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Use of Exergames with Elderly Adults, a Qualitative Study 1

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13th International conference on Methodologies and Intelligent Systems for Technology Enhanced Learning, MIS4TEL 2023

作者： Gutiérrez-Pérez, Bárbara Mariana Martín-García, Antonio víctor García, Juan Luis Cabanillas Gómez-Sánchez, María Cruz University of Salamanca Paseo de Canalejas 169 Salamanca37008 Spain

ISBN: (数字)9783031412264

ISBN: (纸本)9783031412257

The aim of this paper is to delimit the main lines of research on the use of exergames by elderly adults through a literature review. The search for studies was carried out in the Web of Science and SCOPUS databases. A series of inclusion and exclusion criteria were established for the literature review and evaluation. The design used for the content analysis of the selected studies was based on a mixed concurrent triangulation design;the results were analysed from a quantitative and qualitative approach in independent and simultaneous phases. From the quantitative approach, descriptive and frequency analyses of the variables of analysis were performed. For the qualitative analyses, classical content analysis procedures were followed, data reduction was performed through inductive analysis by establishing the subcategories of analysis based on two levels, intracoder and intercoder until agreement was reached among the research team. Subsequently, the data were arranged and grouped through the visual resources offered by Nvivo. Finally, the results were obtained by applying the use of metaphors and analogies, as well as the inclusion of vignettes and narrative fragments. The findings obtained in this study have allowed us to identify different lines of studies on the implementation of exergame systems as resources aimed at physical and cognitive training that promote the improvement of well-being and quality of life in elderly adults. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Serious games

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Review of Systematic Financial Risk Research Based on Knowledge Map 8

Review of Systematic Financial Risk Research Based on Knowle...

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8th International conference on Information Technology and Quantitative Management (ITQM) - Developing Global Digital Economy after COvID-19

作者： Liu, Fangzheng Zhang, Yating Li, Li Yunnan Normal Univ 298 Twelfth Ave Kunming 650500 Yunnan Peoples R China

Systemic financial risk is the risk that causes great losses to financial institutions or losses to the whole fmancial system. Systemic risks are contagious and non-dispersible, with a wide spread and great destructive power to the economy. With CNKI, CSSCI and WOS databases as data sources, this paper makes a visual analysis of domestic systemic financial risk research from 2010 to 2020 by means of scientific measurement and knowledge map analysis, With the help of Citespace v, it explores its research hotspots, combs its evolution path and analyzes its research trends, on this basis, it constructs a systematic integration framework, aiming at promoting the further development of systemic financial risk research. The findings of this paper are as follows: Systematic fmancial risk research is in a vigorous development period, and more attention is paid to the literature research of "risk measurement" and "macro-prudence";Domestic research focuses on financial risks and financial supervision, among which Internet finance, supply-side reform and real economy are the frontier hot spots of systemic financial risks. (C) 2021 The Authors. Published by Elsevier B.v.

关键词： Systemic financial risk knowledge map co-citation analysis

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Complex Networks exploration With Triangles

Complex Networks Exploration With Triangles

引用

IEEE International conference on Big data

作者： Shahadat Hossain Raheem Mir Emam Hossain Department of Computer Science University of Northern British Columbia Prince George Canada Mathematics and Computer Science University of Lethbridge Lethbridge Alberta Canada

Triangles are an essential structure in complex network analysis representing metrics such as clustering coefficient and transitivity. In this work, we employ the recently introduced “Triangle Centrality” in exploring cohesive sub-networks of interest within the maximum $k -$core of complex networks. Motivated by the recent interest in higher-order connectivity patterns to understand fundamental structures that control behaviour of complex systems, we employ triangles as “higher-order” structural units in complex systems. Each triangle in the network is attributed with a numeric score, the so called $k -$count, to indicate its “influence” calculated based on the number of other triangles it is associated with. We demonstrate our proposed method to perform visual exploration of cohesive sub-networks of interest on a well-studied benchmark social network “Lusseau’s Dolphin Social Network”.

