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检索条件"任意字段=Conference on Visual Data Exploration and Analysis II"

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Benign Paroxysmal Positional Vertigo Disorders Classification Using Eye Tracking data 1

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20th International conference on Artificial Intelligence Applications and Innovations (AIAI)

作者： Nguyen, Thang-Anh-Quan Hashmi, Ehtesham Yamin, Muhammad Mudassar Beghdadi, Azeddine Cheikh, Faouzi Alaya Ullah, Mohib Norwegian Univ Sci & Technol Trondheim Norway Univ Sorbonne Paris Nord Inst Galilee L2TI Villetaneuse France

ISBN: (数字)9783031632150

ISBN: (纸本)9783031632174;9783031632150;9783031632143

Nystagmus is a neurological condition characterized by involuntary and rhythmic eye movements. These abnormal eye movements can be indicative of various underlying neurological and vestibular disorders, impacting visual stability and affecting an individual's perception of their surroundings. Benign Paroxysmal Positional Vertigo (BPPV) is a special case of nystagmus where brief episodes of dizziness are triggered by specific head movements. However, the accurate diagnosis of BPPV still heavily relies on the precise interpretation of nystagmus induced by positional tests, which often require specialized expertise. In this paper, we developed an AI framework that detects and tracks the movement of pupil central and dilation overtime. These time-series data are then classified into different types of nystagmus. In contrast to classical image processing approaches, we employ convolutional neural networks as a baseline and use the averaging of numerous models without incurring extra inference or memory costs. The results from experiments demonstrate that when given the patient's eye video data, our framework is capable of classifying the specific BPPV disorder out of six possible types with an average accuracy of 91% on the publically available challenging and unbalanced dataset.

关键词： Classification Ensemble Neurological Disorder Pupil Tracking Time-data analysis Video Nystagmography

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A Conceptual Framework for Immersive Behavioral data exploration Assisted by a Deep Learned Representation of data

A Conceptual Framework for Immersive Behavioral Data Explora...

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IEEE International conference on Artificial Intelligence and Virtual Reality (AIVR)

作者： Victor Duvivier Matthieu Perreira Da Silva Yannick Prié École Centrale Nantes CNRS LS2N UMR 6004 Nantes Université Nantes France

ISBN: (数字)9798331521578

ISBN: (纸本)9798331521585

By allowing to conduct experiments involving eco-logically valid tasks within controlled environments, Virtual Real-ity (VR) offers novel opportunities for studying human behavior. Several modalities can be leveraged, including event logs, motion trajectories, eye-tracking data, or physiological signals. However, analyzing such multimodal data presents considerable challenges due to their inherent complexity, the varied structures they imply, as well as the necessity to use exploratory approaches. There is therefore a need to design visual analytics tools for the exploration of immersive behavioral data without prior knowledge. Our idea is to integrate deep learned computational models -which leverage advanced techniques to extract valuable high-level features from unlabeled data- into exploratory visual Analytics tools. We introduce a conceptual framework for integrating deep learning models into the visual Analytics process of immersive behavioral data analysis, also focusing on the services that such systems should provide to the analysts.

关键词： Deep learning Solid modeling Analytical models visual analytics Biological system modeling Computational modeling Virtual reality Feature extraction Transformers data models

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analysis of Users’ Demand for visual Images of Chinese Medicine Time-Honored Brands Based on the Kano Model 2nd

Analysis of Users’ Demand for Visual Images of Chinese Medi...

引用

2nd International conference on Image, Vision and Intelligent Systems, ICIVIS 2022

作者： Yu, Mengyao He, Changhua Ma, Yue Yunnan University of Business Management Yunnan Kunming650106 China

