Article
Computer Science, Theory & Methods
Mohamad Arafeh, Paolo Ceravolo, Azzam Mourad, Ernesto Damiani, Emanuele Bellini
Summary: This paper proposes a new framework for sampling Online Social Network (OSN) by using domain knowledge to define tailored strategies, which reduces the budget and time required for mining while increasing recall. The experimental results emphasize the importance of the strategy definition step and the application of ontologies on the knowledge graph in the domain of recommendation analysis.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2021)
Article
Computer Science, Hardware & Architecture
Yiyang Fu, Xiaojun Xie, Tao Zhang
Summary: This paper proposes a multi-relational attention network called MRAN for social recommendation, which addresses the issues of data sparsity and over-smoothing. By incorporating user social relations and item homogeneous relations, simulating influence diffusion structure, and using a two-level attention mechanism, the proposed model outperforms previous methods according to experiments.
JOURNAL OF SUPERCOMPUTING
(2023)
Review
Computer Science, Information Systems
Rui Chen, Kangning Pang, Min Huang, Hui Liang, Shizheng Zhang, Lei Zhang, Pu Li, Zhengwei Xia, Jianwei Zhang, Xiangjie Kong
Summary: With the development of online social networks, more and more users are participating and forming rich social relationships. These relationships provide a data source and research basis for recommender systems, driving the development of recommender systems based on social networks.
Article
Computer Science, Artificial Intelligence
Hossein Tahmasebi, Reza Ravanmehr, Rezvan Mohamadrezaei
Summary: Recommender systems aim to provide personalized suggestions based on user interests and behaviors, with collaborative filtering and deep learning being commonly used techniques. The study introduces a hybrid social recommender system utilizing a deep autoencoder network, which improves recommendation accuracy and effectiveness by incorporating social influence. The proposed approach combines collaborative and content-based filtering, along with analyzing users' social characteristics and behaviors for better recommendations.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Wang Hei-Chia, Jhou Hsu-Tung, Tsai Yu-Shan
Summary: A recommender system helps users effectively obtain accurate information, but often faces issues like the cold-start problem and low model scalability. To mitigate these problems, a hybrid recommender system can be used, and extracted features can be integrated into topics to reduce dimensionality.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Vincenzo Moscato, Antonio Picariello, Giancarlo Sperli
Summary: This paper presents a novel music recommendation technique based on the identification of user personality and mood, aiming to improve the performance of recommender systems through the analysis of user behavior. By embedding user personality and mood within a content-based filtering approach, more accurate and dynamic results are obtained, as demonstrated through several experiments.
IEEE INTELLIGENT SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Yang Liu, Liang Chen, Xiangnan He, Jiaying Peng, Zibin Zheng, Jie Tang
Summary: This study focuses on utilizing the indirect influence from high-order neighbors in social networks to enhance the performance of item recommendation. Different from traditional social recommenders, we directly factor social relations in the predictive model to improve user embeddings and recommendation outcomes.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Computer Science, Information Systems
Seungyeon Lee, Dohyun Kim
Summary: In this paper, a recommender system based on convolutional neural network is proposed to capture the complex interactions between users and items, giving greater weight to important features and alleviate the overfitting issue. Experiments show that the proposed method outperforms existing methods.
