Data Mining - Concepts and Techniques.

An empirical model of player motivations in online games provides the foundation to understand and assess how players differ from one another and how motivations of play relate to age, gender, usage patterns, and in-game behaviors. In the current study, a factor analytic approach was used to create an empirical model of player motivations. The analysis revealed 10 motivation subcomponents that grouped into three overarching components (achievement, social, and immersion). Relationships between motivations and demographic variables (age, gender, and usage patterns) are also presented.

http://www.nickyee.com/pubs/Yee%20-%20Motivations%20(2006).pdf

Motivations for play in online games.

Matrix Factorization Techniques for Recommender Systems

Find the secret to improve the quality of life by reading this adaptive blind signal and image processing. This is a kind of book that you need now. Besides, it can be your favorite book to read after having this book. Do you ask why? Well, this is a book that has different characteristic with others. You may not need to know who the author is, how well-known the work is. As wise word, never judge the words from who speaks, but make the words as your good value to your life.

Adaptive blind signal and image processing

An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

Intelligent Automated Assistant

In our previous work, we developed a fuzzy classifier with ellipsoidal regions that has a training capability. In this paper, we extend the fuzzy classifier to diagnosis problems, in which the training data belonging to abnormal classes are difficult to obtain while the training data belonging to normal classes are easily obtained. Assuming that there are no data belonging to abnormal classes, we first train the fuzzy classifier with only the data belonging to normal classes. We then introduce the threshold of the minimum-weighted distance from the centers of the clusters for the data belonging to normal classes. If the unknown data is within the threshold, we classify the data into normal classes and, if not, abnormal classes. The operator checks whether the diagnosis is correct. If the incoming data is classified into the same normal class both by the classifier and the operator, nothing is done. But if the input data is classified into the different normal classes by the classifier and the operator, or if the incoming data is classified into an abnormal class, but the operator classified it into a normal class, the slopes of the membership functions of the fuzzy rules are tuned. If the operator classifies the data into an abnormal class, the classifier is retrained adding the newly obtained data irrespective of the classifier's classification result. The online training is continued until a sufficient number of the data belonging to abnormal classes are obtained. Then the threshold is optimized using the data belonging to both normal and abnormal classes. We evaluate our method using the Fisher iris data, blood cell data, and thyroid data, assuming some of the classes are abnormal.

/pdf/a-fuzzy-classifier-with-ellipsoidal-regions-for-diagnosis-4jwhk4bs6a.pdf

A fuzzy classifier with ellipsoidal regions for diagnosis problems

In this paper, we describe our work on classification of players in Massively Multiplayer Online Games using Hidden Markov Models based on player action sequences. In our previous work, we have discussed a classification approach using a variant of Memory Based Reasoning based on player action frequencies. That approach, however, does not exploit time structures hidden in action sequences of the players. The experimental results given in this paper show that Hidden Markov Models have higher recognition performance than our previous approach, especially for classification of players of different types but having similar action frequencies.

/pdf/mmog-player-classification-using-hidden-markov-models-yzqjzmnv0g.pdf

MMOG Player Classification Using Hidden Markov Models

In this paper, we present a novel approach that uses deep learning techniques for colorizing grayscale images. By utilizing a pre-trained convolutional neural network, which is originally designed for image classification, we are able to separate content and style of different images and recombine them into a single image. We then propose a method that can add colors to a grayscale image by combining its content with style of a color image having semantic similarity with the grayscale one. As an application, to our knowledge the first of its kind, we use the proposed method to colorize images of ukiyo-e a genre of Japanese painting?and obtain interesting results, showing the potential of this method in the growing field of computer assisted art.

Image Colorization Using a Deep Convolutional Neural Network

In their previous work, the authors have developed a method for selecting features based on the analysis of class regions approximated by hyperboxes. They select features analyzing class regions approximated by ellipsoids. First, for a given set of features, each class region is approximated by an ellipsoid with the center and the covariance matrix calculated by the data belonging to the class. Then, similar to their previous work, the exception ratio is defined to represent the degree of overlaps in the class regions approximated by ellipsoids. From the given set of features, they temporally delete each feature, one at a time, and calculate the exception ratio. Then, the feature whose associated exception ratio is the minimum is deleted permanently. They iterate this procedure while the exception ratio or its increase is within a specified value by feature deletion. The simulation results show that the current method is better than the principal component analysis (PCA) and performs better than the previous method, especially when the distributions of class data are not parallel to the feature axes.

/pdf/feature-selection-by-analyzing-class-regions-approximated-by-qniy8q0jsd.pdf

Feature selection by analyzing class regions approximated by ellipsoids

This paper presents a development of our comic generation system that automatically summarizes players' actions and interactions in the virtual world. The feature of the system is that it analyzes the log and screenshots of a game, decides which events are important and memorable, and then generates comics in a fully automatic manner. Also, the interface of our system allows users to customize their own comics. As a result, users can easily use the system to share their stories and create individual comics for archival purposes or storytelling.

Automatic storytelling in comics: a case study on World of Warcraft

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