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27th International Conference on Pattern Recognition, ICPR 2024

作者： Afeefa, P.P. Hazari, Raju Das, Pranesh Machine Learning Laboratory Department of Computer Science and Engineering National Institute of Technology Calicut Kozhikode India

ISBN: (纸本)9783031781186

Automated Short Answer Grading (ASAG) comes under automatic answer script evaluation where the answer length is limited from one phrase to one paragraph. The main task in ASAG is generating a good sentence embedding for both the student and the reference answers. The existing works on the embedding creation perform better when using different deep-learning techniques and language models. However, the deep-learning techniques’ performance mainly depends on the training set size and quality. Most of the publicly available datasets typically have a limited number of reference and student answer pairs. To automate the dataset expansion, text augmentation techniques can be used. Conventional methods like back-translation, synonym replacement, and random deletion may replace some important technical words with other non-relevant terms, resulting in a loss of contextual meaning. We propose a new augmentation strategy for the ASAG datasets using LLM (Large Language Model) prompting. The effect of the proposed strategy is analysed on sentence transformer fine-tuning. We experimented with four different sizes of augmented training sets to determine the impact of the size of augmented training data on fine-tuning the sentence transformer model. Results indicate that sentence transformer fine-tuned using a 50% prompt-driven augmented dataset generates better embeddings. After having good embeddings, the traditional classifiers can be used to classify the student answers to different scores. We introduce "Prompt Adaptive Oversampling (PAO)" to address the class imbalance issue during grade classification. The effectiveness of the proposed strategy is analysed on two different public datasets: SPRAG, and Mohler-ASAG. The proposed method performs better while training highly imbalanced datasets. The source code of this work is available here. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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An Empirical Study on Meta Virtual Reality Applications: Security and Privacy Perspectives

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IEEE Transactions on software engineering 2025年第5期51卷 1437-1454页

作者： Guo, Hanyang Dai, Hong-Ning Luo, Xiapu Xu, Gengyang He, Fengliang Zheng, Zibin Hong Kong Baptist University Department of Computer Science 999077 Hong Kong Sun Yat-sen University School of Software Engineering Zhuhai519082 China The Hong Kong Polytechnic University Department of Computing 999077 Hong Kong

Virtual Reality (VR) has accelerated its prevalent adoption in emerging metaverse applications, but it is not a fundamentally new technology. On the one hand, most VR operating systems (OS) are based on off-the-shelf mobile OS (e.g., Android OS). As a result, VR apps also inevitably inherit privacy and security deficiencies from conventional mobile apps. On the other hand, in contrast to traditional mobile apps, VR apps can achieve an immersive experience via diverse VR devices, such as head-mounted displays, body sensors, and controllers. However, achieving this requires the extensive collection of privacy-sensitive human biometrics (e.g., hand-tracking and face-tracking data). Moreover, VR apps have been typically implemented by 3D gaming engines (e.g., Unity), which also contain intrinsic security vulnerabilities. Inappropriate use of these technologies may incur privacy leaks and security vulnerabilities although these issues have not received significant attention compared to the proliferation of diverse VR apps. In this paper, we develop a security and privacy assessment tool, namely the VR-SP detector for VR apps. The VR-SP detector has integrated program static analysis tools and privacy-policy analysis methods. Using the VR-SP detector, we conduct a comprehensive empirical study on 900 popular VR apps. We obtain the original apps from the popular SideQuest app store and extract Android PacKage (APK) files via the Meta Quest 2 device. We evaluate the security vulnerabilities and privacy data leaks of these VR apps through VR app analysis, taint analysis, privacy policy analysis, and user review analysis. We find that a number of security vulnerabilities and privacy leaks widely exist in VR apps. Moreover, our results also reveal conflicting representations in the privacy policies of these apps and inconsistencies of the actual data collection with the privacy-policy statements of the apps. Further, user reviews also indicate their privacy concerns about rele

关键词： Differential privacy

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T³Planner: Multi-Phase Planning Across Structure-Constrained Optical, IP, and Routing Topologies

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IEEE Journal on Selected Areas in Communications 2025年第5期43卷 1823-1839页

作者： Hao, Yijun Yang, Shusen Li, Fang Zhang, Yifan Zhao, Cong Ren, Xuebin Zhao, Peng Xu, Chenren Wang, Shibo Xi’an Jiaotong University National Engineering Laboratory for Big Data Analytics Xi’an710049 China Xi’an Jiaotong University National Engineering Laboratory for Big Data Analytics Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an710049 China Peking University School of Computer Science Beijing100871 China The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong National Engineering Laboratory for Big Data Analytics Xi'an Jiaotong University Xi'an710049 China