ISBN: (纸本)9789819909223

Objective-based on the relevant research methods of the Kano model, this paper makes a specific exploration of the principles and design ideas of Chinese medicine’s time-honored brand image according to users’ needs. Methods the relevant data of the user needs of the time-honored brand image of Chinese medicine were obtained through the questionnaire survey method. And then, through the Kano model, the user needs were divided into basic needs (m), charm needs (a), no special difference needs (I), reverse needs (R), and expectation needs (o), to explore the internal needs of users for the visual image of the Chinese medicine brand. Moreover, integrate the sensory experience of users for the brand image. The perception process of interactive experience and emotional experience and the most original needs of users are transformed into reliable data. The needs are calculated through the calculation method of better worse coefficient. Results it is proposed that the brand image of time-honored pharmaceutical brands needs to unify the visual images and create the IP images, to make the brand image of time-honored Chinese pharmaceutical brands have the characteristics of the times and interest. Conclusion according to the needs of users, summarizing the methods and strategies suitable for optimizing the visual image of Chinese medicine time-honored brands is not only the inheritance and tolerance of Chinese traditional medicine culture but also provides reference and reference for the continuous growth of Chinese medicine time-honored brands. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Behavioral research

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EXTENDING VISUOSPATIAL analysis IN DESIGN COMPUTING: An exploration with a Novel GPU-Based Algorithm and Form-Based Codes 28th

EXTENDING VISUOSPATIAL ANALYSIS IN DESIGN COMPUTING: An Expl...

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28th International conference on Computer-Aided Architectural Design Research in Asia, CAADRIA 2023

作者： Kimm, Geoff White, Marcus Burry, Mark Swinburne University of Technology Australia

ISBN: (纸本)9789887891796

This paper responds to a gap observed between the contemporary capacity for calculation and analysis of visibility of built environment features, such as buildings, in digital urban and architectural computational research models and the functionality of off-the-shelf software tools available to professionals. The research investigates the potential of visibility analysis to be embedded and extended within computational-based workflows of software tools to better meet urban design and planning industry needs. We introduce a novel method for visibility calculation that exposes output data for further analysis within a computational workflow and implement it in a game development engine used by software tool providers. Based in our engagement with a local government authority, we then use that method to demonstrate a workflow in the context of form-based building codes in which the visual impact of a building is considered rather than prescriptive limits on dimensions and use. Our results indicate the novel method has substantial performance improvements compared to an alternative mode of visibility calculation and that software providers could more thoroughly integrate and extend visibility analysis to meet industry needs. © 2023 and published by the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA), Hong Kong.

关键词： Graphics processing unit

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data DRIVEN INTEGRATED DESIGN SPACE exploration USING iSOM

DATA DRIVEN INTEGRATED DESIGN SPACE EXPLORATION USING iSOM

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ASME International Design Engineering Technical conferences / 42nd Annual Computers and Information in Engineering conference (IDETC-CIE) / 48th Design Automation conference (DAC)

作者： Sushil, Rashmi Rama Baby, Mathew Sharma, Gehendra Nellippallil, Anand Balu Ramu, Palaniappan Indian Inst Technol Madras Dept Engn Design ADOPT Lab Chennai Tamil Nadu India Florida Inst Technol Syst Realizat Lab Melbourne FL 32901 USA Mississippi State Univ Ctr Adv Vehicular Syst Starkville MS USA

ISBN: (纸本)9780791886229

Design preferences or targets are typically available at system level. A designer is usually interested in understanding patches of design space at component levels, across different stages and process that corresponds to such system targets or preferences. This demands a thorough design space exploration permitting both forward and inverse designs. Such exploration becomes cumbersome with large variables and complex systems with conflicting goals. Current work proposes using interpretable self organising map (iSOM) as a visual enabler of decision support system for exploring designs to understand the trade off in system targets/goals. The novelty lies in being able to use a visual form to compare greater than three conflicting goals simultaneously while accounting for design variables. The proposed approach is demonstrated using two test problems: i) hot rolling and cooling process chain design for the production of steel rods and ii) head and neck-injury risk evaluations for vehicular crashworthiness. Using the first problem, we demonstrate the capability of iSOM for the solution space exploration of a many-objective steel manufacturing process chain problem to realize the design of a steel product, in the context of a compromise Decision Support Problem formulation. In the second problem, we demonstrate the capability of iSOM for early-stage design space exploration to identify critical injury-risk regions of interest for different car crash scenarios. These two case studies illustrate the capability to carry out a forward and inverse design, by the proposed approach.

