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IEEE Transactions on Sustainable Computing 2024年第3期10卷 534-546页

作者： Tian, Jiwei Shen, Chao Wang, Buhong Ren, Chao Xia, Xiaofang Dong, Runze Cheng, Tianhao Ministry of Education Key Laboratory for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi'an Jiaotong University Xi'an China Air Traffic Control and Navigation College Air Force Engineering University Xi'an China Information and Navigation College Air Force Engineering University Xi'an China School of Electrical and Electronic Engineering Nanyang Technological University Singapore School of Computer Science and Technology Xidian University Xi'an China

Although conventional false data injection attacks can circumvent the detection of bad data detection (BDD) in sustainable power grid cyber physical systems, they are easily detected by well-trained deep learning-based detectors. Still, state estimation models with deep leaning-based detectors are not secure due to the vulnerabilities and fragility of deep learning models. Using the related laws of conventional false data injection attacks and adversarial sample attacks, this paper proposes the targEted adVersarial fAlse Data injEction (EVADE) strategy to explore targeted adversarial false data injection attacks for state estimation in Smart Grid. The proposed EVADE attack strategy selects key state variables based on adversarial saliency maps to improve the attack efficiency and perturbs as few state variables as possible to reduce the attack cost. In this way, the EVADE attack strategy can bypass the detection of BDD and neural attack detection (NAD) methods (that is, maintaining deep stealthy) with a high success rate and achieve the attack target simultaneously. Experimental results demonstrate the effectiveness of the proposed strategy, posing serious and pressing concerns for sustainable cyber physical power system security. © 2016 IEEE.

关键词： Cyber attacks

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A Decentralized Blockchain-Based Architecture for Healthcare Industry 1

A Decentralized Blockchain-Based Architecture for Healthcare...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Pandey, Shraiyash De, Abhik Kumar Choudhary, Shrishti Asim, Mohammad Computer Science and Engineering Sharda School of Engineering and Technology Sharda University Greater Noida India

ISBN: (纸本)9798350330915

The most prevalent problems in healthcare in India are related to doctors' referral processes, data transmission between health facilities, and patient portals for accessing their medical records. Furthermore, the preservation and distribution of Electronic Medical Records (EMR) is a critical necessity for health organizations since they hold all confidential health data about patients. However, because most Indian health institutions employ centralized outsourced systems, they have issues with EMR security and interoperability. In this paper, we present a Blockchain-based technological architecture for EMR management in Indian health institutions. Due to Blockchain's properties such as privacy, security, transparency, and immutability, we have implemented it in the architecture to address several issues within healthcare. © 2023 IEEE.

关键词： Architecture Blockchain Healthcare

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Deep Imputation Bi-stochastic Graph Regularized Matrix Factorization for Clustering Single-cell RNA-sequencing Data

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IEEE/ACM Transactions on Computational Biology and Bioinformatics 2024年 PP卷 1-11页

作者： Lan, Wei Chen, Jianwei Liu, Mingyang Chen, Qingfeng Liu, Jin Wang, Jianxin Chen, Yi-Ping Phoebe Wei Lan is School of Computer Electronic and Information and Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi University Nanning Guangxi China School of Computer Electronic and Information Guangxi University Nanning Guangxi China School of Computer Electronic and Information and State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources Guangxi University Nanning Guangxi China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha Hunan China Department of Computer Science and Information Technology La Trobe University Melbourne Victoria Australia

By generating massive gene transcriptome data and analyzing transcriptomic variations at the cell level, single-cell RNA-sequencing (scRNA-seq) technology has provided new way to explore cellular heterogeneity and functionality. Clustering scRNA-seq data could discover the hidden diversity and complexity of cell populations, which can aid to the identification of the disease mechanisms and biomarkers. In this paper, a novel method (DSINMF) is presented for single cell RNA sequencing data by using deep matrix factorization. Our proposed method comprises four steps: first, the feature selection is utilized to remove irrelevant features. Then, the dropout imputation is used to handle missing value problem. Further, the dimension reduction is employed to preserve data characteristics and reduce noise effects. Finally, the deep matrix factorization with bi-stochastic graph regularization is used to obtain cluster results from scRNA-seq data. We compare DSINMF with other state-of-the-art algorithms on nine datasets and the results show our method outperformances than other methods. IEEE

关键词： RNA

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Simulation of Social Media-Driven Bubble Formation in Financial Markets using an Agent-Based Model with Hierarchical Influence Network 36

Simulation of Social Media-Driven Bubble Formation in Financ...

