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Robotics and Mechatronics conference (RobMech)

作者： Jude Oyasor Mpho Raborife Pravesh Ranchod School of Computer Science and Applied Mathematics University of the Witwatersrand Johannesburg South Africa School of Consumer Intelligence and Information Systems University of Johannesburg Johannesburg South Africa

ISBN: (数字)9781728141626

ISBN: (纸本)9781728141633

South Africa has one of the highest rates of sexual violence in the world, and with this technology era being the information age, it is not suprising that people are turning to social media to voice their views on this matter. As tweets on sexual violence grow continually, extracting insights from such data demands a robust, real-time, and scalable tools with flexibility in the database schema. Elasticsearch (ES) is an example of a free-license search engine written in Java and developed on Apache Lucene that meets the stated requirements. On the other hand, Kibana facilitates intuitive dashboard development, visual exploration, and real-time analysis of an index in ES through an intuitive graphical user interface. This study demonstrates how gender was inferred and evaluated through the integration of deep neural networks and Google's TensorFlow. We used the AFINN model to infer sentiment analysis as our measure of gender disparity in this instance. The Indexer, built of Node. js, and defined as the hub of the system connects with the Twitter streaming API to ingest tweets found within the boundaries of sexual violence. This system runs persistently with tweets through the ES search engine and visualised in Kibana.

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A blueprint for semantically lifting field trial data: Enabling exploration using knowledge graphs 12

A blueprint for semantically lifting field trial data: Enabl...

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12th International conference on Semantic Web Applications and Tools for Health Care and Life Sciences, SWAT4HCLS 2019

作者： Burkow, D. Hollunder, J. Heinrich, J. Abdallah, F. Rojas-Macias, M.A. Wiljes, C. Cimiano, P. Senger, P. Bayer CropScience CLS Translational R&D Germany Semalytix GmbH Bielefeld Germany Semantic Computing Group Bielefeld University Germany

In many organizations, relevant data is distributed across multiple primary data sources using different data models, schemas, and vocabularies which prevents effective knowledge management at an organizational level. As a result, it is very cumbersome to retrieve and integrate relevant data necessary to answer specific business questions. We propose a semantic lifting architecture that lifts primary data resources into a single corporate knowledge graph, implemented following FAIR and Linked data principles, which assigns unique URIs to entities and maps the data using a uniform shared vocabulary. This structure supports integration of heterogeneous and distributed data sources to enable advanced search and analysis. In this paper we consider the case of field trial data collected and managed by Bayer, present an example of our architecture, and describe an application using this architecture that provides semantic search and visual exploration of field trial data to relevant stakeholders. As a main benefit, our architecture allows domain experts and analysts to retrieve timely and consistently labeled data originating from multiple primary sources while maintaining data context to enable cross-domain analytics. This provides the means to answer more complex questions with minimal data preparation, improving speed and precision by driving decisions with semantically lifted data. Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Knowledge graph

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Sequence Synopsis: Optimize visual Summary of Temporal Event data

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2018年第1期24卷 45-55页

作者： Chen, Yuanzhe Xu, Panpan Ren, Liu Hong Kong Univ Sci & Technol Hong Kong Hong Kong Peoples R China Bosch Res North Amer Palo Alto CA USA

Event sequences analysis plays an important role in many application domains such as customer behavior analysis, electronic health record analysis and vehicle fault diagnosis. Real-world event sequence data is often noisy and complex with high event cardinality, making it a challenging task to construct concise yet comprehensive overviews for such data. In this paper. we propose a novel visualization technique based on the minimum description length (MDL) principle to construct a coarse-level overview of event sequence data while balancing the information loss in it. The method addresses a fundamental trade-off in visualization design: reducing visual clutter vs. increasing the information content in a visualization. The method enables simultaneous sequence clustering and pattern extraction and is highly tolerant to noises such as missing or additional events in the data. Based on this approach we propose a visual analytics framework with multiple levels-of-detail to facilitate interactive data exploration. We demonstrate the usability and effectiveness of our approach through case studies with two real-world datasets. One dataset showcases a new application domain for event sequence visualization, i.e., fault development path analysis in vehicles for predictive maintenance. We also discuss the strengths and limitations of the proposed method based on user feedback.

