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25th International Conference on information Security Applications, WISA 2024

作者： Mbow, Mariama Roman, Rodrigo Seal, Ayan Wang, Kevin I-Kai Mohanty, Sraban Kumar Sakurai, Kouichi Department of Information Science and Technology Kyushu University Fukuoka Japan Lab University of Malaga Málaga Spain PDPM Indian Institute of Information Technology Design and Manufacturing Jabalpur482005 India Department of Electrical Computer and Software Engineering The University of Auckland Auckland1010 New Zealand

ISBN: (纸本)9789819616237

Recently, adversarial attacks against machine learning-based network intrusion detection systems (NIDS) have gained significant attention in cybersecurity. This study investigates the transferability of these adversarial attacks in the context of NIDS. In addition, most existing studies in adversarial learning against NIDS adopted attack strategies designed originally for image classification without considering network traffic characteristics. However, this is impractical and will fail in the real world as network traffic features are constrained, and ignoring the functional behavior of the network traffic features leads to invalid network traffic flow or produces adversarial samples that do not retain their original functionality (malicious or benign). To address these issues, we propose a constrained momentum iterative fast gradient sign method (C-MIFGSM) to generate adversarial network flows that can successfully evade an ML-based IDS through transfer-based attacks while preserving the functional behavior of the network traffic. Our approach was validated using several target NIDS models built with the NSLKDD benchmark dataset. Experimental results demonstrate that even without knowledge of the target model and under feature constraints, it is possible to generate adversarial network traffic flows that achieve a high evasion attack success rate against NIDS built with deep learning and classical ML models. For example, the attack degraded the detection rate of the DoS traffic drops from 92.35% to 20.27% for the MLP model with an evasion increase rate of 78.04%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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OBoctNet: Enhancing Ophthalmic Biomarker Detection Through Active Learning and Explainable AI in Radiological Analysis

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Cognitive Computation 2025年第3期17卷 1-18页

作者： Acharja, Samya Hasan, Md. Zahid Chamok, Farjana Haque Fahim, Kayes Uddin Shuva, Taslima Ferdaus Bulbul, Abdullah Al-Mamun Khan, Risala Tasin Rahman, Md. Tanvir Kaiser, M. Shamim Mahmud, Mufti Moni, Mohammad Ali Health Informatics Research Lab Department of Computer Science and Engineering Daffodil International University Daffodil Smart City Savar Dhaka1341 Bangladesh School of Health and Rehabilitation Sciences The University of Queensland St Lucia BrisbaneQLD4072 Australia Electronics and Communication Engineering Discipline Khulna University Khulna9208 Bangladesh Institute of Information Technology Jahangirnagar University Savar Dhaka1342 Bangladesh Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Tangail1902 Bangladesh Information and Computer Science Department King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia SDAIA-KFUPM Joint Research Center for AI King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia Interdisciplinary Research Center for Bio Systems and Machines King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia AI and Cyber Futures Institute Charles Sturt University BathurstNSW2795 Australia Rural Health Research Institute Charles Sturt University OrangeNSW2800 Australia School of Information Technology Washington University of Science and Technology AlexandriaVA22314 United States

Optical coherence tomography (OCT) scanning is crucial for the diagnosis of widespread ophthalmic diseases. Traditionally, experts manually identify diseases and biomarkers from OCT scans. Recently, modern medical imaging practices have increasingly utilized deep learning techniques to speed up and improve diagnostic accuracy in ophthalmology. However, obtaining accurately labeled datasets is a significant challenge in medical imaging due to the expertise required for precise annotation by trained professionals. This article presents a novel method, named OBoctNet, with a new two-stage training strategy to improve the identification of ophthalmic biomarkers using OCT scans from the OLIVES dataset, which contains only 12% labeled data. This approach leverages a robust methodology that effectively uses labeled and unlabeled data to enhance biomarker identification accuracy, achieving a cumulative performance increase of 23% across 50% of the biomarkers when compared to the previous studies. To better identify biomarkers, the OBoctNet employs an active learning strategy that uses unlabeled data and dynamically ensembles models based on their performance within each experimental setup. Additionally, the usage of Gradient weighted Class Activation Mapping (GradCAM) helps identify regions of interest associated with relevant biomarkers, enhancing interpretability and transparency for potential clinical adoption. © The Author(s) 2025.

关键词： Semi-supervised learning

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High-Performance Synchronization Control of Multi-Axis Motion System Based on Fully-Actuated System Approach 26

High-Performance Synchronization Control of Multi-Axis Motio...

