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17th Wuhan International conference on E-Business, WHICEB 2018

The proceedings contain 74 papers. The special focus in this conference is on E-Business. The topics include: e-Control of Online Customers: When Companies Make Customers Compliant … Lessons for 21st Century China;A Bibliometric Review of Research Trends in Social CRM;a Hybrid Model to Analyze Air Pollution Spread Scales in Xi' an and Surrounding Cities;a Net Loan Monitoring Platform for University Students Based on visual Micro-blog;a Personalization-Privacy Paradox in Usage of Mobile Health Services: A Game Theoretic Perspective;a Research about Consumer’s Usage Intention to Green Finance Products----Taking the Alipay’s Ant forest as the Example;a Research on the Influential Factors of Listing Sales Based on Online Information in Short Rental Markets;a Study on Consumers' Learning Effect in the Price Reduction Auction: a case study of Gongtianxia;a Study on Online-Shopping Opinion Leaders Based on Massive data;a Text Mining Based Approach for Mining Customer Attribute data on Undefined Quality Problem;an analysis on the Development Model of China’s County-level E-commerce;an analysis with Evolutionary Game of the Resource Sharing in Supply Chain Under Cloud Platform;An exploration of Cross-border E-commerce Consumer Feedbacks: An LDA Approach;assessing Customer-Based Brand Equity of E-Commerce Platform Based on Promotion Benefits;can the Adjustment of Consumer Finance Credit Line Cause Impulse Purchase?----An analysis from the Perspective of E-commerce Promotion;considering correlation retarded growth for personalized recommendation in social tagging;consumer Coupon Redemption Behavior Prediction on B2C E-commerce;determinants of online sellers’ advertising: An empirical study on Chinese C2C market.

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Identification of haptic exploration procedures over textile surfaces with a Leap Motion Controller 13

Identification of haptic exploration procedures over textile...

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13th Biennial Norddesign conference, NordDesign 2018

作者： Gussen, Lars C. Ellerich, Max Schmitt, Robert H. Werkzeugmaschinenlabor der RWTH Aachen Department Quality Intelligence Aachen Germany Werkzeugmaschinenlabor der RWTH Aachen Department Quality Intelligence Aachen Germany Werkzeugmaschinenlabor der RWTH Aachen Production Metrology and Quality Management Aachen Germany

ISBN: (纸本)9789176851852

The development of high-quality products, which simultaneously address the customer's needs, is a key challenge for companies nowadays. Besides features and technology a customer assesses a product by its sensory characteristics, i.e. primarily on the basis of visual, acoustics and haptic perception. Although the general importance of the sensory design of products has been recognized by industry, methodical aspects regarding the realization thereof are still insufficient in certain areas. Besides e.g. the simulation of sensory perception, the reproduction of customers' habitus to sensorily approach and explore products is still not fully understood. In this context, especially the manner of haptic exploration of product surfaces plays an important role during the overall assessment of quality. Knowing the haptic exploration is necessary for the technical replication of customers' perception e.g. with special haptic sensors. Haptic characteristics of products or materials are explored performing specific movements of the hands, so-called exploration procedures. An exploration procedure is a movement pattern, which is motivated by the object properties such as shape, size and surface. The aim of the work is to devise an automatic system, which is able to record specific surface exploratory procedures and to effectively identify representative gestures by means of machine learning. It is assumed that customers explore surfaces in their own way;however, the gestures they use are similar between customers to a great extent and can therefore be clustered into homogeneous groups of gestures. For recording the exploratory movements and the human surface interaction, a Leap Motion Controller by the American company Leap Motion, Inc. is applied. To investigate the usability of the Leap Motion Controller for the intended aim, two empirical studies are conducted by asking subjects to explore a textile surface. The extracted data from the controller of the first study is used t

关键词： Textiles

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Gaussian Cubes: Real-Time Modeling for visual exploration of Large Multidimensional datasets

