Analysis of Images, Social Networks and Texts, Wil M. P. van der Aalst; Vladimir Batagelj; Goran

Автор: Dmitry I. Ignatov; Mikhail Yu. Khachay; Alexander
Название: Analysis of Images, Social Networks and Texts
ISBN: 3319125796 ISBN-13(EAN): 9783319125794
Издательство: Springer
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Цена: 54040.00 T
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Описание: This book constitutes the proceedings of the Third International Conference on Analysis of Images, Social Networks and Texts, AIST 2014, held in Yekaterinburg, Russia, in April 2014. They are presented together with 3 short industrial papers, 4 invited papers and tutorials. The papers deal with topics such as analysis of images and videos;

Автор: Wil M. P. van der Aalst; Vladimir Batagelj; Dmitry
Название: Analysis of Images, Social Networks and Texts
ISBN: 3030373339 ISBN-13(EAN): 9783030373337
Издательство: Springer
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Цена: 68930.00 T
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Описание: Invited Opinion Talk.- Double-blind peer-reviewing and inclusiveness in Russian NLP conferences.- Tutorial.- Intel(R) Distribution of OpenVINO(TM) toolkit: a case study of semantic Segmentation.- General Topics of Data Analysis.- Experimental Analysis of Approaches to Multidimensional Conditional Density Estimation.- Histogram-based algorithm for building gradient boosting ensembles of piecewise linear decision trees.- Deep Reinforcement Learning in Match-3 Game.- Distance in Geographic and Characteristics Space for Real Estate Price Prediction.- Fast Nearest-Neighbor Classifier based on Sequential Analysis of Principal Components.- A Simple Method to Evaluate Support Size and Non-uniformity of a Decoder-Based Generative Model.- Natural Language Processing.- Biomedical Entities Impact on Rating Prediction for Psychiatric Drugs.- Combining Neural Language Models for WordSense Induction.- Log-based Reading Speed Prediction: a Case Study on War and Peace.- Cross-lingual argumentation mining for Russian texts.- Dynamic Topic Models for Retrospective Event Detection: A Study on Soviet Opposition-Leaning Media.- Deep Embeddings for Brand Detection in Product Titles.- Wear the Right Head: Comparing Strategies for Encoding Sentences for Aspects Extraction.- Combined Advertising Sign Classifier.- A comparison of algorithms for detection of "figurativeness" in metaphor, irony and puns.- Authorship Attribution in Russian with New High-Performing and Fully Interpretable Morpho-Syntactic Features.- Evaluation of Sentence Embedding Models for Natural Language Understating Problems in Russian.- Noun Compositionality Detection using Distributional Semantics for the Russian Language.- Deep JEDi: Deep Joint Entity Disambiguation to Wikipedia for Russian.- Selecting an optimal feature set for stance detection.- Social network analysis.- Analysis of Students Educational Interests Using Social Networks Data.- Multilevel Exponential Random Graph Models Application to Civil Participation Studies.- The Entity Name Identification in Classification Algorithm: Testing the Advocacy Coalition Framework by Document Analysis (the Case of Russian Civil Society Policy).- Analysis of Images and Video.- Multi-label Image Set Recognition in Visually-Aware Recommender Systems.- Input simplifying as an approach for improving neural network efficiency.- American and Russian Sign Language Dactyl Recognition and Text2Sign Translation.- Data augmentation with GAN: improving chest X-rays pathologies prediction on class-imbalanced cases.- Estimation of non-radial geometric distortions for dash cams.- On Expert-defined versus Learned Hierarchies for Image Classification.- A switching morphological algorithm for depth map recovery.- Learning to Approximate Directional Fields Defined over 2D Planes.- Optimization Problems on Graphs and Network Structures.- Fast and Exact Algorithms for Some NP-Hard 2-Clustering Problems in the One-Dimensional Case.- Efficient PTAS for the Euclidean Capacitated Vehicle Routing Problem with non-uniform non-splittable demand.- Analysis of Dynamic Behavior Through Event Data.- Detection of Anomalies in the Criminal Proceedings Based on the Analysis of Event Logs.- Method to Improve Workow Net Decomposition for Process Model Repair.

Автор: Dmitry I. Ignatov; Mikhail Yu. Khachay; Valeri G.
Название: Analysis of Images, Social Networks and Texts
ISBN: 3319529196 ISBN-13(EAN): 9783319529196
Издательство: Springer
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Цена: 69870.00 T
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Описание: This book constitutes the proceedings of the 5th International Conference on Analysis of Images, Social Networks and Texts, AIST 2016, held in Yekaterinburg, Russia, in April 2016.The 23 full papers, 7 short papers, and 3 industrial papers were carefully reviewed and selected from 142 submissions.

Автор: Mikhail Yu. Khachay; Natalia Konstantinova; Alexan
Название: Analysis of Images, Social Networks and Texts
ISBN: 3319261223 ISBN-13(EAN): 9783319261225
Издательство: Springer
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Цена: 68950.00 T
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Описание: This book constitutes the proceedings of the Fourth International Conference on Analysis of Images, Social Networks and Texts, AIST 2015, held in Yekaterinburg, Russia, in April 2015.The 24 full and 8 short papers were carefully reviewed and selected from 140 submissions.

