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3rd International Conference on Blockchain and Trustworthy Systems, Blocksys 2021

作者： Xue, Gengsheng Li, Yang Zheng, Zibin School of Data and Computer Science Sun Yat-sen University Guangzhou510275 China National Engineering Research Center of Digital Life Sun Yat-sen University Guangzhou510275 China

ISBN: (纸本)9789811679926

Bitcoin exchanges, which are digital platforms for investors to trade Bitcoins freely, operate between digital and fiat currency networks. Thus they provide an opportunity to connect real-world identities to pseudonymous Bitcoin addresses, which is critical to anti-money laundering efforts. To this end, in this paper, we conduct a systematic investigation on the whole Bitcoin exchange ecosystem and seek to characterize, understand, and identify patterns around exchanges (including exchanges’ internal wallet structure and transfer activity). Specifically, we first construct the Bitcoin Transfer Graph (BTG) using the on-chain transaction records and propose an approach to identify the Bitcoin addresses belonging to Bitcoin exchanges. Then, based on the found addresses, we conduct a preliminary analysis of mainstream Bitcoin exchanges and provide an overview of their internal wallet architecture. Lastly, we obtain many observations and findings by analyzing these graphs, which help us obtain a deep understanding of the Bitcoin exchange ecosystem. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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Accessible Authentication Using Keyword Search through Homomorphic Encryption 1

Accessible Authentication Using Keyword Search through Homom...

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1st IEEE International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022

作者： Vekariya, Vipul Manjunath, R. Gill, Jasmeen Gopianand, M. Kumar, Avneesh Jha, Amaresh Parul University Department of CSE PIET Gujarat Vadodara India Department of Data Science & Mathamatics Bangalore India RIMT University Department of CSE Godindgarh Mandi India PSNA College of Engineering and Technology Department of Computer Applications Dindigul India Galgotias University Department of Computer Application Uttar Pradesh Greater Noida India School of Modern Media UPES Dehradun India

ISBN: (纸本)9781665456876

data storage presents substantial issues due to concerns about data security and privacy. In this study, we provide a search-based, interoperable, verified public key cryptography system. The searchability is significantly improved by our recommended method, which allows the cloud servers to create invert crypto indexes while needing a query trap and leveraging asymmetric cryptography developed by van Vries, Miller, Showing different, and Vaikuntanathan. Contrarily, the recommended method offers a brand-new permitted dataset built on the inversion decryption scheme and shows how it may be used to evaluate the thoroughness and correctness of serps. Additionally, using the provided method, many users may do encrypted searches on cryptography. Finally, it is shown that the offered technique is secure utilizing the approximate-GCD problem. The experiment's results showed that the proposed technique had less computational complexity than the methods currently in use. © 2022 IEEE.

关键词： Public key cryptography

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Bayesian Optimization with Stacked Sparse Autoencoder based Cryptocurrency Price Prediction Model 5

Bayesian Optimization with Stacked Sparse Autoencoder based ...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Shaikh, Irfan Abdul Karim Krishna, P. Vamsi Biswal, Swagat Gourav Kumar, A. Sasi Baranidharan, S. Singh, Kamlesh Faculty of Business Bahrain Polytechnic Isa Town Bahrain School of Management Malla Reddy University Telangana Hyderabad India Odisha University of Tecnology and Research Department of Electrical Engineering Orissa Bhubaneswar India Institute of Engineering and Technology Srinivas University Mentor-IT-Inurture Education Solutions Pvt Ltd Bangalore Department of Cloud Technology and Data Science Karnataka Dakshina Kannada India Department of Mba and Research Center Karnataka Bangalore India Graphic Era Hill University Department of Computer Science and Engineering Dehradun Campus Dehradun India

