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IEEE International Symposium on Information Theory

作者： Wael Alghamdi Shahab Asoodeh Flavio P. Calmon Oliver Kosut Lalitha Sankar School of Engineering and Applied Sciences Harvard University Department of Computing and Software McMaster University ( School of Electrical Computer and Energy Engineering Arizona State University

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

We formulate a fundamental limit in differential privacy under growing composition. We introduce the universal composition curve: the best privacy guarantee under repeated composition of a given privacy mechanism given only the sensitivity of the query. We define privacy capacity as the slowest growth rate of this universal composition curve among all privacy mechanisms. We show that, in the limit of large compositions, privacy capacity “single-letterizes” as a minimax KL-divergence term. Our privacy capacity formula extends previous literature results that connect differential privacy and KL-divergence via concentration theorems.

关键词： Privacy Differential privacy Sensitivity Information theory

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A Data-Driven Approach Towards software Regression Testing Quality Optimization 25

A Data-Driven Approach Towards Software Regression Testing Q...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Moubayed, Abdallah Alhindawi, Nouh Alsakran, Jamal Injadat, MohammadNoor Kanan, Mohammad King Fahd University of Petroleum & Minerals Computer Engineering Department Interdisciplinary Research Center for Intelligent Secure Systems Dhahran Saudi Arabia Arizona State University School of Computing and Augmented Intelligence GilbertAZ United States Jadara University Department of Software Engineering Irbid Jordan Fairleigh Dickinson University Canada Zarqa University Data Science & Artificial Intelligence Department Zarqa Jordan University of Business and Technology Department of Industrial Engineering Jeddah Saudi Arabia

ISBN: (纸本)9798331540012

software testing is very important in software development to ensure its quality and reliability. As software systems have become more complex, the number of test cases has increased, which presents the challenge of executing all the tests in a limited time frame. Various test case prioritization techniques have been introduced to solve this problem. These methods aim to identify and implement the most critical tests first. In this paper, we propose an implementation of a dynamic test case prioritization strategy to improve software quality by increasing code coverage with special attention to edge case handling. Edge case test prioritization is a technique that improves test efficiency by selecting extreme case scenarios that can reveal critical bugs or unexpected behavior early in development, improving overall software reliability and dependability. In order to prioritize test cases, this paper presents a regression-based method that makes use of machine learning algorithms. The approach leverages previous performance data to optimize regression testing efficiency by examining variables like test time and execution status. Performance evaluations, when compared against industry standards and cutting-edge techniques, show how effective these algorithms are at correctly prioritizing test cases and identifying faults. This study offers simplified yet reliable solutions for regression testing optimization by shedding light on the efficacy of regression algorithms, such as Random Forest and decision trees. © 2024 IEEE.

关键词： software testing

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Artificial Neural Network using Image Processing for Digital Forensics Crime Scene Object Detection 2

Artificial Neural Network using Image Processing for Digital...

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2nd International Conference on Edge computing and Applications, ICECAA 2023

作者： Devasenapathy, Deepa Raja, M. Dwibedi, Rajat Kumar Vinoth, N. Jayasudha, T. Ganesh, V. Dilli Computing & Software Engineering U.A. Whitaker College of Engineering Florida Gulf Coast University United States Cmr Institute of Technology Department of Computer Science and Engineeting Bengaluru India Aarupadai Veedu Institute of Technology Vmrf Chennai India Chennai Institute of Technology Department of Mechatronics Engineering Chennai India Kpr Institute of Engineering and Technology Department of English Coimbatore India Chennai India

ISBN: (纸本)9798350347579

Digital forensics science places a significant emphasis on the detection of objects as one of the most vital areas of study. Several industries and institutions may benefit from the object detection method, including those concerned with medical diagnostic scanning, traffic monitoring, airport security, law enforcement, and data rescue on a local and global scale. This study aims to detect weapons in video surveillance images by using various enhancement, segmentation, feature extraction, and classification methods by Artificial Neural Network to improve the detection accuracy. Yet, several mathematical and algorithmic models are computed to provide the appropriate approaches. © 2023 IEEE.

