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HypAR: Situated Mineralogy exploration in Augmented Reality 17

HypAR: Situated Mineralogy Exploration in Augmented Reality

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17th ACM SIGGRAPH International conference on Virtual-Reality Continuum and its Applications in Industry (VRCAI)

作者： Engelke, Ulrich Rogers, Casey Klump, Jens Lau, Ian CSIRO Data61 Kensington NSW Australia Curtin Univ Bentley WA Australia CSIRO Mineral Resources Kensington NSW Australia

ISBN: (纸本)9781450370028

Hyperspectral imaging, as a fast and cost effective method of mapping the composition of geological materials in context, is a key enabler for scientific discoveries in the geosciences. Being able to do this in-situ in real world context, possibly in real time, would have profound implications for geology and minerals exploration. This work addresses this important issue by developing an augmented reality application called HypAR that enables in-situ, interactive exploration of mineralogy spatially co-located and embedded with rock surfaces. User centred design is deployed to assure the utility and validity of the system. We describe the requirements analysis and design process for HypAR. We present a prototype using the Microsoft HoloLens that was implemented for a rock wall containing a wide range of minerals and materials from significant geological localities of Western Australia. We briefly discuss several use cases for which HypAR and extensions thereof may prove useful to geoscientists and other end users who have to make effective, informed decisions about the mineralogy of rock surfaces.

关键词： Augmented reality visual analytics interaction design hyperspectral imaging mineralogy geoscience

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Deep Embedded Clustering for data-Driven ECG exploration Using Continuous Wavelet Transforms

Deep Embedded Clustering for Data-Driven ECG Exploration Usi...

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2019 International conference on Information and Digital Technologies, IDT 2019

作者： Wachowiak, Mark P. Moggridge, Jason J. Wachowiak-Smolikova, Renata Computer Science and Mathematics Nipissing University North BayON Canada

ISBN: (纸本)9781728114019

Due to the length, complexity, and inter-subject variation of physiological signals acquired non-invasively, datadriven analysis is increasingly valuable in Smart Health, precision medicine, and in studying physiological dynamics. Knowledge discovery in medical research requires that domain experts analyze complex, high-dimensional data and signals, which is facilitated by dimensionality reduction and clustering. The current paper presents an unsupervised machine learning approach to clustering time-frequency features from ECG records using deep embedded clustering, which optimizes a clustering metric that maps high-dimensional time-frequency data to a lower dimensional latent space. These clusters can be obtained in arbitrarily low dimensions, and are subsequently analyzed with visual analytics to uncover structures and patterns. This technique is applied to time segments of continuous wavelet transforms of ECG records, representing a variety of conditions. Preliminary results on publicly-available ECG records indicate that deep embedded clustering produces a low-dimensional learned representation of time-frequency characteristics that facilitates signal exploration and improves interpretability. © 2019 IEEE.

关键词： Electrocardiograms

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visual Analytics Design To Support Knowledge Generation: The Case Of Outcome Based Education Assessment in Malaysia

Visual Analytics Design To Support Knowledge Generation: The...

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International conference on Computer Applications and Industrial Electronics (ICCAIE)

作者： Leona D. Lumius Muzaffar Hamzah Chin Pei Yee Vincent Pang Goh Say Leng Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Malaysia Faculty of Computing and Informatics Universiti Malaysia Sabah Wilayah Persekutuan Labuan Centre For Teaching Excellence And Academic Quality Universiti Malaysia Sabah Kota Kinabalu Malaysia

ISBN: (数字)9781728150338

ISBN: (纸本)9781728150345

Colleges and universities serve to prepare students for the working world. However, there is a gap between what was learned and taught in colleges and universities with the demands of the working industries. In order to produce graduates equipped with the necessary skills, knowledge and attitude for the working industries, Ministry of Education (MoHE) introduces iCGPA. iCGPA is an outcome based assessment platform that reports students holistic values attainment in the form of a static spider web. However, because students' skills attainment can go beyond the classroom, the existing outcome based education assessment platform output which only shows the students' skills level based on classroom assessment and the static representation in the form of spider chart prevents data exploration for deeper understanding on the students' performance. Besides students' results, higher education institutes stored a number of different students' data which can be leverage to acquire deeper insight to a certain situation. The use of visual analytics to handle different data related to students' skills and values attainment can enhance the outcome-based education assessment platform mechanism by enabling the generation of more insights and knowledge to discover ways to improve students' results. Therefore, in this study, the proposed framework for visual analytics design to support knowledge generation was refined after research requirement analysis was done with experts and the outcome was the finalization of visualization, interaction and data types. The outcome supports the proposed framework's ability to expedite the exploration of students' data result together with their out of classroom activities data thus supporting knowledge generation.

