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Arm motion recognition based on attitude angle sensor 24

Arm motion recognition based on attitude angle sensor

引用

13th international conference on Computing and Pattern Recognition, ICCPR 2024

作者： Niu, Weikun Zhang, Feng Yang, Guang Xie, Qiang Li, Yonggang Key labortory of recognition and intelligence China Electronics Technology Group Corporation Information Science Research Institute Beijing China Beijing Sanchuan Weiwei Technology Co. LTD Beijing100086 China

ISBN: (纸本)9798400717482

In this paper, the data-driven predictive maintenance technology is applied to the motion recognition of assembly line workers. Specifically, the motion data (acceleration, angular velocity and angle) of workers completing three types of arm movements (two counter-clockwise turns, one turn or one and a half turns, and straight line movement) were collected by the attitude angle measurement sensor. For this data set, an algorithm combining continuous wavelet transform and image deep learning model (pre-trained GoogleNet) has been proposed, and the algorithm is compared with the support vector machine based on the statistical characteristics of discrete wavelet transform and the one-dimensional convolutional network model. It is concluded that the proposed scheme can effectively identify the three kinds of typical movements performed by workers (the average test recognition accuracy is around 93%), which is significantly better than the other comparison models. Through feature analysis, it can be concluded that the scheme combining continuous wavelet transform and image processing deep learning model proposed in this paper has better distinguishable feature space compared with the contrastive models. The scheme proposed in this paper can be extended to predictive maintenance scenarios based on time series data type sensors. © 2024 Copyright held by the owner/author(s).

关键词： Predictive maintenance

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Weibo-FA: A Benchmark Dataset for Fake Account Detection in Weibo Platform 25th

Weibo-FA: A Benchmark Dataset for Fake Account Detection in ...

引用

25th international conference on Web Information Systems Engineering, WISE 2024

作者： Li, Zhiqi Fang, Weidong Zhang, Wuxiong Science and Technology on Microsystem Laboratory Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai201899 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Shanghai Research and Development Center for Micro-Nano Electronics Shanghai201210 China

ISBN: (纸本)9789819605750

Weibo is one of the top social media platforms in China, similar to X/Twitter. There are many fake accounts on Weibo who may have various negative impacts on the platform and community. However, most existing work on fake user datasets and detection models is concentrated on X/Twitter, with little consideration given to Weibo. In this paper, we first review some methods for detecting fake accounts on social networking platforms. Secondly, we introduce a dataset called Weibo-FA dataset collected from the Weibo platform, which contains profile information of both fake and genuine accounts. Then Weibo-FA dataset is compared with a baseline dataset using several basic machine learning models and neural networks to evaluate its applicability in traditional models. Subsequently, the Weibo-FA dataset is employed to train two state-of-the-art neural network-based models to demonstrate its suitability for advanced models. The test results are compared with related work. Regarding the results, they indicate that the proposed Weibo-FA dataset exhibits good usability and completeness. It not only enhances the performance of traditional simple models but also achieves high accuracy in detecting fake accounts for advanced complex models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Learning to Program: Mapping Errors and Misconceptions of Python Novices to Support the Design of Intelligent Programming Tutors

Learning to Program: Mapping Errors and Misconceptions of Py...

引用

17th international conference on computer Supported Education, CSEDU 2025

作者： van der Heyden, Lisa Batur, Fatma Chounta, Irene-Angelica Department of Human-centered Computing and Cognitive Science University of Duisburg-Essen Duisburg Germany Computing Education Research Group University of Duisburg-Essen Essen Germany

ISBN: (纸本)9789897587467

Students often struggle with basic programming tasks after their first programming course. Adaptive tutoring systems can support students’ practice by generating tasks, providing feedback, and evaluating students’ progress in real-time. Here, we describe the first step for building such a system focusing on designing tasks that address common errors and misconceptions. To that end, we compiled a collection of Python tasks for novices. In particular, a) we identified errors occurring during introductory programming and mapped them to learning tasks;b) we conducted a survey to validate our mapping;c) we conducted semi-structured interviews with instructors to understand potential reasons for such errors and best practices for addressing them. Synthesizing our findings, we discuss the creation of a tasks’ corpus to serve as a basis for adaptive tutors. This work contributes to the standardization and systematization of computing education and provides insights regarding the design of learning tasks tailored to addressing errors. Copyright © 2025 by SCITEPRESS - science and technology Publications, Lda.

关键词： Python

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Equivariant Diffusion-Based Sequential Hypergraph Neural networks with Co-attention Fusion for Information Diffusion Prediction 25th

Equivariant Diffusion-Based Sequential Hypergraph Neural Net...

