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检索条件"任意字段=International Conference on Information Technology - Coding and Computing"

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Social-enhanced recommendation using graph-based contrastive learning 25

Social-enhanced recommendation using graph-based contrastive...

引用

25th IEEE international conferences on High Performance computing and Communications, 9th international conference on Data Science and Systems, 21st IEEE international conference on Smart City and 9th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Xue, Peng Gao, Qian Fan, Jun Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Jinan250014 China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Shandong Jinan250353 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shandong Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd Shandong Jinan250101 China

ISBN: (纸本)9798350330014

The social network-based recommendation model use social network information to mitigate data sparsity issues and improve the accuracy of recommendation models. However, In most social network-based recommendation algorithms, the social neighbors' contributions are difficult to distinguish from the central user's, neglecting hidden correlations in social infor-mation. In order to solve this problem, this paper presents a graph-based contrastive learning framework for social-enhanced recommendation (SoGCLR) that utilizes implicit social information captured by a new social relation attention mechanism, en-rich user representations, and improve model robustness through graph-based contrastive learning. Specifically, the paper captures the degree of contribution of each neighbor in the social graph to the central user through a social relation attention layer, thus obtaining hidden correlations in social information, and further integrates this with user information in the user-item interaction graph to enrich user representations. In addition, the paper incorporates graph-based contrastive learning into the recommendation task using cross-layer contrastive learning, calculating contrastive loss and mapping nodes with similar but different exposure rates to nearby regions to mitigate exposure bias issues. Results from Ciao and Epinions demonstrate that SoGCLR reduces RMSE and MAE by 1.33% to 1.84% compared with baseline models. © 2023 IEEE.

关键词： Recommender systems

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Assessing the Applicability of Adversarial Machine Learning Approaches for Cybersecurity 2

Assessing the Applicability of Adversarial Machine Learning ...

引用

2nd international conference on Advances in Computation, Communication and information technology, ICAICCIT 2024

作者： Takkalapally, Prashamsh Sharma, Nandan Jaggi, Arjun Hudani, Karim Gupta, Ketan Natarajan, Yuvaraj Department of Engineering Computing and Mathematical Sciences Lewis University United States Senior Security Analyst BC Public Service VictoriaBC Canada HCLTech San DiegoCA United States Tampa FL United States Dept. of Information Technology University of The Cumberlands WilliamsburgKY United States Department of Computer Science Engineering Sri Shakthi institute of engineering and technology Tamil Nadu Coimbatore India

ISBN: (纸本)9798331541217

Adverse machine learning (AML) is a rising area of study that uses system-mastering algorithms to perceive malicious hobbies or to discover malicious adversaries in cybersecurity settings. This research domain combines device-gaining knowledge with game principles to assess and expect the behavior of adversaries. This paper discusses the capacity of using AML tactics for cybersecurity and provides an overview of existing research study findings and safety literature to help the dialogue. Specifically, the paper investigates the security challenges posed by using AML techniques, together with the technical regulations and pointers, to ensure their relaxed and effective deployment. Moreover, the paper offers recommendations and future instructions for furthering studies on AML in cybersecurity. Typical, the evaluation highlights the significance of assessing the applicability of AML techniques for security applications, in addition to its capability to enhance the effectiveness of security systems. © 2024 IEEE.

关键词： Adversarial machine learning

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Deep Learning-based Workload Prediction in Cloud computing to Enhance the Performance 3

Deep Learning-based Workload Prediction in Cloud Computing t...

引用

3rd international conference on Secure Cyber computing and Communications, ICSCCC 2023

作者： Bansal, Sujay Kumar, Mohit Dr. B R Ambedkar National Institute of Technology Computer Science and Engineering Jalandhar India Dr. B R Ambedkar National Institute of Technology Information Technology Jalandhar India

ISBN: (纸本)9798350300710

The cloud paradigm offers a plethora of valuable services to end users through virtualization, aiming to improve resource utilization and reduce services cost. But unpredictable demand of end users degrades the service performance and loss of revenues. The prediction of mixed and inseparable workloads in cloud computing plays a vital role to allocate resources efficiently. Hence, an efficient prediction model is required to give better results in workload management, job scheduling, and overall efficiency of cloud servers. This paper proposes a deep learning-based approach that uses a combination of neural networks to analyze historical workload data and forecast future workloads. The model is evaluated using real existing datasets which are verified and collected by the real-time problems and the output of the proposed algorithm accurately predicts future workloads and improves the user satisfaction level and system efficiency. © 2023 IEEE.

关键词： Cloud computing

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Research on the Application of Power System Cryptography Based on Quantum Key

Research on the Application of Power System Cryptography Bas...

