Structures and lower bounds for binary covering arrays

A $q$-ary $t$-covering array is an $m \times n$ matrix with entries from $\{0, 1, ..., q-1\}$ with the property that for any $t$ column positions, all $q^t$ possible vectors of length $t$ occur at least once. One wishes to minimize $m$ for given $t$ and $n$, or maximize $n$ for given $t$ and $m$. For $t = 2$ and $q = 2$, it is completely solved by R\'enyi, Katona, and Kleitman and Spencer. They also show that maximal binary 2-covering arrays are uniquely determined. Roux found the lower bound of $m$ for a general $t, n$, and $q$. In this article, we show that $m \times n$ binary 2-covering arrays under some constraints on $m$ and $n$ come from the maximal covering arrays. We also improve the lower bound of Roux for $t = 3$ and $q = 2$, and show that some binary 3 or 4-covering arrays are uniquely determined.Comment: 16 page

The Emotional Reactions to Challenging Behavior Scale-Korean (ERCBS-K): Modification and Validation

The purpose of this study was to explore the original version of Mitchell and Hastings\u27s (1998) Emotional Reaction to Challenging Behavior Scale (ERCBS) and estimate validity and reliability of a revised version containing 29 items. The Emotional Reaction to Challenging Behavior Scale-Korean (ERCBS-K) was studied using 445 in-service physical educators (228 females; 217 males). Data were collected using onsite administration as well as mail survey administration procedures. Confirmatory and exploratory factor analyses results supported a five factor, 28-item scale (ERCBS-K). Acceptable internal consistency coefficients were found for each of the subscales of the ERCBS-K (Cronbach\u27s alpha ranged from 0.71 to 0.87)

Image Captioning with Very Scarce Supervised Data: Adversarial Semi-Supervised Learning Approach

Constructing an organized dataset comprised of a large number of images and several captions for each image is a laborious task, which requires vast human effort. On the other hand, collecting a large number of images and sentences separately may be immensely easier. In this paper, we develop a novel data-efficient semi-supervised framework for training an image captioning model. We leverage massive unpaired image and caption data by learning to associate them. To this end, our proposed semi-supervised learning method assigns pseudo-labels to unpaired samples via Generative Adversarial Networks to learn the joint distribution of image and caption. To evaluate, we construct scarcely-paired COCO dataset, a modified version of MS COCO caption dataset. The empirical results show the effectiveness of our method compared to several strong baselines, especially when the amount of the paired samples are scarce.Comment: EMNLP 2019. Project page : https://sites.google.com/view/emnlp19scarcecaptio

Selection of Elevation Models for Flood Inundation Map Generation in Small Urban Stream: Case Study of Anyang Stream

To reduce flood damages, the Ministry of Environment in Korea has provided a flood inundation map so that people can expediently identify flood-prone areas. However, the current flood inundation maps have been produced based on the DEM which makes it difficult to represent realistic situations due to the lack of reproduction of land surface conditions. This study aims to provide more accurate and detailed flood inundation maps for flooding events due to river overflow in small urban areas. In this study, flood inundation analysis is performed using the river analysis system, HEC-RAS 2D, with the DSM and the DEM of urban areas in the Anyang Stream Basin, Korea to examine the differences in terms of terrain data and flooded area. Finally, for urban areas with dense buildings and congested road networks, the flood inundation analysis based on DSM can represent a more realistic flood situation and create an appropriate flood inundation map

Hydraulic and ecological changes under drainage gate operations with coupled model SCHISM-CoSiNE in Saemangeum basin, Korea

The drainage gates have been controlled for desalination under normal conditions and flood defense in  Saemangeum basin, Korea. Recently, it became an issue that the gates have been opened not to deteriorate water quality in the lake. It is, thus, necessary to precisely estimate the changes of water quality characteristics, especially DO, phosphate and nitrate, in the lake according to various gate operations. In this study, Semi-implicit Cross-scale Hydroscience Integrated System Model and Carbon, Silicate, Nitrogen Ecosystem model (SCHISM-CoSiNE) which is cable to simulate dynamic exchange such as gate operation conditions was utilized to obtain reliable and reasonable results including hydrodyanamic and environmental variables. For the verification, the measured data at 6 locations in Saemangeum basin was used to compare  incluidng temperatue and salintiy from 2016 and each relative error became small enough to show high accurary. Also, under various scenarios by changing the designated water surface elevation on flood seasons, this model has been applied to present the best designated water surface elevation in terms of both water quality and water supply in the Saemangeum basin. It becomes possbile to show reliable guidance for dynamic operations and environmental changes with this model as requested in near future