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检索条件"任意字段=Conference on Visual Data Exploration and Analysis II"

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A visual analytics system of data gathered from colonial seabirds 23

A visual analytics system of data gathered from colonial sea...

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23rd International conference on the Information visualisation (IV) - Incorporating Biomedical visualization, Learning Analytics and Geometric Modelling and Imaging

作者： Palleschi, Alessia Crielesi, Matteo Univ Roma La Sapienza Engn Comp Sci Rome Italy

ISBN: (纸本)9781728128382

visual environment techniques can support an easy exploration of large datasets. A visual environment has been developed to explore the breeding data of a monogamous and nest-site faithful seabird, the Scopoli's shearwater. The aim was to provide a tool, easy-to-use for ornithologists, to examine the history of the nests, and the relationships of the various factors affecting the variability in the breeding success during a four year time period. The tool is based on four coordinated views, linked to each other and to the dataset, each showing a different grouping of data according with spatial, temporal, and categorical criteria. This tool can help the ornithologists to pick up easily the similarities and differences between the nests and offers a new instrument for the first exploration of data.

关键词： visual Analytics Shearwater breeding biology analysis of breeding data pair fidelity nest fidelity

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VizCertify: A framework for secure visual data exploration 6

VizCertify: A framework for secure visual data exploration

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6th IEEE International conference on data Science and Advanced Analytics (DSAA)

作者： De Stefani, Lorenzo Spiegelberg, Leonhard F. Upfal, Eli Kraska, Tim Brown Univ Dept Comp Sci Providence RI 02912 USA MIT CSAIL 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (纸本)9781728144931

Recently, there have been several proposals to develop visual recommendation systems. The most advanced systems aim to recommend visualizations, which help users to find new correlations or identify an interesting deviation based on the current context of the user's analysis. However, when recommending a visualization to a user, there is an inherent risk to visualize random fluctuations rather than solely true patterns: a problem largely ignored by current techniques. In this paper, we present VizCertify, a novel framework to improve the performance of visual recommendation systems by quantifying the statistical significance of recommended visualizations. The proposed methodology allows to control the probability of misleading visual recommendations using both classical statistical testing procedures and a novel application of the Vapnik Chervonenkis (VC) dimension towards visualization recommendation which results in an effective criterion to decide whether a recommendation corresponds to a true phenomenon or not.

关键词： visualization Recommendation Systems FWER data exploration visual Recommendation

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An Intelligent visual Big data Analytics Framework for Supporting Interactive exploration and visualization of Big OLAP Cubes

An Intelligent Visual Big Data Analytics Framework for Suppo...

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International conference on Information visualisation (IV)

作者： Carlos Ordonez Zhibo Chen Alfredo Cuzzocrea Javier Garcia-Garcia University of Houston USA Haliburton USA LORIA/University of Calabria France/Italy C3 UNAM Mexico

ISBN: (数字)9781728191348

ISBN: (纸本)9781728191355

visual big data analytics aims at supporting big data analytics via visual metaphors, with a plethora of applications in modern settings and scenarios. In all these domains, visual big data analytics paradigms offer several advantages, among which some noticeable ones are: (i) fast knowledge understanding from big data sets; (ii) pattern and trend discovery from big data sets; (iii) entity and model discovery from big data sets; (iv) sharing insights among organizations. Among several proposals, OLAP-based visual big data analytics methodologies and tools represents a successful case of visual big data analytics frameworks, which is entirely based on OLAP analysis. In this context, an OLAP cube is typically explored with multiple aggregations selecting different subsets of cube dimensions to analyze trends or to discover unexpected results. Unfortunately, such analytic process is generally manual and fails to statistically explain results. On the basis of these considerations, in this paper we propose an innovative OLAP-shaped visual big data analytics framework that incorporates a state-of-the-art statistical technique for supporting exploration and visualization of OLAP data cubes. An experimental evaluation with a medical data set presents statistically significant results and interactive visualizations, which link risk factors and degree of disease.

关键词： visualization Three-dimensional displays data visualization Big data Market research Reliability Medical diagnostic imaging

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Analyzing the Performance of ICN Forwarders on the Wire 7

Analyzing the Performance of ICN Forwarders on the Wire

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7th ACM Annual conference on Information-Centric Networking (ICN)

作者： Drescher, Adam DeHart, John Parwatikar, Jyoti Crowley, Patrick Washington Univ St Louis MO USA Cisco Syst Inc San Jose CA USA

