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检索条件"任意字段=19th Data Compression Conference"

共 11663 条记录，以下是31-40 订阅

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Sensory data Dialogues: A Somaesthetic Exploration of Bordeaux through Five Senses 25

Sensory Data Dialogues: A Somaesthetic Exploration of Bordea...

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19th International conference on Tangible Embedded and Embodied Interaction

作者： Bell, Fiona Cochrane, Karen Anne Haynes, Alice C. Reed, Courtney N. Riggs, Alexandra Teixeira Koelle, Marion Vidal, Laia Turmo Rallabandi, L. Vineetha Univ New Mexico Dept Comp Sci Albuquerque NM 87131 USA Univ Waterloo Waterloo ON Canada KTH Royal Inst Technol Interact Design Stockholm Sweden Loughborough Univ London Inst Digital Technol London England Georgia Inst Technol Digital Media Atlanta GA USA OFFIS Inst Informat Technol Oldenburg Germany KTH Royal Inst Technol Media Technol & Interact Design Stockholm Sweden Saarland Informatics Campus Sensorimotor Interact Max Planck Inst Informat Saarbrucken Germany

ISBN: (纸本)9798400711978

the design of interactive systems and digital artefacts often makes use of digital or analog sensory data as a way to "capture" human senses and sensory experiences. Yet, designing for and with sensory data is complex because of our unique, embodied ways of making sense of our somatosensory experiences. Sensory data does not have one prescribed meaning for everyone. We propose a one-day Studio at TEI to start a dialogue about work with sensory data and its representation of human sensory experience. Specifically, we propose a guided walk and series of sensory explorations in Bordeaux to contemplate the interplay between first-person somatosensory experiences and streams of site-specific data from various sensors. By walking and noticing together, this Studio invites participants to engage in a process of creative reflection on their felt experiences, their connection to their surroundings, and their stance within or outside the design community.

关键词： sensory data soma design somaesthetics first-person perspectives

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A Deep Learning Approach for Improving Waste Classification Accuracy with ResNet50 Feature Extraction 19

A Deep Learning Approach for Improving Waste Classification ...

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19th International conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Darwis, Herdianti Puspitasari, Rahma Purnawansyah Astuti, Wistiani Atmajaya, Dedy Hasnawi, Mardiyyah Faculty of Computer Science Universitas Muslim Indonesia Makassar Indonesia

ISBN: (纸本)9798331507817

this research investigates the use of deep learning for automatic waste classification, specifically using ResNet50 for feature extraction and combining it with various classification algorithms. the dataset comprises 1889 images categorized into four classes: plastic, metal, cardboard, and paper. Two approaches were evaluated: direct classification and feature extraction with ResNet50. the direct classification models, including SVM, KNN, and Random Forest, resulted in low performance, with an average accuracy of 60%. However, using ResNet50 for feature extraction significantly improved the classification accuracy across all models, with the combination of ResNet50 and SVM achieving an accuracy of 91%, and precision, recall, and F1-Score exceeding 92%. this demonstrates the effectiveness of ResNet50's feature extraction capability in enhancing the classification of images. the findings suggest that combining feature extraction and classification models provides a more accurate and efficient solution for automatic waste management systems, supporting the recycling process and waste management efficiency. © 2025 IEEE.

关键词： data accuracy

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From data to data Games: Exploring Public Engagement with Scientific Information through Serious Game Design Workshops 25

From Data to Data Games: Exploring Public Engagement with Sc...

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19th International conference on Tangible Embedded and Embodied Interaction

作者： Clement, Emilie Bats, Raphaelle C. Jansen, Yvonne Garnier, Mathilde Univ Bordeaux Montaigne Bordeaux France Univ Bordeaux Bordeaux France Inria Bordeaux France Univ Bordeaux Ctr Emile Durkheim URFIST Bordeaux Bordeaux France Univ Bordeaux CNRS Inria LaBRI Bordeaux France Univ Bordeaux URFIST Bordeaux MICA Bordeaux France

ISBN: (纸本)9798400711978

We explored the use of Serious Game Design Workshops as a tool for engaging citizens in the dissemination of scientific knowledge to the public. Using research data on forest ecosystems and their dynamics, we ran four workshops which sought to bridge the gap between complex environmental information and public understanding. During these workshops, a total of 88 participants-all non-experts concerning forest ecosystems-created storyboards for 20 diverse game concepts which to varying degrees integrated scientific content into different types of gameplay. A preliminary analysis of the produced game concepts suggest that our workshops were able to foster creativity, enhance environmental education, and promote public engagement with scientific knowledge. However, challenges such as variability in participants' prior knowledge and workshop materials affected outcomes. We conclude by discussing future refinements and suggesting methods to improve the educational and communicative potential of serious games workshops for scientific dissemination.

关键词： Serious Game Design Science Communication Environmental Education Game-based Learning Public Engagement data Visualization Research through Design

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19th International conference on Hybrid Artificial Intelligence Systems, HAIS 2024

19th International Conference on Hybrid Artificial Intellige...