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26th International Conference on Industrial technology, ICIT 2025

作者： Zhang, Dufei Wang, Yao-Wei Gu, Caoyuan Jia, Yu Wu, Qi Wu, Xiang School of Information Science and Engineering Wuhan University of Science and Technology Wuhan30064 China Wuhan University of Science and Technology Engineering Research Center of Metallurgical Automation and Measurement Technology Wuhan430064 China Zhejiang University of Technology Department of Automation China Zhejiang Provincial United Key Laboratory of Embedded Systems Hangzhou310023 China

ISBN: (纸本)9798331521950

This paper investigates a class of synchronization control problem of multi-axis motion systems(MAMSs) with uncertainty and external disturbances. Firstly, a system model that satisfies the full-actuation condition is obtained by performing differential decomposition on each axis(subsystem). The uncertainty and external disturbances are treated as the lumped disturbances of the system. Secondly, a fully-actuated system approach based controller is designed to obtain a linear closed-loop system, which the parameters of the system are determined by parametric approach. Furthermore, the synchronization error model is established and a generalized extended state observer(GESO) is designed to estimate the system state while also estimating the lumped disturbances of the system. Then, the controller with disturbance compensation is designed to achieve high-performance synchronization control of the MSMSs, and system stability analysis is provided. Finally, the effectiveness and superior performance of the proposed approach is verified by simulation experiments. © 2025 IEEE.

关键词： Closed loop control systems

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MeHyper: Accelerating Hypergraph Neural Networks by Exploring Implicit Dataflows 31

MeHyper: Accelerating Hypergraph Neural Networks by Explorin...

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31st IEEE International Symposium on High Performance Computer Architecture, HPCA 2025

作者： Zhao, Wenju Yao, Pengcheng Chen, Dan Zheng, Long Liao, Xiaofei Wang, Qinggang Ma, Shaobo Li, Yu Liu, Haifeng Xiao, Wenjing Sun, Yufei Zhu, Bing Jin, Hai Xue, Jingling Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China National University of Singapore School of Computing 119077 Singapore Guangxi University School of Computer Electronics and Information NanNing530004 China University of New South Wales School of Computer Science and Engineering SydneyNSW2052 Australia

ISBN: (纸本)9798331506476

Hypergraph Neural Networks (HGNNs) are increasingly utilized to analyze complex inter-entity relationships. Traditional HGNN systems, based on a hyperedge-centric dataflow model, independently process aggregation tasks for hyperedges and vertices, leading to significant computational redundancy. This redundancy arises from recalculating shared information across different tasks. For the first time, we identify and harness implicit dataflows (i.e., dependencies) within HGNNs, introducing the microedge concept to effectively capture and reuse intricate shared information among aggregation tasks, thereby minimizing redundant computations. We have developed a new microedge-centric dataflow model that processes shared information as fine-grained microedge aggregation tasks. This dataflow model is supported by the Read-Process-Activate-Generate execution model, which aims to optimize parallelism among these tasks. Furthermore, our newly developed MeHyper, a microedge-centric HGNN accelerator, incorporates a decoupled pipeline for improved computational parallelism and a hierarchical feature management strategy to reduce off-chip memory accesses for large volumes of intermediate feature vectors generated. Our evaluation demonstrates that MeHyper substantially outperforms the leading CPUbased system PyG-CPU and the GPU-based system HyperGef, delivering performance improvements of 1,032.23 × and 10.51 ×, and energy efficiencies of 1,169.03 × and 9.96 ×, respectively. © 2025 IEEE.

关键词： Redundancy

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Enhanced Student Stress Prediction in Higher Education Using a TriBranch CNN Framework 3

Enhanced Student Stress Prediction in Higher Education Using...

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3rd International Conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Kunwar, Fateh Bahadur Srivastava, Satyajee Dhivya, R. Gulati, Monika Amudha, K. Raj, D. Antony Arul Geetanjali Institute of Technical Studies Department of Computer Science and Engineering Udaipur India School of Engineering and Technology Manav Rachna International Institute of Research and Studies Department of Computer Science and Engineering Faridabad India M.Kumarasamy College of Engineering Department of Information Technology Karur India Mullana Ambala India Kongunadu College of Engineering and Technology Department of Electronics and Communication Engineering Trichy India Psg College of Arts & Science Department of Software Systems Coimbatore India

ISBN: (纸本)9798331508456

Students' stress levels have a significant impact on their academic performance in higher education, thus it's important to identify signs of stress early on and intervene to improve academic performance. Machine learning has evolved into a powerful tool for stress prediction, allowing for proactive support of at-risk youngsters. In order to better anticipate college students' stress levels, this study presents an enhanced TriBranch CNN framework. In order to provide the best possible performance, the technique includes data preparation, feature extraction, and model training. The dimensionality and redundancy are reduced using Principal Component Analysis (PCA). After being trained and tested against other models, the Fully Conditional Temporal Bayesian Network (Fully CTBN) outperformed them all with an accuracy of 93.29%, beating both traditional CNN and FCN approaches. These findings demonstrate that the Fully CTBN approach is useful for identifying students who may be struggling academically and providing schools with the information they need to implement data-driven interventions that boost student well-being and performance. © 2025 IEEE.