ISBN: (纸本)9781665474672

Digital currency is a way of currency utilized in the digital world namely electronic devices or digital forms. Many terms are alternative words for digital currency such as cyber cash, digital money, and electronic money. Cryptocurrency is a type of asset that has developed due to the progression of financial technology and it has made a tremendous chance for research workers. Cryptocurrency price prediction is challenging because of the dynamism and price volatility. The electronic economy is severely hazardous and should be advanced with greater caution, to minimize or avoid the risk that occurs in this case. Therefore, this study develops a new Bayesian optimization with Stacked Sparse Autoencoder based Cryptocurrency Price Prediction (BOSSAE-CPP) model. The major intention of the BOSSALCPP technique lies in the effectual prediction of cryptocurrency prices. To attain this, the BOSSAE-CPP technique exploits SSAE model for price prediction process. Moreover, the BO technique is used to optimally choose the hyperparameter values of the SSAE model and results in enhanced predictive outcomes. To deliberate the enhanced outcomes of the BOSSALCPP technique, extensive experimentation study is made. The comparison study highlighted the improved performance of the BOSSAE-CPP technique. © 2023 IEEE.

关键词： Electronic money

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CoMT: A Novel Benchmark for Chain of Multi-modal Thought on Large Vision-Language Models

arXiv

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arXiv 2024年

作者： Cheng, Zihui Chen, Qiguang Zhang, Jin Fei, Hao Feng, Xiaocheng Che, Wanxiang Li, Min Qin, Libo School of Computer Science and Engineering Central South University China Key Laboratory of Data Intelligence and Advanced Computing in Provincial Universities Soochow University China Research Center for SCIR Harbin Institute of Technology Harbin China National University of Singapore Singapore

Large Vision-Language Models (LVLMs) have recently demonstrated amazing success in multi-modal tasks, including advancements in Multi-modal Chain-of-Thought (MCoT) reasoning. Despite these successes, current benchmarks still follow a traditional paradigm with multi-modal input and text-modal output, which leads to significant drawbacks such as missing visual operations and vague expressions. Motivated by this, we introduce a novel Chain of Multi-modal Thought (CoMT) benchmark to address these limitations. Different from the traditional MCoT benchmark, CoMT requires both multi-modal input and multi-modal reasoning output, aiming to mimic human-like reasoning that inherently integrates visual operations. Specifically, CoMT consists of four categories: (1) Visual Creation, (2) Visual Deletion, (3) Visual Update, and (4) Visual Selection to comprehensively explore complex visual operations and concise expression in real scenarios. We evaluate various LVLMs and strategies on CoMT, revealing some key insights into the capabilities and limitations of the current approaches. We hope that CoMT can inspire more breakthroughs on introducing multi-modal generation into the reasoning process. The project page is available at https://***/czhhzc/CoMT. © 2024, CC BY-NC-SA.

关键词： Chains

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FedBlockHealth: A Synergistic Approach to Privacy and Security in IoT-Enabled Healthcare Through Federated Learning and Blockchain

FedBlockHealth: A Synergistic Approach to Privacy and Securi...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Nazar Waheed Ateeq Ur Rehman Anushka Nehra Mahnoor Farooq Nargis Tariq Mian Ahmad Jan Fazlullah Khan Abeer Z. Alalmaie Priyadarsi Nanda School of Electrical and Data Engineering University of Technology Sydney Sydney Australia Department of Information Technology University of Haripur Haripur Pakistan Department of CS and Engineering Thapar University Patiala Patiala India Department of Computer Science University of Sharjah Sharjah United Arab Emirates Department of Computer Engineering Abdul Wali Khan University Mardan Pakistan

The rapid adoption of Internet of Things (IoT) devices in healthcare has introduced new challenges in preserving data privacy, security and patient safety. Traditional approaches need to ensure security and privacy while maintaining computational efficiency, particularly for resource-constrained IoT devices. This paper proposes a novel hybrid approach by combining federated learning and blockchain technology to provide a secured and privacy-preserved solution for IoT-enabled healthcare applications. Our approach leverages a public-key cryptosystem that provides semantic security for local model updates, while blockchain technology ensures the integrity of these updates and enforces access control and accountability. The federated learning process enables a secure model aggregation without sharing sensitive patient data. We implement and evaluate our proposed framework using EMNIST datasets, demonstrating its effectiveness in preserving data privacy and security while maintaining computational efficiency. The results suggest that our hybrid approach can significantly enhance the development of secure and privacy-preserved IoT-enabled healthcare applications, offering a promising direction for future research in this field.

