IF 3 4区计算机科学

International Journal of Imaging Systems and Technology Pub Date : 2025-07-05 DOI: 10.1002/ima.70154

Ningfei Jiang, Yuhu Shi

{"title":"A New Adaptive Sliding Window Method for fMRI Dynamic Functional Connectivity Analysis","authors":"Ningfei Jiang, Yuhu Shi","doi":"10.1002/ima.70154","DOIUrl":"https://doi.org/10.1002/ima.70154","url":null,"abstract":"<div>\u0000 \u0000 <p>The fixed-window sliding time window method is widely used in exploring dynamics functional connectivity of functional magnetic resonance imaging data analysis, but it is difficult to select a suitable window to capture the dynamic changes in brain function. Therefore, a local polynomial regression (LPR) method is proposed to fit the region of interest (ROI) time series in this paper, in which observations are locally modeled by a least-squares polynomial with a kernel of a certain bandwidth that allows for better bias-variance tradeoff. It combines a data-driven variable bandwidth selection mechanism with intersection of confidence intervals (ICI) and a bandwidth optimization algorithm of particle swarm optimization (PSO). Among them, ICI is used to adaptively determine the locally optimal bandwidth that minimizes the mean square error (MSE), and then the bandwidth values at various time points within all ROIs are computed for each subject. Subsequently, the averaged bandwidth values at these time points is regarded as the bandwidth value for that subject at each time point, followed by generating a time-varying bandwidth sequence for each subject, which is used in the PSO-based bandwidth optimization algorithm. Finally, the results of experiments conducted on simulated data showed that the LPR–ICI–PSO method exhibited lower MSE values on time-varying correlation coefficient estimation for different noise scenarios. Furthermore, we applied the proposed method to the autism spectrum disorder (ASD) study, and obtained a classification accuracy of 74.1% from typical controls (TC) through support vector machine (SVM) with the 10-fold cross-validation strategy. These results demonstrated that our proposed method can effectively capture the dynamic changes in brain function, which is valid in clinical diagnosis and helps to reveal the differences in brain functional connectivity patterns.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144558207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

The incentive effects of experts: Evidence from an online mental health platform 专家的激励效应：来自在线心理健康平台的证据

IF 7.4 1区管理学

Information Processing & Management Pub Date : 2025-07-04 DOI: 10.1016/j.ipm.2025.104289

Lini Kuang , Tingting Hou

{"title":"The incentive effects of experts: Evidence from an online mental health platform","authors":"Lini Kuang , Tingting Hou","doi":"10.1016/j.ipm.2025.104289","DOIUrl":"10.1016/j.ipm.2025.104289","url":null,"abstract":"<div><div>A prevalent form of online healthcare comprises hybrid support services, allowing help seekers to pose health-related questions, assess answers from various healthcare experts and ordinary supporters, and vote on the usefulness of answers they find most satisfactory. However, the impact of healthcare experts' engagement on the subsequent supporters' performance within this hybrid service remains unclear. While concerns persist that experts' involvement may diminish subsequent supporters' performance due to a reduced likelihood of recognition, there is a plausible scenario in which it fosters learning and competition, thereby improving performance of subsequent supporters. Leveraging data from a Chinese online mental health platform, we utilize the mediation effects model to investigate the impact of healthcare experts' engagement on the performance of subsequent supporters. Within our model, we focus on the mediating effects of the effort and quality of answers from these subsequent supporters. Our research findings indicate that the engagement of counselors can directly enhance the social recognition obtained by subsequent supporters and can also indirectly boost this recognition by increasing the effort put into their answers. However, the quality of the subsequent supporters' answers does not play a significant mediating role in this relationship. These results fill a gap in the literature on online health services and expert effects, offering valuable insights for online health platforms aiming to enhance the performance of supporters by involving healthcare experts.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 6","pages":"Article 104289"},"PeriodicalIF":7.4,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144548909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

XKanFuse: A novel cross-modal fusion method based on Kolmogorov-Arnold Network for multi-modal medical image fusion XKanFuse：一种基于Kolmogorov-Arnold网络的多模态医学图像融合新方法

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-07-04 DOI: 10.1016/j.knosys.2025.114053

