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Bituminous coal ash, fly ash (FA) and bottom ash (BA), from coal power plant were studied in this research for utilizations in ceramic and pottery industries. Both fly ash and bottom ash mixed with four different ball clays from two kinds of ceramic pottery products such as flowerpot and construction brick, Ratchaburi, Chantaburi, Angthong and Chonburi ball clay. The mixtures were varying ratio between ashes and clays depend on clay sources. After mixing, the bodies were formed to test pieces and were then fired at 800-1200 °C. The results indicated that, added fly ash 0-40 wt% significantly decreased water absorption and increased bending strength when fired at 1150 °C or above. For color effect, CIE L*a*b* color space, both fly ash and bottom ash revealed the samples less lightness, more red and yellow appearance. These results are obvious when fired at 1150oC or above. Following the results, fly ash is possible to be used in ceramic pottery industries replaced with other addition such as sand and pottery stone.

Previous studies have found that a significant number of bug reports are misclassified between bugs and non-bugs, and that manually classifying bug reports is a time-consuming task. To address this problem, we propose a bug reports classification model with N-gram IDF, a theoretical extension of Inverse Document Frequency (IDF) for handling words and phrases of any length. N-gram IDF enables us to extract key terms of any length from texts, these key terms can be used as the features to classify bug reports. We build classification models with logistic regression and random forest using features from N-gram IDF and topic modeling, which is widely used in various software engineering tasks. With a publicly available dataset, our results show that our N-gram IDF-based models have a superior performance than the topic-based models on all of the evaluated cases. Our models show promising results and have a potential to be extended to other software engineering tasks. 5 pages, ICSME 2017

Category 5 Typhoon Haiyan was one of the strongest typhoons to hit the Philippines in modern times, and introduced the term “storm surge” to the vocabulary of many local residents, who had not heard about such phenomena prior to this event. The storm surge manifested itself on the 8th November 2013 as Haiyan made landfall, devastating large areas in the islands of Leyte and Samar. To attempt to gain an understanding of the level of awareness that local residents had about storm surges the authors conducted structured questionnaires (n=172 n=172 ) and focus group interviews with local residents, and discussed the results obtained with key informants (government officers and disaster risk managers). One of the key problems identified during the interviews was how people were not able to clearly understand the concept of a storm surge, with many respondents emphasizing how it would have been better for authorities to describe it as a “tsunami”. A discussion is also made on the recent evolution in world-wide coastal disaster awareness, comparing it to recent developments in storm surge awareness in the Philippines and other countries in the area, such as Vietnam and Japan. The authors conclude that, in terms of its influence on world-wide disaster awareness, typhoon Haiyan constitutes an event of similar importance to major recent high-impact tsunami events such as the 2004 Indian Ocean and 2011 Tohoku events. As a result, the authors will outline a number of lessons learnt from Haiyan, such as the necessity for creating multi-layer safety strategies and improving how information about storm surges is transmitted to local residents.

There are many roles to play in the bug fixing process in open source software development. A developer called ``Committer'', who has a permission to submit a patch into software repository, plays a major role in this process and hold a key to the successfulness of the project. In this work, we have observed each committer activities from the Eclipse-Platform bug tracking system and version archives. Despite the importance of committer's activities, we suspected that sometimes committers can make mistakes, which have negative consequences to the bug fixing process. Our research focus on studying the consequences of each committer's activities to this process. We collected each committer's historical data and evaluated each of them by comparing the more cautiousness to less cautiousness committers. Then we looked deeper into each committer's characteristics to see the reasons why some committers tend to make mistakes more than the others. From our results, we would like to make a humbly suggestion to the OSS's committers to be aware of their importance to the projects and be cautious before doing their jobs.

We propose an automatic utterance type recognizer that distinguishes declarative questions from statements in Indonesian speech. Since utterances in these two types have the same words with the same order and differ only in their intonations, their classification requires not only a speech recognizer, but also an intonation recognizer. In this paper, the most important utterance part for distinguishing those two types is first identified by perceptual experiments. Then, an utterance type recognizer using that part is proposed, where polynomial expansion is used as a feature extractor and a neural network is used as a classifier. We evaluated our method using Indonesian speech database including 29 pairs of sentences of those two types, each of which uttered by 35 speakers. It was proved that final word and final-two-syllables are equally effective for the discrimination of each utterance. The proposed recognizer achieved the best accuracy of 89.1% when the order of polynomial expansion was three and the neural network was a linear perceptron.