IF 3.7 4区计算机科学

Pattern Analysis and Applications Pub Date : 2025-06-01 Epub Date: 2025-02-21 DOI: 10.1007/s10044-025-01423-y

Ali Eslamian, Qiang Cheng

{"title":"TabMixer: advancing tabular data analysis with an enhanced MLP-mixer approach.","authors":"Ali Eslamian, Qiang Cheng","doi":"10.1007/s10044-025-01423-y","DOIUrl":"https://doi.org/10.1007/s10044-025-01423-y","url":null,"abstract":"<p><p>Tabular data, prevalent in relational databases and spreadsheets, is fundamental across fields like healthcare, engineering, and finance. Despite significant advances in tabular data learning, critical challenges remain: handling missing values, addressing class imbalance, enabling transfer learning, and facilitating feature incremental learning beyond traditional supervised paradigms. We introduce TabMixer, an innovative model that enhances the multilayer perceptron (MLP) mixer architecture to address these challenges. TabMixer incorporates a self-attention mechanism, making it versatile across various learning scenarios including supervised learning, transfer learning, and feature incremental learning. Extensive experiments on eight public datasets demonstrate TabMixer's superior performance over existing state-of-the-art methods. Notably, TabMixer achieved substantial improvements in ANOVA AUC across all scenarios: a 4% increase in supervised learning (0.840 to 0.881), 8% in transfer learning (0.803 to 0.872), and 4% in feature incremental learning (0.806 to 0.843). TabMixer demonstrates high computational efficiency and scalability through reduced floating-point operations and learnable parameters. Moreover, it exhibits strong resilience to missing values and class imbalances through both its architectural design and optional preprocessing enhancements. These results establish TabMixer as a promising model for tabular data analysis and a valuable tool for diverse applications.</p>","PeriodicalId":54639,"journal":{"name":"Pattern Analysis and Applications","volume":"28 2","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12053537/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144060749","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}

引用次数: 0

Koopman-based modeling and control of motion systems with hysteresis dynamics using partial state feedback 基于koopman的迟滞运动系统部分状态反馈建模与控制

IF 3.1 3区计算机科学

Mechatronics Pub Date : 2025-05-19 DOI: 10.1016/j.mechatronics.2025.103338

Michael Pumphrey , Almuatazbellah M. Boker , Mohammad Al Janaideh

{"title":"Koopman-based modeling and control of motion systems with hysteresis dynamics using partial state feedback","authors":"Michael Pumphrey , Almuatazbellah M. Boker , Mohammad Al Janaideh","doi":"10.1016/j.mechatronics.2025.103338","DOIUrl":"10.1016/j.mechatronics.2025.103338","url":null,"abstract":"<div><div>Koopman operator theory represents nonlinear dynamical systems as linear systems in an extended state-space. By selecting observable functions composed of derivatives and functions of derivatives derived from the system output, it is possible to model the system without requiring knowledge of its internal states. The Koopman observable functions are iteratively refined to achieve close alignment with the original system dynamics. The resulting linear model in the extended space is then incorporated into a linear quadratic tracker (LQT) framework, enabling the system output to track a desired reference signal. The proposed method is demonstrated on a mechanical motion system with Bouc–Wen hysteresis, where the Koopman-based model and LQT provide robust control of the nonlinear system and can track a smooth trapezoidal step-scan trajectory for wafer scanner machines. The controller was also compared to a traditional PID controller, showing improved performance over the trajectory. Furthermore, simulation results demonstrate the controller’s robustness against varying initial conditions, parameter uncertainties, and external disturbances.</div></div>","PeriodicalId":49842,"journal":{"name":"Mechatronics","volume":"109 ","pages":"Article 103338"},"PeriodicalIF":3.1,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 collaboration scale: A novel approach for assessing robotic systems collaboration capabilities 协作规模：一种评估机器人系统协作能力的新方法

IF 9.1 1区计算机科学

Robotics and Computer-integrated Manufacturing Pub Date : 2025-05-19 DOI: 10.1016/j.rcim.2025.103062

Federico Barravecchia, Riccardo Gervasi, Luca Mastrogiacomo, Fiorenzo Franceschini

