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arXiv 2025年

作者： Xie, Songyan Wen, Jinghang Su, Encheng Yu, Qiucheng School of Computer Science and Technology China University of Mining and Technology Xuzhou China Department of Computer Science City University of Hong Kong Hong Kong Department of Engineering and Design Technische Universität München München Germany

Near-infrared (NIR) face recognition systems, which can operate effectively in low-light conditions or in the presence of makeup, exhibit vulnerabilities when subjected to physical adversarial attacks. To further demonstrate the potential risks in real-world applications, we design a novel, stealthy, and practical adversarial patch to attack NIR face recognition systems in a black-box setting. We achieved this by utilizing human-imperceptible infrared-absorbing ink to generate multiple patches with digitally optimized shapes and positions for infrared images. To address the optimization mismatch between digital and real-world NIR imaging, we develop a light reflection model for human skin to minimize pixel-level discrepancies by simulating NIR light reflection. Compared to state-of-the-art (SOTA) physical attacks on NIR face recognition systems, the experimental results show that our method improves the attack success rate in both digital and physical domains, particularly maintaining effectiveness across various face postures. Notably, the proposed approach outperforms SOTA methods, achieving an average attack success rate of 82.46% in the physical domain across different models, compared to 64.18% for existing methods. The artifact is available at https://***/r/Humanimperceptible-adversarial-patch-0703/. Copyright © 2025, The Authors. All rights reserved.

关键词： Light reflection

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Fast Inverse Model Transformation: Algebraic Framework for Fast Data Plane Verification

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Chen, Shenshen Luo, Jian Guo, Dong Gao, Kai Yang, Yang Richard Tongji University School of Computer Science and Technology Shanghai201804 China Sichuan University Chengdu School of Cyber Science and Engineering Sichuan610065 China Yale University Department of Computer Science New HavenCT06511 United States Fudan University School of Computer Science Shanghai200433 China

Data plane verification (DPV) analyzes routing tables and detects routing abnormalities and policy violations during network operation and planning. Thus, it has become an important tool to harden the networking infrastructure and the computing systems building on top. Substantial advancements have been made in the last decade and state-of-the-art DPV systems can achieve sub-µs verification for an update of a single forwarding rule. In this paper, we introduce fast inverse model transformation (FIMT), the first theoretical framework to systematically model and analyze centralized DPV systems. FIMT reveals the algebraic structure in the model update process, a key step in fast DPV systems. Thus, it can systematically analyze the correctness of several DPV systems and optimization techniques, using algebraic properties. The theory also guides the design and implementation of Uimt, a generic DPV framework with provable optimization techniques. Using Uimt, we create two variants of existing DPV systems, NeoFlash and NeoDeltaNet. Evaluations show that NeoFlash outperforms existing state-of-the-art centralized DPV systems in various datasets and reveal insights to key techniques towards fast DPV. © 2004-2012 IEEE All rights reserved.

关键词： Algebra

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Joint Beamforming and Power Allocation Design for Wirelessly Charged Unmanned Inspecting Vehicles in Power Transmission Systems

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2025年

作者： Huang, Yingjie Abdelhady, Amr M. Wang, Qianggang Ye, Jia Chi, Yuan Eltawil, Ahmed M. El-Sherif, Amr A. ElBatt, Tamer Chongqing University State Key Laboratory of Power Transmission Equipment Technology School of Electrical Engineering Chongqing400044 China King Abdullah University of Science and Technology Computer Electrical and Mathematical Science and Engineering Division Province Makkah23955-6900 Saudi Arabia Electronics and Communications Department Egypt University of Informatics Cairo Governorate Egypt Department of Computer Science and Engineering The American University Cairo Governorate Cairo Egypt

The rapid development of the smart grid has intensified research efforts towards automating power transmission system inspections using unmanned intelligent vehicles (UIVs). A major challenge hindering their large-scale deployment is the short operational endurance of these vehicles. This paper addresses this limitation by proposing the use of an energy transmitter (ET) and a reconfigurable intelligent surface for wirelessly charging the vehicles through radio frequency (RF) signals. Additionally, we tackle the nonlinearity of RF-to-direct current (DC) conversion, which shows efficiency saturation at high power levels, by introducing a novel power-splitting (PS) energy harvesting (EH) receiver architecture in the inspection vehicles. The inspection tasks are divided into multiple time slots, and a power transfer efficiency (PTE) maximization problem is formulated to jointly optimize the beamforming design at both the transmitter and the RIS, as well as the transmit power allocation and receiving power splitter settings for each time slot. This approach aims to meet the energy requirements of the unmanned inspection vehicles while adhering to the total available power constraint at the ET. To solve the non-convex PTE maximization problem, we propose an efficient solution utilizing alternating optimization, the Dinkelbach’s method, and successive convex approximation, considering the unique properties of channel structure and finite phase resolution. Numerical results demonstrate that our proposed system and design significantly extend the lifespan of inspection vehicles and achieve high energy efficiency in a low-complexity manner. © 2017 IEEE.

