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AITU: Digital Generation, IEEE

作者： Veeramanickam M.R.M Aniket Ingavale Vikas Khullar Sneha S Tirth Bishwajeet Pandey Harjit Pal Singh Department of Software Engineering Universidad Nacional Mayor de San Marcos (UNMSM) Lima Peru School of Engineering and Technology DES Pune University Pune India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Engineering Trinity College of Engineering and Research Pune India Department of IS&CS Astana IT University Astana Kazakhstan Department of Physics Faculty of Science and Technology The University of West Indies St Augustine Trinidad & Tobago

ISBN: (数字)9798350364378

ISBN: (纸本)9798350364385

Environmental sound classification is an essential aspect of identifying different classes of sounds using a machine learning approach. This work investigates the efficacy of convolutional neural networks for classifying short environmental sound clips. In this work, a deep 1D-CNN architecture with 2 layers of convolutional employing max-pooling, followed by two fully-connected layers are utilized. The model leverages segmented spectrograms with delta features as input. Evaluated with datasets encompassing environmental and urban recordings, our model surpasses baseline methods utilizing mel-frequency cepstral coefficients (MFCCs) and achieves competitive performance against traditional approaches.

关键词： Training Deep learning Computational modeling computer architecture Feature extraction Data augmentation Recording

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User Acceptance of New HCI Technologies

User Acceptance of New HCI Technologies

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International IEEE Conference and Workshop: Óbuda on Electrical and Power engineering (CANDO-EPE)

作者： Jozsef Katona Attila Kovari Kandó Kálmán Faculty of Electrical Engineering Institute of Electronics and Communication Systems Obuda University Budapest Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemét Hungary Faculty of Computer Science Institute of Digital Technology Eszterházy Károly Catholic University Eger Hungary Department of Natural Sciences Institute of Engineering University of Dunaujvaros Dunaujvaros Budapest Hungary

Human-computer interaction (HCI) is an evolving field of research that focuses on understanding and improving the communication and interaction between humans and computers. Over the past decades, we have seen many significant advances in this field, which have contributed to the widespread adoption and integration of technology into our daily lives. HCI research and development aims to design information technology systems to meet the needs and preferences of users. Usability, efficiency, accessibility and user satisfaction are important considerations in HCI design. This paper presents the results of a pilot study on user acceptance of HCIs. The results show that users are positive about and willing to use HCIs.

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Generative technology for Human Emotion Recognition: A Scope Review

arXiv

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arXiv 2024年

作者： Ma, Fei Yuan, Yucheng Xie, Yifan Ren, Hongwei Liu, Ivan He, Ying Ren, Fuji Yu, Fei Richard Ni, Shiguang Shenzhen China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China Guangzhou China Department of Psychology Faculty of Arts and Sciences Beijing Normal University at Zhuhai Zhuhai China College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

Affective computing stands at the forefront of artificial intelligence (AI), seeking to imbue machines with the ability to comprehend and respond to human emotions. Central to this field is emotion recognition, which endeavors to identify and interpret human emotional states from different modalities, such as speech, facial images, text, and physiological signals. In recent years, important progress has been made in generative models, including Autoencoder, Generative Adversarial Network, Diffusion Model, and Large Language Model. These models, with their powerful data generation capabilities, emerge as pivotal tools in advancing emotion recognition. However, up to now, there remains a paucity of systematic efforts that review generative technology for emotion recognition. This survey aims to bridge the gaps in the existing literature by conducting a comprehensive analysis of over 320 research papers until June 2024. Specifically, this survey will firstly introduce the mathematical principles of different generative models and the commonly used datasets. Subsequently, through a taxonomy, it will provide an in-depth analysis of how generative techniques address emotion recognition based on different modalities in several aspects, including data augmentation, feature extraction, semi-supervised learning, cross-domain, etc. Finally, the review will outline future research directions, emphasizing the potential of generative models to advance the field of emotion recognition and enhance the emotional intelligence of AI systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Emotion Recognition

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Generative technology for Human Emotion Recognition: A Scope Review

SSRN

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SSRN 2024年

关键词： Emotion Recognition

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Stress Detection of Children With ASD Using Physiological Signals

Stress Detection of Children With ASD Using Physiological Si...

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IEEE Signal Processing and Communications Applications (SIU)

作者： Sevgi Nur Bilgin Aktaş Pı nar Uluer Buket Coş kun Elif Toprak Duygun Erol Barkana Hatice Kö se Tatjana Zorcec Ben Robins Agnieszka Landowska Department of AI and Data Engineering Faculty of Computer and Informatics Istanbul Technical University Istanbul Turkey Department of Computer Engineering Faculty of Technology and Engineering Galatasaray University Istanbul Turkey Department of Electrical and Electronics Engineering Faculty of Engineering Yeditepe University Istanbul Turkey University Children&#x2019 s Hospital Skopje North Macedonia School of Computer Science University of Hertfordshire Hatfield UK Department of Software Engineering Faculty of Electronics Telecommunications and Informatics Gdansk University of Technology Gdansk Poland

ISBN: (数字)9781665450928

ISBN: (纸本)9781665450935

This paper proposes a physiological signal-based stress detection approach for children with autism spectrum disorder (ASD) to be used in social and assistive robot intervention. Electrodermal activity (EDA) and blood volume pulse (BVP) signals are collected with an E4 smart wristband from children with ASD in different countries. The peak count and signal amplitude features are derived from EDA signal and used in order to detect the stress of children based on the previously provided reference baselines. Furthermore, a comparison has been made with the stress values determined using low frequency (LF) and high frequency (HF) values extracted from BVP signal.