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36th European Modeling and Simulation Symposium, EMSS 2024, Held at the 21st International Multidisciplinary Modeling and Simulation Multiconference, I3M 2024

作者： Bohorquez, Gonzalo Cartlidge, John School of Computer Science University of Bristol Bristol United Kingdom School of Engineering Mathematics and Technology University of Bristol Bristol United Kingdom

ISBN: (纸本)9791281988026

We propose that a tree-like hierarchical structure represents a simple and effective way to model the emergent behaviour of financial markets, especially markets where there exists a pronounced intersection between social media influences and investor behaviour. To explore this hypothesis, we introduce an agent-based model of financial markets, where trading agents are embedded in a hierarchical network of communities, and communities influence the strategies and opinions of traders. Empirical analysis of the model shows that its behaviour conforms to several stylized facts observed in real financial markets;and the model is able to realistically simulate the effects that social media-driven phenomena, such as echo chambers and pump-and-dump schemes, have on financial markets. © 2024 The Authors.

关键词： Marketplaces

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A Paradigm Shift in Ethereum Network Analysis Through Google BigQuery,Portation, and Visualization 6

A Paradigm Shift in Ethereum Network Analysis Through Google...

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Suresh Kumar, N. Mirdula, S. Singh, Pushpa Gayathri, T. Jayanthi, S. Anandraj, M. School of Computer Science and Engineering Jain Deemed-to-be University Ramanagara India SRM TRP Engineering College Department of Electronic and Communication Engineering Trichy India IILM University Department of Computer Science and Engineering Greater Noida India MVJ College of Engineering Department of Computer Science and Engineering Bangalore India Guru Nanak Institute of Technology Department of Information Technology Ibrahimpatnam India Mahendra College of Engineering Department of Computer Science and Engineering Salem India

ISBN: (纸本)9798350306613

The primary objective of this research is to comprehensively explore and analyze the dynamics of the Ethereum network using innovative methodologies and system architectures. The study aims to extract meaningful statistics from the Ethereum blockchain, focusing on account activity, popularity trends, and the distribution of transactions. Through rigorous data collection, robust query construction, and advanced analytical techniques, the research seeks to provide valuable insights into how the Ethereum network has evolved, particularly in the aftermath of the 'crypto bubble explosion.' The overarching goal is to contribute to the understanding of Ethereum's structural patterns, user behaviors, and the impact of external factors on the network. The research has yielded significant results, unveiling key insights into Ethereum network dynamics. The data collection phase, facilitated by Google BigQuery, successfully captured and filtered relevant information from a specific block range post the 'crypto bubble explosion.' The SQL queries, strategically designed for active account identification and popularity assessment, demonstrated efficiency and accuracy in handling the vast Ethereum dataset. The proposed system, introducing the novel methodology of 'portation,' showcased its efficiency in extracting and interpreting Ethereum blockchain data using Google BigQuery. The system architecture, as illustrated in the diagram, proved to be a well-coordinated and dynamic framework, emphasizing the seamless flow of data and processes. © 2024 IEEE.

关键词： Crypto Bubble Explosion Decentralized Data Transactions Ethereum Blockchain Google BigQuery Power-law Analysis

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UAV-Assisted Data Synchronization for Digital-Twin-Enabled Vehicular Networks

UAV-Assisted Data Synchronization for Digital-Twin-Enabled V...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Wu, Guoquan Nie, Jiangtian Tang, Jianhang Zhang, Yang Xiao, Jiang Xiong, Zehui Yanshan University School of Information Science and Engineering China Nanyang Technological University School of Computer Science and Engineering Singapore Guizhou University State Key Laboratory of Public Big Data China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology China Huazhong University of Science and Technology School of Computer Science and Technology China Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore

ISBN: (纸本)9798350345384

Digital twin (DT) is becoming a promising solution for vehicular networks to improve the interoperability of distributed autonomous driving systems. Mobile edge computing (MEC) has been introduced to provide low-latency services for DT-enabled vehicular networks at the edge of the network. However, it is hard to obtain the dynamic network topology for moving vehicles by the ground-based MEC system, which may deteriorate the service quality for DT synchronization. In this paper, we propose a novel unmanned aerial vehicle (UAV)-assisted synchronization framework for DT-enabled vehicular networks. With the proposed framework, an intelligent resource allocation algorithm is developed to improve UAV resource utilization and maximize the synchronization completion ratio. By leveraging an advantage actor-critic (A2C) algorithm, the synchronization decisions are obtained with low time complexity. Experiment results demonstrate that the proposed algorithm can reduce the synchronization latency and improve the synchronization completion ratio effectively. © 2023 IEEE.

关键词： Resource allocation

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Design and Implementation of an IoT-Based Indoor Hydroponics Farm with Automated Climate and Light Control 4th

Design and Implementation of an IoT-Based Indoor Hydroponics...

