• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

• Journal of KIISE:Databases
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• v.31 no.2
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• pp.174-182
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• 2004

In order to produce information the author usually reference other authors' work. A citation index leads users to papers by citations. Citations lead the user to desired information. In this paper, KSCI(Korea Science Citation Index) which defines the relationships between citing documents and cited documents has been constructed. KSCI System is to solve problems for recursive retrieval in ISI's SCI(Science Citation Index) Path Encoding Indexing technique was used to solve the problems. From the analysis of data, this system has efficiency about 8.98% in the aspect of data storage. In the aspect of retrieval, there was efficiency between citing documents and cited documents, especially there was over 40% of efficiency in the retrieval of cited documents. It is concluded that suggested KSCI system will provide efficient storage and retrieval system.

• Journal of Construction Engineering and Project Management
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• v.8 no.1
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• pp.10-21
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• 2018

Many construction projects involve a plethora of safety-related problems that can cause loss of productivity, diminished revenue, time overruns, and legal challenges. Incorporating data collection and analytics methods can help overcome the root causes of many such problems. However, in a dynamic construction workplace collecting data from a large number of resources is not a trivial task and can be costly, while many contractors lack the motivation to incorporate technology in their activities. In this research, an Android-based mobile application, Preemptive Construction Site Safety (PCS2) is developed and tested for real-time location tracking, trajectory prediction, and prevention of potential collisions between workers and site hazards. PCS2 uses ubiquitous mobile technology (smartphones) for positional data collection, and a robust trajectory prediction technique that couples hidden Markov model (HMM) with risk-taking behavior modeling. The effectiveness of PCS2 is evaluated in field experiments where impending collisions are predicted and safety alerts are generated with enough lead time for the user. With further improvement in interface design and underlying mathematical models, PCS2 will have practical benefits in large scale multi-agent construction worksites by significantly reducing the likelihood of proximity-related accidents between workers and equipment.

• Information Systems Review
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• v.8 no.1
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• pp.141-158
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• 2006

Recently, developing U-City as an integrated set of ubiquitous space services has been regarded as a promising field in realizing ubiquitous computing technology. However, well organized requirement analyses of U-City to declare what kinds of ubiquitous space services are needed and which ubiquitous computing technology should be incorporated to come up with the needs are still insufficient. Hence, the aims of this paper are to propose a set of unique U-City construction philosophies and to analyze which services should be offered in the ubiquitous space services in U-Cities. A field survey to the citizens who are potential end users of the ubiquitous space services was conducted to analyze the success factors of U-City using CSF methodology. Focused group interview with professionals in the field of ubiquitous computing technology in Korea was then performed to identify the relationship of the ubiquitous space services and the critical success factors.

• Geomechanics and Engineering
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• v.5 no.5
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• pp.463-483
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• 2013

Considering the influences of fluid penetration, casing, excavation processes of wellbore and perforation tunnels, the seepage-deformation finite element model of oil and gas well coupled with perforating technique is established using the tensile strength failure criterion, in which the user-defined subroutine is developed to investigate the dynamic evolvement of the reservoir porosity and permeability. The results show that the increases of perforation angle and decreases of perforation density lead to a higher fracture initiation pressure, while the changes of the perforation diameter and length have no evident influences on the fracture initiation pressure. As for initiation location for the fracture in wellbore, it is on the wellbore face while considering the presence of the casing. By contrast, the fractures firstly initiate on the root of the tunnels without considering casing. Besides, the initial fracture position is also related with the perforation angle. The fracture initiation position is located in the point far away from the wellbore face, when the perforation angle is around $30^{\circ}$; however, when the perforation angle is increased to $45^{\circ}$, a plane fracture is initiated from the wellbore face in the maximum horizontal stress direction; no fractures was found around perforation tunnels, when the angel is close to $90^{\circ}$. The results have been successfully applied in an oilfield, with the error of only 1.1% comparing the fracture initiation pressure simulated with the one from on-site experiment.

• The Journal of the Korea Contents Association
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• v.20 no.9
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• pp.397-413
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• 2020

Humor is often considered as a positive phenomenon, and thus frequently employed as an impression management technique for individuals. However, humor may create unexpected outcomes in terms of impression management. This study investigates the relationship between humor and impression management of individuals within three South Korean organizations. A qualitative methodology is employed and data collected through participant observation and semi-structured interviews. Findings suggest that humor may be used more frequently by workers in superior positions, and it may be dangerous for individuals in subordinate positions to initiate humor, as negative impressions such as lack of professionalism and work competence may be crafter through humor. In particular, traditional Confucian values and expectations may lead to perceptions that humor is inappropriate and even rude when it is used by individuals in subordinate positions. However, humor may help to craft an independent identity, and help alter user's impressions as desired. This suggests that while perceptions towards humor as an impression management tool may embed significant risks, humor may help individuals to influence their impressions and diverge from a stereotypical expectations and impressions of workers(according to their hierarchical status), which may be interpreted in multiple ways. This implies that organizations should be careful in encouraging workers to use humor as an impression management tactic, as the relational outcomes may be complex, depending on the cultural understanding of hierarchy and relationships between communicators.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.3
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• pp.215-228
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• 2023

