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Cyber-Syndrome:Concept,Theoretical Characterization,and Control Mechanism

Tsinghua Science and technology 2024年第3期29卷 721-735页

作者： Feifei Shi Huansheng Ning Liming Chen Sahraoui Dhelim School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Faculty of Computing Ulster UniversityNewtownabbeyBT370QBUK School of Computer Science University College DublinDublinD04 V1W8Ireland

The prevalence of social media and mobile computing has led to intensive user engagement in the emergent Cyber-Physical-Social-Thinking(CPST)***,the easy access,the lack of governance,and excessive use has generated a raft of new behaviors within CPST,which affects users’physical,social,and mental *** this paper,we conceive the Cyber-Syndrome concept to denote the collection of cyber disorders due to excessive or problematic Cyberspace interactions based on CPST *** we characterize the Cyber-Syndrome concept in terms of Maslow’s theory of Needs,from which we establish an in-depth theoretical understanding of Cyber-Syndrome from its etiology,formation,symptoms,and ***,we propose an entropy-based Cyber-Syndrome control mechanism for its computation and *** goal of this study is to give new insights into this rising phenomenon and offer guidance for further research and development.

关键词： Cyber-Syndrome cyber-physical systems Cyber-Physical-Social-Thinking(CPST)space Maslow’sHierarchy of Needs entropy

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Vehicle Routing Model Based on Real- Time Road Conditions Considering Carbon Emissions

Vehicle Routing Model Based on Real- Time Road Conditions Co...

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2024 International Conference on New Trends in Computational Intelligence, NTCI 2024

作者： Hou, Ying Wang, Jisu Du, Yongping Han, Honggui School of Information Science and Technology Beijing University of Technology Beijing China Engineering Research Center of Digital Community Ministry of Education Beijing China School of Computing Beijing University of Technology Beijing China

ISBN: (纸本)9798331517021

The vehicle routing problem is an important research topic in the field of intelligent transport. The accurate vehicle routing model is the key to solving the vehicle routing problem. With the awakening awareness of low-carbon logistics and the urgent need for sustainable development, carbon emissions have become a focus of attention in vehicle route modelling. Vehicle routing model based on real-time road conditions considering carbon emission (VRM-RCCE) is proposed in this paper to solve the low-carbon intelligent transport. Firstly, the vehicle speed is identified as a key variable that affects carbon emissions by analyzing the carbon emissions mechanisms. The vehicle speed is predicted accurately based on real-time road conditions. Secondly, a vehicle routing model, considering vehicle numbers, the routing length and the carbon emission, is constructed. The carbon emission is calculated through the predicted vehicle speed. Finally, the validity of VRM-RCCE is verified using Gurobi. Optimal routing schemes are obtained using ant colony optimization algorithm. The VRM-RCCE can effectively assist in solving the vehicle routing problem, providing a powerful theoretical support and practical tool for intelligent transport management. © 2024 IEEE.

关键词： Vehicle routing

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Identifying Acute Disseminated Encephalomyelitis with Time Series Analysis 5th

Identifying Acute Disseminated Encephalomyelitis with Time S...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Pallavi, M. Mishra, Awakash Kudari, Jayashree M. Belwal, Ankit School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India

ISBN: (纸本)9789819780426

Acute disseminated encephalomyelitis (ADEM) is an extraordinary immune-mediated disease that influences the mind and spinal cord, central to the unexpected onset of neurologic symptoms. Time series evaluation can be used to become aware of ADEM in patients. Mainly, statistical analysis of the temporal styles of mind imaging and immunological parameters has proven effective for diagnosing ADEM in medical settings. Such material styles offer essential clues about disease development, which can be leveraged to diagnose ADEM patients appropriately. Acute disseminated encephalomyelitis (ADEM) is a critical degenerative sickness of the imperative nervous device, characterized by a rapid onset of neurologic signs and significant infection of the mind and spinal twine. Early analysis of ADEM is critical to initiate appropriate remedies and reduce long-term incapacity. In this look, we advocate the usage of time collection analysis to discover neurological patterns related to ADEM from the brain indicators of sufferers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Time series

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NumbOD: A Spatial-Frequency Fusion Attack Against Object Detectors 39

NumbOD: A Spatial-Frequency Fusion Attack Against Object Det...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhou, Ziqi Li, Bowen Song, Yufei Yu, Zhifei Hu, Shengshan Wan, Wei Zhang, Leo Yu Yao, Dezhong Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Computer Science and Technology Huazhong University of Science and Technology China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

