Information Sciences最新文献

{"title":"Temporal knowledge graph link predictions with query-guided temporal representation learning","authors":"Linhua Dong ,&nbsp;Xiaofei Zhou ,&nbsp;Bo Wang ,&nbsp;Qiannan Zhu ,&nbsp;Fan Meng","doi":"10.1016/j.ins.2025.122035","DOIUrl":"10.1016/j.ins.2025.122035","url":null,"abstract":"<div><div>Temporal Knowledge Graph (TKG) records real-life events using timestamped facts and is used for the TKG link prediction task which is to answer an incomplete timestamped fact called the query. Existing works predict by learning entity embeddings where they represent entities with entity-related facts guided by queries to emphasize important ones. Although they generalize well, their learning with queries is limited since they guide learning with the average query which merges all queries without considering that queries in TKG represent diverse meanings. Merging diverse queries generates a vague averaged query which will mislead embedding learning and further confuse predictions. To resolve the limitation, we propose individual-query-guided learning (IndiQ) to learn clearer embeddings which faithfully realizes the nature of TKG that its records are diverse and should be modeled individually rather than averaging. Specifically, IndiQ formulates embedding learning as a weighted sum of entity-related facts and calculates weights using queries individually following the total probability theorem. Then, with the novel formulation, IndiQ guides the learning of entity embeddings using queries individually to identify important facts. Finally, IndiQ predicts future links using learned entity embeddings. Experimental results show that we achieve better performance. Visualizations further demonstrate the effectiveness of our IndiQ.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"708 ","pages":"Article 122035"},"PeriodicalIF":8.1,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distributed conflict analysis across varying analysis levels based on fuzzy formal contexts","authors":"Zhenhao Qi ,&nbsp;Huilai Zhi ,&nbsp;Weiping Ding","doi":"10.1016/j.ins.2025.122038","DOIUrl":"10.1016/j.ins.2025.122038","url":null,"abstract":"<div><div>Conflict analysis aims to understand the causes of conflicts and identify effective solutions. While existing studies have thoroughly examined conflict information within individual information systems, the conflict analysis across different systems remains underexplored. In this paper, we utilize fuzzy formal concept analysis to investigate conflict analysis in multi-source information. First, conflict information fusion strategies for distributed fuzzy formal contexts are proposed, including object set extension (vertical merging) and attribute set extension (horizontal merging). Second, algorithms for updating conflict analysis results when adjusting analysis levels are introduced, considering the varying conflict analysis levels inherent in multi-source information. Finally, the fusion strategies for conflict analysis results at varying analysis levels are evaluated, and the selection of analysis levels is analyzed to optimize computational efficiency. The experimental results demonstrate that fusing conflict information is significantly more efficient than recalculation, and it allows for the selection of varying analysis levels to balance time consumption and information volume. This work enhances the efficiency of conflict analysis in fuzzy formal contexts, providing practical methods for managing multi-source information and adjusting analysis levels to meet specific requirements.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"707 ","pages":"Article 122038"},"PeriodicalIF":8.1,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Information diffusion prediction via meta-knowledge learners","authors":"Zhangtao Cheng ,&nbsp;Jienan Zhang ,&nbsp;Xovee Xu ,&nbsp;Wenxin Tai ,&nbsp;Fan Zhou ,&nbsp;Goce Trajcevski ,&nbsp;Ting Zhong","doi":"10.1016/j.ins.2025.122034","DOIUrl":"10.1016/j.ins.2025.122034","url":null,"abstract":"<div><div>Information diffusion prediction is a fundamental task for a vast range of applications, including viral marketing identification and precise recommendation. Existing works focus on modeling limited contextual information from independent cascades while overlooking the diverse user behaviors during the information diffusion: First, users typically have diverse social relationships and pay more attention to their social neighbors, which significantly influences the process of information diffusion. Second, complex temporal influence among different cascade sequences leads to unique and dynamic diffusion patterns between users. To tackle these challenges, we propose MetaCas, a novel