Network topology planning is an essential multi-phase process to build and jointly optimize the multi-layer network topologies in wide-area networks (WANs). Most existing practices target single-phase/layer planning, and are incapable of satisfying all rigorous topological structure constraints (e.g., dual-homing rings) defined by network standards and operators, especially in large-scale networks. These significantly limit their usability and performance in production networks. We consider a general topology planning problem with typical structure constraints over three essential phases (greenfield, reconfiguration, and site expansion) and topological layers (optical, IP, and routing topologies). We present, T3Planner, a novel practical solver to this problem in production. Specifically, we develop a structure-driven encoder based on graph neural network (GNN) for concise structure encoding, and design a new learning framework with optical-centric layer compression/reconstruction and rule-aided reinforcement learning (RL) for fast convergence and high performance. Extensive experiments on nine real topologies demonstrate that T3Planner scales to large optical networks with hundreds of sites, saves 46.6% cost, and supports 3.12x more demand when compared to related existing approaches. © 1983-2012 IEEE.

关键词： Network topology

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ACbot:an IIoT platform for industrial robots

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Frontiers of computer science 2025年第4期19卷 13-27页

作者： Rui WANG Xudong MOU Tianyu WO Mingyang ZHANG Yuxin LIU Tiejun WANG Pin LIU Jihong YAN Xudong LIU School of Computer Science and Engineering Beihang UniversityBeijing 100191China Zhongguancun Laboratory Beijing 100194China School of Software Beihang UniversityBeijing 100191China School of Mechatronics Engineering Harbin Institute of TechnologyHarbin 150001China Hangzhou Innovation Institute Beihang UniversityHangzhou 310051China School of Information Engineering China University of GeosciencesBeijing 100083China

As the application of Industrial Robots(IRs)scales and related participants increase,the demands for intelligent Operation and Maintenance(O&M)and multi-tenant collaboration *** methods could no longer cover the requirements,while the Industrial Internet of Things(IIoT)has been considered a promising ***,there’s a lack of IIoT platforms dedicated to IR O&M,including IR maintenance,process optimization,and knowledge *** this context,this paper puts forward the multi-tenant-oriented ACbot platform,which attempts to provide the first holistic IIoT-based solution for O&M of *** on an information model designed for the IR field,ACbot has implemented an application architecture with resource and microservice management across the cloud and multiple *** this basis,we develop four vital applications including real-time monitoring,health management,process optimization,and knowledge *** have deployed the ACbot platform in real-world scenarios that contain various participants,types of IRs,and *** date,ACbot has been accessed by 10 organizations and managed 60 industrial robots,demonstrating that the platform fulfills our ***,the application results also showcase its robustness,versatility,and adaptability for developing and hosting intelligent robot applications.

关键词： IIoT platform industrial robots cloud-edge collaboration intelligent applications

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RF-Based Sensing and AI Decision Support for Stroke Patient Monitoring: A Digital Twin Approach

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IEEE Access 2025年 13卷 74047-74061页

作者： Khan, Sagheer Anwar, Usman Khan, Aftab Arslan, Tughrul The University of Edinburgh School of Engineering EdinburghEH9 3FF United Kingdom Department of Computer and Software Engineering CoEME Islamabad44000 Pakistan Allama Iqbal Open University Department of Computer Science Islamabad44310 Pakistan

Stroke occurs when the blood flow to a certain region of the brain is disrupted. It is a leading cause of long-term disability and can result in cognitive impairments, speech difficulties, and motor dysfunction. Regular monitoring and timely intervention are critical to minimizing the damage and improving outcomes. This article presents a novel Radio Frequency (RF) sensing and Artificial Intelligence (AI) based Digital Twin (DT) model for effective detection of stroke. Through backscattering RF signals, the proposed Ultra Wide Band (UWB) antenna provides stroke detection. The implementation of Machine Learning (ML) and Deep Learning (DL) technologies for stroke classification provides the necessary decision support to healthcare professionals in DT stroke patient monitoring. The statistical and autonomous (AutoEncoders (AE) and Stacked AutoEncoders (SAE) with structure 32-16-32, 64-32-16-32-64, and 128-64-32-16-32-64-128) feature data is enlarged through Gaussian noise feature data augmentation. The Fine KNN algorithm provides the 93.4% and 92.3% classification accuracies of binary and multi-class classification respectively. Out of the 4 autonomous feature extraction methods, the Fine KNN algorithm with SAE structure 64-32-16-32-64 provided the highest accuracies of 88.2% and 74.8% for binary and multi-class classification. © 2013 IEEE.

