• International Journal of Advanced Culture Technology
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• 제10권4호
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• pp.499-510
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• 2022

This study examines the reporting factors of crime against business in Korea and proposes a corresponding predictive model using machine learning. While many previous studies focused on the individual factors of theft victims, there is a lack of evidence on the reporting factors of crime against a business that serves the public good as opposed to those that protect private property. Therefore, we proposed a crime prevention model for the willingness factor of theft reporting in businesses. This study used data collected through the 2015 Commercial Crime Damage Survey conducted by the Korea Institute for Criminal Policy. It analyzed data from 834 businesses that had experienced theft during a 2016 crime investigation. The data showed a problem with unbalanced classes. To solve this problem, we jointly applied the Synthetic Minority Over Sampling Technique and the Tomek link techniques to the training data. Two prediction models were implemented. One was a statistical model using logistic regression and elastic net. The other involved a support vector machine model, tree-based machine learning models (e.g., random forest, extreme gradient boosting), and a stacking model. As a result, the features of theft price, invasion, and remedy, which are known to have significant effects on reporting theft offences, can be predicted as determinants of such offences in companies. Finally, we verified and compared the proposed predictive models using several popular metrics. Based on our evaluation of the importance of the features used in each model, we suggest a more accurate criterion for predicting var.

• 산업경영시스템학회지
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• 제33권4호
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• pp.130-137
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• 2010

This paper analyzes the main factors affecting user selection of a small-sum electronic payment system using survey data of 396 users. Several findings emerge. First, users consider three pillars and eight factors in adopting a new system : system features(stability, security, and flexibility), transaction cost(payment commission and settlement period), and financial capability of provider(stability of financial structure, risk management capability, and funding capability). Second, the stability of the financial structure of the system provider is the most important factor to user acceptance of a new e-payment system. Users tend to consider uncertainty risk more seriously than transaction cost. This reflects the reality that electronic payment system service industry has not fully fledged yet. Third, some moderating effects exist according to payment methods and business usages. As for payment methods, speedy settlement cycle for wired/wireless phone payment, system stability for credit card and account transfer payment, and security for advance payment means are crucial factors. As for business usages, the stability of financial structure for online game content, system stability for music and video content, proxy payment commission for e-learning content, flexibility of the payment system for digital adult content, and security for public services are decisive ones.

• 인터넷정보학회논문지
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• 제18권1호
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• pp.131-141
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• 2017

본 연구는 대학 이러닝에서 학습자-교수자, 학습자-콘텐츠, 학습자-시스템간의 상호작용이 학습자들이 인지하는 수업만족도 및 학업성취도와 어떤 관련을 갖는지 분석하고자 하였다. 이를 위해 경기도 소재 대규모 대학의 이러닝 강좌 중 학습자-교수자 상호작용이 있었던 수업과 없었던 수업에 참여한 학생 184명을 대상으로 설문조사를 실시하였다. 연구 결과 학습자-교수자와의 상호작용이 있었던 수업에서 학습자-콘텐츠, 학습자-시스템간의 상호작용도 많이 있었고, 상호작용과 수업만족도, 학업성취도 요인간의 상관관계 분석에서는 모두 유의한 관계가 있었다. 수업만족도와 학업성취도 모두 학습자-콘텐츠 상호작용의 영향력이 가장 컸으며, 수업만족도에는 시스템과의 상호작용이, 학업성취도에는 교수자와의 상호작용이 그 다음으로 영향력을 미쳤다. 이러한 연구 결과를 토대로 첫째, 학습자들은 이러닝에서 학습을 혼자 콘텐츠를 이해하는 것으로 인식하고 있기 때문에 질 좋은 교육콘텐츠의 개발지원 및 수업에서의 제공방식에 대한 논의가 필요하며, 둘째, 학습자-교수자 상호작용의 활성화를 위해 교수자 대상의 워크숍 및 교육지원이 필요하다는 것과 셋째, 본 연구에서는 발견되지 않은 학습자간 상호작용도 수업만족도 및 학업성취도에 매우 중요한 상호작용 유형으로, 이를 활성화시킬 수 있는 방안에 대한 논의도 필요함을 제안하였다.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2013년도 추계학술대회
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• pp.873-875
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• 2013