Xinjian Wei , Yafei Xiong , Haotian Lu , Xiaoxuan Xu , Jing Xu

{"title":"XKanFuse: A novel cross-modal fusion method based on Kolmogorov-Arnold Network for multi-modal medical image fusion","authors":"Xinjian Wei , Yafei Xiong , Haotian Lu , Xiaoxuan Xu , Jing Xu","doi":"10.1016/j.knosys.2025.114053","DOIUrl":"10.1016/j.knosys.2025.114053","url":null,"abstract":"<div><div>Multi-modal medical image fusion enables the integration of complementary information from different medical imaging modalities, providing comprehensive insights for clinic applications and diagnosis. Nevertheless, existing methods exhibit considerable potential for improvement in terms of fusion accuracy for insufficient feature representation and cross-modal interaction. To tackle these challenges, a novel cross-modal fusion algorithm based on Kolmogorov–Arnold Network (Kan) for multi-modal medical image fusion is proposed to improve fusion performance, namely, XKanFuse. Specifically, the XKanFuse introduces an innovative Adaptive Kan Convolution (ACKan), which enables precise local-global nonlinear characterization via the adaptive perception mechanism coupled with learnable nonlinear functions, thereby significantly enhancing feature representation. Additionally, the newly designed Cross-Kansformer (XKansformer) in XKanFuse enables effective cross-modal exchange and capture of multi-scale attention flows. By incorporating learnable nonlinear transformations, it reinforces multi-level complementarity and integration, thereby facilitating precise cross-modal interaction and fusion. Extensive quantitative and qualitative experiments on various medical datasets demonstrate the proposed XKanFuse outperforms state-of-the-art methods regarding accuracy on salient regions and edge sharpness, offering superior fusion and significant clinic application potential.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"326 ","pages":"Article 114053"},"PeriodicalIF":7.2,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144556990","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}

引用次数: 0

Classification Hardness Based Adaptive Sampling Ensemble for Imbalanced Data Classification 基于分类硬度的不平衡数据分类自适应抽样集成

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2025-07-04 DOI: 10.26599/TST.2024.9010149

Zenghao Cui;Ziyi Gao;Shuaibing Yue;Rui Wang;Haiyan Zhu

{"title":"Classification Hardness Based Adaptive Sampling Ensemble for Imbalanced Data Classification","authors":"Zenghao Cui;Ziyi Gao;Shuaibing Yue;Rui Wang;Haiyan Zhu","doi":"10.26599/TST.2024.9010149","DOIUrl":"https://doi.org/10.26599/TST.2024.9010149","url":null,"abstract":"Class imbalance can substantially affect classification tasks using traditional classifiers, especially when identifying instances of minority categories. In addition to class imbalance, other challenges can also hinder accurate classification. Researchers have explored various approaches to mitigate the effects of class imbalance. However, most studies focus only on processing correlations within a single category of samples. This paper introduces an ensemble framework called Inter- and Intra-Class Overlapping Ensemble (IICOE), which incorporates two sampling methods. The first method, which is based on classification hardness undersampling, targets majority category samples by using simple samples as the foundation for classification and improving performance by focusing on samples near classification boundaries. The second method addresses the issue of overfitting minority category samples in undersampling and ensemble learning. To mitigate this, an adaptive augment hybrid sampling method is proposed, which enhances the classification boundary of samples and reduces overfitting. This paper conducts multiple experiments on 15 public datasets and concludes that the IICOE ensemble framework outperforms other ensemble learning algorithms in classifying imbalanced data.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 6","pages":"2419-2433"},"PeriodicalIF":6.6,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11072117","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144557916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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}

引用次数: 0

FedCE: A Contrast Enhancement Federated Learning Method for Heterogeneous Medical Named Entity Recognition FedCE：一种用于异构医学命名实体识别的对比增强联邦学习方法

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2025-07-04 DOI: 10.26599/TST.2024.9010186

Kai Chang;Hailong Sun;Jindou Wan;Naiqian Zhang;Yiming Liu;Kuo Yang;Zixin Shu;Jianan Xia;Xuezhong Zhou

