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

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Mask-Guided Region Attention Network for Person Re-Identification 24th

Mask-Guided Region Attention Network for Person Re-Identific...

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24th Pacific-Asia conference on Knowledge Discovery and data Mining (PAKDD)

作者： Zhou, Cong Yu, Han Nanjing Univ Posts & Telecommun Sch Comp Sci & Technol Nanjing 210023 Peoples R China Jiangsu Key Lab Big Data Secur & Intelligent Proc Nanjing 210023 Peoples R China

ISBN: (纸本)9783030474362;9783030474355

Person re-identification (ReID) is an important and practical task which identifies pedestrians across non-overlapping surveillance cameras based on their visual features. In general, ReID is an extremely challenging task due to complex background clutters, large pose variations and severe occlusions. To improve its performance, a robust and discriminative feature extraction methodology is particularly crucial. Recently, the feature alignment technique driven by human pose estimation, that is, matching two person images with their corresponding parts, increases the effectiveness of ReID to a certain extent. However, we argue that there are still a few problems among these methods such as imprecise handcrafted segmentation of body parts, and some improvements can be further achieved. In this paper, we present a novel framework called Mask-Guided Region Attention Network (MGRAN) for person ReID. MGRAN consists of two major components: Mask-guided Region Attention (MRA) and Multi-feature Alignment (MA). MRA aims to generate spatial attention masks and meanwhile mask out the background clutters and occlusions. Moreover, the generated masks are utilized for region-level feature alignment in the MA module. We then evaluate the proposed method on three public datasets, including Market-1501, DukeMTMC-reID and CUHK03. Extensive experiments with ablation analysis show the effectiveness of this method.

关键词： Person re-identification Human pose estimation Mask

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visual analytics for exploring air quality data in an AI-enhanced IoT environment 19

Visual analytics for exploring air quality data in an AI-enh...

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Proceedings of the 11th International conference on Management of Digital EcoSystems

作者： Ilias Kalamaras Ioannis Xygonakis Konstantinos Glykos Sigmund Akselsen Arne Munch-Ellingsen Hai Thanh Nguyen Andreas Jacobsen Lepperod Kerstin Bach Konstantinos Votis Dimitrios Tzovaras Information Technologies Institute - Centre for Research and Technology Hellas Telenor Group Telenor Group & Norwegian University of Science and Technology Norwegian University of Science and Technology

ISBN: (纸本)9781450362382

visual analytics have an important role in the exploration and analysis of large amounts of data in IoT applications. data visualizations can provide overviews of different aspects of data and user interaction can assist exploration. Recent advances in machine learning and Artificial Intelligence have provided methods that can be used in conjunction with visual analytics to enhance user perception. However, AI methods are often used as "black boxes", making them difficult for end-users to trust. In this paper, a novel visual analytics platform is presented, targeting two goals: a) an architecture for the creation of custom interactive visual analytics dashboards using well-defined components linked to each other, and b) the inclusion of components specifically for making AI methods more explainable. The proposed architecture and components are being used in the context of the AI4IoT pilot within the AI4EU project, which targets air quality monitoring through AI and visualization.

关键词： visual analytics reactive workflows IoT explainable AI

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Optimizing Exploratory Workflows for Embedded Platform Trace analysis and Its Application to Mobile Devices 21st

Optimizing Exploratory Workflows for Embedded Platform Trace...

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21st International conference on Human-Computer Interaction (HCii)

作者： Sue, Jonathan Ah Brand, Peter Falk, Joachim Hasholzner, Ralph Teich, Juergen Intel Deutschland GmbH Munich Germany Friedrich Alexander Univ Erlangen Nurnberg Erlangen Germany

ISBN: (纸本)9783030300333;9783030300326

As 5G wireless communication technology is currently deployed, an increasing amount of data is available from mobile devices out in the field. Exploiting this data, also called system traces, recent investigations show the potential to improve the wireless modem design and performance using datacentric approaches. Such data-centric workflows are exploratory and iterative by nature. For instance, time pattern identification is performed by domain experts to derive assumptions on potential optimizations and these assumptions are continuously refined during multiple iterations of data collection, visualization and exploration. In this context, we propose three optimizations to increase the exploration speed in iterative data-centric workflows. First, we present a methodology based on persistent memoization in order to minimize the data processing duration when additional event sequences need to be extracted from a trace. We show that up to 84.5% of the event extraction time can be spared for a typical modem trace data set. Secondly, we present a novel entropy-based data interaction technique for visual exploration of event sequences and finally, a similarity measure to perform subsequence matching in order to assist the user when identifying frequent time patterns in a trace.