关键词： Interactive visualization Predictive Modelling visual Analytics Outcome-Based Education Knowledge Generation visual analytics Students colleges universities knowledge Malaya (Diptera)

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Longitudinal data exploration of upper respiratory tract infection disease in Bandung city 4

Longitudinal data exploration of upper respiratory tract inf...

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4th National conference on Mathematics Research and Its Learning, KNPMP 2019

作者： Tantular, B. Jaya, I.G.N.M. Andriyana, Y. Department of Statistics Faculty of Mathematics and Science Universitas Padjadjaran Sumedang West Java45363 Indonesia

Longitudinal data have a complex structure. Unlike cross-sectional data, there are more associated features with longitudinal data. Associated features to longitudinal data including the heterogeneity between subjects, the serial correlation and the characteristics of changes over time. Therefore exploring longitudinal data is more complicated than that of cross-sectional data. Various methods were developed by many researchers to explore longitudinal data. In general, there are at least three things that could be described from longitudinal data: mean structure, variance structure, and correlation structure. visual methods and subject-specific profile were studied for such datasets to detect these structures. Methods were applied to data sets from upper respiratory tract infection data in Bandung City. From the result, we can describe the growth patterns at both group and individual level and then selecting suitable statistical models which contain within and between groups variations. © Published under licence by IOP Publishing Ltd.

关键词： exploratory analysis longitudinal data subject-specific profile upper respiratory tract infection data visual methods

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Towards visualizing Large Scale Evolving Clones 19

Towards Visualizing Large Scale Evolving Clones

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IEEE/ACM 41st International conference on Software Engineering - Software Engineering in Practice (ICSE-SEIP)

作者： Mondal, Debajyoti Mondal, Manishankar Roy, Chanchal K. Schneider, Kevin Wang, Shisong Li, Yukun Univ Saskatchewan Dept Comp Sci Saskatoon SK Canada

ISBN: (纸本)9781728117645

Software systems in this big data era are growing larger and becoming more intricate. Tracking and managing code clones in such evolving software systems are challenging tasks. To understand how clone fragments are evolving, the programmers often analyze the co-evolution of clone fragments manually to decide about refactoring, tracking, and bug removal. Such manual analysis is infeasible for a large number of clones with clones evolving over hundreds of software revisions. We propose a visual analytics framework, that leverages big data visualization techniques to manage code clones in large software systems. Our framework combines multiple information-linked zoomable views, where users can explore and analyze clones through interactive exploration in real time. We discuss several scenarios where our framework may assist developers in real-life software development and clone maintenance. Experts' reviews reveal many future potentials of our framework.

关键词： Big data data analysis data visualisation program debugging program diagnostics software maintenance

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Facilitating and Managing Machine Learning and data analysis Tasks in Big data Environments using Web and Microservice Technologies 11

Facilitating and Managing Machine Learning and Data Analysis...

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11th International conference on Management of Digital EcoSystems (MEDES)

作者： Shahoud, Shadi Gunnarsdottir, Sonja Khalloof, Hatem Duepmeier, Clemens Hagenmeyer, Veit Karlsruhe Inst Technol 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： Microservice Web-based Applications Big data data Analytic Machine Learning

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STULL: Unbiased Online Sampling for visual exploration of Large Spatiotemporal data

STULL: Unbiased Online Sampling for Visual Exploration of La...

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IEEE Symposium on visual Analytics Science and Technology (VAST)

作者： Guizhen Wang Jingjing Guo Mingjie Tang José Florencio de Queiroz Neto Calvin Yau Anas Daghistani Morteza Karimzadeh Walid G. Aref David S. Ebert Purdue University Chinese Academy of Science Federal University of Ceara Umm Al-Qura University University of Colorado Boulder University of Oklahoma

ISBN: (数字)9781728180090

ISBN: (纸本)9781728180106

Online sampling-supported visual analytics is increasingly important, as it allows users to explore large datasets with acceptable approximate answers at interactive rates. However, existing online spatiotemporal sampling techniques are often biased, as most researchers have primarily focused on reducing computational latency. Biased sampling approaches select data with unequal probabilities and produce results that do not match the exact data distribution, leading end users to incorrect interpretations. In this paper, we propose a novel approach to perform unbiased online sampling of large spatiotemporal data. The proposed approach ensures the same probability of selection to every point that qualifies the specifications of a user's multidimensional query. To achieve unbiased sampling for accurate representative interactive visualizations, we design a novel data index and an associated sample retrieval plan. Our proposed sampling approach is suitable for a wide variety of visual analytics tasks, e.g., tasks that run aggregate queries of spatiotemporal data. Extensive experiments confirm the superiority of our approach over a state-of-the-art spatial online sampling technique, demonstrating that within the same computational time, data samples generated in our approach are at least 50% more accurate in representing the actual spatial distribution of the data and enable approximate visualizations to present closer visual appearances to the exact ones.

