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14th International Conference on Ambient systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Hennebelle, Alain Materwala, Huned Ismail, Leila Melbourne Australia 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 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. © 2023 Elsevier B.V.. All rights reserved.

关键词： Edge computing

Path Planning and information Protection of Mobile Robots Based on Deceptive Reinforcement Learning 23rd

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Path Planning and Information Protection of Mobile Robots Ba...

23rd IFToMM China International Conference on Mechanism and Machine Science and Engineering, IFToMM CCMMS 2022

作者： Xu, Qingfeng Shi, Yingnan Wang, Junchao Miller, Tim Xu, Hangding Wang, Tianmu Lin, Hongbin Liu, Xin-Jun Nie, Zhenguo Ths State Key Laboratory of Tribology Department of Mechanical Engineering Tsinghua University Beijing100084 China Beijing Key Laboratory of Precision/Ultra-Precision Manufacturing Equipments and Control Tsinghua University Beijing100084 China School of Computing and Information Systems Melbourne School of Engineering University of Melbourne Melbourne3000 Australia School of Mathematics and Information Science Guangzhou University Guangzhou510006 China

ISBN: (纸本)9789811993978

Mobile robots have been widely used in military and other fields. By observing the path of the mobile robot, it is easy to infer the destination of its path. However, in some special situations, the user of the mobile robot does not want to reveal its purpose information, such as carrying out military attacks, sneak reconnaissance missions, etc. In order to protect the mobile robot's path information from being leaked, we combine the reinforcement learning path planning method to propose a computationally efficient deceptive reinforcement learning mobile robot path planning method. The path planned by this method balances the path cost and the target deception weight, making it difficult for observers observing the mobile robot path to predict the real target of the mobile robot, while ensuring a lower path cost for the mobile robot. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Mobile robots

Data Engineering and AI-Powered Skin Cancer Identification for Healthcare Applications 28th

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Data Engineering and AI-Powered Skin Cancer Identification f...

28th International Conference on Knowledge Based and Intelligent information and Engineering systems, KES 2024

作者： Ahmed, Imran Ahmad, Misbah Chehri, Abdellah Jeon, Gwanggil School of Computing and Information Science Anglia Ruskin University Cambridge United Kingdom Animal and Agriculture Department Hartpury University Gloucester United Kingdom Centre for Machine Vision Bristol Robotics Laboratory University of the West of England Bristol United Kingdom Canada Department of Embedded Systems Engineering Incheon National University Incheon Korea Republic of

Skin cancer diagnosis, a critical task in the medical domain, can be revolutionized through the application of advanced deep-learning techniques. This work investigates the efficacy of Convolutional Neural Networks (CNNs) in the automated classification of skin cancer. The process begins with a comprehensive explanation of key CNN layers: Conv2D, MaxPool2D, Dropout, and Dense. The Conv2D layers employ learnable filters that transform localized image segments, while MaxPool2D contributes to downsampling, effectively reducing computational cost and overfitting risk. Integrating these layers enables the network to capture local and global characteristics, which is crucial for accurate classification. Adding Dropout layers enhances generalization and mitigates overfitting by introducing randomness during training. ReLU activation functions infuse non-linearity, and the Flatten layer facilitates the transition to fully connected layers. The proposed CNN architecture is meticulously designed considering filter counts, kernel sizes, and pooling dimensions. The trained model demonstrates promising performance by utilizing the HAM10000 dataset, encompassing diverse skin lesion images across seven classes. The CNN model's parameters and architecture are systematically presented, offering insights into its design rationale. The model undergoes optimization with the Adam optimizer and annealing techniques to facilitate convergence. The model's effectiveness is evaluated on validation and test datasets, demonstrating an accuracy of 78.55% and 76.49%, respectively, for skin cancer classification. Data augmentation strategies are introduced to enhance model generalization further. The results underscore CNN's potential as a robust tool for automating skin cancer diagnosis, aligning with the broader trend of leveraging deep learning for medical image analysis © 2024 The Authors.

关键词： Convolutional neural networks

An Improved PI Controller for Stiction Compensation of Control Valves in Process Industry

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An Improved PI Controller for Stiction Compensation of Contr...

第43届中国控制会议

作者： Dong Yang Xun Lang Jiande Wu Yumin Lang Lei Xie Hongye Su Department of Electronic Engineering School of Information Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University State Key Laboratory of Industrial Control Technology Zhejiang University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

In the process industry, limit cycles are mostly induced by stiction-related nonlinearity in control valves, severely deteriorating control loop performance. Such problem highlights the importance of compensating for oscillations due to valve stiction. To this end, this work introduces an improved PI controller(IPIC) for stiction compensation, which retains the advantages of the traditional PI controller with fewer adjustment parameters. The enhancement involves nonlinear variation of the integral gain based on instantaneous error. Additionally, a differential evolution algorithm is utilized for controller *** to state-of-the-art methods in setpoint tracking, disturbance rejection, and robustness to parameter uncertainties, the proposed approach exhibits superior performance.

