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Probabilistic Reliability via Subsystem Structures of Arrangement Graph Networks

IEEE Transactions on Reliability 2024年第1期73卷 279-289页

作者： Huang, Yanze Lin, Limei Xu, Li Hsieh, Sun-Yuan Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Computer Science and Mathematics Fuzhou350118 China Fujian Normal University College of Computer and Cyber Security Key Laboratory of Network Security and Cryptology Fuzhou350117 China National Cheng Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan

With the rapid growth of the number of processors in a multiprocessor system, faulty processors occur in it with a probability that rises quickly. The probability of a subsystem with an appropriate size being fault-free in a definite time interval is a significant and practical measure of the reliability for a multiprocessor system, which characterizes the functionality of a multiprocessor system well. Motivated by the study of subgraph reliability, as well as the attractive structure and fault tolerance properties of (n, k)-arrangement graph A n, k, we focus on the subgraph reliability for A n, k under the probabilistic fault model in this article. First, we investigate intersections of no more than four subgraphs in A n, k, and classify all the intersecting modes. Second, we focus on the probability P(q, A n, k n-1, k-1) with which at least one (n-1, k-1)-subarrangement graph is fault-free in A n, k, when given a uniform probability q with which a single vertex is fault-free, and we establish the P(q, A n, kn-1, k-1) by adopting the principle of inclusion-exclusion under the probabilistic fault model. Finally, we study the probabilistic fault model involving a nonuniform probability with which a single vertex is fault-free, and we prove that the P(q, A n, kn-1, k-1) under both models is very close to the asymptotic value by both theoretical arguments and experimental results. © 1963-2012 IEEE.

关键词： Fault tolerance

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Monte Carlo Tree Search-Based Agent Negotiation Framework for Shared Decision-Making 5

Monte Carlo Tree Search-Based Agent Negotiation Framework fo...

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5th International Conference on Big data and Artificial Intelligence and Software Engineering, ICBASE 2024

作者： Chen, Xin Liu, Yong Lu, Ping Lin, Kai-Biao Xiamen Institute of Technology School of Data Science and Computer Science Xiamen361021 China Xiamen University of Technology School of Economic and Management Xiamen361024 China Xiamen University of Technology Department of Computer Science and Technology Xiamen361024 China

ISBN: (纸本)9798331506612

Shared Decision-Making (SDM) is a collaborative process in which patients and healthcare providers jointly make medical decisions, integrating clinical evidence with the patient's preferences and values. Although previous research has focused on SDM through decision-support tools or manual negotiations, these methods have been limited by time and cost constraints. To overcome these challenges, this study introduces an automated SDM negotiation framework using Monte Carlo Tree Search (MCTS) to support decision-making for both doctors and patients. The proposed MCTSAN model demonstrates superior performance across various scenarios with differing issue complexities. Notably, it maintains high overall satisfaction and reduces satisfaction disparities between patients and healthcare providers, even as negotiation complexity increases. The model typically reaches agreements within 7 to 15 negotiation rounds, effectively enhancing satisfaction and social welfare while minimizing the time and space costs associated with negotiations. This negotiation framework proves to be a valuable tool in facilitating and promoting SDM in clinical settings. © 2024 IEEE.

关键词： Clinical research

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Multi-Stage Predict+Optimize for (Mixed Integer) Linear Programs 38

Multi-Stage Predict+Optimize for (Mixed Integer) Linear Prog...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Hu, Xinyi Lee, Jasper C.H. Lee, Jimmy H.M. Stuckey, Peter J. Department of Computer Science and Engineering The Chinese University of Hong Kong Shatin N.T. Hong Kong Department of Computer Science University of California Davis United States Department of Data Science and AI Monash University Clayton Australia

The recently-proposed framework of Predict+Optimize tackles optimization problems with parameters that are unknown at solving time, in a supervised learning setting. Prior frameworks consider only the scenario where all unknown parameters are (eventually) revealed at the same time. In this work, we propose Multi-Stage Predict+Optimize, a novel extension catering to applications where unknown parameters are instead revealed in sequential stages, with optimization decisions made in between. We further develop three training algorithms for neural networks (NNs) for our framework as proof of concept, all of which can handle mixed integer linear programs. The first baseline algorithm is a natural extension of prior work, training a single NN which makes a single prediction of unknown parameters. The second and third algorithms instead leverage the possibility of updating parameter predictions between stages, and trains one NN per stage. To handle the interdependency between the NNs, we adopt a sequential and parallelized versions of coordinate descent for training. Experimentation on three benchmarks demonstrates the superior learning performance of our methods over classical approaches. © 2024 Neural information processing systems foundation. All rights reserved.

