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Gideon-TS: Efficient exploration and Labeling of Multivariate Industrial Sensor data 24

Gideon-TS: Efficient Exploration and Labeling of Multivariat...

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24th International conference on Enterprise Information Systems (ICEIS)

作者： Langer, Tristan Welbers, Viktor Meisen, Tobias Univ Wuppertal Chair Technol & Management Digital Transformat Lise Meitner Str 27-31 D-42119 Wuppertal Germany

ISBN: (纸本)9789897585692

Modern digitization in industrial production requires the acquisition of process data that is subsequently used in analysis and optimization scenarios. For this purpose, the use of machine learning methods has become more and more established in recent years. However, training advanced machine learning models from scratch requires a lot of labeled data. The creation of such labeled data is a major challenge for many companies, as the generation process cannot be fully automated and is therefore very time-consuming and expensive. Thus, the need for corresponding software tools to label complex data streams, such as sensor data, is steadily increasing. Existing contributions are not designed for handling large datasets and forms common for industrial applications, and offer little support for the labeling of large data volumes. For this reason, we introduce Gideon-TS - an interactive labeling tool for sensor data that is tailored to the needs of industrial use. Gideon-TS can integrate time series datasets in multiple modalities (univariate, multivariate, samples, with and without timestamp) and remains performant even with large datasets. We also present an approach to semi-automatic labeling that reduces the time needed to label large volumes of data. We evaluated Gideon-TS on an industrial exemplary use case by conducting performance tests and a user study to show that it is suitable for labeling large datasets and significantly reduces labeling time compared to traditional labeling methods.

关键词： Labeling Time Series analysis Sensor data visual Analytics

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20th International conference on Computer-Aided Architectural Design Futures, CAAD Futures 2023

20th International Conference on Computer-Aided Architectura...

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20th International conference on Computer-Aided Architectural Design Futures, CAAD Futures 2023

ISBN: (纸本)9783031371882

The proceedings contain 43 papers. The special focus in this conference is on Computer-Aided Architectural Design Futures. The topics include: Social Signals: An Adaptive Installation for Mediating Space During COVID-19 and Beyond;enabling Flexible Architectural Design Re-representations Using a Phenotype-Based Strategy;Towards Human-AI Collaborative Architectural Concept Design via Semantic AI;the Impact of Spatial Layout on Orientation and Wayfinding in Public Housing Estates Using Isovist Polygons and Shape Matching Algorithms;architectural Sketch to 3D Model: An Experiment on Simple-Form Houses;a Physics-Based Constraint Solver for the Site Layout Optimization of Non-convex Buildings with Multiple Requirements;topological Operations: A New Method Toward Retrievable Design Operations;a visual Support Tool for Decision-Making over Federated Building Information;illustrating Algorithmic Design;biologically Informed Design - Towards Additive Biofabrication with Cyanobacteria;a Design-to-Fabrication Workflow for Free-Form Timber Structures Using Offcuts;urban Shaderade. Building Space analysis Method for Energy and Sunlight Consideration in Urban Environments;a Parametric Tool for Outdoor Shade Design: Harnessing Quantitative Indices and visual Feedback for Effective and Efficient Climatic Design of Streets;fine-Grained Long-Term analysis of Resurgent Urban Morphotypes;optimization Strategies of Architecture and Engineering Graduate Students: Responding to data During Design;a Design Ranking Method for Many-Objective Evolutionary Optimization;expanding Performance-Driven Parametric Design Spaces Through data Streams;the Marching Shape 3D: Extensions of the Ice Ray Shape Grammar;AI-Assisted exploration of the Spirit of Place in Chinese Gardens from the Perspective of Spatial Sequences;material-in-the-Loop Fabrication: A Vision-Based Adaptive Clay 3D Printing Workflow on Indeterminate Sand Surfaces;Cost-Effective 3D Urban Massing Reconstruction of Public Aerial LiDAR S

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Interactive Parallel Coordinates visualization Platform for Survey data

Interactive Parallel Coordinates Visualization Platform for ...

