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4th International conference on Big data Analytics, data Mining and Computational Intelligence 2019, BigDaCI 2019 and the 8th International conference on Theory and Practice in Modern Computing 2019, TPMC 2019

作者： Sassi, Imad Anter, Samir FST Mohammedia University of Hassan II Casablanca Morocco

ISBN: (纸本)9789898533920

Big data is an extremely large amount of structured and unstructured data, gathered from a wide range of sources which often require a fast processing and real time analysis. In this new context, the performances of the traditional techniques are limited. However, to handle these bulky quantities of data, new technologies emerged, called Big data technologies. In fact, the characteristics of Big data made the exploration process of these data a painful task. This process is called Big data Analytics. One of the important challenges of Big data is to search new technologies or to improve and extend the existing platforms, infrastructures and standard techniques to manage the Big data. Hadoop / MapReduce paradigm and the Spark framework are among the most prominent solutions for large-scale parallel distributed data processing alongside Machine Learning techniques, in particularly, Deep Learning for performing powerful statistical and predictive analysis. In this paper, we first, give an overview, a classification and a comparison of main Big data technologies. Then, we focus in particular on Machine Learning platforms and libraries, especially those for Deep Learning. The results show that Spark is a general-purpose computation engine thanks to its very generalized solutions. © Multi conference on Computer Science and Information Systems, MCCSIS 2019. All rights reserved.

关键词： Big data

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ImWeb: Cross-Platform ImmersiveWeb Browsing for Online 3D Neuron database exploration 24

ImWeb: Cross-Platform ImmersiveWeb Browsing for Online 3D Ne...

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24th ACM International conference on Intelligent User Interfaces (IUI)

作者： Fulmer, Willis Mahmood, Tahir Li, Zhongyu Zhang, Shaoting Huang, Jian Lu, Aidong Univ N Carolina Charlotte NC 28223 USA Univ Tennessee Knoxville TN USA

ISBN: (纸本)9781450362726

Web services have become one major way for people to obtain and explore information nowadays. However, web browsers currently only offer limited data analysis capabilities, especially for large-scale 3D datasets. This project presents a method of immersive web browsing (ImWeb) to enable effective exploration of multiple datasets over the web with augmented reality (AR) techniques. The ImWeb system allows inputs from both the web browser and AR and provides a set of immersive analytics methods for enhanced web browsing, exploration, comparison, and summary tasks. We have also integrated 3D neuron mining and abstraction approaches to support efficient analysis functions. The architecture of ImWeb system flexibly separates the tasks on web browser and AR and supports smooth networking among the system, so that ImWeb can be adopted by different platforms, such as desktops, large displays, and tablets. We use an online 3D neuron database to demonstrate that ImWeb enables new experiences of exploring 3D datasets over the web. We expect that our approach can be applied to various other online databases and become one useful addition to future web services.

关键词： Immersive web browsing augmented reality visual analytics online database 3D neuron exploration

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Urdu Sentiment Corpus (v1.0): Linguistic exploration and visualization of Labeled dataset for Urdu Sentiment analysis

Urdu Sentiment Corpus (v1.0): Linguistic Exploration and Vis...

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International conference on Information Science and Communication Technology (ICISCT)

作者： Muhammad Yaseen Khan Muhammad Suffian Nizami Center for Language Computing Mohammad Ali Jinnah University Karachi Pakistan

ISBN: (数字)9781728168999

ISBN: (纸本)9781728169002

The significance of the labeled dataset is not obscure from artificial intelligence practitioners. We have seen much phenomenal work, in natural language processing, for many languages (like English, Chinese, and Arabic, etc.), due to the reason for the availability of substantial data. For the Urdu language, despite the third largest spoken language in the world, very little research work is shown; hence, it is adjudged as a `morphologically rich' but `resource-poor' language. Further, the researchers working on Urdu natural language processing are in a quandary due to the lack of availability of labeled/annotated datasets. This paper shares the data, “Urdu Sentiment Corpus” (USC), and insights therein, of Urdu tweets for the sentiment analysis and polarity detection. The dataset is consisting of tweets, such that it casts a political shadow and presents a competitive environment between two separate political parties versus the government of Pakistan. Overall, the dataset is comprising over 17, 185 tokens with 52% records as positive, and 48 % records as negative. This paper shares the visual insights (from document-level to word-level) into the textual similarities, manifold-learning, etc. In addition to it, this paper also presents a Part-of-Speech wise analysis and an unpretentious technique for the extraction of sentiment lexicons from the corpus.