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2017年第1期23卷 681-690页

作者： Wang, Zhe Ferreira, Nivan Wei, Youhao Bhaskar, Aarthy Sankari Scheidegger, Carlos Univ Arizona Tucson AZ 85721 USA Univ Fed Pernambuco Recife PE Brazil

Recently proposed techniques have finally made it possible for analysts to interactively explore very large datasets in real time. However powerful, the class of analyses these systems enable is somewhat limited: specifically, one can only quickly obtain plots such as histograms and heatmaps. In this paper, we contribute Gaussian Cubes, which significantly improves on state-of-the-art systems by providing interactive modeling capabilities, which include but are not limited to linear least squares and principal components analysis (PCA). The fundamental insight in Gaussian Cubes is that instead of precomputing counts of many data subsets (as state-of-the-art systems do), Gaussian Cubes precomputes the best multivariate Gaussian for the respective data subsets. As an example, Gaussian Cubes can fit hundreds of models over millions of data points in well under a second, enabling novel types of visual exploration of such large datasets. We present three case studies that highlight the visualization and analysis capabilities in Gaussian Cubes, using earthquake safety simulations, astronomical catalogs, and transportation statistics. The dataset sizes range around one hundred million elements and 5 to 10 dimensions. We present extensive performance results, a discussion of the limitations in Gaussian Cubes, and future research directions.

关键词： data modeling dimensionality reduction interactive visualization data cubes

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Vision-Based Autonomous Underwater Swimming in Dense Coral for Combined Collision Avoidance and Target Selection

Vision-Based Autonomous Underwater Swimming in Dense Coral f...

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25th IEEE/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Manderson, Travis Higuera, Juan Camilo Gamboa Cheng, Ran Dudek, Gregory McGill Univ Sch Comp Sci Mobile Robot Lab Montreal PQ Canada

ISBN: (纸本)9781538680940

We address the problem of learning vision-based, collision-avoiding, and target-selecting controllers in 3D, specifically in underwater environments densely populated with coral reefs. Using a highly maneuverable, dynamic, six-legged (or flippered) vehicle to swim underwater, we exploit real time visual feedback to make close-range navigation decisions that would be hard to achieve with other sensors. Our approach uses computer vision as the sole mechanism for both collision avoidance and visual target selection. In particular, we seek to swim close to the reef to make observations while avoiding both collisions and barren, coral-deprived regions. To carry out path selection while avoiding collisions, we use monocular image data processed in real time. The proposed system uses a convolutional neural network that takes an image from a forward-facing camera as input and predicts unscaled and relative path changes. The network is trained to encode our desired obstacle-avoidance and reef-exploration objectives via supervised learning from human-labeled data. The predictions from the network are transformed into absolute path changes via a combination of a temporally-smoothed proportional controller for heading targets and a low-level motor controller. This system enables safe and autonomous coral reef navigation in underwater environments. We validate our approach using an untethered and fully autonomous robot swimming through coral reef in the open ocean. Our robot successfully traverses 1000 m of the ocean floor collision-free while collecting close-up footage of coral reefs.

关键词： Navigation Cameras Robot vision systems Task analysis visualization Neural networks

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Multi-Granular Trend Detection for Time-Series analysis

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2017年第1期23卷 661-670页

作者： van Goethem, Arthur Staals, Frank Loffler, Maarten Dykes, Jason Speckmann, Bettina TU Eindhoven Eindhoven Netherlands Aarhus Univ MADALGO DK-8000 Aarhus C Denmark Univ Utrecht NL-3508 TC Utrecht Netherlands City Univ London London England

Time series (such as stock prices) and ensembles (such as model runs for weather forecasts) are two important types of one-dimensional time-varying data. Such data is readily available in large quantities but visual analysis of the raw data quickly becomes infeasible, even for moderately sized data sets. Trend detection is an effective way to simplify time-varying data and to summarize salient information for visual display and interactive analysis. We propose a geometric model for trend-detection in one-dimensional time-varying data, inspired by topological grouping structures for moving objects in two-or higher-dimensional space. Our model gives provable guarantees on the trends detected and uses three natural parameters: granularity, support-size, and duration. These parameters can be changed on-demand. Our system also supports a variety of selection brushes and a time-sweep to facilitate refined searches and interactive visualization of (sub-)trends. We explore different visual styles and interactions through which trends, their persistence, and evolution can be explored.

