Article
Computer Science, Hardware & Architecture
Meirun Chen, Michel Habib, Cheng-Kuan Lin
Summary: This paper investigates the diagnosability of networks under two models of self-diagnosis and completely determines the diagnosability of a certain family of matching composition networks.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Computer Science, Theory & Methods
Yanze Huang, Limei Lin, Sun-Yuan Hsieh
Summary: Cyberspace is not a vacuum space and it is normal for viruses and worms to exist in it. Security threats in cyberspace stem from endogenous security issues caused by the incomplete theoretical system and technology of the information field itself. Improving the self-immunity of networks is crucial, rather than aiming for a completely aseptic cyberspace.
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
(2022)
Article
Mathematics, Applied
Jun Yuan, Huijuan Qiao, Aixia Liu, Xi Wang
Summary: Fault diagnosis is crucial for maintaining the reliability of interconnection networks. This paper focuses on conditional local diagnosis of given nodes in interconnection networks, proposing an algorithm to identify the fault or fault-free status of nodes and demonstrating the conditional diagnosability of certain network structures. The algorithm presented allows for more faults to be detected compared to existing algorithms.
DISCRETE APPLIED MATHEMATICS
(2022)
Article
Multidisciplinary Sciences
Xinyang Wang, Haozhe Li, Qiao Sun, Chen Guo, Hu Zhao, Xinyu Wu, Anqi Wang
Summary: Diagnosability plays a crucial role in evaluating the reliability and fault tolerance of symmetrical multiprocessor systems. The g-good-neighbor conditional diagnosability is suitable for large-scale multiprocessor systems and has received much attention. This paper investigates the relationships between the g-good-neighbor connectivity and diagnosability of graphs under the MM* model, specifically focusing on the exchanged crossed cube (ECO) network structure. The research derives the exact value of the g-good-neighbor diagnosability of ECO under the MM* model and provides a supplement to its diagnosability.
Article
Computer Science, Hardware & Architecture
Ting Tian, Shumin Zhang, Yalan Li
Summary: Diagnosability is an important metric for fault diagnosis in multiprocessor systems. Previous studies only focused on vertex faults, but we considered edge faults as well and obtained the h-edge g-good-neighbor conditional diagnosability of n-dimensional star graphs under the PMC and MM* models.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Computer Science, Theory & Methods
Yalan Li, Jichang Wu, Shumin Zhang, Chengfu Ye
Summary: This paper studies the g-extra connectivity and g-extra conditional diagnosability of round matching composition networks, and obtains some results under the PMC model and MM* model, respectively.
THEORETICAL COMPUTER SCIENCE
(2022)
Article
Computer Science, Interdisciplinary Applications
Nai-Wen Chang, Hsuan-Jung Wu, Sun-Yuan Hsieh
Summary: Due to the growing size of multi-processor systems, processor-fault diagnosis plays a critical role in measuring reliability. This study evaluated the conditional diagnosability of pancake graphs in the PMC model and derived specific numerical results.
JOURNAL OF COMBINATORIAL OPTIMIZATION
(2022)
Article
Computer Science, Hardware & Architecture
Jun Yuan, Aixia Liu, Xi Wang
Summary: The study introduces a new measurement for fault diagnosis in interconnection networks, called g-extra diagnosability, investigating various networks' g-extra diagnosability under the MM* model and proposing a general approach to derive the g-extra diagnosability from the g-extra connectivity. Additionally, a new relationship between the g-extra connectivity and the g-extra diagnosability of networks is proposed based on existing shared practices.
Article
Computer Science, Hardware & Architecture
Shanshan Yin, Liqiong Xu
Summary: This paper investigates the h-restricted vertex diagnosability and the r-restricted edge diagnosability of certain classes of regular networks under the HPMC model.
JOURNAL OF SUPERCOMPUTING
(2022)
Article
Computer Science, Theory & Methods
Limei Lin, Yanze Huang, Yuhang Lin, Sun-Yuan Hsieh, Li Xu
Summary: In this paper, a novel indicator called m-FFNLFD is proposed to describe the diagnosability of a multiprocessor system at a local node, and its properties and applications are studied under different network models.
