计算机科学最新文献

{"title":"STF-LPPVA: Local Privacy-Preserving Method for Vehicle Assignment Based on Spatial–Temporal Fusion","authors":"Lei Tang,&nbsp;Zhengxin Cao,&nbsp;Xin Zhou,&nbsp;Junzhe Zhang,&nbsp;Junchi Ma","doi":"10.1049/ise2/1915019","DOIUrl":"https://doi.org/10.1049/ise2/1915019","url":null,"abstract":"<p>There are user privacy risks in cloud-based vehicle dispatch platforms due to the unauthorized collection, use, and dissemination of data. However, existing data protection methods cannot balance privacy, usability, and efficiency well. To address this, we propose a local privacy-preserving vehicle assignment strategy via spatial–temporal fusion (STF-LPPVA). Specifically, the strategy allows the cloud platform to train and distribute a spatial–temporal representation model to the user side. Encoded by this model, drivers and passengers can privately fuze the spatial–temporal information of their trips and then transmit these fuzed vectors to the cloud platform. Based on the similarity of the vectors, the cloud platform can allocate vehicles using the Kuhn–Monkreth (KM) algorithm. In addition, we analyze the theoretical feasibility of the STF-LPPVA strategy using entropy change and get good performance with a dataset from DiDi in Chengdu, China. The results show that the successful matching rate of the STF-LPPVA strategy is very close to the original data matching with lower time overhead. Our approach can reduce the traveling distance by 66.5% and improve the matching success rate by 36.2% on average.</p>","PeriodicalId":50380,"journal":{"name":"IET Information Security","volume":"2025 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/ise2/1915019","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144853799","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":"Developing a Digital Twin of part cooling in an injection moulding process through a Dynamic Mode Decomposition-Kalman Filter approach","authors":"Mandana Kariminejad ,&nbsp;David Tormey ,&nbsp;Marion McAfee","doi":"10.1016/j.compind.2025.104333","DOIUrl":"10.1016/j.compind.2025.104333","url":null,"abstract":"<div><div>A framework for creating a Digital Twin for spatiotemporal process monitoring is proposed based on Dynamic Mode Decomposition and the Kalman filter (DMD-KF). Many material processes require optimisation of complex spatiotemporal dynamics which are difficult to monitor with limited sensor measurements at accessible locations in the process. The DMD-KF approach facilitates the extraction of a spatiotemporal dynamic model with minimal computation time from numerical simulations, integrated with real-time sensor measurements of the accessible states to correct model errors. The method is demonstrated for real-time spatiotemporal monitoring of component cooling in the injection moulding process. Injection Moulding is a high-volume manufacturing process, which faces challenges in dimensional precision due to shrinkage and warpage defects which manifest post-production due to the gradual relaxation of internal residual stresses. To prevent internal stresses, the component should be sufficiently free of significant temperature differentials prior to ejection from the mould. However, the inaccessible nature of the mould tool limits sensor access for monitoring of the cooling phase. Dynamic Mode Decomposition (DMD) allows a best fit, linear state–space model of the temperature dynamics of 3D spatial nodes of the component to be extracted from a computationally-intensive finite element model of the process. Using only two thermocouple measurements, integrated with the DMD model via a Kalman Filter (KF), allows for construction of the 3D temperature map of the component inside the mould in real time. Simulation of different processing scenarios highlights that even with a DMD model developed under significantly different process conditions than used in implementation, the KF corrections still effectively estimate the temperature of critical states with with a root mean square error of 1.4 °C. The DMD-KF approach shows high potential for real-time spatio-temporal monitoring and quality prediction across diverse manufacturing processes.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"172 ","pages":"Article 104333"},"PeriodicalIF":9.1,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144852480","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":"A novel hybrid clustering and classification framework for multi-level depression detection in social media texts","authors":"Parisa Khodabakhshi,&nbsp;Masoud Mahootchi,&nbsp;Hadi Mosadegh","doi":"10.1016/j.engappai.2025.111952","DOIUrl":"10.1016/j.engappai.2025.111952","url":null,"abstract":"<div><div>Depression is a prevalent psychiatric condition worldwide, with significant social and economic implications. Despite its high incidence, many individuals with depression remain undiagnosed and untreated. Meanwhile, people increasingly use social media platforms to express their emotions and thoughts. Consequently, leveraging these platforms for depression detection may help address several related challenges. This paper proposes a three-stage methodology, based on text mining techniques, to determine the severity of depression in