计算机科学最新文献_第2页

Stabilizing and improving federated learning with highly non-iid data and client dropout

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2024-12-24 DOI: 10.1007/s10489-024-05956-3

Jian Xu, Meilin Yang, Wenbo Ding, Shao-Lun Huang

{"title":"Stabilizing and improving federated learning with highly non-iid data and client dropout","authors":"Jian Xu, Meilin Yang, Wenbo Ding, Shao-Lun Huang","doi":"10.1007/s10489-024-05956-3","DOIUrl":"10.1007/s10489-024-05956-3","url":null,"abstract":"<div><p>The label distribution skew has been shown to be a significant obstacle that limits the model performance in federated learning (FL). This challenge could be more serious when the participating clients are in unstable network circumstances and drop out frequently. Previous works have demonstrated that the classifier head is particularly sensitive to the label skew. Therefore, maintaining a balanced classifier head is of significant importance for building a good and unbiased global model. To this end, we propose a simple yet effective framework by introducing a calibrated softmax function with smoothed prior for computing the cross-entropy loss, and a prototype-based feature augmentation scheme to re-balance the local training, which provide a new perspective on tackling the label distribution skew in FL and are lightweight for edge devices and can facilitate the global model aggregation. With extensive experiments on two benchmark classification tasks of Fashion-MNIST and CIFAR-10, our numerical results demonstrate that our proposed method can consistently outperform the baselines, 2 8% of accuracy over FedAvg in the presence of severe label skew and client dropout.</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":"142875251","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}

引用次数: 0

Efficient knowledge distillation using a shift window target-aware transformer

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2024-12-24 DOI: 10.1007/s10489-024-06207-1

Jing Feng, Wen Eng Ong

引用次数: 0

An attribute reduction algorithm using relative decision mutual information in fuzzy neighborhood decision system

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2024-12-24 DOI: 10.1007/s10489-024-06171-w

Jiucheng Xu, Shan Zhang, Miaoxian Ma, Wulin Niu, Jianghao Duan

{"title":"An attribute reduction algorithm using relative decision mutual information in fuzzy neighborhood decision system","authors":"Jiucheng Xu, Shan Zhang, Miaoxian Ma, Wulin Niu, Jianghao Duan","doi":"10.1007/s10489-024-06171-w","DOIUrl":"10.1007/s10489-024-06171-w","url":null,"abstract":"<div><p>The fuzzy neighborhood rough set integrates the strengths of fuzzy rough set and neighborhood rough set, serving as a pivotal extension of the rough set theory in attribute reduction. However, this model’s widespread application is hindered by its sensitivity to data distribution and limited efficacy in assessing classification uncertainty for datasets with substantial density variations. To mitigate these challenges, this paper introduces an attribute reduction algorithm based on fuzzy neighborhood relative decision mutual information. Firstly, the classification uncertainty of samples is initially defined in terms of relative distance. Simultaneously, the similarity relationship of fuzzy neighborhoods is reformulated, thereby reducing the risk of sample misclassification through integration with variable-precision fuzzy neighborhood rough approximation. Secondly, the notion of representative sample is introduced, leading to a redefinition of fuzzy membership. Thirdly, fuzzy neighborhood relative mutual information from the information view is constructed and combined with fuzzy neighborhood relative dependency from the algebraic view to propose fuzzy neighborhood relative decision mutual information. Finally, an attribute reduction algorithm is devised based on fuzzy neighborhood relative decision mutual information. This algorithm evaluates the significance of attributes by integrating both informational and algebraic perspectives. Comparative tests on 12 public datasets are conducted to assess existing attribute approximation algorithms. The experimental results show that the proposed algorithm achieved an average classification accuracy of 91.28<span>(%)</span> with the KNN classifier and 89.86<span>(%)</span> with the CART classifier. In both classifiers, the algorithm produced an average reduced subset size of 8.54. While significantly reducing feature redundancy, the algorithm consistently maintains a high level of classification accuracy.</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":"142875248","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}

引用次数: 0

Short-term traffic flow prediction based on spatial–temporal attention time gated convolutional network with particle swarm optimization

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2024-12-24 DOI: 10.1007/s10489-024-06117-2