引用

25th international conference on Web Information Systems Engineering, WISE 2024

作者： Lu, Ye Zhang, Ji Yu, Ting Yang, Gaoming Nanjing University of Aeronautics and Astronautics Nanjing China The University of Southern Queensland Darling Heights Australia Zhejiang Lab Hangzhou China Anhui University of Science and Technology Chuzhou China

ISBN: (纸本)9789819605729

Information spread within social networks is a complex process with broad implications. Predicting information diffusion is crucial for understanding information spread within social networks. However, previous research has primarily focused on the homogeneity characteristics of internal cascades, such as temporal and social relationships, neglecting the impact of external information propagation. Additionally, conventional methods of feature integration simply merge cascade and user embeddings, which may introduce excessive redundant information and result in the loss of valuable contextual information critical for accurate predictions. To address these limitations, we present a novel model, the Equivariant Diffusion-based Sequential Hypergraph Neural network with Co-Attention Fusion (EDSHNN-CAF). Within its cascade feature learning module, the model proposes hypergraphs with equivariant diffusion operators to incorporate external cascade influences alongside internal features. This approach effectively captures complex high-order interconnections and accurately reflects the dynamics of information diffusion. In the feature fusion and prediction module, a co-attention mechanism is designed to seamlessly integrate cascade and user embeddings, revealing their complex interdependencies and significantly enhancing predictive capabilities. Experimental results on four real datasets showcase the promising performance of EDSHNN-CAF in predicting information diffusion, outperforming existing state-of-the-art information diffusion prediction models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Spatio-temporal data

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CNN Transformer for the Automated Detection of Rheumatoid Arthritis in Hand Thermal Images 3rd

CNN Transformer for the Automated Detection of Rheumatoid Ar...

引用

3rd international conference on Artificial Intelligence over Infrared Images for Medical Applications, AIIIMA 2024

作者： Ahalya, R.K. Snekhalatha, U. Department of Biomedical Engineering Easwari Engineering College Ramapuram Tamil Nadu Chennai India Department of Biomedical Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur India

ISBN: (纸本)9783031765834

Rheumatoid arthritis (RA) is an instance of arthritis that causes inflammation in the hands, legs, neck, and wrist joints. Signs of RA include joint pain, swelling across multiple joints, weariness, anorexia, and trouble moving in the morning. Early and accurate diagnosis of RA is crucial to manage symptoms and prevent irreversible joint damage. Recently, convolutional neural network (CNN) transformers have gained a crucial role in medical image classification. These advanced deep learning techniques have shown great promise in accurately diagnosing various medical conditions from imaging data. This study explores the application of CNN transformer-based models for the detection and classification of RA using thermal imaging—a passive, non-invasive technique that detects inflammation in the joints by capturing heat patterns. Thermal imaging is a passive, non-invasive method that uses the hand region's joint inflammation to identify RA. The study used 600 thermal hand images of healthy and RA patients to diagnose RA. The current study aim’s to implement a vision transformer (ViT) to identify RA from thermal hand images. The ViT classified the healthy and RA patients with an accuracy of 90%, with a precision, sensitivity and specificity of 0.83, 0.96, and 0.85 respectively. The results of this study underscore the potential of ViT models in the early prediction and diagnosis of RA, offering a non-invasive, efficient, and reliable method for identifying the disease in its early stages. This approach could significantly enhance clinical decision-making and improve patient outcomes by facilitating timely intervention. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Pathology

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Emotion-Aware Synthetic Speech Detection with Multi-level Fusion Features 2nd

Emotion-Aware Synthetic Speech Detection with Multi-level Fu...

引用

2nd international conference on the Frontiers of Robotics and Software Engineering, FRSE 2024

作者： Xiang, Lingyun Zhou, Zhiguo Liu, Yangfan School of Computer and Communication Engineering Changsha University of Science and Technology Changsha410114 China

ISBN: (纸本)9789819631605

Current synthetic speech detection methods often overlook the emotional distinctions between synthetic and real speech. To comprehensively leverage emotional information and deep features to further enhance the accuracy of distinguishing synthetic speeches, this paper proposes an emotion-aware synthetic speech detection method by fusing multi-level different features. Initially, a multi-scale emotional feature extraction model is devised to extract emotional features from 3-D log-Mels. Considering that the emotional features learned by a single model may not be comprehensive enough, the emotion2vec model is utilized to automatically learn additional emotional features from the temporal domain of the original speech. Subsequently, by employing a co-attention network, the extracted two kinds of emotional features are both incorporated into the deep features learned by the end-to-end model Rawnet2, thereby obtaining highly sensitive and weighted deep features. Finally, the extracted emotional features and the weighted deep features are fused to classify synthetic speech from real speech. Experimental results on several datasets demonstrate that the proposed method outperforms baseline methods, with a detection accuracy exceeding 0.97. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Speech recognition

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Review on Architectures of Convolutional Neural network

Review on Architectures of Convolutional Neural Network

引用

Disruptive Technologies (ICDT), international conference on

作者： Sachin Chawla Deepa Gupta Sanjeev Kumar Pippal Dept. Amity School of Engineering and Technology Amity University Greater Noida UP India Dept. Amity Institute of Information Technology Amity University Noida UP India Dept. Computer Science-Artificial Intelligence G.L Bajaj Institute of Technology and Management Greater Noida UP India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

This review provides a broad overview of CNN architectures, focusing on the crucial building blocks and layered structures that form complete networks. It traces the evolution of CNNs, summarizing key publications and highlighting the diverse array of architectures that have emerged in recent years. While simplifying some technical details, the review maintains conceptual rigor to ensure a thorough understanding of CNN principles and development. This review serves as a valuable resource for those seeking to navigate the complexities of CNNs, offering insights into their operational principles, architectural innovations, and potential applications beyond the realm of computer Vision.

