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Journal of Network Intelligence 2023年第3期8卷 839-855页

作者： Sung, Tien-Wen Huang, Zeming Liang, Qiaoxin Lin, Tao Kong, Lingping College of Computer Science and Mathematics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian Fuzhou350118 China Department of Computer Science VSB-Technical University of Ostrava Czech Republic

With the development of smart electricity technology and demand response, optimization of household electricity consumption behavior has become an important research element for energy saving in residential buildings. In the study of smart electricity consumption in households, the differences in users’ lifestyles and their preferences for the use of various appliances can have a great impact on the results. And many existing methods need to rely on users’ awareness, which does not meet the popular demand. In this paper, we propose a new method for residential load scheduling that takes into account the load characteristics of appliances and electricity consumption habits. By analyzing the household electricity consumption data set and mining the personalized needs and us-age preferences of this user for various appliances, we establish an optimization model for electricity consumption behavior that combines the minimization of electricity expenses and user comfort. Finally, an improved artificial bee colony algorithm is proposed for solving the optimization model and generating a personalized dispatching strategy combined with real-time electricity pricing (RTEP) tariff. The proposed improved artificial swarm algorithm is compared with other classical algorithms, including GA, PSO, ABC, and QABC, and the analysis of cases shows that the model can effectively reduce the electricity consumption cost and ensure the customer satisfaction, and the proposed improved ABC-based algorithm outperforms other algorithms in terms of cost and user comfort. © 2023, Journal of Network Intelligence.

关键词： Customer satisfaction

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Machine learning and data augmentation in the proxy means test for poverty targeting

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Statistical Journal of the IAOS 2023年第4期39卷 961-977页

作者： Wobcke, Wayne Mariyah, Siti School of Computer Science and Engineering UNSW Data Science Hub (uDASH) University of New South Wales Sydney NSW Australia

Recent years have seen increased interest in the use of alternative data sources in the definition and production of official statistics and indicators for the UN Sustainable Development Goals. In this paper, we consider the application of data science to the production of official statistics, illustrating our perspective through the use of poverty targeting as an application. We show that machine learning can play a central role in the generation of official statistics, combining a variety of types of data (survey, administrative and alternative). We focus on the problem of poverty targeting using the Proxy Means Test in Indonesia, comparing a number of existing statistical and machine learning methods, then introducing new approaches in the spirit of small area estimation that utilize area-level features and data augmentation at the subdistrict level to develop more refined models at the district level, evaluating the methods on three districts in Indonesia on the problem of estimating 2020 per capita household expenditure using data from 2016-2019. The best performing method, XGBoost, is able to reduce inclusion/exclusion errors on the problem of identifying the poorest 40% of the population in comparison to the commonly used Ridge Regression method by between 4.5% and 13.9% in the districts studied. © 2023 - IOS Press. All rights reserved.

关键词： data augmentation machine learning Poverty targeting

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ReliefSD:Selecting Numerical Features for Fast Subgroup Discovery 41

ReliefSD:Selecting Numerical Features for Fast Subgroup Disc...

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第41届中国控制会议

作者： Zhenfeng He Yin Zhang College of Computer and Data Science Fuzhou University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Subgroup discovery(SD) identifies disproportionally distributed subsets from a dataset according to a target *** features are often discretized before SD to avoid generating too many interval based patterns and aggravating the "pattern flooding" ***,early discretization greatly reduces the quality of *** addition of a few features,especially numerical features,often sharply prolongs the running time of SD,so removing irrelevant features may be a better ***,a recently proposed non-discretization SD approach for numerical features,uses an empirical method to select a subset of ***,the method ignores the labelling information,so it can not remove irrelevant features *** paper analyses Relief based feature selection for SD,and suggests using interval based local subgroups to evaluate the discrimination ability of a *** presents ReliefSD,a novel feature selection method for SD by updating *** interesting subgroups have many positive instances,ReliefSD only selects positive ***,for each feature ReliefSD constructs a single feature based local subgroup whose boundary is defined by the randomly selected instance and its neighbouring positive *** evaluating the purity of the subgroups,ReliefSD iteratively estimates the importance of *** results on 10 UCI datasets suggest ReliefSD is the best in selecting feature subsets for FSSD when compared with the empirical method and ReliefF.

