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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Zhang, Jingyu Hou, Xingxing Teng, Yilong Luo, Lailong Changsha University of Science and Technology School of Computer and Communication Engineering Changsha China National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory School of Systems Engineering Changsha China

ISBN: (纸本)9798350329223

Unspent Transaction Output (UTXO) is part of the transaction data set, which represents the digital cryptocurrency asset in transaction-based blockchain systems. The data management capability, storage method and occupied space of UTXOs will greatly affect the running efficiency and the verification performance of blockchain systems. Especially, with the popularity of blockchain technology, the relevant UTXO data sets have been growing, and all the stored data can no longer be almost completely stored in memory. How should the UTXO transaction data be stored and managed at this time, it is an urgent issue to be solved in bitcoin-like blockchain systems. This paper provides a blockchain transaction data management optimization mechanism based on multi-partitioning. First, we analyze the influencing factors of transactions through real blockchain data. The proposed method can evaluate the time interval and transaction frequency factors, and use the received information to realize the efficient transaction data storage. In our design, UTXOs with lower likelihood to be used in new transactions will be stored in the disk, and the other UTXOs with higher likelihood to be used in new generated transactions should be stored in the cache. This approach aims to minimize memory consumption for the transaction data sets, accelerate UTXO access time during block verification, and ultimately decrease the overall time required for verification, leading to efficient UTXO transaction data management. Finally, the effectiveness of the proposed optimization mechanism is verified through theoretical analysis and simulation experiments, and the UXTO access time has been reduced compared with state-of-the-art methods. © 2023 IEEE.

关键词： Information management

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SvaVoting: A Novel Secret Sharing-Based Verifiable and Anonymous E-Voting Scheme in Blockchain systems 29

SvaVoting: A Novel Secret Sharing-Based Verifiable and Anony...

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29th IEEE International Conference on Parallel and Distributed systems, ICPADS 2023

作者： Zhang, Jingyu Wu, Chenghao Yang, Jin Luo, Lailong Changsha University of Science and Technology School of Computer and Communication Engineering Changsha China National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory School of Systems Engineering Changsha China

ISBN: (纸本)9798350330717

E-Voting systems play a vital role in various communities, aiming to enhance the efficiency, transparency, and citizen engagement in elections. However, as technology advances, ensuring the security and reliability of e-voting systems becomes profound. While researchers have explored the integration of blockchain technology to improve e-voting systems, there still are technical challenges, particularly regarding voters' privacy, ballots' confidentiality, and system performance. To address these challenges, we propose SvaVoting, an innovative blockchain-based e-voting scheme. SvaVoting utilizes the Borda counting method and the identity-based ring signature based on symmetric primitives to anonymously verify the validity of the voter's identity and ballot format. In addition, we introduce secret sharing and a Cloud Service Provider (CSP) to conduct the final counting while preserving the privacy of the ballots. All users in the system can verify the results of CSP calculations, thereby meeting security goals such as correctness, unforgeability, anonymity and verifiability. The identity-based ring signature in our scheme provides 128-bit security, and even it can work in a quantum computing environment. Experimental results demonstrate that our solution significantly enhances the security and performance of e-voting systems, offering a practical path forward for future e-voting systems. © 2023 IEEE.

关键词： cloud service provider e-voting identity-based ring signatures post-quantum cryptography secret sharing

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Machine learning-aided scoring of synthesis difficulties for designer chromosomes

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science China(Life sciences) 2023年第7期66卷 1615-1625页

作者： Yan Zheng Kai Song Ze-Xiong Xie Ming-Zhe Han Fei Guo Ying-Jin Yuan Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering Ministry of EducationTianjin UniversityTianjin300072China School of Chemical Engineering and Technology Tianjin UniversityTianjin300072China School of Computer Science and Engineering Central South UniversityChangsha410083China