INFORMATION SCIENCES
(2022)
Article
Automation & Control Systems
Yassine Afoudi, Mohamed Lazaar, Safae Hmaidi
Summary: Graph-based data is widely used in various applications, and detecting missing links between nodes is a critical challenge. We propose an improved GraphSAGE architecture for link prediction, leveraging advanced aggregation functions and diverse neural network architectures. Our method outperforms standard GraphSAGE in terms of link prediction accuracy, and integrating clustering techniques further enhances its performance in recommender systems.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Yaomin Chang, Lin Shu, Erxin Du, Chuan Chen, Ziyang Zhang, Zibin Zheng, Yuzhao Huang, Xingxing Xing
Summary: Reciprocal Recommender Systems (RRSs) are recommender systems designed for people-to-people recommendation tasks. In this paper, a novel Graph neural network for Reciprocal Recommendation (GraphRR) is proposed to utilize users' multiplex interactions. Experimental results demonstrate the superiority of GraphRR and provide empirical evidence for the benefits of the proposed ego graph augmentation.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Xiang Huang, Yixin He, Bin Yan, Wei Zeng
Summary: In this research, a new session-based recommender system is proposed, which utilizes both the local and global information of item sequences and considers the importance of frequent sub-sequences. By constructing local and global session graphs and using a gated layer to control their contributions, our method is able to learn accurate session-level and global-level item embeddings.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Kun Zhang, Xinwang Liu, Weizhong Wang, Jing Li
Summary: The paper investigates the optimization of multi-criteria recommender systems, aiming to improve accuracy and scalability using social relationships and criteria preferences information. Results show that utilizing a social recommendation model can enhance accuracy, particularly in sparse datasets.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Fahrettin Horasan, Ahmet Hasim Yurttakal, Selcuk Gunduz
Summary: Collaborative filtering is a technique that considers the common characteristics of users and items in recommendation systems. Matrix decompositions, such as SVD and NMF, are widely used in collaborative filtering. In this study, a technique called T-ULVD was used to improve the accuracy and quality of recommendations. Experimental results showed that T-ULVD achieved better results compared to NMF and performed as well as or even better than SVD. This study may provide guidance for future research on solving the cold-start problem and reducing sparsity in collaborative filtering based recommender systems.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Julia Clemente, Hector Yago, Javier De Pedro-Carracedo, Javier Bueno
Summary: This paper proposes a new approach for developing an adaptive competence-based recommender system, using ontological and non-ontological resources to promote improvement in personalized student learning. The importance of flexibility and adaptability in learning modeling and recommendation processes is highlighted.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Chemistry, Multidisciplinary
Soo-Yeon Jeong, Young-Kuk Kim
Summary: This paper proposes a deep learning-based context-aware recommender system that effectively addresses data sparsity and demonstrates superior performance across various datasets.
APPLIED SCIENCES-BASEL
(2022)
Article
Automation & Control Systems
Xiao-Fei Zhang, Le Ou-Yang, Ting Yan, Xiaohua Tony Hu, Hong Yan
Summary: The study introduces a joint graphical model to estimate multiple gene networks simultaneously, leveraging network decomposition and group lasso penalty to examine similarities and differences among different subpopulations and data types, leading to improved accuracy in gene network reconstruction.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Computer Science, Hardware & Architecture
Wei-Pei Huang, Ray C. C. Cheung, Hong Yan
Summary: This article demonstrates an efficient and unified processing element array optimized for 3-D tensor computation, reducing data movement and runtime in various study cases.
IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS
(2021)
Article
Biochemical Research Methods
Ting Xu, Le Ou-Yang, Hong Yan, Xiao-Fei Zhang
Summary: This article proposes a new method to jointly estimate multiple time-varying differential networks for identifying network rewiring over cancer development. Simulation experiments demonstrate that the method outperforms other state-of-the-art techniques in most cases, and real data application shows rediscovery of well-known genes associated with breast cancer development and progression in estimated differential networks.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2021)
Article
Automation & Control Systems
Hu Zhu, Chunfeng Cui, Lizhen Deng, Ray C. C. Cheung, Hong Yan