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19th International conference on Hybrid Artificial Intelligence Systems, HAIS 2024

ISBN: (纸本)9783031741821

the proceedings contain 52 papers. the special focus in this conference is on Hybrid Artificial Intelligence Systems. the topics include: the third Codon Nucleotide’s Role in Genetic Recombination Within SARS-CoV-2 Spike Protein: A Pilot Study;machine Learning for the Identification of Biomarker and Risk Factors associated with Depression in Adult Population: Preliminary Results on a Small Cohort;Unveiling HIV-1 U Sequences: Shedding Light through Transfer Learning on Genomic Spectrograms;model to Early Detection of Autism Spectrum Disorder through Opinion Mining Approach;A Short Analysis of Hybrid Approaches in COVID‑19 for Detection and Diagnosing;A Graph Neural Network with Multi-head Attention for Universal Brain Disease Diagnosis from fMRI Images;a Computer Vision Approach to Detect Facial Characteristics Related to Encephalopathy in Term Infants;SPADE Norms: a Distributed General Framework for Normative Multi-agent Systems;Finding Optimal Classroom Arrangements to Minimize Cheating in Exams Using a Hybrid AI System;resilience to the Flowing Unknown: An Open Set Recognition Framework for data Streams;a Comparison Procedure for the Evaluation of Metaheuristics;Nyström and RFF Ensembles for Large-Scale Kernel Predictions;application of Transfer Learning to Online Models in Malware Detection;a New Training Algorithm for Support Vector Machines;soft Adaptive Segments for Bio-Inspired Temporal Memory;assessing Generative Artificial Intelligence in Fundamental Physics with Gaussian Processes;implementation of Classical Decision Trees in a Quantum Computing Paradigm;machine Learning Methods as Robust Quantum Noise Estimators;the Impact of data Annotations on the Performance of Object Detection Models in Icon Detection for GUI Images;bayesian Model Selection Pruning in Predictive Maintenance;neonates Crying Detection through Feature Extraction and Machine Learning Methods;differentiable Prototypes with Distributed Memory Network for Continual Learning;efficient Continu

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19th International conference on Hybrid Artificial Intelligence Systems, HAIS 2024

19th International Conference on Hybrid Artificial Intellige...

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19th International conference on Hybrid Artificial Intelligence Systems, HAIS 2024

ISBN: (纸本)9783031741852

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A Classification-Based Algorithm to Characterize Euthymia data in Mental Health 19th

A Classification-Based Algorithm to Characterize Euthymia Da...

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19th International conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2024

作者： López, Victoria Llamocca, Pavél Mérida-Nicolich, Alberto CUNEF University Madrid28040 Spain

ISBN: (纸本)9783031750090

Accelerometry data are useful in the diagnosis and treatment of mental illnesses. However, the correct application of these data depends on their quality, and in the case of mental health data, it is essential to characterize the state of euthymia in order to distinguish critical states such as depression or mania. To do this, it is necessary to delimit quality time periods in the follow-up, especially those periods in which the patient is free of seizures. In this work, an algorithm suitable for isolating quality data useful for the characterization of the state of euthymia is presented. the algorithm is based on the robustness of the classification results of the data vectors. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic health record

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19th International conference on Critical Information Infrastructures Security, CRITIS 2024

19th International Conference on Critical Information Infras...

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19th International conference on Critical Information Infrastructures Security, CRITIS 2024

ISBN: (纸本)9783031842597

the proceedings contain 24 papers. the special focus in this conference is on Critical Information Infrastructures Security. the topics include: Enhancing Long-Term Storage Security in Critical Infrastructures Under NIS2 Directive: Addressing Post-quantum Cryptography Challenges;exploratory Trend Analysis of Supply Chain Cybersecurity Management;a Cooperative Feature Removal Mechanism for Cell Outage Detection in Wireless Telecommunication Networks;"Paying the Rent" A Formal Methods Riposte to "Living Off the Land" Attacks;lyapunov-Based Performance Oriented Switching Strategies for Linear Systems;a Re-transmission Algorithm for Phasor data Concentrators for Resilience Enhancement of State Estimation;locally Optimal Information Pathways in the Presence of Static Adversaries for a Hierarchical Smart Distribution Grid Model;inoperability Propagation in Networks with Coupled Logistic Dynamics;Resilience Analysis of Naval Vessels with i2SIM;gender Gap in the Critical Infrastructure Research Community;centrality-Based Security Allocation in Networked Control Systems;using Tabletop Exercises to Raise Cybersecurity Awareness of Decision-Makers;penetrating the Power Grid: Realistic Adversarial Attacks on Smart Grid Intrusion Detection Systems;Cyber Resilience Using ASFA: DORA-Compliant threat-Led Penetration Testing;adaLightLog: Enhancing Application Logs Anomaly Detection via Adaptive Federating Learning;A Cost-Sensitive Approach for Managing Intrusion Alerts in OT Environments;Multicriteria GIS Spatial Analysis for Multi-hazard Assessment of Infrastructures in Case of Natural Events;machine Learning Techniques for Anomaly Detection in the Hydra Testbed: A data-Driven Defense Strategy;Improving Impact Assessment Using Fuzzy Sets in CISIApro 2.0 Model;a Decision Support System for the Damage and Urban Resilience Assessment of Natural Events;urban data Governance: an Interoperability-Based Approach for Monitoring Natural threats at Different Geographic Scales, through Smart

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19th International conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2024

19th International Conference on Soft Computing Models in In...