cascade meta-knowledge learning framework for enhancing information diffusion prediction in an adaptive and dynamic parameter generative manner. Specifically, we design two meta-knowledge-aware topological-temporal modules – Meta-GAT and Meta-LSTM – to extract cascade-specific topological and temporal user interdependencies inherent within the information diffusion process. Model parameters of topological-temporal modules are adaptively generated by the constructed meta-knowledge from three important perspectives: user social structure, user preference, and temporal diffusion influence. Extensive experiments conducted on four real-world social datasets demonstrate that MetaCas outperforms state-of-the-art information diffusion models across several settings (up to 16.6% in terms of Hits@100).</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"707 ","pages":"Article 122034"},"PeriodicalIF":8.1,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Conjunction subspaces test for conformal and selective classification","authors":"Zengyou He,&nbsp;Zerun Li,&nbsp;Junjie Dong,&nbsp;Xinying Liu,&nbsp;Mudi Jiang,&nbsp;Lianyu Hu","doi":"10.1016/j.ins.2025.122037","DOIUrl":"10.1016/j.ins.2025.122037","url":null,"abstract":"<div><div>In this paper, we present a new classifier, which integrates significance testing results over different random subspaces to yield consensus <em>p</em>-values for quantifying the uncertainty of classification decision. The null hypothesis is that the test sample has no association with the target class on a randomly chosen subspace, and hence the classification problem can be formulated as a problem of testing for the conjunction of hypotheses. The proposed classifier can be easily deployed for the purpose of conformal prediction and selective classification with reject and refine options by simply thresholding the consensus <em>p</em>-values. We provide a theoretical analysis of the generalization error bound for the proposed classifier, along with empirical studies on real datasets to demonstrate its effectiveness.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"707 ","pages":"Article 122037"},"PeriodicalIF":8.1,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143534146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel similarity-based taste features-extracted emotions-aware music recommendation algorithm","authors":"Yu Gao ,&nbsp;Shu-Ping Wan ,&nbsp;Jiu-Ying Dong","doi":"10.1016/j.ins.2025.122001","DOIUrl":"10.1016/j.ins.2025.122001","url":null,"abstract":"<div><div>Human music tastes are subjective and difficult to measure, with existing recommendation algorithms often failing to consider music similarity, taste features, and emotions simultaneously. This paper proposes a novel music recommendation algorithm that integrates music similarity, taste features, and emotions, organized into five modules. Motivated by the probabilistic linguistic term set (PLTS), we establish attribute feature vectors of songs by associating attribute values with corresponding probabilities. Module 1 establishes behavior matrix of users to calculate comprehensive behavioral feeling score for obtaining the original music interests of users. Module 2 designs two improved collaborative filtering algorithms to alleviate data sparsity of intuitionistic fuzzy music taste matrix. Module 3 extracts taste features from user listening behavior and the favorite songs of users. Module 4 integrates subjective and objective music emotions to obtain the comprehensive music emotions of user. Considering the dynamic change in users’ music taste features, we incorporate the latest taste features in Module 5 to reorder the song list obtained by the above four modules. The experiment results verify the effectiveness of this recommendation algorithm. It significantly outperforms three popular music recommendation systems in accuracy, excels in ranking quality, new song accuracy, and richness metrics, marginally surpasses them in listening duration.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"708 ","pages":"Article 122001"},"PeriodicalIF":8.1,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143534639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A similar environment transfer strategy for dynamic multiobjective optimization","authors":"Junzhong Ji ,&nbsp;Xiaoyu Zhang ,&nbsp;Cuicui Yang ,&nbsp;Xiang Li ,&nbsp;Guangyuan Sui","doi":"10.1016/j.ins.2025.122018","DOIUrl":"10.1016/j.ins.2025.122018","url":null,"abstract":"<div><div>Solving dynamic multiobjective optimization problems (DMOPs) is extremely challenging due to the need to address multiple conflicting objectives that change over time. Transfer prediction-based strategies typically leverage solutions