关键词： Design Steel Chain Wounds Preferences Accounting Crashworthiness Complex systems Risk Tradeoffs Cooling Manufacturing Steel products Hot rolling Risk assessment Traffic accidents Rods Self-organization

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Development of A Vision-based Ship Behavior Characteristics Extraction Approach and Comparison with K-means Clustering 32nd

Development of A Vision-based Ship Behavior Characteristics ...

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32nd International Ocean and Polar Engineering conference, ISOPE 2022

作者： Cai, Zhiyuan Yu, Long Fan, Qidong Li, Lecheng Collaborative Innovation Center for Advanced Ship and Deep-Sea Exploration State Key Laboratory of Ocean Engineering School of Naval Architecture Ocean and Civil Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781880653814

The poor quality of real ship data and the lack of navigation status marks make it different from the experimental data recorded with experimental conditions, which is difficult to be effectively used in the study of ship performance. In this paper, a visual analysis system of navigation logic is developed to assist in abnormal data processing. At the same time, the visual analysis is used to divide the real ship data sub units, and the small sample navigation state determination set is obtained. The results of K-means clustering are compared with the determined set, and the navigation state class is obtained by semantic classification. Factor analysis is used to analyze the characteristics of navigation state data, so as to reduce the dimension of data and make the classification result better. Taking the inland navigation mark maintenance work boat data as an example, this method is realized to label the ship navigation status for the unknown large sample data set. © 2022 by the International Society of Offshore and Polar Engineers (ISOPE).

关键词： K-means clustering

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3rd International conference of International Society for Intelligent Construction, ISIC 2022

3rd International Conference of International Society for In...

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3rd International conference of International Society for Intelligent Construction, ISIC 2022

ISBN: (纸本)9783031202407

The proceedings contain 38 papers. The special focus in this conference is on International Society for Intelligent Construction. The topics include: Additive Manufactured (3D-Printed) Connections for Thermoplastic Facades;auto(mated)nomous Assembly;BIM-to-FEM: Development of a Software Tool to Increase the Operational Efficiency of Dam Construction Projects;converting Algorithms into Tangible Solutions: A Workflow for Materializing Algorithmic Facade Designs;Development of a BIM Model for Facility Management with Virtual/Augmented Reality Interaction;improv-Structure: Exploring Improvisation in Collective Human-Robot Construction;An exploration of Graph-Based FEM Optimization for Construction Industry;assessing Hazardous Spills Impact on Road Surface Performances by 3D High Resolution Surveying Techniques;integrating Smart and Sustainable Construction: A Review of Present Status, and Possible Opportunities;digital Construction Strategy for Project Management Optimization in a Building Renovation Site: Machine Learning and Big data analysis;integration of Spot Tests and Vibratory Roller data in Rural Road Pavement Layers Made with Recycled Aggregates;multiphysical Coupling analysis of the Transition Zone Behaviour in Corroded Reinforced Concrete Structures;the Influence of the visual Factor on the Efficiency of visualization Method in the Production Environment;waste Elimination and Value Management Framework Based on Lean Design Methods;Aggregating High-Precision GNSS Intelligent Construction data for Quality Asphalt Pavements;construction Sector Transformation: Developing a New Learning Paradigm;digitalization of the Asphalt Paving Workflow and Process – Its Challenges and Opportunities;discrete Aggregate Mass Calculation Method for visual Detection of Aggregate Gradation and Elongated and Flat Aggregate Contents;a New Sustainable System for Piped Water Cooling of Mass Concrete Structures.

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Assisted Physical Interaction: Autonomous Aerial Robots with Neural Network Detection, Navigation, and Safety Layers

Assisted Physical Interaction: Autonomous Aerial Robots with...

