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Institution

Mahidol University International College

About: Mahidol University International College is a based out in . It is known for research contribution in the topics: Tourism & Corporate governance. The organization has 240 authors who have published 485 publications receiving 6095 citations.


Papers
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Proceedings ArticleDOI
01 Nov 2018
TL;DR: The objective is to classify terrain into 5 types: building, green zone, car park, road and canal, and experiment results show that augmenting the training data did not improve the performance.
Abstract: Classification of terrain images taken from an unmanned aerial vehicle (UAV) is presented in this work. The objective is to classify terrain into 5 types: building, green zone, car park, road and canal. The processing flow consists of stitching sets of 4 images to form large field of view images to covers the area of interest. The stitched images were then divided into grids, and each grid were manually labeled as one of the five terrain types. Feature extraction was performed on each grid, where the features consist of percentage of pixels whose color falls with in certain range in the HSV color space, the mean pixel value of each of the BG R channels separately, the mean pixel value of all the channels together, and the number of contours detected from binary images thresholded by simple thresholding and by Otsu's method. Three different classifiers were experimented with: k nearest neighbor, decision tree, and extra tree. Two different dataset were used for training the classifiers: raw dataset where the number of each type of grid were imbalanced due to the nature of the terrains in the area of interest, and an augmented dataset where we artificially increased the number of grids by random flips and rotation such that each class has exactly the same number of grids. A total of six stitched images were reserved for the test set. Experiment results show that best accuracy was achieved by extra tree with accuracy of 85.5%. The results also show that augmenting the training data did not improve the performance.

2 citations

Journal ArticleDOI
TL;DR: The problem of finding the minimum number of monochromatic Schur triples with a fixed positive integer over any 2-coloring of $[1,n] was conjectured by Butler, Costello, and Graham as discussed by the authors.
Abstract: The solution to the problem of finding the minimum number of monochromatic triples $(x,y,x+ay)$ with $a\geq 2$ being a fixed positive integer over any 2-coloring of $[1,n]$ was conjectured by Butler, Costello, and Graham (2010) and Thanathipanonda (2009). We solve this problem using a method based on Datskovsky's proof (2003) on the minimum number of monochromatic Schur triples $(x,y,x+y)$. We do this by exploiting the combinatorial nature of the original proof and adapting it to the general problem.

2 citations

Journal ArticleDOI
TL;DR: In this article, a learner-centred undergraduate business course based on student voice was designed to address learning issues identified in the pretest and help students achieve the course requirements.
Abstract: This study explores how to design a learner-centred undergraduate business course based on student voice. A pretest was adopted to identify existing course-related problems and students' writing products were collected and analysed; the instruction and activities were deliberately designed to address learning issues identified in the pretest and help students achieve the course requirements. By the end of the course, students took the posttest and conducted critical reflections about their study. All 111 Thai students who enrolled in the course participated in the study in trimester two in 2016. The paired t test results indicate significant progress during the learner-centred course. Moreover, the success of the course design was demonstrated by the students' enhanced confidence levels shown in their critical self-reflections. This work has theoretical and practical implications in combining concepts of student voice, creating learner-centred environments, and proposing an effective undergraduate course framework to which educational practitioners can refer.

2 citations

Journal ArticleDOI
TL;DR: This article investigated the effect of religious piety on anti-takeover provisions in U.S. counties and found that religious piety substitutes for corporate governance in alleviating the agency conflict and that strong religious piety leads to weaker governance.
Abstract: Because religious piety induces individuals to be more honest and risk-averse, it makes managers less likely to exploit shareholders, thereby mitigating the agency conflict and potentially influencing governance arrangements. We exploit the variation in religious piety across U.S. counties and investigate the effect of religious piety on anti-takeover provisions. Our results show that religious piety substitutes for corporate governance in alleviating the agency conflict. Effective governance is less necessary for firm with strong religious piety. As a result, religious piety leads to weaker governance, as indicated by more anti-takeover defenses. We exploit historical religious piety as far back as 1952 as our instrumental variable. Religious piety from the distant past is unlikely correlated with current corporate governance directly, except through contemporaneous religious piety. Further analysis shows that religious piety is not merely associated with, but rather brings about, more anti-takeover provisions.

2 citations

Posted Content
TL;DR: In this paper, the authors present parallel algorithms for minibatch-stream sampling in two settings: (1) sliding window, which draws samples from a prespecified number of most-recently observed elements, and (2) infinite window, where samples from all the elements received.
Abstract: This paper investigates parallel random sampling from a potentially-unending data stream whose elements are revealed in a series of element sequences (minibatches). While sampling from a stream was extensively studied sequentially, not much has been explored in the parallel context, with prior parallel random-sampling algorithms focusing on the static batch model. We present parallel algorithms for minibatch-stream sampling in two settings: (1) sliding window, which draws samples from a prespecified number of most-recently observed elements, and (2) infinite window, which draws samples from all the elements received. Our algorithms are computationally and memory efficient: their work matches the fastest sequential counterpart, their parallel depth is small (polylogarithmic), and their memory usage matches the best known.

2 citations


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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20222
202161
202055
201952
201840
201753