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International conference on Artificial Intelligence and data Sciences (AiDAS)

作者： M. Bakri Afrina N.A. M. Hamiz Kolej Pengajian Perkomputeran Information dan Matematik (KPPIM) UiTM Cawangan Melaka Kampus Jasin Melaka Malaysia

ISBN: (数字)9798331528553

ISBN: (纸本)9798331528560

Survey data often encompasses many variables, making it challenging to identify complex relationships. Parallel coordinates visualization offers a powerful solution for exploring these relationships. We propose a web-based application that leverages parallel coordinates to enhance survey data analysis. This user-friendly tool overcomes limitations of traditional methods by enabling interactive exploration of high-dimensional data. The application adheres to the waterfall model for development, ensuring thorough testing and achieving a high System Usability Scale (SUS) score of 86.8%. By providing deeper insights through visual exploration, this application empowers researchers to make more informed decisions. However, well-structured and pre-processed data remains essential for accurate visualizations. Overall, our web-based application with parallel coordinates visualization offers an innovative and efficient approach to survey data analysis.

关键词： Surveys visualization data analysis Accuracy Decision making data visualization data mining Usability Artificial intelligence Testing

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LiDAR Integration with ROS for SLAM Mediated Autonomous Path exploration 1

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International conference on Advanced Computing and Intelligent Technologies, ICACIT 2022

作者： Chaudhuri, Rapti Deb, Suman National Institute of Technology Agartala Agartala India

ISBN: (数字)9789811929809

ISBN: (纸本)9789811929793

Path exploration is a significant problem domain of point-to-point robot navigation. Even the availability of robust sensors has created evolving computational challenges for mobile robot navigation (MRN) specially in GPS denied indoor environments (IE). This paper presents the theoretic and experimental analysis of path exploration challenges and possibilities in an indoor environment by systematic integration of LiDAR with ROS platform for constructing assessable SLAM. The algorithmic solutions have been tested with customized differential drive structure robot platform (CUBOT). This also studies the mapping of the concerned trajectory and environment localization. Consumer grade 2D RP LiDAR is used here as the percept for the edging obstacle periphery, and the associated tangential data is used to construct the localization and mapping. Significant analysis is done on visual representation of LiDAR data in ROS platform for performance evaluation of the path exploration and planning algorithms. This evaluated result would clearly serve as reference to researchers for selecting appropriate SLAM algorithm with an associated memory representation of already explored path. It would also facilitate further combination with optimized global and local path planning techniques to achieve a desired goal by autonomous mobile robot in constrained indoor environment. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Mapping

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visual analysis of Cyclic Time Series with Semantic Zoom

Visual Analysis of Cyclic Time Series with Semantic Zoom

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International conference on Information visualisation (IV)

作者： Patrick Louis Belgin Mutlu Josef Suschnigg Tobias Schreck Pro2Future GmbH Graz Austria Graz University of Technology Graz Austria

ISBN: (数字)9798350380163

ISBN: (纸本)9798350380170

visual analysis (VA) tasks often involve exploring large and complex multi-dimensional datasets to identify trends and anomalies. However, the challenge lies in displaying all the data and maintaining the desired level of detail within the limited screen space. In this paper, we propose a solution that incorporates multiple visualizations and semantic zooming to address this compromise. Our visualization tool focuses on cycle-dependent data, showcasing time series with repetitive behavior. Through semantic zooming, cyclic time series data can be displayed in large quantities and high levels of detail without the need for multiple views. Our proposed tool includes three independent visualizations: line plots, horizon graphs, and adaptive heatmaps. By offering different visualization options, we aim to provide a rich and flexible analytical experience that response to the different user needs and encourages comprehensive data exploration. The tool accommodates both novice and expert users, allowing for intuitive analysis as well as advanced techniques for detailed examination. Our approach follows the mantra of “overview first, zoom and filter, then details-on-demand” facilitating rapid detection and exploration of patterns and trends. In this paper, we present the detailed design, interaction capabilities with semantic zoom, and the results of a user study that demonstrate the effectiveness and usefulness of our proposed tool.