Zhongxing Li, Zenan Li, Chaofeng Pan, Jian Wang

{"title":"Short-term traffic flow prediction based on spatial–temporal attention time gated convolutional network with particle swarm optimization","authors":"Zhongxing Li, Zenan Li, Chaofeng Pan, Jian Wang","doi":"10.1007/s10489-024-06117-2","DOIUrl":"10.1007/s10489-024-06117-2","url":null,"abstract":"<div><p>Recently, the surge in vehicle ownership has led to a corresponding increase in the complexity of traffic data. Consequently, accurate traffic flow prediction has become crucial for effective traffic management. While the advancements in intelligent transportation system (ITS) and internet of things (IoT) technology have facilitated traffic flow prediction, many existing methods overlook the influence of the training process on model accuracy. Traditional approaches often fail to account for this critical aspect. Hence, a new approach to traffic flow prediction is introduced in this paper: a spatial–temporal attention time-gated convolutional network based on particle swarm optimization (PSO-STATG). This method uses the particle swarm algorithm to dynamically optimize the learning rate and epoch parameters throughout the training process. Firstly, spatial–temporal correlations are extracted through spatial map convolution and time-gated convolution, facilitated by an attention mechanism. Subsequently, the learning rate and epoch parameters are dynamically adjusted during the training phase via the particle swarm optimization algorithm. Finally, experiments are conducted with real-world datasets, and the results are compared with those from several existing methods. The experimental results indicate that the accuracy and stability of our proposed model in predicting traffic flow are superior.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 2","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142875291","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}

引用次数: 0

Agenda Formation and Prediction of Voting Tendencies for European Parliament Election using Textual, Social and Network Features

IF 5.9 3区管理学

Information Systems Frontiers Pub Date : 2024-12-23 DOI: 10.1007/s10796-024-10568-w

Gautam Kishore Shahi, Ali Sercan Basyurt, Stefan Stieglitz, Christoph Neuberger

{"title":"Agenda Formation and Prediction of Voting Tendencies for European Parliament Election using Textual, Social and Network Features","authors":"Gautam Kishore Shahi, Ali Sercan Basyurt, Stefan Stieglitz, Christoph Neuberger","doi":"10.1007/s10796-024-10568-w","DOIUrl":"https://doi.org/10.1007/s10796-024-10568-w","url":null,"abstract":"<p>As per agenda-setting theory, political agenda is concerned with the government’s agenda, including politicians and political parties. Political actors utilize various channels to set their political agenda, including social media platforms such as Twitter (now <i>X</i>). Political agenda-setting can be influenced by anonymous user-generated content following the Bright Internet. This is why speech acts, experts, users with affiliations and parties through annotated Tweets were analyzed in this study. In doing so, the agenda formation during the 2019 European Parliament Election in Germany based on the agenda-setting theory as our theoretical framework, was analyzed. A prediction model was trained to predict users’ voting tendencies based on three feature categories: social, network, and text. By combining features from all categories logistical regression leads to the best predictions matching the election results. The contribution to theory is an approach to identify agenda formation based on our novel variables. For practice, a novel approach is presented to forecast the winner of events.</p>","PeriodicalId":13610,"journal":{"name":"Information Systems Frontiers","volume":"92 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142874196","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}

引用次数: 0

Proceedings of the IEEE: Stay Informed. Become Inspired.

IF 23.2 1区计算机科学

Proceedings of the IEEE Pub Date : 2024-12-23 DOI: 10.1109/JPROC.2024.3506203

引用次数: 0

Robust Real-Time Localization System via Semantic Dimensional Chains for Degraded Scenarios

IF 10.6 1区计算机科学

IEEE Internet of Things Journal Pub Date : 2024-12-23 DOI: 10.1109/jiot.2024.3520998

Yunfei Li, Lin Jiang, Bin Lei, Bo Tang, Jianyang Zhu

引用次数: 0

Dual UAV Cluster-Assisted Maritime Physical Layer Secure Communications via Collaborative Beamforming

IF 10.6 1区计算机科学

IEEE Internet of Things Journal Pub Date : 2024-12-23 DOI: 10.1109/jiot.2024.3521977

Jiawei Huang, Aimin Wang, Geng Sun, Jiahui Li, Jiacheng Wang, Hongyang Du, Dusit Niyato

引用次数: 0

Rebalanced Vision-Language Retrieval Considering Structure-Aware Distillation

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2024-12-23 DOI: 10.1109/tip.2024.3518759

Yang Yang, Wenjuan Xi, Luping Zhou, Jinhui Tang

引用次数: 0

Enhancing Federated Learning Performance on Heterogeneous IoT Devices Using Generative Artificial Intelligence With Resource Scheduling

IF 10.6 1区计算机科学

IEEE Internet of Things Journal Pub Date : 2024-12-23 DOI: 10.1109/jiot.2024.3521017

Zezhao Meng, Zhi Li, Xiangwang Hou, Minrui Xu, Yi Xia, Zekai Zhang, Shaoyang Song

引用次数: 0