The State Space Representation (SSR) method provides individual corrections for each Global Navigation Satellite System (GNSS) error components. This method can lead to less bandwidth for transmission and allows selective use of each correction. Precise Point Positioning (PPP) - Real-Time Kinematic (RTK) is one of the carrier-based precise positioning techniques using SSR correction. This technique enables high-precision positioning with a fast convergence time by providing atmospheric correction as well as satellite orbit and clock correction. Currently, the positioning service that supports PPP-RTK technology is the Quazi-Zenith Satellite System Centimeter Level Augmentation System (QZSS CLAS) in Japan. A system that provides correction for each GNSS error component, such as QZSS CLAS, requires monitoring of each error component to provide reliable correction and integrity information to the user. In this study, we conducted an analysis of the performance of residual error bounding for each error component. To assess this performance, we utilized the correction and quality indicators provided by QZSS CLAS. Performance analyses included the range domain, dispersive part, non-dispersive part, and satellite orbit/clock part. The residual root mean square (RMS) of CLAS correction for the range domain approximated 0.0369 m, and the residual RMS for both dispersive and non-dispersive components is around 0.0363 m. It has also been confirmed that the residual errors are properly bounded by the integrity parameters. However, the satellite orbit and clock part have a larger residual of about 0.6508 m, and it was confirmed that this residual was not bounded by the integrity parameters. Users who rely solely on satellite orbit and clock correction, particularly maritime users, thus should exercise caution when utilizing QZSS CLAS.

• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.79-90
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• 2014

User authentication based on ID and PW has been widely used. As the Internet has become a growing part of people' lives, input times of ID/PW have been increased for a variety of services. People have already learned enough to perform the authentication procedure and have entered ID/PW while ones are unconscious. This is referred to as the adaptive unconscious, a set of mental processes incoming information and producing judgements and behaviors without our conscious awareness and within a second. Most people have joined up for various websites with a small number of IDs/PWs, because they relied on their memory for managing IDs/PWs. Human memory decays with the passing of time and knowledges in human memory tend to interfere with each other. For that reason, there is the potential for people to enter an invalid ID/PW. Therefore, these characteristics above mentioned regarding of user authentication with ID/PW can lead to human vulnerabilities: people use a few PWs for various websites, manage IDs/PWs depending on their memory, and enter ID/PW unconsciously. Based on the vulnerability of human factors, a variety of information leakage attacks such as phishing and pharming attacks have been increasing exponentially. In the past, information leakage attacks exploited vulnerabilities of hardware, operating system, software and so on. However, most of current attacks tend to exploit the vulnerabilities of the human factors. These attacks based on the vulnerability of the human factor are called social-engineering attacks. Recently, malicious social-engineering technique such as phishing and pharming attacks is one of the biggest security problems. Phishing is an attack of attempting to obtain valuable information such as ID/PW and pharming is an attack intended to steal personal data by redirecting a website's traffic to a fraudulent copy of a legitimate website. Screens of fraudulent copies used for both phishing and pharming attacks are almost identical to those of legitimate websites, and even the pharming can include the deceptive URL address. Therefore, without the supports of prevention and detection techniques such as vaccines and reputation system, it is difficult for users to determine intuitively whether the site is the phishing and pharming sites or legitimate site. The previous researches in terms of phishing and pharming attacks have mainly studied on technical solutions. In this paper, we focus on human behaviour when users are confronted by phishing and pharming attacks without knowing them. We conducted an attack experiment in order to find out how many IDs/PWs are leaked from pharming and phishing attack. We firstly configured the experimental settings in the same condition of phishing and pharming attacks and build a phishing site for the experiment. We then recruited 64 voluntary participants and asked them to log in our experimental site. For each participant, we conducted a questionnaire survey with regard to the experiment. Through the attack experiment and survey, we observed whether their password are leaked out when logging in the experimental phishing site, and how many different passwords are leaked among the total number of passwords of each participant. Consequently, we found out that most participants unconsciously logged in the site and the ID/PW management dependent on human memory caused the leakage of multiple passwords. The user should actively utilize repudiation systems and the service provider with online site should support prevention techniques that the user can intuitively determined whether the site is phishing.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.29-56
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• 2021

Recently, various types of products have been launched with the rapid growth of the e-commerce market. As a result, many users face information overload problems, which is time-consuming in the purchasing decision-making process. Therefore, the importance of a personalized recommendation service that can provide customized products and services to users is emerging. For example, global companies such as Netflix, Amazon, and Google have introduced personalized recommendation services to support users' purchasing decisions. Accordingly, the user's information search cost can reduce which can positively affect the company's sales increase. The existing personalized recommendation service research applied Collaborative Filtering (CF) technique predicts user preference mainly use quantified information. However, the recommendation performance may have decreased if only use quantitative information. To improve the problems of such existing studies, many studies using reviews to enhance recommendation performance. However, reviews contain factors that hinder purchasing decisions, such as advertising content, false comments, meaningless or irrelevant content. When providing recommendation service uses a review that includes these factors can lead to decrease recommendation performance. Therefore, we proposed a novel recommendation methodology through CNN-based review usefulness score prediction to improve these problems. The results show that the proposed methodology has better prediction performance than the recommendation method considering all existing preference ratings. In addition, the results suggest that can enhance the performance of traditional CF when the information on review usefulness reflects in the personalized recommendation service.