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RGB-D Based Visual Navigation Using Direction Estimation Module 18th

RGB-D Based Visual Navigation Using Direction Estimation Mod...

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18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021

作者： Luo, Chao Bi, Sheng Dong, Min Nie, Hongxu School of Computer Science and Engineering South China University of Technology Guangdong Guangzhou510006 China Key Laboratory of Big Data and Intelligent Robot Ministry of Education Guangdong Guangzhou510006 China

ISBN: (纸本)9783030893699

Target-driven visual navigation without mapping works to solve navigation problems that given a target object, mobile robots can navigate to the target object. Recently, visual navigation has been researched and improved largely by learning-based methods. However, their methods lack depth information and spatial perception, using only single RGB images. To overcome these problems, two methods are presented in this paper. Firstly, we encode visual features of objects by dynamic graph convolutional network and extract 3D spatial features for objects by 3D geometry, a high level visual feature for agent to easily understand object relationship. Secondly, as human beings, they solve this problem in two steps, first exploring a new environment to find the target object and second planning a path to arrive. Inspired by the way of humans navigation, we propose direction estimation module (DEM) based on RGB-D images. DEM provides direction estimation of the target object to our learning model by a wheel odometry. Given a target object, first stage, our agent explores an unseen scene to detect the target object. Second stage, when detected the target object, we can estimate current location of the target object by 3D geometry, after that, each step of the agent, DEM will estimate new location of target object, and give direction information of the target object from a first-view image. It can guide our agent to navigate to the target object. Our experiment results outperforms the result of state of the art method in the artificial environment AI2-Thor. © 2021, Springer Nature Switzerland AG.

关键词： Reinforcement learning

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Debiasing Graph Representation Learning Based on Information Bottleneck

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IEEE Transactions on Neural Networks and Learning Systems 2024年第6期36卷 11092-11105页

作者： Ziyi Zhang Mingxuan Ouyang Wanyu Lin Hao Lan Lei Yang School of Software Engineering South China University of Technology Guangzhou China Department of Computing The Hong Kong Polytechnic University Hong Kong China Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Hong Kong China Department of Computing Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Hong Kong China Department of Computer Science and Technology Tsinghua University Beijing China

Graph representation learning has shown superior performance in numerous real-world applications, such as finance and social networks. Nevertheless, most existing works might make discriminatory predictions due to insufficient attention to fairness in their decision-making processes. This oversight has prompted a growing focus on fair representation learning. Among recent explorations on fair representation learning, prior works based on the adversarial learning usually induce unstable or counterproductive performance. To achieve fairness in a stable manner, we present the design and implementation of graph representation learning based on fairness information bottleneck (GRAFair), a new framework based on a variational graph autoencoder (VGAE). The crux of GRAFair is the conditional fairness bottleneck (CFB), where the objective is to capture the trade-off between the utility of representations and sensitive information of interest. By applying variational approximation, we can make the optimization objective tractable. Particularly, GRAFair can be trained to produce informative representations of tasks while containing little sensitive information without adversarial training. Experiments on various real-world datasets demonstrate the effectiveness of our proposed method in terms of fairness, utility, robustness, and stability.

