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Insider Attack Prevention:LAPUP—Lightweight Authentication Protocol Using PUF

Journal of Communications and Information Networks 2024年第2期9卷 192-206页

作者： Siranjeevi Rajamanickam Satyanarayana Vollala N.Ramasubramanian Department of Computer Engineering Government Polytechnic CollegeTiruchirappalli 620022India Department of Computer Science and Engineering Dr.Shyama Prasad Mukherjee International Institute of Information Technology-Naya RaipurChhattisgarh 493661India Department of Computer Science and Engineering National Institute of TechnologyTiruchirappalli 620015India

The incredible progress in technologies has drastically increased the usage of Web *** share their credentials like userid and password or use their smart cards to get authenticated by the application *** cards are handy to use,but they are susceptible to stolen smart card attacks and few other notable security *** prefer to use Web applications that guarantee for security against several security attacks,especially insider attacks,which is *** of several existing schemes prove the security pitfalls of the protocols from preventing security attacks,specifically insider *** paper introduces LAPUP:a novel lightweight authentication protocol using physically unclonable function(PUF)to prevent security attacks,principally insider *** PUFs are used to generate the security keys,challenge-response pair(CRP)and hardware signature for designing the *** transmitted messages are shared as hash values and encrypted by the keys generated by *** messages are devoid of all possible attacks executed by any attacker,including insider *** is also free from stolen verifier attacks,as the databases are secured by using the hardware signature generated by *** analysis of the protocol exhibits the strength of LAPUP in preventing insider attacks and its resistance against several other security *** evaluation results of the communication and computation costs of LAPUP clearly shows that it achieves better performance than existing protocols,despite providing enhanced security.

关键词： physically unclonable function(PUF) insiderattacks authenticationkeymanagementserver(AKMS) security

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Generalized Two-Point Visual Control Model of Human Steering for Accurate State Estimation1

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ASME Letters in Dynamic Systems and Control 2025年第1期5卷 011004页

作者： Mai, Rene E. Sears, Katherine Roessling, Grace Julius, Agung Mishra, Sandipan Department of Mechanical Aerospace and Nuclear Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Department of Cognitive Science Rensselaer Polytechnic Institute TroyNY12180 United States Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States

We derive and validate a generalization of the two-point visual control model, an accepted cognitive science model for human steering behavior. The generalized model is needed as current steering models are either insufficiently accurate or too complex for online state estimation. We demonstrate that the generalized model replicates specific human steering behavior with high precision (85% reduction in modeling error) and integrate this model into a human-as-advisor framework where human steering inputs are used for state estimation. As a benchmark study, we use this framework to decipher ambiguous lane markings represented by biased lateral position measurements. We demonstrate that, with the generalized model, the state estimator can accurately estimate the true vehicle state, providing lateral state estimates with under 0.15 m error across participants. However, without the generalized model, the estimator cannot accurately estimate the vehicle's lateral state. © 2024 by ASME.

关键词： Man machine systems

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Efficient tensor network contraction with GPU acceleration and parallel strategies 3

Efficient tensor network contraction with GPU acceleration a...

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3rd International Conference on Electronic Information engineering, Big Data, and computer Technology, EIBDCT 2024

作者： Xu, Wei Tong, Weiqin Liu, Xiaoyang School of Computer Engineering and Science Shanghai University China Department of Computer Science Rensselaer Polytechnic Institute United States

ISBN: (纸本)9781510680449

Tensor network contraction is a fundamental operation for dealing with high-dimensional data, which is now widely used in classical simulations of quantum circuits. To improve the performance of simulations, existing methods are implemented by optimizing contraction methods. However, with the increasing size of the tensor network, the contraction efficiency of these methods is difficult to satisfy the performance requirements and also suffers from the challenge of contracting tensor tasks that exceed GPU memory. In this paper, we propose a high-performance chunking method for tensor contraction. By chunking the given tensor, we obtain small tensors or vectors assign the corresponding threads, and contract them in parallel through the GPU. Solving high-order tensor contractions using distributed tensor contraction methods. Compared to the Einsum, our method achieves an average speedup of 22.28×. We also have experiments for large-scale tensor network contractions and our method achieves an average speedup of 13.56× compared to Tensordot. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Tensors