关键词： Time Series data data Transformation and Representation visual Knowledge Representation visual Analytics

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Active Auditory exploration for Identifying Object Contents

Active Auditory Exploration for Identifying Object Contents

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International conference on Advanced Robotics and Mechatronics (ICARM)

作者： Xiang Dou Huaping Liu Xinying Xu Di Guo College of Electrical and Power Engineering Taiyuan University of Technology Taiyuan China Tsinghua University State Key Laboratory of Intelligent Technology and Systems Beijing National Research Center for Information Science and Technology Beijing China

ISBN: (数字)9781728164793

ISBN: (纸本)9781728164809

Intelligent robots need to acquire a target object from the same type of container filled with different kinds of objects. The actions selected by robots are critical to quickly and accurately recognizing the type of objects inside the container. In this paper, we propose an active auditory exploration method based on reinforcement learning, which enables the robot to actively explore the operational behavior of interest and establish the coupling relationship between perception and action to reduce the ambiguity of target recognition. The robot interacts with visually indistinguishable bottles by adopting multiple action behaviors to generate sound data from which the perceptual model learns to classify object contents. This is similar to human beings actively exploring and accumulating experience through sound in the environment where vision cannot be judged. In addition, we also use passive strategies to compare and analyze the impact of inhibition behavior on object recognition. Experimental results show that our method successfully learns effective class recognition strategies and actively selects actions to further improve recognition efficiency.

关键词： Object recognition Containers Robot sensing systems Learning (artificial intelligence) Task analysis visual perception

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Integrated stratigraphic, thermal/burial and uncertainty modeling of the levant basin (East-Mediterranean region)

Integrated stratigraphic, thermal/burial and uncertainty mod...

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Offshore Mediterranean conference and Exhibition 2019, OMC 2019

作者： Nader, Fadi H. IFP Energies nouvelles France

An integrated workflow for modeling sedimentary basins including source to sink stratigraphic forward simulations, thermal and burial evolutions and uncertainty analyses, has been designed and applied on the frontier Levant Basin in the East-Mediterranean region (model covering around 200,000 sq. km, with horizontal cells grid resolution of 5 sq. km and less than 50m thick). The objective of this workflow is to produce reasonable multi-scenario simulations and probabilistic information that can help make sound decisions for less risky hydrocarbon exploration. Alternatively, the same approach can be applied to better understand sedimentary basins for developing hydrothermal energy resources and/or carbon and energy storage elsewhere. The keyword is "integration", and it involves a variety of numerical tools (as well as concept-thinking). Uncertainty has to be quantified ultimately, and minimized for plausible geological and physical simulations. To reach that end, a unique working environment for efficient basin analysis has to secure the following steps: i) basic characterization/integration of all available geological, geophysical and geochemical data (leading to conceptual models);ii) sedimentary facies modeling (with stratigraphic forward numerical engines);iii) structural characterization and modeling (deformation, inversion and faults);iv) burial/thermal and petroleum systems modeling;and v) uncertainty analysis and modeling (risk management). Bridging the gap between sedimentologists, structural geologists, geochemists, petroleum geologists, and geo-modelers proved to be a must for achieving integrated concepts and thereafter numerical models. © 2019 Offshore Mediterranean conference (OMC). All rights reserved.

关键词： Uncertainty analysis

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Facilitating and Managing Machine Learning and data analysis Tasks in Big data Environments using Web and Microservice Technologies 19

Facilitating and Managing Machine Learning and Data Analysis...

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

作者： Shadi Shahoud Sonja Gunnarsdottir Hatem Khalloof Clemens Duepmeier Veit Hagenmeyer Karlsruhe Institute of Technology IAI Karlsruhe Germany

ISBN: (纸本)9781450362382

Driven by the great advance of machine learning in a wide range of application areas, the need for developing machine learning frameworks effectively as well as easily usable by novices increased dramatically. Furthermore, building machine learning models in the context of big data environments still represents a great challenge. In the present paper, we tackle these challenges by introducing a new generic framework for efficiently facilitating the training, testing, managing, storing, and retrieving of machine learning models in the context of big data. The framework makes use of a powerful big data software stack and a microservice architecture for a fully manageable and highly scalable solution. A highly configurable user interface is introduced giving the user the ability to easily train, test, and manage machine learning models. Moreover, it automatically indexes models and allows flexible exploration of them in the visual interface. The performance of the new framework is evaluated on state-of-the-arts machine learning algorithms: it is shown that storing and retrieving machine learning models as well as a respective acceptable low overhead demonstrate an efficient approach to facilitate machine learning in big data environments.

关键词： Big data Web-based Applications data Analytic Microservice Machine Learning

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Spanning the Boundaries of data visualization Work: An exploration of Functional Affordances and Disciplinary Values 1

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14th International conference on Information in Contemporary Society, iconference 2019

作者： Snyder, Jaime Shilton, Katie University of Washington SeattleWA98195 United States University of Maryland College ParkMD20742 United States

ISBN: (数字)9783030157425

ISBN: (纸本)9783030157418

Creating data visualizations requires diverse skills including computer programming, statistics, and graphic design. visualization practitioners, often formally trained in one but not all of these areas, increasingly face the challenge of reconciling, integrating and prioritizing competing disciplinary values, norms and priorities. To inform multidisciplinary visualization pedagogy, we analyze the negotiation of values in the rhetoric and affordances of two common tools for creating visual representations of data: R and Adobe Illustrator. Features of, and discourse around, these standard visualization tools illustrate both a convergence of values and priorities (clear, attractive, and communicative data-driven graphics) side-by-side with a retention of rhetorical divisions between disciplinary communities (statistical analysis in contrast to creative expression). We discuss implications for data-driven work and data science curricula within the current environment where data visualization practice is converging while values in rhetoric remain divided. © 2019, Springer Nature Switzerland AG.

