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

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Pre-Trained Encoder Decoder Transformer Model for Language Processing 6

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

引用

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

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

引用

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

A Simulation Study of Battery Integrated Photovoltaic System...

引用

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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Optimizing Large Language Models with an Enhanced LoRA Fine-Tuning Algorithm for Efficiency and Robustness in NLP Tasks 4

Optimizing Large Language Models with an Enhanced LoRA Fine-...

引用

4th international conference on Communication technology and information technology, ICCTIT 2024

作者： Hu, Jiacheng Liao, Xiaoxuan Gao, Jia Qi, Zhen Zheng, Hongye Wang, Chihang Tulane University New Orleans United States New York University New YorkNY United States Stevens Institute of Technology HobokenNJ United States Northeastern University BostonMA United States The Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9798331528973

This study proposes a large language model optimization method based on the improved LoRA fine-tuning algorithm, aiming to improve the accuracy and computational efficiency of the model in natural language processing tasks. We fine-tune the large language model through a low-rank adaptation strategy, which significantly reduces the consumption of computing resources while maintaining the powerful capabilities of the pre-trained model. The experiment uses the QQP task as the evaluation scenario. The results show that the improved LoRA algorithm shows significant improvements in accuracy, F1 score, and MCC compared with traditional models such as BERT, Roberta, T5, and GPT-4. In particular, in terms of F1 score and MCC, our model shows stronger robustness and discrimination ability, which proves the potential of the improved LoRA algorithm in fine-tuning large-scale pre-trained models. In addition, this paper also discusses the application prospects of the improved LoRA algorithm in other natural language processing tasks, emphasizing its advantages in multi-task learning and scenarios with limited computing resources. Future research can further optimize the LoRA fine-tuning strategy and expand its application in larger-scale pre-trained models to improve the generalization ability and task adaptability of the model. © 2024 IEEE.

关键词： Semantics

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Feasibility of Wide Area Network Traffic Distribution in Cloud computing Enabled Wireless Networks

Feasibility of Wide Area Network Traffic Distribution in Clo...

引用

2024 international conference on E-Mobility, Power Control and Smart Systems, ICEMPS 2024

作者： Mimani, Sushant Pillai, Sanjaikanth E. Vadakkethil Somanathan Jindal, Mayank Jiwani, Nasmin Kiruthiga, T. GoDaddy SnohomishWA United States School of Electrical Engineering and Computer Science University of North Dakota Grand Forks United States San Jose CA United States University of The Cumberlands Dept. of Information Technology WilliamsburgKY United States Vetri Vinayaha College of Engineering and Technology Dept. of Ece Trichy India

ISBN: (纸本)9798350394399

Cloud computing enabled Wi-Fi networks provide an outstanding opportunity for corporations to distribute extensive area community (WAN) site visitors and advantage from reduced latency because of the proliferation of hyper connectivity. The feasibility of implementing a Wide Area Network (WAN) traffic distribution system in cloud computing enabled wireless networks was studied. The study aimed to determine the practicality and effectiveness of this approach in improving network performance. For the experiment, a sample network topology was designed to simulate real-world scenarios and subjected to diverse traffic loads. The results showed that the WAN traffic distribution system was indeed feasible and could effectively distribute and balance network traffic in cloud computing enabled wireless networks. Furthermore, key metrics such as network latency, packet loss, and throughput were significantly improved compared to traditional networks without the WAN distribution system. Further research and development in this area could potentially lead to improved network efficiency and enhanced user experience in the rapidly growing field of cloud computing. Those consequences reveal the feasibility of deploying WAN site visitors distribution in cloud computing-enabled wireless networks, and serves as a starting point for further study aiming to improve the performance of such networks. © 2024 IEEE.

关键词： Wide area networks

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Detecting and Predicting Learner's Dropout Using KNN Algorithm

Detecting and Predicting Learner's Dropout Using KNN Algorit...

引用

2024 OPJU international technology conference on Smart computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Guru Priya, V. Eliyas, Sherin Sathish Kumar, M. Hindustan Institute of Technology and Science Department of Computer Applications Chennai India

ISBN: (纸本)9798350373783

Predicting student dropout is crucial for early intervention and support, addressing a significant loss of potential human capital in the education system. This paper presents a machine-learning approach to predict student dropout using a dataset of student records. Our model leverages various features including demographic information, academic performance, attendance records, and fee payment details to estimate the probability of dropout. A survey conducted among college students in Tamil Nadu sheds light on reasons for discontinuing courses, emphasizing the multifaceted nature of the dropout problem. The proposed predictive model will be evaluated using a large dataset, comparing its effectiveness and accuracy with existing models. This research aims to facilitate proactive interventions by educational institutions, leveraging technology and data analytics to improve retention rates amidst the challenges posed by the pandemic and beyond. Student dropout prediction is paramount for early intervention in the education system, mitigating the loss of potential human capital. This study employs a machine-learning approach to forecast student dropout utilizing a comprehensive dataset comprising demographic information, academic performance, attendance records, and fee payment details. A survey conducted among college students across Tamil Nadu highlights the multifaceted nature of dropout, encompassing socioeconomic and psychological factors. The proposed predictive model will undergo rigorous evaluation against existing benchmarks to assess its efficacy and accuracy. By enabling proactive interventions, this research endeavors to enhance retention rates through the integration of technology and data analytics, particularly in the wake of the pandemic, shaping the future of education. © 2024 IEEE.

关键词： Students

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An Evaluation System for Large Language Models based on Open-Ended Questions 11

An Evaluation System for Large Language Models based on Open...

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11th IEEE international conference on Cyber Security and Cloud computing, CSCloud 2024

作者： Cao, Zhiyuan Ma, Zeyu Chen, Mingang Computer and Information Engineering College Shanghai Polytechnic University Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer Software Technology Shanghai China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer software Technology Mathematical and Science College Shanghai Normal University Shanghai China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer software Technology Shanghai China

ISBN: (纸本)9798350376982

We designed a large language model evaluation system based on open-ended questions. The system accomplished multidimensional evaluation of LLMs using open-ended questions, and it presented evaluation results with evaluation reports. Currently, the evaluation of large-scale language models often exists with two prominent limitations: (1) The evaluation methods are often single-minded, resulting in less credible results. (2) Most evaluations are based on datasets with closed-ended questions, treating generative large language models as discriminative models, which fails to adequately reflect the high output flexibility characteristic of these models. For these two limitations, we proposed an evaluation system for LLMs based on open-ended questions. Our experiments on the adapted open-source datasets demonstrated the effectiveness of this system. © 2024 IEEE.

关键词： Natural language processing systems

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