• Journal of Service Research and Studies
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• v.8 no.3
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• pp.25-35
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• 2018

Initiatives for corporate social responsibility (CSR) have often served as sources of competitive advantage in the business world. Although the adoption of CSR practices in the hotel industry continues to increase, empirical research on the relationship between them and financial performance in the industryremains scarce. The purpose of this study was to ascertain the effects of various dimensions of CSR on the financial performance of corporations in the U.S. hotel industry. Data include Kinder, Lydenburg & Domini social performance scores and Compustat data of hotels from 1991 to 2015 identified using a Standard Industrial Classification code. Results of ordinary least squares regression using Stata revealed that efforts toward CSR have significantly affected the financial performance of numerous hotels. Such findings can initiate discussions and inspire future research on CSR in the hospitality industry.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.32 no.1
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• pp.35-39
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• 2019

In this study, we investigated the optimum calcination temperature of lead-free $0.74(Bi_{0.5}Na_{0.5})TiO_3-0.26SrTiO_3$(BNST) piezoelectric ceramics by analyzing the crystal structure, dielectric properties, and electric field-induced strain behavior. BNST ceramics prepared by conventional solid-state reaction methods at various calcination temperatures according to the industrial standard. All samples of BNST ceramics were subsequently sintered at $1,175^{\circ}C$ for 2 h. Crystal structure classification of the ceramics showed a single perovskite phase, with no second phase detectable for the samples calcined at $750^{\circ}C$ or higher. BNST samples calcined at $850^{\circ}C$ exhibited the most optimal values for itsand the common physical parameters of $density=5.518g/cm^3$, ${\varepsilon}=1,871.837$, $tan{\delta}=0.047$, and ${d_{33}}^*=874pm/V$.

• Journal of Convergence for Information Technology
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• v.9 no.9
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• pp.1-12
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• 2019

A dialog system becomes a new way of communication between human and computer. The dialog system takes human voice as an input, and gives a proper response in voice or perform an action. Although there are several well-known products of dialog system (e.g., Amazon Echo, Naver Wave), they commonly suffer from a problem of out-of-domain utterances. If it poorly detects out-of-domain utterances, then it will significantly harm the user satisfactory. There have been some studies aimed at solving this problem, but it is still necessary to study about this intensively. In this paper, we give an overview of the previous studies of out-of-domain detection in terms of three point of view: dataset, feature, and method. As there were relatively smaller studies of this topic due to the lack of datasets, we believe that the most important next research step is to construct and share a large dataset for dialog system, and thereafter try state-of-the-art techniques upon the dataset.

• Journal of information and communication convergence engineering
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• v.17 no.1
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• pp.74-83
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• 2019

This study designs a squat posture recognition system that can provide correct squat posture guidelines. This system comprises two modules: a Kinect camera for monitoring users' body movements and a Wii Balance Board(WBB) for measuring balanced postures with legs. Squat posture recognition involves two states: "Stand" and "Squat." Further, each state is divided into two postures: correct and incorrect. The incorrect postures of the Stand and Squat states were classified into three and two different types of postures, respectively. The factors that determine whether a posture is incorrect or correct include the difference between shoulder width and ankle width, knee angle, and coordinate of center of pressure(CoP). An expert and 10 participants participated in experiments, and the three factors used to determine the posture were measured using both Kinect and WBB. The acquired data from each device show that the expert's posture is more stable than that of the subjects. This data was classified using a support vector machine (SVM) and $na{\ddot{i}}ve$ Bayes classifier. The classification results showed that the accuracy achieved using the SVM and $na{\ddot{i}}ve$ Bayes classifier was 95.61% and 81.82%, respectively. Therefore, the developed system that used Kinect and WBB could classify correct and incorrect postures with high accuracy. Unlike in other studies, we obtained the spatial coordinates using Kinect and measured the length of the body. The balance of the body was measured using CoP coordinates obtained from the WBB, and meaningful results were obtained from the measured values. Finally, the developed system can help people analyze the squat posture easily and conveniently anywhere and can help present correct squat posture guidelines. By using this system, users can easily analyze the squat posture in daily life and suggest safe and accurate postures.