Designer chromosomes are artificially synthesized ***,these chromosomes have numerous applications ranging from medical research to the development of ***,some chromosome fragments can interfere with the chemical synthesis of designer chromosomes and eventually limit the widespread use of this *** address this issue,this study aimed to develop an interpretable machine learning framework to predict and quantify the synthesis difficulties of designer chromosomes in *** the use of this framework,six key sequence features leading to synthesis difficulties were identified,and an e Xtreme Gradient Boosting model was established to integrate these *** predictive model achieved high-quality performance with an AUC of 0.895 in cross-validation and an AUC of 0.885 on an independent test *** on these results,the synthesis difficulty index(S-index)was proposed as a means of scoring and interpreting synthesis difficulties of chromosomes from prokaryotes to *** findings of this study emphasize the significant variability in synthesis difficulties between chromosomes and demonstrate the potential of the proposed model to predict and mitigate these difficulties through the optimization of the synthesis process and genome rewriting.

关键词： synthetic biology machine learning artificial chromosome chemical synthesis

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Risk Assessment of Satellite Constellations Using Network Theory

Risk Assessment of Satellite Constellations Using Network Th...

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2024 International Annual Conference on Complex systems and Intelligent science, CSIS-IAC 2024

作者： Lin, Qing Li, Ting Li, Yiran Luo, Zong-Fu School of Systems Science and Engineering Sun Yat-sen University Guangzhou China College of Computer Science Inner Mongolia University Hohhot China

ISBN: (纸本)9798331504755

The increasing satellite population in low Earth orbit heightens collision risks and challenges the traditional space situational awareness methods. These methods, typically focused on binary interactions, do not fully capture the complexities of satellite constellation dynamics, especially for smaller objects. This study introduces an innovative satellite constellation safety assessment approach that integrates the collision probability algorithm DilutionMaxPc with complex network analysis. We applied this methodology to actual satellite data and simulated constellation configurations, including objects smaller than 10 cm. This demonstrates its capability to pinpoint critical network nodes and potential collision pathways. Our results indicate that this approach effectively combines intricate interactions and long- term orbital dynamics. It utilizes network centrality metrics alongside collision probabilities, providing a robust framework to enhance the accuracy and resilience of satellite constellation safety assessments. © 2024 IEEE.

关键词： Space debris

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Food Volume Prediction and Classification using Multi-Task Learning 6

Food Volume Prediction and Classification using Multi-Task L...

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6th World Symposium on Artificial Intelligence, WSAI 2024

作者： Sari, Yuita Arum Nakazawa, Atsushi Gofuku, Akio Graduate School of Interdisciplinary Science and Engineering in Health Systems Okayama University Okayama Japan Brawijaya University Faculty of Computer Science Malang Indonesia

ISBN: (纸本)9798350374162

Food volume prediction is critical for calculating calories in food analysis and nutritional evaluation. However, obtaining data and measuring volume from the 3D sensor camera concurrently is time-consuming. Additionally, high computing power is necessary to compute the volume. Therefore, machine learning techniques are employed to extract specific data collected from the data collection process for learning purposes to save time. In this study, we employ Multi-Task Learning to predict volume and categorize food due to their interdependence simultaneously. We evaluate several backbones and find that ResNet50 attains the best performance with an MAE of 4.87 for volume prediction and 100% food classification accuracy. Our proposed method showcases outstanding results, aided by an efficient volume measurement methodology © 2024 IEEE.