Автор: van der Aalst
Название: Analysis of Images, Social Networks and Texts
ISBN: 3319730126 ISBN-13(EAN): 9783319730127
Издательство: Springer
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Цена: 46570.00 T
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Описание: This book constitutes the proceedings of the 6th International Conference on Analysis of Images, Social Networks and Texts, AIST 2017, held in Moscow, Russia, in July 2017. The 29 full papers and 8 short papers were carefully reviewed and selected from 127 submissions. general topics of data analysis;

Автор: Adams Niall
Название: Data Analysis for Network Cyber-Security
ISBN: 1783263741 ISBN-13(EAN): 9781783263745
Издательство: World Scientific Publishing
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Цена: 86590.00 T
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Описание: There is increasing pressure to protect computer networks against unauthorized intrusion, and some work in this area is concerned with engineering systems that are robust to attack. However, no system can be made invulnerable. Data Analysis for Network Cyber-Security focuses on monitoring and analyzing network traffic data, with the intention of preventing, or quickly identifying, malicious activity.Such work involves the intersection of statistics, data mining and computer science. Fundamentally, network traffic is relational, embodying a link between devices. As such, graph analysis approaches are a natural candidate. However, such methods do not scale well to the demands of real problems, and the critical aspect of the timing of communications events is not accounted for in these approaches.This book gathers papers from leading researchers to provide both background to the problems and a description of cutting-edge methodology. The contributors are from diverse institutions and areas of expertise and were brought together at a workshop held at the University of Bristol in March 2013 to address the issues of network cyber security. The workshop was supported by the Heilbronn Institute for Mathematical Research.

Автор: Kjarulff, Uffe B. Madsen, Anders L.
Название: Bayesian networks and influence diagrams: a guide to construction and analysis
ISBN: 1461451035 ISBN-13(EAN): 9781461451037
Издательство: Springer
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Цена: 111790.00 T
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Описание: In a Second Edition offering six new sections, new examples, tables, figures and more, this book shows how to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. Includes more than 140 examples.

Автор: Rokia Missaoui; Sergei O. Kuznetsov; Sergei Obiedk
Название: Formal Concept Analysis of Social Networks
ISBN: 3319641662 ISBN-13(EAN): 9783319641669
Издательство: Springer
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Цена: 102480.00 T
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Описание: The book studies the existing and potential connections between Social Network Analysis (SNA) and Formal Concept Analysis (FCA) by showing how standard SNA techniques, usually based on graph theory, can be supplemented by FCA methods, which rely on lattice theory.

Автор: Uffe B. Kj?rulff; Anders L. Madsen
Название: Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
ISBN: 1493900293 ISBN-13(EAN): 9781493900299
Издательство: Springer
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Цена: 121110.00 T
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Описание: In a Second Edition offering six new sections, new examples, tables, figures and more, this book shows how to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. Includes more than 140 examples.

Автор: Uffe B. Kj?rulff; Anders L. Madsen
Название: Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
ISBN: 1441925465 ISBN-13(EAN): 9781441925466
Издательство: Springer
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Цена: 71690.00 T
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Описание: This book provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. The theory and methods presented are illustrated through more than 140 examples.

Автор: Simon Parkinson; Andrew Crampton; Richard Hill
Название: Guide to Vulnerability Analysis for Computer Networks and Systems
ISBN: 3030064743 ISBN-13(EAN): 9783030064747
Издательство: Springer
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Цена: 51230.00 T
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Описание: This professional guide and reference examines the challenges of assessing security vulnerabilities in computing infrastructure. Various aspects of vulnerability assessment are covered in detail, including recent advancements in reducing the requirement for expert knowledge through novel applications of artificial intelligence. The work also offers a series of case studies on how to develop and perform vulnerability assessment techniques using start-of-the-art intelligent mechanisms.Topics and features: provides tutorial activities and thought-provoking questions in each chapter, together with numerous case studies; introduces the fundamentals of vulnerability assessment, and reviews the state of the art of research in this area; discusses vulnerability assessment frameworks, including frameworks for industrial control and cloud systems; examines a range of applications that make use of artificial intelligence to enhance the vulnerability assessment processes; presents visualisation techniques that can be used to assist the vulnerability assessment process.In addition to serving the needs of security practitioners and researchers, this accessible volume is also ideal for students and instructors seeking a primer on artificial intelligence for vulnerability assessment, or a supplementary text for courses on computer security, networking, and artificial intelligence.

Автор: Arindam Chaudhuri
Название: Visual and Text Sentiment Analysis through Hierarchical Deep Learning Networks
ISBN: 9811374732 ISBN-13(EAN): 9789811374739
Издательство: Springer
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Цена: 46570.00 T
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Описание: This book presents the latest research on hierarchical deep learning for multi-modal sentiment analysis. Further, it analyses sentiments in Twitter blogs from both textual and visual content using hierarchical deep learning networks: hierarchical gated feedback recurrent neural networks (HGFRNNs). Several studies on deep learning have been conducted to date, but most of the current methods focus on either only textual content, or only visual content. In contrast, the proposed sentiment analysis model can be applied to any social blog dataset, making the book highly beneficial for postgraduate students and researchers in deep learning and sentiment analysis. The mathematical abstraction of the sentiment analysis model is presented in a very lucid manner. The complete sentiments are analysed by combining text and visual prediction results. The book’s novelty lies in its development of innovative hierarchical recurrent neural networks for analysing sentiments; stacking of multiple recurrent layers by controlling the signal flow from upper recurrent layers to lower layers through a global gating unit; evaluation of HGFRNNs with different types of recurrent units; and adaptive assignment of HGFRNN layers to different timescales. Considering the need to leverage large-scale social multimedia content for sentiment analysis, both state-of-the-art visual and textual sentiment analysis techniques are used for joint visual-textual sentiment analysis. The proposed method yields promising results from Twitter datasets that include both texts and images, which support the theoretical hypothesis.