{"title":"The collaboration scale: A novel approach for assessing robotic systems collaboration capabilities","authors":"Federico Barravecchia, Riccardo Gervasi, Luca Mastrogiacomo, Fiorenzo Franceschini","doi":"10.1016/j.rcim.2025.103062","DOIUrl":"10.1016/j.rcim.2025.103062","url":null,"abstract":"<div><div>In the transformative landscape of Industry 4.0 and the impending transition to Industry 5.0, the paradigm of collaborative robotics is emerging as a cornerstone, combining human and robotic distinctive abilities. This intersection is leading to a new era of 'human-centric' manufacturing, where the integration of human with robots is not just an option, but a need. In particular, the shift towards Industry 5.0 highlights the return of the human element to technological processes, emphasising adaptability, customization, and collaboration between humans and machines.</div><div>In this context, this study introduces the <em>Collaboration Scale,</em> a metric designed to evaluate the collaborative capabilities of robotic systems within this human-centred framework. This scale provides clear levels of collaboration across five foundational dimensions: <em>Situation awareness, Adaptivity, Communication, Learning</em>, and <em>Mobility</em>.</div><div>The proposed scale has three objectives: (i) establishing a common language for practitioners and researchers, (ii) promoting innovation and standardisation in collaborative robotics, and (iii) providing a practical tool for assessing and comparing the collaborative capabilities of different systems.</div><div>The framework aims to bridge the gap between current capabilities and future aspirations in robotics, while also promoting a human-centric approach for Industry 5.0.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"96 ","pages":"Article 103062"},"PeriodicalIF":9.1,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084529","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

Weak Population–Empowered Large-Scale Multiobjective Immune Algorithm 弱种群授权大规模多目标免疫算法

IF 5 2区计算机科学

International Journal of Intelligent Systems Pub Date : 2025-05-19 DOI: 10.1155/int/6462697

Yang Chen, Wenshan Li, Junjiang He, Tao Li, Wenbo Fang, Xiaolong Lan

{"title":"Weak Population–Empowered Large-Scale Multiobjective Immune Algorithm","authors":"Yang Chen, Wenshan Li, Junjiang He, Tao Li, Wenbo Fang, Xiaolong Lan","doi":"10.1155/int/6462697","DOIUrl":"https://doi.org/10.1155/int/6462697","url":null,"abstract":"<div>\u0000 <p>The multiobjective immune optimization algorithms (MOIAs) utilize the principle of clonal selection, iteratively evolving by replicating a small number of superior solutions to optimize decision vectors. However, this method often leads to a lack of diversity and is particularly ineffective when facing large-scale optimization problems. Moreover, an overemphasis on elite solutions may result in a large number of redundant offspring, reducing evolutionary efficiency. By delving into the causes of these issues, we find that a key factor is that existing algorithms overlook the role of weak solutions during the evolutionary process. With this in mind, we propose a weak population–empowered large-scale multiobjective immune algorithm (WP–MOIA). The core of this algorithm is to construct, in addition to the traditional elite population, a cooperative evolutionary population based on a portion of the remaining solutions, referred to as the weak population. During the evolution, both populations work together: the elite population maximizes its advantageous status for local searches, focusing on exploitation, while the weak population seeks greater variation to escape its disadvantaged position, engaging in broader exploration. At the same time, the sizes of both populations are dynamically adjusted to collaboratively maintain the balance of evolution. Through comparisons with nine state-of-the-art multiobjective evolutionary algorithms (MOEAs) and four powerful MOIAs on 30 benchmark problems, the proposed algorithm demonstrates superior performance in both small-scale and large-scale multiobjective optimization problems (MOPs), and exhibits better convergence efficiency. Especially in large-scale MOPs, the new algorithm’s performance nearly surpasses all 13 advanced algorithms being compared.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/6462697","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144085388","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}

引用次数: 0

Measuring the impact of predictive models on the software project: A cost, service time, and risk evaluation of a metric-based defect severity prediction model 测量预测模型对软件项目的影响：基于度量的缺陷严重性预测模型的成本、服务时间和风险评估

IF 2 2区计算机科学

Automated Software Engineering Pub Date : 2025-05-19 DOI: 10.1007/s10515-025-00519-3