关键词： Deep learning

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A Hierarchical Progressive Perception System for Autonomous Luggage Trolley Collection

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IEEE Transactions on Automation science and engineering 2025年 22卷 14484-14497页

作者： Sun, Zhirui Zhang, Zhe Zhao, Jieting Ye, Hanjing Wang, Jiankun Southern University of Science and Technology Shenzhen Key Laboratory of Robotics Perception and Intelligence Department of Electronic and Electrical Engineering Shenzhen518055 China Southern University of Science and Technology Jiaxing Research Institute Jiaxing314000 China Southern University of Science and Technology Shenzhen Key Laboratory of Robotics and Computer Vision Department of Electronic and Electrical Engineering Shenzhen518055 China

Advancements in intelligent vehicle technologies and autonomous driving are now used for tasks like collecting luggage trolleys at airports. The robotic autonomous luggage trolley collection system employs robots to gather and transport scattered luggage trolleys. However, existing methods for detecting and locating luggage trolleys often fail when they are not fully visible. To address this, we introduce the hierarchical progressive perception system, which enhances the detection and localization of luggage trolleys under partial occlusion. The proposed system integrates the hierarchical process structure and progressive perception strategy. This innovative structure processes the luggage trolley’s position and orientation separately. It can accurately determine the luggage trolley’s position with just one well-detected keypoint and estimate its orientation when partially occluded. Once the luggage trolley’s initial pose is detected, the progressive perception strategy continuously refines this information until the robot begins grasping. The proposed system only needs RGB images for labeling and training, eliminating the need for complex data collection and annotation. The experiments on detection and localization demonstrate that the proposed system is more reliable under partial occlusion compared to existing methods. Its effectiveness and robustness have also been confirmed through practical tests in actual luggage trolley collection tasks. © 2004-2012 IEEE.

关键词： Intelligent robots

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Uni4DAL: A Unified Baseline for Multi-dataset 4D Auto-Labeling 27th

Uni4DAL: A Unified Baseline for Multi-dataset 4D Auto-Label...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Yang, Zhiyuan Wang, Xuekuan Zhang, Wei Tan, Xiao Lu, Jinchen Wang, Jingdong Ding, Errui Lai, Zhihui Zhao, Cairong Department of Computer Science and Technology Tongji University Shanghai201804 China Baidu Inc. Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9783031781124

The 4D auto-labeling system, with its potential to enhance data annotation efficiency for 3D object detection, has garnered significant attention. However, its adoption has been hampered by the high costs associated with temporally annotated long-sequential training data and limited generalization capabilities across diverse scenarios. In this paper, we hypothesize that a multi-dataset approach can address these challenges and, accordingly, first introduce a Unified training pipeline for multi-dataset 4D Auto-Labeling, namely Uni4DAL. We recognize that this is a challenging task, primarily due to data-level variations and feature-level inconsistencies among various datasets. Motivated by this understanding, we initially propose a series of Data-Level Alignment (DLA) operations to mitigate potential discrepancies between diverse datasets and ensure synchronized training progress across samples from multiple datasets. Furthermore, to address feature-level inconsistencies, we introduce the Mixed Expert Models Voxel Feature Encoding (MoE-VFE) module, which aims to extract both domain-specific and domain-generalizable features. Additionally, we employ a Domain-Adaptive Hard Example Mining (DA-HEM) technique to leverage both data-level and feature-level consistencies, ensuring that the model pays enhanced attention to the challenging samples during multi-dataset training. Finally, comprehensive experiments demonstrate that Uni4DAL significantly improves performance on the nuScenes, Argoverse2, and Waymo datasets, and exhibits greater robustness with insufficient training data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： 4D auto-labeling Multi-dataset training Offline 3D object detection

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Fixed-point quantum continuous search algorithm with optimal query complexity

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science China(Physics,Mechanics & Astronomy) 2025年第6期Mechanics & Astronomy) .卷 84-95页

作者： Shan Jin Yuhan Huang Shaojun Wu Guanyu Zhou Chang-Ling Zou Luyan Sun Xiaoting Wang Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Key Laboratory of Quantum Information CAS University of Science and Technology of China Hefei National Laboratory Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University

Continuous search problems(CSPs), which involve finding solutions within a continuous domain, frequently arise in fields such as optimization, physics, and engineering. Unlike discrete search problems, CSPs require navigating an uncountably infinite space, presenting unique computational challenges. In this work, we propose a fixed-point quantum search algorithm that leverages continuous variables to address these challenges, achieving a quadratic speedup. Inspired by the discrete search results, we manage to establish a lower bound on the query complexity of arbitrary quantum search for CSPs, demonstrating the optimality of our approach. In addition, we demonstrate how to design the internal structure of the quantum search oracle for specific problems. Furthermore, we develop a general framework to apply this algorithm to a range of problem types, including optimization and eigenvalue problems involving continuous variables.