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Raw Instinct: Trust Your Classifiers and Skip the Conversion 6

Raw Instinct: Trust Your Classifiers and Skip the Conversion

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6th IEEE International conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Kantas, Christos Antoniussen, Bjork Andersen, Mathias v. Munkso, Rasmus Kotnala, Shobhit Jensen, Simon B. Mogelmose, Andreas Norgaard, Lau Moeslund, Thomas B. Aalborg University Visual Analysis and Perception Lab Aalborg Denmark Phase One A/S Copenhagen Denmark

ISBN: (纸本)9798350325485

Using RAW-images in computer vision problems is surprisingly underexplored considering that converting from RAW to RGB does not introduce any new capture information. In this paper, we show that a sufficiently advanced classifier can yield equivalent results on RAW input compared to RGB and present a new public dataset consisting of RAW images and the corresponding converted RGB images. Classifying images directly from RAW is attractive, as it allows for skipping the conversion to RGB, lowering computation time significantly. Two CNN classifiers are used to classify the images in both formats, confirming that classification performance can indeed be preserved. We furthermore show that the total computation time from RAW image data to classification results for RAW images can be up to 8.46 times faster than RGB. These results contribute to the evidence found in related works, that using RAW images as direct input to computer vision algorithms looks very promising. © 2023 IEEE.

关键词： Classification (of information)

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CONTEXTUAL: evaluating context-sensitive text-rich visual reasoning in large multimodal models 24

CONTEXTUAL: evaluating context-sensitive text-rich visual re...

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Proceedings of the 41st International conference on Machine Learning

作者： Rohan Wadhawan Hritik Bansal Kai-Wei Chang Nanyun Peng Department of Computer Science University of California Los Angeles

Many real-world tasks require an agent to reason jointly over text and visual objects, (e.g., navigating in public spaces), which we refer to as context-sensitive text-rich visual reasoning. Specifically, these tasks require an understanding of the context in which the text interacts with visual elements within an image. However, there is a lack of existing datasets to benchmark the state-of-the-art multimodal models' capability on context-sensitive text-rich visual reasoning. In this paper, we introduce CONTEXTUAL, a novel dataset featuring human-crafted instructions that require context-sensitive reasoning for text-rich images. We conduct experiments to assess the performance of 14 foundation models (GPT-4v, Gemini-Pro-vision, LLavA-Next) and establish a human performance baseline. Further, we perform human evaluations of the model responses and observe a significant performance gap of 30.8% between GPT-4v (the current best-performing Large Multimodal Model) and human performance. Our fine-grained analysis reveals that GPT-4v encounters difficulties interpreting time-related data and infographics. However, it demonstrates proficiency in comprehending abstract visual contexts such as memes and quotes. Finally, our qualitative analysis uncovers various factors contributing to poor performance including lack of precise visual perception and hallucinations. Our dataset, code, and leaderboard can be found on the project page https://***/.

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AMOSI:Attention-Based Multimodal Sentiment Intensity analysis with LSTM-CNN Architecture for Online Opinion videos

AMOSI:Attention-Based Multimodal Sentiment Intensity Analysi...

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International conference on Computing and Networking Technology (ICCNT)