Umamaheswara Sharma B, Ravichandra Sadam

{"title":"Measuring the impact of predictive models on the software project: A cost, service time, and risk evaluation of a metric-based defect severity prediction model","authors":"Umamaheswara Sharma B, Ravichandra Sadam","doi":"10.1007/s10515-025-00519-3","DOIUrl":"10.1007/s10515-025-00519-3","url":null,"abstract":"<div><p>In a critical software system, the testers have to spend an enormous amount of time and effort maintaining the software due to the continuous occurrence of defects. To reduce the time and effort of a tester, prior works in the literature are limited to using documented defect reports to automatically predict the severity of the defective software modules. In contrast, in this work, we propose a metric-based software defect severity prediction (SDSP) model that is built using a decision-tree incorporated self-training semi-supervised learning approach to classify the severity of the defective software modules. Empirical analysis of the proposed model on the AEEEM datasets suggests using the proposed approach as it successfully assigns suitable severity class labels to the unlabelled modules. On the other hand, numerous research studies have addressed the methodological aspects of SDSP models, but the gap in estimating the performance of a developed prediction using suitable measures remains unattempt. For this, we propose the risk factor, per cent of the saved budget, loss in the saved budget, per cent of remaining edits, per cent of remaining edits, remaining service time, and gratuitous service time, to interpret the predictions in terms of project objectives. Empirical analysis of the proposed approach shows the benefit of using the proposed measures in addition to the traditional measures.</p></div>","PeriodicalId":55414,"journal":{"name":"Automated Software Engineering","volume":"32 2","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084984","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

HKT: Hierarchical structure-based knowledge tracing HKT：基于层次结构的知识追踪

IF 7.4 1区管理学

Information Processing & Management Pub Date : 2025-05-19 DOI: 10.1016/j.ipm.2025.104206

Qing Li , Zhijun Huang , Jianwen Sun , Xin Yuan , Shengyingjie Liu , Zhonghua Yan

{"title":"HKT: Hierarchical structure-based knowledge tracing","authors":"Qing Li , Zhijun Huang , Jianwen Sun , Xin Yuan , Shengyingjie Liu , Zhonghua Yan","doi":"10.1016/j.ipm.2025.104206","DOIUrl":"10.1016/j.ipm.2025.104206","url":null,"abstract":"<div><div>Knowledge tracing (KT) is a fundamental task in Intelligent Tutoring Systems, aiming to predict learners’ performance on specific questions and trace their evolving knowledge state. With the advancement of deep learning in this field, various methods have been applied to model the relations between knowledge. However, most existing knowledge tracing methods focus on modeling knowledge at a single level, neglecting the inherent hierarchical structure of knowledge, which limits their ability to capture complex relations. In this paper, we propose a novel hierarchical knowledge tracing model (HKT), which integrates influences of multiple knowledge levels to predict learners’ performance. Specifically, we construct different types of hierarchical graphs to capture both intra-hierarchy dependencies and cross-hierarchy relations. To effectively combine information from multiple levels, we design weight allocation networks that dynamically assign weights to different knowledge levels, thereby synthesizing their effects for accurate performance prediction. Experimental results demonstrate that HKT outperforms baseline methods on multiple benchmark datasets, validating the effectiveness of integrating knowledge across all levels compared to single-level models.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 5","pages":"Article 104206"},"PeriodicalIF":7.4,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084250","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

Correction to “Securing the Road Ahead: A Survey on Internet of Vehicles Security Powered by a Conceptual Blockchain-Based Intrusion Detection System for Smart Cities” 更正“确保前方道路安全：基于概念区块链的智慧城市入侵检测系统驱动的车联网安全调查”

IF 2.5 4区计算机科学

Transactions on Emerging Telecommunications Technologies Pub Date : 2025-05-19 DOI: 10.1002/ett.70154

引用次数: 0

Fractional Kalman filters 分数卡尔曼滤波器

IF 4.8 2区计算机科学

Automatica Pub Date : 2025-05-19 DOI: 10.1016/j.automatica.2025.112383

Xusheng Yang, Wen-An Zhang, Li Yu

{"title":"Fractional Kalman filters","authors":"Xusheng Yang, Wen-An Zhang, Li Yu","doi":"10.1016/j.automatica.2025.112383","DOIUrl":"10.1016/j.automatica.2025.112383","url":null,"abstract":"<div><div>The nonlinear Kalman filters usually suffer from degradation when the measurements appear in the tail of prior distribution. This article presents a new nonlinear filter named the fractional Kalman filter (FKF) for high robustness against linearization errors of both the time and the measurement updates. Based on the Bayesian theory and Gaussian assumption, an optimal fractional Gaussian filter (OFGF) is developed by maximizing the posterior PDFs, which consists of three basic steps namely time update, fractional measurement update and fractional state fusion. Specifically, the prior probability density function (PDF) and the likelihood function are factored into <span><math><mi>N</mi></math></span> parts with fractional exponents, which increases the <em>overlapping area</em> of the prior and the likelihood distributions for linearization improvement. With the OFGF and the Kullback–Leibler divergence (KLD) analysis, the FKF is developed by a deterministic sampling-based linearization for approximation of means and covariances. From the performance analysis, it reveals that the risk of destroying the stabilities of both the time update and the measurement update is reduced by introducing the fractional exponents. Finally, the effectiveness and superiority of the proposed FKF method are verified by simulations of a target tracking example.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"178 ","pages":"Article 112383"},"PeriodicalIF":4.8,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084300","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