关键词： Exploratory workflow Iterative feature extraction Workflow optimization Time-oriented data visualization visual interaction Zoom plus Slant Event sequence similarity measure Categorical event sequence

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International conference on Design Tools and Methods in Industrial Engineering, ADM 2019

International Conference on Design Tools and Methods in Indu...

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International conference on Design Tools and Methods in Industrial Engineering, ADM 2019

ISBN: (纸本)9783030311537

The proceedings contain 83 papers. The special focus in this conference is on Design Tools and Methods in Industrial Engineering. The topics include: Shape and Texture analysis of Radiomic data for Computer-Assisted Diagnosis and Prognostication: An Overview;informing the Use of visual Assets in Industrial Augmented Reality;integrated Design Tools for Model-Based Development of Innovative Vehicle Chassis and Powertrain Systems;a Handheld Mobile Augmented Reality Tool for On-Site Piping Assembly Inspection;multisensory Augmented Reality Experiences for Cultural Heritage Exhibitions;optical Stereo-System for Full-Field High-Frequency 3D Vibration Measurements Based on Low-Frame-Rate Cameras;CAD Reconstruction: A Study on Reverse Modelling Strategies;3D Scanning Procedure for the Evaluation of Lymphedema of Upper Limbs Using Low-Cost Technolgy: A Preliminary Study;low Cost Device to Perform 3D Acquisitions Based on ChAruCo Markers;automatic Segmentation of Constant Radius Secondary Features from Real Objects;comparison of Algorithms for Recognition of Cylindrical Features in a Voxel-Based Approach for Tolerance Inspection;mandible Morphing Through Principal Components analysis;tolerance Prediction for Determinate Assembly Approach in Aeronautical Field;robust Parameter analysis of Compliant Part Models for Computer Aided Tolerancing;an Improved Design Method for Net-Shape Manufacturing in Powder Metallurgy;design for Assembly in the Conceptual Development of Aircraft Systems;a Knowledge Formalization Approach for Manufacturing Cost Estimation;vibration-Assisted Face Grinding of Mould Steel;virtual Design for Assembly Improving the Product Design of a Two-Way Relief Valve;improving the Shoes Customization Process Through a Digitally-Enabled Framework;Conceptual Design of a Functional Orthodontic Appliance for the Correction of Skeletal Class ii Malocclusion.

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Avoiding Drill-down Fallacies with VisPilot: Assisted exploration of data Subsets 24

Avoiding Drill-down Fallacies with VisPilot: Assisted Explor...

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24th ACM International conference on Intelligent User Interfaces (IUI)

作者： Lee, Doris Jung-Lin Dev, Himel Hu, Huizi Elmeleegy, Hazem Parameswaran, Aditya Univ Illinois Urbana IL 61801 USA Google Inc Redwood City CA USA

ISBN: (纸本)9781450362726

As datasets continue to growin size and complexity, exploring multidimensional datasets remain challenging for analysts. A common operation during this exploration is drill-down-understanding the behavior of data subsets by progressively adding filters. While widely used, in the absence of careful attention towards confounding factors, drill-downs could lead to inductive fallacies. Specifically, an analyst may end up being "deceived" into thinking that a deviation in trend is attributable to a local change, when in fact it is a more general phenomenon;we term this the drill-down fallacy. One way to avoid falling prey to drill-down fallacies is to exhaustively explore all potential drill-down paths, which quickly becomes infeasible on complex datasets with many attributes. We present VisPilot, an accelerated visual data exploration tool that guides analysts through the key insights in a dataset, while avoiding drill-down fallacies. Our user study results show that VisPilot helps analysts discover interesting visualizations, understand attribute importance, and predict unseen visualizations better than other multidimensional data analysis baselines.