关键词： Pediatrics Autism Social robots Signal processing Feature extraction Assistive robots Physiology

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Temporal Feature Matters: A Framework for Diffusion Model Quantization

arXiv

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arXiv 2024年

作者： Huang, Yushi Gong, Ruihao Liu, Xianglong Liu, Jing Li, Yuhang Lu, Jiwen Tao, Dacheng Electrical and Computer Engineering Department Hong Kong University of Science and Technology Hong Kong State Key Laboratory of Software Development Environment Beihang University China Department of Data Science and AI Faculty of IT Monash University Australia Electrical Engineering Department Yale University United States Department of Automation Tsinghua University China School of Computer Science and Engineering Nanyang Technological University Singapore

The Diffusion models, widely used for image generation, face significant challenges related to their broad applicability due to prolonged inference times and high memory demands. Efficient Post-Training Quantization (PTQ) is crucial to address these issues. However, unlike traditional models, diffusion models critically rely on the time-step for the multi-round denoising. Typically, each time-step is encoded into a hypersensitive temporal feature by several modules. Despite this, existing PTQ methods do not optimize these modules individually. Instead, they employ unsuitable reconstruction objectives and complex calibration methods, leading to significant disturbances in the temporal feature and denoising trajectory, as well as reduced compression efficiency. To address these challenges, we introduce a novel quantization framework that includes three strategies: 1) TIB-based Maintenance: Based on our innovative Temporal Information Block (TIB) definition, Temporal Information-aware Reconstruction (TIAR) and Finite Set Calibration (FSC) are developed to efficiently align original temporal features. 2) Cache-based Maintenance: Instead of indirect and complex optimization for the related modules, pre-computing and caching quantized counterparts of temporal features are developed to minimize errors. 3) Disturbance-aware Selection: Employ temporal feature errors to guide a fine-grained selection between the two maintenance strategies for further disturbance reduction. This framework preserves most of the temporal information and ensures high-quality end-to-end generation. Extensive testing on various datasets, diffusion models and hardware confirms our superior performance and acceleration. © 2024, CC BY.

关键词： Spatio-temporal data

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Light-weight Sketch Recognition with Knowledge Distillation

Light-weight Sketch Recognition with Knowledge Distillation

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International Conference on Multimedia Analysis and Pattern Recognition (MAPR)

作者： Viet-Tham Huynh Tam.V Nguyen Minh-Triet Tran Software Engineering Laboratory and Faculty of Information Technology University of Science VNU-HCM Vietnam Vietnam National University Ho Chi Minh City Vietnam Department of Computer Science University of Dayton U.S.A.

Recognizing hand-drawn sketches is a promising starting point for various applications, such as assisting artists in creating 3D environments for games or virtual environment scenes quickly and efficiently from concept arts. In addition, by understanding drawings, we can generate 3D models that can be used for further design and development. Thus, in this paper, we aim to develop a novel lightweight network that can accurately recognize sketch drawings. We propose a lightweight-yet-efficient neural network based on MobileNetV2 for sketch recognition and employ knowledge distillation to train the proposed model from EfficientNet-B4. To evaluate the accuracy of the proposed method, we collect a dataset of sketches comprising 1800 drawings in 12 categories, ranging from furniture to animals. The experimental results show that our network model achieves an accuracy of 96.7%, with 96.9% precision, 96.7% recall, and 96.7% F1-score. These results demonstrate that the proposed approach has great potential for practical sketch recognition applications, such as interior design or VR scene generation.

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A Blockchain-Based Distributed Collaborative Sensing and Spectrum Access Approach for Consumer Electronics

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IEEE Transactions on Consumer Electronics 2025年

作者： Peng, Yuhuai Li, Yuan Guo, Yu Zhang, Dawei Khan, Fazlullah Alturki, Ryan Alshawi, Bandar Northeastern University College of Computer Science and Engineering Shenyang110819 China Zhiyuan Research Institute Xihu District Hangzhou China Zhengzhou University School of Computer and Artificial Intelligence Henan Zhengzhou450001 China University of Nottingham Ningbo China School of Computer Science Faculty of Science and Engineering Zhejiang Ningbo315104 China KKR and KSR Institute of Technology and Sciences Department of Computer Science and Engineering Andhra Pradesh Guntur India Division of Research and Development Lovely Professional University Phagwara India University Centre of Research and Development Chandigarh University Mohali India Umm Al-Qura University Department of Software Engineering College of Computing Makkah Saudi Arabia Umm Al-Qura University College of Computing Department of Computer and Network Engineering Makkah Saudi Arabia

