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52ndHawaii International conference on System Sciences (HICSS)

作者： Luo, Jianxi Singapore Univ Technol & Design SUTD SUTD MIT Int Design Ctr Data Driven Innovat Lab Singapore Singapore

ISBN: (纸本)9780998133126

A strand of recent studies utilized complete patent databases and classification systems to construct large network maps of patent technology classes, which might approximate the total technology space. It has been argued that such maps are useful for competitive intelligence analysis, technology road mapping, innovation decision support, and so on in the literature. In this paper, we illustrate the InnoGPS system to integrate such a map with various map-based visual analytic functions for technology navigation, positioning, neighborhood exploration, path finding and information retrieval. These analytics are either descriptive, predictive or prescriptive. During the process of developing InnoGPS, we have conceived a wide spectrum of other potential applications of the total technology space map for consumers, business, education and so on. These possibilities together with the difficulty to construct an accurate technology space representation suggest the strategic value to develop the total technology space map as a digital platform for any applications to discover, manage or represent any data, information and knowledge related to technologies, and to nurture an ecosystem of developers and users.

关键词： Competitive intelligence

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SMAP: A Joint Dimensionality Reduction Scheme for Secure Multi-Party visualization

SMAP: A Joint Dimensionality Reduction Scheme for Secure Mul...

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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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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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ImWeb: Cross-Platform ImmersiveWeb Browsing for Online 3D Neuron database exploration 24

ImWeb: Cross-Platform ImmersiveWeb Browsing for Online 3D Ne...

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

作者： Fulmer, Willis Mahmood, Tahir Li, Zhongyu Zhang, Shaoting Huang, Jian Lu, Aidong Univ N Carolina Charlotte NC 28223 USA Univ Tennessee Knoxville TN USA

ISBN: (纸本)9781450362726

Web services have become one major way for people to obtain and explore information nowadays. However, web browsers currently only offer limited data analysis capabilities, especially for large-scale 3D datasets. This project presents a method of immersive web browsing (ImWeb) to enable effective exploration of multiple datasets over the web with augmented reality (AR) techniques. The ImWeb system allows inputs from both the web browser and AR and provides a set of immersive analytics methods for enhanced web browsing, exploration, comparison, and summary tasks. We have also integrated 3D neuron mining and abstraction approaches to support efficient analysis functions. The architecture of ImWeb system flexibly separates the tasks on web browser and AR and supports smooth networking among the system, so that ImWeb can be adopted by different platforms, such as desktops, large displays, and tablets. We use an online 3D neuron database to demonstrate that ImWeb enables new experiences of exploring 3D datasets over the web. We expect that our approach can be applied to various other online databases and become one useful addition to future web services.

关键词： Immersive web browsing augmented reality visual analytics online database 3D neuron exploration

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Urdu Sentiment Corpus (v1.0): Linguistic exploration and visualization of Labeled dataset for Urdu Sentiment analysis

Urdu Sentiment Corpus (v1.0): Linguistic Exploration and Vis...

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International conference on Information Science and Communication Technology (ICISCT)

作者： Muhammad Yaseen Khan Muhammad Suffian Nizami Center for Language Computing Mohammad Ali Jinnah University Karachi Pakistan

ISBN: (数字)9781728168999

ISBN: (纸本)9781728169002

The significance of the labeled dataset is not obscure from artificial intelligence practitioners. We have seen much phenomenal work, in natural language processing, for many languages (like English, Chinese, and Arabic, etc.), due to the reason for the availability of substantial data. For the Urdu language, despite the third largest spoken language in the world, very little research work is shown; hence, it is adjudged as a `morphologically rich' but `resource-poor' language. Further, the researchers working on Urdu natural language processing are in a quandary due to the lack of availability of labeled/annotated datasets. This paper shares the data, “Urdu Sentiment Corpus” (USC), and insights therein, of Urdu tweets for the sentiment analysis and polarity detection. The dataset is consisting of tweets, such that it casts a political shadow and presents a competitive environment between two separate political parties versus the government of Pakistan. Overall, the dataset is comprising over 17, 185 tokens with 52% records as positive, and 48 % records as negative. This paper shares the visual insights (from document-level to word-level) into the textual similarities, manifold-learning, etc. In addition to it, this paper also presents a Part-of-Speech wise analysis and an unpretentious technique for the extraction of sentiment lexicons from the corpus.

关键词：

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BlackWidow: Monitoring the Dark Web for Cyber Security Information 11

BlackWidow: Monitoring the Dark Web for Cyber Security Infor...

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11th Annual International conference on Cyber Conflict (CyCon) - Silent Battle

作者： Schaefer, Matthias Fuchs, Markus Strohmeier, Martin Engel, Markus Liechti, Marc Lenders, Vincent Univ Kaiserslautern Dept Comp Sci Kaiserslautern Germany SeRo Syst Kaiserslautern Germany Armasuisse Cyber Def Campus Thun Switzerland Trivo Syst Bern Switzerland

ISBN: (纸本)9789949990450

The Dark Web, a conglomerate of services hidden from search engines and regular users, is used by cyber criminals to offer all kinds of illegal services and goods. Multiple Dark Web offerings are highly relevant for the cyber security domain in anticipating and preventing attacks, such as information about zero-day exploits, stolen datasets with login information, or botnets available for hire. In this work, we analyze and discuss the challenges related to information gathering in the Dark Web for cyber security intelligence purposes. To facilitate information collection and the analysis of large amounts of unstructured data, we present BlackWidow, a highly automated modular system that monitors Dark Web services and fuses the collected data in a single analytics framework. BlackWidow relies on a Docker-based micro service architecture which permits the combination of both preexisting and customized machine learning tools. BlackWidow represents all extracted data and the corresponding relationships extracted from posts in a large knowledge graph, which is made available to its security analyst users for search and interactive visual exploration. Using BlackWidow, we conduct a study of seven popular services on the Deep and Dark Web across three different languages with almost 100,000 users. Within less than two days of monitoring time, BlackWidow managed to collect years of relevant information in the areas of cyber security and fraud monitoring. We show that BlackWidow can infer relationships between authors and forums and detect trends for cybersecurity-related topics. Finally, we discuss exemplary case studies surrounding leaked data and preparation for malicious activity.

关键词： Dark Web analysis open source intelligence cyber intelligence

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M-QuBE3: Querying big multilayer graph by evolutive extraction and exploration

M-QuBE3: Querying big multilayer graph by evolutive extracti...

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2019 conference on visualization and data analysis, VDA 2019

作者： Laumond, Antoine Melancon, Guy Pinaud, Bruno Ghoniem, Mohammad Univ. Bordeaux LaBRI UMR 5800 Talence F-33400 France Luxembourg

Although node-link representations of graphs are widespread and even sometimes preferred to other approaches, they suffer from obvious limitations when graphs become large or dense, inducing visual cluttering and impeding the traditional visual information seeking process. This article presents a new strategy of exploration particularly suitable when graphs are large and dense. Users iteratively drive the exploration through the visualization of small sub-networks of interest. Our technique is particularly useful with multilayer networks, where layers typically combine into a large and dense network. Our iterative exploration process called M-QuBE3 computes a score for each node of a graph based on structural and semantic information where more interesting nodes from a user point of view have higher scores. This in turn translates into a procedure to select sub-networks of interest. Within each sub-network, the user can select nodes to enhance the semantic context (and thus impact their interest score) and iteratively refine the exploration towards more relevant sub-networks. The M-QuBE3 process natively handles multilayer network and allows the use of layers as a semantic apparatus when driving the navigation. © 2019 Society for Imaging Science and Technology. All rights reserved.

关键词： Semantics

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