ISBN: (纸本)157735897X

With the advancement of deep learning, object detectors (ODs) with various architectures have achieved significant success in complex scenarios like autonomous driving. Previous adversarial attacks against ODs have been focused on designing customized attacks targeting their specific structures (e.g., NMS and RPN), yielding some results but simultaneously constraining their scalability. Moreover, most efforts against ODs stem from image-level attacks originally designed for classification tasks, resulting in redundant computations and disturbances in object-irrelevant areas (e.g., background). Consequently, how to design a model-agnostic efficient attack to comprehensively evaluate the vulnerabilities of ODs remains challenging and unresolved. In this paper, we propose NumbOD, a brand-new spatial-frequency fusion attack against various ODs, aimed at disrupting object detection within images. We directly leverage the features output by the OD without relying on its internal structures to craft adversarial examples. Specifically, we first design a dual-track attack target selection strategy to select high-quality bounding boxes from OD outputs for targeting. Subsequently, we employ directional perturbations to shift and compress predicted boxes and change classification results to deceive ODs. Additionally, we focus on manipulating the high-frequency components of images to confuse ODs’ attention on critical objects, thereby enhancing the attack efficiency. Our extensive experiments on nine ODs and two datasets show that NumbOD achieves powerful attack performance and high stealthiness. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep learning

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Unraveling Disease-Specific Diffusion Patterns in Human Brain Using Diffusion Tensor Image Analysis 5th

Unraveling Disease-Specific Diffusion Patterns in Human Brai...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Prajapati, Sandhya Bhagawati, Rupam Alam, Intekhab Jebakumari, S. Adlin Department of Electrical Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India

ISBN: (纸本)9789819780426

Diffusion tensor imaging (DTI) is a feasible approach to have a look at the structural connectivity of neuronal networks in vivo, as it presents insights into the microstructural and molecular properties of white depend fiber bundles. On this study, we used superior DTI and diffusion tensor image evaluation to analyze the unique diffusion patterns of several distinct diseases in human mind. We obtained statistics from healthful controls and cohorts of sufferers with a range of sicknesses, along with stroke, annoying mind injury, Alzheimer’s ailment, and Parkinson’s ailment, in addition to from postmortem brains of each wholesome and diseased people. Our findings emphasize the capability of the use of this technique to increase ailment-precise structural networks biomarkers for diagnosis and control of neurological illnesses. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Diffusion tensor imaging

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Battery pack design based on an origami sandwich structure for the enhanced cooling and mechanical performances in electric vehicles 6

Battery pack design based on an origami sandwich structure f...

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6th International Future Energy Electronics Conference, IFEEC 2023

作者： Li, Ruifeng Lu, Guoxing Shen, Weixiang School of Engineering Swinburne University of Technology Hawthorn Melbourne3122 Australia School of Science Computing and Engineering Technologies Swinburne University of Technology Hawthorn Melbourne3122 Australia

ISBN: (纸本)9798350339888

This paper introduces an integrated design approach for electric vehicles (EVs) to enhance cooling and crashworthiness performances simultaneously. The proposed design incorporates an origami sandwich structure (OSS) within a battery module consisting of three cells connected in series. The study explores the effects of the folding angle (0) on the performances, and it is revealed that implementing OSS-based cooling channels with θ=40° results in a reduction of maximum temperature by 4.3°C under 5C fast discharging condition and a remarkable improvement in peak crushing force and impact energy absorption compared to a flat channel. Moreover, the performances can be further improved with the rising folding angle (0). A comparison of different origami channels and a flat channel from various perspectives indicates that the origami channel with a folding angle of 20° or 30° yield the most optimized designs. © 2023 IEEE.

关键词： Crashworthiness

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Application Deployment: The Game-Changing Impact of Containerization 4

Application Deployment: The Game-Changing Impact of Containe...

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4th International Conference on Power, Control and computing Technologies, ICPC2T 2025

作者： Galhotra, Parth Singh, Kumar Aniruddh Hasan, Zafarul Safa, M. Department of Networking and Communications School of Computing Faculty of Engineering and Technology SRM IST SRM Nagar Kattankulathur TN Chennai603203 India

ISBN: (纸本)9798331530402

This study will introduce the use of Docker for simple application packaging to investigate the impact of packaging on application deployment and management. Containerization is a technological method for packaging and consuming applications in a self-contained environment that is more efficient than virtualization. Docker is a well-known containerization platform that enables efficient creation, deployment, and management of applications by focusing and efficiently utilizing resources across multiple locations. Delivering programs and investigating their similarities in terms of consistency, efficiency, and effectiveness. It also solves security issues and multi-container issues. The approach will be to package a simple web application, deploy it via Docker, and then measure how performance and manageability are improved compared to traditional deployments. Using Docker for application deployment helps you better understand how containerization can improve and simplify deployment and management in today's software development. © 2025 IEEE.

