• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2020년도 제62차 하계학술대회논문집 28권2호
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• pp.697-699
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• 2020

2020년의 게임 시장은 스마트폰 기기의 발전과 Pay To Win을 사용하는 BM모델의 감소 등의 변화로 인해 플레이어의 플레이 타임과 게임에 대한 이해도를 핵심 요소로 잡고 있다. 이러한 상황 속에서 게임에 대한 실력과 이해도가 높은 일부 유저들이 대신 게임을 플레이하여 타 유저들의 요구 사항을 충족 해주고 부당한 이익과 게임 내 성장, 경쟁 불균형을 발생시키는 'Game-Boosting'의 모습을 확인할 수 있었다. 본 논문에서는 'Game-boosting' 유저들에 대해 설명하고, 해당 유저들에 대한 기준을 정의하여, '대리게임 금지법' 에 적용될 수 있는 기준을 제시한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권7호
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• pp.1794-1806
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• 2023

This study presents a method for capturing photographs of users as input and converting them into 2D character animation sprites using a generative adversarial network-based artificial intelligence network. Traditionally, 2D character animations have been created by manually creating an entire sequence of sprite images, which incurs high development costs. To address this issue, this study proposes a technique that combines motion videos and sample 2D images. In the 2D sprite generation process that uses the proposed technique, a sequence of images is extracted from real-life images captured by the user, and these are combined with character images from within the game. Our research aims to leverage cutting-edge deep learning-based image manipulation techniques, such as the GAN-based motion transfer network (impersonator) and background noise removal (U² -Net), to generate a sequence of animation sprites from a single image. The proposed technique enables the creation of diverse animations and motions just one image. By utilizing these advancements, we focus on enhancing productivity in the game and animation industry through improved efficiency and streamlined production processes. By employing state-of-the-art techniques, our research enables the generation of 2D sprite images with various motions, offering significant potential for boosting productivity and creativity in the industry.

• 한국콘텐츠학회논문지
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• 제22권5호
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• pp.48-56
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• 2022

야구에서 데이터분석의 중요성은 점점 더 커지고 있는데, 이는 Billy Beane의 머니볼 이론을 적용한 MLB의 오클랜드 구단과 2020년 KBO 우승팀인 NC다이노스의 사례에서도 알 수 있다. 미국 뿐 만 아니라 우리나라에서도 데이터를 이용한 다양한 연구이 이루어지고 있는데, 특히 경기의 승패를 예측하기 딥러닝 및 머신러닝을 이용한 연구가 활발하게 진행되고 있다. 그러나 기존 연구의 경우 경기 승패 예측에 초점이 맞추어져 있으며, 어떠한 요인이 경기에 중요한 영향을 주었는지에 대한 결과 해석이 어렵다는 한계점이 존재한다. 이에 따라 본 논문에서는 포지션별 최고의 선수에게 수여되는 골든글러브 수상 예측 모형을 개발하고, 어떠한 요인이 수상에 영향을 주는지를 파악함으로써, 우수한 선수로 구분되는 척도를 찾고자 한다. 분석 모형으로는 Boosting 기법 중의 하나인 XGBoost를 이용하였으며, 어떠한 요인이 골든글러브 수상에 중요한 영향을 주었는지 파악하기 위하여 XGBoost의 변수 중요도를 이용하였다. 이와 같은 분석을 통해 각 포지션별 중요한 지표를 파악할 수 있었다.

• 초등상담연구
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• 제4권1호
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• pp.263-284
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• 2005

The purpose of this study was to examine the effect of game-centered empathy training on the prosocial behaviors of elementary schoolers in a bid to suggest how their prosocial behaviors could be boosted. The research questions were posed as below: 1. Does game-based empathy training improve the overall empathy of school children? 2. Does game-based empathy training have a better effect on the cognitive empathy of school children or their emotional empathy? 3. Does game-based empathy training enhance the prosocial behaviors of school children? The subjects in this study were 62 children in their fourth year of D elementary school in Eumseong-gun, north Chungcheong province. They were divided into an experimental group and a control group, and a survey was conducted before and after the experimental group under-went empathy training for about six weeks. The instrument used in this study was David(1980)'s Interpersonal Reactivity Index adopted by Park Sung-hee(1996) to suit school children. Another instrument was Park Sung-hee (1997)'s inventory to assess the prosocial behaviors of children. The collected data were analyzed by SPSS 10.0 for Windows program, and reliability analysis and t-test were employed. The findings of the study were as follows: First, as for the effects of the game-based empathy training on the overall empathy of the elementary school youngsters that included both emotional and cognitive empathy, both groups got lower scores in posttest than in pretest. The experiment produced unexpected results, as the experimental group got significantly lower scores. This fact indicated that the game-based empathy training was ineffective. Second, the game-centered empathy training didn't exercise any influences on their cognitive and emotional empathy. The experiment had a reverse impact on the cognitive and emotional empathy of the experimental group, which implied that the training served as a factor to deteriorate the two types of empathy, and the hypothesis posed in this study was rejected. Therefore, which type of empathy could make a better progress by being exposed to the training couldn't definitely be determined. Third, the game-based empathy training didn't serve to Improve the prosocial behaviors of the elementary schoolers. There was no change in the experimental group, and this fact signified that there's something wrong with the attempt to develop school children's empathy to step up their prosocial behaviors. Based on the above-mentioned findings, the following conclusion was reached: First, the game-centered empathy training had no effects on boosting the overall empathy of the school children. Second, the game-centered empathy training couldn't be said to be effective in improving either cognitive empathy or emotional one. From a viewpoint of relativity, that could be said to affect emotional empathy more than cognitive one. Third, the game-based empathy training wasn't effective in improving prosocial behaviors. Rather, that resulted in interrupting the promotion of prosocial behaviors.