关键词： food volume prediction machine learning multi-task learning

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Privacy preserving large language models: ChatGPT case study based vision and framework

IET Blockchain

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IET Blockchain 2024年第S1期4卷 706-724页

作者： Ullah, Imdad Hassan, Najm Gill, Sukhpal Singh Suleiman, Basem Ahanger, Tariq Ahamed Shah, Zawar Qadir, Junaid Kanhere, Salil S. School of Computer Science Faculty of Engineering The University of Sydney SydneyNSW Australia Higher Colleges of Technology Abu Dhabi United Arab Emirates School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Management Information Systems Department College of Business Administration Prince Sattam bin Abdulaziz University Al-Kharj Saudi Arabia Department of Information Technology Sydney International School of Technology and Commerce SydneyNSW Australia Department of Computer Science and Engineering College of Engineering Qatar University Doha Qatar Sydney Australia

The generative Artificial Intelligence (AI) tools based on Large Language Models (LLMs) use billions of parameters to extensively analyse large datasets and extract critical information such as context, specific details, identifying information, use this information in the training process, and generate responses for the requested queries. The extracted data also contain sensitive information, seriously threatening user privacy and reluctance to use such tools. This article proposes the conceptual model called PrivChatGPT, a privacy-preserving model for LLMs consisting of two main components, that is, preserving user privacy during the data curation/pre-processing and preserving private context and the private training process for large-scale data. To demonstrate the applicability of PrivChatGPT, it is shown how a private mechanism could be integrated into the existing model for training LLMs to protect user privacy;specifically, differential privacy and private training using Reinforcement Learning (RL) were employed. The privacy level probabilities are associated with the document contents, including the private contextual information, and with metadata, which is used to evaluate the disclosure probability loss for an individual's private information. The privacy loss is measured and the measure of uncertainty or randomness is evaluated using entropy once differential privacy is applied. It recursively evaluates the level of privacy guarantees and the uncertainty of public databases and resources during each update when new information is added for training purposes. To critically evaluate the use of differential privacy for private LLMs, other mechanisms were hypothetically compared such as Blockchain, private information retrieval, randomisation, obfuscation, anonymisation, and the use of Tor for various performance measures such as the model performance and accuracy, computational complexity, privacy vs. utility, training latency, vulnerability to attacks, and

关键词： Blockchain

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Calligraphy Alphabet Perception Using Artificial Intelligence 9

Calligraphy Alphabet Perception Using Artificial Intelligenc...

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Venkatesh, S. Jeevitha, D. Gnanaselvi, J. Anitha Srm Institute of Science And Technology Department of Data Science And Business Systems School of Computing Chennai Kattankulathur India Jeppiaar Engineering College Department of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350365092

The human brain has a simple time analyzing and processing images. The brain is able to rapidly deconstruct and distinguish an image's various components when the eye perceives it. With the Convolutional Neural Network (CNN) as its foundation, this research suggests deep learning conceptual models. When the algorithms are compared, it becomes clear that CNN-based classification of handwritten alphabets performs better than other algorithms in terms of accuracy. The Manual Net, Alex Net, and LeNet Architectures are among the CNN algorithms employed in this research. The convulational layer, max pooling, flattening, feature assortment, rectifier lined unit, and completely linked softmaxx layers are each components of the aforementioned designs. The proposed network is tested using an image dataset comprising 530 training photos and 2756 testing images. The top precision and cost-efficient model will be used in the Django context to build a handler line for supplying the appeal to be recognized and obtaining the productivity outcome of recognized appeal. © 2024 IEEE.

关键词： Convolutional neural networks

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A Comprehensive Study on the Role of Artificial Intelligence in Animal Husbandry

A Comprehensive Study on the Role of Artificial Intelligence...

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2023 Intelligent Computing and Control for engineering and Business systems, ICCEBS 2023

作者： Surya Deveswar Reddy, Kovvuri Uday Shaik, Ayesha Balasundaram, A. Nivedita, M. Ram Kakarla, Lakshmi Sai Nithin, Mattapally Sai School of Computer Science and Engineering Vellore Institute of Technology Chennai Chennai India School of Computer Science and Engineering Vellore Institute of Technology Center for Cyber Physical Systems Chennai India

ISBN: (纸本)9798350394580

Distinguishing the breeds of animals is one of the major examination areas in animal welfare. Recognition of animal breeds demands numerous determining elements which are necessary to be explored and following classification of variety. Animals such as cows, buffaloes, sheep, goats, and other diverse creatures play a huge role in the production of milk, and sometimes meat. Every creature has different species and provide unique benefits which contribute a lot to the Indian economy. India is currently holding the first rank in milk export. Hence, it is compulsory to introduce automated methods that will differentiate breeds of various species. In addition, identifying diseases that effected to animals is also an important area related to animal protection and it is necessary to automate the detection as it would be effective for identify the diseases in primary stages. This paper carries out a full-scale study on how deep learning techniques are used in animal husbandry. © 2023 IEEE.