关键词： Representation learning Adversarial machine learning Graph neural networks Optimization Electronic mail Training Predictive models Message passing Encoding Vectors

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OCI Runtime Comparison and Analysis Study 5

OCI Runtime Comparison and Analysis Study

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5th IEEE International Conference on Artificial Intelligence and Knowledge engineering, AIKE 2022

作者： Jeon, Sung Hwan Shim, Simon Chung, Haejin Nah, Yunmook Dept. of Data Science Graduate School Dankook University Yongin-si Korea Republic of Dept. of Computer Engineering San Jose State University San Jose United States Cha Mirisa College Duksung Women's University Seoul Korea Republic of Dept. of Applied Computer Eng. Dankook University Yongin-si Korea Republic of

ISBN: (数字)9781665471206

ISBN: (纸本)9781665471206

Containers are one of the technologies required for cloud services. Because containers use resources directly on the host, they are lighter and faster than traditional virtual machines. However, it has the disadvantage of poor security and isolation. To solve this problem, security-oriented runtimes such as Kata, gVisor, and Firecracker have emerged. Each runtime is created differently, so performance and resource usage are different. In this study, performance and evaluation of each lowlevel runtime are conducted. © 2022 IEEE.

关键词： Containers

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Inferring Prerequisite Relationships Among Learning Resources for HPC Education 11th

Inferring Prerequisite Relationships Among Learning Resource...

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11th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2020

作者： Wu, Run Luo, Chenyu Hu, Miao Wu, Di School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9789811600098

For online HPC (High-Performance Computing) education, identifying the prerequisite relationships among different HPC learning resources is important for generating learning paths and personalized course recommendations for students. Previously, such prerequisite relationships were often created by domain experts manually. In this paper, we propose a novel framework to infer both concept and learning resource prerequisite relationships and integrate such a framework into an online HPC education platform. We first construct a bi-directional long short-term (BiLSTM) neural network with attention mechanism to automatically mine the latent semantic features from the formal description of concepts. Then, we input two kinds of features into a fully connected neural network for classification and obtain the relationships among concepts. Considering the asymmetry and directivity of the prerequisite relation, we adopt a network embedding model to learn the vector representations of each concept as the prerequisite and subsequent roles. The concept vectors are weighted and summed to generate the feature vectors of learning resources. For a given pair of learning resources, their feature vectors are input into a classifier that outputs the result of relation classification. Finally, we conduct a series of experiments on two large benchmark datasets. The experimental results show that our method outperforms existing methods with significant improvements. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Vectors

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Research on comprehensive evaluation of data link based on G1 method and entropy weight method

Research on comprehensive evaluation of data link based on G...

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2021 International Conference on Mechanical engineering, Intelligent Manufacturing and Automation Technology, MEMAT 2021

作者： Jin, Ming Zhang, Junyan Cui, Shuo Kang, Mei Xiao, Yi Xiang, Rongxin Yan, Zhenye Big Data Center of State Grid Corporation of China Xicheng District Beijing China School of Control and Computer Engineering North China Electric Power University Changping District Beijing China

With the rapid development of mobile Internet technology and the advent of the 21st century Internet and information age, data has become the most important strategic core resources. With the rapid development of ubiquitous power Internet of things construction, there are still many problems in the data link monitoring and data management: the low effectiveness of data transmission affects the normal operation of the data link;there are few data evaluation models, which lead to the problem of one sidedness and subjectivity in the understanding of the operation status of the data link. Aiming at the problem that the current data link cannot directly show the link quality, this position proposes a comprehensive evaluation method for the entire data link based on the order relationship analysis(G1) method and the entropy weight method. First, establish a complete data link indicator system, and then separately Use the order relationship analysis method to determine the subjective weight, use the entropy weight method to determine the objective weight, and finally combine the two to determine the comprehensive weight, and obtain a comprehensive score that reflects both the expert level and experience and the actual situation related data. The data and transmission conditions of each link are shown, and the comprehensiveness, rationality and scientificity of the method are also demonstrated. © Published under licence by IOP Publishing Ltd.

关键词： Information management

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