Conference Papers

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    Potential Z-Fighting Conflict Detection System in 3D Level Design Tools
    ( 2015) Pisal Setthawong
    Z-Fighting is an effect that happens in 3D scenes when two co-planar surfaces share similar values in the z-buffer which leads to flicking and visual artifacts during the rendering process due to conflicting order of rendering the surface. However in 3D level design, scenes created by the tools can be complex, in which level designers can inadvertently place co-planar surfaces that would be susceptible to z-fighting. Level designers typically notice the z-fighting artifact through visual inspection through the usage of a 3D walkthrough test on the scene which is time-consuming and easy to miss. To solve the issue, a proposal of a zfighting detection system for level design tools is proposed to streamline the process of detecting potential hotspots where z-fighting conflicts may occur from co-planar objects.
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    Optimizing Player Throughput for Interactive Motion Based Kiosk Games – A Case Study from the PTT Technobots Campaign
    ( 2015) Pisal Setthawong
    Maintaining booths that are attractive to exhibition attendees is one of the main goals of exhibitors. One of the popular tactics to attract attention is to utilize new and emerging technology to create fresh new types of interactive booths, in which motion-based kiosk games are gaining popularity in this domain. However motion-based kiosk games are usually designed using conventional computer game design principles and are not optimized for exhibitions which results in a low player throughput. This paper examines the issues behind conventional game design principles, and proposes changes that would improve player throughput and is empirically validated upon a real world case study in which the author has worked on. The case study selected is the Petroleum Authority of Thailand (PTT) Technobots Campaign, in which a series of motion-based kiosk games were deployed at a number of popular department stores in Bangkok, Thailand during the period of May-Aug 2013.
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    Domain-oriented two-stage aggregation: generating baseball play-by-play narratives
    ( 2015) Baldwin, James ; Songsak Channarukul
    This paper presents an end-to-end natural language generation system that performs aggregation in two stages: the first takes advantage of the information implicit in the source knowledge base in order to aggregate event components into complex sentences. The second stage examines the developing context of the text in order to aggregate similar adjacent events into more fluent text. The source knowledge base is the Retrosheet collection of play-by-play baseball scoresheets encoded in machine-readable form. The output is reasonably fluent and natural, human-readable play-by-play narratives of historical baseball games. The system was tested against all regular season major league games played from 1950 to 1969, taking less than a second to produce three to five pages of text for each game. The aggregation achieved resulted in a substantial improvement in native speaker judgments of fluency and readability.
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    Alternating Least Squares with Incremental Learning Bias
    ( 2015) Aung, Than Htike ; Rachsuda Jiamthapthaksin
    Recommender systems provide personalized suggestions for every individual user in the system. Many recommender systems use collaborative filtering approach in which the system collects and analyzes users' past behaviors, activities or preferences to produce high quality recommendations for the users. Among various collaborative recommendation techniques, model-based approaches are more scalable than memory-based approaches for large scale data sets in spite of large offline computation and difficulty to update the model in real time. In this paper, we introduce Alternating Least Squares with Incremental Learning Bias (ALS++) algorithm to improve over existing matrix factorization algorithms. These learning biases are treated as additional dimensions in our algorithm rather than as additional weights. As the learning process begins after regularized matrix factorization, the algorithm can update incrementally over the preference changes of the data set in constant time without rebuilding the new model again. We set up two different experiments using three different data sets to measure the performance of our new algorithm.
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    Feature Envy Factor A Metric for Automatic Feature Envy Detection
    ( 2015) Kwankamol Nongpong
    As a software system evolves, its design get deteriorated and the system becomes difficult to maintain. In order to improve such an internal quality, the system must be restructured without affecting its external behavior. The process involves detecting the design flaws (or code smells) and applying appropriate refactorings that could help remove such flaws. One of the design flaws in many object-oriented systems is placing members in the wrong class. This code smell is called Feature Envy and it is a sign of inappropriate coupling and cohesion. This work proposes a metric to detect Feature Envy code smell that can be removed by relocating the method. Our evaluation shows promising results as the overall system’s complexity is reduced after suggested Move Method refactorings are applied.