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2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Doulani, Khushbu Sharma, Samarth Adhikari, Mainak Vijayalata, Y. Indian Institute of Information Technology Lucknow Department of Computer Science Lucknow India Indian Institute of Information Technology Lucknow Department of Computer Science and Artificial Intelligence Lucknow India School of Data Science Indian Institute of Science Education and Research Thiruvananthapuram Thiruvananthapuram India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350364866

Vector-borne diseases have created critical world-wide public health hazards that require innovative approaches for prevention and management. Within the context of IoT-enabled edge networks, this study offers a unique framework to build a reliable and straightforward distant prediction model for vector-borne diseases. The suggested strategy incorporates the use of edge computing capabilities to predict the onset and spread of diseases caused by vectors by using real-time data streams from mobile applications. Employing widely used techniques from machine learning, including interpretable models, our system not only achieves high prediction accuracy but also provides insight into the most important factors influencing the spread of disease. Moreover, this approach overcomes the resource constraints essential to edge networks, ensuring the prediction model's dependability and scalability. © 2024 IEEE.

关键词： Prediction models

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A Lightweight and Trustworthy Authentication Protocol for UAV Networks Based on PUF 24

A Lightweight and Trustworthy Authentication Protocol for UA...

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14th International Conference on Communication and Network Security, ICCNS 2024

作者： Zhu, Juntao Peng, Jin Wang, Long College of Computer Science Beijing University of Technology Beijing China The 6th Research Institute of China Electronics Corporation Beijing China

ISBN: (纸本)9798400717994

With the continuous development of drone remote sensing technology, drone networks have garnered widespread attention. The application of drones has expanded from military uses to industrial and civilian sectors, facilitating tasks such as automated combat, production, and rescue operations. However, since drones operate in public environments, their communications are vulnerable to physical attacks and tampering, which can lead to privacy leaks and pose security risks. Additionally, as resource-constrained devices, drones find it difficult to support complex encryption algorithms. To address this issue, this paper proposes a lightweight authentication protocol based on Physical Unclonable Functions (PUF). This protocol leverages PUF to prevent physical capture attacks and enhances system efficiency through hash and XOR operations. Furthermore, trust management technology is applied to further evaluate the network access behavior of drones, ensuring trustworthy access to the drone network. The security of this protocol is verified using the ROR model and AVISPA tool, and informal analysis demonstrates that it meets the security requirements of drone networks. Compared with existing protocols, this protocol shows significant advantages in terms of computational and communication overhead, achieving a good balance between security and efficiency. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Drones

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SSD object detection algorithm based on knowledge map

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International Journal of Wireless and Mobile Computing 2024年第2期27卷 152-160页

作者： Huang, Li Wang, Xiaofeng Lu, Jianhua Hu, Wei Zhang, Changrong College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan China China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan China Key Laboratory of Metallurgical Equipment and Control Technology of the Ministry of Education Wuhan University of Science and Technology Hubei Wuhan China Precision Manufacturing Research Institute Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Hubei Wuhan China

With the pervasive integration of artificial intelligence into all aspects of human life, talent emerges as a primary resource. Upon analysing the current state of talent training in higher education institutions, issues such as dispersed knowledge points, overlapping content, a singular practice approach and ineffective evaluation have been identified. In response to these challenges, this paper proposes a multidisciplinary and comprehensive practical teaching methodology grounded in the knowledge graph framework. It delves into diverse paths for practical teaching and assessment, including aspects like teaching objectives, problem decomposition, resource integration, implementation methods and performance evaluation. The practical application of the SSD algorithm in researching service robot indoor object detection serves as an illustrative example. Employing the holistic practical teaching approach facilitated by the knowledge graph, this model guides students in acquiring object detection expertise, thereby enhancing their comprehensive development. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Object detection

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Design Thinking and Innovative Tool Use in Business Model