individuals who post textual content on social media. In the proposed framework, each post is transformed into a vector of numerical features using established feature extraction methods. Principal Component Analysis is then applied to select the most informative features for identifying whether a post indicates depression via a classification algorithm. If depression is detected, the method clusters the relevant posts based on their characteristics, grouping similar texts together. A second classification model is then applied within each cluster to determine the level of depression. To equip the model with effective algorithms at each stage, the Taguchi method is used to identify the best combination of feature extraction, clustering, and classification techniques. Specifically, Bidirectional Encoder Representations from Transformers (BERT) is used for deep contextual feature extraction, Deep Embedded Clustering (DEC) is employed for clustering, and Support Vector Machine (SVM) is used for classification. Numerical results show that the proposed approach can accurately classify individuals’ posts into one of four depression levels: non-depressed, low, moderate, and severe. These findings suggest that social networks offer a platform for assessing mental health through textual analysis.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"160 ","pages":"Article 111952"},"PeriodicalIF":8.0,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144858426","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":"Adaptive adversarial pattern contrast algorithm for black-box model and domain attack","authors":"Weidong Wang ,&nbsp;Yi Wang ,&nbsp;Zhi Li,&nbsp;Long Zheng,&nbsp;Li Zhang","doi":"10.1016/j.engappai.2025.111980","DOIUrl":"10.1016/j.engappai.2025.111980","url":null,"abstract":"<div><div>The transferability of adversarial attacks in deep neural networks (DNNs) is a significant challenge, especially for achieving effective attacks across models and data domains. Unfortunately, existing attack approaches primarily focus on cross-model transferability, often overlooking the potential for black-box attacks across diverse data domains. This paper proposes the <strong>A</strong>daptive adversarial <strong>P</strong>att<strong>e</strong>rn <strong>C</strong>ontrast (APEC) algorithm, designed to achieve cross-model and domain adversarial attacks with high transferability. Firstly, APEC generates transferable adversarial examples by leveraging spatial characteristics such as regional homogeneity, repetition, and density, thereby increasing classifier misclassification rates. Secondly, a key innovation in APEC is the similarity contrast loss inspired by contrastive learning. It guides the model to learn discriminative adversarial features by aligning adversarial examples with adversarial patterns and distancing them from clean examples. Importantly, this optimization is performed label-free, enhancing APEC’s practicality in real-world black-box scenarios. Additionally, we introduce a Gaussian low-pass filter in APEC to generate adversarial perturbation patterns adaptively. This operation suppresses high-frequency information while preserving the low-frequency characteristics of natural examples, enhancing APEC’s attack capabilities. The APEC algorithm shows relative improvement across models and data domains compared to state-of-the-art transferability attacks. Our code is available at <span><span>https://github.com/cs-igps/APEC-TransferAttack</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"160 ","pages":"Article 111980"},"PeriodicalIF":8.0,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144858427","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":"CNN based intra channel nonlinear equalization method for WDM CO-OFDM systems","authors":"Xi Fang,&nbsp;YunZhang Wang,&nbsp;LingYu Liu,&nbsp;YueDing Xu,&nbsp;YuXiang Liu","doi":"10.1016/j.yofte.2025.104359","DOIUrl":"10.1016/j.yofte.2025.104359","url":null,"abstract":"<div><div>Polarization-division multiplexing wavelength-division multiplexing coherent optical orthogonal frequency-division multiplexing (PDM-WDM CO-OFDM) systems have emerged as a key technology in optical communications due to their high spectral efficiency and ability to resist frequency selective fading. However, in long-distance or high-data-rate transmission scenarios, signal distortion caused by fiber nonlinear effects significantly degrades system performance. Traditional nonlinear compensation methods, such as the high-order Volterra series, can address some of these challenges but are limited by high computational complexity and insufficient accuracy, making them unsuitable for real-time transmission requirements. To address these challenges, we propose a convolutional neural network (CNN)-based nonlinear equalization method. By introducing a sliding window mechanism, the global signal is divided into local segments, enabling the model to efficiently capture local nonlinear features while maintaining low computational complexity. Additionally, CNNs leverage parameter sharing and parallel computation to significantly improve training and inference speeds, overcoming the computational inefficiencies and sequential nature of recurrent neural networks (e.g., Bi-LSTM) in handling time-series data. Simulation results demonstrate that, for a 16-QAM modulated PDM-WDM CO-OFDM system with a data rate of 850 Gb/s, the proposed method improves the maximum transmission distance by 100 % compared to the Volterra series model. Furthermore, while achieving comparable bit error rate (BER) performance to the Bi-LSTM model, the computational complexity of the CNN-based method is significantly decreased. The proposed CNN method exhibits superior robustness and performance in nonlinear equalization.