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11th Annual International conference on Cyber Conflict (CyCon) - Silent Battle

作者： Schaefer, Matthias Fuchs, Markus Strohmeier, Martin Engel, Markus Liechti, Marc Lenders, Vincent Univ Kaiserslautern Dept Comp Sci Kaiserslautern Germany SeRo Syst Kaiserslautern Germany Armasuisse Cyber Def Campus Thun Switzerland Trivo Syst Bern Switzerland

ISBN: (纸本)9789949990450

The Dark Web, a conglomerate of services hidden from search engines and regular users, is used by cyber criminals to offer all kinds of illegal services and goods. Multiple Dark Web offerings are highly relevant for the cyber security domain in anticipating and preventing attacks, such as information about zero-day exploits, stolen datasets with login information, or botnets available for hire. In this work, we analyze and discuss the challenges related to information gathering in the Dark Web for cyber security intelligence purposes. To facilitate information collection and the analysis of large amounts of unstructured data, we present BlackWidow, a highly automated modular system that monitors Dark Web services and fuses the collected data in a single analytics framework. BlackWidow relies on a Docker-based micro service architecture which permits the combination of both preexisting and customized machine learning tools. BlackWidow represents all extracted data and the corresponding relationships extracted from posts in a large knowledge graph, which is made available to its security analyst users for search and interactive visual exploration. Using BlackWidow, we conduct a study of seven popular services on the Deep and Dark Web across three different languages with almost 100,000 users. Within less than two days of monitoring time, BlackWidow managed to collect years of relevant information in the areas of cyber security and fraud monitoring. We show that BlackWidow can infer relationships between authors and forums and detect trends for cybersecurity-related topics. Finally, we discuss exemplary case studies surrounding leaked data and preparation for malicious activity.

关键词： Dark Web analysis open source intelligence cyber intelligence

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M-QuBE3: Querying big multilayer graph by evolutive extraction and exploration

M-QuBE3: Querying big multilayer graph by evolutive extracti...

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2019 conference on visualization and data analysis, VDA 2019

作者： Laumond, Antoine Melancon, Guy Pinaud, Bruno Ghoniem, Mohammad Univ. Bordeaux LaBRI UMR 5800 Talence F-33400 France Luxembourg

Although node-link representations of graphs are widespread and even sometimes preferred to other approaches, they suffer from obvious limitations when graphs become large or dense, inducing visual cluttering and impeding the traditional visual information seeking process. This article presents a new strategy of exploration particularly suitable when graphs are large and dense. Users iteratively drive the exploration through the visualization of small sub-networks of interest. Our technique is particularly useful with multilayer networks, where layers typically combine into a large and dense network. Our iterative exploration process called M-QuBE3 computes a score for each node of a graph based on structural and semantic information where more interesting nodes from a user point of view have higher scores. This in turn translates into a procedure to select sub-networks of interest. Within each sub-network, the user can select nodes to enhance the semantic context (and thus impact their interest score) and iteratively refine the exploration towards more relevant sub-networks. The M-QuBE3 process natively handles multilayer network and allows the use of layers as a semantic apparatus when driving the navigation. © 2019 Society for Imaging Science and Technology. All rights reserved.

关键词： Semantics

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CyteGuide: visual Guidance for Hierarchical Single-Cell analysis

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2018年第1期24卷 739-748页

作者： Hollt, Thomas Pezzotti, Nicola van Unen, Vincent Koning, Frits Lelieveldt, Boudewijn P. F. Vilanova, Anna Delft Univ Technol Comp Graph & Visualizat Grp Delft Netherlands Leiden Univ Med Ctr Computat Biol Ctr Leiden Netherlands Leiden Univ Med Ctr Dept Immunohematol & Blood Transfus Leiden Netherlands Leiden Univ Med Ctr Div Image Proc Dept Radiol Leiden Netherlands Delft Univ Technol Pattern Recognit & Bioinformat Grp Delft Netherlands