Design Thinking and Innovative Tool Use in Business Model

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Singhi, Nitika Verma, Ankush Agrawal, Atul Khan, Irfan Computer Science Engineering Shri Vaishnav Vidhyapeeth Vishwavidyalava Indore India Data Analytics and Information System Prestige Institute of Management and Research Indore India Computer Science Engineering Sushila Devi Bansal College of Technology Indore India Computer Science Engineering Bansal Institute of Science and Technology Bhopal India

ISBN: (纸本)9798350375237

In past few year designs thinking has worked on several new effective problem and has generated innovative solution for new business model. For the scenario of today's demand, to better understand how design thinking can work on different perspectives in business models and how it can bring new innovative solutions for any problem is essential However, studies of existing literature explore the application, potential, and challenges of Design Thinking towards the comprehensive aim of this paper, which regards business model innovation as a fundamental subject of Design Thinking. The paper provides practical tools for exploring Design Thinking along with various critical aspects for the researchers to explore how Design Thinking can be applied to innovative business models. The main focus of this paper is to explain the basics of Design Thinking, how to frame the mind through different techniques used, the Design Techniques used for a business model, the methodology of a design thinker, and different tools like BPM, Blue Work Live, Mind Mapping, Brainstorming, and RICPS. © 2024 IEEE.

关键词： Mapping

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A Generic Blockchain-based Steganography Framework with High Capacity via Reversible GAN

A Generic Blockchain-based Steganography Framework with High...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Zhuo Chen Liehuang Zhu Peng Jiang Jialing He Zijian Zhang School of Cyberspace Science and Technology Beijing Institute of Technology China College of Computer Science Chongqing University China

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Blockchain-based steganography enables data hiding via encoding the covert data into a specific blockchain transaction field. However, previous works focus on the specific field-embedding methods while lacking a consideration on required field-generation embedding. In this paper, we propose GBSF, a generic framework for blockchain-based steganography. The sender generates the required fields, where the additional covert data is embedded to enhance the channel capacity. Based on GBSF, we design R-GAN that utilizes the generative adversarial network (GAN) with a reversible generator to generate the required fields and encode additional covert data into the input noise of the reversible generator. We then explore the performance flaw of R-GAN and introduce CCR-GAN as an improvement. CCR-GAN employs a counter-intuitive data preprocessing mechanism to reduce decoding errors in covert data. It incurs gradient explosion for model convergence and we design a custom activation function. We conduct experiments using the transaction amount of the Bitcoin mainnet as the required field. The results demonstrate that R-GAN and CCR-GAN allow to embed 11-bit (embedding rate of 17.2%) and 24-bit (embedding rate of 37.5%) covert data within a transaction amount, and enhance the channel capacity of state-of-the-art works by 4.30% to 91.67% and 9.38% to 200.00%, respectively.

关键词： Steganography Channel capacity Noise Data preprocessing Generative adversarial networks Generators Explosions

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Managing Health Treatment by Optimizing Complex Lab-Developed Test Configurations: A Health Informatics Perspective

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computers, Materials & Continua 2023年第6期75卷 6251-6267页

作者： Uzma Afzal Tariq Mahmood Ali Mustafa Qamar Ayaz H.Khan Department of Computer Science Federal Urdu University of Arts Science and TechnologyKarachiPakistan Department of Computer Science Institute of Business AdministrationKarachiPakistan Department of Computer Science College of ComputerQassim UniversityBuraydahSaudi Arabia Computer Engineering Department King Fahd University of Petroleum and MineralsDhahranSaudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence Dhahran31261Saudi Arabia