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
(2022)
Article
Computer Science, Hardware & Architecture
Asghar A. Asgharian Sardroud, Mohsen Ghasemi
Summary: This paper investigates the diagnosability of interconnection networks in supercomputers and provides some conclusions based on the analysis of triangle-free graphs.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Computer Science, Hardware & Architecture
Xinyang Wang, Lijuan Huang, Qiao Sun, Naqin Zhou, Yuehong Chen, Weiwei Lin, Keqin Li
Summary: This paper focuses on the g-extra diagnosability of the balanced hypercube, proving upper and lower bounds using the contradiction method and providing specific formulas under the PMC and MM* models. Simulation experiments were conducted to verify the effectiveness of the proposed theories, contributing certain theoretical and practical value to the research of BHn fault diagnosis.
JOURNAL OF SUPERCOMPUTING
(2022)
Article
Computer Science, Theory & Methods
Xianyong Li, Jiaming Huang, Yajun Du, Yongquan Fan, Xiaoliang Chen
Summary: Processor fault diagnosis aims to identify faulty processors in a multicomputer system, ensuring high reliability and availability. The g-good-neighbor conditional diagnosability is a novel method for fault diagnosis in various networks. The diagnostic capabilities of hypermesh optical interconnection networks are determined under the PMC model and comparison model, and the results are applied to derive the diagnosability of hypercubes under the PMC and comparison models.
INTERNATIONAL JOURNAL OF FOUNDATIONS OF COMPUTER SCIENCE
(2023)
Article
Computer Science, Information Systems
Jiarong Liang, Qian Zhang, Changzhen Li
Summary: The paper proposes a precise fault diagnosis algorithm for a star network system, consisting of three main parts. The theoretical analysis and simulation results demonstrate the accuracy and time complexity of the algorithm.
Article
Computer Science, Theory & Methods
Ping Li, Shurong Zhang, Xiaomin Hu, Weihua Yang
Summary: Fault diagnosability is crucial for network reliability. This paper proposes a novel measure called structural diagnosability, which focuses on the diagnosis ability of specific structures rather than individual faulty vertices. The study calculates the structural diagnosability of n-dimensional hypercube under the PMC and MM* models, considering various specific structures.
THEORETICAL COMPUTER SCIENCE
(2023)
Article
Computer Science, Information Systems
Xia Liang, Jie Guo, Peide Liu
Summary: This paper investigates a novel consensus model based on social networks to manage manipulative and overconfident behaviors in large-scale group decision-making. By proposing a novel clustering model and improved methods, the consensus reaching is effectively facilitated. The feedback mechanism and management approach are employed to handle decision makers' behaviors. Simulation experiments and comparative analysis demonstrate the effectiveness of the model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiang Li, Haiwang Guo, Xinyang Deng, Wen Jiang
Summary: This paper proposes a method based on class gradient networks for generating high-quality adversarial samples. By introducing a high-level class gradient matrix and combining classification loss and perturbation loss, the method demonstrates superiority in the transferability of adversarial samples on targeted attacks.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Lingyun Lu, Bang Wang, Zizhuo Zhang, Shenghao Liu
Summary: Many recommendation algorithms only rely on implicit feedbacks due to privacy concerns. However, the encoding of interaction types is often ignored. This paper proposes a relation-aware neural model that classifies implicit feedbacks by encoding edges, thereby enhancing recommendation performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jaehong Yu, Hyungrok Do
Summary: This study discusses unsupervised anomaly detection using one-class classification, which determines whether a new instance belongs to the target class by constructing a decision boundary. The proposed method uses a proximity-based density description and a regularized reconstruction algorithm to overcome the limitations of existing one-class classification methods. Experimental results demonstrate the superior performance of the proposed algorithm.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Hui Tu, Shifei Ding, Xiao Xu, Haiwei Hou, Chao Li, Ling Ding
Summary: Border-Peeling algorithm is a density-based clustering algorithm, but its complexity and issues on unbalanced datasets restrict its application. This paper proposes a non-iterative border-peeling clustering algorithm, which improves the clustering performance by distinguishing and associating core points and border points.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Long Tang, Pan Zhao, Zhigeng Pan, Xingxing Duan, Panos M. Pardalos
Summary: In this work, a two-stage denoising framework (TSDF) is proposed for zero-shot learning (ZSL) to address the issue of noisy labels. The framework includes a tailored loss function to remove suspected noisy-label instances and a ramp-style loss function to reduce the negative impact of remaining noisy labels. In addition, a dynamic screening strategy (DSS) is developed to efficiently handle the nonconvexity of the ramp-style loss.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Raghunathan Krishankumar, Sundararajan Dhruva, Kattur S. Ravichandran, Samarjit Kar