关键词： Neural networks

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Process Mining representation using Communication Structured Acyclic Nets (CSA-nets)

Process Mining representation using Communication Structured...

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2024 International Workshop on Petri Nets and software engineering, PNSE 2024

作者： Almutairi, Nadiyah Alshammari, Tuwailaa Alahmadi, Mohammed School of Computing Newcastle University Science Square Newcastle upon TyneNE4 5TG United Kingdom Department of Science and Technology University of Hafr Albatin Hafr Al-Batin39524 Saudi Arabia Department of Software Engineering University of Jeddah Jeddah21493 Saudi Arabia

Communication Structured Acyclic Nets (CSA-nets) are derived from Structured Occurrence Nets SON which are a Petri net-based formalism used to represent the behaviour of Complex Evolving Systems (CESs). It consists of sets of Acyclic Nets ANs that communicate with each other through a set of buffer places. It supports the analysis and visualisation of activities of CESs. In this work, we use SONs formalism to represent processes that have been decomposed using approaches in [1], we used SONs to compose the subnets and use its robust representation to analyse and visualise the same model, however, without any existing cycles. Our approach provides a mechanism for composing a SON model by using the decomposed subnets. We argue that the resulting model exhibits similar behaviour to the original model. © 2024 Copyright for this paper by its authors.

关键词： Petri nets

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Security Issues and Application of Blockchain 2nd

Security Issues and Application of Blockchain

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2nd International Conference on Machine Learning, Advances in computing,Renewable Energy and communication, MARC 2020

作者： Saxena, Sandeep Gupta, Umesh Kumar Renu Dwivedi, Vimal Galgotias College of Engineering and Technology Greater Noida India School of Computing Science and Engineering Galgotias University Greater Noida India Department of Software Science Tallinn University of Technology Tallinn Estonia

ISBN: (纸本)9789811623530

Blockchain is the most important IT invention in current technology. Blockchain is much secured, easily shared and works with various distributed ledgers. Blockchain is changing the working environment from centralized to decentralize. Blockchain has removed the problem of single point of failure (SPOF). In the blockchain, you can easily track all previous transactions and can validate future transactions. Due to various benefits in blockchain, the number of organizations works on blockchain technology for providing solutions and many others are working on security issues in blockchain like 51% vulnerability, private key security, double spending, criminal activities, transaction leakage, smart contracts and many others. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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Prepartition: Load Balancing Approach for Virtual Machine Reservations in a Cloud Data Center

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Journal of Computer Science & Technology 2023年第4期38卷 773-792页

作者：田文洪徐敏贤周光耀吴逵须成忠 Rajkumar Buyya School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu 610054China Yangtze Delta Region Institute(Huzhou) University of Electronic Science and Technology of ChinaHuzhou 313001China Institute of Advanced Computing and Digital Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Department of Computer Science University of VictoriaVictoriaBCV8W 3P6Canada State Key Laboratory of Internet of Things for Smart City University of MacaoMacao 999078China School of Computing and Information Systems University of MelbourneMelbourne 3010Australia

Load balancing is vital for the efficient and long-term operation of cloud data *** virtualization,post(reactive)migration of virtual machines(VMs)after allocation is the traditional way for load balancing and consoli***,it is not easy for reactive migration to obtain predefined load balance objectives and it may interrupt services and bring ***,we provide a new approach,called Prepartition,for load *** partitions a VM request into a few sub-requests sequentially with start time,end time and capacity demands,and treats each sub-request as a regular VM *** this way,it can proactively set a bound for each VM request on each physical machine and makes the scheduler get ready before VM migration to obtain the predefined load balancing goal,which supports the resource allocation in a fine-grained *** with real-world trace and synthetic data show that our proposed approach with offline version(PrepartitionOff)scheduling has 10%–20%better performance than the existing load balancing baselines under several metrics,including average utilization,imbalance degree,makespan and Capacity_*** also extend Prepartition to online load *** results show that our proposed approach also outperforms state-of-the-art online algorithms.

