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

作者： Huang, Qihao Li, Chao Wu, Mincheng Zhejiang University College of Control Science and Engineering Hangzhou China Zhejiang University International Business School Center for Data Science Hangzhou China Zhejiang University of Technology College of Information Engineering Hangzhou China

ISBN: (纸本)9798350345384

The accurate multi-step prediction of passenger flow is crucial for the efficient operation of intercity Intelligent Transportation Systems (ITS). However, the presence of large-scale anomalous events can significantly impact the accuracy of these predictions, particularly in the case of intercity events. In this paper, we propose a novel approach that utilizes a spatio-temporal graph to predict passenger flow. Our method incorporates population migration data, allowing us to analyze the influence of large-scale anomalous events on intercity public transportation. Additionally, we introduce a large-scale events aware module (LEAM) designed to detect and evaluate the impact of anomalous events on passenger flow. Our analysis provided evidence to support the rationality of the proposed architecture. To evaluate the performance of our approach, we employ three popular deep learning models for multi-step prediction. The experimental results demonstrate that our architecture significantly improves the accuracy of anomalous event prediction and enhances the global optimization of predictions, compared to the same models without the integration of LEAM. © 2023 IEEE.

关键词： Forecasting

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Research on Distributed Competition big data Hierarchical Storage Method Based on Ceph Architecture

Research on Distributed Competition Big Data Hierarchical St...

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2022 International Conference on Signal Processing and Communication Security, ICSPCS 2022

作者： Chen, Xiangzhou Liu, Qing Zhang, Bijun Li, Gangzhu Wu, Shaoping Big Data Center of State Grid Corporation of China Beijing100031 China State Grid Info-Telecom Great Power Science and Technology Fujian Fuzhou350003 China

ISBN: (纸本)9781510660151

In the face of intensive data access requests, the current big data storage methods have the problem of poor concurrent read-write performance. Based on Ceph architecture, a distributed big data hierarchical storage method is designed to realize the robust storage service of data files. According to the frequency and popularity of the access object, the cold data with low frequency is eliminated, and the access cache pool or back-end storage pool is selected. Standardize the I/O performance, load and capacity values in the OSD set, calculate the comprehensive weight, and determine the priority of node access. The entire Ceph architecture balances the data distribution. When highly concurrent data access operations occur, a random number is selected to execute the read service of the storage node to ensure the interconnection communication in different clusters. The test results show that the write and read time of the hierarchical storage method based on Ceph architecture is greatly reduced, and good results are achieved in terms of cache hit rate, which can effectively manage and store competitive big data. © 2022 SPIE.

关键词： big data

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SpaDE: Semantic Locality Preserving Biclustering for Neuroimaging data

SpaDE: Semantic Locality Preserving Biclustering for Neuroim...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Md Abdur Rahaman Zening Fu Armin Iraji Vince Calhoun Center for Translational Research in Neuroimaging and Data Science (TReNDS) School of Computational Science and Engineering Georgia Institute of Technology

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

The most discriminative and revealing patterns in the neuroimaging population are often confined to smaller subdivisions of the samples and features. Especially in neuropsychiatric conditions, symptoms are expressed within micro subgroups of individuals and may only underly a subset of neurological mechanisms. As such, running a whole-population analysis yields suboptimal outcomes leading to reduced specificity and interpretability. Biclustering is a potential solution since subject heterogeneity makes one-dimensional clustering less effective in this realm. Yet, high dimensional sparse input space and semantically incoherent grouping of attributes make post hoc analysis challenging. Therefore, we propose a deep neural network called semantic locality preserving auto decoder (SpaDE), for unsupervised feature learning and biclustering. SpaDE produces coherent subgroups of subjects and neural features preserving semantic locality and enhancing neurobiological interpretability. Also, it regularizes for sparsity to improve representation learning. We employ SpaDE on human brain connectome collected from schizophrenia (SZ) and healthy control (HC) subjects. The model outperforms several state-of-the-art biclustering methods. Our method extracts modular neural communities showing significant (HC/SZ) group differences in distinct brain networks including visual, sensorimotor, and subcortical. Moreover, these bi-clustered connectivity substructures exhibit substantial relations with various cognitive measures such as attention, working memory, and visual learning.

关键词： Neuroimaging Representation learning Visualization Semantics Network analyzers Brain modeling Feature extraction Physiology Performance analysis Engineering in medicine and biology

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Certainty Equivalence Control of Discrete-Time Multiagent Systems: A Framework for Uniform Global Exponential Stability

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IEEE Transactions on Automatic Control 2023年第12期68卷 7660-7675页

作者： Su, Youfeng Lee, Ti-Chung Cai, He Chen, Xiaofeng Fuzhou University College of Computer and Data Science Fuzhou350116 China National Sun Yat-sen University Department of Electrical Engineering Kaohsiung804201 Taiwan South China University of Technology School of Automation Science and Engineering Guangzhou510641 China Fuzhou University School of Mathematics and Statistics Fuzhou350116 China

