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Unveiling Multivariate EEG Features: A Novel Approach to Enhancing ADHD Diagnosis Through visual and Auditory Attention Tests

Unveiling Multivariate EEG Features: A Novel Approach to Enh...

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2023 International conference on Fuzzy Theory and Its Applications, iFUZZY 2023

作者： Fan, Zuo-Cian Lin, Ro-Wei Tsai, Ching-Shu Chou, Wen-Jiun Li, Chia-Jung Huang, Zih-Jun Shih, Bin-Yu Wang, Liang-Jen Ko, Li-Wei Department of Biological Science & Technology Hsinchu300 Taiwan National Yang Ming Chiao Tung University International Ph.D. Program in Interdisciplinary Neuroscience Hsinchu300 Taiwan Kaohsiung Chang Gung Memorial Hospit Department of Child and Adolescent Psychiatry National Yang Ming Chiao Tung University Institute of Electrical and Control Engineering Department of Electronics and Electrical Engineering Hsinchu300 Taiwan

ISBN: (纸本)9798350305791

We sought to revolutionize the classification of Attention-Deficit/Hyperactivity Disorder (ADHD) by pioneering an innovative approach that seamlessly integrated auditory and visual tests with electroencephalogram (EEG) coherence analysis, forming the foundation for our Support Vector Machine (SVM) models. Our primary objective was to achieve precise differentiation between individuals with ADHD and those without, aiming to reshape the landscape of ADHD diagnosis and early intervention. We constructed an SVM model based on CATA coherence, incorporating a carefully selected set of 180 features, and another grounded in CPT coherence with 70 features. Astonishingly, our exploration uncovered that a model incorporating CATA's coherence and Power Spectral Density (PSD) as features achieved an astounding accuracy rate of 97.7%, using only half the number of features. This revelation hints at untapped potential for multivariate advancements in the realm of ADHD classification. The landscape of ADHD diagnosis and intervention is perpetually evolving, most notably with the FDA's approval of game therapy for ADHD. Leveraging the transformative power of our findings, we envision the development of user-friendly game therapies for in-home use, empowering both clinicians and parents alike. This marks a paradigm shift in the realm of ADHD intervention, offering objective, data-driven tools for the early management of ADHD. Gamified therapy not only enhances accessibility but also fosters engagement and compliance, all of which are pivotal in the long-term care of individuals with ADHD. In summary, our study represents a significant advancement in our comprehension and classification of ADHD. By fusing auditory and visual tests with EEG coherence analysis, fortified by state-of-the-art machine learning techniques, we have achieved an unprecedented level of accuracy in identifying individuals with ADHD. Our findings, aligned with the FDA-approved game therapy, offer a glimpse into a

关键词： Electroencephalography

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Research on Early Warning of Drug Crime Behaviors Based on data-driven Method 24

Research on Early Warning of Drug Crime Behaviors Based on D...

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4th International conference on Big data, Artificial Intelligence and Risk Management, ICBAR 2024

作者： Wu, Shaobing Wang, Changmei Institute of Information Security Key Laboratory of Intelligent Drug Control Yunnan Police College Yunnan Kunming China Education Ministry Key Laboratory of Advanced Technology and Preparation for Renewable Energy Materials Yunnan Normal University Yunnan Kunming China

ISBN: (纸本)9798400709753

The identification of target suspects in images and videos from multi-source heterogeneous data is a difficult task in criminal investigation. In criminal scene analysis during law enforcement, images and videos from surveillance applications and security systems play an important role in providing visual files for the scene. With the rise of big data, artificial intelligence, the Internet of Things, blockchain, and cyberspace security, it is particularly important to improve the application of computer technology in drug crime cases and achieve data-driven drug crime behavior prediction. This paper builds police subject-specific databases such as drug addicts, drug traffickers, key personnel, and fugitives, and achieves drug crime behavior prediction and warning based on identity information, facial recognition and storage and picture comparison using a combination of Python and OpenCV. It provides a useful exploration for combating Drug Crime. © 2024 Copyright held by the owner/author(s).

