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Understanding a sequence of sequences: visual exploration of categorical states in lake sediment cores

IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2018年第1期24卷 66-76页

作者： Unger, Andrea Drager, Nadine Sips, Mike Lehmann, Dirk J. GFZ German Res Ctr Geosci Potsdam Germany Univ Magdeburg Magdeburg Germany

This design study focuses on the analysis of a time sequence of categorical sequences. Such data is relevant for the geoscientific research field of landscape and climate development. It results from microscopic analysis of lake sediment cores. The goal is to gain hypotheses about landscape evolution and climate conditions in the past. To this end, geoscientists identify which categorical sequences are similar in the sense that they indicate similar conditions. Categorical sequences are similar if they have similar meaning (semantic similarity) and appear in similar time periods (temporal similarity). For data sets with many different categorical sequences, the task to identify similar sequences becomes a challenge. Our contribution is a tailored visual analysis concept that effectively supports the analytical process. Our visual interface comprises coupled visualizations of semantics and temporal context for the exploration and assessment of the similarity of categorical sequences. Integrated automatic methods reduce the analytical effort substantially. They (1) extract unique sequences in the data and (2) rank sequences by a similarity measure during the search for similar sequences. We evaluated our concept by demonstrations of our prototype to a larger audience and hands-on analysis sessions for two different lakes. According to geoscientists, our approach fills an important methodological gap in the application domain.

关键词： visualization in Earth Science Time Series data Categorical data Design Study

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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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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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Development of character-filled junior high school teachers books based on integrated learning threaded models with visual sense themes and optical instruments

Development of character-filled junior high school teachers ...

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2018 International conference on Research and Learning of Physics, ICRLP 2018

作者： Refmainawita Ratnawulan Department of Physics Universitas Negeri Padang Padang25131 Indonesia

The general objective of this study was to develop integrated science-based teacher books with character based threaded model integrated learning. At the Preliminary Research stage, a need analysis is conducted. The method used to do need assessment is a descriptive research method. Respondents selected in this study were science teachers at SMPN 2 Nan Sabaris in the academic year 2017/2018. The type of data in this study is primary data. The type of data in this study is primary data. Where the teacher's book is a guideline for teachers in carrying out learning including preparation, implementation, and assessment as well as guidelines for the use of student books. The purpose of this study was to develop the results of a character-based science-based teacher book on integrated threaded learning with the senses of vision and optical devices. This book was prepared to get a clear and detailed picture of carrying out learning activities. The development phase is now from the design of the teacher's book, then the teacher's book is validated by experts and practitioners. This type of research is research and development using a Plomp model consisting of preliminary research (preliminary analysis) prototyping and assessment. in this study are data analysis, validation data, and practicality, and effectiveness data. The research instruments consisted of sheets of interview sheets, and teacher response questionnaires. This data analysis technique is descriptive percentage. The results of the research at the preliminary research stage are the results of a needs analysis and context analysis of the opportunities for SMP-based science teacher books with integrated threaded model-based learning with the senses of vision and optical devices. The result of prototype I is the teacher's book is valid with a value of 0.86. The result of prototype ii is the teacher's book is practical, and the result of prototype iiI is the teacher's book is very practical. Furthermore, the result

关键词： data handling

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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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Multi-target Image Detection Using Pigeon-Inspired Optimization based Species Mechanism

Multi-target Image Detection Using Pigeon-Inspired Optimizat...