ISBN: (纸本)9781450380409

Information Centric Networking (ICN) is growing in both popularity and maturity. Two highly-related architectures under the ICN umbrella, Content Centric Networking (CCNx) and Named data Networking (NDN), received significant effort to improve their forwarding performance. Despite this focus, little work has been done to evaluate ICN forwarders in a comprehensive and rigorous manner. Furthermore, the preexisting literature in IP can only apply broadly due to the substantial differences between the architectures. In this paper, we provide a methodology to analyze the performance of ICN forwarders. Our testing methodology has two key focuses: (i) packet processing performance is the primary metric of exploration, as bytes are usually cheap;and (ii) the PIT, FIB, and Content Store are the primary structures to probe when considering performance impact. With these focuses in mind, we present a series of behavioral microbenchmarks that can probe the performance of CCNx/NDN forwarders in a rigorous way. To show the efficacy of these experiments, we apply them to the reference forwarders of the CCNx and NDN architectures, Metis and NFD, giving us a careful understanding of their performance characteristics. Additionally, these microbenchmarks should readily apply to high performance forwarders in the space.

关键词： Named data Networking Content Centric Networking Forwarder Performance analysis ICN Packet Processing

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exploration of anomalies in cyclic multivariate industrial time series data for condition monitoring 23

Exploration of anomalies in cyclic multivariate industrial t...

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Workshops of the 23rd International conference on Extending database Technology/23rd International conference on database Theory, EDBT-ICDT-WS 2020

作者： Suschnigg, Josef Mutlu, Belgin Fuchs, Anna Katharina Sabol, Vedran Thalmann, Stefan Schreck, Tobias Pro2Future Avl List Graz Austria Pro2Future Know-Center GmbH Graz Austria Avl List Graz Austria Know-Center GmbH Graz Austria University of Graz Graz Austria Graz University of Technology Graz Austria

Industrial product testing is frequently performed in cycles, resulting in cycle-dependent test data. Monitoring the condition of products under test involves analysis of large and complex test data sets. Main tasks are to detect anomalies and dependencies between observation variables, which appears to be challenging to engineers. In this paper, we present a flexible and extendable visual analytics approach for anomaly detection focusing on cycle-depended data. It is based on a glyph representation to visualize anomaly scores of cycles with respect to interactively selected reference data. Our approach is built on a design study in collaboration with an industrial engineering corporation, and is demonstrated on real data from engines tested on automotive testbeds. Based on findings from evaluation results, we provide a discussion and an outlook for future work. © 2020 Copyright for this paper by its author(s).

关键词： Condition monitoring

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Breaking the Curse of visual data exploration: Improving Analyses by Building Bridges between data World and Real World 10

Breaking the Curse of Visual Data Exploration: Improving Ana...

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14th International Joint conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) / 10th International conference on Information visualization Theory and Applications (IVAPP)

作者： Kraus, Matthias Weiler, Niklas Breitkreutz, Thorsten Keim, Daniel A. Stein, Manuel Univ Konstanz Data Anal & Visualizat Constance Germany

ISBN: (纸本)9789897583544

visual data exploration is a useful means to extract relevant information from large sets of data. The visual analytics pipeline processes data recorded from the real world to extract knowledge from gathered data. Subsequently, the resulting knowledge is associated with the real world and applied to it. However, the considered data for the analysis is usually only a small fraction of the actual real-world data and lacks above all in context information. It can easily happen that crucial context information is disregarded, leading to false conclusions about the real world. Therefore, conclusions and reasoning based on the analysis of this data pertain to the world represented by the data, and may not be valid for the real world. The purpose of this paper is to raise awareness of this discrepancy between the data world and the real world which has a high impact on the validity of analysis results in the real world. We propose two strategies which help to identify and remove specific differences between the data world and the real world. The usefulness and applicability of our strategies are demonstrated via several use cases.

关键词： visualization Theory Uncertainty Validation visual Analytics

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Deep learning in the ultrasound evaluation of neonatal respiratory status

Deep learning in the ultrasound evaluation of neonatal respi...

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International conference on Pattern Recognition

作者： Michela Gravina Diego Gragnaniello Luisa Verdoliva Giovanni Poggi Iuri Corsini Carlo Dani Fabio Meneghin Gianluca Lista Salvatore Aversa Francesco Raimondi Fiorella Migliaro Carlo Sansone University of Naples Federico II Naples Italy Hospital Careggi of Florence Italy Hospital “Vittore Buzzi” Milan Italy Civil Hospital of Brescia Italy Division of Neonatology Section of Pediatrics University of Naples Federico II Naples Italy

ISBN: (纸本)9781728188089;9781728188096

Lung ultrasound imaging is reaching growing interest from the scientific community. On one side, thanks to its harmlessness and high descriptive power, this kind of diagnostic imaging has been largely adopted in sensitive applications, like the diagnosis and follow-up of preterm newborns in neonatal intensive care units. On the other side, state-of-the-art image analysis and pattern recognition approaches have recently proven their ability to fully exploit the rich information contained in these data, making them attractive for the research community. In this work, we present a thorough analysis of recent deep learning networks and training strategies carried out on a vast and challenging multicenter dataset comprising 87 patients with different diseases and gestational ages. These approaches are employed to assess the lung respiratory status from ultrasound images and are evaluated against a reference marker. The conducted analysis sheds some light on this problem by showing the critical points that can mislead the training procedure and proposes some adaptations to the specific data and task. The achieved results sensibly outperform those obtained by a previous work, which is based on textural features, and narrow the gap with the visual score predicted by the human experts.