{"title":"FedCE: A Contrast Enhancement Federated Learning Method for Heterogeneous Medical Named Entity Recognition","authors":"Kai Chang;Hailong Sun;Jindou Wan;Naiqian Zhang;Yiming Liu;Kuo Yang;Zixin Shu;Jianan Xia;Xuezhong Zhou","doi":"10.26599/TST.2024.9010186","DOIUrl":"https://doi.org/10.26599/TST.2024.9010186","url":null,"abstract":"Medical Named Entity Recognition (NER) plays a crucial role in attaining precise patient portraits as well as providing support for intelligent diagnosis and treatment decisions. Federated Learning (FL) enables collaborative modeling and training across multiple endpoints without exposing the original data. However, the statistical heterogeneity exhibited by clinical medical text records poses a challenge for FL methods to support the training of NER models in such scenarios. We propose a Federated Contrast Enhancement (FedCE) method for NER to address the challenges faced by non-large-scale pre-trained models in FL for label-heterogeneous. The method leverages a multi-view encoder structure to capture both global and local semantic information, and employs contrastive learning to enhance the interoperability of global knowledge and local context. We evaluate the performance of the FedCE method on three real-world clinical record datasets. We investigate the impact of factors, such as pooling methods, maximum input text length, and training rounds on FedCE. Additionally, we assess how well FedCE adapts to the base NER models and evaluate its generalization performance. The experimental results show that the FedCE method has obvious advantages and can be effectively applied to various basic models, which is of great theoretical and practical significance for advancing FL in healthcare settings.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 6","pages":"2384-2398"},"PeriodicalIF":6.6,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11072110","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144557926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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}

引用次数: 0

Generalizing and Classifying From Few Samples: A Comprehension of Approaches to Few-Shot Visual Learning 少数样本泛化与分类：对少数镜头视觉学习方法的理解

IF 1.8 4区计算机科学

Computational Intelligence Pub Date : 2025-07-04 DOI: 10.1111/coin.70098

Nadeem Yousuf Khanday, Shabir Ahmad Sofi

{"title":"Generalizing and Classifying From Few Samples: A Comprehension of Approaches to Few-Shot Visual Learning","authors":"Nadeem Yousuf Khanday, Shabir Ahmad Sofi","doi":"10.1111/coin.70098","DOIUrl":"https://doi.org/10.1111/coin.70098","url":null,"abstract":"<div>\u0000 \u0000 <p>Unlike traditional machine learning techniques, few-shot learning (FSL) represents a paradigm aimed at acquiring new tasks from just a handful of labeled examples. The challenge in FSL lies in its requirement for models to generalize effectively from a small dataset to previously unseen examples. Various approaches have been developed for FSL, encompassing techniques such as metric learning, meta-learning, and hybrid methods, among others. These approaches have found success in numerous computer vision tasks, including image and video classification, object detection, object segmentation, robotics, natural language processing, and various real-world applications such as medical diagnosis and self-driving cars. This comprehensive survey offers an in-depth exploration of recent advancements and the current state-of-the-art in FSL. The study presents a thorough examination of different FSL approaches, categorizing them primarily into meta-learning and non-meta-learning methods. It also delves into benchmark datasets for FSL, highlights existing research challenges, and explores the diverse applications of FSL. Furthermore, the survey identifies and discusses open research challenges within the field of FSL.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 4","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144558122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Deep Learning–Based Automatic Modulation Classification Using Hybrid CNN–XGBoost Model for Wireless Communication Systems 基于CNN-XGBoost混合模型的无线通信系统深度学习自动调制分类

IF 1.7 4区计算机科学

International Journal of Communication Systems Pub Date : 2025-07-04 DOI: 10.1002/dac.70160

Salem Titouni, Idris Messaoudene, Boualem Hammache, Massinissa Belazzoug, Farouk Chetouah, Yassine Himeur