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Full-model-free Adaptive Graph Deep Deterministic Policy Gradient Model for Multi-terminal Soft Open Point Voltage Control in Distribution Systems

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Journal of Modern Power Systems and Clean Energy 2024年第6期12卷 1893-1904页

作者： Huayi Wu Zhao Xu Minghao Wang Youwei Jia Department of Electrical and Electronic Engineering The Hong Kong Polytechnic UniversityHong KongChina Shenzhen Research Institute The Hong Kong Polytechnic UniversityShenzhenChina Department of Electrical and Computer Engineering University of MacaoMacaoChina Department of Electrical and Electronic Engineering Southern University of Science and TechnologyShenzhenChina

High penetration of renewable energy sources(RESs)induces sharply-fluctuating feeder power,leading to volt-age deviation in active distribution *** prevent voltage violations,multi-terminal soft open points(M-sOPs)have been integrated into the distribution systems to enhance voltage con-trol ***,the M-SOP voltage control recalculated in real time cannot adapt to the rapid fluctuations of photovol-taic(PV)power,fundamentally limiting the voltage controllabili-ty of *** address this issue,a full-model-free adaptive graph deep deterministic policy gradient(FAG-DDPG)model is proposed for M-SOP voltage ***,the attention-based adaptive graph convolutional network(AGCN)is lever-aged to extract the complex correlation features of nodal infor-mation to improve the policy learning ***,the AGCN-based surrogate model is trained to replace the power flow cal-culation to achieve model-free ***,the deep deterministic policy gradient(DDPG)algorithm allows FAG-DDPG model to learn an optimal control strategy of M-SOP by continuous interactions with the AGCN-based surrogate *** tests have been performed on modified IEEE 33-node,123-node,and a real 76-node distribution systems,which demonstrate the effectiveness and generalization ability of the proposed FAG-DDPGmodel.

关键词： Soft open point graph attention graph convolutional network reinforcement learning voltage control distribution system deep deterministic policy gradient

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AI-enabled dental caries detection using transfer learning and gradient-based class activation mapping

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Journal of Ambient Intelligence and Humanized Computing 2024年第7期15卷 3009-3033页

作者： Inani, Hardik Mehta, Veerangi Bhavsar, Drashti Gupta, Rajeev Kumar Jain, Arti Akhtar, Zahid Department of Computer Science and Engineering Pandit Deendayal Energy University Gujarat Gandhinagar India Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Uttar Pradesh Noida India Department of Network and Computer Security State University of New York Polytechnic Institute NY United States

Dental caries detection holds the key to unlocking brighter smiles and healthier lives by identifying one of the most common oral health issues early on. This vital topic sheds light on innovative ways to combat tooth decay, empowering individuals to take control of their oral health and maintain radiant smiles. This research paper delves into the realm of transfer learning techniques, aiming to elevate the precision and efficacy of dental caries diagnosis. Utilizing Keras ImageDataGenerator, a rich and balanced dataset is crafted by augmenting teeth images from the Kaggle teeth dataset. Five cutting-edge pre-trained architectures are harnessed in the transfer learning approach: EfficientNetV2B3, VGG19, InceptionResNetV2, Xception, and ResNet50, with each model, initialized using ImageNet weights and tailored top layers. A comprehensive set of evaluation metrics, encompassing accuracy, precision, recall, F1-score, and false negative rates are employed to gauge the performance of these architectures. The findings unveil the unique advantages and drawbacks of each model, illuminating the path to an optimal choice for dental caries detection using Grad-CAM (Gradient-weighted Class Activation Mapping). The testing accuracies achieved by EfficientNetV2B3, VGG19, InceptionResNetV2, Xception, and ResNet50 models stand at 95.89%, 96.58%, 93.15%, 93.15%, and 94.18%, respectively. The Training accuracies stood at 100%, 99.91%, 100%, 100% and 100%, meanwhile on validation we achieved 97.63%, 96.68%, 98.82%, 96.68%, and 100% accuracies for EfficientNetV2B3, VGG19, InceptionResNetV2, Xception, and ResNet50 models respectively. Capitalizing on transfer learning and juxtaposing diverse pre-trained architectures, this research paper paves the way for substantial advancements in dental diagnostic capabilities, culminating in enhanced patient outcomes and superior oral health. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Chemical activation

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Mutual Retinex: Combining Transformer and CNN for Image Enhancement

