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Smart contract token-based privacy-preserving access control system for industrial Internet of Things

Digital Communications and Networks 2023年第2期9卷 337-346页

作者： Weizheng Wang Huakun Huang Zhimeng Yin Thippa Reddy Gadekallu Mamoun Alazab Chunhua Su Department of Computer Science City University of Hong KongHong Kong SAR999077China Division of Computer Science University of AizuFukushima965-8580Japan School of Information Technology and Engineering Vellore Institute of TechnologyTamil NaduIndia College of Engineering IT and EnvironmentCharles Darwin UniversityAustralia School of Computer Science and Cyber Engineering Guangzhou UniversityChina

Due to mobile Internet technology's rapid popularization,the Industrial Internet of Things(IIoT)can be seen everywhere in our daily *** IIoT brings us much convenience,a series of security and scalability issues related to permission operations rise to the surface during device ***,at present,a reliable and dynamic access control management system for IIoT is in urgent *** till now,numerous access control architectures have been proposed for ***,owing to centralized models and heterogeneous devices,security and scalability requirements still cannot be *** this paper,we offer a smart contract token-based solution for decentralized access control in IIoT ***,there are three smart contracts in our system,including the Token Issue Contract(TIC),User Register Contract(URC),and Manage Contract(MC).These three contracts collaboratively supervise and manage various events in IIoT *** also utilize the lightweight and post-quantum encryption algorithm-Nth-degree Truncated Polynomial Ring Units(NTRU)to preserve user privacy during the registration ***,to evaluate our proposed architecture's performance,we build a prototype platform that connects to the local ***,experiment results show that our scheme has achieved secure and dynamic access control for the IIoT system compared with related research.

关键词： Blockchain Privacy preservation Smart contract Industrial IoT

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SiamS3C: spatial-channel cross-correlation for visual tracking with centerness-guided regression

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Multimedia Systems 2024年第5期30卷 1-14页

作者： Zhang, Jianming Chen, Wentao He, Yufan Kuang, Li-Dan Sangaiah, Arun Kumar School of Computer and Communication Engineering Changsha University of Science and Technology Changsha410076 China International Graduate School of AI National Yunlin University of Science and Technology Douliu64002 Taiwan

Visual object tracking can be divided into the object classification and bounding-box regression tasks, but only one sharing correlation map leads to inaccuracy. Siamese trackers compute correlation map by cross-correlation operation with high computational cost, and this operation performed either on channels or in spatial domain results in weak perception of the global information. In addition, some Siamese trackers with a centerness branch ignore the associations between the centerness branch and the bounding-box regression branch. To alleviate these problems, we propose a visual object tracker based on Spatial-Channel Cross-Correlation and Centerness-Guided Regression. Firstly, we propose a spatial-channel cross-correlation module (SC3M) that combines the search region feature and the template feature both on channels and in spatial domain, which suppresses the interference of distractors. As a lightweight module, SC3M can compute dual independent correlation maps inputted to different subnetworks. Secondly, we propose a centerness-guided regression subnetwork consisting of the centerness branch and the bounding-box regression branch. The centerness guides the whole regression subnetwork to enhance the association of two branches and further suppress the low-quality predicted bounding boxes. Thirdly, we have conducted extensive experiments on five challenging benchmarks, including GOT-10k, VOT2018, TrackingNet, OTB100 and UAV123. The results show the excellent performance of our tracker and our tracker achieves real-time requirement at 48.52 fps. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Object tracking

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Search if you don't know! Knowledge-Augmented Korean Grammatical Error Correction with Large Language Models

Search if you don't know! Knowledge-Augmented Korean Grammat...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Koo, Seonmin Kim, Jinsung Park, Chanjun Lim, Heuiseok Department of Computer science and Engineering Korea University Korea Republic of Upstage AI Korea Republic of

ISBN: (纸本)9798891761681

Grammatical error correction (GEC) system is a practical task used in the real world, showing high achievements alongside the development of large language models (LLMs). However, these achievements have been primarily obtained in English, and there is a relative lack of performance for non-English data, such as Korean. We hypothesize that this insufficiency occurs because relying solely on the parametric knowledge of LLMs makes it difficult to thoroughly understand the given context in the Korean GEC. Therefore, we propose a Knowledge-Augmented GEC (KAGEC) framework that incorporates evidential information from external sources into the prompt for the GEC task. KAGEC first extracts salient phrases from the given source and retrieves non-parametric knowledge based on these phrases, aiming to enhance the context-aware generation capabilities of LLMs. Furthermore, we conduct validations for fine-grained error types to identify those requiring a retrieval-augmented manner when LLMs perform Korean GEC. According to experimental results, most LLMs, including ChatGPT, demonstrate significant performance improvements when applying KAGEC. © 2024 Association for Computational Linguistics.