关键词： exploratory data analysis visualization recommendation drill-down data analysis

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visual analysis Scenarios for Oceanographic Buoy data 23

Visual Analysis Scenarios for Oceanographic Buoy Data

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23rd International conference in Information visualization (IV) / 16th International conference Computer Graphics, Imaging and visualization (CGiV)

作者： Monteiro, Walbert Cunha Monteiro, Marcela Cunha Miranda, Brunelli Pinto Sampaio Neto, Nelson Cruz Resque dos Santos, Carlos Gustavo Meiguins, Bianchi Serique Fed Univ Para Fac Comp Sci Belem Para Brazil Fed Univ Para Inst Coastal Studies Braganca Para Brazil

ISBN: (纸本)9781728128504

The analysis of oceanographic data is a difficult task due to the large volume and multidimensionality of the data. Often is it necessary to aggregate other datasets to establish relationships and more complex analysis in order to answer environmental questions. Thus, this work aims to present different information visualization techniques (InfoVis) for oceanographic data concerning the most frequently used techniques, such as 2D and 3D Charts, Surface Map, Choropleth Map, and Line Map. For this purpose, a dataset of oceanographic buoys from the National Oceanic and Atmospheric Administration (NOAA) covering a period of 02 years (from 2015 to 2016) was used. This set of data was treated and classified according to the seasonal periods (winter, spring, summer, and autumn). The Rose Wind technique revealed that the wind speed average was between 1012 m/s. The Circle Parking technique showed a predominance of wind and waves from NE. In 2015, highest waves were recorded, mainly during the winter. The obtained results can be considered a useful tool for the assessment of the wind and waves relationship and help to coastal management strategies.

关键词： Information visualization visual analysis Oceanographic Buoy data Temporal Correlation

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Modelling of SDG's Achievement in East Java Using Bi-responses Nonparametric Regression with Mixed Estimator Spline Truncated and Kernel 15

Modelling of SDG's Achievement in East Java Using Bi-respons...

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15th International Symposium on Geometric Function Theory and Applications, GFTA 2019

作者： Dewanti, Paramita Nyoman Budiantara, I. Rumiati, Agnes Tuti Department of Statistics Sepuluh Nopember Institute of Technology Surabaya Indonesia

The Government of Indonesia has grouped 17 objectives of SDG's into 4 pillars, among others: social pillars, economic pillars, the pillars of environmental development and the pillars of legal development and Governance. Some strategic indicators are used to know the achievement of SDG'S especially achievement in social and economic pillars such as the Human Development Index (HDI) and Gini Ratio. There are several variables affecting then. A method used to model the effects of its variables is regression analysis. The results of the initial exploration through the scatter plot shows a pattern that changes and disconnects at a certain point for a variable IPM with a variable that is allegedly influencing it. With a scatter plot, it can be found that between the Gini ratio variables with some of its predictive variables, does not have a specific pattern. Bi-responses nonparametric regression is an analysis that appropriates with that case. Bi-responses nonparametric regression analysis is a type of nonparametric regression analysis used when the pattern between two response variables and some of the predictor variables is unknown or if there is no prior information regarding the pattern of them. Most of researchers use bi-responses nonparametric regression approached by spline truncated, kernel and etc. Most of the research, use kernel and spline truncated because of their advantages such as: have a good capability to develop model from unknown pattern data and have a good visual representation. The first aim of this paper is obtaining the estimator of mixed estimator spline truncated and kernel in bi-responses nonparametric regression with weighted least square. The second aim is gaining a best model to explain the relationship indicators of SDG's achievement with some variables affecting them. To obtain the best model, it is necessary to select the optimal of knots point and bandwidth parameters using a smallest Generalized Cross Validation (GCV). © Published under

关键词： Regression analysis

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ConceptExplorer: visual analysis of Concept Drifts in Multi-source Time-series data

ConceptExplorer: Visual Analysis of Concept Drifts in Multi-...

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IEEE Symposium on visual Analytics Science and Technology (VAST)

作者： Xumeng Wang Wei Chen Jiazhi Xia Zexian Chen Dongshi Xu Xiangyang Wu Mingliang Xu Tobias Schreck State Key Lab of CAD&CG Zhejiang University School of Computer Science and Engineering Central South University College of Information Engineering China Jiliang University Institute of Graphics and Image Hangzhou Dianzi University Zhengzhou University Graz University of Technology

ISBN: (数字)9781728180090

ISBN: (纸本)9781728180106

Time-series data is widely studied in various scenarios, like weather forecast, stock market, customer behavior analysis. To comprehensively learn about the dynamic environments, it is necessary to comprehend features from multiple data sources. This paper proposes a novel visual analysis approach for detecting and analyzing concept drifts from multi-sourced time-series. We propose a visual detection scheme for discovering concept drifts from multiple sourced time-series based on prediction models. We design a drift level index to depict the dynamics, and a consistency judgment model to justify whether the concept drifts from various sources are consistent. Our integrated visual interface, ConceptExplorer, facilitates visual exploration, extraction, understanding, and comparison of concepts and concept drifts from multi-source time-series data. We conduct three case studies and expert interviews to verify the effectiveness of our approach.