• Imaging Science in Dentistry
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• v.49 no.2
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• pp.115-122
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• 2019

Purpose: The aims of this study were first, to compare panoramic radiography with cone-beam computed tomography (CBCT) for evaluating topographic relationships, such as the classification of maxillary posterior teeth and their distance to the maxillary sinus floor; and second, to determine the relationship between maxillary sinus pathology and the presence of apical lesions. Materials and Methods: In total, 285 paired CBCT and panoramic radiography records of patients (570 maxillary sinuses) were retrospectively analyzed. Both imaging modalities were used to determine the topographic relationship of the maxillary posterior teeth to the sinus floor. Mucosal thickening >2 mm was considered a pathological state. Data were analyzed using the chi-square, Wilcoxon, and Mann-Whitney U tests. Odds ratios (ORs) and confidence intervals(CIs) were calculated. Results: The closest vertical distance measurements made between posterior maxillary teeth roots and the maxillary sinus on panoramic radiography and CBCT scans showed statistically significant differences from each other(P<0.05). Compared to panoramic radiography, CBCT showed higher mean values for the distance between the maxillary sinus floor and maxillary posterior teeth roots. The CBCT images showed that at least 1 apical lesion adjacent to the right maxillary sinus increased the risk of maxillary sinus pathology by 2.37 times(OR, 2.37; 95% CI, 1.58-3.55, P<0.05). Conclusion: Panoramic radiography might lead to unreliable diagnoses when evaluating the distance between the sinus floor and posterior roots of the maxillary teeth. Periapical lesions anatomically associated with maxillary sinuses were a risk factor for sinus mucosal thickening.

• Journal of Species Research
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• v.8 no.2
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• pp.197-214
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• 2019

The taxonomic hierarchy of the phylum Proteobacteria was assessed, after which the isolation and classification state of Proteobacteria species with valid names for Korean indigenous isolates were studied. The hierarchical taxonomic system of the phylum Proteobacteria began in 1809 when the genus Polyangium was first reported and has been generally adopted from 2001 based on the road map of Bergey's Manual of Systematic Bacteriology. Until February 2018, the phylum Proteobacteria consisted of eight classes, 44 orders, 120 families, and more than 1,000 genera. Proteobacteria species isolated from various environments in Korea have been reported since 1999, and 644 species have been approved as of February 2018. In this study, all novel Proteobacteria species from Korean environments were affiliated with four classes, 25 orders, 65 families, and 261 genera. A total of 304 species belonged to the class Alphaproteobacteria, 257 species to the class Gammaproteobacteria, 82 species to the class Betaproteobacteria, and one species to the class Epsilonproteobacteria. The predominant orders were Rhodobacterales, Sphingomonadales, Burkholderiales, Lysobacterales and Alteromonadales. The most diverse and greatest number of novel Proteobacteria species were isolated from marine environments. Proteobacteria species were isolated from the whole territory of Korea, with especially large numbers from the regions of Chungnam/Daejeon, Gyeonggi/Seoul/Incheon, and Jeonnam/Gwangju. Most Halomonadaceae species isolated from Korean fermented foods and solar salterns were halophilic or halotolerant. Air-borne members of the genera Microvirga, Methylobacterium, and Massilia had common characteristics in terms of G+C content, major respiratory quinones, and major polar lipids.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.281-291
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• 2021

The Islamic jurisprudential legal system represents an essential component of the Islamic religion, that governs many aspects of Muslims' daily lives. This creates many questions that require interpretations by qualified specialists, or Muftis according to the main sources of legislation in Islam. The Islamic jurisprudence is usually classified into branches, according to which the questions can be categorized and classified. Such categorization has many applications in automated question-answering systems, and in manual systems in routing the questions to a specialized Mufti to answer specific topics. In this work we tackle the problem of automatic categorisation of Islamic jurisprudential legal questions using deep learning techniques. In this paper, we build a hierarchical deep learning model that first extracts the question text features at two levels: word and sentence representation, followed by a text classifier that acts upon the question representation. To evaluate our model, we build and release the largest publicly available dataset of Islamic questions and answers, along with their topics, for 52 topic categories. We evaluate different state-of-the art deep learning models, both for word and sentence embeddings, comparing recurrent and transformer-based techniques, and performing extensive ablation studies to show the effect of each model choice. Our hierarchical model is based on pre-trained models, taking advantage of the recent advancement of transfer learning techniques, focused on Arabic language.