关键词： data visualization Spatiotemporal phenomena visual analytics Indexes Task analysis Spatial databases Time factors

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BD 2019 - Proceedings of the 3rd conference on Biographical data in a Digital World 2019

BD 2019 - Proceedings of the 3rd Conference on Biographical ...

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3rd conference on Biographical data in a Digital World, BD 2019

The proceedings contain 10 papers. The topics discussed include: a quantitative analysis of biographical data from Ainm, the Irish-language biographical database;reconstructing data for modelling collective biography: a case of zemstvo deputies in Russia in the second half of XIX century;linked data - a paradigm shift for publishing and using biography collections on the semantic web;data exchange in practice: towards a prosopographical API;mining the biographical dictionary of Republican China. From print to network exploration;large-scale data harvesting for biographical data;reassembling the lives of Finnish prisoners of the second world war on the semantic web;and exiled but not forgotten: investigating commemoration of musicians in Vienna after 1945 through visual analytics.

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SilkViser: A visual Explorer of Blockchain-based Cryptocurrency Transaction data

SilkViser: A Visual Explorer of Blockchain-based Cryptocurre...

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IEEE Symposium on visual Analytics Science and Technology (VAST)

作者： Zengsheng Zhong Shuirun Wei Yeting Xu Ying Zhao Fangfang Zhou Feng Luo Ronghua Shi School of Computer Sciences and Engineering Central South University China Chongqing Engineering Technology Research Center for Information Management in Development Chongqing Technology and Business University China

ISBN: (数字)9781728180090

ISBN: (纸本)9781728180106

Many blockchain-based cryptocurrencies provide users with online blockchain explorers for viewing online transaction data. However, traditional blockchain explorers mostly present transaction information in textual and tabular forms. Such forms make understanding cryptocurrency transaction mechanisms difficult for novice users (NUsers). They are also insufficiently informative for experienced users (EUsers) to recognize advanced transaction information. This study introduces a new online cryptocurrency transaction data viewing tool called SilkViser. Guided by detailed scenario and requirement analyses, we create a series of appreciating visualization designs, such as paper ledger-inspired block and blockchain visualizations and ancient copper coin-inspired transaction visualizations, to help users understand cryptocurrency transaction mechanisms and recognize advanced transaction information. We also provide a set of lightweight interactions to facilitate easy and free data exploration. Moreover, a controlled user study is conducted to quantitatively evaluate the usability and effectiveness of SilkViser. Results indicate that SilkViser can satisfy the requirements of NUsers and EUsers. Our visualization designs can compensate for the inexperience of NUsers in data viewing and attract potential users to participate in cryptocurrency transactions.

关键词： Blockchain Bitcoin data visualization Tools data analysis visual analytics data mining

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Explainable Rule-Based Clustering based on Cyclic Probabilistic Causal Models

Explainable Rule-Based Clustering based on Cyclic Probabilis...

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International conference on Information visualisation (iv)

作者： Evgenii E. Vityaev Bayar Pak Sobolev institute of mathematics of SD RAS Novosibirsk State University Novosibirsk Russia Novosibirsk State University Novosibirsk Russia

ISBN: (数字)9781728191348

ISBN: (纸本)9781728191355

Discovering and visualizing data clusters is an important AI/ML and visual knowledge discovery task. This paper proposes a new data clustering approach inspired by the concept of causal models used in cognitive science. This approach is based on the causal relations between features, instead of similarity of features in traditional clustering approaches. The concept of the center of the cluster is formalized in accordance with prototype theory of concepts explored in the cognitive science in terms of a correlational structure of perceived attributes. Traditionally in AI and cognitive science, causal models are described using Bayesian networks. However, Bayesian networks do not support cycles. This paper proposes a novel mathematical apparatus probabilistic generalization of formal concepts - for describing causal models via cyclical causal relations (fixpoints of causal relations) that form a clusters and generate a clusters prototypes. This approach is illustrated with a case study.

关键词： visualization Prototypes Probabilistic logic Cognitive science Bayes methods Mathematical model Task analysis

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