关键词： Training Technological innovation computer vision Reviews Architecture Buildings computer architecture Self-supervised learning Time measurement Convolutional neural networks

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Camouflaged Object Detection with CNN-Transformer Harmonization and Calibration

Camouflaged Object Detection with CNN-Transformer Harmonizat...

引用

2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhao, Yilin Zhang, Qing Li, Yuetong College of Information Engineering Shanghai Institute of Technology Shanghai China

ISBN: (纸本)9798350368741

Camouflaged object detection (COD) aims to segment objects that visually blend into their surroundings. However, the subtle differences between camouflaged objects and the background make this task highly challenging. Therefore, how to represent and learn local details and global contexts is crucial for improving detection performance. In this paper, we propose a novel COD network which synergistically leverages the distinct but complementary local and global knowledge to capture the camouflaged objects and identify imperceptible boundaries. Specifically, we design a Feature Coherence Harmonization module to integrate intra-layer features by bridging the knowledge gap between convolutional neural network (CNN) features, which focus on local patterns, and Transformer features, which capture global relationships. Furthermore, we propose a Cross-layer Feature Calibration Module that adaptively aligns inter-layer features, progressively aggregating diverse information to achieve an accurate prediction. Experimental results on COD benchmark datasets demonstrate that the proposed network significantly outperforms state-of-the-art approaches. © 2025 IEEE.

关键词： Camouflaged object detection inter-layer aggregation intra-layer integration

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SHIKSHA: Smart Hybrid Intelligent Knowledge System for Helping Academia 2nd

SHIKSHA: Smart Hybrid Intelligent Knowledge System for Help...

引用

2nd international conference on Applied Algorithms, ICAA 2025

作者： Paul, Aishik Shaha, Abhisri Mukherjee, Arijit Guha, Debajit Das, Priyam Das, Ankan Tarafdar, Anurina Sen, Anindya Department of Electronics and Communication Engineering Heritage Institute of Technology West Bengal Kolkata700107 India Department of Computer Science and Engineering Heritage Institute of Technology West Bengal Kolkata700107 India Heritage Institute of Technology West Bengal Kolkata700107 India

ISBN: (纸本)9783031845420

Our Smart Hybrid Intelligent Knowledge System for Helping Academia (SHIKSHA) addresses critical challenges in classroom and examination management by offering a fully automated solution. Key features consist automated attendance tracking with integrated intruder detection which eliminates manual errors and enhances security. The system also improves examination management using the Coordinate and Area-based Row Column Mapping (CAB-RCM) algorithm ensuring accurate seat-matrix mapping via face recognition. SHIKSHA automates student performance monitoring by analyzing behavior with face tracking and seating patterns to help educators make data-driven decisions. It supports resource management by sending maintenance alerts based on projector usage with a brightness-based algorithm. It monitors classroom audio to assess attentiveness and track syllabus progress. Utilizing existing CCTV cameras and microphones, SHIKSHA operates with minimal hardware requirements and offers an eco-friendly, cost-effective and scalable solution that enhances academic efficiency across a wide range of educational institutions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Knowledge engineering

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A Federated Learning Method for Non-intrusive Load Monitoring Based on Fed-Prox and Bi-GRU 5th

A Federated Learning Method for Non-intrusive Load Monitorin...

引用

5th international conference on Neural Computing for Advanced Applications (NCAA)

作者： Xu, Jinghao Li, Ding Hu, Wenkai Cheng, Xudong China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Wuhan Inst Technol Sch Elect & Informat Engn Wuhan 430205 Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan 430074 Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geoexplorat Wuhan 430074 Peoples R China

ISBN: (纸本)9789819770069;9789819770076

In recent studies, Non-Intrusive Load Monitoring (NILM) methods based on deep learning have received widespread attentions and achieved promising results. Most existing NILM models are typically trained on data from an individual user, and thus may have poor performance when applied for load recognition of other users. Achieving load recognition across different households requires joint training of models with data collected from different users. However, due to concerns regarding user data privacy, it is challenging to directly access and utilize electricity consumption data from various users in practice. Motivated by such a problem, this study designs a federated learning method for NILM based on Fed-Prox and Bi-GRU. By aggregating and optimizing models trained by various local households at the central server multiple times, the resulting global model achieves load recognition across different households. Experiments are conducted using the UK-DALE and REFIT datasets to validate the proposed framework's effectiveness in load recognition across multiple users. The experimental results demonstrate that compared to NILM methods tailored for an individual user, the proposed approach exhibits better generalization performance for load recognition across multiple diverse users.

关键词： Non-intrusive load monitoring Federated learning Load recognition Deep neural network

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