关键词： Predictive models data visualization data models Adaptation models Indexes Analytical models visual analytics

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VISIGRAPP 2019 - Proceedings of the 14th International Joint conference on Computer Vision, Imaging and Computer Graphics Theory and Applications

VISIGRAPP 2019 - Proceedings of the 14th International Joint...

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10th International conference on Information visualization Theory and Applications, IVAPP 2019 - Part of the 14th International Joint conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2019

ISBN: (纸本)9789897583544

The proceedings contain 37 papers. The topics discussed include: breaking the curse of visual data exploration: improving analyses by building bridges between data world and real world;visual analytics of multidimensional projections for constructing classifier decision boundary maps;equivalence of turn-regularity and complete extensions;compensation of simultaneous orientation contrast in superimposed textures;visual growing neural gas for exploratory data analysis;parallel coordinates-based visual analytics for materials property;synthesizing light field volumetric visualizations in real-time using a compressed volume representation;lightweight coordination of multiple independent visual analytics tools;and coordinated image- and feature-space visualization for interactive magnetic resonance spectroscopy imaging data analysis.

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ImageCLEF 2020: Multimedia Retrieval in Lifelogging, Medical, Nature, and Internet Applications 1

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42nd European conference on IR Research

作者： Ionescu, Bogdan Muller, Henning Peteri, Renaud Duc-Tien Dang-Nguyen Zhou, Liting Piras, Luca Riegler, Michael Halvorsen, Pal Tran, Minh-Triet Lux, Mathias Gurrin, Cathal Chamberlain, Jon Clark, Adrian Campello, Antonio de Herrera, Alba G. Seco Ben Abacha, Asma Datla, Vivek Hasan, Sadid A. Liu, Joey Demner-Fushman, Dina Pelka, Obioma Friedrich, Christoph M. Cid, Yashin Dicente Kozlovski, Serge Liauchuk, Vitali Kovalev, Vassili Berari, Raul Brie, Paul Fichou, Dimitri Dogariu, Mihai Stefan, Liviu Daniel Constantin, Mihai Gabriel Univ Politehn Bucuresti Bucharest Romania Univ Appl Sci Western Switzerland HES SO Delemont Switzerland Univ La Rochelle La Rochelle France Univ Bergen Bergen Norway Dublin City Univ Dublin Ireland Pluribus One Cagliari Italy Univ Cagliari Cagliari Italy SimulaMet Oslo Norway Univ Sci Ho Chi Minh City Vietnam Klagenfurt Univ Klagenfurt Austria Univ Essex Colchester Essex England Wellcome Trust Res Labs London England Natl Lib Med Bethesda MD USA Philips Res Cambridge Cambridge MA USA CVS Hlth Monroeville PA USA Univ Appl Sci & Arts Dortmund Dortmund Germany Univ Warwick Coventry W Midlands England United Inst Informat Problems Minsk BELARUS teleportHQ Cluj Napoca Romania

ISBN: (数字)9783030454425

ISBN: (纸本)9783030454418;9783030454425

This paper presents an overview of the 2020 ImageCLEF lab that will be organized as part of the conference and Labs of the Evaluation Forum-CLEF Labs 2020 in Thessaloniki, Greece. ImageCLEF is an ongoing evaluation initiative (run since 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval of visual data with the aim of providing information access to large collections of images in various usage scenarios and domains. In 2020, the 18th edition of ImageCLEF will organize four main tasks: (i) a Lifelog task (videos, images and other sources) about daily activity understanding, retrieval and summarization, (ii) a Medical task that groups three previous tasks (caption analysis, tuberculosis prediction, and medical visual question answering) with new data and adapted tasks, (iii) a Coral task about segmenting and labeling collections of coral images for 3D modeling, and a new (iv) Web user interface task addressing the problems of detecting and recognizing hand drawn website UIs (User Interfaces) for generating automatic code. The strong participation, with over 235 research groups registering and 63 submitting over 359 runs for the tasks in 2019 shows an important interest in this benchmarking campaign. We expect the new tasks to attract at least as many researchers for 2020.

关键词： Lifelogging retrieval and summarization Medical image classification Coral image segmentation and classification Recognition of hand drawn website UIs ImageCLEF benchmarking Annotated data

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