关键词： Interactive exploration Trend Detection Time Series

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Outcrop and subsurface geology applied to drilling, sweet spot and target zone detection of resource shales: The Woodford example

Outcrop and subsurface geology applied to drilling, sweet sp...

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SPE/AAPG/SEG Unconventional Resources Technology conference 2018, URTC 2018

作者： Slatt, Roger M. Galvis-Portillo, Henry Becerra-Rondon, Daniella Ekwunife, Ifunanya C. Brito, Richard Zhang, Jing Molinares, Carlos Torres, Emilio Duarte, David Milad, Benmadi Institute of Reservoir Characterization School of Geology and Geophysics University of Oklahoma United States Department of Geosciences University of Calgary Calgary Canada Baker Hughes GE company HoustonTX United States Newfield Exploration Co. The WoodlandsTX United States

Outcrop and subsurface cores are sampled for a variety of low-cost rock analyses, including: Chemical (XRF) and mineralogical (XRF, XRD, petrography) composition and stratification, hardness (Leeb rebound hardness tester);microfabric, porosity and permeability (SEM);organic content (TOC, RockEval, biomarkers);fractures/folds (visual observations);2D and 3D seismic volumes, and biostratigraphy (if biota are present) This data set can then be combined to determine depositional environment, measure long-/short-distance lateral continuity of strata away from the wellbore, determine fracturability (brittle vs. ductile rock), categorize reservoir quality (porosity and permeability) and predict the potential for fractures to hold open or to close on proppant. These features provide important screening criteria for geoscientists and decision-makers. An example of the analysis, utility and application of these techniques is provided of the Woodford Formation, which is currently the 3rd most active hydrocarbon play in the U.S. Copyright 2018, Unconventional Resources Technology conference (URTeC).

关键词： Porosity

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A Synthetic visual analysis System for Time-Varying Network

A Synthetic Visual Analysis System for Time-Varying Network

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IEEE International conference on Computer and Communication Engineering Technology (CCET)

作者： Jian Huang Xiushan Zhang Department of Computer Science Naval University of Engineering Wuhan China

ISBN: (纸本)9781538674383

In recent years, with the rapid development of computer science and information technology, the world is filled and propelled of time-varying and multivariate data. The enormous growth of data in the last decades led to big data challenge in the network security field. Traditional visual analysis method for the time-varying network is inadequate. Efficient methods for visual clutter reduction, network structure exploration and network behavior detection are needed. In this paper, we propose two methods: A triangular-based algorithm for time-varying behavior presentation and an improved brush PCP aims to assist the visual analysis task in time-varying network pattern detection. A synthetic visual analysis system is designed and implemented on the basis of these two novel methods. Our system is capable of visually analyzing vast amounts of network data. To better describe and demonstrate the usefulness and performance of our system, we utilize the ChinaVis2015 Challenge dataset as a case study.

关键词： Layout visualization Heuristic algorithms data visualization Telecommunication traffic Big data Clutter

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Interactive data exploration through Multiple visual Contexts with Different data Models and Dimensions 21

Interactive Data Exploration through Multiple Visual Context...

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21st International conference on Information visualisation (IV)

作者： Phi Giang Pham Huang, Mao Lin Quang Vinh Nguyen Univ Technol Sydney NSW Australia Cantho Tech Econ Coll Can Tho Vietnam Western Sydney Univ Sydney NSW Australia

ISBN: (纸本)9781538608319

visual analytics plays a key role in bringing insights to audiences who are interested and dedicated in data exploration. In the area of relational data, many advanced visualization tools and frameworks are proposed in order to dealing with such data features. However, the majority of those have not greatly considered the whole process from data-model mining to query utilizing on dimensions and data values, which might cause interruption to exploration activities. This paper presents a new interactive exploration framework for relational data analysis through automatic interconnection of data models, data dimensions and data values. The basic idea is to construct a relative and switchable chain of those context representations by integrating our previous techniques on node-link, parallel coordinate and scatterplot graphics. This approach enables users to flexibly make relative queries on desired contexts at any stage of exploration for deep data understanding. The result from a typical case study for the framework demonstration indicates that our approach is able to handle the addressed challenge.

