计算机科学最新文献

{"title":"KINEMATIC CONVEX COMBINATIONS OF MULTIPLE POSES OF A BOUNDED PLANAR OBJECT BASED ON AN AVERAGE-DISTANCE MINIMIZING MOTION SWEEP.","authors":"Huan Liu, Qiaode Jeffrey Ge, Mark P Langer","doi":"10.1115/1.4069154","DOIUrl":"10.1115/1.4069154","url":null,"abstract":"<p><p>Convex combination of points is a fundamental operation in computational geometry. By considering rigid-body displacements as points in the image spaces of planar quaternions, quaternions and dual quaternions, respectively, the notion of convexity in Euclidean three-space has been extended to kinematic convexity in <math><mi>S</mi> <mi>E</mi> <mo>(</mo> <mn>2</mn> <mo>)</mo> <mo>,</mo> <mi>S</mi> <mi>O</mi> <mo>(</mo> <mn>3</mn> <mo>)</mo></math> , and <math><mi>S</mi> <mi>E</mi> <mo>(</mo> <mn>3</mn> <mo>)</mo></math> in the context of computational kinematic geometry. This paper deals with computational kinematic geometry of bounded planar objects rather than that of infinitely large moving spaces. In this paper, we present a new formulation for kinematic convexity based on an average-distance minimizing motion sweep of a bounded planar object. The resulting 1-DOF motion sweep between two planar poses is represented as a convex combination in the configuration space defined by <math><mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>,</mo> <mi>z</mi> <mo>)</mo></math> where <math><mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo></math> is associated with the location of the centroid of the planar object and <math><mi>z</mi> <mo>=</mo> <mtext>sin</mtext> <mspace></mspace> <mi>θ</mi></math> with <math><mi>θ</mi></math> being the angle of rotation. For three poses, a 2-DOF motion sweep is developed that not only minimizes the combined average squared distances but also attains a convex-combination representation so that existing algorithms for convex hull of points can be readily applied to the construction and analysis of kinematic convex hulls. This results in a new type of convex hull for planar kinematics such that its boundaries are defined by the average-distance minimizing sweeps of the bounded planar object.</p>","PeriodicalId":49155,"journal":{"name":"Journal of Mechanisms and Robotics-Transactions of the Asme","volume":"17 11","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12349904/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144856880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"S-YOLO: An enhanced small object detection method based on adaptive gating strategy and dynamic multi-scale focus module.","authors":"Zengnan Wang, Feng Yan, Liejun Wang, Yabo Yin, Jiahuan Lin","doi":"10.1016/j.neunet.2025.107782","DOIUrl":"10.1016/j.neunet.2025.107782","url":null,"abstract":"<p><p>Detecting small objects in drone aerial imagery presents significant challenges, particularly when algorithms must operate in real-time under computational constraints. To address this issue, we propose S-YOLO, an efficient and streamlined small object detection framework based on YOLOv10. The S-YOLO architecture emphasizes three key innovations: (1) Enhanced Small Object Detection Layers: These layers augment semantic richness to improve detection of diminutive targets. (2) C2fGCU Module: Incorporating Gated Convolutional Units (GCU), this module adaptively modulates activation strength through deep feature analysis, enabling the model to concentrate on salient information while effectively mitigating background interference. (3) Dynamic Multi-Scale Fusion (DMSF) Module: By integrating SE-Norm with multi-scale feature extraction, this component dynamically recalibrates feature weights to optimize cross-scale information integration and focus. S-YOLO surpasses YOLOv10-n, achieving mAP50:95 improvements of 5.3%, 4.4%, and 1.4% on the VisDrone2019, AI-TOD, and DOTA1.0 datasets, respectively. Notably, S-YOLO maintains fewer parameters than YOLOv10-n while processing 285 images per second, establishing it as a highly efficient solution for real-time small object detection in aerial imagery.</p>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"191 ","pages":"107782"},"PeriodicalIF":6.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144668866","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":"Curriculum negative mining for temporal networks.","authors":"Ziyue Chen, Tongya Zheng, Mingli Song","doi":"10.1016/j.neunet.2025.107858","DOIUrl":"10.1016/j.neunet.2025.107858","url":null,"abstract":"<p><p>Temporal networks are effective in capturing the evolving interactions of networks over time, such as social networks and e-commerce networks. In recent years, researchers have primarily concentrated on developing specific model architectures for Temporal Graph Neural Networks (TGNNs) in order to improve the representation quality of temporal nodes and edges. However, limited attention has been given to the quality of negative samples during the training of TGNNs. When compared with static networks, temporal networks present two specific challenges for negative sampling: positive sparsity and positive shift. Positive sparsity refers to the presence of a single positive sample amidst numerous negative samples at each timestamp, while positive shift relates to the variations in positive samples across different timestamps. To robustly address these challenges in training TGNNs, we introduce Curriculum Negative Mining (CurNM), a model-aware curriculum learning framework that adaptively adjusts the