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2024 international conference on Innovative Design and Intelligent Manufacturing, IDIM 2024

作者： Dong, Wu Liu, Xu Peng, Linyu Liu, Kang Peng, Dili Wang, Tao Shi, Ji Zuo, Jun Power Dispatching Control Center of Guizhou Power Grid Guiyang550002 China

ISBN: (纸本)9781643685427

The research and test of the security protection technology of distribution network quantum encryption information based on 5G multi access edge computing applies the communication technology and quantum encryption technology of 5G power private network to the terminal environment of distribution network. Through the integration of quantum key and power service master station, terminal and quantum key, 5G communication and quantum encryption equipment, It enables safe communication between the distribution terminal equipment and the distribution master station, and the system response time is controllable. The results show that in the 5G deployment environment, the maximum delay in 1000 tests is 121.23 ms, while in the 5G+quantum environment, the maximum delay is 200.23 ms. © 2024 The Authors.

关键词： Distribution network information security multi-access edge computing quantum encryption

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Vehicular Network Security: Threats, Vulnerabilities and Countermeasures 15

Vehicular Network Security: Threats, Vulnerabilities and Cou...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Dhabliya, Dharmesh Thangarasu, N. Khurana, Shivam Raajini, X. Mercilin Goyal, Shobhit Arunkumar, D.T. Vishwakarma Institute of Technology Department of Information Technology Maharashtra Pune India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research for Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Quantum University Research Center Quantum University India School of Engineering and Technology Mechaical Engineering Karnataka Bangalore India

ISBN: (纸本)9798350370249

Vehicular community protection (VNS) covers a diffusion of security measures to ensure the secure, cozy, and dependable operation of motors on the street. VNS is an essential software of the Internet of Things, and its safety features are critical in the prevention of injuries, intrusion, and malicious interference. Most of the prevailing VNS solutions depend on verbal exchange protocols, consisting of 802.11p and DSRC, with the purpose of shielding the integrity, privacy, and protection of the records transmitted in the network. However, there needs to be more comprehensive studies on the threats, vulnerabilities, and countermeasures associated with VNS. The primary threats to VNS encompass guy-in-the-middle assaults, spoofing, and denial-of-provider assaults. These attacks take advantage of vulnerabilities in the protection mechanisms that defend vehicular network verbal exchange and may lead to privacy breaches, lack of statistics, or maybe hijacking of the car. Capacity vulnerabilities include insufficient encryption and authentication of the information transmitted inside the vehicular community, lax authentication processes, vulnerable memory control, and inadequate access control. Furthermore, the multiplied connectivity of cars to the internet and the global Wi-Fi will increase the hazard from malware and other malicious code. © 2024 IEEE.

关键词： Authentication

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Application of Kernel Density Estimation in Stock Trading Volume Distribution 24

Application of Kernel Density Estimation in Stock Trading Vo...

引用

2024 international conference on Artificial Intelligence of Things and computing, AITC 2024

作者： Fan, Yaqiang Cheng, Ximing School of Science Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9798400709869

Stock trading volume is an important indicator reflecting market activity and investor behavior. It is of great significance to study its distribution characteristics and dynamic changes for understanding market behavior. This study uses kernel density estimation to analyze the probability distribution characteristics of trading volume in A-share market, and combines long-term short-term memory network model to model the time dynamic change of trading volume. The results show that the stock trading volume shows significant right-skewed and fat-tailed characteristics, which is suitable for the generalized inverse gamma distribution. The long-term short-term memory network model can effectively capture the time trend of trading volume, and the time slice and rolling window analysis of kernel density estimation reveal the high activity of the market in the opening and closing periods. This study provides data support and method reference for market volatility analysis and transaction risk management. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Risk management

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Pre-Trained Encoder Decoder Transformer Model for Language Processing 6

Pre-Trained Encoder Decoder Transformer Model for Language P...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Farhan Akthar, K. Deepaksakthi, V.K. Tamizhselvi, A. Department of Information Technology St.Joseph’s College of Engineering Chennai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522667

Tamil language processing in NLP has yet to be outstanding, mainly because of the absence of high-quality resources. In this project, a novel approach to address these limitations is to build an encoder-decoder transformer model specifically for the Tamil language. The comprehensive Tamil text dataset is augmented along with transformer architectures to further enhance the performance on tasks such as machine translation, text summarization, and generation. Character-level processing further allows Tamil to some extent to capture subtleties, while the multi-head self-attention mechanisms bring in a new dimension to unveil complex linguistic dependencies of the text. Preliminary results look promising for gaining this efficiency in language processing as it outperformed prevailing approaches in key Tamil NLP tasks. Using the Transformer model for processing the Tamil language holds potential for filling an important gap for underrepresented languages in NLP, and further contributes to developing more inclusive AI. © 2025 IEEE.

