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检索条件"任意字段=International Conference on Measurement and Modeling of Computer Systems"

共 90981 条记录，以下是411-420 订阅

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What do the Face and Voice Reveal? Investigating Trust Dynamics During Human-Robot Interaction

What do the Face and Voice Reveal? Investigating Trust Dynam...

引用

20th Annual ACM/IEEE international conference on Human-Robot Interaction, HRI 2025

作者： Alzahrani, Abdullah Nasir, Jauwairia Tayeb, Ahmad J. André, Elisabeth Ahmad, Muneeb I. Swansea University Department of Computer Science Swansea United Kingdom University of Augsburg Augsburg Germany King Abdulaziz University Department of Information Technology Jeddah Saudi Arabia

ISBN: (纸本)9798350378931

Existing research has shown that vocal and non-vocal human cues correlate with human trust and distrust behaviours, suggesting their potential to measure human trust in robots in real-time. However, there is a lack of research in Human-Robot Interaction that integrates vocal and non-vocal cues into a comprehensive model to measure trust. This paper aims to estimate human trust in robots by examining vocal and non-vocal cues differences between trust and distrust states across multiple sessions of collaborative game-based HRI with 40 participants. Our analysis revealed that vocal and non-vocal human cues can indeed predict trust in HRI, with certain facial expressions, facial movements, and pitch being significant factors. Random Forest classifier achieved the highest accuracy (84 %) in classifying trust states, with key features such as facial expressions (fear, angry), facial blendshapes (cheekSquintRight, jawRight), and vocal characteristics (Duration, Harmonicity std) being the most predictive of trust. These findings demonstrate the importance of combining vocal and non-vocal cues for accurate trust measurement and highlight the potential for real-time trust assessment in robotic systems. © 2025 IEEE.

关键词： Collaborative robots

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Advanced Deep Learning Framework for Diagnosing Autism Spectrum Disorder Through Facial Expression Analysis 5

Advanced Deep Learning Framework for Diagnosing Autism Spect...

引用

5th IEEE international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Balasubramani, Jagadesh Surendran, R. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350356236

Autism Spectrum Disorder is a neurodevelopmental disorder that presents with persistent deficits in social communication and social or emotional reciprocity. Timely intervention may result from an early and precise diagnosis of autism spectrum disorder (ASD). This study proposes a paradigm that uses deep learning systems to analyze facial expressions and emotions to classify children with autism spectrum disorder. The proposed framework utilizes a hybrid model combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks (CNN-BiLSTM) model to learn and perform classification tasks, To enhance image quality, input data undergoes pre-processing techniques, including histogram equalization, which improves contrast, and adaptive median filtering, which reduces noise while preserving essential details. Model optimization is a self-adaptive Black-Winged kite optimization method for hyperparameter tuning, ensuring that the model's parameters are optimal for improved classification accuracy. Performance measurement results such as Accuracy, recall, precision, specificity, and confusion matrix shown from evaluating the framework in ASD datasets obtained from the Kaggle repository are implemented in Python. This approach enhances the speed and precision of Autism Spectrum Disorder diagnosis accordingly and gives a very powerful instrument for real-time clinical application. © 2025 IEEE.

关键词： Image enhancement

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UMLify: Instant UML Diagrams from Scenario Descriptions

UMLify: Instant UML Diagrams from Scenario Descriptions

引用

Data Science, Agents & Artificial Intelligence (ICDSAAI), international conference on

作者： Tejaswini N Sharath S H Sanjitha A Tanushree S Veena A Rajshekhar Ghogge Department of Computer Science and Engineering Dr Ambedkar Institute of Technology Bengaluru Karnataka India Department of Physics Dr Ambedkar Institute of Technology Bengaluru Karnataka India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Unified modeling Language (UML) is a visual modeling language for expressing the architecture and dynamics of software systems. Traditionally, creating UML diagrams required significant expertise and effort to translate user stories into visual representations. However, advancements in tools like PlantUML and Natural Language Processing (NLP) have revolutionized this process. This paper explores how these technologies can automatically generate UML diagrams, simplifying the transformation of natural language user stories into structured visual models. By focusing on the integration of NLP and UML generation tools, the paper aims to demonstrate their potential to streamline software development, improve accuracy, and enhance scalability in modeling processes.

关键词： Visualization Accuracy Translation Scalability Unified modeling language Focusing Software systems Natural language processing Optimization Software development management

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3rd international conference on Trends in Electronics and Health Informatics, TEHI 2023

3rd International Conference on Trends in Electronics and He...

