• Journal of the Korean Society for information Management
• /
• 제29권4호
• /
• pp.143-163
• /
• 2012

The dominant model of acquiring scholarly contents at academic libraries is so called big deal where libraries subscribe to a bundle of hundreds, if not thousands of journals in a multi-year contract with fixed annual rate increase. The bid deal, started in the mid-1990s, offered a number of advantages for academic libraries and their users. However, escalating prices for these packages have become a serious issue casting doubts about the sustainability of the subscription-based model. At the moment, it appears there is no viable alternative other than pay-per-view method that is being tested at some libraries. Libraries' budget situation will remain a key factor that might change the situation. Open access started in the 2000s as a vehicle to eliminate barriers to publishing and distributing peer-reviewed scholarly journal articles. Open access publishing is witnessing two-digit growth annually. Open access articles now occupy close to 20% of two major citation databases: Scopus and Web of Science. Google Scholar service, debuted in late 2004, is now a popular tool for discovering and accessing scholarly articles from a vast selection of journals around the world. There is a call for taking Google Scholar seriously as a potential replacement of library databases amid concerns regarding the quality of journals indexed, limited search capabilities vis-$\grave{a}$-vis library databases, and monopoly of public goods. Escalating budget problems, rapid growth of open access publishing and the emergence of powerful free tool, such as Google Scholar, need to be taken seriously as these forces might bring disruptive changes to the existing subscription-based model of scholarly contents at academic libraries.

• Journal of Digital Convergence
• /
• 제16권8호
• /
• pp.169-179
• /
• 2018

Recently, web search query information has been applied in advanced predictive model research. Google dominates the global web search market in the Korean market; however, Naver possesses a dominant market share. Based on this characteristic, this study intends to compare the utility of the Korean web search query information of Google and Naver using predictive models. Therefore, this study develops three time-series predictive models to estimate the youth unemployment rate in Korea using the ARIMA model. Model 1 only used the youth unemployment rate in Korea, whereas Models 2 and 3 added the Korean web search query information of Naver and Google, respectively, to Model 1. Compared to the predictability of the models during the training period, Models 2 and 3 showed better fit compared with Model 1. Models 2 and 3 correlated different query information. During predictive periods 1 (continuous with the training period) and 2 (discontinuous with the training period), Model 3 showed the best performance. During predictive period 2, only Model 3 exhibited a significant prediction result. This comparative study contributes to a general understanding of the usefulness of Korean web query information using the Naver and Google search engines.

• Journal of Digital Contents Society
• /
• 제17권5호
• /
• pp.317-327
• /
• 2016

Flipped learning is a new learning technique which can maximize the learning effect by mixing two or more different learning environments including online & offline, and recently introduced system: 'Google classroom' is the optimized internet platform for flipped learning. This study tried to apply flipped learning to regular course 2nd grade dental students(n=70) and evaluated the satisfaction of students. The subjects of periodontology and operative dentistry were chosen to evaluate flipped learning model for regular course 2nd grade dental students(n=70). Each class consisted of six classes, and three times of them were performed in conventional classes and the other three times were in flipped learning method by using Google classroom. Evaluation of satisfaction progressed at the end of class. In this study, application of flipped learning in the dental college classes showed high efficiency in terms of degree of understanding, self-directed learning and motivation. Collectively, it was shown that flipped learning using Google classroom can be a reliable platform in dental classes.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• 제26권5호
• /
• pp.1235-1241
• /
• 2016

Personalized App recommendation system is recently famous since the number of various apps that can be used in smart phones that increases exponentially. However, the site users using google play site with malwares have experienced severe damages of privacy exposure and extortion as well as a simple damage of satisfaction descent at the same time. In addition, Sybil attack (Sybil) manipulating the score (rating) of each app with falmay also present because of the social networks development. Up until now, the sybil detection studies and malicious apps studies have been conducted independently. But it is important to determine finally the existence of intelligent attack with Sybil and malware simultaneously when we consider the intelligent attack types in real-time. Therefore, in this paper we experimentally evaluate the relationship between malware and sybils based on real cralwed dataset of goodlplay. Through the extensive evaluations, the correlation between malware and sybils is low for malware providers to hide themselves from Anti-Virus (AV).

• Journal of Intelligence and Information Systems
• /
• 제29권2호
• /
• pp.129-148
• /
• 2023

Bitcoin is a blockchain technology-based digital currency that has been recognized as a representative cryptocurrency and a financial investment asset. Due to its highly volatile nature, Bitcoin has gained a lot of attention from investors and the public. Based on this popularity, numerous studies have been conducted on price and trend prediction using machine learning and deep learning. This study employed LSTM (Long Short Term Memory) and CNN (Convolutional Neural Networks), which have shown potential for predictive performance in the finance domain, to enhance the classification accuracy in Bitcoin price trend prediction. XAI(eXplainable Artificial Intelligence) techniques were applied to the predictive model to enhance its explainability and interpretability by providing a comprehensive explanation of the model. In the empirical experiment, CNN was applied to technical indicators and Google trend data to build a Bitcoin price trend prediction model, and the CNN model using both technical indicators and Google trend data clearly outperformed the other models using neural networks, SVM, and LSTM. Then SHAP(Shapley Additive exPlanations) was applied to the predictive model to obtain explanations about the output values. Important prediction drivers in input variables were extracted through global interpretation, and the interpretation of the predictive model's decision process for each instance was suggested through local interpretation. The results show that our proposed research framework demonstrates both improved classification accuracy and explainability by using CNN, Google trend data, and SHAP.

• Communications of the Korean Institute of Information Scientists and Engineers
• /
• 제33권4호
• /
• pp.8-12
• /
• 2015

• Review of Korea Contents Association
• /
• 제7권2호
• /
• pp.29-36
• /
• 2009

• JOURNAL OF ELECTRICAL WORLD
• /
• 통권405호
• /
• pp.87-90
• /
• 2010

• Journal of Korea Fair Competition Federation
• /
• 제163호
• /
• pp.86-93
• /
• 2012

• Water for future
• /
• 제48권4호
• /
• pp.84-89
• /
• 2015