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IEEE Symposium on visual Analytics Science and Technology (VAST)

作者： Jiazhi Xia Tianxiang Chen Lei Zhang Wei Chen Yang Chen Xiaolong Zhang Cong Xie Tobias Schreck School of Computer Science and Engineering Central South University State Key Lab of CAD&CG Zhejiang University I4 data Pennsylvania State University University Park PA Facebook Graz University of Technology

ISBN: (数字)9781728180090

ISBN: (纸本)9781728180106

Nowadays, as data becomes increasingly complex and distributed, data analyses often involve several related datasets that are stored on different servers and probably owned by different stakeholders. While there is an emerging need to provide these stakeholders with a full picture of their data under a global context, conventional visual analytical methods, such as dimensionality reduction, could expose data privacy when multi-party datasets are fused into a single site to build point-level relationships. In this paper, we reformulate the conventional t-SNE method from the single-site mode into a secure distributed infrastructure. We present a secure multi-party scheme for joint t-SNE computation, which can minimize the risk of data leakage. Aggregated visualization can be optionally employed to hide disclosure of point-level relationships. We build a prototype system based on our method, SMAP, to support the organization, computation, and exploration of secure joint embedding. We demonstrate the effectiveness of our approach with three case studies, one of which is based on the deployment of our system in real-world applications.

关键词： data visualization Dimensionality reduction data privacy Task analysis Privacy Hospitals Distributed databases

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GeoMultivis: Helping decision-making through interactive visualizations from geospatial multivariate data 25

GeoMultivis: Helping decision-making through interactive vis...

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25th Americas conference on Information Systems, AMCIS 2019

作者： Signoretti, Wagner Manssour, Isabel Silveira, Milene Pontifícia Universidade Católica do Rio Grande do Sul Brazil

The amount of geospatial data that are daily generated through mobile phones, GPS and other technologies, could help in the process of decision-making. We can find datasets with several attributes associated with specific geographic coordinates, such as open government, meteorological and social network data. The visual exploration of such data facilitates to communicate how different attributes correlate to geographical locations by layering these attributes over maps. However, the analysis and knowledge discovery of several attributes associated with a specific location is not a trivial task. In this scenario, we introduce GeoMultiVis, a set of interactive visualizations that combines multivariate data and geospatial localization. It integrates a map with clustered charts and coordinated multiple views, offering a way to interactively apply different filters and analyze several attributes, updating all the related visual representations. We demonstrate its applicability through three real-cases studies. Our main contribution is the interactive visualizations to support the visual analysis of geospatial multivariate data and its application to real cases, helping to find patterns and insights that could support decisions makers to improve social issues in the future. © 2019 Association for Information Systems. All rights reserved.

关键词： Decision making

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visual Inspection with Federated Learning 16th

Visual Inspection with Federated Learning

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16th International conference on Image analysis and Recognition (ICIAR)

作者： Han, Xu Yu, Haoran Gu, Haisong VisionX Fdn San Jose CA 95134 USA

ISBN: (纸本)9783030272722;9783030272715

In industrial applications of AI, challenges for visual inspection include data shortage and security. In this paper, we propose a Federated Learning (FL) framework to address these issues. This method is incorporated with our novel dataonomy(SM) approach which can overcome the limited size of industrial dataset in each inspection task. The models pre-trained in the server can continuously and regularly update, and help each client upgrade its inspection model over time. The FL approach only requires clients to send to the server certain information derived from raw images, and thus does not sacrifice data security. Some preliminary tests are done to examine the workability of the proposed framework. This study is expected to bring the field of automated inspection to a new level of security, reliability, and efficiency, and to unlock significant potentials of deep learning applications.

关键词： visual Inspection Federated Learning dataonomy(SM)

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Behavioral Sentiment analysis of Depressive States

Behavioral Sentiment Analysis of Depressive States

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International conference on Cognitive Infocommunications (CogInfoCom)

作者： Anna Esposito Gennaro Raimo Mauro Maldonato Carl Vogel Massimiliano Conson Gennaro Cordasco Universita degli Studi della Campania “L. Vanvitelli” International Institute for Advanced Scientific Studies (IIASS) Italy Universita di Napoli “Federico II” Naples Italy School of Computer Science and Statistics Trinity College Dublin Ireland

ISBN: (数字)9781728182131

ISBN: (纸本)9781728182148

The need to release accurate and incontrovertible diagnoses of depression has fueled the search for new methodologies to obtain more reliable measurements than the commonly adopted questionnaires. In such a context, research has sought to identify non-biased measures derived from analyses of behavioral data such as voice and language. For this purpose, sentiment analysis techniques were developed, initially based on linguistic characteristics extracted from texts and gradually becoming more and more sophisticated by adding tools for the analyses of voice and visual data (such as facial expressions and movements). This work summarizes the behavioral features accounted for detecting depressive states and sentiment analysis tools developed to extract them from text, audio, and video recordings.

