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International Conference on Platform technology and Service (PlatCon)

作者： Xinyue Zhang Yuxuan Zhao Ka Lok Man Jeremy S. Smith Young-Ae Jung Yutao Yue School of Advanced Technology Xi'an Jiaotong-Liverpool University Suzhou China School of AI and Advanced Computing Xi'an Jiaotong-Liverpool University Entrepreneur College (Taicang) Suzhou China Department of Electrical Engineering and Electronics University of Liverpool Liverpool United Kingdom Department of Artificial Intelligence Software Technology Sun Moon University Chung-nam South Korea Thrust of Artificial Intelligence and Thrust of Intelligent Transportation The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Institute of Deep Perception Technology JITRI Wuxi China XJTLU-JITRI Academy of Industrial Technology Xi'an Jiaotong-Liverpool University Suzhou China Department of Mathematical Science Technology University of Liverpool Liverpool United Kingdom

ISBN: (数字)9798350367874

ISBN: (纸本)9798350367881

Internet of Things (IoT) technology can provide real-time public facilities data to intelligent Transportation Systems (ITS), especially the surveillance cameras' video data. With the popularity of video surveillance systems, Video Anomaly Detection (VAD) has become more important in society and traffic management. However, the traditional, highly manual-dependent VAD method and the neural network model-based VAD method (such as Recurrent Neural Networks, RNN) face a significant challenge in temporal stream processing. This paper analyses current specific temporal challenges and proposes a Transformer-based spatial-temporal VAD model to alleviate the influence of temporal limitations. With the development of Transformer models, global information consideration and long-term relationship building have become more accessible, and the processing of temporal information in video data has become more efficient.

关键词： Recurrent neural networks Transportation Manuals Streaming media Transformers Video surveillance Real-time systems Internet of Things Anomaly detection Faces

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Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls

arXiv

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arXiv 2025年

作者： Gao, Can Tan, Xiaofeng Zhou, Jie Ding, Weiping Pedrycz, Witold The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China The Faculty of Data Science City University of Macau Macau999078 China The Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland The Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia The Department of Computer Engineering Istinye University Istanbul Turkey

Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks. However, most unsupervised outlier detection methods are carefully designed to detect specified outliers, while real-world data may be entangled with different types of outliers. In this study, we propose a fuzzy rough sets-based multi-scale outlier detection method to identify various types of outliers. Specifically, a novel fuzzy rough sets-based method that integrates relative fuzzy granule density is first introduced to improve the capability of detecting local outliers. Then, a multi-scale view generation method based on granular-ball computing is proposed to collaboratively identify group outliers at different levels of granularity. Moreover, reliable outliers and inliers determined by the three-way decision are used to train a weighted support vector machine to further improve the performance of outlier detection. The proposed method innovatively transforms unsupervised outlier detection into a semi-supervised classification problem and for the first time explores the fuzzy rough sets-based outlier detection from the perspective of multi-scale granular balls, allowing for high adaptability to different types of outliers. Extensive experiments carried out on both artificial and UCI datasets demonstrate that the proposed outlier detection method significantly outperforms the state-of-the-art methods, improving the results by at least 8.48% in terms of the Area Under the ROC Curve (AUROC) index. The source codes are released at https://***/Xiaofeng-Tan/MGBOD. © 2025, CC BY.

关键词： Granulation

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Localized collocation schemes and their applications

引用

Acta Mechanica Sinica 2022年第7期38卷 1-28,I0002页

作者： Zhuojia Fu Zhuochao Tang Qiang Xi Qingguo Liu Yan Gu Fajie Wang Key Laboratory of Ministry of Education for Coastal Disaster and Protection Hohai UniversityNanjing210098China Center for Numerical Simulation Software in Engineering and Sciences College of Mechanics and MaterialsHohai UniversityNanjing211100China Department of Fluid Dynamics and Thermodynamics Faculty of Mechanical EngineeringUniversity of LjubljanaLjubljana1000Slovenia Laboratory for Simulation of Materials and Processes Institute of Metals and TechnologyLjubljana1000Slovenia School of Mathematics and Statistics Qingdao UniversityQingdaoShandong266071China College of Mechanical and Electrical Engineering National Engineering Research Center for Intelligent Electrical Vehicle Power SystemQingdao UniversityQingdao266071China