关键词： cloud computing physical machine(PM) virtual machine(VM) reservation load balancing Prepartition

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The Role of AI in Predictive UX Shaping the Future of Metaverse Interactions

The Role of AI in Predictive UX Shaping the Future of Metave...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Nedal Nwasra Khalil Omar Zahid Hussain Qaisar Software Engineering Department University of Petra Amman Jordan Faculty of Computing & Emerging Technologies Multan Pakistan Computer Science Department University of Petra Amman Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

The Metaverse is defined as a collectively shared, persistent, and contextualized virtual space that will drive next-generation digital interaction facilitated by enhanced extended reality (XR) and edge computing. One of the major challenges within this development area is the proper anticipation and fulfillment of user context needs, preferences, and intentions in real-time. This paper proposes the concept of Predictive User Experience through AI for a turn to solve this challenge. With enabled predictive analytics, machine learning, and real-time data processing, the proposed AI-based predictive UX model dynamically adjusts metaverse interface features, content delivery, and modes of interaction to enhance immersion, all while maximizing user satisfaction. In this paper, we discuss AI's potential for explanatory mechanics in predictive UX and how it shapes the future of metaverse interactions. We propose a conceptual framework that describes how data collection, data processing, and adaptive interface design can be integrated within a feedback-driven architecture that seamlessly predicts and adjusts to user requirements. In this paper, we present a research methodology that integrates qualitative and technical approaches-from data modeling and architecture design up to scenario-based assessments-to validate our UX framework's predictiveness. Hence, we proceed with a use case that exemplifies the applicability of AI-based predictive UX systems for ensuring user presence and interaction consistency within a metaverse educational scenario. The discussion synthesizes and finally concludes the most pertinent findings relevant to user engagement and personalization regarding privacy and ethical considerations without compromising how these technologies can be safely and effectively scaled. We conclude our paper with the opinion that XR interactions driven by AI predictive UX mark a milestone in how applications are created, not just for entertainment and gaming but also

关键词： Adaptation models Metaverse Extended reality Machine learning Computer architecture Predictive models Data collection Data models User experience Real-time systems

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A Secured and Optimized Broadcast Authentication Scheme for the Internet of Medical Things 4

A Secured and Optimized Broadcast Authentication Scheme for ...

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4th IEEE International Conference on Electrical, Computer, and Energy Technologies, ICECET 2024

作者： Riaz, Syed Waleed Khan, Fazlullah Ullah, Insaf Alshawi, Bandar Alturki, Ryan Wedyan, Mohammad Abdul Wali Khan University Mardan Computer Science Department KPK Pakistan School of Computer Science University of Nottingham Ningbo China Faculty of Science and Engineering Zhejiang Ningbo315104 China Institute for Analytics and Data Science University of Essex ColchesterCO4 3SQ United Kingdom College of Computing Umm Al-Qura University Department of Computer and Network Engineering Makkah Saudi Arabia College of Computing Umm Al-Qura University Department of Software Engineering Makkah Saudi Arabia Faculty of Information Technology and Computer Sciences Department of Computer Sciences Irbid21163 Jordan

ISBN: (纸本)9798350395914

This Internet of Medical Things (IoMT), facilitates the medical stop regarding real-time monitoring of patients, medical emergency management, remote surgery, patient information management, medical equipment, drug monitoring, etc. However, IoMT devices communicate with each other in an open environment that makes them vulnerable to a wide range of malicious threats from malicious entities. To protect the devices and their associated data, we designed a broadcast authentication scheme for IoMT devices using identity-based public key cryptography that exploits the lightweight features of the Hyper-Elliptic Curve (HEC). We then performed the security analysis based on the Random Oracle Model (ROM), in which we have proved that the proposed scheme is unforgeable under the hardiness of the hyperelliptic curve discrete logarithm problem. The proposed scheme is analyzed in terms of computational and communication overhead and the experimental results justify the superiority of the proposed work in comparison to the existing schemes. © 2024 IEEE.