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4th International Conference on Computing, Communications, and Cyber-Security, IC4S 2022

作者： Jain, Swati Kaur, Mandeep Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India

ISBN: (纸本)9789819914784

With the boom of industrialization and the escalation of world population, there is a significant decline in the land suitable for farming and an increase in demand for food. Hydroponics, a branch of agriculture, facilitates the growth of plants in water without the use of soil. This offers a lifeline of sorts by making vertical farming possible. Hydroponics gives us the freedom to wholly control the parameters that affect plant growth, and if done correctly, it can be beneficial both commercially and environmentally. Manual monitoring of all the variables of plant growth is difficult and can lead to growth failures. This paper proposes how the Internet of Things (IoT), a revolutionary technology can be integrated with hydroponics to automate the process of climate monitoring and control. A practical model is implemented in this paper where IoT sensors are used to control the climate conditions and ambient lights of the hydroponic farm. Sensors monitor the climate and light conditions throughout the day and automatically maintain suitable growth conditions. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Hydroponics

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Systematic Review of Dyslexia Detection Approaches based on Physiological Traits 10

Systematic Review of Dyslexia Detection Approaches based on ...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Joshi, Sunaina Shah, Aatif Chhetri, Neetesh Abidi, Ali Imam School of Engineering and Technology Sharda University Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9789380544472

The procedure of detecting dyslexia is difficult and needs a multidisciplinary approach. Early dyslexia detection is essential for providing people with this learning disability with adequate assistance and intervention. This study categorizes most relevant and recently published approaches proposed for dyslexia detection on the basis of physiological traits used as dataset and the algorithms used for the same. The analysis showed application of various traits such as handwriting, neurological scans and eye-based movements. A detailed analysis has been presented in terms of accuracy as well. The study has successfully condensed a multitude of the state of the arts methods for dyslexia detection leading towards possibly more physiological traits based study. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Eye movements

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Multi-Task Learning for Game Review Classification with Emotion and Sarcasm Detection 8

Multi-Task Learning for Game Review Classification with Emot...

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8th International Conference on Data science in Cyberspace, DSC 2023

作者： Xia, Xinchen Huang, Yuee Zhang, Yiwen School of Computer Science and Technology Anhui University Hefei China School of Public Health Wannan Medical College Wuhu China

ISBN: (纸本)9798350331035

Sentiment analysis is a widely used technique to identify the emotion tendency of a piece of text. As the number of video games and players continues to increase, the task of automating emotion computing for game reviews is essential for both game platforms and developers. The former can recommend good and suitable games to users, while the latter can provide game developers with suggestions for improvement. However, there are few affective computing studies and models in the field of game reviews. Therefore, this paper investigates the performance of several traditional sentiment classification models on game review datasets and deduces several possible reasons for model performance degradation based on misclassified samples. Based on these preliminary studies, we believe that sarcasm, game context, and comparison are the three main reasons leading to the decline in classifier performance. We conclude that a satisfactory model requires the following characteristics: sarcasm detection, game contextualization, and key emotion capture. Based on this, we design a multi-tasking game review classification model integrating sarcasm detection and emotion analysis modules. Experiments on two real datasets from the Steam platform demonstrate that our approach has superior performance compared to mainstream sentiment classification models. © 2023 IEEE.

关键词： game reviews sarcasm detection sentiment analysis Steam

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Online Learning Behavior Analysis and Prediction Based on Spiking Neural Networks

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Journal of Social Computing 2024年第2期5卷 180-193页

作者： Yanjing Li Xiaowei Wang Fukun Chen Bingxu Zhao Qiang Fu the Institute of Education Science Research Heilongjiang UniversityHarbin 150080China the School of Cyberspace Security Shandong University of Political Science and LawJinan 250014China the School of Computer Science and Technology Harbin Engineering UniversityHarbin 150001China the Faculty of Electrical and Computer Engineering University of VictoriaVictoriaV8P 5C2Canada. the Guangxi Key Lab of Brain-Inspired Computing and Intelligent Chips School of Electronic and Information EngineeringGuangxi Normal UniversityGuilin 541004China

The vast amount of data generated by large-scale open online course platforms provide a solid foundation for the analysis of learning behavior in the field of *** study utilizes the historical and final learning behavior data of over 300000 learners from 17 courses offered on the edX platform by Harvard university and the Massachusetts Institute of technology during the 2012-2013 academic *** have developed a spike neural network to predict learning outcomes,and analyzed the correlation between learning behavior and outcomes,aiming to identify key learning behaviors that significantly impact these *** goal is to monitor learning progress,provide targeted references for evaluating and improving learning effectiveness,and implement intervention measures *** results demonstrate that the prediction model based on online learning behavior using spiking neural network achieves an impressive accuracy of 99.80%.The learning behaviors that predominantly affect learning effectiveness are found to be students’academic performance and level of participation.

关键词： online learning learning outcomes prediction learning behavior analysis spiking neural network

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