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IEEE Transactions on Emerging Topics in Computational Intelligence 2024年第3期8卷 2240-2252页

作者： Jiang, Kui Wang, Qiong An, Zhaoyi Wang, Zheng Zhang, Cong Lin, Chia-Wen Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Wuhan University National Engineering Research Center for Multimedia Software School of Computer Science Wuhan430072 China Wuhan Polytechnic University School of Mathematics and Computer Science Wuhan430024 China National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu30013 Taiwan

Images captured in low-light or underwater environments are often accompanied by significant degradation, which can negatively impact the quality and performance of downstream tasks. While convolutional neural networks (CNNs) and Transformer architectures have made significant progress in computer vision tasks, there are few efforts to harmonize them into a more concise framework for enhancing such images. To this end, this study proposes to aggregate the individual capability of self-attention (SA) and CNNs for accurate perturbation removal while preserving background contents. Based on this, we carry forward a Retinex-based framework, dubbed as Mutual Retinex, where a two-branch structure is designed to characterize the specific knowledge of reflectance and illumination components while removing the perturbation. To maximize its potential, Mutual Retinex is equipped with a new mutual learning mechanism, involving an elaborately designed mutual representation module (MRM). In MRM, the complementary information between reflectance and illumination components are encoded and used to refine each other. Through the complementary learning via the mutual representation, the enhanced results generated by our model exhibit superior color consistency and naturalness. Extensive experiments have shown the significant superiority of our mutual learning based method over thirteen competitors on the low-light task and ten methods on the underwater image enhancement task. In particular, our proposed Mutual Retinex respectively surpasses the state-of-the-art method MIRNet-v2 by 0.90 dB and 2.46 dB in PSNR on the LOL 1000 and FIVEK datasets, while with only 19.8% model parameters. © 2017 IEEE.

关键词： Image enhancement

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Research on the closed-loop supply chain of intelligent products considering government subsidies in the context of the internet of things

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Discover Internet of Things 2025年第1期5卷 1-20页

作者： Zhao, Jinghua Cui, Shaoyun Xu, Zheng School of Business University of Shanghai for Science and Technology Shanghai China School of Computer and Information Engineering Institute for Artificial Intelligence Shanghai Polytechnic University Shanghai201209 China

The rapid development of Internet of Things technology and the continuous improvement of consumer demand have spawned the emergence of intelligent products based on Internet of Things technology. The existing research on the closed-loop supply chain of intelligent products under the background of the Internet of Things still lacks consideration of different subsidy objects and recyclers. Therefore, for different subsidy objects in the three-stage closed-loop supply chain of intelligent products, a pricing decision model involving manufacturers, recyclers, retailers and consumers under the minimum standard supervision of recycling technology is constructed, and different recyclers are distinguished according to the characteristics of intelligent products. By comparing the profits of each subject in different models, the optimal model of government subsidy is obtained. It is found that under the four modes, subsidizing recyclers without self-recovery technology makes the total profit of the supply chain reach the optimal solution;capital recyclers can obtain the largest number of old intelligent product recycling by using self-recovery technology;when subsidizing manufacturers, the total profit and product recovery rate of the supply chain are at the bottom. In addition, recycling subsidies, subsidy impact coefficients and technical standards will affect the best profits of enterprises. Through the analysis of the closed-loop supply chain of intelligent products, the research provides theoretical support and decision-making basis for the supply chain members to formulate relevant strategies and the government to take relevant measures to promote resource utilization and environmental protection. © The Author(s) 2025.

关键词： Smart manufacturing

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Loop Subgraph-Level Greedy Mapping Algorithm for Grid Coarse-Grained Reconfigurable Array

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Tsinghua science and Technology 2023年第2期28卷 330-343页

作者： Naijin Chen Fei Cheng Chenghao Han Jianhui Jiang Xiaoqing Wen School of Computer and Information Science Anhui Polytechnic UniversityWuhu 241000China School of Software Engineering Tongji UniversityShanghai 201804China Department of Computer Science and Networks Kyushu Institute of TechnologyFukuoka 820-8502Japan