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Information Technology,Big data and Artificial Intelligence (ICIBA), International conference on IEEE International conference on

作者： Long Wang Jing Fan Xiao Wei Xincheng Cui Dongjie Wu National Computer System Engineering Research Institute of China Beijing China State-owned Changhong Machinery Factory Guilin China

ISBN: (数字)9781728152240

ISBN: (纸本)9781728152257

Multi-target detection (MTD) has a wide range of applications in target recognition, visual tracking, and other fields. The task of MTD is to find the sub-image set with the highest matching degree using the template image set from the source image. Therefore, this problem can be mapped to a multi-modal optimization problem. This paper combines the modified pigeon-inspired optimization (PIO) and species-based mechanism to perform multi-template matching tasks. To enhance the performance of our proposed method, the PIO is modified as follows: (1) the fitness of the seed is chosen as the switch signal of the map and compass operator and landmark operator; (2) in landmark operator, the halve mechanism is removed and a perturbation factor is introduced to enhance the exploration and exploitation ability. The species-based mechanism is utilized to search for multiple extreme points in parallel with multiple subpopulations. In addition, an expel mechanism is proposed to avoid the unnecessary fitness evaluation around the detected optima. Simulation results demonstrate that our proposed method performs better in detection accuracy and efficiency.

关键词： Optimization Statistics Sociology Compass Particle swarm optimization Switches Task analysis

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HiPiler: visual exploration of Large Genome Interaction Matrices with Interactive Small Multiples

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2018年第1期24卷 522-531页

作者： Lekschas, Fritz Bach, Benjamin Kerpedjiev, Peter Gehlenborg, Nils Pfister, Hanspeter Harvard Univ Cambridge MA 02138 USA Harvard Med Sch Boston MA USA

This paper presents an interactive visualization interface HiPiler for the exploration and visualization of regions-of-interest in large genome interaction matrices. Genome interaction matrices approximate the physical distance of pairs of regions on the genome to each other and can contain up to 3 million rows and columns with many sparse regions. Regions of interest (ROls) can be defined, e.g., by sets of adjacent rows and columns, or by specific visual patterns in the matrix. However, traditional matrix aggregation or pan-and-zoom interfaces fail in supporting search, inspection, and comparison of ROls in such large matrices. In HiPiler, ROls are first-class objects, represented as thumbnail-like "snippets". Snippets can be interactively explored and grouped or laid out automatically in scatterplots, or through dimension reduction methods. Snippets are linked to the entire navigable genome interaction matrix through brushing and linking. The design of HiPiler is based on a series of semi-structured interviews with 10 domain experts involved in the analysis and interpretation of genome interaction matrices. We describe six exploration tasks that are crucial for analysis of interaction matrices and demonstrate how HiPiler supports these tasks. We report on a user study with a series of data exploration sessions with domain experts to assess the usability of HiPiler as well as to demonstrate respective findings in the data.

关键词： Interactive Small Multiples Matrix Comparison Biomedical visualization Genomics

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CCVis: visual analytics of student online learning behaviors using course clickstream data

CCVis: Visual analytics of student online learning behaviors...

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2019 conference on visualization and data analysis, VDA 2019

作者： Goulden, Maggie Celeste Gronda, Eric Yang, Yurou Zhang, Zihang Tao, Jun Wang, Chaoli Duan, Xiaojing Ambrose, G. Alex Abbott, Kevin Miller, Patrick Trinity College Dublin Ireland University of Maryland Baltimore County United States Zhejiang University China University of Notre Dame United States

As more and more college classrooms utilize online plat- forms to facilitate teaching and learning activities, analyzing stu- dent online behaviors becomes increasingly important for instruc- tors to effectively monitor and manage student progress and per- formance. In this paper, we present CCVis, a visual analytics tool for analyzing the course clickstream data and exploring student online learning behaviors. Targeting a large college introductory course with over two thousand student enrollments, our goal is to investigate student behavior patterns and discover the possi- ble relationships between student clickstream behaviors and their course performance. We employ higher-order network and struc- tural identity classification to enable visual analytics of behavior patterns from the massive clickstream data. CCVis includes four coordinated views (the behavior pattern, behavior breakdown, clickstream comparative, and grade distribution views) for user interaction and exploration. We demonstrate the effectiveness of CCVis through case studies along with an ad-hoc expert evalua- tion. Finally, we discuss the limitation and extension of this work. © 2019, Society for Imaging Science and Technology.

关键词： Students

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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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