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3rd international conference on Trends in Electronics and Health Informatics, TEHI 2023

ISBN: (纸本)9789819739363

The proceedings contain 52 papers. The special focus in this conference is on Trends in Electronics and Health Informatics. The topics include: An Effective Combination of Deep and Machine Learning Models for Monkeypox Detection from Dermatographic Image;a Time-Efficient and Effective Image Contrast Enhancement Technique Using Fuzzification and Defuzzification;An Ensemble Machine Learning-Based Approach for Detecting Malicious Websites Using URL Features;The Multi-class Paradigm: How Transformers Are Reshaping Language Analysis in NLP;deep Learning Precision Farming: Identification of Bangladeshi-Grown Fruits Using Transfer Learning-Based Detection;Deep Learning Solutions for Detecting Bangla Fake News: A CNN-Based Approach;a Two-Stage Stacking Ensemble Learning for Employee Attrition Prediction;ensemble Learning Approaches for Alzheimer’s Disease Classification in Brain Imaging Data;pseudo-Knighted Cocktail Shaker Sort;sentiment Analysis in Twitter Data Using Machine Learning-Based Approach;road Object Detection for Visually Impaired People in Bangladesh;newBreeze: A Comprehensive Solution to a Beginner-Friendly Arch Linux Distribution with Zen Kernel;deep Ensemble Learning Approach for Multimodal Emotion Recognition;Tri Focus Net: A CNN-Based Model with Integrated Attention Modules for Pest and Insect Detection in Agriculture;detection and Classification of Spam Email: A Machine Learning-Based Experimental Analysis;predictive modeling and Early Detection of White Spot Disease in Shrimp Farming Using Machine Learning: A Case Study in Bangladesh;bangla License Plate Detection and Recognition Approach Based on computer Vision for Authentic Vehicle Identification;feature Techniques with a Custom Convolutional Model for Breast Tumor Surveillance in Mammograms;An AI-Based Clinical Recommendation System Using Ensemble-Based Soft Voting Classifier;machine Learning-Based Approach to Predict Heart Diseases Using Fused Dataset;an Optimal Feature Selection-Based Approach to P

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25th international conference on Intelligent Data Engineering and Automated Learning, IDEAL 2024

25th International Conference on Intelligent Data Engineerin...

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25th international conference on Intelligent Data Engineering and Automated Learning, IDEAL 2024

ISBN: (纸本)9783031777370

The proceedings contain 92 papers. The special focus in this conference is on Intelligent Data Engineering and Automated Learning. The topics include: Model-Based Meta-reinforcement Learning for Hyperparameter Optimization;towards Sustainable Precision: Machine Learning for Laser Micromachining Optimization;association Rules Mining with Auto-encoders;using Contrastive Learning to Map Stylistic Similarities in Narrative Writers;automatic Classification of Signal and Noise in Functional Magnetic Resonance Imaging Scans Using Convolutional Neural Networks;how Resilient are Language Models to Text Perturbations?;emotional Sequential Influence modeling on False Information;CSSDH: An Ontology for Social Determinants of Health to Operational Continuity of Care Data Interoperability;padel Two-Dimensional Tracking Extraction from Monocular Video Recordings;drowsiness Detection Using Vital Sign Sensors and Deep Learning on Smartwatches;Benchmarking Out of the Box Open-Source LLMs for Malware Detection Based on API Calls Sequences;multimodal Visio-Lingual Content Analysis to Detect Fake Content on Reddit;MetaLIRS: Meta-learning for Imputation and Regression Selection;pipeline for Semantic Segmentation of Large Railway Point Clouds;preliminary Investigation on Machine Learning and Deep Learning Models for Change of Direction Classification in Running;efficient Radar Scheduling Using Genetic Algorithms and Stochastic Heuristic Initialization;towards a Communication Specification Language for Heterogeneous Service Orchestration Based on Process Calculus and Holonic Multi-agent systems;counterfactual Explanations for Sustainable Tourism Indicators;Tracking Healthy Organs in Medical Scans to Improve Cancer Treatment by Using UW-Madison GI Tract Image Segmentation;low Consumption Models for Disease Diagnosis in Isolated Farms;fast and Scalable Recommendation Retrieval Model with Mixed Attention and Knowledge Distillation;Federated Learning for Vietnamese SMS Spam Detection Using Pre

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Guided View Approach for Lane Detection and Caution Analysis using computer Vision and Deep Learning Techniques

Guided View Approach for Lane Detection and Caution Analysis...

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Artificial Intelligence and Data Engineering (AIDE), international conference on

作者： Manjula Gururaj Rao Vinyas S Gururaj Rao H Priyanka Vishal Aisiri NITTE (Deemed to be University) NMAM Institute of Technology Nitte Karkala Sasken Technologies Limited Bangaluru Computer Science & Engineering PESU Bangaluru

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

In recent years, advancements in computer vision technology have enabled innovative applications in various fields, including transportation safety. One such application is a Lane Detection and Caution measurement Analyzer system aimed at enhancing road safety. This system uses computer vision techniques to provide real-time analysis of road conditions, helping drivers navigate more safely. The Lane Detection module employs image processing algorithms such as edge detection, Hough transform, and perspective transformation to identify lane boundaries accurately. Additionally, the Caution measurement Analyzer module utilizes object detection and tracking algorithms to identify potential hazards, such as potholes, and measures the caution required for safe driving. The proposed model integrates CNN, OpenCV, and YOLO, achieving 87% accuracy in object detection and caution measurement, and 94% accuracy in lane boundary detection.