• International Journal of Computer Science & Network Security
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• 제22권6호
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• pp.390-399
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• 2022

Cyber security and resilience are phrases that describe safeguards of ICTs (information and communication technologies) from cyber-attacks or mitigations of cyber event impacts. The sole purpose of Risk models are detections, analyses, and handling by considering all relevant perceptions of risks. The current research effort has resulted in the development of a new paradigm for safeguarding services offered online which can be utilized by both service providers and users. customers. However, rather of relying on detailed studies, this approach emphasizes task selection and execution that leads to successful risk treatment outcomes. Modelling intelligent CSGs (Cyber Security Games) using MLTs (machine learning techniques) was the focus of this research. By limiting mission risk, CSGs maximize ability of systems to operate unhindered in cyber environments. The suggested framework's main components are the Threat and Risk models. These models are tailored to meet the special characteristics of online services as well as the cyberspace environment. A risk management procedure is included in the framework. Risk scores are computed by combining probabilities of successful attacks with findings of impact models that predict cyber catastrophe consequences. To assess successful attacks, models emulating defense against threats can be used in topologies. CSGs consider widespread interconnectivity of cyber systems which forces defending all multi-step attack paths. In contrast, attackers just need one of the paths to succeed. CSGs are game-theoretic methods for identifying defense measures and reducing risks for systems and probe for maximum cyber risks using game formulations (MiniMax). To detect the impacts, the attacker player creates an attack tree for each state of the game using a modified Extreme Gradient Boosting Decision Tree (that sees numerous compromises ahead). Based on the findings, the proposed model has a high level of security for the web sources used in the experiment.

• The Journal of Asian Finance, Economics and Business
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• 제9권5호
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• pp.213-224
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• 2022

In recent years, gamification has been a hot issue due to its positive impact on organizational success. The proper application of game elements in an organizational context is required for gamification implementations. Gamification remains an area of active research for its behavior molding potential. Employee engagement is a critical component in assessing employee behavior and is considered crucial for organizational success. Research questionnaires were completed online between March 2021 and February 2022. Our targeted sample encompassed low and mid-level personnel of Asian and Middle eastern employees working in Hungary. The questionnaire was introduced using google forms. Our sample size consisted of 203 respondents (N = 203). Research results indicated gamification's significance in increasing employees' intrinsic motivation and therefore boosting organizational engagement levels. Gamification improved employees' task performance and the overall quality of work. Organizational culture had a mediating role between gamification and employees' behavior. Organizational culture and employee behavior are in close correlation. Research findings also proved engagements' mediating effect on employees' behavior. The results of the research showed that gamification in human resources has risen in popularity, especially in terms of its impact on employee behavior and performance. The study's findings demonstrated that gamification has a positive impact on organizational performance and collaboration.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권12호
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• pp.6069-6091
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• 2017

Augmented Reality (AR) overlays virtual information on real world data, such as displaying useful information on videos/images of a scene. This paper presents an Enhanced AR (EAR) system that displays useful statistical players' information on captured images of a sports game. We focus on the situation where the input image is degraded by strong sunlight. Proposed EAR system consists of an image enhancement technique to improve the accuracy of subsequent player and face detection. The image enhancement is followed by player and face detection, face recognition, and players' statistics display. First, an algorithm based on multi-scale retinex is proposed for image enhancement. Then, to detect players' and faces', we use adaptive boosting and Haar features for feature extraction and classification. The player face recognition algorithm uses boosted linear discriminant analysis to select features and nearest neighbor classifier for classification. The system can be adjusted to work in different types of sports where the input is an image and the desired output is display of information nearby the recognized players. Simulations are carried out on 2096 different images that contain players in diverse conditions. Proposed EAR system demonstrates the great potential of computer vision based approaches to develop AR applications.