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FacIt: Factorizing Tensors into Interpretable and Scrutinizable Patterns

FacIt: Factorizing Tensors into Interpretable and Scrutiniza...

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16th IEEE Symposium on visualization for Cyber Security (VizSec) / IEEE visualization conference (IEEE VIS)

作者： Wen, Xidao Lin, Yu-Ru Ahn, Yongsu Pelechrinis, Konstantinos Liu, Xi Cao, Nan Univ Pittsburgh Sch Comp & Informat Pittsburgh PA 15260 USA Texas A&M Univ Program Comp Engn College Stn TX 77843 USA TongJi Univ Coll Design & Innovat Shanghai Peoples R China

ISBN: (纸本)9781728149417

Tensor Factorization has been widely used in many fields to discover latent patterns from multidimensional data. Interpreting or scrutinizing the tensor factorization results are, however, by no means easy. We introduce FacIt, a generic visual analytic system that directly factorizes tensor-formatted data into a visual representation of patterns to facilitate result interpretation, scrutinization, information query, as well as model selection. Our design consists of (i) a suite of model scrutinizing and inspection tools that allows efficient tensor model selection (commonly known as rank selection problem) and (ii) an interactive visualization design that empowers users with both characteristics- and content-driven pattern discovery. We demonstrate the effectiveness of our system through usage scenarios with policy adoption analysis.

关键词： Human-centered computing visualization visualization application domains visual analytics

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Exploring the Power of visual Features for the Recommendation of Movies 19

Exploring the Power of Visual Features for the Recommendatio...

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27th ACM conference on User Modeling, Adaptation and Personalization (ACM UMAP)

作者： Rimaz, Mohammad Hossein Elahi, Mehdi Bakhshandegan, Farshad Trattner, Christoph Hosseini, Reza Tkalcic, Marko KN Toosi Univ Technol Tehran Iran Free Univ Bozen Bolzano Bolzano Bozen Italy Karlsruhe Inst Technol Karlsruhe Germany Univ Bergen Bergen Norway PmOne Analyt GmbH Paderborn Germany

ISBN: (纸本)9781450360210

In this paper, we explore the potential of using visual features in movie Recommender Systems. This type of content features can be extracted automatically without any human involvement and have been shown to be very effective in representing the visual content of movies. We have performed the following experiments, using a large dataset of movie trailers: (i) Experiment A: an exploratory analysis as an initial investigation on the data, and (ii) Experiment B: building a movie recommender based on the visual features and evaluating the performance. The observed results have shown promising potential of visual features in representing the movies and the excellency of recommendation based on these features.

关键词： movie recommender systems multimedia content-based recommendation cold start film video

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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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Interactive visualization of Urban Areas of Interest: A Parameter-Free and Efficient Footprint Method 19

Interactive Visualization of Urban Areas of Interest: A Para...

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12th ACM International conference on Web Search and data Mining (WSDM)

作者： Li, Mingzhao Bao, Zhifeng Choudhury, Farhana Sellis, Timos RMIT Univ Melbourne Vic Australia Swinburne Univ Technol Hawthorn Vic Australia

ISBN: (纸本)9781450359405

Understanding urban areas of interest (AOIs) is essential to decision making in various urban planning and exploration tasks. Such AOIs can be computed based on the geographic points that satisfy the user query. In this demo, we present an interactive visualization system of urban AOIs, supported by a parameter-free and efficient footprint method called AOI-shapes. Compared to state-of-the-art footprint methods, the proposed AOI-shapes (i) is parameter-free, (ii) is able to recognize multiple regions/outliers, (iii) can detect inner holes, and (iv) supports the incremental method. We demonstrate the effectiveness and efficiency of the proposed AOI-shapes based on a real-world real estate dataset in Australia. A preliminary version of the online demo can be accessed at http://***/.

关键词： area of interest geographic visualization aoi-shapes footprint visual search

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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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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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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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Undergraduates building spacecraft: Using inquiry-based methods to teach spacecraft engineering

Undergraduates building spacecraft: Using inquiry-based meth...

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Accelerating Space Commerce, exploration, and New Discovery conference, ASCEND 2020

作者： Bering, Edgar A. Talbot, Robert Khan, Shuhab Moges, Mequanint Jacobs, Laura T. Gamblin, Rachel B. Greer, Michael Gunawan, Bryan Hernandez, Elizabeth Lehnen, Jamie Piña, Megan Porat, Itay Prince, John R. Pessoa, Ana Gabriela Ulinski, Alexandra Hampton, Donald Mölders, Nicole Thorsen, Denise University of Houston HoustonTX77204 United States STEM Center University of Houston HoustonTX77204 United States Geophysical Institute University of Alaska Fairbanks FairbanksAK99775 United States University of Alaska Fairbanks FairbanksAK99775 United States

ISBN: (数字)9781624106088

ISBN: (纸本)9781624106088

The Undergraduate Student Instrumentation Project (USIP) was a NASA program to engage undergraduate students in rigorous scientific research, for the purposes of innovation and developing the next generation of professionals in space research. The program is student led and executed from initial ideation of research objectives to the design and deployment of scientific payloads. The University of Houston was selected twice to participate in the USIP programs. The first program (USIP_UH I) ran from 2013 to 2016. USIP_UH ii ran from 2016 to 2019. USIP_UH I (USIP_UH ii) at the University of Houston was (is) composed of eight (seven) research teams developing six (seven), distinct, balloon-based scientific instruments. This project was a for-credit course two years in duration. During the six years in the program so far, 39 students participated. This program was transformative for these students. Most of these students have graduated. More than half graduated with honors. All graduates are employed or entered grad school (~11). Five are employed at NASA/JSC. The program is continuing with internal support and 23 students enrolled. For the faculty, the project was one long exercise in professional development. This paper reviews the pedagogical approaches used and lessons learned. © 2020 The MITRE Corporation. All Rights Reserved.

关键词： Spacecrafts NASA Programs Total Electron Content Systems Engineering Gravity Waves Atmospheric Conditions Radio Telemetry Balloon Flight data analysis MATLAB

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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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