关键词： Animal Breeds Automate Methods Classification Deep learning Diseases Machine Learning

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Prediction of Covid-19 Recovery Chances Using Supervised Machine Learning Classification 5th

Prediction of Covid-19 Recovery Chances Using Supervised Mac...

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5th International Conference on Innovative Product Design and Intelligent Manufacturing systems, ICIPDIMS 2023

作者： Dhanalakshmi, R. Nivashini, A. Vijayaraghavan, N. Centre for Cyber Physical Systems School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering MNM Jain Engineering College Chennai India Department of Mathematics KCG College of Technology Chennai India

ISBN: (纸本)9789819767311

Predictive analytics if combined with machine learning approaches have the potential to play a significant role in forecasting the spread of respiratory infections. Machine learning approaches aid in the mining of data to better assess and forecast the presence or absence of covid-19 infection. In this paper, XGBoost Ensemble Classification using a fine-tuned approach is being used. Various machine learning algorithms are being analyzed and XGBoost Ensemble Classification algorithm is used to forecast and predict the covid-19 infected patient recovery chances that is the possible outcome of a covid-19 infected patient along with the prediction probability of recovery. The suggested categorization model is applied to a variety of situations. When compared to other ensemble models like random forest, hybrid ensemble and other individual classifiers such as decision tree, logistic regression, K-nearest neighbor, support vector machine and Naive Bayes, the XGBoost Ensemble Classification model gave the highest accuracy. Predicting the correct class is important. Thus, the proposed system discusses the possibility of automated prediction for covid-19 infected patient recovery chances along with the probability of recovery and the effectiveness of such predictions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： COVID-19

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Federated Knowledge Transfer Fine-Tuning Large Server Model with Resource-Constrained IoT Clients

Federated Knowledge Transfer Fine-Tuning Large Server Model ...

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International Workshop on Trustworthy Federated Learning, FL 2024

作者： Chen, Shaoyuan You, Linlin Liu, Rui Yu, Shuo Abdelmoniem, Ahmed M. School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen China Shenzhen Fangle Technology Co. Ltd. Shenzhen China School of Computer Science and Engineering Nanyang Technological University Singapore Singapore School of Computer Science and Technology Dalian University of Technology Dalian China School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom

ISBN: (纸本)9783031822391

The training of large models, involving fine-tuning, faces the scarcity of high-quality data. Compared to the solutions based on centralized data centers, updating large models in the Internet of Things (IoT) faces challenges in coordinating knowledge from distributed clients by using their private and heterogeneous data. To tackle such a challenge, we propose KOALA (Federated Knowledge Transfer Fine-tuning Large Server Model with Resource-Constrained IoT Clients) to impel the training of large models in IoT. Since the resources obtained by IoT clients are limited and restricted, it is infeasible to locally execute large models and also update them in a privacy-preserving manner. Therefore, we leverage federated learning and knowledge distillation to update large models through collaboration with their small models, which can run locally at IoT clients to process their private data separately and enable large-small model knowledge transfer through iterative learning between the server and clients. Moreover, to support clients with similar or different computing capacities, KOALA is designed with two kinds of large-small model joint learning modes, namely to be homogeneous or heterogeneous. Experimental results demonstrate that compared to the conventional approach, our method can not only achieve similar training performance but also significantly reduce the need for local storage and computing power resources. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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