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

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A platform to support the visual analysis of the SALMANTICOR study outcomes: conveying cardiological data to lay users 21

A platform to support the visual analysis of the SALMANTICOR...

引用

9th International conference on Technological Ecosystems for Enhancing Multiculturality (TEEM)

作者： Vazquez-Ingelmo, Andrea Garcia-Penalvo, Francisco Jose Sanchez-Puente, Antonio Vicente-Palacios, Victor Laffond, Ana Garcia-Galindo, Alberto Sampedro-Gomez, Jesus Ignacio Dorado-Diaz, P. Sanchez, Pedro L. Univ Salamanca Res Inst Educ Sci Dept Comp Sci GRIAL Res Grp Salamanca Spain Hosp Univ Salamanca SACyL Cardiol Dept Salamanca Spain Univ Salamanca Fac Med IBSAL Salamanca Spain CIBERCV ISCiii Madrid Spain Philips Healthcare Salamanca Spain Hosp Univ Salamanca Cardiol Dept Salamanca Spain IBSAL Salamanca Spain

ISBN: (纸本)9781450390668

Cardiovascular diseases are the largest risk factor for mortality in developed countries, and hospitals are facing an increasing workload in relation to cardiovascular diseases. In this regard, policymaking related to these diseases and based on epidemiological knowledge of the population can be a powerful means to prevent and treat them. The SALMANTICOR study was conceived in this context. This study had the purpose of collecting data concerning the prevalence and incidence of structural heart disease in the province of Salamanca (Spain). However, the amount of data and variables collected (more than 300), can make the understanding of the results cumbersome for non-experience users. This work overviews the design and architecture of a data visualization platform to support the exploration of the SALMANTICOR study results, with a special focus on conveying this information to lay users.

关键词： data visualization Health domain Cardiology Human-Computer Interaction

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Toonnote: Improving communication in computational notebooks using interactive data comics 21

Toonnote: Improving communication in computational notebooks...

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2021 CHI conference on Human Factors in Computing Systems: Making Waves, Combining Strengths, CHI 2021

作者： Kang, Daye Ho, Tony Marquardt, Nicolai Industrial Design Kaist Daejeon Korea Republic of

ISBN: (纸本)9781450380966

Computational notebooks help data analysts analyze and visual- ize datasets, and share analysis procedures and outputs. However, notebooks typically combine code (e.g., Python scripts), notes, and outputs (e.g., tables, graphs). The combination of disparate materi- als is known to hinder the comprehension of notebooks, making it difcult for analysts to collaborate with other analysts unfamiliar with the dataset. To mitigate this problem, we introduce ToonNote, a JupyterLab extension that enables the conversion of notebooks into data comics. ToonNote provides a simplifed view of a Jupyter notebook, highlighting the most important results while supporting interactive and free exploration of the dataset. This paper presents the results of a formative study that motivated the system, its im- plementation, and an evaluation with 12 users, demonstrating the efectiveness of the produced comics. We discuss how our fndings inform the future design of interfaces for computational notebooks and features to support diverse collaborators. © 2021 ACM.

关键词： Python

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Towards Accurate Text-based Image Captioning with Content Diversity exploration

Towards Accurate Text-based Image Captioning with Content Di...

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IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Xu, Guanghui Niu, Shuaicheng Tan, Mingkui Luo, Yucheng Du, Qing Wu, Qi South China Univ Technol Guangzhou Peoples R China Pazhou Lab Guangzhou Peoples R China Univ Adelaide Adelaide SA Australia Minist Educ Key Lab Big Data & Intelligent Robot Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Text-based image captioning (TextCap) which aims to read and reason images with texts is crucial for a machine to understand a detailed and complex scene environment, considering that texts are omnipresent in daily life. This task, however, is very challenging because an image often contains complex texts and visual information that is hard to be described comprehensively. Existing methods attempt to extend the traditional image captioning methods to solve this task, which focus on describing the overall scene of images by one global caption. This is infeasible because the complex text and visual information cannot be described well within one caption. To resolve this difficulty, we seek to generate multiple captions that accurately describe different parts of an image in detail. To achieve this purpose, there are three key challenges: 1) it is hard to decide which parts of the texts of images to copy or paraphrase;2) it is non-trivial to capture the complex relationship between diverse texts in an image;3) how to generate multiple captions with diverse content is still an open problem. To conquer these, we propose a novel Anchor-Captioner method. Specifically, we first find the important tokens which are supposed to be paid more attention to and consider them as anchors. Then, for each chosen anchor, we group its relevant texts to construct the corresponding anchor-centred graph (ACG). Last, based on different ACGs, we conduct the multi-view caption generation to improve the content diversity of generated captions. Experimental results show that our method not only achieves SOTA performance but also generates diverse captions to describe images.

关键词： visualization Computer vision Image resolution Benchmark testing Pattern recognition Proposals Optical character recognition software

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visualization of similarity queries in an immersive virtual reality environment 24

Visualization of similarity queries in an immersive virtual ...

