Article
Computer Science, Artificial Intelligence
Yao Chen, Jiangang Liu, Zhe Zhang, Shiping Wen, Wenjun Xiong
Summary: In this work, a novel Knowledge Graph Embedding (KGE) strategy called MobiusE is proposed, which embeds entities and relations to the surface of a Mobius ring. Compared to the classic TorusE, MobiusE exhibits more nonlinearity and generates more precise embedding results. The experiments show that MobiusE outperforms TorusE and other classic embedding strategies in several key indicators.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Mathematics, Applied
Yan-Ting Xie, Yong-De Feng, Shou-Jun Xu
Summary: This paper studies a class of Cayley graphs generated by transpositions and proves that this class of graphs is a partial cube if and only if it is a bubble sort graph. Furthermore, analytical expressions for the Wiener indices of bubble sort graphs are provided.
MATHEMATICAL METHODS IN THE APPLIED SCIENCES
(2022)
Article
Mathematics, Applied
Wen -Han Zhu, Rong-Xia Hao, Yan-Quan Feng, Jaeun Lee
Summary: In this paper, we investigate the Omega-paths and path connectivity in a connected simple graph G. By deeply exploring the structural properties of the k-ary n-cube Q(n)(k), we completely determine its 3-path connectivity.
APPLIED MATHEMATICS AND COMPUTATION
(2023)
Article
Computer Science, Information Systems
Hongyan Ran, Caiyan Jia, Pengfei Zhang, Xuanya Li
Summary: The study proposes an end-to-end Multi-channel Graph ATtention network with Event-Sharing Module for rumor detection, which achieves state-of-the-art performance.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Sheng Tian, Guibing Guo, Yifei Li, Yuan Liu, Xingwei Wang
Summary: Recommender systems aim to suggest items of interest based on historical interactions between users and items. Existing methods often overlook the global view and relation directions, and fail to consider the influential factors of path length and number of relation types. To address these issues, a recommendation model is proposed that can capture high-order local and global user-item interactions.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Hardware & Architecture
Tzu-Liang Kung
Summary: This article is dedicated to establishing a deep analysis on the exact formula of super path-connectivity for the crossed cube interconnection network. A sufficient and necessary condition is presented to classify whether or not crossed cubes can be super path-connected.
JOURNAL OF SUPERCOMPUTING
(2022)
Article
Chemistry, Multidisciplinary
Ming Liu, Lei Chen, Zihao Zheng
Summary: This paper addresses the challenge of measuring text similarity in web applications and proposes a passage-level event connection graph to model the relationships between events mentioned in the text. The core event is revealed from the graph and used to measure text similarity. Two improvements are also provided to better model the relationships between events. Experimental results demonstrate that our calculation outperforms unsupervised methods and achieves comparable results to some supervised neuron-based methods in measuring text similarity.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Theory & Methods
Xirong Xu, Huifeng Zhang, Ziming Wang, Qiang Zhang, Peng Zhang
Summary: This study examines the fault-tolerant edge-pancyclicity of the n-dimensional crossed cube, showing that in some cases, at most n-2 faulty vertices and/or edges can be tolerated, with optimal results in certain senses.
INTERNATIONAL JOURNAL OF FOUNDATIONS OF COMPUTER SCIENCE
(2021)
Article
Mathematics
S. Rajeshwari, M. Rajesh
Summary: This paper discusses the efficiency of a graph embedding problem in simulating one interconnection network in another interconnection network, which is characterized by the influential parameter of wirelength. The convex edge partition of 3-Ary n-Cubes and the minimized wirelength of both 3-Ary n-Cubes and circulant networks are obtained to determine the quality of the embeddings.
Article
Computer Science, Artificial Intelligence
Yingying Xue, Aibo Song, Xiaolin Fang, Jiahui Jin, Xiangguo Sun, Yingxue Zhang
Summary: Dynamics and heterogeneity are two major challenges in recent graph learning research, and addressing these challenges is crucial for real-world applications. Existing approaches usually decompose heterogeneous graphs into different semantics and learn separate representations for each space. However, there are still two open problems: the neglect of the mutual influence between different semantics and the reliance on smoothness assumption for graph evolving. This paper proposes a cross-view mechanism to capture the mutual influence and a graph attention network to learn multi-scaled features at both local and global levels, addressing these problems and demonstrating superior performance in various graph tasks.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Theory & Methods
Micheal Arockiaraj, Arul Jeya Shalini, J. Nancy Delaila
Summary: In this paper, the importance of graph embedding in the field of data science is discussed, along with the characteristics and improvements of network-based architectures like binary cube and spined cube. Furthermore, an algorithm for embedding the spined cube into a grid and computing the minimum wirelength is proposed using the edge congestion technique.
