计算机科学最新文献

{"title":"Outer synchronization and outer H_∞ synchronization for coupled fractional-order reaction-diffusion neural networks with multiweights.","authors":"Jin-Liang Wang, Si-Yang Wang, Yan-Ran Zhu, Tingwen Huang","doi":"10.1016/j.neunet.2024.106893","DOIUrl":"10.1016/j.neunet.2024.106893","url":null,"abstract":"<p><p>This paper introduces multiple state or spatial-diffusion coupled fractional-order reaction-diffusion neural networks, and discusses the outer synchronization and outer H_∞ synchronization problems for these coupled fractional-order reaction-diffusion neural networks (CFRNNs). The Lyapunov functional method, Laplace transform and inequality techniques are utilized to obtain some outer synchronization conditions for CFRNNs. Moreover, some criteria are also provided to make sure the outer H_∞ synchronization of CFRNNs. Finally, the derived outer and outer H_∞ synchronization conditions are validated on the basis of two numerical examples.</p>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"181 ","pages":"106893"},"PeriodicalIF":6.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142639979","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":"Enabling deformation slack in tracking with temporally even correlation filters.","authors":"Yuanming Zhang, Huihui Pan, Jue Wang","doi":"10.1016/j.neunet.2024.106839","DOIUrl":"10.1016/j.neunet.2024.106839","url":null,"abstract":"<p><p>Discriminative correlation filters with temporal regularization have recently attracted much attention in mobile video tracking, due to the challenges of target occlusion and background interference. However, rigidly penalizing the variability of templates between adjacent frames makes trackers lazy for target evolution, leading to inaccurate responses or even tracking failure when deformation occurs. In this paper, we address the problem of instant template learning when the target undergoes drastic variations in appearance and aspect ratio. We first propose a temporally even model featuring deformation slack, which theoretically supports the ability of the template to respond quickly to variations while suppressing disturbances. Then, an optimal derivation of our model is formulated, and the closed form solutions are deduced to facilitate the algorithm implementation. Further, we introduce a cyclic shift methodology for mirror factors to achieve scale estimation of varying aspect ratios, thereby dramatically improving the cross-area accuracy. Comprehensive comparisons on seven datasets demonstrate our excellent performance: DroneTB-70, VisDrone-SOT2019, VOT-2019, LaSOT, TC-128, UAV-20L, and UAVDT. Our approach runs at 16.9 frames per second on a low-cost Central Processing Unit, which makes it suitable for tracking on drones. The code and raw results will be made publicly available at: https://github.com/visualperceptlab/TEDS.</p>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"181 ","pages":"106839"},"PeriodicalIF":6.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142607187","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":"Corrigendum to \"Hydra: Multi-head Low-rank Adaptation for Parameter Efficient Fine-tuning\" [Neural Networks Volume 178, October (2024), 1-11/106414]].","authors":"Sanghyeon Kim, Hyunmo Yang, Younghyun Kim, Youngjoon Hong, Eunbyung Park","doi":"10.1016/j.neunet.2024.106878","DOIUrl":"10.1016/j.neunet.2024.106878","url":null,"abstract":"","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"181 ","pages":"106878"},"PeriodicalIF":6.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142644985","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":"Reinforcement learning for online testing of autonomous driving systems: a replication and extension study.","authors":"Luca Giamattei, Matteo Biagiola, Roberto Pietrantuono, Stefano Russo, Paolo Tonella","doi":"10.1007/s10664-024-10562-5","DOIUrl":"https://doi.org/10.1007/s10664-024-10562-5","url":null,"abstract":"<p><p>In a recent study, Reinforcement Learning (RL) used in combination with many-objective search, has been shown to outperform alternative techniques (random search and many-objective search) for online testing of Deep Neural Network-enabled systems. The empirical evaluation of these techniques was conducted on a state-of-the-art Autonomous Driving System (ADS). This work is a replication and extension of that empirical study. Our replication shows that RL does not outperform pure random test generation in a comparison conducted under the same settings of the original study, but with no confounding factor coming from the way collisions are measured. Our extension aims at eliminating some of the possible reasons for the poor performance of RL observed in our replication: (1) the presence of reward components providing contrasting feedback to the RL agent; (2) the usage of an RL algorithm (Q-learning) which requires discretization of an intrinsically continuous state space. Results show that our new RL agent is able to converge to an effective policy that outperforms random search. Results also highlight other possible improvements, which open to further investigations on how to best leverage RL for online ADS testing.