关键词： Software design

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M3GNAS: Multi-modal Multi-view Graph Neural Architecture Search for Medical Outcome Predictions

M3GNAS: Multi-modal Multi-view Graph Neural Architecture Sea...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Al-Sabri, Raeed Gao, Jianliang Chen, Jiamin Oloulade, Babatounde Moctard Wu, Zhenpeng Abdullah, Monir Hu, Xiaohua Central South University School of Computer Science and Engineering Changsha China College of Science and Engineering Hamad Bin Khlifa University Division of Information and Computing Technology Doha Qatar College of Computing and Information Technology University of Bisha Saudi Arabia College of Computing & Informatics Drexel University United States

ISBN: (纸本)9798350386226

Multi-modal multi-view graph learning models have achieved significant success in medical outcome prediction, combining various modalities to enhance the performance of various medical tasks. However, current architectures for multi-modal multi-view graph learning (M3GL) models heavily depend on manual design, demanding significant effort and expert experience. Meanwhile, significant advancements have been achieved in the field of graph neural architecture search (GNAS), contributing to the automated design of learning architectures based on graphs. However, GNAS faces challenges in automating multimodal multi-view graph learning (M3GL) models, as existing frameworks cannot handle M3GL architecture topology, and current search spaces do not consider M3GL models. To address the above challenges, we propose, for the first time, a multi-modal multi-view graph neural architecture search (M3GNAS) framework that automates the construction of the optimal M3GL models, enabling the integration of multi-modal features from different views. We also design an effective multi-modal multi-view learning (M3L) search space to develop inner-view and outer-view graph representation learning in the context of graph learning, obtaining a latent graph representation tailored to the specific requirements of downstream tasks. To examine the effectiveness of M3GNAS, it is evaluated on medical outcome prediction tasks. The experimental findings demonstrate our proposed framework's superior performance compared to state-of-the-art models. © 2024 IEEE.

关键词： Contrastive Learning

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Maximizing Profit of the Service Providers through Efficient Content Placement and Retrieval in an IoT-enabled Edge-Cloud Platform 18

Maximizing Profit of the Service Providers through Efficient...

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Gandhi, Preet Mahanta, Abhishriya Ghose, Manojit Sahu, Aryabartta Indian Institute of Technology Department of Computer Science and Engineering Guwahati India Indian Institute of Technology School of Computing and Electrical Engineering Mandi India Indian Institute of Information Technology Department of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798350391725

In today's 5G era, the amount of data generated by the Internet of Things (IoT) devices is enormous. Data is processed and stored in the cloud under a traditional cloud computing architecture, and real-time processing and retrieval of these data is time-consuming. Therefore, an edge-cloud framework comes in handy where the content generated is placed on the edge nodes of the network, and it can reach the endusers in a time-bound manner. A majority of the previous research works on content placement under the edge-cloud architecture primarily aimed at improving the latency of the system. However, the approach proposed in this paper aims to maximize the profit of the service provider by effectively placing the content in a hierarchically organized edge-cloud network. We propose two strategies and compare them with random and greedy approaches for both synthetic and real data sets. The experimental results demonstrate the effectiveness of the proposed strategies, and the profit of the service providers increases by 38.81% (maximum) and 10.51% (on average) for the synthetic dataset and by 65.74% (maximum) and 22.83% (on average) for the real-world dataset when compared with the greedy approach. © 2024 IEEE.

关键词： Cloud platforms

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Examining Unsupervised Learning Techniques for Economic Forecasting 5th

Examining Unsupervised Learning Techniques for Economic Fore...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Negi, Bichitra Singh Minish, T. N. Sterlin Mishra, Awakash Karthikeyan, M. Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bengaluru India

ISBN: (纸本)9789819780426

This newsletter examines a ramification of unsupervised mastering strategies for financial forecasting. The number one consciousness has been using strategies inclusive of k-suggest clustering and principal thing evaluation (PCA) to derive meaning from facts and generate forecasts for macroeconomic indicators consisting of gross home product (GDP) and stability of bills. We also discuss using neural networks (NNs) as software for unsupervised learning for monetary forecasting. Via a sequence of empirical experiments, we analyze and examine the forecasts generated through every unsupervised, gaining knowledge of methods for various macroeconomic indicators. The outcomes of the experiments suggest that NNs and PCA are most effective for monetary forecasting and discover that PCA is a promising tool for making forecasts with more excellent accurate effects than okay-manner. Moreover, the results offer insights on a way to use unsupervised learning for financial forecasting and suggest ability ways in which to enhance the accuracy of those forecasts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decentralized finance

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