from historical environments to generate an initial population for a new environment. However, these strategies often overlook the similarity between the historical and new environments, which can negatively impact the quality of the initial population. To address this issue, we propose a similar environment transfer strategy. Firstly, we select Pareto-optimal solutions from a randomly generated population in the new environment to form a prior Pareto set (PS). The prior PS is expand by oversampling sparse solutions. Then, we apply the maximum mean discrepancy (MMD) to measure the discrepancy between the prior PS and the PS from each historical environment. The historical environment with the smallest MMD is identified as the similar environment. Finally, we use solutions from this similar environment to establish a kernelized easy transfer learning model, which is employed to predict the quality of random solutions in the new environment. The initial population is formed by combining excellent solutions predicted by the model with the prior PS. Experimental results demonstrate that the proposed strategy significantly outperforms several state-of-the-art strategies.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"707 ","pages":"Article 122018"},"PeriodicalIF":8.1,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143512612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Edge-enabled personalized fitness recommendations and training guidance for athletes with privacy preservation","authors":"Yuncheng Li ,&nbsp;Cong Li ,&nbsp;Fan Wang","doi":"10.1016/j.ins.2025.122032","DOIUrl":"10.1016/j.ins.2025.122032","url":null,"abstract":"<div><div>In the contemporary era of technological advancements, the demand for personalized fitness solutions tailored to individual athletes' needs and training goals has surged, becoming a pivotal aspect of competitive science and athletic development. This growing trend underscores the need for sophisticated fitness recommendation systems capable of providing customized training regimes. However, such personalization requires the processing of sensitive health and performance data, raising significant privacy concerns among athletes wary of unauthorized data access or misuse. To address these dual challenges, this paper introduces an innovative edge-enabled personalized fitness recommendation system designed specifically for athletes, aiming to harmonize the optimization of personalized training plans with stringent privacy preservation measures. Using the localized processing capabilities of edge computing, our system minimizes latency, enhances real-time data analysis, and significantly reduces the risk of privacy breaches by keeping sensitive data on the athlete's device or in close proximity. We present a comprehensive evaluation of the system's performance through extensive experiments, demonstrating its superior ability to provide personalized fitness recommendations while ensuring robust privacy protection compared to traditional cloud-based solutions. Our findings indicate a promising avenue for adopting edge computing in competitive technology, offering a scalable, efficient, and secure approach to fostering athletic excellence through personalized training.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"707 ","pages":"Article 122032"},"PeriodicalIF":8.1,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143512610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gaussian belief propagation for dynamic obstacle avoidance and formation control in second-order multi-agent systems","authors":"Zexin Huang ,&nbsp;Zhi Liu ,&nbsp;Meijian Tan ,&nbsp;C.L. Philip Chen","doi":"10.1016/j.ins.2025.122022","DOIUrl":"10.1016/j.ins.2025.122022","url":null,"abstract":"<div><div>This paper proposes a control strategy that combines Gaussian Belief Propagation (GBP) with the Artificial Potential Field (APF) method, enabling Multi-Agent Systems (MASs) to achieve global consensus in formation control while flexibly responding to dynamic obstacles within the GBP-based framework. Existing APF-based methods are difficult to cope with fast-moving obstacles exceeding the speed threshold, while the adaptive formation control and stochastic dynamic obstacle avoidance methods proposed in this paper effectively address this challenge. By utilizing the control method proposed in this paper, the MASs are able to accurately predict the future position of such obstacles and pre-plan the obstacle avoidance path. In addition, they are also able to seamlessly return to the desired formation trajectory after effectively solving the collision avoidance challenge, which proves the generalizability of our newly proposed method in various dynamic scenarios. This research offers novel insights and approaches for adaptive control and dynamic obstacle avoidance in MASs.