The solvability of certainty equivalence control problems for discrete-time multiagent systems over uniformly jointly connected switching networks relies on the stability/attractivity analysis for a class of perturbed switched systems with respect to a set of switching signals. To tackle this issue, in this article, a novel systematic and unified framework is established consisting of three key stability/attractivity results. First, the uniform global exponential stability for two dual cases of the nominal dynamics is analyzed in a unified way by checking the weak zero-state detectability from the limiting systems. Second, the uniform global exponential attractivity of the perturbed switched systems accommodating both first-order and zero-order time-varying perturbations is studied without resorting to any Lyapunov function for the entire perturbed switched systems. Third, the uniform global κ-exponential stability for a class of cascaded switched systems is investigated. These stability/attractivity results together lay the foundation to thoroughly solve two kinds of typical certainty equivalence control problems, and also improve two specific cooperative control designs. Moreover, a relaxed dwell-time condition is considered, which makes the obtained results tolerable in the extreme case of instantaneous link failure and restoration. Finally, the control performances are shown by numerical simulations. © 1963-2012 IEEE.

关键词： Multi agent systems

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Recognize Illegal Transactions in the Bitcoin Network Using Graph Attention with DIKW 23

Recognize Illegal Transactions in the Bitcoin Network Using ...

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on data science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and big data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Zheng, Haixiao Wen, Bin Li, Yuhan School of Information Science and Technology Hainan Normal University Key Laboratory of Data Science and Smart Education of the Ministry of Education of China Haikou China

ISBN: (纸本)9781665494571

Bitcoin has become the choice of many illegal transactions due to its anonymity. To fight crime and maintain the order of the financial market, it is necessary to identify illegal transaction activities in the Bitcoin network. On the basis of referring to the relationship between data, information, and knowledge in the DIKW architecture, this article proposes a new method called MP-GAT to convert discrete data in the Bitcoin network into usable information and knowledge to help identify illegal transactions. MP-GAT uses a combination of multi-layer perceptron and graph attention networks to build a model for identifying illegal transactions. Specifically, the concept of artificial intelligence is used to simulate the human reasoning system and learn from the data. Use the MP-GAT model to convert Bitcoin data into information and knowledge. The method of artificial intelligence is used to complete the transformation of data, information, and knowledge in the DIKW system to achieve the purpose of detecting and identifying illegal transactions in the Bitcoin network. © 2021 IEEE.

关键词： Knowledge engineering Smart cities Bitcoin data models Cognition

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A Deep Biclustering Framework for Brain Network Analysis

A Deep Biclustering Framework for Brain Network Analysis

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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Md Abdur Rahaman Zening Fu Armin Iraji Vince Calhoun Center for Translational Research in Neuroimaging and Data Science (TRenDS) School of Computational Science and Engineering Georgia Institute of Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Brain functional connectivity (FC) analysis has emerged as a compelling quest to understand human brain dynamics and clarify disorder-related aberrations. Typically, FC can be portrayed as a graph of brain components (nodes) and their functional links (edges) known as the brain network (BN). The brain operates as a modular unit, with different regions forming semantically cohesive submodules to execute essential neuronal processing. Identifying these granules can provide insights into the underlying neurobiological mechanisms. Consequently, substantial research efforts have been directed toward clustering the constituents of brain networks. However, the inherent subject heterogeneity in the biological population and the wide spectrum of brain disease manifestation significantly impede cluster generalization. Thus, it often delivers a suboptimal solution and misses insightful nuances of neural systems. Therefore, we propose a deep neural network (DNN) framework for a more granular subgrouping of brain networks by simultaneously stratifying subjects and feature dimensions. The framework adapts discrete learning of BN edges and jointly optimizes instance and feature assignment probability distributions for a novel bicluster retrieval. Extensive experiments on multiple neuroimaging datasets show our model outperforms state-of-the-art biclustering methods. In addition, the extracted biclusters render more modular and semantically meaningful communities in the brain network highlighting significant neuroscientific relevance.

关键词： Neuroimaging Artificial neural networks Network analyzers Brain modeling Feature extraction Probability distribution Physiology

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Artificial intelligence based machine learning algorithm for prediction of cancer in female anatomy

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Multimedia Tools and Applications 2025年第16期84卷 15735-15761页

作者： Ghantasala, G S Pradeep Hung, Bui Thanh Chakrabarti, Prasun R, Sathiyaraj Pellakuri, Vidyullatha Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Karnataka Bangalore India Data Science Laboratory Faculty of Information Technology Industrial University of Ho Chi Minh city Ho Chi Minh City Viet Nam Deputy Provost ITM SLS Baroda University Gujarat Vadodara India Department of CSE GITAM School of Technology GITAM University Bengaluru India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur India