Summary: The paper proposed an elastic net constraint-based tensor model for high-order graph matching, introducing a tradeoff between sparsity and accuracy. A nonmonotone spectral projected gradient method was derived for optimization, proving global convergence and superiority of the method through experiments.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Engineering, Electrical & Electronic
Jianjun Liu, Dunbin Shen, Zebin Wu, Liang Xiao, Jun Sun, Hong Yan
Summary: This paper proposes a patch-aware deep fusion approach for hyperspectral image fusion, aiming to improve the fusion result by utilizing patch information under subspace representation. The proposed approach builds a fusion model and solves it using an optimization algorithm, resulting in a structured deep fusion network. The performance is further improved by an aggregation fusion technique.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2022)
Article
Biochemical Research Methods
Yu-Ting Tan, Le Ou-Yang, Xingpeng Jiang, Hong Yan, Xiao-Fei Zhang
Summary: Learning how gene regulatory networks change under different conditions is important. Existing methods for inferring differential networks have limitations. In this study, a new method is proposed and shown to outperform other methods in simulation studies and applications.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2022)
Article
Biochemical Research Methods
Rizwan Qureshi, Avirup Ghosh, Hong Yan
Summary: This study examines the complete structure of the multi-domain EGFR protein and its mutants using molecular dynamics simulations and normal mode analysis. The findings reveal different patterns of correlated motions in each domain of EGFR mutants compared to the wildtype, and the mutant structures have fewer hydrogen bonds. These findings are important for understanding the dynamics and communications in EGFR domains.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2022)
Article
Computer Science, Artificial Intelligence
You-Wei Luo, Chuan-Xian Ren, Dao-Qing Dai, Hong Yan
Summary: This paper proposes a Riemannian manifold learning framework for achieving transferability and discriminability simultaneously in unsupervised domain adaptation. A probabilistic discriminant criterion is established on the target domain using soft labels, and manifold metric alignment is used to be compatible with the embedding space. Experimental results demonstrate the superiority of the proposed method.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Biochemical Research Methods
Meng-Guo Wang, Le Ou-Yang, Hong Yan, Xiao-Fei Zhang
Summary: In this study, a novel method called prior network-dependent gene network inference (pGNI) is proposed to estimate gene co-expression networks by integrating gene expression data and prior protein interaction network data. The method successfully captures the modular structures in the networks and is demonstrated to be effective through simulation studies and real datasets.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2022)
Article
Biochemical Research Methods
Bo Li, Ke Jin, Le Ou-Yang, Hong Yan, Xiao-Fei Zhang
Summary: The single-cell RNA sequencing (scRNA-seq) technique is used to analyze gene expression patterns in complex tissues at single-cell resolution, but dropout events can hinder downstream analyses. We developed a new imputation method, scTSSR2, which combines matrix decomposition with two-side sparse self-representation to effectively impute dropout events in scRNA-seq data. Comparative experiments show that scTSSR2 outperforms existing imputation methods in terms of computational speed and memory usage. We also provide a user-friendly R package, scTSSR2, for denoising scRNA-seq data and improving data quality.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Computer Science, Artificial Intelligence
Ali Raza Shahid, Mehmood Nawaz, Xinqi Fan, Hong Yan
Summary: This article proposes a view-adaptive mechanism that transforms the skeleton view into a more consistent virtual perspective, reducing the influence of view variations.
IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS
(2023)
Article
Biochemical Research Methods
Subin Qian, Huiyi Liu, Xiaofeng Yuan, Wei Wei, Shuangshuang Chen, Hong Yan
Summary: This paper proposes a biclustering method called RCSBC, which aims to find checkerboard patterns within gene expression data. By exploiting the relationship between the row/column structure of a gene expression matrix and the structure of biclusters, the method achieves low time and space complexity and outperforms existing algorithms in terms of clustering accuracy and time/space complexity.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2022)
Article
Computer Science, Information Systems
Xinqi Fan, Mingjie Jiang, Hong Yan
Summary: This paper proposes a new deep learning-based face mask detector to meet the low computational requirements for embedded systems. By introducing a novel residual context attention module and an auxiliary task, the feature extraction ability of the model is enhanced, achieving state-of-the-art results on two public datasets.