关键词： YOLO computer vision Accuracy Lane detection Image edge detection Transforms Road safety Real-time systems Hazards Vehicles

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25th international conference on Intelligent Data Engineering and Automated Learning, IDEAL 2024

25th International Conference on Intelligent Data Engineerin...

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25th international conference on Intelligent Data Engineering and Automated Learning, IDEAL 2024

ISBN: (纸本)9783031777301

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13th CCF international conference on Natural Language Processing and Chinese Computing, NLPCC 2024

13th CCF International Conference on Natural Language Proces...

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13th CCF international conference on Natural Language Processing and Chinese Computing, NLPCC 2024

ISBN: (纸本)9789819794393

The proceedings contain 194 papers. The special focus in this conference is on Natural Language Processing and Chinese Computing. The topics include: Hierarchical Knowledge Aggregation for Personalized Response Generation in Dialogue systems;multi-hop Reading Comprehension Model Based on Abstract Meaning Representation and Multi-task Joint Learning;Leveraging Large Language Models for QA Dialogue Dataset Construction and Analysis in Public Services;MCFC: A Momentum-Driven Clicked Feature Compressed Pre-trained Language Model for Information Retrieval;integrating Syntax Tree and Graph Neural Network for Conversational Question Answering over Heterogeneous Sources;pqE: Zero-Shot Document Expansion for Dense Retrieval with Large Language Models;CKF: Conditional Knowledge Fusion Method for CommonSense Question Answering;MPPQA: Structure-Aware Extractive Multi-span Question Answering for Procedural Documents;GraphLLM: A General Framework for Multi-hop Question Answering over Knowledge Graphs Using Large Language Models;local or Global Optimization for Dialogue Discourse Parsing;structure and Behavior Dual-Graph Reasoning with Integrated Key-Clue Parsing for Multi-party Dialogue Reading Comprehension;enhancing Emotional Support Conversation with Cognitive Chain-of-Thought Reasoning;a Simple and Effective Span Interaction modeling Method for Enhancing Multiple Span Question Answering;FacGPT: An Effective and Efficient Method for Evaluating Knowledge-Based Visual Question Answering;PAPER: A Persona-Aware Chain-of-Thought Learning Framework for Personalized Dialogue Response Generation;towards Building a Robust Knowledge Intensive Question Answering Model with Large Language Models;model-Agnostic Knowledge Distillation Between Heterogeneous Models;exploring Multimodal Information Fusion in Spoken Off-Topic Degree Assessment;integrating Hierarchical Key Information and Semantic Difference Features for Long Text Matching;CausalAPM: Generalizable Literal Disentanglement for NLU

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13th CCF international conference on Natural Language Processing and Chinese Computing, NLPCC 2024

13th CCF International Conference on Natural Language Proces...

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13th CCF international conference on Natural Language Processing and Chinese Computing, NLPCC 2024

ISBN: (纸本)9789819794331

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Integration of computer Simulation and Bim in Water Conservancy Flood Control Simulation and Early Warning System

Integration of Computer Simulation and Bim in Water Conserva...

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Electronics, Energy systems and Power Engineering (EESPE), IEEE international conference on

作者： Zekun Chang Shandong Vocational College of Industry Zibo China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

This paper proposes an innovative flood control simulation and early warning system integrating computer simulation and building information modeling (BIM). The system combines deep learning technology with water flow simulation, uses long short-term memory network (LSTM) to model and predict historical water level data, and combines the three-dimensional terrain information provided by the BIM model to dynamically evaluate the water flow and flood spread risks. Through this innovative algorithm, the system can achieve accurate flood prediction according to different scenarios and conditions, and improve the timeliness and accuracy of early warning. In the experimental part, a reservoir was used as a case for verification. The results showed that the LSTM-based algorithm improved the prediction accuracy by $\mathbf{2 0 \%}$ compared with the traditional regression prediction method, and the computational efficiency was improved by 15 %. Compared with traditional methods, the water conservancy flood control early warning system integrating BIM and deep learning is more stable and efficient under complex terrain. This study provides new ideas for intelligent decision-making in the field of water conservancy and flood control. It is not only highly practical, but also provides technical support for further promoting intelligent water conservancy management and disaster prevention and control.

关键词： Deep learning Accuracy computer simulation Computational modeling Water conservation Alarm systems Predictive models Prediction algorithms Real-time systems Floods

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