关键词： Stiction Limit Cycle Nonlinear PI controller Differential Evolution Algorithm

Federated Learning Architectures: A Performance Evaluation with Crop Yield Prediction Application

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arXiv 2024年

作者： Mukherjee, Anwesha Buyya, Rajkumar Department of Computer Science Mahishadal Raj College West Bengal Mahishadal721628 India Laboratory School of Computing and Information Systems The University of Melbourne VIC3052 Australia

Federated learning has become an emerging technology for data analysis for IoT applications. This paper implements centralized and decentralized federated learning frameworks for crop yield prediction based on Long Short-Term Memory Network. For centralized federated learning, multiple clients and one server is considered, where the clients exchange their model updates with the server that works as the aggregator to build the global model. For the decentralized framework, a collaborative network is formed among the devices either using ring topology or using mesh topology. In this network, each device receives model updates from the neighbour devices, and performs aggregation to build the upgraded model. The performance of the centralized and decentralized federated learning frameworks are evaluated in terms of prediction accuracy, precision, recall, F1-Score, and training time. The experimental results present that ≥97% and >97.5% prediction accuracy are achieved using the centralized and decentralized federated learning-based frameworks respectively. The results also show that the using centralized federated learning the response time can be reduced by ∼75% than the cloud-only framework. Finally, the future research directions of the use of federated learning in crop yield prediction are explored in this paper. © 2024, CC BY.

关键词： Federated learning

PRAGA: Prototype-aware Graph Adaptive Aggregation for Spatial Multi-modal Omics Analysis

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arXiv 2024年

作者： Huang, Xinlei Ma, Zhiqi Meng, Dian Liu, Yanran Ruan, Shiwei Sun, Qingqiang Zheng, Xubin Qiao, Ziyue School of Computing and Information Technology Great Bay University Dongguan523000 China Guangdong Provincial Key Laboratory of Mathematical and Neural Dynamical Systems Dongguan523000 China

Spatial multi-modal omics technology, highlighted by Nature Methods as an advanced biological technique in 2023, plays a critical role in resolving biological regulatory processes with spatial context. Recently, graph neural networks based on K-nearest neighbor (KNN) graphs have gained prominence in spatial multi-modal omics methods due to their ability to model semantic relations between sequencing spots. However, the fixed KNN graph fails to capture the latent semantic relations hidden by the inevitable data perturbations during the biological sequencing process, resulting in the loss of semantic information. In addition, the common lack of spot annotation and class number priors in practice further hinders the optimization of spatial multi-modal omics models. Here, we propose a novel spatial multi-modal omics resolved framework, termed PRototype-Aware Graph Adaptative Aggregation (PRAGA). PRAGA constructs a dynamic graph to capture latent semantic relations and comprehensively integrate spatial information and feature semantics. The learnable graph structure can also denoise perturbations by learning cross-modal knowledge. Moreover, a dynamic prototype contrastive learning is proposed based on the dynamic adaptability of Bayesian Gaussian Mixture Models to optimize the multi-modal omics representations for unknown biological priors. Quantitative and qualitative experiments on simulated and real datasets with 7 competing methods demonstrate the superior performance of PRAGA. Code is available at https://***/Xubin-s-Lab/PRAGA. © 2024, CC BY-NC-ND.

关键词： Contrastive Learning

A Cross-Platform Instant Messaging User Association Method Based on Spatio-temporal Trajectory 8th

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A Cross-Platform Instant Messaging User Association Method B...

8th International Conference on Artificial Intelligence and Security , ICAIS 2022

作者： Zhou, Pei Luo, Xiangyang Du, Shaoyong Li, Lingling Yang, Yang Liu, Fenlin School of Cyber Science and Engineering Zhengzhou University Zhengzhou450000 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450000 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Intelligent Engineering Zhengzhou University of Aeronautics Zhengzhou450000 China School of Computing and Information Systems Singapore Management University Singapore188065 Singapore

ISBN: (纸本)9783031067600

The current research on cross-platform instant messaging user association is mainly divided into two categories: based on user attributes and based on user behavior. Methods based on user attributes mainly identify users through multiple attributes such as user name and multi-platform user information association based on cell phone number, but user association is not possible when multi-platform user information is inconsistent and users do not grant their address book permissions. Methods based on user behavior mainly calculate the similarity between user trajectories features such as geographic location frequency and co-occurrence, but this method lacks the user’s information, which leads to the inability to fully excavate the sequential features of the trajectory and affects the accuracy of trajectory matching. In order to solve this problem, this paper proposes a cross-platform instant messaging user association algorithm based on temporal trajectories (CPTrajst). We firstly place probes in the area where the target may appear so as to obtain user information, gets user trajectory, then processes the trajectory and performs two trajectory matches, finally associate users of different platforms whose trajectories match, thus increasing the accuracy and reliability of cross-platform instant messaging user association. We conducts specific experiments for users of WeChat and Momo, the most commonly used instant messengers in China. The results show that we can achieve reliable association for these two types of instant messaging users and the user association accuracy can reach 99.5%, which is better than the existing user association algorithms based on trajectory matching. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Trajectories