Cognitive radio (CR) provides key technical support for the seamless communication of consumer electronic (CE) devices through distributed cooperative sensing and spectrum access. However, its openness and dynamic nature introduce potential security risks and challenges. Due to the openness of wireless communication, spectrum sensing and access are vulnerable to malicious attacks, and resource-constrained devices cannot effectively implement artificial intelligence (AI)-driven encryption and access control strategies. To address these limitations, this study proposes a blockchain-based distributed collaborative sensing method for CE devices. In the proposed method, after sensing, the smart contracts verify node legitimacy using the ring signature method. The malicious users are rejected by the outlier detection method, which improves the collaborative anti-interference ability. In addition, by using a centralized training and distributed execution architecture, a secure spectrum sensing strategy based on multi-agent reinforcement learning is designed. The proposed strategy ensures that each node makes independent decisions, which enhances the adaptability of the CE ecosystem. The proposed method is verified by extensive simulations, and the results show that it can maintain spectrum awareness of more than 95.72% and an access success rate of 91.19% under malicious attacks. Moreover, communication overhead is significantly reduced by approximately 93.53%. © 1975-2011 IEEE.

关键词： Blockchain

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Virtual Reality Semiconductor Laboratory: Usability Testing to Improve User Experience (UX) in Virtual Environments

Virtual Reality Semiconductor Laboratory: Usability Testing ...

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Sciences and Techniques of Automatic Control and computer engineering MI-STA, IEEE International Maghreb Meeting of the Conference on

作者： Ahmed Jamah Ahmed Alnagrat Muhannad Ismail Alkhallas Abdalslam S Imhmed Mohamed Rizalafande Che Ismail Syed Zulkarnain Syed Idrus Halifah Salem Saed Department of Communication Faculty of Business & Communication (FPK) Department of Computer Science and Information Technology Higher Institute of Science and Technology Wadi al-Shati Universiti Malaysia Perlis(UniMAP) Wadi al-Shati Fezzan Libya Kangar Perlis Malaysia Department of Software Engineering Faculty of Engineering Technology in Zuwarah Zuwarah Libya Faculty of Information Technology Aljufra University Aljufra Libya Faculty of Electronic Engineering Technology (FTKEN) Universiti Malaysia Perlis (UniMAP) Arau Malaysia Department of Communication Faculty of Business & Communication (FPK) Universiti Malaysia Perlis(UniMAP) Kangar Perlis Malaysia Department of Computer Science and Information Technology Higher Institute of Science and Technology Wadi al-Shati Wadi al-Shati Fezzan Libya

The Virtual Reality (VR) Semiconductor Laboratory, a computer-based virtual lab designed for training engineering students in semiconductor concepts, was evaluated to enhance user experience (UX) in virtual environments. A sample of 97 participants, including undergraduate, Master's, and PhD students, participated in the study. The User Experience Questionnaire (UEQ) was developed to collect data from users to evaluate their experience with the VR lab. The results of the study showed that the participants had a positive experience with the VR learning environment. They found the experience to be enjoyable, easy to understand, and helpful in completing learning tasks. The majority of participants rated the experience as very easy or easy in terms of learning and understanding the material, enjoyment, ease of using the interface, comfort with body movement during learning, and assistance with tasks. Additionally, most participants rated the experience as very easy in terms of task completion and comfort with the VR experiment. The study found high reliability in terms of usability, indicating that the UEQ is a valid and reliable tool for assessing virtual laboratory usability. These findings suggest that virtual laboratories can be effective learning and training tools in various fields, such as science and engineering. This research highlights the potential of VR technology in improving the training and learning experience for students.

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Character, Word, or Both? Revisiting the Segmentation Granularity for Chinese Pre-trained Language Models

arXiv

引用

arXiv 2023年

作者： Liang, Xinnian Zhou, Zefan Huang, Hui Wu, Shuangzhi Xiao, Tong Yang, Muyun Li, Zhoujun Bian, Chao State Key Lab of Software Development Environment Beihang University Beijing China School of Computer Science and Engineering Northeastern University Shenyang China Faculty of Computing Harbin Institute of Technology Harbin China Lark Platform Engineering-AI Beijing China

Pretrained language models (PLMs) have shown marvelous improvements across various NLP tasks. Most Chinese PLMs simply treat an input text as a sequence of characters, and completely ignore word information. Although Whole Word Masking can alleviate this, the semantics in words is still not well represented. In this paper, we revisit the segmentation granularity of Chinese PLMs. We propose a mixed-granularity Chinese BERT (MigBERT) by considering both characters and words. To achieve this, we design objective functions for learning both character and word-level representations. We conduct extensive experiments on various Chinese NLP tasks to evaluate existing PLMs as well as the proposed MigBERT. Experimental results show that MigBERT achieves new SOTA performance on all these tasks. Further analysis demonstrates that words are semantically richer than characters. More interestingly, we show that MigBERT also works with Japanese. Our code has been released here 1 and you can download our model here 2 © 2023, CC BY.

关键词： Semantics

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