A Review of Deepfake and Its Detection: From Generative Adversarial Networks to Diffusion Models Deepfake及其检测综述：从生成对抗网络到扩散模型

IF 5 2区计算机科学

International Journal of Intelligent Systems Pub Date : 2025-05-19 DOI: 10.1155/int/9987535

Baoping Liu, Bo Liu, Tianqing Zhu, Ming Ding

{"title":"A Review of Deepfake and Its Detection: From Generative Adversarial Networks to Diffusion Models","authors":"Baoping Liu, Bo Liu, Tianqing Zhu, Ming Ding","doi":"10.1155/int/9987535","DOIUrl":"https://doi.org/10.1155/int/9987535","url":null,"abstract":"<div>\u0000 <p>Deepfake technology, leveraging advanced artificial intelligence (AI) algorithms, has emerged as a powerful tool for generating hyper-realistic synthetic human faces, presenting both innovative opportunities and significant challenges. Meanwhile, the development of Deepfake detectors represents another branch of models striving to recognize AI-generated fake faces and protect people from the misinformation of Deepfake. This ongoing cat-and-mouse game between generation and detection has spurred a dynamic evolution in the landscape of Deepfake. This survey comprehensively studies recent advancements in Deepfake generation and detection techniques, focusing particularly on the utilization of generative adversarial networks (GANs) and diffusion models (DMs). For both GAN-based and DM-based Deepfake generators, we categorize them based on whether they synthesize new content or manipulate existing content. Correspondingly, we examine various strategies employed to identify synthetic and manipulated Deepfake, respectively. Finally, we summarize our findings by discussing the unique capabilities and limitations of GANs and DM in the context of Deepfake. We also identify promising future directions for research, including the development of hybrid approaches that leverage the strengths of both GANs and DM, the exploration of novel detection strategies utilizing advanced AI techniques, and the ethical considerations surrounding the development of Deepfake. This survey paper serves as a valuable resource for researchers, practitioners, and policymakers seeking to understand the state-of-the-art in Deepfake technology, its implications, and potential avenues for future research and development.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/9987535","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144085389","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}

引用次数: 0

Multi-class Agent Trajectory Prediction with Selective State Spaces for autonomous driving 基于选择状态空间的自动驾驶多类智能体轨迹预测

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-05-19 DOI: 10.1016/j.engappai.2025.111027

Jin Fan , Zhanwen Liu , Yong Fang , Zeyu Huang , Yang Liu , Shan Lin

{"title":"Multi-class Agent Trajectory Prediction with Selective State Spaces for autonomous driving","authors":"Jin Fan , Zhanwen Liu , Yong Fang , Zeyu Huang , Yang Liu , Shan Lin","doi":"10.1016/j.engappai.2025.111027","DOIUrl":"10.1016/j.engappai.2025.111027","url":null,"abstract":"<div><div>Understanding and predicting multi-class agents’ movement has become more critical and challenging in diverse applications such as autonomous driving and urban intelligent monitoring. The current research mainly focuses on the motion trajectory of single-class agents. However, due to real traffic scenarios’ complexity and interactive behaviors’ variability, the motion patterns displayed by various classes of agents show inherent randomness. In this paper, inspired by the linear-time sequence model Mamba, we propose a Multi-class Agent Trajectory Prediction with Selective State Spaces (MTPSS) to model the interaction between different agents and better predict the trajectory of an individual. Specifically, MTPSS includes modeling relationships in both temporal and spatial dimensions. When encoding the spatial correlation within the trajectory graph, we construct a category-based sorting approach, which puts large-size category nodes behind to enhance contextual access. Then the sorted nodes are bi-directionally scanned through Mamba blocks, which makes the model more robust to permutations. In terms of temporal, considering the highly dynamic nature of rapidly moving agents, we utilize Mamba’s remarkable performance on sequential data to effectively conduct temporal scans to capture long-range temporal dependencies. Finally, to compute physically feasible trajectories, MTPSS employs the Neural Ordinary Differential Equation to smooth the predicted trajectory of the agent. We conducted extensive experiments on two publicly available traffic datasets and compared our method with state-of-the-art methods. Quantitative experiments show that our performance metrics are superior to state-of-the-art methods, and qualitative experiments demonstrate that the predicted trajectories have good diversity, which shows its potential in real-world traffic scenarios.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"156 ","pages":"Article 111027"},"PeriodicalIF":7.5,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084069","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

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