作者： v v v Bhagya Sree G Bharathi Mohan R Prasanna Kumar M Rithani Gundala Pallavi Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Through online video sharing websites, people share their thoughts, experiences, and reviews on a daily basis. Researching the subjectivity and sentiment in these opinion videos is becoming more and more popular among academics and industry professionals. Although sentiment analysis works well for text, there isn’t much research done on it when it comes to videos and other multimedia. The largest obstacles to research in this area are the absence of appropriate methodology, baselines, datasets,and statistical analysis of the relationships between data from various modality sources. The Multimodal Opinionlevel Sentiment Intensity dataset (MOSI), the first opinion-level annotated corpus of sentiment and subjectivity analysis in online videos, is presented to the scientific community in this paper. With labels for subjectivity, sentiment intensity, per-frame and per-opinion annotated visual features, the dataset is meticulously annotated. User-generated opinion videos are increasingly prevalent, necessitating advanced sentiment analysis techniques. We present an attention-based LSTM-2D CNN model for multimodal sentiment intensity prediction. Our contributions are three-fold: 1) We collect new annotations of visual gestures and acoustic effects in online videos to augment the MOSI dataset. 2) We design an end-to-end neural architecture to model textual, visual and acoustic modalities. Bi-directional LSTMs capture temporal dependencies in text and audio. 2D CNNs extract spatial visual features. A hierarchical attention mechanism identifies important utterances and frames. 3) Extensive experiments demonstrate 74.7% accuracy, outperforming prior BC-LSTM models by 1.7%. Ablation studies validate the impact of multimodal fusion and attention. Our work provides an improved neural baseline on MOSI, with detailed analysis of multi-view sequential modelling for opinion videos. The additional annotations and strong results will aid future research in this emerging domain.

关键词： visualization Sentiment analysis Attention mechanisms Annotations Computer architecture Predictive models Streaming media Feature extraction Acoustics data models

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GraphFederator: Federated visual analysis for Multi-party Graphs

GraphFederator: Federated Visual Analysis for Multi-party Gr...

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Pacific (formerly Asia-Pacific APvIS) visualization Symposium

作者： Dongming Han Haiyang Zhu Wei Chen Rusheng Pan Yijing Liu Jiehui Zhou Haozhe Feng Tianye Zhang Xumeng Wang Minfeng Zhu Jianrong Tao Changjie Fan Xiaolong Luke Zhang State Key Lab of CAD&CG Zhejiang University China Hithink RoyalFlush Information Network Co. Ltd. China Wuchan Zhongda Digital Technology Co. Ltd. China Ministry of Education Laboratory of Art and Archaeology Image (Zhejiang University) China College of Computer Science Nankai University China Zhejiang University China Fuxi AI Lab Netease China Pennsylvania State University USA

ISBN: (数字)9798350393804

ISBN: (纸本)9798350393811

This paper presents GraphFederator, a novel approach to construct federated representations of multi-party graphs and supports privacy-preserving visual analysis of graphs. Inspired by the concept of federated learning, we reformulate the analysis of multi-party graphs into a decentralization process. The new federation framework consists of a shared module that is responsible for federated modeling and analysis, and a set of local modules that run on respective graph data. Specifically, we propose a Federated Graph Representation Model (FGRM) that is learned from encrypted characteristics of multi-party graphs in local modules. We also design multiple visualization tools for federated visualization, exploration, and analysis of multi-party graphs. Experimental results on two datasets demonstrate the effectiveness of our approach.

关键词： visualization Analytical models Federated learning data visualization data models Encryption

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RMExplorer: A visual Analytics Approach to Explore the Performance and the Fairness of Disease Risk Models on Population Subgroups

RMExplorer: A Visual Analytics Approach to Explore the Perfo...

引用

IEEE visualization conference (vIS)

作者： Kwon, Bum Chul Kartoun, Uri Khurshid, Shaan Yurochkin, Mikhail Maity, Subha Brockman, Deanna G. Khera, Amit v. Ellinor, Patrick T. Lubitz, Steven A. Ng, Kenney IBM Res Armonk NY 10504 USA Broad Inst Cambridge MA USA Univ Michigan Ann Arbor MI 48109 USA

ISBN: (数字)9781665488129

ISBN: (纸本)9781665488129

Disease risk models can identify high-risk patients and help clinicians provide more personalized care. However, risk models developed on one dataset may not generalize across diverse subpopulations of patients in different datasets and may have unexpected performance. It is challenging for clinical researchers to inspect risk models across different subgroups without any tools. Therefore, we developed an interactive visualization system called RMExplorer (Risk Model Explorer) to enable interactive risk model assessment. Specifically, the system allows users to define subgroups of patients by selecting clinical, demographic, or other characteristics, to explore the performance and fairness of risk models on the subgroups, and to understand the feature contributions to risk scores. To demonstrate the usefulness of the tool, we conduct a case study, where we use RMExplorer to explore three atrial fibrillation risk models by applying them to the UK Biobank dataset of 445,329 individuals. RMExplorer can help researchers to evaluate the performance and biases of risk models on subpopulations of interest in their data.