关键词： Modeling languages

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Climate Geoscience–Based Disaster Management in Healthcare Analysis by Markov Q-Transfer Adversarial with Cloud Computing

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Remote Sensing in Earth Systems sciences 2025年第2期8卷 474-483页

作者： Srinivas, Jhade Rebecca, B. Bhargavi, Kovvuri N Swetha, Velma Reddy Spandana, S. Degala, Divya Priya Department of Computer Science and Engineering-Data Science KG Reddy College of Engineering and Technology Moinabad Telangana Hyderabad India Computer Science and Engineering Marri Laxman Reddy Institute of Technology and Management Telangana Dundigal 500043 India Department of CSE Aditya University AP Surampalem India Department of Computer Science and Engineering (AI-ML) Malla Reddy Engineering College Hyderabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Bowrampet Telangana Hyderabad India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Analysis and reaction to natural disasters have made extensive use of deep learning methods using semantic segmentation networks. These implementations’ foundation is based on convolutional neural networks (CNNs), which are capable of precisely identifying and locating the relevant areas of interest within satellite imagery or other types of remote sensing data. This helps with rescue planning, restoration efforts, and disaster evaluation. Difficulties in the provision of healthcare services, due to natural disasters, are made worse by the existing problems prevalent in most healthcare systems. The aim of this research is to propose a novel technique in climate change–based disaster management with their public healthcare analysis using cloud computing with deep learning techniques. In this research, the cloud network has been used in LiDAR image collection for climate change–based disaster-struck regions and their public healthcare analysis. This collected image has been classified and optimized using a deep Markov Q-transfer adversarial neural network with binary grey whale swarm optimization. Experimental analysis has been carried out for various disaster management–based public healthcare datasets in terms of classification accuracy, specificity, NSE, sensitivity, and F-1 score. The proposed technique attained a classification accuracy of 97%, F-1 score of 94%, specificity of 96%, sensitivity of 95%, and NSE of 57%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Adversarial neural network Cloud computing Disaster management LiDAR image Whale swarm optimization

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Energy Efficient Green Routing for UAVs Ad-Hoc Network

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Intelligent Automation & Soft Computing 2023年第7期37卷 1111-1127页

作者： M.Muthukumar Rajasekar Rangasamy Irshad Hussain Salman A.AlQahtani Department of Computer Science and Engineering Sree Sowdambika College of EngineeringTamil NaduIndia Department of Computer Science and Engineering-AI&ML School of EngineeringMalla Reddy UniversityHyderabadIndia Faculty of Electrical and Computer Engineering University of Engineering and Technology Peshawar25000PeshawarPakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Kingdom of Saudi Arabia

The purpose of this article is to propose Stability-based Energy-Efficient Link-State Hybrid Routing(S-ELHR),a low latency routing proto-col that aims to provide a stable mechanism for routing in unmanned aerial vehicles(UAV).The S-ELHR protocol selects a number of network nodes to create a Connected Dominating Set(CDS)using a parameter known as the Stability Metric(SM).The SM considers the node’s energy usage,connectivity time,and node’s *** the highest SM nodes are chosen to form *** node declares a Willingness to indicate that it is prepared to serve as a relay for its neighbors,by employing its own energy state.S-ELHR is a hybrid protocol that stores only partial topological information and routing tables on CDS *** of relying on the routing information at each intermediary node,it uses source routing,in which a route is generated on-demand,and data packets contain the addresses of the nodes the packet will transit.A route recovery technique is additionally utilized,which first locates a new route to the destination before forwarding packets along *** simulation for various network sizes and mobility speeds,the efficiency of S-ELHR is *** findings demonstrate that S-ELHR performs better than Optimized Link State Routing(OLSR)and Energy Enhanced OLSR(EE-OLSR)in terms of packet delivery ratio,end-to-end delay,and energy consumption.

