Proceeding Papers

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    Energy Efficient Topology Control in Multi-hop Wireless Networks based on One-hop and Two-hop Neighbor Information
    Topology control in multi-hop wireless networks is a technique used to control nodes' activities to construct a topology with an acceptable level of network connectivity while minimizing energy consumption. It can be broadly classified into two main paradigms which are State Scheduling and Transmission Power Control. Both of them have received lots of attention from many researchers for decades. According to Transmission Power Control technique, most of them take into account only one-hop neighbor information to optimize transmission power and control topology in multi-hop wireless networks. In this paper, an energy efficient topology control based on both one-hop and two-hop neighbor information is proposed to reduce energy consumption without degrading network performances. A number of simulation scenarios are constructed in this work to study energy consumption as well as other network performances that are endto- end delay, throughput, and packet delivery ratio of the proposed algorithm.
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    IT Design Skills Selection for Professional Development
    ( 2016-02)
    IT professionals in these days need to obtain multidisciplinary skill sets in order to succeed in their career. Design skill is considered as one of important characteristic for IT professionals. Many researches addressed design skill sets differently. This results in an increasing numbers of design skill items, in which some of those items have their meaning slightly different from each other. These long lists of design skill items can cause to long range of questionnaire. This study evaluates three techniques used for items selection which help reducing the number of skill items but with expect to remain their value within the selected items. The comparative studies used in this work are random selection, statistics selection, and effective coverage-based selection. The study found that the proposed technique provides the most satisfy result comparing to the other two techniques. According to this, the reduction rate by using this model is better than random and statistics algorithms about 47.83% and 34.78% respectively. Moreover, the percent coverage by using the proposed model is higher than the traditional methods approximately 13-26 %.
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    Exploring the Effects of Openness to Experience and Theory of Consumption Values on Online Learning Adoption
    Personality traits and perceived product values are increasingly used to explain how people adopt innovative technologies. However, their relationships and effects on online learning adoption are ill-defined. This study investigates one of the common personality trait - openness to experience - and the value dimensions of theory of consumption values - functional (quality and monetary) value, social value, emotional value, epistemic value, and conditional value - to understand students’ intentions to adopt online learning. The study used a structural equation modelling technique (SEM) to analyze the data gathered from university students. The results indicate that students who are highly open to experience pay attention to monetary value and conditional value of online learning. Students, who perceived online learning as a quality method of learning (quality value), an interesting method of learning (emotional value), and an online learning community (social value), will have good intention to adopt online learning. Interestingly, the results contend that the five values of theory of consumption values are interrelated and contributed to online learning adoption differently. This study provides guidance to universities for planning and developing online courses/programs that will be considered valuable by students who are open to new experience leading to the increment in online learning students. The analysis results and implications for theory and practice are discussed. The paper concludes with the study limitations and directions for future studies.
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    Dimension Independent Cosine Similarity for Collaborative Filtering using MapReduce
    DIMSUM, an efficient and accurate all-pair similarity algorithm for real-world large scale dataset, tackles shuffle size problem of several similarity measures using MapReduce. The algorithm uses a sampling technique to reduce `power items' and preserves similarities. This paper presents an improved algorithm DIMSUM+ with a complex sampling technique to enhance DIMSUM so that it is able to further reduce `power users'. The algorithm generates k-nearest-neighbor matrix that are used in collaborative based Recommender systems. The evaluations of algorithm on MovieLens dataset with 1 million movie ratings and Yahoo! Music dataset with 700 million song ratings show significant improvement that DIMSUM+ outperforms DIMSUM at least 1.4x faster.
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    A System for Popular Thai Slang Extraction from Social Media Content with N-Gram Based Tokenization (KST 2016)
    With increased penetration of smart devices and internet connectivity, many Thais are more readily engaged in social media, online forums, and chat groups. As there is an increased consumption of social media content, there is a shift from the consumption of traditional medias in which formal language are used regularly such as broadcast and traditional print medias. Social media posts are a reflection of the trend, where posts usually made by younger generations usually involve communication in slang and non-formal language which is not typically available in formalized dictionaries. As the Thai population like to follow trends, one of behaviors of that many Thai social media users engage in, is to follow the latest popular social media trends in slang and word usage. As slang are changed and evolved over time, it is usually useful to have an online mining tool in which could capture the trends of emerging and popular slang. This paper proposes an approach that extracts popular Thai slang by comparing social media posts and utilizing tokenization, a dictionary based approach to extract unknown words, before expanding it by using n-gram approach to figure what are currently trending and popular slang words.