Single-cell analysis through mass cytometry has become an increasingly important tool for immunologists to study the immune system in health and disease. Mass cytometry creates a high-dimensional description vector for single cells by time-of-flight measurement. Recently, t-Distributed Stochastic Neighborhood Embedding (t-SNE) has emerged as one of the state-of-the-art techniques for the visualization and exploration of single-cell data. Ever increasing amounts of data lead to the adoption of Hierarchical Stochastic Neighborhood Embedding (HSNE), enabling the hierarchical representation of the data. Here, the hierarchy is explored selectively by the analyst, who can request more and more detail in areas of interest. Such hierarchies are usually explored by visualizing disconnected plots of selections in different levels of the hierarchy. This poses problems for navigation, by imposing a high cognitive load on the analyst. In this work. we present an interactive summary-visualization to tackle this problem. CyteGuide guides the analyst through the exploration of hierarchically represented single-cell data, and provides a complete overview of the current state of the analysis. We conducted a two-phase user study with domain experts that use HSNE for data exploration. We first studied their problems with their current workflow using HSNE and the requirements to ease this workflow in a field study. These requirements have been the basis for our visual design. In the second phase, we verified our proposed solution in a user evaluation.

关键词： Hierarchical data HSNE Single-Cell analysis visual Guidance

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visual analysis Based on Research on Mergers and Acquisitions Performance

Visual Analysis Based on Research on Mergers and Acquisition...

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International conference on Economic Management and Model Engineering (ICEMME)

作者： Jianwen Yan Xingli Cai Yu Fan School of managemeat Hefei University of Technology Hefei Anhui China

Mergers and acquisitions(M& A) is one of the strategic behaviors of corporate growth and evolution, and M& A performance is an important index to evaluate whether the function of corporate M& A is achieved and the effect of M& A. As the M& A market becomes more and more active, scholars are doing more research on related aspects, but few studies use bibliometrics and visualization methods to conduct in-depth exploration and show the full picture of the field. This article uses bibliometric research methods to analyze the data samples of 5065 articles in the core collection of Web of Science from 2000 to 2019, and the document type is Article, and the time distribution, spatial distribution, knowledge evolution, and literature of the domain knowledge Co-cited and research hotspots were analyzed in depth and visualized. It reveals the development status of related research on mergers and acquisitions and performance, analyzes and discusses research hotspots and future development trends in this field, and provides important knowledge support for researchers in this field to carry out related research.

关键词： Knowledge engineering Economics visualization Graphical models Corporate acquisitions Bibliometrics data visualization

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Surprise! Predicting Infant visual Attention in a Socially Assistive Robot Contingent Learning Paradigm 28

Surprise! Predicting Infant Visual Attention in a Socially A...

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28th IEEE International conference on Robot and Human Interactive Communication (RO-MAN)

作者： Klein, Lauren Itti, Laurent Smith, Beth A. Rosales, Marcelo Nikolaidis, Stefanos Mataric, Maja J. Univ Southern Calif Interact Lab Dept Comp Sci Los Angeles CA 90089 USA Univ Southern Calif iLab Dept Comp Sci Los Angeles CA 90007 USA Univ Southern Calif Infant Neuromotor Control Lab Div Biokinesiol & Phys Therapy Los Angeles CA 90007 USA Univ Southern Calif Interact & Collaborat Autonomous Robot Syst Lab Dept Comp Sci Los Angeles CA 90007 USA

ISBN: (纸本)9781728126227

Early intervention to address developmental disability in infants has the potential to promote improved outcomes in neurodevelopmental structure and function [1]. Researchers are starting to explore Socially Assistive Robotics (SAR) as a tool for delivering early interventions that are synergistic with and enhance human-administered therapy. For SAR to be effective, the robot must be able to consistently attract the attention of the infant in order to engage the infant in a desired activity. This work presents the analysis of eye gaze tracking data from five 6-8 month old infants interacting with a Nao robot that kicked its leg as a contingent reward for infant leg movement. We evaluate a Bayesian model of low-level surprise on video data from the infants' head-mounted camera and on the timing of robot behaviors as a predictor of infant visual attention. The results demonstrate that over 67% of infant gaze locations were in areas the model evaluated to be more surprising than average. We also present an initial exploration using surprise to predict the extent to which the robot attracts infant visual attention during specific intervals in the study. This work is the first to validate the surprise model on infants;our results indicate the potential for using surprise to inform robot behaviors that attract infant attention during SAR interactions.