</div></div>","PeriodicalId":19663,"journal":{"name":"Optical Fiber Technology","volume":"94 ","pages":"Article 104359"},"PeriodicalIF":2.7,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144858144","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}

{"title":"SeAF-PrS: Effective and adaptive priority scheduling scheme for improving the quality of service in wireless body area networks","authors":"Shaik Afzal Ahammed M S ,&nbsp;Manjaiah D H","doi":"10.1016/j.compeleceng.2025.110616","DOIUrl":"10.1016/j.compeleceng.2025.110616","url":null,"abstract":"<div><div>The Wireless Body Area Networks (WBAN) hold immense capability in medical services with the significant benefits of remote patient health monitoring. Since data transmission is the major step for real-time diagnosis, conventional healthcare applications exhibited lower adaptability and continuous transmission of data resulting in higher energy losses. Therefore, the research focuses on improving the quality of services through an efficient technique Known as Sensitive and Adaptive Flitter optimized priority scheduler (SeAF-PrS). The data transmission is performed when deviations from the original signal levels is noticed. The application of the Sensitive and Adaptive Flitter Optimization (SeAFO) algorithm improves data scheduling by assisting in the selection of high-priority signals for patients that require immediate diagnosis. In addition, the modified shift rows (MSR) encryption scheme provides higher security through the encrypting of data packets as well as the authentication check allows only the verified users to access the patient data. The proposed approach is compared against the traditional healthcare monitoring systems which reveal improved efficiency with a throughput of 0.74 kbps, minimum delay of 0.100 ms, and energy loss of 7.37 J for 10 nodes with 250 users.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"127 ","pages":"Article 110616"},"PeriodicalIF":4.9,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144858292","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}

{"title":"Multi-head divide-and-conquer residual-attention mechanism with pointer network for multimodal question summarization in healthcare","authors":"S. Priskilla Manonmani,&nbsp;S. Malathi","doi":"10.1016/j.ipm.2025.104348","DOIUrl":"10.1016/j.ipm.2025.104348","url":null,"abstract":"<div><div>In contemporary medicine, summaries of medical questions are vital for effective and precise patient care. Current techniques handle only text-based summarization without considering the merit of incorporating visual information. To meet this, this research presents a multimodal summarization system that combines textual queries with medical images to support the extraction of meaningful details. The proposed system has three phases. In the first step, a gradual fusion decoder bidirectional encoder representation from transformers with vision transformers is utilized to produce fine-grained feature maps and diagnose diseases. The Multi-Agent Contextualized Diffusion Model (MACDM) is then utilized to contextualize knowledge using cross-modal information. Lastly, a Multi-head Divide-and-Conquer Residual-Attention mechanism with Pointer Network (MDCRAPN) is utilized to provide brief and relevant summaries. Furthermore, the hermit crab shell exchange algorithm is integrated to optimize hyperparameters for improved performance. The experimental results indicate that this proposed approach performs better than existing approaches with a recall-oriented understudy for gisting evaluation-1 score of 48.11 on the Multimodal Medical Question Summarization (MMQS) dataset. This approach significantly enhances the identification and summarization of medical disorders, demonstrating the potential to enhance healthcare communication and decision-making.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"63 1","pages":"Article 104348"},"PeriodicalIF":6.9,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144858241","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}