This paper presents a summary of various localized collocation schemes and their engineering *** basic concepts of localized collocation methods(LCMs)are first introduced,such as approximation theory,semianalytical collocation methods and localization *** on these basic concepts,five different formulations of localized collocation methods are introduced,including the localized radial basis function collocation method(LRBFCM)and the generalized finite difference method(GFDM),the localized method of fundamental solutions(LMFS),the localized radial Trefftz collocation method(LRTCM),and the localized collocation Trefftz method(LCTM).Then,several additional schemes,such as the generalized reciprocity method,Laplace and Fourier transformations,and Krylov deferred correction,are introduced to enable the application of the LCM to large-scale engineering and scientific computing for solving inhomogeneous,nonisotropic and time-dependent partial differential *** typical benchmark examples are presented to show the recent developments and applications on the LCM solution of some selected boundary value problems,such as numerical wave flume,potential-based inverse electrocardiography,wave propagation analysis and 2D phononic crystals,elasticity and in-plane crack problems,heat conduction problems in heterogeneous material and nonlinear time-dependent Burgers’***,some conclusions and outlooks of the LCMs are summarized.

关键词： Localization Collocation method Semianalytical Computational mechanics

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A Novel Motion Accumulation and Selection Network for Rgb-Based Action Recognition

SSRN

引用

SSRN 2022年

作者： Wang, Huafeng Li, Hanlin Liu, Wanquan Gu, Xianfeng School of Information Technology North China University of Technology Beijing China School of Software Beihang University Beijing China School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou China Department of Computer Science Stony BrookNY11794-2424 United States

A large number of existing methods have demonstrated the significance of motion information for action recognition in video. In the literature, most of the previous methods rely heavily on the temporal differences of features extracted by convolutional networks (CNN) to represent the motion. However, this type of motion representation approach may have two potential drawbacks: 1) The difference operation may cause the incompleteness of the extracted moving target contour;2) Treating all the extracted motion features equally may lead to situations where some motion features will not contribute to the classification or even produce negative incentives. Inspired by the fact that the human eye is often a cumulative and selective process of visual attention when observing a video sequence, we propose a motion accumulation and selection network (MAS-Net) based on a novel embedded 2D CNN. According to the experimental results on typical video datasets such as Something-Something V1&V2 and Kinetics-400, it is shown that MAS-Net has achieved the state-of-the-arts on Something-Something V1&V2 and the competitive results on Kinetics-400, while the computational load is kept at a relatively low level. © 2022, The Authors. All rights reserved.

关键词： Motion estimation

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HealthEdge: A Machine Learning-Based Smart Healthcare Framework for Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and Cloud Computing System

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Procedia Computer Science 2023年 220卷 331-338页

作者： Alain Hennebelle Huned Materwala Leila Ismail Independent Researcher Melbourne Australia Intelligent Distributed Computing and Systems (INDUCE) Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates Cloud Computing and Distributed Systems (CLOUDS) Lab School of Computing and Information Systems The University of Melbourne Australia

Diabetes Mellitus has no permanent cure to date and is one of the leading causes of death globally. The alarming increase in diabetes calls for the need to take precautionary measures to avoid/predict the occurrence of diabetes. This paper proposes HealthEdge, a machine learning-based smart healthcare framework for type 2 diabetes prediction in an integrated IoT-edge-cloud computing system. Numerical experiments and comparative analysis were carried out between the two most used machine learning algorithms in the literature, Random Forest (RF) and Logistic Regression (LR), using two real-life diabetes datasets. The results show that RF predicts diabetes with 6% more accuracy on average compared to LR.