关键词： data-driven drug crime behavior prediction identity recognition

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Transforming Asset Maintenance and Optimisation Through Autonomous Operations

Transforming Asset Maintenance and Optimisation Through Auto...

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Offshore Technology conference, OTC 2022

作者： Pierson, Nicholas Clarke, Richard Mikhailov, Oleg Wood plc Exploration Robotics Technologies Inc

ISBN: (纸本)9781613998526

Autonomous Operations is about utilising the latest available fit-for-purpose technology in operations, maintenance, inspection, and asset optimisation. It involves collecting operating data such as visual, thermal, low-and high-frequency acoustic, FLIR (Forward Looking InfraRed), and gas-sensing through devices mounted on autonomous robots or drones, possibly mixed with stand-alone fixed devices. This "mobile sensor" approach is ideal for existing infrastructure where adding enhanced sensing capabilities with an IoT solution is cost prohibitive. It is flexible: if a site is modified, the robots can be given a new route to monitor. It is also future proof as sensors can be easily upgraded as technology advances. Autonomous robot inspection is enabled by a software platform designed for full processing "at Edge" as a typical robotics inspection generates tens of gigabytes of data per hour. data pre-processing, construction of detailed 3D temperature, vibration amplitude and gas concentration maps, identification of anomalies, their classification by criticality and communication to operators is done by computers at the facility. Time lapse analysis is used to process the data either in-situ, or remotely, depending on site connectivity. Reports are automatically generated indicating whether the site is functioning within normal working conditions, or an anomaly has been detected. The electronic reports contain the 3D model and are viewable on mobile devices providing the field engineer with detailed diagnostic data literally at their fingertips. The process builds and maintains the living digital twin without having to expend engineering hours to update 3D models. This process not only removes the need for people, which reduces cost and safety risk, but also improves reliability by increasing the available data set and reducing inspection intervals. By utilising data analytics and artificial intelligence, the anomalies detected can be risk ranked, prioritised and sche

关键词： Robots

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Knowledge-based Graph exploration analysis on Customer's Preference of Insurance Services 10

Knowledge-based Graph Exploration Analysis on Customer's Pre...

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10th International conference on Digital Arts, Media and Technology and 8th ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI DAMT and NCON 2025

作者： Chen, Yingfan Kongjit, Chalermpon Media and Technology Chiang Mai University College of Arts Chiang Mai Thailand

ISBN: (纸本)9798331543273

With the increasing number of Chinese students in Thailand, the demand for insurance services from these students has gradually shown diversity and personalized characteristics. However, the current research on the insurance demand of overseas students is relatively insufficient. Based on text analysis and knowledge graph technology, this study focuses on the needs and preferences of Chinese students in Thailand in terms of insurance choices, and presents the research results in a visual form. The researchers collected chat history content of some target groups, as well as public discussion data on social media platforms and forums frequently used by Chinese students in Thailand. The researchers used the text analysis method to extract high-frequency keywords related to international student insurance, and carried out sentiment analysis to obtain the main concerns of current international students in insurance selection. Then, a knowledge graph centered on insurance demand is constructed to visualize key nodes and relationships such as insurance categories, budget preferences, and demand characteristics. © 2025 IEEE.

关键词： Knowledge graph

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AutoGaze: A Very Initial exploration in A SAM2-based Pipeline for Automated Eye-Object Interaction analysis in First-Person Videos

AutoGaze: A Very Initial Exploration in A SAM2-based Pipelin...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE conference on

作者： Qing Zhang Yifei Huang Jun Rekimoto The University of Tokyo Sony CSL Kyoto

ISBN: (数字)9798331514846

ISBN: (纸本)9798331525637

This paper presents a novel automated workflow for analyzing eye-tracking data in first-person videos. Our system uses the Segment Anything Model 2 (SAM2) to segment and track objects, correlating them with gaze information to provide a detailed understanding of visual attention and hand-object interactions. The pipeline outputs a structured JSON file containing rich information about gaze behavior, object interactions, and their temporal relationships. We demonstrate the system’s effectiveness in a life science operational context and compare its performance with a multimodal Large Language Model (LLM) for automated annotation. Our findings highlight the potential of combining advanced computer vision techniques with LLMs for comprehensive and scalable analysis of gaze behavior in real-world scenarios.