关键词： Inductive power transmission

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Smart Energy Management Leveraging Twin Adaptive Pulse Coupled Networks for Dynamic Energy Optimization in IoT-based Electrical WSN

Smart Energy Management Leveraging Twin Adaptive Pulse Coupl...

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Trends in Material science and Inventive Materials (ICTMIM), International Conference on

作者： Ravi Kumar M B. Md. Irfan Sugunadevi C Umang Soni Madhu B K Ramya Maranan Department of Electronics and Communication Engineering Cambridge Institute of Technology Bengaluru Karnataka Department of Information Technology Nalsar University of Law Hyderabad Telengana Department of Information Science and Engineering CMR Institute of Technology Bengaluru Karnataka Electronics & Communication Engineering Faculty of Engineering and Technology Parul Institute of Engineering and Technology Parul University Gujarat Department of Computer Science and Engineering Vidya Vikas Institute of Engineering and Technology Mysore Karnataka Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Internet of Things (IoT)-driven Wireless Sensor Networks (WSNs) undergo fast growth hence requiring sophisticated energy optimization methods to keep the networks operational longer with reliable data handling. Traditional energy management practices lead to early node failure combined with inefficient network routes and non-even energy distribution which blocks network development and operational performance expansion. The three main factors that cause WSNs to be energy inefficient are improper node positioning along with excessive routing overhead and uneven distribution of power consumption across sensor nodes. The current network management approaches do not provide adequate dynamic energy distribution which results in premature network failure. This research establishes "Smart Energy Management leveraging Twin Adaptive Pulse Coupled Networks for Dynamic Energy Optimization in IoT-Based WSN (MG-TwinAPC-ReP)" to address these challenges. The proposed framework MG-TwinAPC-ReP features four layers which (1) strategic node deployment coverage, (2) Cluster-Based Routing Protocol Using Modified Greylag-Goose Optimization, and (3) Energy management through adaptive load balancing using Twin Adaptive Pulse Coupled Network's dual synchronization model and (4) uses Reformed Poplar Optimization to optimize networking parameters. Experimental results indicate remarkable performance capabilities which lead to a 99.82% increase in network lifetime and 99.74% energy conservation together with 99.91% reliable data delivery and 99.65% reduced latency compared to traditional IoT-WSN systems. The proposed scalable self-adapting energy-efficient WSN model provides an optimal solution for smart cities together with healthcare and agriculture and industrial IoT applications which drives sustainable IoT-driven WSN deployment into the future.

关键词： Wireless sensor networks Adaptive systems Smart cities Medical services Routing Load management Mathematical models Reliability Optimization Industrial Internet of Things

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Deep Learning Applications for Detecting Crop Diseases from Image Data

Deep Learning Applications for Detecting Crop Diseases from ...

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2025 IEEE International Conference on Computational, Communication and Information technology, ICCCIT 2025

作者： Praveen, R.V.S. Mittal, Mudit Parida, Prasanta Begum, Rowsonara Kumar, Yogendra Nijhawan, Ginni LTIMindtree Limited Digital Engineering and Assurance Telangana Hyderabad India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India School of Rural Management KIIT University Odisha Bhubaneswar India MLR Institute of Technology Department of Comp. Sc. & Engg. Telangana Hyderabad India GLA University Department of Electrical Engineering Mathura India Lovely Professional University Dept. of Comp. Sc. & Engineering Phagwara India

ISBN: (纸本)9798331512965

In order to optimize the productivity of crops and ensure food security the crop diseases should be identified as soon as possible. In this study, we study the image based crop disease identification using Deep Learning Methods. However, the traditional illnesses detection techniques are typically based on specialist knowledge and human inspection, and the latter also has the disadvantages of being very time consuming and error prone. Use of convolutional neural networks (CNNs), in particular, have made deep learning powerful for many imaging problems such as precise and automated identification of disease. The ultimate goal of this research is to build a CNN based model which, through the analysis of photos of a damaged leaf, can identify and categorize various crop diseases. With this large data set with excellent images of both healthy and sick crops, we take advantage of. The model is trained and tested based on F1 scores, accuracy, precision and recall. The findings indicate that the model proposed in this research outperforms generic machine learning techniques in diagnosing diseases such as rust, mildew, and blight at high accuracy. The real time implementation of the system is also scalable enough to monitor big areas in the smart farming set up. I show in this study that deep learning has the potential to significantly enhance agricultural disease management and to pave the way to new and more effective and sustainable ways to grow crops. © 2025 IEEE.

