Data Mining - Concepts and Techniques.

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Social Network Analysis

It is shown that, particularly for terrestrial cellular telephony, the interference-suppression feature of CDMA (code division multiple access) can result in a many-fold increase in capacity over analog and even over competing digital techniques. A single-cell system, such as a hubbed satellite network, is addressed, and the basic expression for capacity is developed. The corresponding expressions for a multiple-cell system are derived. and the distribution on the number of users supportable per cell is determined. It is concluded that properly augmented and power-controlled multiple-cell CDMA promises a quantum increase in current cellular capacity. >

On the capacity of a cellular CDMA system

Recommender systems have developed in parallel with the web. They were initially based on demographic, content-based and collaborative filtering. Currently, these systems are incorporating social information. In the future, they will use implicit, local and personal information from the Internet of things. This article provides an overview of recommender systems as well as collaborative filtering methods and algorithms; it also explains their evolution, provides an original classification for these systems, identifies areas of future implementation and develops certain areas selected for past, present or future importance.

http://romisatriawahono.net/lecture/rm/survey/information%20retrieval/Bobadilla%20-%20Recommender%20Systems%20-%202013.pdf

Recommender systems survey

Most information in the world exists in the format of text, such as news articles and web pages. Different lines of research have been conducted to discover, understand and access knowledge about real-world entities and relations from text. However, the application of these word oriented methods to ontology integration tasks has not been yet explored. In this paper, we apply these word oriented methods to ontology integration tasks in which we analyze a noun phrase (NP) to identify its head noun, which is useful to avoid wrong relations between entities. We also propose a collaborative acquisition algorithm that combines WordNet-based and Text corpus.

A Method for Integration across Text Corpus and WordNet-Based Ontologies

With the increasing interest in multimedia annotation, emotion annotation is being recognized as an essential resource which can be applied for a variety of purposes, including video information retrieval and dialogue systems. Here we introduce an automatic emotion annotation schema for dialogues, which could be used for the retrieval of specific scenes in film. Distinguished from previous works, we apply a new approach using the hypernym/hyponym relations and synonyms of WordNet, which enables us to organize a novel set of emotional concepts and to automatically detect whether a specific emotional word is associated with a specified emotional concept through measuring the conceptual distance between them.

https://link.springer.com/content/pdf/10.1007%2F978-3-642-20042-7_14.pdf

Automatic emotion annotation of movie dialogue using WordNet

Multi-agent systems have been attacking the challenges of distributed information retrieval. In this paper, we propose a consensus method-based framework to evaluate the performance of cooperative information retrieval tasks of the agents. Two well-known measurements, precisionand recall, are extended to handle consensual closeness (i.e., local and global consensus) between the retrieved results. We show in a motivating example that the proposed criteria are prone to solve the problem of rigidity of classical precisionand recall. More importantly, the retrieved results can be ranked with respect to the consensual score.

Consensus-Based Evaluation Framework for Cooperative Information Retrieval Systems

The influence of social relationships has received considerable attention in recommendation systems. In this paper, we propose a personalized advertisement recommendation system based on user preference and social network information. The proposed system uses collaborative filtering and frequent pattern network techniques using social network information to recommend personalized advertisements. Frequent pattern network is employed to alleviate cold-start and sparsity problems of collaborative filtering. For the social relationship modeling, direct and indirect relations are considered and relation weight between users is calculated by using six degrees of Kevin Bacon. Weight `1' is given to those who have connections directly, and weight `0' is given to those who are over six steps away and hove no relation to each other. According to a research of Kevin Bacon, everybody can know certain people through six depths of people. In order to improve prediction accuracy, we apply social relationship to user modeling. In our experiments, advertisement information is collected and item rating and user information including social relations are extracted from a social network service. The proposed system applies user modeling between collaborative filtering and frequent pattern network model to recommend advertisements according to user condition. User's types are composed with combinations of both techniques. We compare the performance of the proposed method with that of other methods. From the experimental results, a proposed system applying user modeling using social relationships can achieve better performance and recommendation quality than other recommendation systems.

Personalized advertisement system using social relationship based user modeling

The book consists of 35 extended chapters which have been selected and invited from the submissions to the 4th International Conference on Computational Collective Intelligence Technologies and Applications (ICCCI 2012) held on November 28-30, 2012 in Ho Chi Minh City, Vietnam. The book is organized into six parts, which are semantic web and ontologies, social networks and e-learning, agent and multiagent systems, data mining methods and applications, soft computing, and optimization and control, respectively. All chapters in the book discuss theoretical and practical issues connected with computational collective intelligence and related technologies. The editors hope that the book can be useful for graduate and Ph.D. students in Computer Science, in particular participants in courses on Soft Computing, Multiagent Systems, and Data Mining. This book can be also useful for researchers working on the concept of computational collective intelligence in artificial populations. It is the hope of the editors that readers of this volume can find many inspiring ideas and use them to create new cases of intelligent collectives. Many such challenges are suggested by particular approaches and models presented in individual chapters of this book. The editors hope that readers of this volume can find many inspiring ideas and influential practical examples and use them in their future work.

Geun-Sik Jo

Papers

A Method for Integration across Text Corpus and WordNet-Based Ontologies

Automatic emotion annotation of movie dialogue using WordNet

Consensus-Based Evaluation Framework for Cooperative Information Retrieval Systems

Personalized advertisement system using social relationship based user modeling

Advanced Methods for Computational Collective Intelligence