep learning method has been developed in this research. Different algorithms are used for object detection and intrusion detection. Subsequently, the proposed model can be implemented and verified in NVIDIA Jetson TX2 platform. The proposed model attains better rate of performance than the existing models. The Performance of the proposed model can be validated with accuracy, precision, recall, MSE and F-measure. Higher performance rate indicates that the system can detect intrusion more accurately than the existing mechanisms. 99% of detection accuracy can be attained through the proposed model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Intrusion detection

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Research on the Current State of Privacy Risks in Children's Mobile Applications 4

Research on the Current State of Privacy Risks in Children's...

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4th International Symposium on Computer Technology and Information Science, ISCTIS 2024

作者： Geng, Xinrui Liu, Tao Shang, Chong Chen, Jianxiang Lu, Yueran Zhang, Yan Wang, Lifeng Xi'an Jiaotong-Liverpool University School of AI and Advanced Computing Suzhou China MIIT Key Laboratory of Mobile Application Innovation and Governance Technology Beijing China Chinese Academy of Sciences Institute of Information Engineering Beijing China Shanxi Agricultural University School of Agricultural Engineering China Tianjin Normal University School of Software Engineering Tianjin China University of Chinese Academy of Sciences Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security Beijing China Shandong Yeda Smart City Operation Technology Co. Ltd. Shandong China

ISBN: (纸本)9798350354560

In the digital age, children increasingly access a vast world of information through mobile applications. Despite global regulations and protective measures by app stores, privacy breaches in these apps persist. This study investigates privacy issues and threats in children's mobile applications by comparing APKs and legal provisions across different countries. It examines 70 representative children's apps from China, Europe, the U.S., and Japan through static code analysis and dynamic runtime testing. Using a mixed-methods approach, the research identifies integrated third-party services, data transmission protocols, permission requests, and conducts code audits to assess privacy compliance. It also simulates real usage scenarios to track data flow and detect unauthorized data collection and sharing practices. Furthermore, the study compares national privacy laws, focusing on data minimization, parental consent mechanisms, data encryption, and storage duration regulations. The goal is to provide empirical evidence and recommendations for optimizing global children's online privacy protection policies. © 2024 IEEE.

关键词： Differential privacy

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Container Performance in Space Systems 25th

Container Performance in Space Systems

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25th International Conference on Internet computing and IoT, ICOMP 2024, and 22nd International Conference on Embedded Systems, Cyber-physical Systems, and Applications, ESCS 2024, held as part of the World Congress in Computer Science, Computer engineering and Applied computing, CSCE 2024

作者： Alamgir, Shariar M. Bohra, Dhruv L. Nguyen, Elisabeth A. Embedded and Specialized Computing Department The Aerospace Corporation El SegundoCA United States Software Architecture and Engineering Department The Aerospace Corporation El SegundoCA United States

ISBN: (纸本)9783031859229

Critical embedded software, such as spacecraft flight software, requires high reliability, efficiency, and real-time performance while being capable enough to meet application needs. The increasing prevalence of Linux, and in particular Linux containers, presents opportunities for space systems but also poses potential risks since Linux was not designed to be a real-time operating system. In this study, we conducted a variety of experiments to look at both the overhead imposed by containers and the latency experienced in Linux using various combinations of patched OS and containers running on a Raspberry Pi 4. We found that containers have little impact on CPU utilization but do incur penalties in RAM, disk storage, and startup time. We also found the unpatched Linux kernel performs well for real time;and that, while the real-time kernel patch reduces both overall and maximum latency, it can negatively impact performance, particularly on single-core systems running non-real-time tasks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Linux

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A Systematic Literature Review on Large Language Models for Automated Program Repair

arXiv

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arXiv 2024年

作者： Zhang, Quanjun Fang, Chunrong Xie, Yang Ma, Yuxiang Sun, Weisong Yang, Yun Chen, Zhenyu State Key Laboratory for Novel Software Technology Nanjing University China School of Computer Science and Engineering Nanyang Technological University Singapore Department of Computing Technologies Swinburne University of Technology Australia

Automated Program Repair (APR) attempts to patch software bugs and reduce manual debugging efforts. Very recently, with the advances in Large Language Models (LLMs), a rapidly increasing number of APR techniques have been proposed, significantly facilitating software development and maintenance and demonstrating remarkable performance. However, due to ongoing explorations in the LLM-based APR field, it is challenging for researchers to understand the current achievements, challenges, and potential opportunities. This work provides the first systematic literature review to summarize the applications of LLMs in APR between 2020 and 2024. We analyze 127 relevant papers from LLMs, APR and their integration perspectives. First, we categorize existing popular LLMs that are applied to support APR and outline three types of utilization strategies for their deployment. Besides, we detail some specific repair scenarios that benefit from LLMs, e.g., semantic bugs and security vulnerabilities. Furthermore, we discuss several critical aspects of integrating LLMs into APR research, e.g., input forms and open science. Finally, we highlight a set of challenges remaining to be investigated and the potential guidelines for future research. Overall, our paper provides a systematic overview of the research landscape to the APR community, helping researchers gain a comprehensive understanding of achievements and promote future research. Our artifacts are publicly available at the GitHub repository: https://***/iSEngLab/AwesomeLLM4APR. © 2024, CC BY.