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Graph Contrastive Learning for Truth Inference 40

Graph Contrastive Learning for Truth Inference

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Liu, Hao Liu, Jiacheng Tang, Feilong Li, Peng Chen, Long Yu, Jiadi Zhu, Yanmin Gao, Min Yang, Yanqin Hou, Xiaofeng Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong East China Normal University School of Data Science and Engineering Shanghai China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China University of Aizu School of Computer Science and Engineering Japan Simon Fraser University School of Computing Science Canada

ISBN: (纸本)9798350317152

Crowdsourcing has become a popular paradigm for collecting large-scale labeled datasets by leveraging numerous annotators. However, these annotators often provide noisy labels due to varying expertise. Truth inference aims to infer accurate consensus labels from noisy crowdsourced annotations. Existing approaches rely heavily on hand-engineered assumptions or ground truth data, limiting their applicability. To address this, we propose GOVERN, a graph contrastive learning framework for truth inference without such external supervision. GOVERN employs a novel graph data augmentation strategy to generate views capturing worker coordination patterns. A contrastive objective then encourages invariant representations across views, enabling the discovery of features related to the hidden consensus. Further, a label correction method based on k-nearest neighbors refines noisy pseudo-labels to supervise model training. Comprehensive experiments on 9 real-world datasets demonstrate that GOVERN outperforms state-of-the-art truth inference techniques. © 2024 IEEE.

关键词： Crowdsourcing

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Latent Logic Tree Extraction for Event Sequence Explanation from LLMs 41

Latent Logic Tree Extraction for Event Sequence Explanation ...

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41st International Conference on Machine Learning, ICML 2024

作者： Song, Zitao Yang, Chao Wang, Chaojie An, Bo Li, Shuang School of Computer Science and Engineering Nanyang Technological University Singapore School of Data Science The Chinese University of Hong Kong Shenzhen China Skywork AI Singapore

Modern high-stakes systems, such as healthcare or robotics, often generate vast streaming event sequences. Our goal is to design an efficient, plug- and-play tool to elicit logic tree-based explanations from Large Language Models (LLMs) to provide customized insights into each observed event sequence. Built on the temporal point process model for events, our method employs the likelihood function as a score to evaluate generated logic trees. We propose an amortized Expectation-Maximization (EM) learning framework and treat the logic tree as latent variables. In the E-step, we evaluate the posterior distribution over the latent logic trees using an LLM prior and the likelihood of the observed event sequences. LLM provides a high-quality prior for the latent logic trees, however, since the posterior is built over a discrete combinatorial space, we cannot get the closed-form solution. We propose to generate logic tree samples from the posterior using a learnable GFlowNet, which is a diversity-seeking generator for structured discrete variables. The M-step employs the generated logic rules to approximate marginalization over the posterior, facilitating the learning of model parameters and refining the tunable LLM prior parameters. In the online setting, our locally built, lightweight model will iteratively extract the most relevant rules from LLMs for each sequence using only a few iterations. Empirical demonstrations showcase the promising performance and adaptability of our framework. Copyright 2024 by the author(s)

关键词： Trees (mathematics)

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Adaptive Three-Level BDDC Using Frugal Constraints 27th

Adaptive Three-Level BDDC Using Frugal Constraints

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27th International Conference on Domain Decomposition Methods in science and Engineering, DD 2022

作者： Klawonn, Axel Lanser, Martin Weber, Janine Department of Mathematics and Computer Science University of Cologne Weyertal 86-90 Koln50931 Germany Center for Data and Simulation Science University of Cologne Cologne Germany

ISBN: (纸本)9783031507687

The convergence rate of both the FETI-DP (Finite Element Tearing and Interconnecting-Dual Primal) and the BDDC (Balancing Domain Decomposition by Constraints) domain decomposition methods strongly depend on the spectrum, i.e., the eigenvalues of the preconditioned system [7, 2]. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Domain decomposition methods

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Secure Online Banking Authentication System Using Time Bound Password 5

Secure Online Banking Authentication System Using Time Bound...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Danthy, Ronson Pratham Pai, K. Rao, Vidya Manipal Institute of Technology Manipal Academy of Higher Education Department of Data Science and Computer Applications Manipal India Manipal Institute of Technology Manipal Academy of Higher Education Department of Computer Science and Computer Applications Manipal India

ISBN: (纸本)9798350383522

The banking system is a key component of financial transactions and economic expansion in the modern world. The introduction of internet banking, however, has created some brand-new difficulties, particularly in providing safe user authentication. This study investigates the flaws in conventional authentication techniques, including passwords and SMS-based One-Time Passwords (OTP), and suggests a creative fix. By using Time-Based One-Time Password (TOTP) created by an authenticator app and encrypting the user credentials with this as key, the proposed solution improves security. By addressing problems including poor password habits, SMS theft, OTP delivery delays, phishing, and social engineering attacks, this method strengthens the security of online banking. The approach provides a thorough framework for building this secure authentication system by detailing TOTP generation and encryption. This system successfully defends against popular attacks such as replay attacks, brute force attacks, rainbow tables, packet sniffing, and random guessing. © 2024 IEEE.