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"707 ","pages":"Article 122022"},"PeriodicalIF":8.1,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143512320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ensuring privacy and correlation awareness in multi-dimensional service quality prediction and recommendation for IoT","authors":"Weiyi Zhong ,&nbsp;Wei Fang ,&nbsp;Yifan Zhao ,&nbsp;Sifeng Wang ,&nbsp;Chao Yan ,&nbsp;Rong Jiang ,&nbsp;Maqbool Khan ,&nbsp;Xuan Yang ,&nbsp;Wajid Rafique","doi":"10.1016/j.ins.2025.122017","DOIUrl":"10.1016/j.ins.2025.122017","url":null,"abstract":"<div><div>Edge computing, with its advantages in terms of lightweight data transmission between users and cloud platforms, has become a promising solution for alleviating the heavy burden of timely data processing in many IoT scenarios, such as smart commerce and smart healthcare. However, several challenges arise when fusing multi-source IoT data recorded by different edge servers. First of all, data repetition within each edge server can greatly reduce the efficiency of various edge-based smart applications. Besides, IoT data fusion associated with multiple distributed edge servers can compromise user privacy. In addition, the multi-dimensional and interrelated nature of IoT data complicates precise data mining and analysis. To tackle these issues, a novel edge data fusion method (named <em>TLTM</em>) for cross-platform service recommendation is brought forth, which considers data dimensions, data correlation, and data privacy simultaneously. Finally, to validate the effectiveness and efficiency of the <em>TLTM</em> method, we have designed extensive experiments on the popular WS-DREAM dataset. The reported experimental results show that our <em>TLTM</em> method is superior to other related methods in terms of popular performance metrics including MAE, RMSE, Precision, Recall, F1-Score, and Time cost.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"706 ","pages":"Article 122017"},"PeriodicalIF":8.1,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Relaxed naïve Bayesian classifier based on maximum dependent attribute groups","authors":"Gui-Liang Ou ,&nbsp;Yu-Lin He ,&nbsp;Ying-Chao Cheng ,&nbsp;Joshua Zhexue Huang","doi":"10.1016/j.ins.2025.122013","DOIUrl":"10.1016/j.ins.2025.122013","url":null,"abstract":"<div><div>The utilisation of effective dependent attribute groups (DAGs) can benefit the construction of a high-performance naïve Bayesian classifier (NBC), to alleviate the conditional independence assumption of naïve Bayes. An NBC with optimised DAGs retains the simple NBC structure and significantly enhances NBC generalisation performance. However, it is extremely difficult to determine the appropriate DAGs for a given dataset when training an NBC with good generalisation capability. Therefore, this study proposes a relaxed NBC (RNBC) based on the maximum DAGs (MDAGs), that relaxes the attribute independence assumption by constructing an NBC with a series of MDAGs generated from the original condition attribute set. To determine the MDAGs, the RNBC includes an effective objective function to determine the degree of membership of conditional attributes belonging to different DAGs. Unlike the regular computation of class-conditional probability in traditional NBCs with whole condition attributes, the RNBC calculates multiple class-conditional probabilities corresponding to non-overlapping MDAGs and their products are utilised to construct the classification system. Exhaustive experiments were conducted to systematically verify the feasibility, rationality, and effectiveness of RNBC. The results demonstrate that (1) the objective function used to determine the MDAGs is convergent, and that MDAGs can be obtained with low time consumption; (2) the RNBC with MDAGs achieves a lower classification risk than traditional NBCs with the independence assumption; and (3) the RNBC achieves statistically higher training/testing accuracy and probability estimation quality with lower classification risk compared with eight representative Bayesian classifiers spanning 22 benchmark datasets. The best average testing accuracy, probability mean square error, and area under the curve for the RNBC were 0.76, 0.35, and 0.85, respectively. These results systematically confirmed that the proposed RNBC is an efficient NBC variant with high structural stability, strong correlation expression, and good generalisability.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"707 ","pages":"Article 122013"},"PeriodicalIF":8.1,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}