</p>","PeriodicalId":11525,"journal":{"name":"Empirical Software Engineering","volume":"30 1","pages":"19"},"PeriodicalIF":3.5,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11538197/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142602130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The effect of data complexity on classifier performance.","authors":"Jonas Eberlein, Daniel Rodriguez, Rachel Harrison","doi":"10.1007/s10664-024-10554-5","DOIUrl":"10.1007/s10664-024-10554-5","url":null,"abstract":"<p><p>The research area of Software Defect Prediction (SDP) is both extensive and popular, and is often treated as a classification problem. Improvements in classification, pre-processing and tuning techniques, (together with many factors which can influence model performance) have encouraged this trend. However, no matter the effort in these areas, it seems that there is a ceiling in the performance of the classification models used in SDP. In this paper, the issue of classifier performance is analysed from the perspective of data complexity. Specifically, data complexity metrics are calculated using the Unified Bug Dataset, a collection of well-known SDP datasets, and then checked for correlation with the defect prediction performance of machine learning classifiers (in particular, the classifiers C5.0, Naive Bayes, Artificial Neural Networks, Random Forests, and Support Vector Machines). In this work, different domains of competence and incompetence are identified for the classifiers. Similarities and differences between the classifiers and the performance metrics are found and the Unified Bug Dataset is analysed from the perspective of data complexity. We found that certain classifiers work best in certain situations and that all data complexity metrics can be problematic, although certain classifiers did excel in some situations.</p>","PeriodicalId":11525,"journal":{"name":"Empirical Software Engineering","volume":"30 1","pages":"16"},"PeriodicalIF":3.5,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11527945/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142570943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Higher-order topology for collective motions","authors":"Zijie Sun, Tianjiang Hu","doi":"10.1007/s40747-024-01665-z","DOIUrl":"https://doi.org/10.1007/s40747-024-01665-z","url":null,"abstract":"<p>Collective motions are prevalent in various natural groups, such as ant colonies, bird flocks, fish schools and mammal herds. Physical or mathematical models have been developed to formalize and/or regularize these collective behaviors. However, these models usually follow pairwise topology and seldom maintain better responsiveness and persistence simultaneously, particularly in the face of sudden predator-like invasion. In this paper, we propose a specified higher-order topology, rather than the pairwise individual-to-individual pattern, to enable optimal responsiveness-persistence trade-off in collective motion. Then, interactions in hypergraph are designed between both individuals and sub-groups. It not only enhances connectivity of the interaction network but also mitigates its localized feature. Simulation results validate the effectiveness of the proposed approach in achieving a subtle balance between responsiveness and persistence even under external disturbances.</p>","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"24 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142879934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Online healthcare platform doctors’ fatigue and continuance use intention based on JD-R model","authors":"Lingling Yu, Yuewei Zhong, Nan Chen","doi":"10.1108/intr-09-2023-0822","DOIUrl":"https://doi.org/10.1108/intr-09-2023-0822","url":null,"abstract":"<h3>Purpose</h3>\u0000<p>The online healthcare platform (OHP) has become an essential element of the healthcare system, representing a technological shift in the job responsibilities of medical professionals. Drawing on a technology-based job demands–resources (JD-R) model, this study aims to examine how the technological characteristics of OHP affect doctors’ OHP use psychology and behavior.</p><!--/ Abstract__block -->\u0000<h3>Design/methodology/approach</h3>\u0000<p>This empirical study was based on a survey conducted among 423 doctors with OHP use experience. The proposed model underwent assessment through partial least squares structural equation modeling (PLS-SEM) to reveal the effects of technology-based job demands (i.e. technology-based work overload and technology-based work monitoring) and resources (i.e. perceived usefulness, facilitating conditions and IT mindfulness) on doctors’ OHP fatigue and continuance use intention.</p><!--/ Abstract__block -->\u0000<h3>Findings</h3>\u0000<p>Results suggest that technology-based work monitoring, perceived usefulness and facilitation conditions have significant impacts on doctors’ psychological and behavioral responses to using OHP, whereas technology-based work overload and IT mindfulness have a single impact on continuance use intention and fatigue of OHP.</p><!--/ Abstract__block -->\u0000<h3>Research limitations/implications</h3>\u0000<p>It assists doctors, healthcare administrators, policymakers and technology developers in understanding OHPs’ technological characteristics, enabling them to harness its benefits and mitigate potential challenges. Additionally, given the self-reported cross-sectional data from China, future studies can improve generalizability and adopt experimental methods or longitudinal designs with objective data.</p><!--/ Abstract__block -->\u0000<h3>Originality/value</h3>\u0000<p>It extends the research on OHP by employing a technology-based JD-R model to explore work attributes and dual effects associated with OHP’s technological characteristics. It also enriches existing research by examining the role of OHP’s technological characteristics in doctors’ psychological and behavioral responses.</p><!--/ Abstract__block -->","PeriodicalId":54925,"journal":{"name":"Internet Research","volume":"275 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142869963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Facebook vs Twitter: the differential relationship with mass attitudes about democracy in Latin Americas","authors":"Richard Kornrumpf, Jason Gainous, Kevin M. Wagner, Tricia J. Gray","doi":"10.1108/intr-09-2023-0850","DOIUrl":"https://doi.org/10.1108/intr-09-2023-0850","url":null,"abstract":"<h3>Purpose</h3>\u0000<p>We argue that the information flow on Twitter is largely driven by elite communication with a top-down flow, while Facebook’s bottom-up flow is driven by mass public communication. Both are crucial news sources for democratic processes in Latin America. We explore how exposure to these flows affects opinions on democracy across 18 countries with varying democratic conditions.