关键词： Artificial Intelligence (AI) Cloud Computing Diagnosis Digital Health Edge Computing eHealth Internet of Things (IoT) Logisitic Regression Machine Learning Prognosis Random Forest Risk Factors Smart Connected Healthcare Type 2 Diabetes Mellitus

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Explore Contextual Information for 3D Scene Graph Generation

arXiv

引用

arXiv 2022年

作者： Liu, Yuanyuan Long, Chengjiang Zhang, Zhaoxuan Liu, Bokai Zhang, Qiang Yin, Baocai Yang, Xin The Department of Electronic Information and Electrical Engineering Dalian University of Technology Dalian116024 China Meta Reality Labs BurlingameCA94010 United States The Key Lab of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian116622 China The Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial Intelligence Faculty of Information Technology Beijing University of Technology Beijing100124 China

3D scene graph generation (SGG) has been of high interest in computer vision. Although the accuracy of 3D SGG on coarse classification and single relation label has been gradually improved, the performance of existing works is still far from being perfect for fine-grained and multi-label situations. In this paper, we propose a framework fully exploring contextual information for the 3D SGG task, which attempts to satisfy the requirements of fine-grained entity class, multiple relation labels, and high accuracy simultaneously. Our proposed approach is composed of a Graph Feature Extraction module and a Graph Contextual Reasoning module, achieving appropriate information-redundancy feature extraction, structured organization, and hierarchical inferring. Our approach achieves superior or competitive performance over previous methods on the 3DSSG dataset, especially on the relationship prediction sub-task. Copyright © 2022, The Authors. All rights reserved.

关键词： Feature extraction

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SmartEdge: Smart Healthcare End-to-End Integrated Edge and Cloud Computing System for Diabetes Prediction Enabled by Ensemble Machine Learning

SmartEdge: Smart Healthcare End-to-End Integrated Edge and C...

引用

IEEE International Conference on Cloud Computing technology and Science (CloudCom)

作者： Alain Hennebelle Qifan Dieng Leila Ismail Rajkumar Buyya Independent Researcher in Information Technology Systems and Artificial Intelligence Applications Abu Dhabi United Arab Emirates Cloud Computing and Distributed Systems (CLOUDS) Lab School of Computing and Information Systems University of Melbourne Melbourne Australia Department of Computer Science and Software Engineering Intelligent Distributed Computing and Systems (INDUCE) Lab College of Information Technology Emirates Center for Mobility Research National Water and Energy Center UAE University Al-Ain Abu Dhabi United Arab Emirates

ISBN: (数字)9798331507589

ISBN: (纸本)9798331507596

The Internet of Things (IoT) revolutionizes smart city domains such as healthcare, transportation, industry, and education. The Internet of Medical Things (IoMT) is gaining prominence, particularly in smart hospitals and Remote Patient Monitoring (RPM). The vast volume of data generated by IoMT devices should be analyzed in real-time for health surveillance, prognosis, and prediction of diseases. Current approaches relying on Cloud computing to provide the necessary computing and storage capabilities do not scale for these latency-sensitive applications. Edge computing emerges as a solution by bringing cloud services closer to IoMT devices. This paper introduces SmartEdge, an AI-powered smart healthcare end-to-end integrated edge and cloud computing system for diabetes prediction. This work addresses latency concerns and demonstrates the efficacy of edge resources in healthcare applications within an end-to-end system. The system leverages various risk factors for diabetes prediction. We propose an Edge and Cloud-enabled framework to deploy the proposed diabetes prediction models on various configurations using edge nodes and main cloud servers. Performance metrics are evaluated using, latency, accuracy, and response time. By using ensemble machine learning voting algorithms we can improve the prediction accuracy by 5% versus a single model prediction.

关键词： Cloud computing Accuracy Smart cities Computational modeling Transportation industry Surveillance Machine learning Predictive models Diabetes Time factors

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Driving Behavior Safety Assessment in Edge Computing Using Multitask Discriminative TS Fuzzy Model

引用

IEEE Transactions on Consumer Electronics 2024年

作者： Gu, Yi Chen, Zhiyu Wang, Lu Shen, Jinsong Bashir, Ali Kashif Wang, Wei Jiangnan University School of Artificial Intelligence and Computer Science Wuxi214122 China Jiangnan University Engineering Research Center of Intelligent Technology for Healthcare Ministry of Education Wuxi214122 China Chongqing University School of Big Data and Software Engineering Chongqing400044 China Jiangsu Keli Internet Information Technology Co. Ltd Jingjiang214500 China Manchester Metropolitan University Department of Computing and Mathematics United Kingdom Woxsen University Woxsen School of Business Hyderabad502 345 India Lebanese American University Department of Computer Science and Mathematics Beirut Lebanon Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China