关键词： Authentication

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CRAYM: neural field optimization via camera RAY matching 24

CRAYM: neural field optimization via camera RAY matching

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Liqiang Lin Wenpeng Wu Chi-Wing Fu Hao Zhang Hui Huang College of Computer Science and Software Engineering Shenzhen University Department of Computer Science and Engineering The Chinese University of Hong Kong School of Computing Science Simon Fraser University

ISBN: (纸本)9798331314385

We introduce camera ray matching (CRAYM) into the joint optimization of camera poses and neural fields from multi-view images. The optimized field, referred to as a feature volume, can be "probed" by the camera rays for novel view synthesis (NVS) and 3D geometry reconstruction. One key reason for matching camera rays, instead of pixels as in prior works, is that the camera rays can be parameterized by the feature volume to carry both geometric and photometric information. Multi-view consistencies involving the camera rays and scene rendering can be naturally integrated into the joint optimization and network training, to impose physically meaningful constraints to improve the final quality of both the geometric reconstruction and photorealistic rendering. We formulate our per-ray optimization and matched ray coherence by focusing on camera rays passing through keypoints in the input images to elevate both the efficiency and accuracy of scene correspondences. Accumulated ray features along the feature volume provide a means to discount the coherence constraint amid erroneous ray matching. We demonstrate the effectiveness of CRAYM for both NVS and geometry reconstruction, over dense- or sparse-view settings, with qualitative and quantitative comparisons to state-of-the-art alternatives.

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IP2FL: Interpretation-Based Privacy-Preserving Federated Learning for Industrial Cyber-Physical Systems

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2024年 2卷 321-330页

作者： Namakshenas, Danyal Yazdinejad, Abbas Dehghantanha, Ali Parizi, Reza M. Srivastava, Gautam University of Guelph Cyber Science Lab Canada Cyber Foundry GuelphONN1G2W1 Canada Kennesaw State University Decentralized Science Lab College of Computing and Software Engineering MariettaGA30060 United States Brandon University Department of Math and Computer Science ManitobaMBR7A6A9 Canada China Medical University Research Centre for Interneural Computing Taichung40402 Taiwan Lebanese American University Department of Computer Science and Mathematics Beirut1102 Lebanon

The expansion of Industrial Cyber-Physical Systems (ICPS) has introduced new challenges in security and privacy, highlighting a research gap in effective anomaly detection while preserving data confidentiality. In the ICPS landscape, where vast amounts of sensitive industrial data are exchanged, ensuring privacy is not just a regulatory compliance issue but a critical shield against industrial espionage and cyber threats. Existing solutions often compromise data privacy for enhanced security, leaving a significant void in protecting sensitive information within ICPS networks. Addressing this, our research presents the IP2FL model, an Interpretation-based Privacy-Preserving Federated Learning approach tailored for ICPS. This model combines Additive Homomorphic Encryption (AHE) for privacy with advanced feature selection methods and Shapley Values (SV) for enhanced explainability. The proposed solution mitigates privacy concerns in federated learning, where traditional methods fall short due to computational constraints and lack of interpretability. By integrating AHE, the IP2FL model minimizes computational overhead and ensures data privacy. Our dual feature selection approach optimizes system performance while incorporating SV to provide critical insights into model decisions, advancing the field towards more transparent and understandable AI systems in ICPS. The validation of our model using ICPS-specific datasets demonstrates its effectiveness and potential for practical applications. © 2023 IEEE.

关键词： Embedded systems

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