关键词： visualization Solid modeling Three-dimensional displays Pipelines Gaze tracking Virtual reality User interfaces Life sciences Trajectory Videos

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visual Analyze the Impact of Structural Features and Node Attributes on Collaborative Relationships 19th

Visual Analyze the Impact of Structural Features and Node...

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19th CCF conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2024

作者： Lu, Qiang Ma, Cheng Wu, Hongfeng Zhang, Gaofeng Yu, Ye School of Computer Science and Information Hefei University of Technology Hefei China School of Software Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China

ISBN: (纸本)9789819623754

Collaborative relationships are becoming more complex, diversified and multi-organization cross, which poses new challenges to collaborative relationship analysis. In order to analyze the effects of different structural features and node attributes on the overall cooperative relationships, and to support the exploration at the global, group and individual levels to promote the comprehensive analysis of cooperative relationships, we propose a visual analysis method based on graph contrast learning(GCL). The first step is to perturb the structural features and node attributes of the collaboration relationships through data augmentation. The second step is to use the graph convolutional network(GCN) to represent the structural features and node attributes as dense vectors in the high-dimensional vector space. The third step is to explore the influence of different structural features and node attributes through classification task outcome indicators. Based on this method, GCL4Vis, an interactive visual analysis system is designed, which is equipped with a variety of visual views to help users analyze the effects of different structural features and node attributes. Finally, this paper verified the usability and effectiveness of the GCL4Vis visual analysis system through specific case studies and structured interviews with users. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Stunning Doodle: A Tool for Joint visualization and analysis of Knowledge Graphs and Graph Embeddings 19th

Stunning Doodle: A Tool for Joint Visualization and Analysis...

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19th International European Semantic Web conference (ESWC)

作者： Ettorre, Antonia Bobasheva, Anna Michel, Franck Faron, Catherine Univ Cote Azur CNRS INRIA I3S Sophia Antipolis France

ISBN: (纸本)9783031069819;9783031069802

In recent years, the growing application of Knowledge Graphs to new and diverse domains has created the need to make these resources accessible and understandable by users with increasingly diverse backgrounds. visualization techniques have been widely employed as means to facilitate the exploration and comprehension of such data sources. Moreover, the emerging use of Knowledge Graph Embeddings as input features of Machine Learning methods has given even more visibility to this kind of representation, but raising the new issue of understandability and interpretability of such embeddings. In this paper, we show how visualization techniques can be used to jointly explore and interpret both Knowledge Graphs and Graph Embeddings. We present Stunning Doodle, a tool that enriches the classical visualization of Knowledge Graphs with additional information meant to enable the visual analysis and comprehension of Graph Embeddings. The idea is to help the user figure out the logical connection between (1) the information captured by the Graph Embeddings and (2) the structure and semantics of the Knowledge Graph from which they are generated. We detail the use of Stunning Doodle in a real-world scenario and we show how it has been helpful to interpret different Graph Embeddings and to choose the most suitable with respect to a specific final goal.

关键词： Knowledge Graphs visualization Graph Embeddings

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data Virtual Machines: Simplifying data Sharing, exploration & Querying in Big data Environments

Data Virtual Machines: Simplifying Data Sharing, Exploration...