关键词： Advanced Encryption Standard - Galois Counter Mode (AES-GCM) Authentication Encryption Online Banking Time-Based One-Time Password (TOTP) Two-Factor authentication (2FA)

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What's the Real: A Novel Design Philosophy for Robust AI-Synthesized Voice Detection 24

What's the Real: A Novel Design Philosophy for Robust AI-Syn...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Hai, Xuan Liu, Xin Tan, Yuan Liu, Gang Li, Song Niu, Weina Zhou, Rui Zhou, Xiaokang School of Information Science and Engineering Lanzhou University Lanzhou China The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Business Data Science Kansai University Osaka Japan

ISBN: (纸本)9798400706868

Voice is one of the most widely used media for information transmission in human society. While high-quality synthetic voices are extensively utilized in various applications, they pose significant risks to content security and trust building. Numerous studies have concentrated on AI-synthesized voice detection to mitigate these risks, with many claiming to achieve promising performance. However, recent research has demonstrated that fake voice detectors suffer from serious overfitting to speaker-irrelative features (SiFs) and cannot be used in real-world scenarios. In this paper, we analyze the limitations of existing fake voice detectors and propose a new design philosophy, guiding the detection model to prioritize learning human voice features rather than the difference between the human voice and the synthetic voice. Based on this philosophy, we propose a novel AI-synthesized voice detection framework named SiFSafer, which uses pre-trained speech representation models to enhance the learning of feature distribution in human voices and the adapter fine-tuning to optimize the performance. The evaluation shows that the average EERs of existing fake voice detectors in the ASVspoof datasets can exceed 20% if the SiFs like silence segments are removed, while SiFSafer achieves an EER of less than 8%, indicating that SiFSafer is robust to SiFs and strongly resistant to existing attacks. © 2024 ACM.

关键词： Fake detection

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Prediction of Rice Leaf Diseases at an Early Stage Using Deep Neural Networks 1

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8th International Conference on computers, Management and Mathematical sciences, ICCM 2022

作者： Mahanty, Mohan Vamsi, Bandi Srilatha, Y. Doppala, Bhanu Prakash Department of Computer Science and Engineering Vignan’s Institute of Information Technology-Duvvada Andhra Pradesh Visakhapatnam India Department of Artificial Intelligence and Data Science Madanapalle Institute of Technology & Science Andhra Pradesh Madanapalle India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur India Data Analytics Generation Australia SydneyNSW Australia

ISBN: (数字)9783031511677

ISBN: (纸本)9783031511660

Rice is the major crop in India, and India has been the biggest exporter and the second-largest producer in the entire world, so it is heavily reliant on rice for its economy and food supply. There has been an increase in rice production from 53.6 million tons in fiscal to 120 million tons over the last 40 years. Healthy and proper rice plant growth is required to maintain food production and supply in accordance with people’s needs and demands. The major diseases affecting these rice plants are leaf blast, hispa, and brown spot. The advancement of deep learning paved the way for these diseases to be detected using computer vision. Many researches have been conducted in the detection of rice leaf diseases, where the rice leaves have been deeply affected at a later stage. In this chapter, we propose an effective deep learning model for an early-stage disease detection of rice leaves. The proposed model was developed by an ensemble of the pretrained DenseNet-201 with the Naïve Inception module. The model was trained and tested over a Rice Diseases Image dataset;it attained an overall disease detection accuracy of 87.69% and surpasseed the existing models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Food supply

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Mobility-aware Seamless Service Migration and Resource Allocation in Multi-edge IoV Systems

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6315-6332页

作者： Chen, Zheyi Huang, Sijin Min, Geyong Ning, Zhaolong Li, Jie Zhang, Yan Fuzhou University College of Computer and Data Science Fuzhou350116 China Ministry of Education Engineering Research Center of Big Data Intelligence Fuzhou350002 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China University of Exeter Department of Computer Science Faculty of Environment Science and Economy ExeterEX4 4QF United Kingdom Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China University of Oslo Department of Informatics Oslo0316 Norway

Mobile Edge Computing (MEC) offers low-latency and high-bandwidth support for Internet-of-Vehicles (IoV) applications. However, due to high vehicle mobility and finite communication coverage of base stations, it is hard to maintain uninterrupted and high-quality services without proper service migration among MEC servers. Existing solutions commonly rely on prior knowledge and rarely consider efficient resource allocation during the service migration process, making it hard to reach optimal performance in dynamic IoV environments. To address these important challenges, we propose SR-CL, a novel mobility-aware seamless Service migration and Resource allocation framework via Convex-optimization-enabled deep reinforcement Learning in multi-edge IoV systems. First, we decouple the Mixed Integer Nonlinear Programming (MINLP) problem of service migration and resource allocation into two sub-problems. Next, we design a new actor-critic-based asynchronous-update deep reinforcement learning method to handle service migration, where the delayed-update actor makes migration decisions and the one-step-update critic evaluates the decisions to guide the policy update. Notably, we theoretically derive the optimal resource allocation with convex optimization for each MEC server, thereby further improving system performance. Using the real-world datasets of vehicle trajectories and testbed, extensive experiments are conducted to verify the effectiveness of the proposed SR-CL. Compared to benchmark methods, the SR-CL achieves superior convergence and delay performance under various scenarios. © 2025 IEEE.

关键词： Convex optimization

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