THEORETICAL COMPUTER SCIENCE
(2022)
Article
Computer Science, Artificial Intelligence
Sean Bin Yang, Chenjuan Guo, Bin Yang
Summary: Ranking paths in transportation services is an important functionality, and this study proposes a regression modeling approach to assign ranking scores to paths based on historical trajectories. The study introduces effective training data enrichment and a multi-task learning framework to improve the ranking estimation. Empirical studies validate the effectiveness and practicality of the proposed framework.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Computer Science, Hardware & Architecture
Tianyu Zhao, Shuai Huang, Yong Wang, Chengliang Chai, Guoliang Li
Summary: This paper proposes a learning-based method RNE and RNE+ for computing the shortest paths between two vertices on road networks, with features of fast speed, high efficiency, and low error rate, which significantly outperform existing methods in experiments.
Article
Computer Science, Information Systems
Chenji Huang, Yixiang Fang, Xuemin Lin, Xin Cao, Wenjie Zhang
Summary: In this article, we propose a novel prediction model called ABLE, which utilizes the Attention mechanism and BiLSTM for Embedding, to improve the performance of meta-path prediction in heterogeneous information networks. Experimental results show that ABLE outperforms existing methods on multiple real datasets.
ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
(2022)
Article
Computer Science, Artificial Intelligence
Jianxin Li, Hao Peng, Yuwei Cao, Yingtong Dou, Hekai Zhang, Philip S. Yu, Lifang He
Summary: Graph neural networks (GNNs) are widely used in deep learning for graph analysis tasks. However, current methods ignore heterogeneity in real-world graphs and fail to capture content-based correlations between nodes. In this paper, we propose a novel HAE framework and a HAE(GNN) model that incorporates meta-paths and meta-graphs for rich, heterogeneous semantics and leverages self-attention mechanism for exploring content-based interactions between nodes.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Hardware & Architecture
Yanan Chang, Xiaohua Jia, Jianqun Cui
COMPUTERS & ELECTRICAL ENGINEERING
(2015)
Article
Computer Science, Theory & Methods
Ruitao Xie, Yonggang Wen, Xiaohua Jia, Haiyong Xie
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
(2015)
Article
Computer Science, Theory & Methods
Baolei Cheng, Jianxi Fan, Xiaohua Jia
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
(2015)
Article
Computer Science, Theory & Methods
Kan Yang, Xiaohua Jia, Kui Ren
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
(2015)
Article
Computer Science, Interdisciplinary Applications
Marjan Marzban, Qian-Ping Gu, Xiaohua Jia
JOURNAL OF COMBINATORIAL OPTIMIZATION
(2016)
Article
Computer Science, Information Systems
Xu Xu, Weifa Liang, Xiaohua Jia, Wenzheng Xu
WIRELESS COMMUNICATIONS & MOBILE COMPUTING
(2016)
Article
Telecommunications
Kunxiao Zhou, Liming Xie, Xiaohua Jia
WIRELESS PERSONAL COMMUNICATIONS
(2016)
Article
Computer Science, Information Systems
Weifa Liang, Wenzheng Xu, Xiaojiang Ren, Xiaohua Jia, Xiaola Lin
ACM TRANSACTIONS ON SENSOR NETWORKS
(2016)
Article
Computer Science, Information Systems
Kan Yang, Zhen Liu, Xiaohua Jia, Xuemin Sherman Shen
IEEE TRANSACTIONS ON MULTIMEDIA
(2016)
Article
Computer Science, Hardware & Architecture
Zhao Liu, Jianxi Fan, Xiaohua Jia
JOURNAL OF COMPUTER AND SYSTEM SCIENCES
(2016)
Article
Computer Science, Information Systems
Kuai Xu, Feng Wang, Xiaohua Jia
SECURITY AND COMMUNICATION NETWORKS
(2016)
Article
Computer Science, Theory & Methods
Xi Wang, Jianxi Fan, Xiaohua Jia, Cheng-Kuan Lin
THEORETICAL COMPUTER SCIENCE
(2016)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Hongwei Du, Rongrong Zhu, Xiaohua Jia, Chuang Liu
COMBINATORIAL OPTIMIZATION AND APPLICATIONS, (COCOA 2015)
(2015)
Proceedings Paper
Computer Science, Theory & Methods
Peng-Jun Wan, Fahad Al-Dhelaan, Xiaohua Jia, Baowei Wang, Guowen Xing
2015 IEEE CONFERENCE ON COMPUTER COMMUNICATIONS (INFOCOM)
(2015)
Proceedings Paper
Computer Science, Information Systems
Dongping Deng, Hongwei Du, Xiaohua Jia, Qiang Ye
WIRELESS ALGORITHMS, SYSTEMS, AND APPLICATIONS
(2015)
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)