关键词： Students

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Analysis of Light-Weight Cryptography Algorithms for UAV-Networks

arXiv

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arXiv 2025年

作者： Patel, Aanchal Cherukuri, Aswani Kumar School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore632014 India

Unmanned Aerial Vehicles (UAVs) are increasingly utilized across various domains, necessitating robust security measures for their communication networks. The ASCON family, a NIST finalist in lightweight cryptography standards, is known for its simplistic yet resilient design, making it well-suited for resource-constrained environments characterized by limited processing capabilities and energy reservoirs. This study focuses on understanding the integration and assessment of the ASCON encryption algorithm in UAV networks, emphasizing its potential as a lightweight and efficient cryptographic solution. The research objectives aim to evaluate ASCON variants' effectiveness in providing security comparable to AES-128 while exhibiting lower computational cost and energy consumption within simulated UAV network environments. Comparative analysis is conducted to assess performance metrics such as encryption and decryption speeds, resource utilization, and resistance to cryptographic vulnerabilities against established algorithms like AES. The proposed architecture outlines the setup for evaluating ASCON and AES in a simulated UAV network environment, focusing on performance monitoring, security analysis, and comparative studies. Performance metrics including peak and average execution times, overall throughput, and security properties against various cryptographic attacks are measured and analysed to determine the most suitable cryptographic algorithm for UAV communication systems. Performance results indicate that ASCON-128a as the optimal choice for UAV communication systems requiring a balance between efficiency and security. Its superior performance metrics, robust security properties, and suitability for resource-constrained environments position it as the preferred solution for securing UAV communication networks. By leveraging the strengths of ASCON-128a, UAV communication systems can achieve optimal performance and security, ensuring reliable and secure communicati

关键词： Unmanned aerial vehicles (UAV)

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ALScN Lamb Wave Resonator Based on NbN Superconducting Electrode at Cryogenic Temperature 38

ALScN Lamb Wave Resonator Based on NbN Superconducting Elect...

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38th IEEE International Conference on Micro Electro Mechanical systems, MEMS 2025

作者： Li, Wenzhen Xu, Xuankai Li, Jiawei Dong, Peng Wang, Yiwei Xiong, Ruihong Li, Jun Wu, Tao School of Information Science and Technology ShanghaiTech University Shanghai China State Key Lab of Transducer Technology Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China University of Chinese Academy of Sciences Beijing China School of Physical Science and Technology ShanghaiTech University Shanghai China Shanghai Engineering Research Center of Energy Efficient and Custom Ai Ic Shanghai China

ISBN: (纸本)9798331508890

This work presents a S0 mode Lamb wave resonator (LWR) leveraging niobium nitride (NbN) as a superconducting electrode material on the Al0.7Sc0.3N platform. A substantial improvement in the resonator's quality factor (Q) was observed near the superconducting transition temperature of NbN, indicating a reduction in energy dissipation within the superconducting state. Analysis based on the special modified Butterworth-Van Dyke (MBVD) model revealed that electrode loss followed the same decreasing trend as the resistance of NbN at temperatures below 10 K, with both parameters approaching zero at 4K. When operating at this low temperature, the quality factor of the resonator reaches 1524, which is approximately 7.29 times higher than room temperature. Additionally, the figure-of-merit (Fom = Q∗kt2) reaches 57.89 was attained, which is approximately 5.76 times higher than room temperature. These advancements underline the promise of superconducting NbN electrodes in LWRs for next-generation hybrid quantum networks. © 2025 IEEE.