关键词： Sentiment analysis databases Tools Linguistics Depression Feature extraction Stress

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HPC Enabled Immersive and non-Immersive visualization of Large Scale Scientific data 2

HPC Enabled Immersive and non-Immersive Visualization of Lar...

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conference on SPIE Disruptive Technologies in Information Sciences ii

作者： Su, Simon Perry, Vincent An, Michael Bravo, Luis Dasari, Venkat Us Army Res Lab 320 Hopkins Rd Aberdeen Proving Ground MD 21005 USA

ISBN: (数字)9781510626928

ISBN: (纸本)9781510626928

Advancement in the areas of high performance computing and computational sciences have facilitated the generation of an enormous amount of research data by computational scientists -the volume, velocity and variability of Big 'Research' data has increased across all disciplines. An immersive and non-immersive analytics platform capable of handling extreme-scale scientific data will enable scientists to visualize unwieldy simulation data in an intuitive manner and guide the development of sophisticated and targeted analytics to obtain usable information. Our immersive and non-immersive visualization work is an attempt to provide computational scientists with the ability to analyze the extreme-scale data generated. The main purpose of this paper is to identify different characteristics of a scientific data analysis process to provide a general outline for the scientists to select the appropriate visualization systems to perform their data analytics. In addition, we will include some of the details on how to how the immersive and non-immersive visualization hardware and software are setup. We are confident that the findings in our paper will provide scientists with a streamlined and optimal visual analytics workflow.

关键词： Scientific visualization Immersive Analytics Large-Scale Simulation data

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Learning to Perform visual Tasks from Human Demonstrations 9th

Learning to Perform Visual Tasks from Human Demonstrations

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9th Iberian conference on Pattern Recognition and Image analysis (IbPRIA)

作者： Nunes, Afonso Figueiredo, Rui Moreno, Plinio Univ Lisbon Inst Super Tecn Inst Syst & Robot Lisbon Portugal

ISBN: (纸本)9783030313210;9783030313203

The human visual system makes extensive use of saccadic eye movements to cope with decaying resolution towards the periphery of the visual field, and point the highest acuity region of the retina (i.e. the fovea) to regions of interest, while searching for objects in natural scenes. Experimental evidence has shown that, when searching for objects, humans exploit the advantage of a priori known relations between object classes (i.e. context) to prune the search space, which results in oculo-motor behaviours that are optimal both in terms of effectiveness (i.e. success rate) and efficiency (i.e. energy and time consumed) during search tasks execution. In this work we propose a biologically plausible system that learns from human demonstrations provided by eye tracking data to perform visual search tasks. The proposed framework leverages the recognition accuracy of state-of-the-art pre-trained CNNs with the sequential predictive power of RNNs, trained in an end-to-end manner to predict saliency maps (task-independent) and locate objects of interest (task-dependent).

关键词： Active perception visual search Eye tracking Selective visual attention

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Towards a Graphical User Interface for Exploring and Fine-Tuning Crowd Simulations

Towards a Graphical User Interface for Exploring and Fine-Tu...

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

作者： Andrea Bönsch Marcel Jonda Jonathan Ehret TorstenW. Kuhlen Visual Computing Institute RWTH Aachen University Germany

ISBN: (数字)9781728165325

ISBN: (纸本)9781728165332

Simulating a realistic navigation of virtual pedestrians through virtual environments is a recurring subject of investigations. The various mathematical approaches used to compute the pedestrians’ paths result, i.a., in different computation-times and varying path characteristics. Customizable parameters, e.g., maximal walking speed or minimal interpersonal distance, add another level of complexity. Thus, choosing the best-fitting approach for a given environment and use-case is non-trivial, especially for novice users. To facilitate the informed choice of a specific algorithm with a certain parameter set, crowd simulation frameworks such as Menge [3] provide an extendable collection of approaches with a unified interface for usage. However, they often miss an elaborated visualization with high informative value accompanied by visual analysis methods to explore the complete simulation data in more detail - which is yet required for an informed choice. Benchmarking suites such as SteerBench [14] are a helpful approach as they objectively analyze crowd simulations, however they are too tailored to specific behavior details. To this end, we propose a preliminary design of an advanced graphical user interface providing a 2D and 3D visualization of the crowd simulation data as well as features for time navigation and an overall data exploration.

