Visual Analysis of Multilayer Networks, McGee Fintan, Renoust Benjamin, Archambault Daniel
Автор: McGee Fintan, Renoust Benjamin, Archambault Daniel Название: Visual Analysis of Multilayer Networks ISBN: 1636391435 ISBN-13(EAN): 9781636391434 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 9563.00 р. Наличие на складе: Нет в наличии.
Описание: The emergence of multilayer networks as a concept from the field of complex systems provides many new opportunities for the visualization of network complexity, and has also raised many new exciting challenges. The multilayer network model recognizes that the complexity of relationships between entities in real-world systems is better embraced as several interdependent subsystems (or layers) rather than a simple graph approach. Despite only recently being formalized and defined, this model can be applied to problems in the domains of life sciences, sociology, digital humanities, and more. Within the domain of network visualization there already are many existing systems, which visualize data sets having many characteristics of multilayer networks, and many techniques, which are applicable to their visualization. In this Synthesis Lecture, we provide an overview and structured analysis of contemporary multilayer network visualization. This is not only for researchers in visualization, but also for those who aim to visualize multilayer networks in the domain of complex systems, as well as those solving problems within application domains. We have explored the visualization literature to survey visualization techniques suitable for multilayer network visualization, as well as tools, tasks, and analytic techniques from within application domains. We also identify the research opportunities and examine outstanding challenges for multilayer network visualization along with potential solutions and future research directions for addressing them.
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 Рейтинг: Цена: 10480.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.
An accessible introduction to the artificial intelligence technology that enables computer vision, speech recognition, machine translation, and driverless cars.
Deep learning is an artificial intelligence technology that enables computer vision, speech recognition in mobile phones, machine translation, AI games, driverless cars, and other applications. When we use consumer products from Google, Microsoft, Facebook, Apple, or Baidu, we are often interacting with a deep learning system. In this volume in the MIT Press Essential Knowledge series, computer scientist John Kelleher offers an accessible and concise but comprehensive introduction to the fundamental technology at the heart of the artificial intelligence revolution.
This book starts with setting up a Python virtual environment with the deep learning framework TensorFlow and then introduces the fundamental concepts of TensorFlow. Before moving on to Computer Vision, you will learn about neural networks and related aspects such as loss functions, gradient descent optimization, activation functions and how backpropagation works for training multi-layer perceptrons.
To understand how the Convolutional Neural Network (CNN) is used for computer vision problems, you need to learn about the basic convolution operation. You will learn how CNN is different from a multi-layer perceptron along with a thorough discussion on the different building blocks of the CNN architecture such as kernel size, stride, padding, and pooling and finally learn how to build a small CNN model.
Next, you will learn about different popular CNN architectures such as AlexNet, VGGNet, Inception, and ResNets along with different object detection algorithms such as RCNN, SSD, and YOLO. The book concludes with a chapter on sequential models where you will learn about RNN, GRU, and LSTMs and their architectures and understand their applications in machine translation, image/video captioning and video classification
Описание: This book constitutes the proceedings of the 8th International Conference on Analysis of Images, Social Networks and Texts, AIST 2019, held in Kazan, Russia, in July 2019.The 24 full papers and 10 short papers were carefully reviewed and selected from 134 submissions (of which 21 papers were rejected without being reviewed).
Автор: Rokia Missaoui; Sergei O. Kuznetsov; Sergei Obiedk Название: Formal Concept Analysis of Social Networks ISBN: 3319877399 ISBN-13(EAN): 9783319877396 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Поставка под заказ.
Автор: Dmitry I. Ignatov; Mikhail Yu. Khachay; Alexander Название: Analysis of Images, Social Networks and Texts ISBN: 3319125796 ISBN-13(EAN): 9783319125794 Издательство: Springer Рейтинг: Цена: 8106.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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 Рейтинг: Цена: 10340.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.
Описание: Provides a central source of reference on visual information processing in wireless sensor network environments and its technology, application, and society issues. This book is an important resource for researchers and academics working in the interdisciplinary domains of wireless sensor network technology and multimedia technology and its related areas.
Автор: Tominski, Christian , Schumann, Heidrun Название: Interactive Visual Data Analysis ISBN: 0367898756 ISBN-13(EAN): 9780367898755 Издательство: Taylor&Francis Рейтинг: Цена: 9186.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The book provides a comprehensive overview on information visualization and visual exploration. The top-down view on the problem illustrated with numerous examples based on real data and settings will help people from these domains to get a sound knowledge about key challenges, concepts and methodologies in this regard.
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.
Автор: van der Aalst Название: Analysis of Images, Social Networks and Texts ISBN: 3319730126 ISBN-13(EAN): 9783319730127 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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;
Описание: This book offers comprehensive coverage of information retrieval by considering both Text Based Information Retrieval (TBIR) and Content Based Image Retrieval (CBIR), together with new research topics.
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