关键词： Subgroup discovery Feature selection Relief Numerical feature

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Agreements of Nonlinear Opinion Dynamics in Switching Social Networks

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IEEE Transactions on Automatic Control 2024年第6期69卷 4174-4181页

作者： Lee, Ti-Chung Su, Youfeng National Sun Yat-sen University Department of Electrical Engineering Kaohsiung80424 Taiwan Fuzhou University College of Computer and Data Science Fuzhou350116 China

This article formulates and analyzes agreements of the nonlinear opinion dynamics in social networks according to switching interactions, where the agents' susceptibilities depend on current states. These switching interactions are formulated by switching directed graphs with some mild joint connectivity, and depicted by nonlinear switching model. An extended LaSalle invariance principle is established for analyzing the agreements of this switching model as well as three specialized scenarios, where the limiting equations formed by the weak∗ convergence are used to study the steady-state behavior so that the stability analysis is substantially simplified in the sense that the 'max - min' Lyapunov function may be assumed to be constant along any bounded forward complete solutions of limiting equations. Examples and simulations are used to verify the obtained results. © 1963-2012 IEEE.

关键词： Social networking (online)

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Interactive Physiotherapy Game Using React Three Fiber for Hand Motion Rehabilitation

Interactive Physiotherapy Game Using React Three Fiber for H...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Yadav, Ram Kumar Soni, Ashish Kumar Sakare, Apeksha Manipal University Department of Data Science and Engineering Jaipur India G H Raisoni College of Engineering Department of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798331529833

This article describes an interactive physical activity system designed to use React Three Fiber to assist patients with hand rehabilitation. The game simulates a virtual environment where rods controlled by motion-sensing devices represent the patient's hand. Through the "Create"function, a sphere can be created from the previous position and the patient must hold their hand firmly within the sphere's boundaries. Adjustable sliders allow optimal control of hold time and sensitivity to adapt to the patient's growth and treatment needs. The game provides immediate visual feedback, encouraging patients to practice stability and mobility, which are essential elements of good physical therapy. The app simulates real-world movements in a virtual environment, creating a controlled environment that provides immediate visual feedback to encourage patients to take action. This technology uses React Three Fiber to create a 3D environment, including writing information for precise control of the stick. This interactive model is designed to not only develop motor skills through consistent, guided exercises, but also to improve overall patient compliance. Through this application, we explore the intersection of play and physical therapy by examining the potential for digital therapy tools to complement and enhance traditional practices This approach uses the appeal of interactive games to potentially increase treatment compliance, making it an important tool in digital therapy. This article discusses the use of React Three Fiber's techniques, methods, and therapeutic potential for physical therapy. © 2025 IEEE.

关键词： Virtual environments

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Merging approach of pre-trained deep learning models for edge-cloud collaborative inference

Merging approach of pre-trained deep learning models for edg...

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2024 International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, AHPCAI 2024

作者： Yan, Wansong College of Computer and Data Science Fuzhou University Xueyuan Road Minhou Fuzhou350108 China

ISBN: (纸本)9781510685840

Artificial Intelligence (AI)-based Internet of Things (IoT) applications benefit greatly from advanced deep learning models. However, the increasing complexity and resource requirements of deep learning models pose challenges to computational efficiency and deployment on resource-constrained devices. To address these challenges, the paper proposes a merging technique to efficiently merge multiple single-task deep learning models into a unified multi-task model. The proposed method merges models in a serial fashion, potentially replacing various layers with a smaller deep neural sub-network to connect the layers of the previous model and the latter model's layers. This method improves computational efficiency, and can create larger models to perform the functions of the original models with minimal training overhead. The paper designs and implements a merging model that supports end-to-end compressed sensing (CS) sampling and object detection, and the experimental results verify the efficacy of the proposed method in generating fine-tuned multi-task deep neural models with minimal training time and resource costs, making it a cost-efficient solution for edge-cloud collaborative inference. © 2024 SPIE.

关键词： data modeling Clouds Object detection Education and training Deep learning Performance modeling data transmission Feature extraction Instrument modeling Sampling rates

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Many-Objective Evolutionary Algorithm Based on Improved Dominance Relation 18th

Many-Objective Evolutionary Algorithm Based on Improved Domi...

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18th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2022

作者： Pei, Zhen-Yu Yang, Ze-Yuan Liu, Geng-Geng Pan, Jeng-Shyang College of Computer and Data Science Fuzhou University Fujian350116 China College of Computer Science and Engineering Shandong University of Science and Technology Shandong266590 China

ISBN: (纸本)9789819906048

With the increase of the number of objectives in multi-objective optimization problems, the proportion of non-dominated individuals in the population will increase exponentially, and the multi-objective evolutionary algorithm based on the traditional Pareto dominance relation lacks sufficient selection pressure, which degenerates the convergence of the algorithm. In addition, the effectiveness of existing density estimation approaches to distinguish the individuals in high-dimensional space decreases, resulting in poor performance in maintaining diversity. In view of the above problems, this paper proposes a many-objective evolutionary algorithm based on improved dominance relation. First, an improved dominance relation is proposed, the individual with the best convergence is kept in each niche, which increases the selection pressure. Second, a dynamic fitness function is proposed so that individuals with better convergence and diversity can be retained adaptively. Third, an adaptive student distribution crossover operator is proposed, which further improves the convergence and diversity of our algorithm. The experimental results show that the population obtained by MaOEA-IDR is better than the existing algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Evolutionary algorithms