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International conference on Unmanned Aircraft Systems (ICUAS)

作者： Berra, Andrea Sankaranarayanan, Viswa Narayanan Seisa, Achilleas Santi Mellet, Julien Gamage, Udayanga G. W. K. N. Satpute, Sumeet Gajanan Ruggiero, Fabio Lippiello, Vincenzo Tolu, Silvia Fumagalli, Matteo Nikolakopoulos, George Soto, Miguel Angel Trujillo Heredia, Guillermo CATEC Adv Ctr Aerosp Technol Seville Spain Lulea Univ Technol Dept Comp Elect & Space Engn Robot & AI Team Lulea Sweden Univ Naples Federico II Naples Dept Elect Engn & Informat Technol PRISMA Lab Naples Italy Tech Univ Denmark Dept Elect Engn & Photon Automat & Control Grp Lyngby Denmark Univ Seville Robot Vis & Control Grp Sch Engn Seville Spain

ISBN: (纸本)9798350357899;9798350357882

The paper introduces a novel framework for safe and autonomous aerial physical interaction in industrial settings. It comprises two main components: a neural network-based target detection system enhanced with edge computing for reduced onboard computational load, and a control barrier function (CBF)-based controller for safe and precise maneuvering. The target detection system is trained on a dataset under challenging visual conditions and evaluated for accuracy across various unseen data with changing lighting conditions. Depth features are utilized for target pose estimation, with the entire detection framework offloaded into low-latency edge computing. The CBF-based controller enables the UAV to converge safely to the target for precise contact. Simulated evaluations of both the controller and target detection are presented, alongside an analysis of real-world detection performance.

关键词： Aerial Physical Interaction UAVs Control Barrier Function Neural Network Edge Computing

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An Interactive visual System for data Analytics of Social Media 21st

An Interactive Visual System for Data Analytics of Social Me...

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21st International conference on Algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Zhang, Yi Li, Zhaohui Xi, Dewei Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China Tianjin Univ Sch New Media & Commun Tianjin Peoples R China

ISBN: (纸本)9783030953911;9783030953904

With the development of the Internet, Big data analysis of social media has become a hot research topic in recent years. However, a challenging problem in social media analysis is how to intelligently detect event information from massive media data and how to help users quickly understand event content. Therefore, we propose a method to extract important time periods of events from media data to analyze the media event information, and develop an interactive visual system based on the "5W" principle. The system provides an interactive analysis platform for exploring media Big data (e.g., Twitter data). The system uses a dynamic topic model to extract topics, a Naive Bayesian classifier to distinguish emotions, and explores events based on the evolution of time, topics, and emotions. In terms of visualization, the system allows users to add some annotations based on segmentation of complex information and highlight important events by adding different story cards to the timeline so that users can get a quick overview of events. Users can also interactively modify the story cards during the exploration process. Finally, several case studies show that our system can effectively reduce the time required to understand social media data and allow users to quickly explore the full picture of an event through an interactive approach.

关键词： Social media analysis Big data analysis Important time period extraction Interactive visualization Social media event

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A visual Tool for Interactively Privacy analysis and Preservation on Order-Dynamic Tabular data 18th

A Visual Tool for Interactively Privacy Analysis and Preserv...

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18th European-Alliance-for-Innovation (EAI) International conference on Collaborative Computing - Networking, Applications and Worksharing (CollaborateCom)

作者： Liang, Fengzhou Liu, Fang Zhou, Tongqing Sun Yat Sen Univ Guangzhou 510000 Peoples R China Hunan Univ Changsha 410000 Peoples R China Natl Univ Def Technol Changsha 410000 Peoples R China

ISBN: (纸本)9783031243851;9783031243868

The practice of releasing individual data, usually in tabular form, is obligated to prevent privacy leakage. With rendered privacy risks, visualization techniques have greatly prompted the user-friendly data sanitization process. Yet, we point out, for the first time, the attribute order (i.e., schema) of tabular data inherently determines the risk situation and the output utility, while is ignored in previous efforts. To mitigate this gap, this work proposes the design and pipeline of a visual tool (TPA, Tabular Privacy Assistant) for nuanced privacy analysis and preservation on order-dynamic tabular data. By adapting data cube structure as the flexible backbone, TPA manages to support real-time risk analysis in response to attribute order adjustment. Novel visual designs, i.e., data abstract, risk tree, integrated privacy enhancement, are developed to explore data correlations and acquire privacy awareness. We demonstrate TPA's effectiveness with a case study on the prototype and qualitatively discuss the pros and cons with domain experts for future improvement.

关键词： Interactive system visualization Privacy analysis

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