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19th International conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2024

ISBN: (纸本)9783031750090

the proceedings contain 61 papers. the special focus in this conference is on Soft Computing Models in Industrial and Environmental Applications. the topics include: Improving the Efficiency of Production Processes by Reducing Human Errors Using Intelligent Methods;automated Semantic Labelling of Images Generated with Deep Diffusion Probabilistic Models;wastewater Quality Indicator Estimation Using Machine Learning and data Augmentation Techniques;a Virtual Avatar for Sign Language Signing;comparison of D-Wave Quantum Computing Environment Solvers for a Two-Machine Jobs Scheduling Problem;hybrid Fuzzy-Based Method for Optimally Selecting Aircraft for Low-Cost Carriers Operating Medium-Haul Flights;two Way Ranging Positioning Methods Analysis Under Optimized Sensor Deployments;human Acoustic Events Detection as Anomalies in Industrial Environments Using Shallow Unsupervised Techniques;Machine Learning in the Last-Mile Delivery: Modified Q-Learning for the TSP;IoT and AI Based Security System – Design and Implementation;characterization of Cattle Behavior Based on Dimensional Reduction Techniques;time Series Forecasting in Agriculture: Explainable Deep Learning with Lagged Feature Selection;artificial Intelligence Methodology for Run-to-Target Manufacturing;multi-robot Path Planning Problem Applied to Real World Enviroment;an Approach of Optimization in Capacitated Vehicle Routing Problem in Logistics Distribution;bayesian Generation of Synthetic data;learning-Based Models for Intelligent Control Over Air Conditioning Units in a Smart Building;predicting Power Generation from Photovoltaic Energy System;photovoltaic Generation Forecasting for Intelligent Management in Energy Communities;application of Neural Models in Solar Radiation Measurements for a Better data Gathering Quality;caravels: A Decentralized Container-Based Infrastructure for Sustainable Human-Centric Intelligent Energy Communities;Production Scheduling for Energy Cost Optimization with Constraints: Com

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Segmentation and data Extraction from Carte du Ciel Astrographic Maps 20th

Segmentation and Data Extraction from Carte du Ciel Astrog...

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20th EAI International conference on Computer Science and Education in Computer Science, CSECS 2024

作者： Laskov, Lasko M. Nikolova, Ivon New Bulgarian University Informatics Department Sofia Bulgaria

ISBN: (纸本)9783031843112

the goal of our research is to develop a set of methods and algorithms for automatic data extraction from images of historical astrographic plates. We focus on scanned astrographic maps that are result of the 19th century huge astronomical project Carte du Ciel. the goal of Carte du Ciel, along with Astrographic Catalogue, was to map the stars up to 14th magnitude in the entire visible sky. the result is stored in the form of photographic plates and their paper copies, called astrographic maps. they contain valuable information for researchers in the field of astronomy, mainly because of the age of the data, and are a subject of extensive research. In this paper we present our approach for segmentation of images of astrographic maps and automatic extraction of the triple stars images (called asterisms) that are contained in them. the presented method creates a data set of asterisms whose purpose is to train a convolutional neural network for automatic flare stars detection in the astrographic maps. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Image segmentation

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GBC: Gaussian-Based Colorization and Super-Resolution for 3D Reconstruction 19

GBC: Gaussian-Based Colorization and Super-Resolution for 3D...

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19th ACM SIGGRAPH International conference on Virtual-Reality Continuum and its Applications in Industry, VRCAI 2024

作者： Liao, Jiecheng He, Shi Yuan, Yichen Zhang, Hui Department of Computer Science and Technology BNU-HKBU United International College ZhuHai China Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International College Zhuhai China

ISBN: (纸本)9798400713484

In this paper, we introduce GBC, an advanced framework for transforming low-resolution, monochrome video sequences into high-resolution, colorized, and geometrically accurate 3D models. GBC combines Bidirectional Optical Flow Super-Resolution (BOF-SR) for temporal super-resolution with a novel colorization approach, Temporal Optical Flow-based Colorization (TOF-CO), designed to enhance frame-to-frame temporal consistency. the integration of these modules with COLMAP-based 3D Gaussian splatting further extends GBC's capability to reconstruct high-fidelity 3D scenes. Additionally, we created a custom dataset tailored to the challenges of low-light, low-quality historical footage, enabling robust evaluation alongside public datasets. this solution offers a new approach to video restoration, advancing temporal coherence, color accuracy, and 3D scene fidelity. the source code is publicly available at https://***/ffftuanxxx/GBC. © 2024 ACM.

关键词： 3D reconstruction

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