关键词： Heating systems visualization Semantics Time series analysis data visualization Market research Usability

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Leveraging Content Based Image Retrieval Using data Mining for Efficient Image exploration 1

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4th International conference on Mobile Radio Communications and 5G Networks, MRCN 2023

作者： Kaur, Jaspreet Gupta, Divya Singh, Amrinder Shah, Syed Hassan Ahmed Department of Computer Science and Engineering Chandigarh University Mohali 140413 India University Institute of Teachers Training and Research ChandigarhUniversity Punjab Mohali India California State University FullertonCA United States

ISBN: (数字)9789819707003

ISBN: (纸本)9789819706990

A content-based image Retrieval (CBIR) has become an essential tool for managing and searching large-scale images. However, the accuracy and performance of CBIR systems can be improved by combining data mining techniques. Content-based retrieval (CBR) uses the properties and characteristics of the content itself to search for and retrieve information from a big database instead of depending on text or metadata. CBR is very helpful in research, where it is necessary to swiftly and effectively examine vast amounts of data. According to the results, data mining techniques can considerably increase the retrieval process accuracy and effectiveness. Similar photos can be grouped together using clustering algorithms, common patterns of visual features can be found using association rule mining, and images can be classified using classification techniques. In order to create a system for content-based image retrieval and processing, we studied the retrieval of images from huge databases using a variety of feature extraction and matching techniques. The demand for CBIR development came as a result of the sharp rise in image database volumes and their widespread use in several applications. The description of basic feature extraction methods including texture, color, and form is provided in this study. Once these features are retrieved and then used for comparing photos based on similarity. This study suggests a cutting-edge system design for CBIR system that integrates content-based picture and color analysis with data mining methods. This work is intended to develop a segmentation module for the CBIR system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Feature extraction

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Rich Screen Reader Experiences for Accessible data visualization

Rich Screen Reader Experiences for Accessible Data Visualiza...

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24th Eurographics/IEEE VGTC conference on visualization (EuroVis)

作者： Zong, Jonathan Lee, Crystal Lundgard, Alan Jang, JiWoong Hajas, Daniel Satyanarayan, Arvind MIT Cambridge MA 02139 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA UCL London England

Current web accessibility guidelines ask visualization designers to support screen readers via basic non-visual alternatives like textual descriptions and access to raw data tables. But charts do more than summarize data or reproduce tables;they afford interactive data exploration at varying levels of granularity-from fine-grained datum-by-datum reading to skimming and surfacing high-level trends. In response to the lack of comparable non-visual affordances, we present a set of rich screen reader experiences for accessible data visualization and exploration. Through an iterative co-design process, we identify three key design dimensions for expressive screen reader accessibility: structure, or how chart entities should be organized for a screen reader to traverse;navigation, or the structural, spatial, and targeted operations a user might perform to step through the structure;and, description, or the semantic content, composition, and verbosity of the screen reader's narration. We operationalize these dimensions to prototype screen-reader-accessible visualizations that cover a diverse range of chart types and combinations of our design dimensions. We evaluate a subset of these prototypes in a mixed-methods study with 13 blind and visually impaired readers. Our findings demonstrate that these designs help users conceptualize data spatially, selectively attend to data of interest at different levels of granularity, and experience control and agency over their data analysis process. An accessible HTML version of this paper is available at: http://***/pubs/rich-screen-reader-vis-experiences.

关键词： data visualization

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WHEN IS THE CLEANING OF SUBJECTIVE data RELEVANT TO TRAIN UGC VIDEO QUALITY METRICS? 29

WHEN IS THE CLEANING OF SUBJECTIVE DATA RELEVANT TO TRAIN UG...