difficulty of negative samples. Within this framework, we first establish a dynamically updated negative pool that balances random, historical, and hard negatives to address the challenges posed by positive sparsity. Secondly, we implement a temporal-aware negative selection module that focuses on learning from the disentangled factors of recently active edges, thus accurately capturing shifting preferences. Finally, the selected negatives are combined with annealing random negatives to support stable training. Extensive experiments on 12 datasets and 3 TGNNs demonstrate that our method outperforms baseline methods by a significant margin. Additionally, thorough ablation studies and parameter sensitivity experiments verify the usefulness and robustness of our approach. Our code is available at https://github.com/zziyue83/CurNM.</p>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"191 ","pages":"107858"},"PeriodicalIF":6.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144660884","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":"Pixel adaptive deep-unfolding neural network with state space model for image deraining.","authors":"Yao Xiao, Youshen Xia","doi":"10.1016/j.neunet.2025.107845","DOIUrl":"10.1016/j.neunet.2025.107845","url":null,"abstract":"<p><p>Rain streaks affects the visual quality and interfere with high-level vision tasks on rainy days. Removing raindrops from captured rainy images becomes important in computer vision applications. Recently, deep-unfolding neural networks (DUNs) are shown their effectiveness on image deraining. Yet, there are two issues that need to be further addressed : 1) Deep unfolding networks typically use convolutional neural networks (CNNs), which lack the ability to perceive global structures, thereby limiting the applicability of the network model; 2) Their gradient descent modules usually rely on a scalar step size, which limits the adaptability of the method to different input images. To address the two issues, we proposes a new image de-raining method based on a pixel adaptive deep unfolding network with state space models. The proposed network mainly consists of both the adaptive pixel-wise gradient descent (APGD) module and the stage fusion proximal mapping (SFPM) module. APGD module overcomes scalar step size inflexibility by adaptively adjusting the gradient step size for each pixel based on the previous stage features. SFPM module adopts a dual-branch architecture combining CNNs with state space models (SSMs) to enhance the perception of both local and global structures. Compared to Transformer-based models, SSM enables efficient long-range dependency modeling with linear complexity. In addition, we introduce a stage feature fusion with the Fourier transform mechanism to reduce information loss during the unfolding process, ensuring key features are effectively propagated. Extensive experiments on multiple public datasets demonstrate that our method consistently outperforms state-of-the-art deraining methods in terms of quantitative metrics and visual quality. The source code is available at https://github.com/cassiopeia-yxx/PADUM.</p>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"191 ","pages":"107845"},"PeriodicalIF":6.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144668864","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":"Real-time data-efficient portrait stylization via geometric alignment.","authors":"Xinrui Wang, Zhuoru Li, Xuanyu Yin, Xiao Zhou, Yusuke Iwasawa, Yutaka Matsuo, Jiaxian Guo","doi":"10.1016/j.neunet.2025.107774","DOIUrl":"10.1016/j.neunet.2025.107774","url":null,"abstract":"<p><p>Portrait Stylization aims to imbue portrait photos with vivid artistic effects drawn from style examples. Despite the availability of enormous training datasets and large network weights, existing methods struggle to maintain geometric consistency and achieve satisfactory stylization effects due to the disparity in facial feature distributions between facial photographs and stylized images, limiting the application on rare styles and mobile devices. To alleviate this, we propose to establish meaningful geometric correlations between portraits and style samples to simplify the stylization by aligning corresponding facial characteristics. Specifically, we integrate differentiable Thin-Plate-Spline (TPS) modules into an end-to-end Generative Adversarial Network (GAN) framework to improve the training efficiency and promote the consistency of facial identities. By leveraging inherent structural information of faces, e.g., facial landmarks, TPS module can establish geometric alignments between the two domains, at global and local scales, both in pixel and feature spaces, thereby overcoming the aforementioned challenges. Quantitative and qualitative comparisons on a range of portrait stylization tasks demonstrate that our models not only outperforms existing models in terms of fidelity and stylistic consistency, but also achieves remarkable improvements in 2× training data efficiency and 100× less computational complexity, allowing our lightweight model to achieve real-time inference (30 FPS) at 512*512 resolution on mobile devices.</p>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"191 ","pages":"107774"},"PeriodicalIF":6.