关键词： Bayesian networks

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ICMR 2019 - Proceedings of the 2019 ACM International conference on Multimedia Retrieval

ICMR 2019 - Proceedings of the 2019 ACM International Confer...

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2019 ACM International conference on Multimedia Retrieval, ICMR 2019

ISBN: (纸本)9781450367653

The proceedings contain 60 papers. The topics discussed include: a geographical-temporal awareness hierarchical attention network for next point-of-interest recommendation;the focus-aspect-value model for explainable prediction of subjective visual interpretation;context-aware embeddings for automatic art analysis;methods of multi-modal data exploration;benchmarking search and annotation in continuous human skeleton sequences;a genetic programming approach for searching on nearest neighbors graphs;weakly supervised image retrieval via coarse-scale feature fusion and multi-level attention blocks;an unsupervised genetic algorithm framework for rank selection and fusion on image retrieval;and integrity verification in medical image retrieval systems using spread spectrum steganography.

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Use of wavelet transform and signal processing techniques for inferring interwell connectivity in waterflooding operations

Use of wavelet transform and signal processing techniques fo...

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SPE Annual Technical conference and Exhibition 2019, ATCE 2019

作者： Unal, Ebru Siddiqui, Fahd Rezaei, Ali Eltaleb, Ibrahim Kabir, Shah Soliman, Mohamed Y. Dindoruk, Birol University of Houston United States Shell International Exploration and Production Inc.

ISBN: (纸本)9781613996638

Inter-well connectivity (IWC) is one of the most significant properties when evaluating the success of a waterflood. This connectivity has been obtained from various physics-based methods such as simulations, tracers and using heuristics and semi-analytical tools like capacitance-resistance model (CRM). Production and injection data are a key piece of information required to compute the IWC. In this study, we present a new method for estimating IWC using signal processing techniques on the wavelet transform of the injection and production rate data. First, the injection and production rates are subjected to multiresolution analysis using the wavelet transform to determine the detail coefficients. The variance of the detail coefficients is then computed and is ready to be processed using various signal processing techniques. Signal processing techniques such as cross-correlation, time lag, Spearman correlation, and Kendal correlation are used to identify the level of relationship between the processed injection and production data in wavelet scale space. Based on the correlation coefficients, a new IWC link parameter is proposed for characterizing the IWC between well pairs. The IWC link parameters between well pairs are then plotted for visual representation. We created several simulation models for multi-well systems, established water-flood patterns, and for randomly placed wells to establish the new IWC link parameter. The resulting injection and production rates were analyzed using the methodology above and the new IWC link parameter is established in terms of cross-correlation coefficient. We also performed several simulations for a heterogenous reservoir to compute and compare the accuracy of the new IWC link parameter. Finally, the methodology is subjected to real field waterflooding, and compared against the CRM results, which shows a good agreement. The visual representation gives new insight into whether the connectivity is being affected by the reservoir

关键词： Heuristic methods

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A Framework for Sedimentological data Structure Extraction in Gold Mining Facies Delineation

A Framework for Sedimentological Data Structure Extraction i...