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BlackWidow: Monitoring the Dark Web for Cyber Security Information 11

BlackWidow: Monitoring the Dark Web for Cyber Security Infor...

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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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visual Grounding in Video for Unsupervised Word Translation

Visual Grounding in Video for Unsupervised Word Translation

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conference on Computer Vision and Pattern Recognition (CVPR)

作者： Gunnar A. Sigurdsson Jean-Baptiste Alayrac Aida Nematzadeh Lucas Smaira Mateusz Malinowski João Carreira Phil Blunsom Andrew Zisserman Carnegie Mellon University DeepMind Department of Engineering Science University of Oxford

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

There are thousands of actively spoken languages on Earth, but a single visual world. Grounding in this visual world has the potential to bridge the gap between all these languages. Our goal is to use visual grounding to improve unsupervised word mapping between languages. The key idea is to establish a common visual representation between two languages by learning embeddings from unpaired instructional videos narrated in the native language. Given this shared embedding we demonstrate that (i) we can map words between the languages, particularly the 'visual' words; (ii) that the shared embedding provides a good initialization for existing unsupervised text-based word translation techniques, forming the basis for our proposed hybrid visual-text mapping algorithm, MUVE; and (iii) our approach achieves superior performance by addressing the shortcomings of text-based methods -- it is more robust, handles datasets with less commonality, and is applicable to low-resource languages. We apply these methods to translate words from English to French, Korean, and Japanese -- all without any parallel corpora and simply by watching many videos of people speaking while doing things.

关键词： visualization Training Grounding Task analysis Dogs data models Adaptation models

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

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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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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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Source(s) of produced water in the permian Delaware Basin, West Texas: A geochemical analysis

Source(s) of produced water in the permian Delaware Basin, W...

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SPE/AAPG/SEG Unconventional Resources Technology conference 2020, URTeC 2020

作者： Taras Bryndzia, L. Hows, Amie M. Day-Stirrat, Ruarri J. Nikitin, Anton Huvaz, Ozkan Shell International Exploration and Production Inc. HoustonTX United States

The Permian Delaware Basin (DB) is one of the most desirable regions for production of unconventional oil in the United States. While extended horizontal wells, stimulated with hydraulic fracturing, can recover economic volumes of oil in the DB, this production is often associated with large volumes of water. Relatively high water-oil ratios (WORs) can Erode the value of producing wells. This of course begs the questions: where is the water coming from and why is so much being produced? This study shows that the produced waters (PWs) are primarily in-situ Wolfcamp shale formation water and not water associated with hydraulic fracturing or well completions. This conclusion is based on the observation that the Wolfcamp shale formation water has an oxygen isotopic composition of ~6.5 ± 0.5 ‰ (SMOW) and a salinity of ~23 kppm. These oxygen isotopic data and salinities are consistent with illite-water equilibrium at peak burial conditions. However, in some areas of the DB, PWs have much higher salinities (~50-125 kppm). The PWs also have a characteristic geochemical fingerprint of highly radiogenic 87/86Sr ratios of ~0.7085-0.7095. The source of this highly radiogenic strontium is believed to be the Salado salt in the overlying shallow Ochoan evaporites, with 87/86Sr of ~0.7090-0.7095. Dissolution of the Ochoan evaporites and salt is the likely source of high salinity brines in Guadalupian and Leonardian age sands and silts within the DB. These high salinity PWs are mixtures of Wolfcamp formation water and dissolved Ochoan evaporites and salt that infiltrated deep into the DB during uplift of the western edge of the DB. Uplift was closely related to the formation of the "Alvarado Ridge", beginning at ~20 Ma, with peak uplift at ~7 to 4 Ma, creating conditions hydrologically favorable for ingress of the high salinity brines deep into the DB. Due to the high illite content in the Wolfcamp shale, the shale-silt interface likely behaved as a clay membrane. Differences in sal

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