Automation 5.0: The Key to systems Intelligence and Industry 5.0

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IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1723-1727页

作者： Ljubo Vlacic Hailong Huang Mariagrazia Dotoli Yutong Wang Petros A.Ioannou Lili Fan Xingxia Wang Raffaele Carli Chen Lv Lingxi Li Xiaoxiang Na Qing-Long Han Fei-Yue Wang Institute of Intelligent and Integrated Systems and the School of Engineering and Built Environment Griffith UniversityNathanQLD 4111Australia Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic UniversityHong KongChina Department of Electrical and Information Engineering Polytechnic of Bari70126 BariItaly State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190and also with the Qingdao Academy of Intelligent IndustriesQingdao 266114China Department of Electrical Engineering-Systems University of Southern CaliforniaLos AngelesCA 90007 USA School of Automation Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049 Beijing Huairou Academy of Parallel Sensing Beijing 101499China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University IndianapolisIndianapolisIN 46202 USA Department of Engineering University of CambridgeCB21TN CambridgeU.K. School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Dazhou Artificial Intelligence Institute Dazhouand the Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China

AUTOMATION has come a long way since the early days of mechanization,i.e.,the process of working exclusively by hand or using animals to work with *** rise of steam engines and water wheels represented the first generation of industry,which is now called Industry Citation:***,***,***,***,***,***,***,***,***,***,***,Q.-***,and F.-***,“Automation 5.0:The key to systems intelligence and Industry 5.0,”IEEE/CAA ***,vol.11,no.8,pp.1723-1727,Aug.2024.

关键词： Automation machinery intelligence

INCREASE: Inductive Graph Representation Learning for Spatio-Temporal Kriging

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arXiv 2023年

作者： Zheng, Chuanpan Qi, Jianzhong Fan, Xiaoliang Chen, Chaochao Wang, Cheng Chen, Longbiao Fujian Key Laboratory of Sensing and Computing for Smart Cities School of Informatics Xiamen University China School of Computing and Information Systems University of Melbourne Australia College of Computer Science and Technology Zhejiang University China

Spatio-temporal kriging is an important problem in web and social applications, such as Web or Internet of Things, where things (e.g., sensors) connected into a web often come with spatial and temporal properties. It aims to infer knowledge for (the things at) unobserved locations using the data from (the things at) observed locations during a given time period of interest. This problem essentially requires inductive learning. Once trained, the model should be able to perform kriging for different locations including newly given ones, without retraining. However, it is challenging to perform accurate kriging results because of the heterogeneous spatial relations and diverse temporal patterns. In this paper, we propose a novel inductive graph representation learning model for spatio-temporal kriging. We first encode heterogeneous spatial relations between the unobserved and observed locations by their spatial proximity, functional similarity, and transition probability. Based on each relation, we accurately aggregate the information of most correlated observed locations to produce inductive representations for the unobserved locations, by jointly modeling their similarities and differences. Then, we design relation-aware gated recurrent unit (GRU) networks to adaptively capture the temporal correlations in the generated sequence representations for each relation. Finally, we propose a multi-relation attention mechanism to dynamically fuse the complex spatio-temporal information at different time steps from multiple relations to compute the kriging output. Experimental results on three real-world datasets show that our proposed model outperforms state-of-the-art methods consistently, and the advantage is more significant when there are fewer observed locations. Our code is available at https://***/zhengchuanpan/INCREASE. Copyright © 2023, The Authors. All rights reserved.

关键词： Kriging

MixSA: Training-free Reference-based Sketch Extraction via Mixture-of-Self-Attention

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arXiv 2025年

作者： Yang, Rui Wu, Xiaojun He, Shengfeng The School of Computer Sciences Shaanxi Normal University Xi'An China The School of Computer Science The Key Laboratory of Intelligent Computing and Service Technology for Folk Song of the Ministry of Culture and Tourism Shaanxi Normal University Xi'An710119 China The School of Computing and Information Systems Singapore Management University Singapore

Current sketch extraction methods either require extensive training or fail to capture a wide range of artistic styles, limiting their practical applicability and versatility. We introduce Mixture-of-Self-Attention (MixSA), a training-free sketch extraction method that leverages strong diffusion priors for enhanced sketch perception. At its core, MixSA employs a mixture-of-self-attention technique, which manipulates self-attention layers by substituting the keys and values with those from reference sketches. This allows for the seamless integration of brushstroke elements into initial outline images, offering precise control over texture density and enabling interpolation between styles to create novel, unseen styles. By aligning brushstroke styles with the texture and contours of colored images, particularly in late decoder layers handling local textures, MixSA addresses the common issue of color averaging by adjusting initial outlines. Evaluated with various perceptual metrics, MixSA demonstrates superior performance in sketch quality, flexibility, and applicability. This approach not only overcomes the limitations of existing methods but also empowers users to generate diverse, high-fidelity sketches that more accurately reflect a wide range of artistic expressions. Copyright © 2025, The Authors. All rights reserved.

关键词： Image texture