关键词： Computational modeling data models Graphical user interfaces Analytical models Navigation data visualization Solid modeling

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CyteGuide: visual Guidance for Hierarchical Single-Cell analysis

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2018年第1期24卷 739-748页

作者： Hollt, Thomas Pezzotti, Nicola van Unen, Vincent Koning, Frits Lelieveldt, Boudewijn P. F. Vilanova, Anna Delft Univ Technol Comp Graph & Visualizat Grp Delft Netherlands Leiden Univ Med Ctr Computat Biol Ctr Leiden Netherlands Leiden Univ Med Ctr Dept Immunohematol & Blood Transfus Leiden Netherlands Leiden Univ Med Ctr Div Image Proc Dept Radiol Leiden Netherlands Delft Univ Technol Pattern Recognit & Bioinformat Grp Delft Netherlands

Single-cell analysis through mass cytometry has become an increasingly important tool for immunologists to study the immune system in health and disease. Mass cytometry creates a high-dimensional description vector for single cells by time-of-flight measurement. Recently, t-Distributed Stochastic Neighborhood Embedding (t-SNE) has emerged as one of the state-of-the-art techniques for the visualization and exploration of single-cell data. Ever increasing amounts of data lead to the adoption of Hierarchical Stochastic Neighborhood Embedding (HSNE), enabling the hierarchical representation of the data. Here, the hierarchy is explored selectively by the analyst, who can request more and more detail in areas of interest. Such hierarchies are usually explored by visualizing disconnected plots of selections in different levels of the hierarchy. This poses problems for navigation, by imposing a high cognitive load on the analyst. In this work. we present an interactive summary-visualization to tackle this problem. CyteGuide guides the analyst through the exploration of hierarchically represented single-cell data, and provides a complete overview of the current state of the analysis. We conducted a two-phase user study with domain experts that use HSNE for data exploration. We first studied their problems with their current workflow using HSNE and the requirements to ease this workflow in a field study. These requirements have been the basis for our visual design. In the second phase, we verified our proposed solution in a user evaluation.

关键词： Hierarchical data HSNE Single-Cell analysis visual Guidance

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Estimating wheel slip of a planetary exploration rover via unsupervised machine learning

Estimating wheel slip of a planetary exploration rover via u...

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IEEE Aerospace conference

作者： Kruger, Justin Rogg, Arno Gonzalez, Ramon Stanford Univ Stanford CA 94305 USA NASA Ames Res Ctr Moffett Field CA 94035 USA Robon Worldwide Tech Startup Calle Extremadura 5 Roquetas De Mar 04740 Almeria Spain

ISBN: (纸本)9781538668542

Planetary exploration rovers often encounter imperfect traction and wheel slip, which negatively impacts navigation and in the worst case can result in permanent immobilization. Recent studies have applied machine learning to estimate rover wheel slip, which this paper extends via the implementation of three unsupervised learning algorithms: self-organizing maps, k-means clustering, and autoencoding. Unsupervised learning is preferred since labelled training data may be risky or time-consuming to obtain on site;each algorithm classifies the rover's current slip state into one of several discrete categories. Proprioceptive sensors are used to avoid added complexity and prevent a reliance on visual odometry. The algorithms are validated using sensor data from a planetary rover driving on a sandy incline, and performance is evaluated for different velocities, sensor inputs, slip classes, algorithm parameters, and data filters. Self-organizing maps (SOM) demonstrate the best slip classification accuracy, achieving 97% immobilization detection in the ideal two-class case. At rover-like speeds of 0.10 m/s, 88% accuracy is demonstrated for three classes. For ten slip classes, 71% accuracy is obtainable. Compared to SOM, k-means loses 530% accuracy and autoencoders lose 2-10% accuracy. SOM is most computationally intensive while k-means is least. An analysis of significant parameters for algorithm tuning displays accuracy benefits of up to 25%, and mis-classifications can be further reduced by modifying class boundaries. The algorithms are generic and can be trained for different terrain, environment or vehicle parameters, and although some labelled data is needed to directly associate unsupervised clusters with slip classes, it is significantly less than what a fully-supervised algorithm requires. Unsupervised learning is thus considered promising for robust real-time rover slip estimation.

关键词： Neurons Clustering algorithms Wheels Self-organizing feature maps Sensors Estimation Machine learning

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VISTILES: Coordinating and Combining Co-located Mobile Devices for visual data exploration

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2018年第1期24卷 626-636页

作者： Langner, Ricardo Horak, Tom Dachselt, Raimund Tech Univ Dresden Interact Media Lab Dresden Germany

We present VISTILES, a conceptual framework that uses a set of mobile devices to distribute and coordinate visualization views for the exploration of multivariate data. In contrast to desktop-based interfaces for information visualization, mobile devices offer the potential to provide a dynamic and user-defined interface supporting co-located collaborative data exploration with different individual workflows. As part of our framework, we contribute concepts that enable users to interact with coordinated & multiple views (CMV) that are distributed across several mobile devices. The major components of the framework are: (i) dynamic and flexible layouts for CMV focusing on the distribution of views and (ii) an interaction concept for smart adaptations and combinations of visualizations utilizing explicit side-by-side arrangements of devices. As a result, users can benefit from the possibility to combine devices and organize them in meaningful spatial layouts. Furthermore, we present a web-based prototype implementation as a specific instance of our concepts. This implementation provides a practical application case enabling users to explore a multivariate data collection. We also illustrate the design process including feedback from a preliminary user study, which informed the design of both the concepts and the final prototype.

关键词： Mobile devices coordinated & multiple views multi-display environment cross-device interaction

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