Summary: Health 4.0 is gaining global attention for better healthcare through digital technologies. This study proposes a new decision-making framework for selecting viable blockchain service providers in the Internet of Medical Things (IoMT). The framework addresses the limitations in previous studies and demonstrates its applicability in the Indian healthcare sector. The results show the top ranking BSPs, the importance of various criteria, and the effectiveness of the developed model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Tao Tan, Hong Xie, Liang Feng
Summary: This paper proposes a heterogeneous update idea and designs HetUp Q-learning algorithm to enlarge the normalized gap by overestimating the Q-value corresponding to the optimal action and underestimating the Q-value corresponding to the other actions. To address the limitation, a softmax strategy is applied to estimate the optimal action, resulting in HetUpSoft Q-learning and HetUpSoft DQN. Extensive experimental results show significant improvements over SOTA baselines.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Chao Yang, Xianzhi Wang, Lina Yao, Guodong Long, Guandong Xu
Summary: This paper proposes a dynamic transformer-based architecture called Dyformer for multivariate time series classification. Dyformer captures multi-scale features through hierarchical pooling and adaptive learning strategies, and improves model performance by introducing feature-map-wise attention mechanisms and a joint loss function.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiguang Li, Baolu Feng, Yunhe Sun, Ammar Hawbani, Saeed Hammod Alsamhi, Liang Zhao
Summary: This paper proposes an enhanced scatter search strategy, using opposition-based learning, to solve the problem of automated test case generation based on path coverage (ATCG-PC). The proposed ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path and generates new test cases to cover the target path by modifying the dimensions of existing test cases. Experimental results show that the ESSENT algorithm outperforms other state-of-the-art algorithms, achieving maximum path coverage with fewer test cases.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Shirin Dabbaghi Varnosfaderani, Piotr Kasprzak, Aytaj Badirova, Ralph Krimmel, Christof Pohl, Ramin Yahyapour
Summary: Linking digital accounts belonging to the same user is crucial for security, user satisfaction, and next-generation service development. However, research on account linkage is mainly focused on social networks, and there is a lack of studies in other domains. To address this, we propose SmartSSO, a framework that automates the account linkage process by analyzing user routines and behavior during login processes. Our experiments on a large dataset show that SmartSSO achieves over 98% accuracy in hit-precision.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Renchao Wu, Jianjun He, Xin Li, Zuguo Chen
Summary: This paper proposes a memetic algorithm with fuzzy-based population control (MA-FPC) to solve the joint order batching and picker routing problem (JOBPRP). The algorithm incorporates batch exchange crossover and a two-level local improvement procedure. Experimental results show that MA-FPC outperforms existing algorithms in terms of solution quality.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Guoxiang Zhong, Fagui Liu, Jun Jiang, Bin Wang, C. L. Philip Chen
Summary: In this study, we propose the AMFormer framework to address the problem of mixed normal and anomaly samples in deep unsupervised time-series anomaly detection. By refining the one-class representation and introducing the masked operation mechanism and cost sensitive learning theory, our approach significantly improves anomaly detection performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jin Zhou, Kang Zhou, Gexiang Zhang, Ferrante Neri, Wangyang Shen, Weiping Jin
Summary: In this paper, the authors focus on the issue of multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) in practical problem-solving. They propose a dual data-driven method for solving this problem, which consists of eliminating redundant variables, constructing objective functions, selecting evolution operators, and using a multi-objective evolutionary algorithm. The experiments conducted on two different problem domains demonstrate the effectiveness, practicality, and scalability of the proposed method.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Georgios Charizanos, Haydar Demirhan, Duygu Icen
Summary: This article proposes a new fuzzy logistic regression framework that addresses the problems of separation and imbalance while maintaining the interpretability of classical logistic regression. By fuzzifying binary variables and classifying subjects based on a fuzzy threshold, the framework demonstrates superior performance on imbalanced datasets.
INFORMATION SCIENCES
(2024)