A complex Laboratory Developed Test(LDT)is a clinical test developed within a single *** is typically configured from many fea-ture constraints from clinical repositories,which are part of the existing Lab-oratory Information Management System(LIMS).Although these clinical repositories are automated,support for managing patient information with test results of an LDT is also integrated within the existing ***,the support to configure LDTs design needs to be made available even in standard LIMS *** manual configuration of LDTs is a complex process and can generate configuration inconsistencies because many constraints between features can remain *** is a risky process and can lead patients to undergo unnecessary *** proposed an optimized solution(opt-LDT)based on Genetic Algorithms to automate the configuration and resolve the inconsistencies in ***-LDT encodes LDT configuration as an optimization problem and generates a consistent configuration that satisfies the constraints of the *** tested and validated opt-LDT for a local secondary care hospital in a real healthcare *** results,averaged over ten runs,show that opt-LDT resolves 90%of inconsistencies while taking between 6 and 6.5 s for each ***,positive feedback based on a subjective questionnaire from clinicians regarding the performance,acceptability,and efficiency of opt-LDT motivates us to present our results for regulatory approval.

关键词： Artificial intelligence health informatics evolutionary algorithms genetic algorithms feature selection laboratory developed test

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SP-ADMM: a distributed optimization method of SFC placement for 5G-MEC networks 3

SP-ADMM: a distributed optimization method of SFC placement ...

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3rd International Conference on Algorithms, Microchips, and Network Applications, AMNA 2024

作者： Zhang, Zhibo Wang, Huiqiang Niu, Shuangyue Lv, Hongwu College of Computer Science and Technology Harbin Engineering University Harbin China AVIC Chengdu Aircraft Design and Research Institute Chengdu China

ISBN: (纸本)9781510680098

Recently, the combination of a service function chain (SFC) with network function virtualization (NFV) and software-defined networking (SDN) has provided customers with flexible and efficient services. The emergence of multi-access edge computing (MEC) further enhances the level of service customization. However, achieving joint optimization of virtual network function (VNF) deployment and flow allocation in resource-constrained scenarios while meeting the diverse requirements of 5G verticals is challenging. Current research rarely addresses dedicated service provisioning for edge servers and considers the additional instantiation overhead introduced by adjusting cloud server parameters. In fact, this is a non-negligible issue during SFC deployment in 5G-MEC scenarios. Based on the above considerations, this paper constructs a joint SFC deployment problem for edge-cloud networks with the goal of maximizing network utility. We first propose a univariate modeling method based on meta-links that effectively avoids the variable coupling problem in traditional multivariate modeling approaches and reduce the problem size by at least half. Subsequently, to solve the NP-hard integer nonlinear problem (INLP), we propose a distributed computing architecture named SP-ADMM, which improves the speed and quality of SFC deployment in large-scale scenarios via convex combinatorial formulations and a Viterbi-based heuristic algorithm (PAC-GREP). Finally, we experimentally verify the convergence and approximation of the algorithms. Our solution demonstrates advantages in terms of network utility and convergence speed under the same network resources, increasing service capacity by at least 39%. © 2024 SPIE.

关键词： Network function virtualization

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New MRA Schemes Based on the CRT for Polynomial Rings

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IEEE Transactions on Information Theory 2025年第6期71卷 4671-4685页

作者： Yang, Jing Wang, Xianfang Xiang, Can Fu, Fang-Wei Xia, Shu-Tao Shandong Technology and Business University School of Computer Science and Technology Shandong Yantai264005 China Westone Cryptologic Research Center CETC Cyberspace Security Technology Company Ltd. Beijing100070 China South China Agricultural University College of Mathematics and Informatics Guangdong Guangzhou510642 China Nankai University Chern Institute of Mathematics LPMC Tianjin300071 China Tsinghua University Tsinghua Shenzhen International Graduate School Guangdong Shenzhen518055 China