Driving behavior safety analysis in edge computing involves the real-time collection and processing of vehicle driving data. Fuzzy models have advantages in analyzing driving behavior due to their excellent ability to handle uncertain information. This study develops a multitask discriminative Takagi-Sugeno fuzzy model (MD-TS-FM) for driving behavior safety assessment. To comprehensively analyze the correlation and differences in multitask driving behaviors, the designed consequent part consists of two parts: task-shared part reflects the shared intrinsic structure information that are consistent across different driving tasks, and task-specific part reflects distinct characteristics and variations specific to each task, allowing the model to address the unique aspects of different driving behaviors. Accordingly, the task-shared consequent part is characterized by low-rank property, which mines global structural information within the fuzzy space;whereas the task-specific consequent part exhibits sparsity, which removes non-discriminative and irrelevant information. This sparsity ensures that the model focuses on the most critical features for each specific task. Furthermore, a discriminative diversity term is introduced to enhance the diversity between tasks, which explores the consistent information of task-shared consequents while reducing the overlap of task-specific consequents. Experimental results indicate that the MD-TS-FM model can be effectively applied to driving behavior safety assessment. © 1975-2011 IEEE.

关键词： driving behavior safety assessment edge computing fuzzy model multitask learning

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Energy-Aware Edge-Cloud Computation Offloading for Smart Connected Health 8

Energy-Aware Edge-Cloud Computation Offloading for Smart Con...

引用

8th International Conference on Future Internet of Things and Cloud, FiCloud 2021

作者： Materwala, Huned Ismail, Leila United Arab Emirates University Intelligent Distributed Computing and Systems Research Laboratory College of Information Technology Department of Computer Science and Software Engineering Abu Dhabi Al Ain15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Abu Dhabi Al Ain United Arab Emirates

ISBN: (纸本)9781665425742

Smart connected healthcare is an emerging technology in the context of smart cities. The connected network aims to provide efficient and effective remote patient care. In such scenarios, edge and clouds come into the picture to offload computation from sensors and mobile devices, monitoring patient's health conditions, having limited processing capabilities to the edge and/or the cloud. The problem of optimizing the energy consumption of the edge and cloud servers in this offloading scenario is crucial. However, existing research efforts focus on sensors or cloud energy optimization. They do not consider the edge and cloud as part of the offloading strategy. In this paper, we address this void by proposing a novel energy-aware offloading algorithm for smart connected healthcare, which optimizes the energy of the edge-cloud computing platform for compute-intensive applications. The experimental results show that our proposed algorithm is a promising approach to energy savings. © 2021 IEEE.

关键词： Energy efficiency

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Secure and Privacy-Preserving Lightweight Blockchain for Energy Trading 8

Secure and Privacy-Preserving Lightweight Blockchain for Ene...

引用

8th International Conference on Future Internet of Things and Cloud, FiCloud 2021

作者： Materwala, Huned Ismail, Leila College of Information Technology United Arab Emirates University Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering Abu Dhabi Al Ain15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Abu Dhabi Al Ain United Arab Emirates

ISBN: (纸本)9781665425742

Renewable energy is gaining wide attention to address the issues, such as depleting resources and environmental degradation, linked with the current non-renewable energy. However, storing renewable energy is challenging. Consequently, energy trading is required to transfer the surplus amount of renewable energy from the prosumers to the consumers. The current centralized energy trading system is less secure and involves large transmission losses and high overheads. The decentralization, replication, immutability, and transparency characteristics of blockchain show a promising potential to address the inherent issues of centralized energy trading. However, the existing blockchain-based energy trading suffers from the drawbacks such as limited scalability, high energy loss, and low transaction throughput. In this paper, we propose a secure and privacy-preserving lightweight blockchain for energy trading that reduces the computational and communication overhead compared to the bitcoin network. This is by clustering the prosumers and perform the energy trading through a cluster head. Our experimental results show the potential of our architecture. © 2021 IEEE.

关键词： Scalability

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