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24th International conference Information visualisation (iv)

作者： Malaquias Junior, Roseval Donisete Paiva, Claudio Eduardo Bueno, Renato Univ Fed Sao Carlos Sao Carlos SP Brazil Sao Paulo State Technol Coll Franca SP Brazil

ISBN: (纸本)9781728191348

The virtual reality provides new possibilities for data interaction in visualization systems, assisting users in the analysis of data through improved intuition and perception. In content-based image retrieval systems the visualization of queries results allows adding the human visual perception in the analysis, improving the knowledge discovery process. visualization allows users to better understand the distribution of data related to the queries and to incorporate their prior knowledge in the process of data retrieval and analysis. This work proposes the interactive visualization of similarity queries on complex data in a low cost immersive virtual reality environment. Thus, we proposed rendering options and techniques for interaction to avail advantages of the immersive environment and enable the cheap and practical visualization of similarity queries. We present visualization examples, through the virtual reality prototype produced, to demonstrate the advantages of its use in data analysis.

关键词： Similarity Query Complex data visualization Virtual reality Multidimensional mapping

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RoseTrajVis: visual Analytics of Trajectories with Rose Diagrams 24

RoseTrajVis: Visual Analytics of Trajectories with Rose Diag...

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24th International conference Information visualisation (iv)

作者： Paula Afonso, Ana Ferreira, Antonio Ferreira, Luis Vaz, Ricardo Univ Lisbon LASIGE Fac Ciencias Lisbon Portugal

ISBN: (纸本)9781728191348

In this paper we propose RoseTrajVis, a visual analytics system for studying trajectories using rose diagrams, inspired in a classic visualization technique for aggregating data. The rose diagram is composed of sectors (petals) that aggregate trajectories given their direction and show their speeds within an area around a point in a map. It also includes colored arrows on the border that point to the origin and destination of the trajectories and show their average speed. RoseTrajVis enables users to place rose diagrams over a map view and interact with them by applying time, speed and distance to origin/destination filters, and by adjusting the aggregation radius, allowing the visualization of commuting patterns in a city. We prepared a prototype of RoseTrajVis with taxi data, and evaluated the completion times and errors of users carrying out trajectory analysis tasks, and also collected feedback from a domain expert. Results revealed that completion times varied with task complexity, being longer for tasks requiring exploratory analysis of the trajectories on a map, and that almost no errors were made by users. The expert mentioned that RoseTrajVis provides an innovative way of analysing aggregate trajectories and has the potential to be useful in several professional areas.

关键词： visual analytics trajectories aggregation rose diagrams taxi trajectories

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A comparative analysis of different terrain correction methods based on Landsat-8 OLI data 2

A comparative analysis of different terrain correction metho...

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2021 2nd International conference on Geology, Resources exploration and Development, ICGRED 2021

作者： Zhang, Ying Ai, Yunfei Su, Fei Su, Hang He, Zhixian Wang, Changhai Ying, Jun Zhou, Minlu China Traffic Telecommunication and Information Center Beijing100011 China College of Forestry Beijing Forestry University Beijing100083 China GuangXi Communications Design Group CO. LTD. 530029 China

The topographic effect has long been recognized as one of problems for quantitative remote sensing. The paper compares the correction results of OLI data based on C model, Cosine model, SCS model, Minnaert model and pixel-based Minnaert Correction Method. The visual inspection, statistical analysis and regression analysis of images were used to analyze the results before and after correction of image. The results show that the C model has a better correction effect. The Cosine model and SCS model were significantly overcorrected. However, C model, Minnaert model and slope-based Minnaert model weakened the influence of terrain. By comparative analysis, the C correction method was optimal model for OLI image. © Published under licence by IOP Publishing Ltd.

关键词： Regression analysis

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Breast Cancer Classification: In-depth exploration of Different Paradigms and Ensemble Technique

Breast Cancer Classification: In-depth Exploration of Differ...

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International conference on Computing for Sustainable Global Development (INDIACom)

作者： Abdulahi Mahammed Adem Ravi Kant Sonia Sudesh Kumari C. Naga Swaroopa Gaurav Gupta Yogananda School of AI Computer and Data Sciences Shoolini University Solan India ECE Department Maharishi Markandeshwar (Deemed to be University) Mullana Ambala BPSMV Khanpur Kalan Sonipat India Annamacharya Institute of Technology and Sciences Rajampet Andhra Pradesh India