关键词： visual analytics dynamic visual query interactive user interface

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4th International conference of Pioneer Computer Scientists, Engineers and Educators, ICPCSEE 2018

4th International Conference of Pioneer Computer Scientists,...

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4th International conference of Pioneer Computer Scientists, Engineers and Educators, ICPCSEE 2018

ISBN: (纸本)9789811322020

The proceedings contain 116 papers. The special focus in this conference is on Pioneer Computer Scientists, Engineers and Educators. The topics include: The construction approach of statutes database;weighted clustering coefficients based feature extraction and selection for collaboration relation prediction;a representation-based pseudo nearest neighbor classifier;Research on network intrusion data based on KNN and feature extraction algorithm;PSHCAR: A position-irrelevant scene-aware human complex activities recognizing algorithm on mobile phones;visual-based character embedding via principal component analysis;a novel experience-based exploration method for q-learning;overlapping community detection based on community connection similarity of maximum clique;heterogeneous network community detection algorithm based on maximum bipartite clique;the competence of volunteer computing for mapreduce big data applications;novel algorithm for mining frequent patterns of moving objects based on dictionary tree improvement;malCommunity: A graph-based evaluation model for malware family clustering;negative influence maximization in social networks;mining correlation relationship of users from trajectory data;context-aware network embedding via variation autoencoders for link prediction;SFSC: Segment feature sampling classifier for time series classification;Fuzzy C-mean clustering based: LEO satellite handover;an improved apriori algorithm based on matrix and double correlation profit constraint;mining and ranking important nodes in complex network by K-shell and degree difference;representation learning for knowledge graph with dynamic step;research on the security protection scheme for container-based cloud platform node based on blockchain technology;an improved K-means parallel algorithm based on cloud computing;statistical learning-based prediction of execution time of data-intensive program under hadoop2.0;E-CAT: Evaluating crowdsourced android testing.

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4th International conference of Pioneer Computer Scientists, Engineers and Educators, ICPCSEE 2018

4th International Conference of Pioneer Computer Scientists,...

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4th International conference of Pioneer Computer Scientists, Engineers and Educators, ICPCSEE 2018

ISBN: (纸本)9789811322051

The proceedings contain 54 papers. The special focus in this conference is on Pioneer Computer Scientists, Engineers and Educators. The topics include: The construction approach of statutes database;weighted clustering coefficients based feature extraction and selection for collaboration relation prediction;a representation-based pseudo nearest neighbor classifier;Research on network intrusion data based on KNN and feature extraction algorithm;PSHCAR: A position-irrelevant scene-aware human complex activities recognizing algorithm on mobile phones;visual-based character embedding via principal component analysis;a novel experience-based exploration method for q-learning;overlapping community detection based on community connection similarity of maximum clique;heterogeneous network community detection algorithm based on maximum bipartite clique;the competence of volunteer computing for mapreduce big data applications;novel algorithm for mining frequent patterns of moving objects based on dictionary tree improvement;malCommunity: A graph-based evaluation model for malware family clustering;negative influence maximization in social networks;mining correlation relationship of users from trajectory data;context-aware network embedding via variation autoencoders for link prediction;SFSC: Segment feature sampling classifier for time series classification;Fuzzy C-mean clustering based: LEO satellite handover;an improved apriori algorithm based on matrix and double correlation profit constraint;mining and ranking important nodes in complex network by K-shell and degree difference;representation learning for knowledge graph with dynamic step;research on the security protection scheme for container-based cloud platform node based on blockchain technology;an improved K-means parallel algorithm based on cloud computing;statistical learning-based prediction of execution time of data-intensive program under hadoop2.0;E-CAT: Evaluating crowdsourced android testing.

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