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144668865","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":"Practicing in quiz, assessing in quiz: A quiz-based neural network approach for knowledge tracing.","authors":"Shuanghong Shen, Qi Liu, Zhenya Huang, Linbo Zhu, Junyu Lu, Kai Zhang","doi":"10.1016/j.neunet.2025.107797","DOIUrl":"10.1016/j.neunet.2025.107797","url":null,"abstract":"<p><p>Online learning has demonstrated superiority in connecting high-quality educational resources to a global audience. To ensure an excellent learning experience with sustainable and opportune learning instructions, online learning systems must comprehend learners' evolving knowledge states based on their learning interactions, known as the Knowledge Tracing (KT) task. Generally, learners practice through various quizzes, each comprising several exercises that cover similar knowledge concepts. Therefore, their learning interactions are continuous within each quiz but discrete across different quizzes. However, existing methods overlook the quiz structure and assume all learning interactions are uniformly distributed. We argue that learners' knowledge states should also be assessed in quiz since they practiced in quiz. To achieve this goal, we present a novel Quiz-based Knowledge Tracing (QKT) model, which effectively integrates the quiz structure of learning interactions. This is achieved by designing two distinct modules by neural networks: one for intra-quiz modeling and another for inter-quiz fusion. Extensive experimental results on public real-world datasets demonstrate that QKT achieves new state-of-the-art performance. The findings of this study suggest that incorporating the quiz structure of learning interactions can efficiently comprehend learners' knowledge states with fewer quizzes, and provides valuable insights into designing effective quizzes with fewer exercises.</p>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"191 ","pages":"107797"},"PeriodicalIF":6.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144660885","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":"KF-OLSR: A Novel Routing Protocol for Mobile Ad Hoc Networks Utilizing Kalman Filter and Fuzzy Logic Based on OLSR Routing Protocol","authors":"Fazel Irani","doi":"10.1002/cpe.70351","DOIUrl":"https://doi.org/10.1002/cpe.70351","url":null,"abstract":"<div>\u0000 \u0000 <p>Mobile ad hoc networks (MANETs), and especially Flying ad hoc networks (FANETs), operate in highly dynamic 3D environments that demand routing protocols capable of adapting to rapid topology changes. This paper presents KF-OLSR, a novel OLSR extension that combines an Extended Kalman Filter (EKF) for predictive mobility estimation with Mamdani-style fuzzy inference systems to compute fuzzy costs for both Multipoint Relay (MPR) selection and routing-table construction. The EKF processes historical GPS positions and velocities to produce accurate current and short-term predicted positions, from which we derive new mobility-aware metrics: Predicted Relative Displacement (PRD), Predicted Link Lifetime (PLL), and Mobility Variance (MV). These are fused with traditional topology and link indicators—node degree, centrality, and a Link Quality Indicator (LQI, e.g., ETX/Hello reception)—to produce (i) an MPR suitability cost that selects stable, well-positioned relays and (ii) composite link costs used by a modified Dijkstra algorithm to build routing tables favoring long-lived, high-quality paths. Hello and TC messages are extended to carry compact EKF predictions and metric summaries so nodes can compute fuzzy costs locally without additional message types. We also present an analytical modeling and formal analysis framework that derives theoretical performance bounds on packet delivery ratio, end-to-end delay, and route stability as functions of prediction accuracy, node density, and mobility dynamics, and quantify the protocol's computational and communication overhead. These analyzes show that KF-OLSR's gains persist under bounded prediction errors and identify operational regions where the protocol provides provable improvements over baselines. NS-2 simulations using a Gauss–Markov mobility model validate the analytical results and show that KF-OLSR significantly outperforms E-OLSR, ETX-OLSR, ML-OLSR, and MD-OLSR—reducing end-to-end delay by up to 28.57%, increasing packet delivery ratio by up to 79.13%, and improving throughput by up to 120.41%—demonstrating the effectiveness of combining predictive analytics with fuzzy decision-making for airborne ad hoc networks.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 25-26","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145317567","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}

{"title":"Medical Domain Knowledge Collaborative Graph Learning for Healthcare Event Prediction","authors":"Usman Naseem,&nbsp;Junaid Rashid,&nbsp;Haohui Lu,&nbsp;Dominic Ng,&nbsp;Zain Hussain,&nbsp;Amir Hussain","doi":"10.1111/exsy.70151","DOIUrl":"https://doi.org/10.1111/exsy.70151","url":null,"abstract":"<div>\u0000 \u0000 <p>Electronic health records have become more prevalent worldwide, and with this, the opportunity for more accurate and automated prediction of health events has grown. Such predictions are crucial for providing preventive and proactive healthcare to patients. Although various advanced methods have been explored, they often fail to fully leverage medical domain knowledge, understand interrelations between diseases and patients comprehensively, and efficiently integrate unstructured clinical notes into predictive models. To address these challenges, we propose the Medical Domain Knowledge