• Journal of Digital Convergence
• /
• v.11 no.2
• /
• pp.235-242
• /
• 2013

Reputation systems aggregate users' feedback after the completion of a transaction and compute the "reputation" of products, services, or providers, which can assist other users in decision-making in the future. With the rapid growth of online e-Learning content providing services, a suitable reputation system for more credible e-Learning content delivery has become important and is essential if educational content providers are to remain competitive. Most existing reputation systems focus on generating ratings only for user reputation; they fail to consider the reputations of products or services(item reputation). However, it is essential for B2C e-Learning services to have a reliable reputation rating mechanism for items since they offer guidance for decision-making by presenting the ranks or ratings of e-Learning content items. To overcome this problem, we propose a novel collaborative filtering based reputation rating method. Collaborative filtering, one of the most successful recommendation methods, can be used to improve a reputation system. In this method, dual information sources are formed with groups of co-oriented users and expert users and to adapt it to the reputation rating mechanism. We have evaluated its performance experimentally by comparing various reputation systems.

• Information Systems Review
• /
• v.25 no.1
• /
• pp.27-46
• /
• 2023

Research on restaurant recommender systems has been proposed due to the development of the food service industry and the increasing demand for restaurants. Existing restaurant recommendation studies extracted consumer preference information through quantitative information or online review sensitivity analysis, but there is a limitation that it cannot reflect consumer semantic preference information. In addition, there is a lack of recommendation research that reflects the detailed attributes of restaurants. To solve this problem, this study proposed a model that can learn the interaction between consumer preferences and restaurant attributes by applying deep learning techniques. First, the convolutional neural network was applied to online reviews to extract semantic preference information from consumers, and embedded techniques were applied to restaurant information to extract detailed attributes of restaurants. Finally, the interaction between consumer preference and restaurant attributes was learned through the element-wise products to predict the consumer preference rating. Experiments using an online review of Yelp.com to evaluate the performance of the proposed model in this study confirmed that the proposed model in this study showed excellent recommendation performance. By proposing a customized restaurant recommendation system using big data from the restaurant industry, this study expects to provide various academic and practical implications.

• IEIE Transactions on Smart Processing and Computing
• /
• v.5 no.4
• /
• pp.256-266
• /
• 2016

Concurrently detected and annotated abnormal events can have a significant impact on surveillance systems. By considering the specific domain of pedestrian trajectories, this paper presents two main contributions. First, as introduced in much of the work on trajectory-based anomaly detection in the literature, only information about pedestrian paths, such as direction and speed, is considered. Differing from previous work, this paper proposes a framework that deals with additional types of trajectory-based anomalies. These abnormal events take places when a person enters prohibited areas. Those restricted regions are constructed by an online learning algorithm that uses surrounding information, including detected pedestrians and background scenes. Second, a simple data-boosting technique is introduced to overcome a lack of training data; such a problem particularly challenges all previous work, owing to the significantly low frequency of abnormal events. This technique only requires normal trajectories and fundamental information about scenes to increase the amount of training data for both normal and abnormal trajectories. With the increased amount of training data, the conventional abnormal trajectory classifier is able to achieve better prediction accuracy without falling into the over-fitting problem caused by complex learning models. Finally, the proposed framework (which annotates tracks that enter prohibited areas) and a conventional abnormal trajectory detector (using the data-boosting technique) are integrated to form a united detector. Such a detector deals with different types of anomalous trajectories in a hierarchical order. The experimental results show that all proposed detectors can effectively detect anomalous trajectories in the test phase.

• Educational Technology International
• /
• v.13 no.1
• /
• pp.79-100
• /
• 2012

The focus of this research is on identifying the problems that learners experience during online problem-based learning (e-PBL) from a cognitive perspective. The study is concentrated on learners' cognitive load level at each stage of e-PBL. The research questions are specifically as follows: What is the level of cognitive load at each stage of e-PBL and what is the relationship between cognitive load and group performance? What cognitive difficulties are experienced by learners in e-PBL and what causes cognitive difficulties? In this study, we found that cognitive load was the highest in stage 1 and there was negative relationship between cognitive load at stage 1 and group performance. In addition, learners experienced difficulties during e-PBL such as the complexity of task, the difficulty in collaboration, and the lack of appropriate references. For further study, we will investigate some strategies regarding adjusting learners' cognitive load in the early stages of e-PBL.