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KeyEE:Enhancing Low-Resource Generative Event Extraction with Auxiliary Keyword Sub-Prompt

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Big data Mining and Analytics 2024年第2期7卷 547-560页

作者： Junwen Duan Xincheng Liao Ying An Jianxin Wang Hunan Key Laboratory of Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China Big Data Institute Central South UniversityChangsha 410083China

Event Extraction(EE)is a key task in information extraction,which requires high-quality annotated data that are often costly to *** classification-based methods suffer from low-resource scenarios due to the lack of label semantics and fine-grained *** recent approaches have endeavored to address EE through a more data-efficient generative process,they often overlook event keywords,which are vital for *** tackle these challenges,we introduce KeyEE,a multi-prompt learning strategy that improves low-resource event extraction by Event Keywords Extraction(EKE).We suggest employing an auxiliary EKE sub-prompt and concurrently training both EE and EKE with a shared pre-trained language *** the auxiliary sub-prompt,KeyEE learns event keywords knowledge implicitly,thereby reducing the dependence on annotated ***,we investigate and analyze various EKE sub-prompt strategies to encourage further research in this *** experiments on benchmark datasets ACE2005 and ERE show that KeyEE achieves significant improvement in low-resource settings and sets new state-of-the-art results.

关键词： natural language processing Event Extraction(EE) Multi-Prompt Learning(MPL) low-resource

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Facial Image-Based Autism Detection:A Comparative Study of Deep Neural Network Classifiers

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computers, Materials & Continua 2024年第1期78卷 105-126页

作者： Tayyaba Farhat Sheeraz Akram Hatoon SAlSagri Zulfiqar Ali Awais Ahmad Arfan Jaffar Faculty of Computer Science and Information Technology The Superior UniversityLahore54600Pakistan Intelligent Data Visual Computing Research(IDVCR) Faculty of Computer Science and Information TechnologyThe Superior UniversityLahore54600Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh12571Saudi Arabia School of Computer Science and Electronic Engineering(CSEE) University of EssexWivenhoe ParkColchesterCO43SQUK

Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive *** and precise ASD detection is crucial,particularly in regions with limited diagnostic resources like *** study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local *** research involves experimentation with VGG16 and MobileNet models,exploring different batch sizes,optimizers,and learning rate *** addition,the“Orange”machine learning tool is employed to evaluate classifier performance and automated image processing capabilities are utilized within the *** findings unequivocally establish VGG16 as the most effective classifier with a 5-fold cross-validation ***,VGG16,with a batch size of 2 and the Adam optimizer,trained for 100 epochs,achieves a remarkable validation accuracy of 99% and a testing accuracy of 87%.Furthermore,the model achieves an F1 score of 88%,precision of 85%,and recall of 90% on test *** validate the practical applicability of the VGG16 model with 5-fold cross-validation,the study conducts further testing on a dataset sourced fromautism centers in Pakistan,resulting in an accuracy rate of 85%.This reaffirms the model’s suitability for real-world ASD *** research offers valuable insights into classifier performance,emphasizing the potential of machine learning to deliver precise and accessible ASD diagnoses via facial image analysis.

关键词： Autism Autism Spectrum Disorder(ASD) disease segmentation features optimization deep learning models facial images classification

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A Novel Dynamic Practical Byzantine Fault Tolerance Protocol Based on Node Grouping 24

A Novel Dynamic Practical Byzantine Fault Tolerance Protocol...

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12th International Conference on Communications and Broadband Networking, ICCBN 2024

作者： Tan, Bingbing Chen, Yanli Zhou, Yonghui Dong, Zhicheng School of Big Data and Computer Science Guizhou Normal University China Tibet Univerity China

ISBN: (纸本)9798400717109

The Practical Byzantine Fault Tolerance protocol (PBFT) has been widely deployed in the blockchain network. However, two main issues, the communication complexity and the inability of nodes to join/exit the network without restarting the system, significantly degrade consensus efficiency. A novel dynamic practical byzantine fault tolerance protocol based on node grouping (NG-PBFT) is proposed as a solution to these problems. This protocol is realized through the grouping of nodes into consensus and observation groups. The nodes in observation group preprocess nodes' joining/exiting requests, while the nodes in consensus group reach consensus about the preprocessed results. Furthermore, an adaptive three phases or two phases consensus mechanism that can effectively improve consensus efficiency is proposed. We provide theoretical complexity to show that our protocol reduces the communication complexity of consensus and improves consensus efficiency significantly. © 2024 ACM.

关键词： Authentication Protocol

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