关键词： visual analytics health informatics fairness subgroup analysis explainability interpretability electronic health records

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Literature analysis on the research status of mass entrepreneurship and innovation training projects : Document metrology based on CNKI database 3

Literature analysis on the research status of mass entrepren...

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3rd International conference on Education, Knowledge and Information Management, ICEKIM 2022

作者： Zhou, Liang Zhang, Wen Jing Lanzhou University of Finance and Economics Information Engineering Institute Lanzhou China Lanzhou University of Finance and Economics Financial Institute Lanzhou China

ISBN: (纸本)9781665416665

Taking the literature of CNKI as the data source and using the literature metrological visual analysis function of CNKI, this paper makes a quantitative analysis on the relevant literature in the research field of "College Students' innovation and entrepreneurship training program"from 2011 to 2021 from the aspects of literature quantity and annual distribution, subject analysis, discipline distribution, source journals, and author distribution, Get the research status and development trend of innovation and entrepreneurship training projects. The results show that in recent ten years, Chinese scholars have paid more and more attention to the research of "College Students' innovation and entrepreneurship training program,"and the keywords with high frequency are "innovation and entrepreneurship,""innovation and entrepreneurship education,""practical teaching"and so on;Education and teaching forum, with 4705 papers published as relevant research papers, has the most significant number of papers, followed by education modernization and laboratory research and exploration, with 393 and 333 papers respectively. Huang Suoyi, Jian Guoming, Zhang Liucheng, and the other three authors wrote a lot, but the distribution of authors was scattered. Higher education, medical education, marginal medical disciplines, computer software, and computer applications ranked among the top three in the number of documents issued. © 2022 IEEE.

关键词： Medical education

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NEXAS: A visual Tool for Navigating and Exploring Argumentation Solution Spaces 9

NEXAS: A Visual Tool for Navigating and Exploring Argumentat...

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9th International conference on Computational Models of Argument (COMMA)

作者： Dachselt, Raimund Gaggl, Sarah Alice Kroetzsch, Markus Mendez, Julian Rusovac, Dominik Yang, Mei Tech Univ Dresden Interact Media Lab Dresden Dresden Germany Tech Univ Dresden Log Programming & Argumentat Grp Dresden Germany Tech Univ Dresden Knowledge Based Syst Grp Dresden Germany Tech Univ Dresden Ctr Tactile Internet CeTI Dresden Germany Tech Univ Dresden Cluster Excellence Phys Life PoL Dresden Germany

ISBN: (纸本)9781643683065;9781643683072

Recent developments on solvers for abstract argumentation frameworks (AFs) made them capable to compute extensions for many semantics efficiently. However, for many input instances these solution spaces can become very large and incomprehensible. So far, for the further exploration and investigation of the AF solution space the user needs to use post-processing methods or handcrafted tools. To compare and explore the solution spaces of two selected semantics, we propose an approach that visually supports the user, via a combination of dimensionality reduction of argumentation extensions and a projection of extensions to sets of accepted or rejected arguments. We introduce the novel web-based visualization tool NEXAS that allows for an interactive exploration of the solution space together with a statistical analysis of the acceptance of individual arguments for the selected semantics, as well as provides an interactive correlation matrix for the acceptance of arguments. We validate the tool with a walk-through along three use cases.

关键词： abstract argumentation visualization solution space exploration

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