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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： S. Nithya D. Deepa Vunnava Dinesh Babu Pallavi G Hamed Fawareh R. D. Kayalvizhy Department of Computer Science Engineering Saveetha Engineering College Chennai Department of CSE St. Joseph's College of Engineering Chennai Department of CSE RV Institute of Technology Guntur Department of Civil Engineering Laxminarayan Innovation Technological University Nagpur India Software Engineering Dept Faculty of Information Technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia Department of Electronics and Communication Engineering R.M.K. Engineering College Kavaraipettai Tamil Nadu

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Trust is essential in the constantly developing environment of online social networks. The Nexus Terroism Impact and Intro Trust facilitate the communication, decision-making, and maintenance of digital communities among individuals. Trust-based Learning with Classification Principle (TLCP) is introduced in this research to improve the assessment of trust in social networks. To ascertain its efficacy, our proposed methodology undergoes a 10-fold cross-validation against the Trust Assessment Methodology (TAM). The traditional Trust Assessment Model (TAM) evaluates and regulates trust in Internet social networking. This method incorporates trust assessment factors to generate a user dependability judgment that is both discriminatory and dynamic. The Trustworthy Account Management (TAM) challenge is designed to enhance digital trust by addressing disinformation, deception, and hostile conduct. The TLCP is able to accurately evaluate trust by utilizing reputation scoring, community analysis, and user behavior metrics. Trust levels are dynamically adjusted as a result of the identification of trustworthy behavioral patterns and danger indicators by machine learning algorithms and social media data. This research examines the connections between trust management and digital interactions and suggests a general solution to alleviate specific trust evaluation issues across online social networks.

关键词： Measurement Machine learning algorithms Social networking (online) Privacy breach Real-time systems Hazards Maintenance Internet Fake news Trust management

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Metric Spaces in Which Many Triangles Are Degenerate

arXiv

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arXiv 2024年

作者： Chvátal, Vašek de Rancourt, Noé Quintero, Guillermo Gamboa Kantor, Ida Szabó, Péter G.N. Department of Computer Science and Software Engineering Concordia University Montreal Canada Department of Applied Mathematics Charles University Prague Czech Republic Univ. Lille CNRS UMR 8524 Laboratoire Paul Painlevé LilleF-59000 France Computer Science Institute of Charles University Prague Czech Republic Department of Engineering Mathematics Faculty of Engineering and Information Technology University of Pécs Pécs Hungary Department of Computer Science and Information Theory Budapest University of Technology and Economics Hungary

Richmond and Richmond (American Mathematical Monthly 104 (1997), 713–719) proved the following theorem: If, in a metric space with at least five points, all triangles are degenerate, then the space is isometric to a subset of the real line. We prove that the hypothesis is unnecessarily strong: In a metric space on n points, fewer than 7n2/6 suitably placed degenerate triangles suffice. However, fewer than n(n − 1)/2 degenerate triangles, no matter how cleverly placed, never *** Codes 30L99, 51F99, 54E99 © 2024, CC BY.

关键词： Topology

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Smart UML - Assignment Management Tool for UML Diagrams

Smart UML - Assignment Management Tool for UML Diagrams

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Advancements in Computing (ICAC), International Conference on

作者： D.N.H Weerasinghe K.A.T Thiwanka H.B.C Jayasith P.A.D Onella Natalie U.U. Samantha Rajapaksha Anuradha Karunasena Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398106

The great majority of manual processes have been automated in the current day. Nonetheless, there is no reliable approach for evaluating UML diagrams for plagiarism and correctness. Unified Modeling Language (UML) provides the basis for UML diagrams. UML Diagrams are typically used to graphically express a system’s stakeholders, activities, objects, and classes to get a deep knowledge of a particular system. UML diagrams are widely used by industry experts as well as beginners. Class diagrams and use case diagrams are the most commonly used diagrams in UML. Though most computer science-related students get assessed in UML modelling, diagram plagiarism isn’t considered in most exams since it’s a tedious task to perform. The proposed system automates capturing details in UML diagram components and generating a model answer from the given question scenario to compare the diagrams and calculate the plagiarism percentage with the correction percentage.

关键词： Industries Plagiarism Computational modeling Unified modeling language Manuals Stakeholders Reliability

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Identification of Sounds Using Deep Learning with MFCC Features Extraction

Identification of Sounds Using Deep Learning with MFCC Featu...

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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

引用

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

引用

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

引用

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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