{"title":"A robust counterpart model and matheuristic-oriented evolutionary algorithm for evaluating energy consumption of project scheduling with uncertainty","authors":"Zheng Gao ,&nbsp;Liping Zhang ,&nbsp;Zikai Zhang ,&nbsp;Zixiang Li ,&nbsp;Yingli Li","doi":"10.1016/j.swevo.2025.102124","DOIUrl":"10.1016/j.swevo.2025.102124","url":null,"abstract":"<div><div>Makespan is a key metric for evaluating project progress, while energy consumption directly impacts green performance metrics. These are the key metrics that managers focus on. Based on this, an energy-aware multi-mode resource-constrained project scheduling problem is proposed. However, activity durations in real project scheduling are often uncertain. Energy consumption and makespan cannot be accurately evaluated due to uncertain activity durations. In response to this, a multi-objective mixed-integer linear programming (MILP) model is proposed to trade off makespan and total energy consumption with uncertainty. Then, the uncertainty level and reliability level are introduced to quantify uncertain activity durations. Finally, the MILP model is transformed into a robust counterpart model to obtain robust non-dominated solutions for small-scale instances. Additionally, a matheuristic-oriented multi-objective evolutionary algorithm is designed to address large-scale instances. Finally, extensive numerical experiments are conducted to validate the proposed model and algorithm. The experimental results demonstrate that the robust counterpart model can quickly obtain a set of robust non-dominated solutions for small-scale instances. The matheuristic local optimization approach can indeed rapidly improve the quality of robust non-dominated solutions. Furthermore, the matheuristic-oriented multi-objective evolutionary algorithm outperforms state-of-the-art algorithms in terms of several multi-objective evaluation indicators.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102124"},"PeriodicalIF":8.5,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144858251","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":"Novel meta learning-artificial intelligence model for systematically detecting potential pan cancer-related targets","authors":"Jiang Qi-yu ,&nbsp;Zeng hui-yan","doi":"10.1016/j.engappai.2025.111976","DOIUrl":"10.1016/j.engappai.2025.111976","url":null,"abstract":"<div><h3>Aims</h3><div>This study developed a novel artificial intelligence-driven computational framework to systematically detect potential PPOG (pan cancer-related targets).</div></div><div><h3>Methods</h3><div>Ribonucleic acid (RNA) transcriptome data and Deoxyribonucleic acid (DNA) methylation data of seven cancers were collected from the Cancer Genome Atlas database. A novel model “Few shot-Graph Convolutional Neural Network Model” (FSGCNM) integrating graph neural convolutional networks, meta learning, and newly developed algorithms was designed to systematically detect PPOG. The predictive performances of FSGCNM on cancer classifications with different numbers of support sets and query sets were compared, respectively. The performances of FSGCNM on systematically detecting PPOG were evaluated by comparing with Random Forest and Extreme Gradient Boosting. To further show the reliability of the model, differential expression analysis and Receiver Operating Characteristic Curve fitting analysis were further performed to demonstrate the pan cancer likelihoods of the top 10 PPOG.</div></div><div><h3>Findings</h3><div>Total of 84 potential PPOG was identified based on FSGCNM.</div></div><div><h3>Consequences</h3><div>The novel artificial intelligence method helps to systematically detect PPOG. <strong>The model and algorithm used in this study are the contribution in artificial intelligence, and the detection of pan cancer genes is the application in engineering.</strong></div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"160 ","pages":"Article 111976"},"PeriodicalIF":8.0,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144858428","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":"Making hierarchically aware decisions on short findings for automatic summarisation","authors":"Emrah Inan","doi":"10.1016/j.jocs.2025.102692","DOIUrl":"10.1016/j.jocs.2025.102692","url":null,"abstract":"<div><div>An impression in a typical radiology report emphasises critical information by providing a conclusion and reasoning based on the findings. However, the findings and impression sections of these reports generally contain brief texts, as they highlight crucial observations derived from the clinical radiograph. In this scenario, abstractive summarisation models often experience a degradation in performance when generating short impressions. To address this challenge in the summarisation task, our work proposes a method that combines well-known fine-tuned text classification and abstractive summarisation language models. Since fine-tuning a language model requires an extensive, well-defined training dataset and is a time-consuming task dependent on high GPU resources, we employ prompt engineering, which uses prompt templates to programme language models and improve their performance. Our method first predicts whether the given findings text is normal or abnormal by leveraging a fine-tuned language model. Then, we apply a radiology-specific BART model to generate the summary for abnormal findings. In the zero-shot setting, our method achieves remarkable results compared to existing approaches on a real-world dataset. In particular, our method achieves scores of 37.43 for ROUGE-1, 21.72 for ROUGE-2, and 35.52 for ROUGE-L.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"91 ","pages":"Article 102692"},"PeriodicalIF":3.7,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144852163","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}