关键词： Connected dominatingset hybridrouting UAVAdhocnetworks link state stability

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Etching-free pixel definition in InGaN green micro-LEDs

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Light(science & Applications) 2024年第8期13卷 1655-1665页

作者： Zhiyuan Liu Yi Lu Haicheng Cao Glen Isaac Maciel Garcia Tingang Liu Xiao Tang Na Xiao Raul Aguileta Vazquez Mingtao Nong Xiaohang Li Advanced Semiconductor Laboratory Electrical and Computer Engineering ProgramCEMSE DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Kingdom of Saudi Arabia

The traditional plasma etching process for defining micro-LED pixels could lead to significant sidewall *** near sidewall regions act as non-radiative recombination centers and paths for current leakage,significantly deteriorating device *** this study,we demonstrated a novel selective thermal oxidation(STO)method that allowed pixel definition without undergoing plasma damage and subsequent dielectric *** annealing in ambient air oxidized and reshaped the LED structure,such as p-layers and InGaN/GaN multiple quantum ***,the pixel areas beneath the pre-deposited SiO_(2)layer were selectively and effectively *** was demonstrated that prolonged thermal annealing time enhanced the insulating properties of the oxide,significantly reducing LED leakage ***,applying a thicker SiO_(2)protective layer minimized device resistance and boosted device efficiency *** the STO method,InGaN green micro-LED arrays with 50-,30-,and 10-μm pixel sizes were manufactured and *** results indicated that after 4 h of air annealing and with a 3.5-μm SiO_(2)protective layer,the 10-μm pixel array exhibited leakage currents density 1.2×10^(-6)A/cm^(2)at-10 V voltage and a peak on-wafer external quantum efficiency of~6.48%.This work suggests that the STO method could become an effective approach for future micro-LED manufacturing to mitigate adverse LED efficiency size effects due to the plasma etching and improve device ***-LEDs fabricated through the STO method can be applied to micro-displays,visible light communication,and optical interconnect-based *** planar pixel geometry will provide more possibilities for the monolithic integration of driving circuits with ***,the STO method is not limited to micro-LED fabrication and can be extended to design other III-nitride devices,such as photodetectors,laser diodes,high-electron-mobility transistors

关键词： annealing protective quantum

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Object Detection Framework Using OpenCV for Low Cost and High Performance 11th

Object Detection Framework Using OpenCV for Low Cost and Hig...

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11th International Conference on Intelligent Computing and Applications, ICRTC 2023

作者： Bhardwaj, Sarthak Soni, Pratyaksh Ul Haq, Furqan Masih, Alvin Precious Sharma, Puneet Dept. of Computer Science & Engineering Amity University Uttar Pradesh Noida India School of Computer Science & AI SR University Telangana Warangal India

ISBN: (纸本)9789819717231

In contemporary times, advancements in technology have led to a renewed interest in the development of autonomous vehicles, commonly referred to as self-driving cars. These vehicles are equipped with sensors and computer vision tools that enable them to detect and respond to their environment, reducing the need for human intervention in the navigation process. Given the transformative potential of this technology, there has been a growing emphasis on researching and developing autonomous vehicles as a way of revolutionizing the transportation industry. The goal of this research work is to code highly optimized plus parameterized Python algorithms that would drive the object detection systems like Canny edge, pedestrian, vehicle, etc., with the help of pre-existing object models with greater emphasis on reducing the cost-performance trade-off. Various object models like EfficientNet, YOLOv4, R-CNN, etc. were deployed and tested for creating comparison in terms of ease of deployment, efficiency, accuracy, computational, and hardware costs to filter out the most efficient model for the coded computer vision tools. These innovative systems will possess the capability to detect lane markings, accurately identify pedestrians and other vehicles, and continually monitor the vehicle's immediate surroundings, thereby providing real-time information to the autonomous driving system through highly customizable parameters that can be changed according to the needs. By default, the codes in the research work will be customized for Indian roads, which are known for their unique challenges, such as lack of lane markings, heavy traffic, and poor road conditions. However, challenges remain, including legal and regulatory issues, code robustness, hardware integration, public perception, acceptance, and ensuring the safety and security of the autonomous driving system. Despite these challenges, the potential of autonomous vehicles to revolutionize transportation is significant. © The A