Article
Engineering, Electrical & Electronic
Yi Yang, Lixin Han, Yuanzhen Liu, Jun Zhu, Hong Yan
Summary: Inspired by the accuracy and efficiency of the gamma-norm of a matrix, the study generalizes the gamma-norm to tensors and proposes a new tensor completion approach within the tensor singular value decomposition framework. An efficient algorithm, combining the augmented Lagrange multiplier and closed-resolution of a cubic equation, is developed to solve the associated nonconvex tensor multi-rank minimization problem. Experimental results demonstrate that the proposed approach outperforms current state of the art algorithms in recovery accuracy.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2021)
Article
Computer Science, Information Systems
Mengxu Zhu, Rizwan Qureshi, Hong Yan
Summary: EGFR plays a vital role in lung cell proliferation and mutations in its kinase domain may lead to cancer. This study investigated the binding mechanism of EGFR drug mutant complex through molecular dynamics simulation and geometrical properties analysis. The results showed that drug-sensitive mutants have tighter interactions, while drug-resistant mutants have looser bindings.
Review
Computer Science, Software Engineering
Orvila Sarker, Asangi Jayatilaka, Sherif Haggag, Chelsea Liu, M. Ali Babar
Summary: This study provides a comprehensive view of the challenges and critical success factors in the design, implementation, and evaluation stages of phishing education, training, and awareness (PETA). The findings highlight the need to address human-centric issues, bridge users' knowledge gaps, and adopt personalized approaches to enhance defense against phishing attacks.
JOURNAL OF SYSTEMS AND SOFTWARE
(2024)
Article
Computer Science, Software Engineering
Carlos Araujo, Meuse Oliveira Jr., Bruno Nogueira, Paulo Maciel, Eduardo Tavares
Summary: This paper proposes a method based on stochastic Petri nets for evaluating the consistency levels of storage systems based on NoSQL DBMS. The method takes into account different consistency levels and redundant nodes, and estimates the system's availability, throughput, and the probability of accessing the newest data. Experimental results demonstrate the practical feasibility of this approach.
JOURNAL OF SYSTEMS AND SOFTWARE
(2024)
Review
Computer Science, Software Engineering
L. Giamattei, A. Guerriero, R. Pietrantuono, S. Russo, I. Malavolta, T. Islam, M. Dinga, A. Koziolek, S. Singh, M. Armbruster, J. M. Gutierrez-Martinez, S. Caro-Alvaro, D. Rodriguez, S. Weber, J. Henss, E. Fernandez Vogelin, F. Simon Panojo
Summary: This article presents the results of a systematic study on the available monitoring tools for DevOps and microservices. It provides a classification and analysis of these tools, aiming to be a useful reference for researchers and practitioners in this field.
JOURNAL OF SYSTEMS AND SOFTWARE
(2024)
Article
Computer Science, Software Engineering
Jessica Diaz, Jorge Perez, Isaque Alves, Fabio Kon, Leonardo Leite, Paulo Meirelles, Carla Rocha
Summary: This paper presents empirical research on the structure of DevOps teams in software-producing organizations to better understand the organizational structure and characteristics of teams adopting DevOps. A theory of DevOps taxonomies is built through analysis, and its consistency with other taxonomies is tested.
JOURNAL OF SYSTEMS AND SOFTWARE
(2024)
Article
Computer Science, Software Engineering
Sinan Sigurd Tanilkan, Jo Erskine Hannay
Summary: When deciding to develop new software, it is important to have a clear understanding of the intended benefits. However, our research shows that stakeholders' understanding of benefits often fluctuates during the development process, leading to uncertainty. Therefore, we recommend focusing on helping practitioners embrace changes in their understanding of benefits.
JOURNAL OF SYSTEMS AND SOFTWARE
(2024)
Article
Computer Science, Software Engineering
Pingyan Wang, Shaoying Liu, Ai Liu, Wen Jiang
Summary: This paper presents an approach that combines static analysis tools and manual audits to effectively detect various types of security vulnerabilities. By using a special Petri net representation, the proposed method is able to assist in the detection of taint-style vulnerabilities.