Collaborative Graph Learning (MED-CGL) model. MED-CGL incorporates external medical knowledge bases to enhance the predictive power of unstructured clinical notes and extracts learnable features from the MIMIC-III health record dataset using medical domain knowledge and collaborative graph learning. We introduce the Enhanced Medical Knowledge Integration (EMKI) module, which employs a novel attention mechanism to connect clinical notes with disease descriptions precisely. It also enhances the system's performance by integrating medical knowledge from the semantically labelled knowledge-enhanced (SLAKE) dataset during the training phase. Furthermore, our model considers the complexities of unstructured clinical notes, providing a nuanced perspective on the interplay between diseases and patient profiles. Our experiments show that the MED-CGL model exhibited outstanding performance in diagnosis prediction, achieving an F1 score of 27.32%, and in heart failure prediction, where it attained an accuracy of 91.39%. This significant improvement demonstrates the robustness and effectiveness of our model, which is further supported by our in-depth ablation study.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 12","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145317334","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}

{"title":"Development of Efficient Control Charts for Monitoring Mean of Paired Differences of Quality Characteristics","authors":"Muhammad Wasim Amir,&nbsp;Hafiz Zafar Nazir,&nbsp;Zameer Abbas,&nbsp;Noureen Akhtar,&nbsp;Babar Zaman","doi":"10.1002/cpe.70349","DOIUrl":"https://doi.org/10.1002/cpe.70349","url":null,"abstract":"<div>\u0000 \u0000 <p>In the manufacturing process, numerous quality characteristics are pair-correlated, which influence the output of quality of products. Examining these characteristics and their interactions can help identify the root cause of quality defects and maintain product quality consistency. The natural correlation between the paired quality characteristics provides the basis for using their differences as a potential measure for assessment and comparison. Control charts are a vital tool in statistical process control (SPC), enabling the oversight of the manufacturing process output and helping to identify variations, thereby ensuring product quality. The Shewhart and exponentially weighted moving average (EWMA) schemes are good for noticing the large and small changes in understudy quality characteristics. The control charts for monitoring paired quality characteristics are uncommon and rare in the literature. In this study, we develop two new EWMA and combined Shewhart-EWMA (CSE) schemes to observe the location of paired differences in quality characteristics. The Monte Carlo simulation method is used to evaluate the run-length properties of the proposed schemes. The performance of the developed schemes is compared with that of their existing classical counterparts. The numerical results show that the developed schemes are more powerful than their counterparts in detecting the changes in the understudy process parameter. A practical and two hypothetical examples are also given to implement the proposed structures for the practitioners.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 25-26","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145317568","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}

{"title":"SER Analysis of Spatial Modulation and PLNC-Based Multicast Relay Network in mmWave Communications","authors":"Murugavalli E,&nbsp;Rajeswari K,&nbsp;Suresh M.N,&nbsp;Thiruvengadam S. J","doi":"10.1002/dac.70286","DOIUrl":"https://doi.org/10.1002/dac.70286","url":null,"abstract":"<div>\u0000 \u0000 <p>Millimeter-wave (mmWave) communication has wide applications in 5G broadband cellular communication, wireless backhaul connections, wireless personal area networks, vehicular area networks, and mobile ad hoc networks. Major issues in using mmWave communication for multicasting are blockage and penetration losses in signal propagation. To overcome these losses, the proposed system model includes decode-and-forward (DF) relay nodes between source and destination nodes in the multicast network. The performance of the relay network is further improved by adopting physical layer network coding (PLNC) at the relay node. In addition, spatial modulation (SM) is adopted as a modulation technique due to its inherent advantage of improved spectral efficiency. In this paper, a multicast relay network using SM and PLNC in mmWave communications is proposed for indoor line-of-sight (LoS) environments. The error performance analysis of the proposed system is investigated in terms of deriving analytical expressions by considering orthogonal channel conditions among the nodes. The overall end-to-end pairwise error probability (PEP) and symbol error rate (SER) performances of the proposed system are derived from the performances at the links between the source nodes to relay and destination nodes during the first time slot and relay nodes to destination nodes during the second time slot. The advantage of the PLNC-based system is identified by comparing it with the non-PLNC-based multicast system. Analytical results are compared with Monte Carlo simulations.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"38 17","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145317618","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}