关键词： quantum search quantum algorithm quantum computation optimization spectral problems

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Evaluating AI-powered predictive solutions for MRI in lumbar spinal stenosis: a systematic review

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Artificial Intelligence Review 2025年第8期58卷 1-78页

作者： Al-antari, Mugahed A. Salem, Saied Raza, Mukhlis Elbadawy, Ahmed S. Bütün, Ertan Aydin, Ahmet Arif Aydoğan, Murat Ertuğrul, Bilal Talo, Muhammed Gu, Yeong Hyeon Department of Artificial Intelligence and Data Science College of AI Convergence Daeyang AI Center Sejong University Seoul05006 Korea Republic of Department of Artificial Intelligence College of AI Convergence Daeyang AI Center Sejong University Seoul05006 Korea Republic of Department of Computer Engineering Faculty of Engineering Fırat University Elazığ Turkey Department of Computer Engineering Faculty of Engineering Inonu University Malatya Turkey Department of Software Engineering Faculty of Technology Fırat University Elazığ Turkey Department of Neurosurgery Faculty of Medicine Fırat University Elazığ Turkey Department of Computer Science & Engineering University of North Texas DentonTX76205 United States

Lumbar spinal stenosis (LSS) involves the narrowing of the spinal canal, leading to compression of the spinal cord and nerves in the lower back. Common causes include injuries, degenerative age-related changes, congenital conditions, and tumors, all of which contribute to back pain. Early diagnosis is critical for symptom management, preventing progression, and preserving quality of life. This study systematically reviews AI-based approaches for predicting LSS using MRI axial and sagittal imaging. The review focuses on various AI tasks: detection, segmentation, classification, hybrid approaches, spinal index measurements (SIM), and explainable AI frameworks. The aim is to highlight current knowledge, identify limitations in existing models, and propose future research directions. Following PRISMA guidelines and the PICO method (Population, Intervention, Comparison, Outcome), the review collects data from databases like PubMed, Web of science, scienceDirect, and IEEE Xplore (2005–2024). The Rayyan AI tool is used for duplicate removal and screening. The screening process includes an initial review of titles and abstracts, followed by full-text appraisal. The Meta Quality Appraisal Tool (MetaQAT) assesses the quality of selected articles. Of 1323 records, 97 duplicates were removed. After screening, 895 records were excluded, leaving 331 for full-text review. Among these, 184 articles were excluded for lacking AI relevance. Ultimately, 95 key articles (91 technical papers and 4 reviews) were identified for their contributions to AI-based LSS prediction. This review provides a comprehensive analysis of AI techniques in LSS prediction, guiding future research and advancing understanding in areas like explainable AI and large language models (LLMs). © The Author(s) 2025.

关键词： Explainable artificial intelligence (XAI) Harmonization Large language models (LLMs) LSS prediction Lumbar spinal stenosis (LSS) Spinal LSS indices measurements

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Parkinson’s Disease Detection from Resting State EEG Using Multi-head Graph Structure Learning with Gradient Weighted Graph Attention Explanations 7th

Parkinson’s Disease Detection from Resting State EEG Using...

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7th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2024, Held in Conjunction with 27th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Neves, Christopher Zeng, Yong Xiao, Yiming Department of Computer Science and Software Engineering Concordia University MontrealQC Canada Concordia Institute for Information Systems Engineering Concordia University MontrealQC Canada

ISBN: (纸本)9783031787607

Parkinson’s disease (PD) is a debilitating neurodegenerative disease that has severe impacts on an individual’s quality of life. Compared with structural and functional MRI-based biomarkers for the disease, electroencephalography (EEG) can provide more accessible alternatives for clinical insights. While deep learning (DL) techniques have provided excellent outcomes, many techniques fail to model spatial information and dynamic brain connectivity, and face challenges in robust feature learning, limited data sizes, and poor explainability. To address these issues, we proposed a novel graph neural network (GNN) technique for explainable PD detection using resting state EEG. Specifically, we employ structured global convolutions with contrastive learning to better model complex features with limited data, a novel multi-head graph structure learner to capture the non-Euclidean structure of EEG data, and a head-wise gradient-weighted graph attention explainer to offer neural connectivity insights. We developed and evaluated our method using the UC San Diego Parkinson’s disease EEG dataset, and achieved 69.40% detection accuracy in subject-wise leave-one-out cross-validation while generating intuitive explanations for the learnt graph topology. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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