关键词： Smart agriculture

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Beyond Vanilla Fine-Tuning: Leveraging Multistage, Multilingual, and Domain-Specific Methods for Low-Resource Machine Translation

arXiv

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arXiv 2025年

作者： Thillainathan, Sarubi Yuan, Songchen Lee, En-Shiun Annie Jayasena, Sanath Ranathunga, Surangika Department of Computer Science and Engineering University of Moratuwa Sri Lanka Department of Language Science and Technology Saarland Informatics Campus Saarland University Germany Department of Computer Science University of Toronto Canada Faculty of Science University of Ontario of Technology Canada School of Mathematical and Computational Sciences Massey University New Zealand

Fine-tuning multilingual sequence-to-sequence large language models (msLLMs) has shown promise in developing neural machine translation (NMT) systems for low-resource languages (LRLs). However, conventional single-stage fine-tuning methods struggle in extremely low-resource NMT settings, where training data is very limited. This paper contributes to artificial intelligence by proposing two approaches for adapting msLLMs in these challenging scenarios: (1) continual pre-training (CPT), where the msLLM is further trained with domain-specific monolingual data to compensate for the under-representation of LRLs, and (2) intermediate task transfer learning (ITTL), a method that fine-tunes the msLLM with both in-domain and out-of-domain parallel data to enhance its translation capabilities across various domains and tasks. As an application in engineering, these methods are implemented in NMT systems for Sinhala, Tamil, and English (six language pairs) in domain-specific, extremely low-resource settings (datasets containing fewer than 100,000 samples). Our experiments reveal that these approaches enhance translation performance by an average of +1.47 bilingual evaluation understudy (BLEU) score compared to the standard single-stage fine-tuning baseline across all translation directions. Additionally, a multi-model ensemble further improves performance by an additional BLEU score. © 2025, CC BY.

关键词： Neural machine translation

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A Survey on Security Control and Estimation for Cyber-Physical Systems Under Cyber-Attacks: Advances, Challenges and Future Directions

Artificial Intelligence Science and Engineering

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Artificial Intelligence science and engineering 2025年第1期1卷 1-16页

作者： Haoyang YU Zidong WANG Lei ZOU Yezheng WANG Jun LI College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao Shandong China Department of Computer Science Brunel University London Uxbridge U.K. College of Information Science and Technology Donghua University Shanghai China Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of Education Shanghai China School of Automation Nanjing University of Information Science and Technology Nanjing China

Cyber-physical systems (CPSs) are regarded as the backbone of the fourth industrial revolution, in which communication, physical processes, and computer technology are integrated. In modern industrial systems, CPSs are widely utilized across various domains, such as smart grids, smart healthcare systems, smart vehicles, and smart manufacturing, among others. Due to their unique spatial distribution, CPSs are highly vulnerable to cyber-attacks, which may result in severe performance degradation and even system instability. Consequently, the security concerns of CPSs have attracted significant attention in recent years. In this paper, a comprehensive survey on the security issues of CPSs under cyber-attacks is provided. Firstly, mathematical descriptions of various types of cyber-attacks are introduced in detail. Secondly, two types of secure estimation and control processing schemes, including robust methods and active methods, are reviewed. Thirdly, research findings related to secure control and estimation problems for different types of CPSs are summarized. Finally, the survey is concluded by outlining the challenges and suggesting potential research directions for the future.

关键词： Surveys Graphical models Estimation Process control Medical services Cyber-physical systems Smart grids Security Cyberattack Smart manufacturing

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Damage-Free Neutral Beam Etching for Gate Recess in E-Mode AlGaN/GaN HEMTs

IEEE Electron Device Letters

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IEEE Electron Device Letters 2025年第5期46卷 705-708页

作者： Chen, Yi-Ho Chu, Fu-Chuan Aslam, Muhammad Lee, Yao-Jen Li, Yiming Samukawa, Seiji National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Graduate Degree Program of College of Electrical and Computer Engineering Hsinchu300093 Taiwan National Chung-Shan Institute of Science and Technology Solid State Devices Section Electronic System Research Division Taoyuan325 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation Institute of Artificial Intelligence Innovation National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Institute of Communications Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Communications Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan

Recess gate etching is a critical technique for achieving enhancement-mode (E-mode) AlGaN/GaN high-electron mobility transistors (HEMTs) because the interface is susceptible to the etching damage. This study fabricates recess gates using the neutral beam etching (NBE) technique. By adjusting the aperture thickness in the NBE apparatus, we simulate both NB-mode and plasma-mode etching. The electrical characteristics of E-mode HEMTs fabricated using these two modes are analyzed and compared through DC, noise, and pulsed IV measurements. The results demonstrate that NB-recessed HEMTs exhibit superior performance. © 1980-2012 IEEE.