JOURNAL OF SYSTEMS AND SOFTWARE
(2024)
Article
Computer Science, Software Engineering
Edgar Sarmiento-Calisaya, Julio Cesar Sampaio do Prado Leite
Summary: This research introduces an automated requirements analysis approach that combines natural language processing, Petri-nets, and visualization techniques to improve the quality of scenario-based specifications, identify defects, and anticipate inconsistencies.
JOURNAL OF SYSTEMS AND SOFTWARE
(2024)
Article
Computer Science, Software Engineering
Jian Hu
Summary: This paper proposes a two-stage trace matrix optimization method for fault localization, which addresses the challenges of coincidental correctness and data imbalance in the current trace matrix. Through extensive experiments, significant improvements in fault localization effectiveness are demonstrated.
JOURNAL OF SYSTEMS AND SOFTWARE
(2024)
Article
Computer Science, Software Engineering
Fan Zhang, Manman Peng, Yuanyuan Shen, Qiang Wu
Summary: This study proposes a novel method called HFEDR that utilizes the hierarchical features of Transformer models and reorganizes training data to improve code search performance. Experimental results demonstrate the effectiveness and rationality of the proposed approach.
JOURNAL OF SYSTEMS AND SOFTWARE
(2024)
Article
Computer Science, Software Engineering
Tong Wang, Bixin Li
Summary: Software architecture erosion has a negative impact on software quality, performance, and evolution cost. This paper proposes an approach called EsArCost to locate the causes of architecture erosion and estimate the repair cost of each erosion problem. Experimental results show that EsArCost can effectively and efficiently estimate repair costs.
JOURNAL OF SYSTEMS AND SOFTWARE
(2024)
Article
Computer Science, Software Engineering
Xiajing Wang, Rui Ma, Wei Huo, Zheng Zhang, Jinyuan He, Chaonan Zhang, Donghai Tian
Summary: This paper proposes a new potential-aware fuzzing scheme called SYNTONY that measures seed potential using multiple objectives and prioritizes promising seeds to increase the number of unique crashes and coverage. Experimental results show that SYNTONY outperforms other fuzzing tools and has high compatibility and expansibility.
JOURNAL OF SYSTEMS AND SOFTWARE
(2024)
Article
Computer Science, Software Engineering
Stefano Lambiase, Gemma Catolino, Fabiano Pecorelli, Damian A. Tamburri, Fabio Palomba, Willem-Jan van den Heuvel, Filomena Ferrucci
Summary: This paper contributes to the existing body of knowledge on factors affecting productivity in software development by studying the cultural and geographical dispersion of a development community. The results show that cultural and geographical dispersion significantly impact productivity, suggesting that managers and practitioners should consider these aspects throughout the software development lifecycle.
JOURNAL OF SYSTEMS AND SOFTWARE
(2024)
Article
Computer Science, Software Engineering
Elaine Venson, Bradford Clark, Barry Boehm
Summary: The software industry has been under pressure to adopt security practices and reduce software vulnerabilities. This study quantifies the effort required to develop secure software in increasing levels of rigor and scope and provides validated cost multipliers for practitioners to estimate proper resources for adopting security practices.
JOURNAL OF SYSTEMS AND SOFTWARE
(2024)
Article
Computer Science, Software Engineering
Yangyang Zhao, Mingyue Jiang, Yibiao Yang, Yuming Zhou, Hanjie Ma, Zuohua Ding
Summary: Previous studies have ignored the potential associations between modules involved in the same defect, and this comprehensive study explores the implications of intra-defect associations for defect prediction. The majority of defects occur across functions, with implicit dependencies between the modules. By considering intra-defect associations and merging modules, the proposed data processing approach significantly improves defect prediction performance.
JOURNAL OF SYSTEMS AND SOFTWARE
(2024)
Article
Computer Science, Software Engineering
Meira Levy, Irit Hadar
Summary: This research sheds new light on how students learn and practice hybrid work in educational settings through two educational studies. The findings show the benefits of new educational programs in fostering empathy and innovation among students, while also highlighting the challenges and opportunities in addressing real challenges.
JOURNAL OF SYSTEMS AND SOFTWARE
(2024)