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IEEE International conference on Image Processing (ICIP)

作者： Perrin, Anne-Flore Dormeval, Charles Wang, Yilin Birkbeck, Neil Adsumilli, Balu Le Callet, Patrick Nantes Univ Ecole Cent Nantes CAPACITES SAS CNRSLS2NUMR 6004 F-44000 Nantes France Google Inc Mountain View CA USA

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Outlier analysis and spammer detection recently gained momentum in order to reduce uncertainty of subjective ratings in image & video quality assessment tasks. The large proportion of unreliable ratings from online crowdsourcing experiments and the need for qualitative and quantitative large-scale studies in the deep-learning ecosystem played a role in this event. We study the effect that data cleaning has on trainable models predicting the visual quality for videos, and present results demonstrating when cleaning is necessary to reach higher efficiency. To this end, we present and analyze a benchmark on clean and noisy User Generated Content (UGC) large-scale datasets on which we re-trained models, followed by an empirical exploration of the constraint of data removal. Our results show that a dataset presenting between 7 and 30% of outliers benefits from cleaning before training.

关键词： Outlier Cleaning Video Quality Metrics User Generated Content Training metrics

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24th International conference on Artificial Intelligence in Education, AIED 2023

24th International Conference on Artificial Intelligence in ...

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24th International conference on Artificial Intelligence in Education, AIED 2023

ISBN: (纸本)9783031362712

The proceedings contain 79 papers. The special focus in this conference is on Artificial Intelligence in Education. The topics include: The Road Not Taken: Preempting Dropout in MOOCs;does Informativeness Matter? Active Learning for Educational Dialogue Act Classification;can Virtual Agents Scale Up Mentoring?: Insights from College Students’ Experiences Using the *** Platform at an American Hispanic-Serving Institution;Real-Time AI-Driven Assessment and Scaffolding that Improves Students’ Mathematical Modeling during Science Investigations;Improving Automated Evaluation of Student Text Responses Using GPT-3.5 for Text data Augmentation;the Automated Model of Comprehension Version 3.0: Paying Attention to Context;smartPhone: Exploring Keyword Mnemonic with Auto-generated Verbal and visual Cues;analysing Verbal Communication in Embodied Team Learning Using Multimodal data and Ordered Network analysis;improving Adaptive Learning Models Using Prosodic Speech Features;neural Automated Essay Scoring Considering Logical Structure;"Why My Essay Received a 4?": A Natural Language Processing Based Argumentative Essay Structure analysis;leveraging Deep Reinforcement Learning for Metacognitive Interventions Across Intelligent Tutoring Systems;enhancing Stealth Assessment in Collaborative Game-Based Learning with Multi-task Learning;how Peers Communicate Without Words-An Exploratory Study of Hand Movements in Collaborative Learning Using Computer-Vision-Based Body Recognition Techniques;scalable Educational Question Generation with Pre-trained Language Models;involving Teachers in the data-Driven Improvement of Intelligent Tutors: A Prototyping Study;reflexive Expressions: Towards the analysis of Reflexive Capability from Reflective Text;Implementing and Evaluating ASSISTments Online Math Homework Support At large Scale over Two Years: Findings and Lessons Learned;algebra Error Classification with Large Language Models;exploration of Annotation Strategies for Automati

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Empowering Social Sciences with data visualization - An Insights and explorations into Behavioral Patterns and Urban Dynamics

Empowering Social Sciences with Data Visualization - An Insi...

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Pervasive Computational Technologies (ICPCT), International conference on

作者： Raihana Parveen Yeo Boon Chin K. Naveen B. Sonia B. Ramana G. LakshmiAlekhya Visual Media Science Research Laboratory QIS College of Engineering and Technology Ongole India Faculty of Engineering and Technology Multimedia University

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

visual data is an integral and essential aspect of any science, a powerful tool for description and exploration in social sciences005. Abstract: In this study, we apply new advanced ways of visualization to bring light on steps helping policy makers better understand multifaceted social phenomena using datasets publicly available. Through combining geospatial, temporal and network visualizations, the framework provides an intimate understanding of urban growth patterns of i-Social networks and behavior characteristics. Findings show that attractive visualizations make interpreting and revealing otherwise hidden patterns easier and provide insight into policy. Such an approach would finally close the gap between data complexity and actionable insights, offering social scientists a solid foundation for research with real impact.

关键词： visualization Fluctuations data analysis Correlation Geology Urban planning data visualization Solids Market research Geospatial analysis

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