关键词： Aluminum gallium nitride

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A Deep Learning Framework for Arabic Cyberbullying Detection in Social Networks

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computers, Materials & Continua 2025年第5期83卷 3113-3134页

作者： Yahya Tashtoush Areen Banysalim Majdi Maabreh Shorouq Al-Eidi Ola Karajeh Plamen Zahariev Computer Science Department Jordan University of Science and TechnologyIrbid22110Jordan Department of Information Technology Faculty of Prince AI-Hussein Bin AbdallahⅡFor Information TechnologyThe Hashemite UniversityZarqa13133Jordan Computer Science Department Tafila Technical UniversityTafila6110Jordan Department of Digital Media Software Engineering Ferris State UniversityBig RapidsMI 49503USA Department of Telecommuniations University of Ruse“Angel Kanchev”Ruse7017Bulgaria

Social media has emerged as one of the most transformative developments on the internet,revolu-tionizing the way people communicate and ***,alongside its benefits,social media has also given rise to significant challenges,one of the most pressing being *** issue has become a major concern in modern society,particularly due to its profound negative impacts on the mental health and well-being of its *** the Arab world,where social media usage is exceptionblly high,cyberbullying has become increasingly prevalent,necessitating urgent *** detection of harmful online behavior is critical to fostering safer digital environments and mitigating the adverse efcts of *** underscores the importance of developing advanced tools and systems to identify and address such behavior *** paper investigates the development of a robust cyberbullying detection and classifcation system tailored for Arabic comments on *** study explores the efectiveness of various deep learning models,including Bi-LSTM(Bidirectional Long Short Term Memory),LSTM(Long Short-Term Memory),CNN(Convolutional Neural Networks),and a hybrid CNN-LSTM,in classifying Arabic comments into binary classes(bullying or not)and multiclass categories.A comprehensive dataset of 20,000 Arabic YouTube comments was collected,preprocessed,and labeled to support these *** results revealed that the CNN and hybrid CNN-LSTM models achieved the highest accuracy in binary classification,reaching an impressive 91.9%.For multiclass dlassification,the LSTM and Bi-LSTM models outperformed others,achieving an accuracy of 89.5%.These findings highlight the efctiveness of deep learning approaches in the mitigation of cyberbullying within Arabic online communities.

关键词： Arabic text lassification arabic text mining cyberbullying detection neural networks deep learning CNN LSTM YouTube Bi-LSTM

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Big Data Clustering Algorithms: Improving Efficiency and Scalability for Massive Data Mining and Pattern Recognition

Big Data Clustering Algorithms: Improving Efficiency and Sca...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： Shanmugam Muthu G Ramesh Kalyan V. Samuthira Pandi Shobana D Lakshmi Priya J Jaishika S Shipt Inc Birmingham USA Department of Computer Science and Engineering SNS College of Technology Coimbatore Tamilnadu India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Department of Mechatronics Engineering Rajalakshmi Engineering College Chennai Department of ECE Saveetha Engineering College Chennai Department of Computer Science and Engineering Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

The emergence of big data has completely changed the way data analysis is done. As a result, there is a pressing need for effective clustering algorithms that can handle large datasets from different fields. Efficiency, scalability, and finding meaningful patterns in enormous data are some of the important difficulties that this research tackles in relation to big data clustering. We thoroughly examine and evaluate a range of clustering algorithms, from more conventional ones like hierarchical and K-Means clustering to more cutting-edge ones like density-based spatial clustering (DBSCAN) and model-based clustering. To further improve the performance and scalability of clustering algorithms, we investigate new methods that make use of distributed computing frameworks like Apache Spark and MapReduce. We offer a thorough assessment of their efficacy in terms of clustering quality, computational efficiency, and resource use by using and evaluating these methods on large-scale datasets. While conventional clustering algorithms may fail miserably when faced with extremely big data sets, our research shows that newer methods that make use of optimization techniques and distributed processing greatly outperform their predecessors. The effect of feature selection and dimensionality reduction techniques on improving clustering results is also shown. By shedding light on how to choose and implement clustering algorithms for effective data mining and pattern identification, this study adds to the continuing endeavors in the domain of big data analytics. Progress in areas like customer segmentation, anomaly detection, and social network analysis can be achieved with the help of adaptive algorithms that can handle large datasets while keeping clustering results accurate and dependable, according to the study.

关键词： Dimensionality reduction Machine learning algorithms Accuracy Social networking (online) Scalability Heuristic algorithms Clustering algorithms Adaptive algorithms Big Data Computational efficiency

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