关键词： Superconducting transition temperature

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Automated explainable and interpretable framework for anomaly detection and human activity recognition in smart homes

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Neural Computing and Applications 2025年 1-14页

作者： Kim, Tae Hoon Ojo, Stepehn Krichen, Moez Alamro, Meznah A. Mihoub, Alaeddine Sampedro, Gabriel Avelino School of Information and Electronic Engineering Zhejiang University of Science and Technology No. 318 Zhejiang Hangzhou China Department of Electrical and Computer Engineering College of Engineering Anderson Anderson University AndersonSC29621 United States Al-Baha University Al-Baha65528 Saudi Arabia ReDCAD Laboratory University of Sfax Sfax3038 Tunisia Department of Information Technology College of Computer and Information Science Princess Nourah Bint Abdul Rahman University Riyadh11564 Saudi Arabia Department of Management Information Systems College of Business and Economics Qassim University P.O. Box: 6640 Buraidah51452 Saudi Arabia College of Computer Studies De La Salle University 2401 Taft Ave. Malate Manila1004 Philippines Faculty of Information and Communication Studies University of the Philippines Open University Los Baños4031 Philippines

This paper presents a comprehensive framework for activity recognition and anomaly detection in smart home environments, targeting applications in convenience, efficiency, responsiveness, and healthcare. The proposed framework incorporates explainable artificial intelligence (XAI) to interpret feature impacts on learning models and optimize feature selection for activity recognition and anomaly detection. The study examines the recognition of two activity sets: Activities Set I, comprising 8 activities, and Activities Set II, comprising 11 activities. Various machine learning models (random forest (RF), decision tree (DT), and XGBoost (XGB)) are employed, along with an ensemble voting classifier (EVC) with these machine learning. EVC achieved 91% accuracy for Activities Set I and 90–91% accuracy for Activities Set II. The model demonstrated strong precision, recall, and F1-score across most classes. For anomaly detection, the Isolation Forest and H2O Isolation Forest Estimator algorithms are utilized to uncover irregularities in daily routines. The Isolation Forest identified anomalies in activities such as chores and desk-related tasks, while the H2O Isolation Forest Estimator detected no anomalies in either activity set. This study underscores the potential of the proposed framework to enhance smart home functionality and highlights avenues for future research and system improvements. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Adversarial machine learning

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VisioSLR: A Vision Data-Driven Framework for Sign Language Video Recognition and Performance Evaluation on Fine-Tuned YOLO Models

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Procedia Computer science 2025年 257卷 85-92页

作者： Leila Ismail Nada Shahin Henok Tesfaye Alain Hennebelle Intelligent Distributed Computing and Systems (INDUCE) Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates Independent Researcher

With the emergence of AI for good, there has been an increasing interest in building computer vision data-driven deep learning inclusive AI solutions. Sign language Recognition (SLR) has gained attention recently. It is an essential component of a sign-to-text translation system to support the deaf and hard-of-hearing population. This paper presents a computer VISIOn data-driven deep learning framework for Sign Language video Recognition (VisoSLR). VisioSLR provides a precise measurement of translating signs for developing an end-to-end computational translation system. Considering the scarcity of sign language datasets, which hinders the development of an accurate recognition model, we evaluate the performance of our framework by fine-tuning the very well-known YOLO models, which are built from a signs-unrelated collection of images and videos, using a small-sized sign language dataset. Gathering a sign language dataset for signs training would involve an enormous amount of time to collect and annotate videos in different environmental setups and multiple signers, in addition to the training time of a model. Numerical evaluations of VisioSLR show that our framework recognizes signs with a mean average precision of 97.4%, 97.1%, and 95.5% and 11, 12, and 12 milliseconds of recognition time on YOLOv8m, YOLOv9m, and YOLOv11m, respectively.

关键词： Artificial intelligence Computer vision Deep learning Fine-tuning Machine translation Neural machine translation Sign language Sign language recognition Transfer learning YOLO

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LLMs as Repositories of Factual Knowledge: Limitations and Solutions

arXiv

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arXiv 2025年

作者： Mousavi, Seyed Mahed Alghisi, Simone Riccardi, Giuseppe Signals and Interactive Systems Lab Department of Information Engineering and Computer Science University of Trento Italy

LLMs’ sources of knowledge are data snapshots containing factual information about entities collected at different timestamps and from different media types (e.g. wikis, social media, etc.). Such unstructured knowledge is subject to change due to updates through time from past to present. Equally important are the inconsistencies and inaccuracies occurring in different information sources. Consequently, the model’s knowledge about an entity may be perturbed while training over the sequence of snapshots or at inference time, resulting in inconsistent and inaccurate model performance. In this work, we study the appropriateness of Large Language Models (LLMs) as repositories of factual knowledge. We consider twenty-four state-of-the-art LLMs that are either closed-, partially (weights), or fully (weight and training data) open-source. We evaluate their reliability in responding to time-sensitive factual questions in terms of accuracy and consistency when prompts are perturbed. We further evaluate the effectiveness of state-of-the-art methods to improve LLMs’ accuracy and consistency. We then propose "ENtity-Aware Fine-tuning" (ENAF), a soft neurosymbolic approach aimed at providing structured representation of entities during fine-tuning to improve the model’s performance. © 2025, CC BY-NC-SA.

关键词： Structured Query Language

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