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Learning from Multiple Social Networks
With the proliferation of social network services, more and more social users, such as individuals and organizations, are simultaneously involved in multiple social networks for various purposes. In fact, multiple social networks characterize the same social users from different perspectives, and their contexts are usually consistent or complementary rather than independent. Hence, as compared to using information from a single social network, appropriate aggregation of multiple social networks offers us a better way to comprehensively understand the given social users. Learning across multiple social networks brings opportunities to new services and applications as well as new insights on u...
Visual and Multimedia Information Management
Current research in Visual Database Systems can be characterized by scalability, multi-modality of interaction, and higher semantic levels of data. Visual interfaces that allow users to interact with large databases must scale to web and distributed applications. Interaction with databases must employ multiple and more diversified interaction modalities, such as speech and gesture, in addition to visual exploitation. Finally, the basic elements managed in modern databases are rapidly evolving, from text, images, sound, and video, to compositions and now annotations of these media, thus incorporating ever-higher levels and different facets of semantics. In addition to visual interfaces and mu...
Image and Video Retrieval
Here are the refereed proceedings of the 5th International Conference on Image and Video Retrieval, CIVR 2006, held in Singapore in July 2006. Presents 18 revised full papers and 30 poster papers, together with extended abstracts of 5 papers of 1 special session and those of 10 demonstration papers. These cover interactive image and video retrieval, semantic image retrieval, visual feature analysis, learning and classification, image and video retrieval metrics, and machine tagging.
Recommender Systems
Recommender Systems: A Multi-Disciplinary Approach presents a multi-disciplinary approach for the development of recommender systems. It explains different types of pertinent algorithms with their comparative analysis and their role for different applications. This book explains the big data behind recommender systems, the marketing benefits, how to make good decision support systems, the role of machine learning and artificial networks, and the statistical models with two case studies. It shows how to design attack resistant and trust-centric recommender systems for applications dealing with sensitive data. Features of this book: Identifies and describes recommender systems for practical us...
Mining User Generated Content
Originating from Facebook, LinkedIn, Twitter, Instagram, YouTube, and many other networking sites, the social media shared by users and the associated metadata are collectively known as user generated content (UGC). To analyze UGC and glean insight about user behavior, robust techniques are needed to tackle the huge amount of real-time, multimedia, and multilingual data. Researchers must also know how to assess the social aspects of UGC, such as user relations and influential users. Mining User Generated Content is the first focused effort to compile state-of-the-art research and address future directions of UGC. It explains how to collect, index, and analyze UGC to uncover social trends and...
ECAI 2020
This book presents the proceedings of the 24th European Conference on Artificial Intelligence (ECAI 2020), held in Santiago de Compostela, Spain, from 29 August to 8 September 2020. The conference was postponed from June, and much of it conducted online due to the COVID-19 restrictions. The conference is one of the principal occasions for researchers and practitioners of AI to meet and discuss the latest trends and challenges in all fields of AI and to demonstrate innovative applications and uses of advanced AI technology. The book also includes the proceedings of the 10th Conference on Prestigious Applications of Artificial Intelligence (PAIS 2020) held at the same time. A record number of ...
MultiMedia Modeling
The two-volume set LNCS 8935 and 8936 constitutes the thoroughly refereed proceedings of the 21st International Conference on Multimedia Modeling, MMM 2015, held in Sydney, Australia, in January 2015. The 49 revised regular papers, 24 poster presentations, were carefully reviewed and selected from 189 submissions. For the three special session, a total of 18 papers were accepted for MMM 2015. The three special sessions are Personal (Big) Data Modeling for Information Access and Retrieval, Social Geo-Media Analytics and Retrieval and Image or video processing, semantic analysis and understanding. In addition, 9 demonstrations and 9 video showcase papers were accepted for MMM 2015. The accepted contributions included in these two volumes represent the state-of-the-art in multimedia modeling research and cover a diverse range of topics including: Image and Video Processing, Multimedia encoding and streaming, applications of multimedia modelling and 3D and augmented reality.
Multimedia Content Analysis
Multimedia Content Analysis: Theory and Applications covers the latest in multimedia content analysis and applications based on such analysis. As research has progressed, it has become clear that this field has to appeal to other disciplines such as psycho-physics, media production, etc. This book consists of invited chapters that cover the entire range of the field. Some of the topics covered include low-level audio-visual analysis based retrieval and indexing techniques, the TRECVID effort, video browsing interfaces, content creation and content analysis, and multimedia analysis-based applications, among others. The chapters are written by leading researchers in the multimedia field.
Representation Learning for Natural Language Processing
This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing.
Advances in Multimedia Modeling
The 16th international conference on Multimedia Modeling (MMM2010) was held in the famous mountain city Chongqing, China, January 6–8, 2010, and hosted by Southwest University. MMM is a leading international conference for researchersand industry practitioners to share their new ideas, original research results and practicaldevelopment experiences from all multimedia related areas. MMM2010attractedmorethan160regular,specialsession,anddemosession submissions from 21 countries/regions around the world. All submitted papers were reviewed by at least two PC members or external reviewers, and most of them were reviewed by three reviewers. The review process was very selective. From the total of...