关键词： Decoding

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A Review on Blockchain Based Security in Education 3

A Review on Blockchain Based Security in Education

引用

3rd international conference on Pervasive computing and Social Networking, ICPCSN 2023

作者： Sheela Rani, P. Kaveri, C. Nivedha, M. Niveda, S. Lakshi, K. Sandhiya, R. Panimalar Engineering College Information Technology Dept Chennai India

ISBN: (纸本)9798350322842

Blockchain technology is very well positioned, and the education industry may greatly benefit from blockchain technology, which can be used for digital credentials, academic records, learning management systems, funding and payments, research, and collaboration. The development of distributed registries in blockchain technology is employed in educational institutions to provide digital credentials. Learning platforms built on the blockchain can offer individualized learning experiences by monitoring student progress and adapting the content to suit each student's requirements. This study examined the use of blockchain in academic institutions through a review of the literature. It aims to examine the difficulties encountered by educational organizations while deploying an innovative application that provides unchangeable, trustworthy records of students' lifelong learning. Implementing blockchain to address educational obstacles like scalability, integrity, privacy, and security problems. The most problematic and contentious issues relating to blockchain security, challenges, and legislation have been discussed in detail. For blockchain technology to be implemented by higher education as a digital technology, this study offers a translucent exploration of blockchain. © 2023 IEEE.

关键词： Smart contract

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Optimizing Hyperspectral Anomaly Detection with a Scalable Quantum Snow Geese Non-Local Neural Network Algorithm 4

Optimizing Hyperspectral Anomaly Detection with a Scalable Q...

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4th international conference on Ubiquitous computing and Intelligent information Systems, ICUIS 2024

作者： Ramkumar, M.Siva Emayavaramban, G. Latha, G.Charlyn Pushpa Giri, Jayant Al-Qawasmi, Khaled Sns College of Technology Department of Ece India Jct College of Engineering and Technology Department of Ece India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Dept of Information Technology Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India Saveetha University Department of Vlsi Microelectronics TN Chennai602105 India Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331529635

Hyperspectral Anomaly Detection (HAD) focuses on identifying unusual objects or patterns in hyperspectral imagery (HSI) that differ from the normal background or surrounding pixels. In the past two decades, anomaly detection (AD) has become essential in hyperspectral image analysis, offering a novel approach to differentiate targets from the background without requiring prior information. Current methods face multiple drawbacks, either by neglecting the problem of low resolution or by depending on super-resolution models that aim to reconstruct the entire image, which can result in ineffective detection of abnormal pixels. To address these challenges, a Scalable Quantum Snow Geese Non-local Neural Network Algorithm (ASQSGNon-LNNet) is introduced for hyperspectral anomaly detection, aimed at achieving high accuracy. In this approach, input images are sourced from the ABU-urban dataset. First, the features of these input images are extracted using the Memory Efficient Vision Transformer (ME-ViT) method. Subsequently, the extracted features undergo Hyperspectral Super Resolution using the Deform-Mamba Network. After that, anomaly detection is conducted using a Scalable Quantum Non-local Neural Network (SQNonL-NNet), optimized by the Snow Geese Algorithm (SGA), leading to automated hyperspectral anomaly detection with improved efficiency and accuracy. The performance of the proposed ASQSGNon-LNNet is assessed using the ABU-urban dataset, achieving an accuracy of 99.8% and surpassing existing methods. The results of this technique reveal unusual patterns or objects in hyperspectral images by analyzing their spectral signatures and enhancing accuracy in detecting hidden or subtle anomalies in remote sensing, environmental monitoring, and military applications. This technology provides greater accuracy than traditional methods. © 2024 IEEE.

关键词： Snow

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A Simulation Study of Battery Integrated Photovoltaic System for Gram Panchayat Development information Kiosk 5

A Simulation Study of Battery Integrated Photovoltaic System...

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5th international conference on Circuits, Control, Communication and computing, I4C 2024

作者： Hemachandra, G. Arakeri, Megha P Lakshmana Samprathi, H.S. Shilpa, H.B. Nanditha, S. Ramaiah Institute of Technology Department of Eee Bengaluru India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Computer Science and Engineering Manipal India Seamovations Labs Pvt.Ltd Bengaluru India

ISBN: (纸本)9798331528539

In rural India, gram panchayats serve as vital administrative bodies that require access to accurate information on government initiatives aimed at fostering community growth. However, these gram panchayats frequently encounter issues with electricity access, which hampers the setting up and functioning of information systems, for sharing important details with farmers. They need a secure power supply to discharge their functions in a time-bound manner and thereby help fast-track integrated community development. The present work is a feasibility and performance study of an integrated battery photovoltaic power system in developing a gram panchayat information kiosk. This study employs simulations to analyze the performance of battery-integrated PV systems for continuous power supply to information kiosk. Solar irradiance, battery capacity, and energy demand patterns are analysed to optimize the system design and reliable operation. The findings of this simulation study offers valuable insights to rural agencies, and energy practitioners involved in gram panchayat development initiatives. By demonstrating the technical feasibility and economic advantages of battery-integrated PV systems for information kiosks, contributes to advancing sustainable energy solutions tailored to the specific needs of rural communities, ultimately fostering inclusive development and empowerment at the grassroots level. © 2024 IEEE.

关键词： battery gram panchayat information kiosk photovoltaic system rural development

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