• Journal of the Korea Convergence Society
• /
• v.10 no.11
• /
• pp.33-39
• /
• 2019

Edutech, which combines education and information technology, is in the spotlight. Core technologies of the 4th Industrial Revolution have been actively used in education. Students use an AI-based learning platform to self-diagnose their needs. And get personalized training online with a cloud learning platform. Recently, a new educational medium called smart speaker that combines artificial intelligence technology and voice recognition technology has emerged and provides various educational services. The purpose of this study is to suggest a way to use smart speaker educationally to overcome the limitation of existing education. To this end, the concept and characteristics of smart speakers were analyzed, and the implications were derived by analyzing the contents provided by smart speakers. Also, the problem of using smart speaker was considered.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.9
• /
• pp.2573-2589
• /
• 2023

As 5G mobile systems carry multiple services and applications, numerous user, and application types with varying quality of service requirements inside a single physical network infrastructure are the primary problem in constructing 5G networks. Radio Access Network (RAN) slicing is introduced as a way to solve these challenges. This research focuses on optimizing RAN slices within a singular physical cell for vehicle-to-everything (V2X) and enhanced mobile broadband (eMBB) UEs, highlighting the importance of adept resource management and allocation for the evolving landscape of 5G services. We put forth two unique strategies: one being offline network slicing, also referred to as standard network slicing, and the other being Online reinforcement learning (RL) network slicing. Both strategies aim to maximize network efficiency by gathering network model characteristics and augmenting radio resources for eMBB and V2X UEs. When compared to traditional network slicing, RL network slicing shows greater performance in the allocation and utilization of UE resources. These steps are taken to adapt to fluctuating traffic loads using RL strategies, with the ultimate objective of bolstering the efficiency of generic 5G services.

• The Journal of Information Systems
• /
• v.30 no.2
• /
• pp.29-56
• /
• 2021

Purpose Cyber deviance of adolescents has become a serious social problem. With a widespread use of smartphones, incidents of cyber deviance have increased in Korea and both quantitative and qualitative damages such as suicide and depression are increasing. Research has been conducted to understand diverse factors that explain adolescents' delinquency in cyber space. However, most previous studies have focused on a single theory or perspective. Therefore, this study aims to comprehensively analyze motivations of juvenile cyber deviance and to develop a predictive model for delinquent adolescents by integrating four different theories on cyber deviance. Design/methodology/approach By using data from Korean Children & Youth Panel Survey 2010, this study extracts 27 potential factors for cyber deivance based on four background theories including general strain, social learning, social bonding, and routine activity theories. Then this study employs econometric analysis to empirically assess the impact of potential factors and utilizes a machine learning approach to predict the likelihood of cyber deviance by adolescents. Findings This study found that general strain factors as well as social learning factors have positive effects on cyber deviance. Routine activity-related factors such as real-life delinquent behaviors and online activities also positively influence the likelihood of cyber diviance. On the other hand, social bonding factors such as community commitment and attachment to community lessen the likelihood of cyber deviance while social factors related to school activities are found to have positive impacts on cyber deviance. This study also found a predictive model using a deep learning algorithm indicates the highest prediction performance. This study contributes to the prevention of cyber deviance of teenagers in practice by understanding motivations for adolescents' delinquency and predicting potential cyber deviants.

• Journal of Distribution Science
• /
• v.13 no.6
• /
• pp.105-118
• /
• 2015

Purpose - Currently, strengthening small and medium-sized enterprises (SME) in terms of competitiveness is a key economic issue. However, the problem is that many SMEs lack the internal competence required to cope with a rapidly changing market structure. Such problems can act as an obstacle to economic development, yet most SMEs in Korea are dealing with this problem today. A company's source of competitive advantage is changing from quantity to quality, facility to knowledge, and hardwork to creativity. Under such circumstances, a company should place learning and sharing of knowledge and continuously creating new knowledge as its priority. This study aims to identify the effect of a chief executive officer's (CEO) compassion and forgiveness - positive factors in organizational emotion - on learning organization activities and organizational performance, through a theoretical comparison. Research design, data, and methodology - For this study, SMEs based in Daejeon and Chungcheong area were selected. To secure credibility of the data, the subjects were selected among those who have been working at the business for six months or longer. The survey was conducted for 30 days from March 5, 2015 to April 5, 2015. Both offline and online surveys were conducted. Fifty companies were chosen and 700 questionnaires were distributed, with 506 used for analysis. Fifty subject companies (25 from Daejeon, 10 from Chungnam, 10 from Chungbuk, and five from Sejong) were selected and the objective, target, and survey content were explained to a manager at each company either face-to-face or on the phone. Of the total of 700 questionnaires distributed via mail or e-mail, 78.6% or 550 copies were returned. Excluding 44 insufficient questionnaires, the remainder, 506 questionnaires, were used for analysis. Results - This study analyzed how the CEO's compassion and forgiveness affects learning organization activities and organizational performance. First, compassion of the CEO at the SMEs directly affected the learning organization activities and indirectly affected the organizational performance. Second, forgiveness of the CEO at the SMEs did not affect the learning organization activities and organizational performance directly or indirectly. Conclusions - The study conclusions are as follows. First, CEO compassionate behavior at the SMEs was a significant variable that directly and indirectly affected learning organization activities and organizational performance. Therefore, the CEO of an SME can create a positive organizational atmosphere through compassionate behaviors in the organization. Second, the forgiving behavior of the CEO did not have direct or indirect effects on learning organization activities and organizational performance. However, the reason for a CEO to continue his or her forgiving behavior is because it strengthens employee resilience, commitment, and self-efficacy to protect the organization from negative influences such as layoffs, risks, and wrongdoings. The action of forgiveness does not have direct or indirect effects. However, the CEO shall continue such behavior to strengthen members' physiological resilience, commitment, and self - effectiveness, and to protect the organization from risks including layoff and external negative factors.