关键词： Autonomous vehicles

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Secure blockchain bidding auction protocol against malicious adversaries

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High-Confidence Computing 2024年第3期4卷 58-63页

作者： Xiaobing Dai Jiajing Liu Xin Liu Xiaofen Tu Ruexue Wang Department of Computer and Information Engineering Baotou Vocational&Technical CollegeBaotou 014010China Department of Computer Tianjin Ren’ai CollegeTianjin 301636China School of Information Engineering Inner Mongolia University of Science and TechnologyBaotou 014010China

In recent years,with the development of blockchain,electronic bidding auction has received more and more *** at the possible problems of privacy leakage in the current electronic bidding and auction,this paper proposes an electronic bidding auction system based on blockchain against malicious adversaries,which uses the secure multi-party computation to realize secure bidding auction protocol without any trusted third *** protocol proposed in this paper is an electronic bidding auction scheme based on the threshold elliptic curve *** can be implemented without any third party to complete the bidding auction for some malicious behaviors of the participants,which can solve the problem of resisting malicious adversary *** security of the protocol is proved by the real/ideal model paradigm,and the efficiency of the protocol is *** efficiency of the protocol is verified by simulating experiments,and the protocol has practical value.

关键词： Blockchain Secure multi-party computation Electronic bidding auction Malicious model Real/ideal model paradigm

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ML-Orchestrated Malware Detection Framework for Windows APIs in Critical Infrastructure Systems 2

ML-Orchestrated Malware Detection Framework for Windows APIs...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Vaghasiya, Shayalkumar Gupta, Rajesh Mohapatra, Santoshinee Tanwar, Sudeep Garg, Deepak Institute of Technology Nirma University Department of Computer Science and Engineering Gujarat 382481 India Sr University School of Computer Science and Ai Telangana Warangal India

ISBN: (纸本)9798350387933

The security of critical infrastructure systems is crucial as they provide basic amenities and services necessary for functioning of a community, also they represent important part of country's economy. An attack on this critical infrastructure systems can have significant consequences. In this paper we discuss the This paper addresses the increasing security challenges in critical infrastructure systems due to cyber threats, particularly malware and propose a framework based on machine learning for robust malware classification. Our proposed framework includes a dataset of Windows API functions used by the software, preprocessing techniques, and the XGBoost model which achieves an accuracy of 86.98%. Using this model, we can detect and categorize five different types of malwares to provide security against malware attacks. The results show model's ability to successfully classify different kinds of malware. Realtime monitoring is another feature of this framework which is important for actively protecting critical infrastructure systems. © 2024 IEEE.

关键词： Machine learning

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ML-based Vehicle Accident Severity Prediction Framework for Traffic Management Systems 2

ML-based Vehicle Accident Severity Prediction Framework for ...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Patel, Kavya Gondalia, Unnati Kakkar, Riya Gupta, Rajesh Tanwar, Sudeep Garg, Deepak Institute of Technology Nirma University Department of Computer Science and Engineering Gujarat 382481 India Sr University School of Computer Science and Ai Telangana Warangal India

ISBN: (纸本)9798350387933

The present research examines the current issues arises with the growing usage of personal vehicles in the transportation industry. This adoption is due to the technical advancements in the automobile sector. Although personal automobiles have many benefits, such as convenience and safety features, the ensuing increase in traffic congestion has become a serious problem and is contributing to an increase in traffic accidents. In today's dynamic transportation scene, striking a balance between the advantages and problems provided by this paradigm change is essential. We suggest an all-encompassing paradigm for accident severity prediction in response to these difficulties. The proposed framework makes use of cutting-edge sensor technologies and machine learning methods including KNearest Neighbours, Random Forest, XGBoost, and Bagging. This all-encompassing strategy seeks to improve overall transportation efficiency, optimise traffic management, and proactively predict the severity of accidents. The goal of this research is to improve road safety and accident management by providing useful information to emergency services, law enforcement, and decisionmakers. © 2024 IEEE.

关键词： Motor transportation

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