{"title":"Deep Learning–Based Automatic Modulation Classification Using Hybrid CNN–XGBoost Model for Wireless Communication Systems","authors":"Salem Titouni, Idris Messaoudene, Boualem Hammache, Massinissa Belazzoug, Farouk Chetouah, Yassine Himeur","doi":"10.1002/dac.70160","DOIUrl":"https://doi.org/10.1002/dac.70160","url":null,"abstract":"<div>\u0000 \u0000 <p>Accurate modulation classification is a key challenge in wireless communication systems, directly influencing signal decoding and system reliability. This paper presents a hybrid model that combines both convolutional neural networks (CNNs) and extreme gradient boosting (XGBoost) to enhance modulation classification performance. Firstly, CNN extracts high-level features from input data, leveraging a fully connected architecture with dropout regularization to prevent overfitting. These features are then used by the XGBoost classifier for robust decision-making. The proposed framework was evaluated on a modulation characteristic dataset, achieving a test accuracy of 98.3%. Performance metrics, including precision, recall, and F1 score, were calculated for each modulation class, with the average F1 score exceeding 0.9834. Furthermore, the hybrid model demonstrated resilience in noisy conditions, as shown by receiver operating characteristic (ROC) curves with the area under a curve (AUC) values greater than 0.98 for most classes. These results highlight the efficiency of the CNN–XGBoost hybrid approach in addressing complex signal classification tasks and its potential for deployment in real-world communication systems.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"38 12","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144551012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Integrated path planning and control for autonomous vehicle platooning 自动驾驶车辆队列的综合路径规划与控制

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-07-04 DOI: 10.1016/j.conengprac.2025.106470

Kiyun Gil, Jinsoo Yuk, Jongho Shin

{"title":"Integrated path planning and control for autonomous vehicle platooning","authors":"Kiyun Gil, Jinsoo Yuk, Jongho Shin","doi":"10.1016/j.conengprac.2025.106470","DOIUrl":"10.1016/j.conengprac.2025.106470","url":null,"abstract":"<div><div>Platooning provides various benefits, such as improving traffic system efficiency and reducing fuel consumption. Existing research on platooning has primarily concentrated on longitudinal control in highways or dedicated road environments. However, these approaches can significantly reduce platooning performance during scenarios requiring lateral maneuvers, such as cornering or obstacle avoidance. To address these limitations, this study proposes a comprehensive platooning system that considers both longitudinal and lateral dynamics. The proposed platooning approach comprises model predictive control (MPC)-based path planning incorporating the constant time gap (CTG) strategy, and integral control-based path tracking. The MPC-based path planning is formulated as an optimal control problem aimed at minimizing the total cost, which includes the candidate path cost based on CTG policy-generated velocity commands and environmental costs. The optimal control input is obtained using particle swarm optimization (PSO), resulting in the generation of an optimal path. For path tracking, an integral error for yaw rate is defined, and an integral control-based method is employed, considering the differential equation of the integral error and the vehicle’s dynamics model. Numerical simulations and indoor experiments are conducted to validate the feasibility of the proposed approach, with an analysis of the results. A validation video is available at: <span><span>https://youtu.be/-F4xney2Yjc</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"164 ","pages":"Article 106470"},"PeriodicalIF":5.4,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144556787","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

Total Contents 全部内容

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2025-07-04

引用次数: 0

Output Type Guided Random Test Case Generation for String Validation Routines 字符串验证例程的输出类型引导随机测试用例生成

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2025-07-04 DOI: 10.26599/TST.2024.9010023

Chenhui Cui;Rubing Huang;Jinfu Chen;Yunan Zhou

{"title":"Output Type Guided Random Test Case Generation for String Validation Routines","authors":"Chenhui Cui;Rubing Huang;Jinfu Chen;Yunan Zhou","doi":"10.26599/TST.2024.9010023","DOIUrl":"https://doi.org/10.26599/TST.2024.9010023","url":null,"abstract":"String validation routines have been widely used in many real-world applications, such as email validation and postcode validation. String test cases are adopted to test these validation routines, to identify potential defects and security risks. Random Testing (RT) is a well-known testing approach to randomly generate string test cases from the input domain (i.e., the set of all possible test inputs), which is simple to implement at a low cost. However, its testing effectiveness may be unsatisfactory for string validation routines. The main reason for this is that RT may have a high probability to generate invalid rather than valid string test cases, due to its randomness property. This research proposes a new RT approach based on the output types (i.e., valid and invalid strings) for string validation routines, namely Output-type-guided Random Testing (RT-O), which attempts to randomly generate both valid and invalid string test cases with a certain probability. This research performed an empirical study involving several real-world string validation routines collected from ten Java open-source projects, to investigate and compare testing performances of RT-O against the previous two widely-used RT methods. The results show that the generated string test cases by RT-O outperform test cases generated by other RT methods.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 6","pages":"2467-2486"},"PeriodicalIF":6.6,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11072066","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144557876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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}

引用次数: 0

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