Many lives are lost to breast cancer every year. Prognosis and timely identification of cancer types are vital components of cancer research, as early diagnosis is critical to effective treatment. This study presents a comprehensive investigation into breast cancer classification, employing an extensive suite of datasets from Wisconsin Breast Cancer for machine learning models. Rigorous preprocessing techniques were applied to refine the dataset, segmented into groups of testing and training. The ensemble of models, comprising Logistic Regression, Decision Tree, Random Forest, XGBoost, Support Vector Machine (SVM), Gaussian Naive Bayes, K-Nearest Neighbors (KNN), AdaBoost, together with Deep Neural Network (DNN), underwent meticulous evaluation. For a more in-depth analysis, performance metrics like ROC-AUC scores, F1 Score, accuracy, precision, and recall were used. The ROC curve, or receiver operating characteristic provided visual insights, highlighting the trade-offs involving specificity and sensitivity. The ensemble model emerged as a promising approach, showcasing collaborative strengths across diverse algorithms, and achieves an accuracy of 98.2%. Confusion matrices and running time comparisons further elucidated model intricacies and computational efficiencies. This study will contribute valuable insights into breast cancer diagnostics, emphasizing the importance of tailored model selection and collaboration in clinical decision-making.

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visual analysis of Intertextuality Based on Knowledge Graph 2021

Visual Analysis of Intertextuality Based on Knowledge Graph

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2021 3rd International conference on Artificial Intelligence and Advanced Manufacture

作者： Yi Gao Chenming Duan Qiuyang Cao Dalian Polytechnic University China Dalian University of Technology China

ISBN: (纸本)9781450385046

The rapid growth of the scientific literature is presenting researchers with increasing challenges. That means relying on human resources to complete the mining of massive resources or the exploration of cutting-edge trends in a short period of time is impossible. To address this problem, this paper presents a method for visual literature research based on knowledge graph. The method is to use the software CiteSpace to make a comprehensive visual analysis of the origin, development, themes and frontier trends of intertextuality theory, and use artificial intelligence to process data, information and images, so as to complete the construction of scientific knowledge graph of intertextuality theory, enrich literature resources and improve the scientific research environment. The knowledge graph is a scientific measurement method which can visually present literature research results and ultimately realize the grand goal of artificial intelligence serving the development of scientific research.

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Sparsity exploration for Structured and Unstructured Weight Formations in CNN Architecture

Sparsity Exploration for Structured and Unstructured Weight ...

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Asia-Pacific Signal and Information Processing Association Annual Summit and conference (APSIPA)

作者： Devi Noor Endrawati Infall Syafalni Nana Sutisna Trio Adiono University Center of Excellence on Microelectronics Bandung Institute of Technology School of Electrical Engineering and Informatics

The Convolutional Neural Network (CNN) is a frequently used algorithm used technique for processing pixel data and visual images. Nevertheless, the utilization of extensive network sizes poses significant obstacles to the underlying technology’s throughput and energy efficiency. Various methods have been devised to enhance the efficiency and computing speed of Convolutional Neural Network (CNN) acceleration, improving the accuracy of the CNN process. Compression techniques have been employed in the algorithmic aspect to minimize the number of parameters in each layer, enhancing the detection process’s efficiency. Pruning is a compression technique that involves the removal of neurons and synapses that do not play a substantial role in the detection process, hence minimizing their impact on the Neural Network layer. The compression method generates sparse data, wherein a significant amount of data has a value of zero. Various pruning techniques provide distinct types of sparse data, encompassing organized structured sparse and unstructured sparse *** consequent form of sparse data structure has many advantages and downsides. Based on an analysis of various extant sparse data structures, an innovative accelerator has been developed to perform convolution operations using various types of sparse data obtained via distinct pruning procedures. The design architecture outlined in this study involves the removal of all zero-valued data weights through a pruning technique following the training process. The accelerator will process all non-zero weights by performing the convolution process promptly, utilizing the "weight stream" data. The findings from the conducted design testing indicate that the utilization of trimmed sparse data leads to improved performance in terms of faster output during the convolution process. The researchers conducted experiments utilizing diverse types of sparse weighted data, encompassing both structured and unstructured forms. These experi

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visual Recognition and Recommendation System for Cultural Tourism Attractions Based on Deep Learning 1st

Visual Recognition and Recommendation System for Cultural To...

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1st International conference on Pattern analysis and Machine Intelligence, ICPAMI 2024

作者： Li, Si Yang, Xiaoqi Guangzhou College of Commerce Guangzhou China

ISBN: (纸本)9789819633487

This article designs and constructs a deep prediction model that combines deep neural networks and Transformer encoders. Build a prediction model using deep neural networks and Transformer encoders. Due to the powerful feature extraction ability of deep neural networks and their ability to fuse heterogeneous data from multiple sources, they are used to process basic information about users and scenic spots. The Transformer encoder is used to process user comment data, modeling long-distance dependencies between words in sentences through position encoding and self attention at the encoding end, thereby fully mining the comprehensive features of comment data and obtaining more potential user interest features. The features of the last three networks are input into the factorization machine to further learn their potential interaction relationships, thereby improving the prediction accuracy of the model and alleviating the problem of data sparsity. Finally, the effectiveness and accuracy of this recommendation model were verified through experimental comparison with traditional recommendation models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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