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2019 IEEE Asia-Pacific conference on Computer Science and data Engineering, CSDE 2019

作者： Khoza, Phumlani Nhlanganiso University of Witwatersrand Scilinx Research School of Computer Science Applied Mathematics Johannesburg South Africa

ISBN: (纸本)9781728163031

Quartz-pebble conglomerate (reef) horizons of the Witwatersrand Basin are the most important sources of gold (Au) in South Africa. Concentration of gold to economic ore-grades in the Witwatersrand-type gold deposits has been linked to sedimentary processes having operated as principal driving forces. Therefore, in Witwatersrand-type gold deposits, sedimentological data are often utilized to make qualitative assessment of in-situ gold-grade. This paper presents a framework that prescribes a normative and principled analysis of sedimentological data in order to quantify in-situ gold-grades. Its significance is the provision of a scalable process for extracting correlational structures, regardless of the attribute value-types. The framework applies network analysis, and a bespoke clustering algorithm to support visual analysis of spatial correlation between sedimentological data samples. A key result is the preservation of the semantic interpretation of the resulting cluster assignments via a topologically invariant transformation. This is achieved by applying non-uniform weighting to the attributes to emphasize the influence of non-numeric attributes in cluster assignment. Additionally, sedimentological attributes that are strongly correlated with gold-grade are identified by adopting total variance reduction from decision tree regression as a guiding principle. Using this approach, and data collected from a mining company located in the Witwatersrand Basin, spatial correlation structures defined over sedimentological data attributes are reported. As a byproduct, observations that lack spatial correlation with the rest of the data are identified. These isolated observations can inform quality assurance and quality control departments about potential irregularities in the reporting of sedimentological data within exploration and mining companies. © 2019 IEEE.

关键词： Quality control

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A Surface-Theoretic Approach for Statistical Shape Modeling 22nd

A Surface-Theoretic Approach for Statistical Shape Modeling

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10th International Workshop on Machine Learning in Medical Imaging (MLMI) / 22nd International conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)

作者： Ambellan, Felix Zachow, Stefan von Tycowicz, Christoph Zuse Inst Berlin Visual Data Anal Berlin Germany

ISBN: (纸本)9783030322519;9783030322502

We present a novel approach for nonlinear statistical shape modeling that is invariant under Euclidean motion and thus alignment-free. By analyzing metric distortion and curvature of shapes as elements of Lie groups in a consistent Riemannian setting, we construct a framework that reliably handles large deformations. Due to the explicit character of Lie group operations, our non-Euclidean method is very efficient allowing for fast and numerically robust processing. This facilitates Riemannian analysis of large shape populations accessible through longitudinal and multi-site imaging studies providing increased statistical power. We evaluate the performance of our model w.r.t. shape-based classification of pathological malformations of the human knee and show that it outperforms the standard Euclidean as well as a recent nonlinear approach especially in presence of sparse training data. To provide insight into the model's ability of capturing natural biological shape variability, we carry out an analysis of specificity and generalization ability.

关键词： Statistical shape analysis Principal Geodesic analysis Lie groups Classification Manifold valued statistics

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visual analysis and exploration of COVID-19 Based on Multi-source Heterogeneous data

Visual Analysis and Exploration of COVID-19 Based on Multi-s...

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IEEE/ACM Int'l conference on & Int'l conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Yun Zhou Hu He Jieqi Rong Yun Cheng YongChang Li Wei Zhong Fu Jiang Hu Nan University of Finance and Economics Changsha China School of Computer Science and Engineering Central South University Changsha China

ISBN: (数字)9781728176475

ISBN: (纸本)9781728176482

The novel coronavirus pneumonia (COVID-19) has raged in many countries around the world. In the process of fighting against the COVID-19, unprecedented large-scale epidemic data have been produced such as case data, spatio-temporal data, public opinion data and so on. The increasingly complex data poses a significant challenge to understand. A two-level interactive visualization system named COVID-19Vis is proposed in this paper, which collects epidemic data from multiple sources and provides an interactive mode of multi-graph linkage. Users can not only easily analyze and interpret the spatial-temporal characteristics and potential rules of the epidemic, but also find the relationship between policy, online public opinion and the development of the epidemic situation. Through a large number of visualization effects and user feedback, the effectiveness and practicability of the COVID-19Vis are further verified.

关键词： COVID-19 Epidemics visualization Market research data visualization Hospitals Urban areas

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