• International Journal of Computer Science & Network Security
• /
• v.22 no.7
• /
• pp.451-458
• /
• 2022

This article is devoted to the problem of national and patriotic education of young students by means of digital technologies in the conditions of distance learning environment. It is emphasized that national and patriotic education is a powerful means of strengthening the unity and integrity of Ukraine. It is proved that national and patriotic education will be effective under the condition of systematic and purposeful activity on formation of patriotic consciousness in youth, sense of national dignity, necessity of service of ideals and values of the country. Various forms of educational work of national and patriotic orientation at Pavlo Tychyna Uman State Pedagogical University, which were conducted by digital technologies: online thematic lectures, educational classes, round tables, workshops, guest online meetings with famous researchers of historical heritage of Ukraine, online tours of historical places, virtual exhibitions of art, participation in the national-patriotic student camp "Diia" (Action) and etc. The activity of the University Library and V. O. Sukhomlinsky State Scientific and Pedagogical Library of Ukraine of the National Academy of Pedagogical Sciences of Ukraine, which has a significant impact on the formation of national consciousness and social and political activity of students by modern means of information and communication technologies. It is determined that the project "Inclusive 3D map" helps to broaden the horizons and deepen the knowledge of young students, education of a true citizen, the formation of cognitive interest in the subjects studied, motivation to study, raising awareness of Ukrainians on historical and cultural heritage. The study showed that young students take an active social attitude: they speak Ukrainian, want to live and work in Ukraine, respect their homeland, its traditions, cultural and historical past, love to travel and they are tolerant of people with special needs. Promising areas of educational work with students based on the use of a wide range of information and communication technologies, namely 3D games, TV tandems, podcasts, social networks, video resources in national and patriotic education of youth.

• Asian review of World Histories
• /
• v.4 no.1
• /
• pp.3-34
• /
• 2016

This introduction is both a statement of a research problem and an account of the first research results for its solution. As more historical databases come online and overlap in coverage, we need to discuss the two main issues that prevent 'big' results from emerging so far. Firstly, historical data are seen by computer science people as unstructured, that is, historical records cannot be easily decomposed into unambiguous fields, like in population (birth and death records) and taxation data. Secondly, machine-learning tools developed for structured data cannot be applied as they are for historical research. We propose a complex network, narrative-driven approach to mining historical databases. In such a time-integrated network obtained by overlaying records from historical databases, the nodes are actors, while thelinks are actions. In the case study that we present (the world as seen from Venice, 1205-1533), the actors are governments, while the actions are limited to war, trade, and treaty to keep the case study tractable. We then identify key periods, key events, and hence key actors, key locations through a time-resolved examination of the actions. This tool allows historians to deal with historical data issues (e.g., source provenance identification, event validation, trade-conflict-diplomacy relationships, etc.). On a higher level, this automatic extraction of key narratives from a historical database allows historians to formulate hypotheses on the courses of history, and also allow them to test these hypotheses in other actions or in additional data sets. Our vision is that this narrative-driven analysis of historical data can lead to the development of multiple scale agent-based models, which can be simulated on a computer to generate ensembles of counterfactual histories that would deepen our understanding of how our actual history developed the way it did. The generation of such narratives, automatically and in a scalable way, will revolutionize the practice of history as a discipline, because historical knowledge, that is the treasure of human experiences (i.e. the heritage of the world), will become what might be inherited by machine learning algorithms and used in smart cities to highlight and explain present ties and illustrate potential future scenarios and visionarios.