Breast and cervical cancers account for more than 85 percent of all cancer-related fatalities in developing nations, according to the World Cancer Research Fund. As a result, breast and cervical cancer have become one of the leading causes of mortality among women worldwide. This field is still in its infancy, with only a few studies in gynaecology and computer science looking into the detection of breast and cervical cancer. According to the researchers, medical records and early testing from individuals with breast and cervical cancer will be used in this study to determine the prognosis of those suffering from the diseases. To assess our cervical cancer predictions, we employed machine learning models such as Optimized Hybrid Ensemble Classifier (OHEC), which were trained on patient behavior and variables revealed to be associated with patient behavior. The datasets in this study have a substantial number of missing values, and the distribution of those values has been altered as a function of the missing values. OHEC classifier performance has been shown to improve when the number of features is reduced and the problem of high-class imbalance is resolved, because the accuracy, sensitivity, and specificity of the classifier, as well as the number of false positives, were used to demonstrate the success of feature selection in the suggested model's predictive analysis. This has been demonstrated through the use of numerous tests involving categorization challenges. The study underscores the critical significance of early detection and prognosis in combating breast and cervical cancers, which remain leading causes of mortality worldwide. Through the utilization of machine learning models like the OHEC, the authors have demonstrated the potential for improved predictive accuracy and clinical outcomes. The findings highlight the importance of addressing challenges such as missing data and class imbalance in enhancing the performance of predictive models for effective

关键词： Diseases

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Obstacle detection in railway tracks: vehicle autonomous system application

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International Journal of Vehicle Autonomous Systems 2025年第2期18卷 105-117页

作者： Sarkar, Swagata Baptist, Leo John Valarmathi, K. Degadwala, Sheshang Department of Artificial intelligence and Data Science Sri Sairam Engineering College West Tambaram Tamil Nadu Chennai India School of IT and Management BlueCrest University College Monrovia Liberia Department of Information Technology S.A. Engineering College Chennai Tamil Nadu India Department of Computer Engineering Sigma Institute of Engineering Vadodara Gujarat India

For the purpose of preventing accidents on railroads that might result in casualties and perhaps cause the train to be damaged or derailed, reliable obstacle detection could be of great assistance. The present system has drawbacks such as the generic object detectors do not have sufficient classes to account for all of the conceivable circumstances, and it is difficult to acquire data sets that include items on trains. This paper proposes the use of a shallow network for the purpose of learning railway segmentation from regular railway photos. The small receptive field of the network avoids overconfident predictions and enables the network to concentrate on the locally extremely distinct and recurring patterns of the train environment. Our system novelty gives the network the ability to maximise its effectiveness. The approach that we use is evaluated using a specialised data set that contains railways images that have been intentionally supplemented with obstructions. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Obstacle detectors

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A Robust Key Exchange and Tamper-Resistant Protocol for HAN and NAN Networks in Smart Grids

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IEEE Internet of Things Journal 2025年第12期12卷 21394-21403页

作者： Faizan Ayub, Muhammad Li, Xiong Mahmood, Khalid Shamshad, Salman Kumar Das, Ashok Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan International Institute of Information Technology Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India Korea University College of Informatics Department of Computer Science and Engineering Seoul02841 Korea Republic of Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China

Smart grids (SGs) rely on home area networks (HANs) and neighborhood area networks (NANs) to ensure efficient power distribution, real-time monitoring, and seamless communication between smart devices. Despite these advantages, the use of public communication channels in HAN and NAN networks introduces critical challenges, such as vulnerability to impersonation, physical tampering, and scalability issues in resource-constrained environments. These issues compromise the stability, reliability, and security of SG environments. Existing protocols often fail to adequately address these challenges, particularly in ensuring resistance to physical tampering of supply lines and impersonation attacks. Additionally, they struggle to minimize the computation, communication, and energy costs associated with securing a resource-constrained SG environment. Therefore, we propose a robust key exchange and tamper-resistant protocol for HAN and NAN networks to address these limitations. The proposed protocol leverages the physical unclonable function (PUF) mechanism to offer physical tampering resistance to smart meters. We validate our protocol formally using the Random or Real (RoR) model, which confirms its security robustness. Additionally, our informal security analysis highlights the protocol’s resilience against impersonation, physical tampering attacks, etc. We analyze and compare the performance of the proposed protocol, which further demonstrates our protocol’s effectiveness and security compared to competing protocols. Moreover, the proposed protocol achieves resource efficiency with 45.99% and 58.85% substantial reductions in computation overhead and energy overhead, respectively, compared to competing protocols. These results showcase the protocol’s enhanced security and practicality for resource-constrained SG environments. © 2014 IEEE.

关键词： Authentication Protocol

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Latest Technologies on dataset Distillation: A Survey

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Procedia Computer science 2024年 242卷 1112-1117页

作者： Muyang Li Yi Qu Yong Shi Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing 100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China College of Information Science and Technology University of Nebraska at Omaha Omaha NE 68182 USA

dataset distillation refers to the process of constructing a smaller dataset based on a larger dataset, so that the training model with the smaller dataset can obtain similar results to the larger dataset. In this paper, we first briefly review the basic methods and some recent advances in this field from the perspectives of meta-learning framework and data matching framework, including the backpropagation through time(BPTT), kernel ridge regression(KRR), decoupled techniques, gradient matching(DC), trajectory matching(MTT) and distribution matching(DM). The results of the plug-and-play approach in recent years are then presented. Finally, we discuss possible future developments from the perspectives of data distillation algorithms on large datasets, the relationship between compress ratio and information contained, more tasks and data modalities, and the use of generative models to improve the distillation results of datasets.

关键词： dataset Distillation Survey Meta-learning framework data matching framework

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