• Journal of Digital Convergence
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• v.19 no.4
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• pp.25-31
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• 2021

Moving toward an aged society, traffic accidents involving elderly drivers have also attracted broader public attention. A rapid increase of senior involvement in crashes calls for developing appropriate crash-severity prediction models specific to senior drivers. In that regard, this study leverages machine learning (ML) algorithms so as to predict the severity of vehicle-pedestrian collisions induced by elderly drivers. Specifically, four ML algorithms (i.e., Logistic model, K-nearest Neighbor (KNN), Random Forest (RF), and Support Vector Machine (SVM)) have been developed and compared. Our results show that Logistic model and SVM have outperformed their rivals in terms of the overall prediction accuracy, while precision measure exhibits in favor of RF. We also clarify that driver education and technology development would be effective countermeasures against severity risks of senior driver-induced collisions. These allow us to support informed decision making for policymakers to enhance public safety.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.19-30
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• 2021

Diabetes Mellitus (DM) is one of common chronic diseases leading to severe health complications that may cause death. The disease influences individuals, community, and the government due to the continuous monitoring, lifelong commitment, and the cost of treatment. The World Health Organization (WHO) considers Saudi Arabia as one of the top 10 countries in diabetes prevalence across the world. Since most of the medical services are provided by the government, the cost of the treatment in terms of hospitals and clinical visits and lab tests represents a real burden due to the large scale of the disease. The ability to predict the diabetic status of a patient without the laboratory tests by performing screening based on some personal features can lessen the health and economic burden caused by diabetes alone. The goal of this paper is to investigate the prediction of diabetic and prediabetic patients by considering factors other than the laboratory tests, as required by physicians in general. With the data obtained from local hospitals, medical records were processed to obtain a dataset that classified patients into three classes: diabetic, prediabetic, and non-diabetic. After applying three machine learning algorithms, we established good performance for accuracy, precision, and recall of the models on the dataset. Further analysis was performed on the data to identify important non-laboratory variables related to the patients for diabetes classification. The importance of five variables (gender, physical activity level, hypertension, BMI, and age) from the person's basic health data were investigated to find their contribution to the state of a patient being diabetic, prediabetic or normal. Our analysis presented great agreement with the risk factors of diabetes and prediabetes stated by the American Diabetes Association (ADA) and other health institutions worldwide. We conclude that by performing class-specific analysis of the disease, important factors specific to Saudi population can be identified, whose management can result in controlling the disease. We also provide some recommendations learnt from this research.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.3
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• pp.223-230
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• 2021

Currently, self-driving vehicles are on the verge of commercialization after testing. However, even though autonomous vehicles have not been fully commercialized, 81 accidents have occurred, and the driving method of vehicles to avoid accidents relies heavily on LiDAR. In order for the currently commercialized 3-level autonomous vehicle to develop into a 4-level autonomous vehicle, more information must be collected than previously collected information. Therefore, this paper proposes a Driving Situation Judgment System (DSJS) that accurately calculates the crisis situation the vehicle is in by useing the roughness of the road and the state of the passengers of surrounding vehicles including road information and weather information collected from existing autonomous vehicles. As a result of DSJS's PDM experiment, PDM was able to classify passengers 15.52% more accurately on average than the existing vehicle's passenger recognition system. This study can be a basic research to achieve the 4th level autonomous vehicle by collecting more various types than the data collected by the existing 3rd level autonomous vehicle.