关键词： data visualization

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Using visualization to Improve Clustering analysis on Heterogeneous Information Network 22

Using Visualization to Improve Clustering Analysis on Hetero...

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22nd International conference Information visualisation (iv) - Biomedical visualization, visualisation on Built and Rural Environments and Geometric Modelling and Imaging

作者： Wang, Wenbo Li, Yuwei Wang, Feng Liu, Xiaopei Zheng, Youyi ShanghaiTech Univ Shanghai Peoples R China Jilin Univ Changchun Jilin Peoples R China Zhejiang Univ Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781538672020

The exploration and analysis of data mining methodologies is an important task for effective knowledge discovery, especially in today's heterogeneous information networks. Previously presented approaches for mining optimization aim primarily at the improvements of time complexity, space complexity, accuracy, and robustness. We extend the state-of-the-art method by concentrating on user-availability and algorithm understandability. Specifically, we use Rankclus, a classic clustering algorithm as an example. After uncovering the unseen computing processes to be displayed in a visual form, the whole clustering processes are transparent to the users, which may help them more clearly and quickly understand how the algorithms are computed, how does each object influence one another. In addition, we use a density approach to intuitively simplify the discovery of data patterns, and through the visualized results, users can adjust algorithm parameters with or without professional training. Finally, we use another two visual techniques to improve the visualization quality: a heatmap matrix designed for checking the similarities of objects which are in the same cluster, and a DOItree implemented to further analyze the accuracy of the algorithms.

关键词： data mining heterogeneous information networks visualization Rankclus

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Big data visualisation and visual Analytics for Music data Mining 22

Big Data Visualisation and Visual Analytics for Music Data M...

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22nd International conference Information visualisation (iv) - Biomedical visualization, visualisation on Built and Rural Environments and Geometric Modelling and Imaging

作者： Barkwell, Katrina E. Cuzzocrea, Alfredo Leung, Carson K. Ocran, Ashley A. Sanderson, Jennifer M. Stewart, James Ayrton Wodi, Bryan H. Univ Manitoba Dept Comp Sci Winnipeg MB Canada Univ Trieste Dept Engn & Architecture DIA Trieste TS Italy

ISBN: (纸本)9781538672020

As high volumes of a wide variety of valuable data of different veracities can be easily generated or collected at a high velocity nowadays, big data visualisation and visual analytics are in demand in various real-life applications. Musical data are examples of big data. Embedded in these big data are useful information and valuable knowledge. Many existing big data mining algorithms return useful information and valuable knowledge in textual or tabular forms. Knowing that "a picture is worth a thousand words", big data visualisation and visual analytics are also in demand. In this paper, we present a system for visualising and analysing big data. In particular, our system focuses on the big data science task of the discovery and exploration of frequent patterns (i.e., collections of items that frequently occurring together) from musical data. Evaluation results show the applicability of our system in big data visualisation and visual analytics for music data mining.

关键词： big data data visualisation visual analytics visualiser frequent patterns musical data music data analytics music data mining

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PolyVis: Cross-Device Framework for Collaborative visual data analysis

PolyVis: Cross-Device Framework for Collaborative Visual Dat...

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IEEE International conference on Systems, Man and Cybernetics

作者： Abeer Alsaiari Andrew Johnson Arthur Nishimoto Electronic Visualization Laboratory University of Illinois at Chicago Chicago IL USA

Multi device environments present new opportunities for collaborative visual data analysis and sense making by utilizing each device's strengths and capabilities. However, one of the associated challenges with visual data analysis in multi device environments is the sharing of visual components across devices. We present a framework developed on top of SAGE2 platform for cross-device collaborative visual data exploration. As part of our framework, we contribute the concept of rapid development and assembling of visualizations that can span multiple devices of different modalities. It provides the users with an environment for visualization compositions that delegate the rendering to the target device, allowing them to augment their large display workspace with portable devices for further exploration territories. Facilitated by its intuitive visualization composition pipeline, users with no programming skills such as data analysts can enhance their analytical scope with no coding barriers. We describe the framework, its implementation with a use case, and the rationale behind its design.

关键词： Collaboration data visualization data analysis visual analytics Task analysis Headphones

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