关键词： Training Deep learning Pediatrics visualization Ultrasonic imaging Correlation Lung

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Inferring Student Engagement in Collaborative Problem Solving from visual Cues

Inferring Student Engagement in Collaborative Problem Solvin...

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International conference on Multimodal Interaction (ICMI)

作者： Kasparova, Angelika Celiktutan, Oya Cukurova, Mutlu Kings Coll London Dept Engn London England UCL UCL Knowledge Lab London England

ISBN: (纸本)9781450380027

Automatic analysis of students' collaborative interactions in physical settings is an emerging problem with a wide range of applications in education. However, this problem has been proven to be challenging due to the complex, interdependent and dynamic nature of student interactions in real-world contexts. In this paper, we propose a novel framework for the classification of student engagement in open-ended, face-to-face collaborative problem-solving (CPS) tasks purely from video data. Our framework i) estimates body pose from the recordings of student interactions;ii) combines face recognition with a Bayesian model to identify and track students with a high accuracy;and iii) classifies student engagement leveraging a Team Long Short-Term Memory (Team LSTM) neural network model. This novel approach allows the LSTMs to capture dependencies among individual students in their collaborative interactions. Our results show that the Team LSTM significantly improves the performance as compared to the baseline method that takes individual student trajectories into account independently.

关键词： Collaborative problem solving Complex group interactions Team Long Short-Term Memory networks

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A study on EEG-NIRS testing of drug users' brain function under visual induction 9

A study on EEG-NIRS testing of drug users' brain function un...

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9th International conference on Computing and Pattern Recognition, ICCPR 2020

作者： Yang, Banghua Gu, Xuelin Xu, Ding Fan, Chengcheng School of Mechanical and Electrical Engineering and Automation Shanghai University Shanghai China Shanghai Drug Rehabilitation Administration Bureau Shanghai China

ISBN: (纸本)9781450387835

Long-term drug abuse may change the brain function. In this paper, a new experimental paradigm combining EEG and NIRSIT is designed to explore target stimulus pictures-induced changes in electroencephalograms, as well as the changes in blood oxygen under near-infrared spectroscopy in drug users. The experiment divides into three stages. The first stage denotes the resting state. The task in the second stage includes induction by target stimulation pictures and neutral pictures. And in the third stage, the resting state pictures are all neutral pictures. The electroencephalograms and near-infrared data were collected in the three stages. Then an analysis was made of the ERP potential P300 of the electroencephalograms, the changes in hemoglobin concentration under near-infrared spectroscopy, and the channel time series. An analysis was made of the EEG and NIRSIT data on 120 people who were tested in a rehabilitation center according to the paradigm designed for the experiment. An analysis of the electroencephalograms in the second stage of the experiment shows that target stimulus pictures evoked more obvious P300 volatility in electroencephalograms than neutral pictures. Meanwhile, an analysis of the NIRSIT data shows that the concentration of oxyhemoglobin increased and the concentration of deoxyhemoglobin decreased. exploration of drug rehabilitation studies, laying a foundation for further predicting pathological changes in the human brain, as well as targeted rehabilitation. © 2020 ACM.

关键词： Detoxification

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Synthesis and structure research of Bis[N, N-bis(2-hydroxyethyl)dithiocarbamato-κ2S,S']nickel(ii) 4

Synthesis and structure research of Bis[N, N-bis(2-hydroxyet...

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4th International Symposium on Resource exploration and Environmental Science, REES 2020

作者： Hou, Lanfeng Chen, Shaofeng Maoming Polytechnic Maoming Guangdong China

New metal dithiocarbamate complex [(HOC2H4)2NCSS]2 Ni was synthesized by method of One-Pot synthesis and characterized by elemental analysis, IR spectra, melting point. The crystal structure was determined by X-ray single crystal diffraction. Complex [(HOC2H4)2NCSS]2Ni is monoclinic with space group P2(1)/c. Lattice parameters: A=6.3798(8) Å, b=11.8298(14) Å, c=11.4586(13) Å, β=93.63(0) °. Parameter ratio: A/b=0.5393, b/c=1.0324, c/a=1.7961. The data agreed well with the structure determined by X-ray single crystal diffraction. © Published under licence by IOP Publishing Ltd.

关键词： Crystal structure

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