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2022 IEEE International conference on Big data, Big data 2022

作者： Chatziantoniou, Damianos Kantere, Verena Antoniou, Nikos Gantzia, Angeliki Aueb Greece Ntua Greece

ISBN: (纸本)9781665480451

Today's analytics environments are characterized by a high degree of heterogeneity in terms of data systems, formats and types of analysis. Many occasions call for rapid, ad hoc, on demand construction of a data model that represents (parts of) the data infrastructure of an organization, including ML tasks. This data model is given to data scientists to play with (express reports, build ML models, explore, etc.) We present a novel graph-based conceptual model, the data Virtual Machine (DVM) representing data (persistent, transient, derived) of an organization. A DVM can be built quickly and agilely, offering schema flexibility. It is amenable to visual interfaces for schema and query management. dataframing, a frequent querying/preprocessing task in analytics applications, is usually carried out by experienced data engineers employing SQL (in the presence of a relational data warehouse) or Python/R: a procedural approach with all the known drawbacks. dataframes over DVMs are expressed declaratively - and visually, via a simple and intuitive tool. This way, non-IT experts can be involved in dataframing. In addition, query evaluation takes place within an algebraic framework with all the known benefits. I.e. a DVM enables the delegation of data engineering tasks to simpler users. We have seen analogous cases in the past, e.g. with the introduction of SQL. Finally, a DVM offers a formalism that facilitates data sharing, data portability and a single view of any entity - because a DVM's node is an attribute and an entity at the same time. In this respect, DVMs can excellently serve as a data virtualization technique, an emerging trend in the industry. We argue that DVMs can have a significant practical impact in today's big data environments. © 2022 IEEE.

关键词： Big data

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Semantic Explanation of Interactive Dimensionality Reduction

Semantic Explanation of Interactive Dimensionality Reduction

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IEEE visualization conference (IEEE VIS)

作者： Bian, Yali North, Chris Krokos, Eric Joseph, Sarah Virginia Tech Blacksburg VA 24061 USA Dept Def Ft Lee VA USA

ISBN: (纸本)9781665433358

Interactive dimensionality reduction helps analysts explore the high-dimensional data based on their personal needs and domain-specific problems. Recently, expressive nonlinear models are employed to support these tasks. However, the interpretation of these human-steered nonlinear models during human-in-the-loop analysis has not been explored. To address this problem, we present a new visual explanation design called semantic explanation. Semantic explanation visualizes model behaviors in a manner that is similar to users' direct projection manipulations. This design conforms to the spatial analytic process and enables analysts better understand the updated model in response to their interactions. We propose a pipeline to empower interactive dimensionality reduction with semantic explanation using counterfactuals. Based on the pipeline, we implement a visual text analytics system with nonlinear dimensionality reduction powered by deep learning via the BERT model. We demonstrate the efficacy of semantic explanation with two case studies of academic article exploration and intelligence analysis.

关键词： Interactive Dimensionality Reduction Projection Explanation Counterfactual Explanation Human-in-the-loop analysis

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Semantic Tracklets: An Object-Centric Representation for visual Multi-Agent Reinforcement Learning

Semantic Tracklets: An Object-Centric Representation for Vis...

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IEEE/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Liu, Iou-Jen Ren, Zhongzheng Yeh, Raymond A. Schwing, Alexander G. Univ Illinois Champaign IL 61820 USA

ISBN: (纸本)9781665417143

Solving complex real-world tasks, e.g., autonomous fleet control, often involves a coordinated team of multiple agents which learn strategies from visual inputs via reinforcement learning. Many existing multi-agent reinforcement learning (MARL) algorithms however don't scale to environments where agents operate on visual inputs. To address this issue, algorithmically, recent works have focused on non-stationarity and exploration. In contrast, we study whether scalability can also be achieved via a disentangled representation. For this, we explicitly construct an object-centric intermediate representation to characterize the states of an environment, which we refer to as 'semantic tracklets.' We evaluate 'semantic tracklets' on the visual multi-agent particle environment (VMPE) and on the challenging visual multi-agent GFootball environment. 'Semantic tracklets' consistently outperform baselines on VMPE, and achieve a +2.4 higher score difference than baselines on GFootball. Notably, this method is the first to successfully learn a strategy for five players in the GFootball environment using only visual data. For more, please see our project page: https://***/SemanticTracklets

关键词： visualization Scalability Semantics Reinforcement learning Task analysis Intelligent robots

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