</p><!--/ Abstract__block -->\u0000<h3>Design/methodology/approach</h3>\u0000<p>Using mixed-effects models, our analysis draws on survey data from the 2018 Latinobarómetro paired with democracy measures from the 2018 Varieties of Democracy.</p><!--/ Abstract__block -->\u0000<h3>Findings</h3>\u0000<p>The results indicate that Facebook’s bottom-up communication correlates with negative perceptions of democracy, whereas Twitter’s top-down model correlates with more favorable views, especially among mass consumers. However, these differences are inconsistent across demographic factors.</p><!--/ Abstract__block -->\u0000<h3>Research limitations/implications</h3>\u0000<p>Cross-sectional survey data limits causal claims. Longitudinal data could provide stronger insights into the mechanisms underlying the observed relationships.</p><!--/ Abstract__block -->\u0000<h3>Practical implications</h3>\u0000<p>Understanding how different platforms influence democratic attitudes can inform strategies for political communication and digital governance in Latin America. Policymakers should consider platform-specific interventions to promote democratic engagement.</p><!--/ Abstract__block -->\u0000<h3>Originality/value</h3>\u0000<p>This study adds to the limited research on platform differences in political public opinion, particularly in Latin America, and highlights the need to explore mechanisms of change across various social media platforms.</p><!--/ Abstract__block -->","PeriodicalId":54925,"journal":{"name":"Internet Research","volume":"1 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142869960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning state-action correspondence across reinforcement learning control tasks via partially paired trajectories","authors":"Javier García,&nbsp;Iñaki Rañó,&nbsp;J. Miguel Burés,&nbsp;Xosé R. Fdez-Vidal,&nbsp;Roberto Iglesias","doi":"10.1007/s10489-024-06190-7","DOIUrl":"10.1007/s10489-024-06190-7","url":null,"abstract":"<div><p>In many reinforcement learning (RL) tasks, the state-action space may be subject to changes over time (e.g., increased number of observable features, changes of representation of actions). Given these changes, the previously learnt policy will likely fail due to the mismatch of input and output features, and another policy must be trained from scratch, which is inefficient in terms of <i>sample complexity</i>. Recent works in transfer learning have succeeded in making RL algorithms more efficient by incorporating knowledge from previous tasks, thus partially alleviating this problem. However, such methods typically must provide an explicit state-action correspondence of one task into the other. An autonomous agent may not have access to such high-level information, but should be able to analyze its experience to identify similarities between tasks. In this paper, we propose a novel method for automatically learning a correspondence of states and actions from one task to another through an agent’s experience. In contrast to previous approaches, our method is based on two key insights: i) only the first state of the trajectories of the two tasks is <i>paired</i>, while the rest are <i>unpaired</i> and randomly collected, and ii) the transition model of the source task is used to predict the dynamics of the target task, thus aligning the <i>unpaired</i> states and actions. Additionally, this paper intentionally decouples the learning of the state-action corresponce from the transfer technique used, making it easy to combine with any transfer method. Our experiments demonstrate that our approach significantly accelerates transfer learning across a diverse set of problems, varying in state/action representation, physics parameters, and morphology, when compared to state-of-the-art algorithms that rely on cycle-consistency.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 3","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142875250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 domain-aware model with multi-perspective contrastive learning for natural language understanding","authors":"Di Wang,&nbsp;Qingjian Ni","doi":"10.1007/s10489-024-06154-x","DOIUrl":"10.1007/s10489-024-06154-x","url":null,"abstract":"<div><p>Intent detection and slot filling are core tasks in natural language understanding (NLU) for task-oriented dialogue systems. However, current models face challenges with numerous intent categories, slot types, and domain classifications, alongside a shortage of well-annotated datasets, particularly in Chinese. Therefore, we propose a domain-aware model with multi-perspective, multi-positive contrastive learning. First, we adopt a self-supervised contrastive learning with multiple perspectives and multiple positive instances, which is capable of spacing the vectors of positive and negative instances from the domain, intent, and slot perspectives, and fusing more positive instance information to increase the classification effectiveness of the model. Our proposed domain-aware model defines domain-level units at the decoding layer, allowing the model to predict intent and slot information based on domain features, which greatly reduces the search space for intent and slot. In addition, we design a dual-stage attention mechanism for capturing implicitly shared information between intents and slots. We propose a data augmentation method that adds noise to the embedding layer, applies fine-grained augmentation techniques, and filters biased samples based on a similarity threshold. Our model is applied to real task-oriented dialogue systems and compared with other NLU models. Experimental results demonstrate that our proposed model outperforms other models in terms of NLU performance.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 3","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142875296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}