关键词： Repair

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Performance Verification of State Feedback H∞ State Molecular Controller Via Chemical Reaction Networks 5

Performance Verification of State Feedback H∞ State Molecul...

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5th International Conference on Industrial Artificial Intelligence, IAI 2023

作者： Yao, Jingwang Wang, Xing'An Lv, Hui Li, Yang Zhang, Wujie Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China Dalian University College of Environmental and Chemical Engineering Dalian116622 China Dalian Chivy Biotechnology Co. Ltd Dalian116023 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110004 China

ISBN: (纸本)9798350325294

An important goal in synthetic biology is in the design and implementation of control strategies embedded in biochemical reaction systems to enable control of the process at the molecular level. The problem of set-point tracking and perturbation disturbances in the biochemical reaction process can be solved by a state feedback control strategy and a more stable and better dynamic performance of synthetic controllers. First, it is shown how a set of CRNs can provide state feedback to the biomolecular implementation of the H∞ controller. Then, to facilitate the modularity of biochemical process design, CRNs of trigonometric functions and repetitive sequence step signals are defined for the first time as input signals to the controller. Finally, numerical simulation experiments are designed to verify the effectiveness of the whole scheme from several perspectives, such as setpoint tracking and external perturbations. The results show that the state feedback H∞ molecular controller outperforms the conventional linear controller because it has a faster tracking response and better perturbation rejection performance. © 2023 IEEE.

关键词： Controllers

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A Selection Method of Effective Metamorphic Relations 13

A Selection Method of Effective Metamorphic Relations

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13th International Conference on Reliability, Maintainability, and Safety, ICRMS 2022

作者： Zhang, Jie Hong, Jie Huang, Dafei Li, Meng Yan, Shiyu Gong, Helin School of Computing University of South China Hunan Hengyang China Engineering and Technology Research Center of Software Evaluation and Testing for Intellectual Hunan Hengyang China CNNC Key Laboratory on High Trusted Computing Hunan Hengyang China Nuclear Power Institute of China Science and Technology on Reactor System Design Technology Laboratory Chengdu China

ISBN: (数字)9781665486903

ISBN: (纸本)9781665486903

Metamorphic testing can effectively alleviate the test oracle problem. However, the program under test needs to be executed one more time;it is particularly significant to select the metamorphic relation with the highly fault-detecting ability and improve the test cost-effectiveness. Due to the limitations of existing evaluation methods in engineering applications, a validity evaluation method for metamorphic relation is presented based on single factor and multi-factor mutation scores. It can guide their priority ranking and selection. Experiments show that this method can significantly reduce test costs. © 2022 IEEE.

关键词： Cost effectiveness

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Selecting and Evaluating Key MDS-UPDRS Activities Using Wearable Devices for Parkinson's Disease Self-Assessment

IEEE Journal of Selected Areas in Sensors

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IEEE Journal of Selected Areas in Sensors 2024年 1卷 177-189页

作者： Zhao, Yuting Wang, Xulong Peng, Xiyang Li, Ziheng Nan, Fengtao Zhou, Menghui Qi, Jun Yang, Yun Zhao, Zhong Xu, Lida Yang, Po Yunnan University National Pilot School of Software Kunming650106 China University of Sheffield Department of Computer Science SheffieldS10 2TN United Kingdom Northwest A&F University College of Information Engineering Xianyang712100 China Xi'an JiaoTong-Liverpool University Department of Computing Suzhou215000 China Yunnan First People's Hospital Yunnan650021 China Old Dominion University NorfolkVA23529 United States

Parkinson's disease (PD) is a complex neurodegenerative disease in the elderly. This disease has no cure, but assessing these motor symptoms will help slow down that progression. Inertial sensing-based wearable devices, such as mobile phones and smartwatches have been widely employed to analyze the condition of PD patients. However, most studies purely focused on a single activity or symptom, which may ignore the correlation between activities and complementary characteristics. In this article, a novel technical pipeline is proposed for fine-grained classification of PD severity grades, which identify the most representative activities. We also propose a multiactivities combination scheme based on MDS-UPDRS. Utilizing this scheme, symptom-related and complementary activities are captured. We collected 85 PD subjects of different severity grades using a single wrist sensor. Our best results demonstrate F1 scores of 95.75% for PD diagnosis and the fine-grained classification accuracy of PD disease grade is 82.41% when combing four activities which improved by 11.02% over a single activity. The experiments and theoretical analyses can serve as a useful foundation for future investigations into the effect of proposed solutions for PD diagnosis in uncontrolled environment setup, ultimately leading to self-PD assessment in the home environment. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Fed-CVLC: Compressing Federated Learning Communications with Variable-Length Codes