To solve the problem of grid coarse-grained reconfigurable array task mapping under multiple constraints,we propose a Loop Subgraph-Level Greedy Mapping(LSLGM)algorithm using parallelism and processing element *** the constraint of a reconfigurable array,the LSLGM algorithm schedules node from a ready queue to the current reconfigurable cell array *** mapping a node,its successor’s indegree value will be dynamically *** its successor’s indegree is zero,it will be directly scheduled to the ready queue;otherwise,the predecessor must be dynamically *** the predecessor cannot be mapped,it will be scheduled to a blocking *** dynamically adjust the ready node scheduling order,the scheduling function is constructed by exploiting factors,such as node number,node level,and node *** with the loop subgraph-level mapping algorithm,experimental results show that the total cycles of the LSLGM algorithm decreases by an average of 33.0%(PEA44)and 33.9%(PEA_(7×7)).Compared with the epimorphism map algorithm,the total cycles of the LSLGM algorithm decrease by an average of 38.1%(PEA_(4×4))and 39.0%(PEA_(7×7)).The feasibility of LSLGM is verified.

关键词： Grid Coarse-Grained Reconfigurable Array(GCGRA) mapping loop subgraph scheduling

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WiFi Received Signal Strength (RSS) Based Automated Attendance System for Educational Institutions 24

WiFi Received Signal Strength (RSS) Based Automated Attendan...

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11th International Conference on Networking, Systems and Security, NSysS 2024

作者： Khan, Sidratul Muntaha Maliha, Mehreen Tabassum Haque, Md Shahedul Rahman, Ashikur Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh Department of Computer Science Virginia Polytechnic Institute State University Arlington United States

ISBN: (纸本)9798400711589

Smartphones are becoming part of people’s day-to-day activities now-a-days. Globally, almost 90% of cellular phones are smartphones. "Personnel tracking" is a viable usage of smartphones. Automated attendance system, an application of personnel tracking, is efficient and essential for enhancing productivity and streamlining operations in modern workplaces and educational institutions. While traditional methods of attendance tracking are prone to inaccuracies and inefficiencies, smartphone-based systems offer a smoother approach. In this paper, we present a smartphone-based attendance system that leverages Wi-Fi signal strength for indoor localization. Instead of requiring precise positioning, we propose a system with zone-based localization approach. We divide the whole area into distinct smaller zones and determine the users’ location within these zones. Through real-world deployment, we demonstrate that our novel approach reduces the complexity of exact positioning while still achieving high accuracy in identifying a user’s presence within specific areas, which are often enclosed by boundaries. Comparative evaluations with implementing an algorithm show the superiority of our proposed method in terms of accuracy and practicality, making it suitable for deployment in large-scale organizational settings. © 2024 Copyright held by the owner/author(s).

关键词： Wi-Fi

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Human-computer Interaction Using Deep Fusion Model-Based Facial Expression Recognition System

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computer Modeling in engineering & sciences 2023年第5期135卷 1165-1185页

作者： Saiyed Umer Ranjeet Kumar Rout Shailendra Tiwari Ahmad Ali AlZubi Jazem Mutared Alanazi Kulakov Yurii Department of Computer Science&Engineering Aliah UniversityKolkata700156India Department of Computer Science and Engineering National Institute of TechnologySrinagarJammu and Kashmir190006India Department of Computer Science&Engineering Thapar UniversityPatiala147004India Computer Science Department King Saud UniversityRiyadh11451Saudi Arabia Department of Computer Engineering National Technical University of UkraineIgor Sikorsky Kyiv Polytechnic InstituteKyiv03056Ukraine

A deep fusion model is proposed for facial expression-based human-computer Interaction ***,image preprocessing,i.e.,the extraction of the facial region from the input image is ***,the extraction of more discriminative and distinctive deep learning features is achieved using extracted facial *** prevent overfitting,in-depth features of facial images are extracted and assigned to the proposed convolutional neural network(CNN)*** CNN models are then ***,the performance of each CNN model is fused to obtain the final decision for the seven basic classes of facial expressions,i.e.,fear,disgust,anger,surprise,sadness,happiness,*** experimental purposes,three benchmark datasets,i.e.,SFEW,CK+,and KDEF are *** performance of the proposed systemis compared with some state-of-the-artmethods concerning each *** performance analysis reveals that the proposed system outperforms the competitive methods in terms of various performance ***,the proposed deep fusion model is being utilized to control a music player using the recognized emotions of the users.

关键词： Deep learning facial expression emotions recognition CNN

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