At present, existing multi-receiver authentication (MRA) schemes can only handle situations where the capacities of all receivers in the schemes are the same. However, in reality, different receivers may need to have different storage capacities. In this paper, inspired by the secret sharing scheme based on the Chinese Remainder Theorem (CRT) for polynomial rings where each participant holds the share with different sizes, we propose three new constructions of unconditionally secure MRA schemes for multiple messages using the CRT for polynomial rings, including a (k,n) -threshold MRA scheme, a (k, n, ω)-weighted threshold MRA scheme, and a (Q, F)-general MRA scheme. As far as we know, our proposed MRA schemes are the first MRA schemes with different storage capacities for different receivers and the first ones based on the CRT for polynomial rings. Moreover, the proposed schemes can be seen as extensions of the MRA scheme in Safavi-Naini and Wang. In particular, as for our (Q, F)-general MRA scheme, it has generally more communication complexity and much less computation complexity than the existing (Q, F)-general MRA scheme. © 1963-2012 IEEE.

关键词： Authentication

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Knowledge-Enhanced Recommendation System Design for Mechatronics IoT Cloud Platform 1

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5th International Conference on Artificial Intelligence in China, AIC 2023

作者： Dong, Shaohua Ma, Xinchun Fan, Xiaochao Wang, Shoutong College of Computer Science and Technology Xinjiang Normal University Urumqi830054 China XinJiang Electronic Research Institute Urumqi830013 China

ISBN: (数字)9789819975457

ISBN: (纸本)9789819975440

In recent years, with the rapid development of the Internet and the continuous maturation of artificial intelligence technology, mechatronics has embraced the Internet of Things and is progressively advancing towards digitalization and intelligentization. The exponential growth of the network has facilitated seamless sharing and communication of information across the mechatronics engineering field and other industrial sectors, resulting in the accumulation of extensive multimodal data comprising text, images, and electromagnetic waves. However, conventional recommendation algorithms have predominantly focused on textual information, overlooking the valuable insights present within multimodal data. Additionally, mechatronics encounters challenges pertaining to information transmission, processing, and security. To tackle these challenges, we propose a knowledge-enhanced recommendation system based on a mechatronics-oriented Internet of Things cloud architecture. This system harnesses the rich multimodal information generated within the mechatronics domain, employs entity recognition and mapping techniques to integrate images and textual data into a comprehensive knowledge graph, and employs a multi-hop cross-modal attention mechanism to significantly enhance product recommendations and "virtual assembly line construction." Furthermore, the system facilitates key technological research in the Internet of Things for the mechatronics industry, enables the development of large-scale distributed application software and products, ensures control information security, and facilitates product inspection services. It also fosters close collaboration with relevant research projects and technical services, providing technological support for the continuous improvement of quality and efficiency in the mechatronics industry in Xinjiang. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Internet of things

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Revolutionizing Content Identification: A Comprehensive Study on YouTube Live and Video-on-Demand Detection

Revolutionizing Content Identification: A Comprehensive Stud...

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2024 International Conference on Smart Systems for Electrical, Electronics, Communication and computer Engineering, ICSSEEC 2024

作者： Srija R, Keerthana Kavya, D. Agarwal, Viraj Krithik, R. Pranav, S. Arul Anand, N. Bhat, Raviraj Gunasekaran, Shyam Prabhu PSG College of Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Samsung Research Institute Karnataka Bengaluru India

ISBN: (纸本)9798350378177

With the growing popularity of over-the-top (OTT) streaming services, distinguishing between live and video-on-demand (VoD) streams is becoming increasingly important for network operators and OEMs [1]. However, traditional Deep Packet Inspection (DPI) techniques struggle to differentiate between the two as live and VoD streams share similar network markers. To address this, two machine learning-based strategies are proposed. The first involves training a convolutional neural network (CNN) model using IO graphs of live and VoD video streams and making predictions for new streams using their IO graphs. The second involves using the nfstream library to extract and identify YouTube live and VoD streams from packet capture (pcap) files, followed by building a machine learning model that can identify live and VoD streams with an accuracy of over 96% [2]. Our proposed solutions have important implications for monitoring and improving customer experience, as well as better traffic management. © 2024 IEEE.

关键词： Convolutional neural networks

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