Fed-CVLC: Compressing Federated Learning Communications with...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Xiaoxin Su Yipeng Zhou Laizhong Cui John C. S. Lui Jiangchuan Liu College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computing Faculty of Science and Engineering Macquarie University Sydney Australia Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen University Shenzhen China Department of Computer Science and Engineering The Chinese University of Hong Kong HKSAR School of Computing Science Simon Fraser University Canada

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

In Federated Learning (FL) paradigm, a parameter server (PS) concurrently communicates with distributed participating clients for model collection, update aggregation, and model distribution over multiple rounds, without touching private data owned by individual clients. FL is appealing in preserving data privacy; yet the communication between the PS and scattered clients can be a severe bottleneck. Model compression algorithms, such as quantization and sparsification, have been suggested but they generally assume a fixed code length, which does not reflect the heterogeneity and variability of model updates. In this paper, through both analysis and experiments, we show strong evidences that variable-length is beneficial for compression in FL. We accordingly present Fed-CVLC (Federated Learning Compression with Variable-Length Codes), which fine-tunes the code length in response of the dynamics of model updates. We develop optimal tuning strategy that minimizes the loss function (equivalent to maximizing the model utility) subject to the budget for communication. We further demonstrate that Fed-CVLC is indeed a general compression design that bridges quantization and sparsification, with greater flexibility. Extensive experiments have been conducted with public datasets to demonstrate that Fed-CVLC remarkably outperforms state-of-the-art baselines, improving model utility by 1.50%-5.44%, or shrinking communication traffic by 16.67%-41.61%.

关键词： Training Data privacy Codes Quantization (signal) Federated learning Data models Servers

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Leveraging API Specifications for Scaffolding Quantum Applications

Leveraging API Specifications for Scaffolding Quantum Applic...

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Quantum computing and engineering (QCE), IEEE International Conference on

作者： Javier Romero-Álvarez Jaime Alvarado-Valiente Enrique Moguel Carlos Canal Jose García-Alonso Juan M. Murillo Quercus Software Engineering Group Universidad de Extremadura Cáceres Spain Computing and Advanced Technologies Foundation of Extremadura Cáceres Spain ITIS Software Universidad de Málaga Málaga Spain

Quantum computing is still in its early stages, and developers used to high-level tools are encountering difficulties in developing hybrid classical-quantum systems. This has created the need for the adaptation of classical tools or the development of new tools to approach the quantum world and enable more efficient development. Quantum computers are accessed through cloud providers, which has led to the use of Service-Oriented Architectures. Although there are currently tools and standards available for improving the development of classical services, they need to be adapted for quantum services. In this work, we propose the adaptation of classic tools to be used in the development of hybrid systems, extending classic specifications such as OpenAPI and AsyncAPI to offer developers a set of tools closer to the environment they are used to. The provided implementation is focused on Amazon Braket as the quantum computing service provider.

关键词：

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VCFGAN: A Two-stage Collaborative Filtering Framework based on Variational Autoencoders and Generative Adversarial Networks

VCFGAN: A Two-stage Collaborative Filtering Framework based ...

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Information Systems and computing Technology (ISCTech), International Conference on

作者： Zhiqiang Huang Jinzhong Li Xueyu Huang Junqi Liu School of Software Engineering Jiangxi University of Science and Technology Nanchang China College of Electronics and Information Engineering Jinggangshan University Ji’an China Ministry of Education Key Laboratory of Embedded System and Service Computing (Tongji University) Shanghai China

ISBN: (数字)9798350379860

ISBN: (纸本)9798350379877

Deep generative models, such as Generative Adversarial Networks (GAN) and Variational Autoencoders (VAE), are widely used in collaborative filtering. They usually learn users’ preferences for items directly from a highly sparse user rating matrix (URM), and then recommend the top-N items to users. Due to the high sparsity of URM, GAN has limited ability to handle sparse data, while VAE’s encoder can extract features. Therefore, we propose a two-stage collaborative filtering framework based on variational autoencoders and generative adversarial networks, named VCFGAN. It first uses two VAEs to extract features from URM and side information (SI), and then uses the extracted latent vectors to train the GAN network. To evaluate the performance of our proposed VCFGAN model, some experiments are conducted on two real datasets, and the experimental results show that our model outperforms other representative models.

关键词： Training Collaborative filtering Autoencoders Generative adversarial networks Feature extraction Vectors Data mining Sparse matrices Recommender systems Information systems

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