정보기술 발전에 따라 정보 활용 및 처리 역량이 상승하면서 교육 환경의 지능화, 네트워크화로 기술간, 서비스 간 융 복합을 통한 다양한 학습 내용 및 방법이 출현하였으며, 최근 e-러닝 산업에서 스마트기기 보급 확산과 상황 적응적이고 자기 주도적 학습에 대한 소비자의 니즈가 증가하면서 새로운 형태의 교육시스템인 스마트러닝이 부각되고 있다. 이러한 교육 패러다임의 변화에 따라 기존의 교육 콘텐츠를 스마트기기에 적용하기 위해서는 콘텐츠 및 솔루션 구조의 개선이 요구되며, 또한 서비스 제공의 측면에서 다양한 교육 콘텐츠 연동과 교육 서비스 융합을 위한 표준 플랫폼 적용이 필요하다. 이에 본 논문에서는 JVM 환경의 PC 인터페이스를 통해 ePub 표준의 교육용 멀티미디어 콘텐츠 제작기능과 기존 서책형 파일 포맷의 자료 정보를 응용하기 위한 정보변환 모듈, 스마트 기기용 ePub 전자책 뷰어를 포함하는 통합 솔루션 소프트웨어인 ePub Solution을 설계하였다.

• 대한본초학회지
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• 제22권4호
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• pp.51-58
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• 2007

Objectives : Investigation of the memory and cognitive enhancing effect of HT008-1 in scopolamine induced amnesia mice. Methods : At 60 min before acquisition trials, HT008-1 (30, 100, 300 mg/kg p.o.) was administered, and 30 min later, mice were injected with scopolamin (1.0 mg/kg, i.p.). In the passive avoidance test, acquisition trials were carried out 30 min after a single scopolamine treatment. Retention trials were carried out 24h after acquisition trials. Y-maze test was carried out 30 min after a single scopolamine treatment. Spontaneous alternation behavior during an 8-min session was recorded. Inhibitory effects of HT008-1 (0.01, 0.1, 1.0 mg/ml) on AChE activity was measured. Result : HT008-1 ameliorated scopolamine-induced learning impairments and spatial cognitive function in passive avoidance and Y-maze test, respectively. Moreover HT008-1 showed a significant inhibitory effect on AChE activity. Discussion: This study presented that eMultiherb mixture HT008-1 enhanced learning memory and spatial cognitive function in scopolamine-induced amnesia mice. These results suggest that the effect of HT008-1 may be dependent on the inhibition of AChE activity.

• 정보보호학회논문지
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• 제34권1호
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• pp.71-82
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• 2024

정보통신기술(ICT) 고도화에 따라 PDF, MS Office, HWP 파일로 대표되는 전자 문서형 파일의 활용이 많아졌고, 공격자들은 이 상황을 놓치지 않고 문서형 악성코드를 이메일과 메신저를 통해 전달하여 감염시키는 피해사례가 많아졌다. 이러한 피해를 막고자 AI를 사용한 악성코드 탐지 연구가 진행되고 있으나, PDF나 MS-Office와 같이 전 세계적으로 활용성이 높은 전자 문서형 파일에 비해 주로 국내에서만 활용되는 HWP(한글 워드 프로세서) 문서 파일은 양질의 정상 또는 악성 데이터가 부족하여 지속되는 공격에 강건한 모델 생성에 한계점이 존재한다. 이러한 한계점을 해결하기 위해 기존 수집된 데이터를 변형하여 학습 데이터 규모를 늘리는 데이터 증강 방식이 제안 되었으나, 증강된 데이터의 유용성을 평가하지 않아 불확실한 데이터를 모델 학습에 활용할 가능성이 있다. 본 논문에서는 HWP 악성코드 탐지에 있어 데이터의 유용성을 정량화하고 이에 기반하여 학습에 유용한 증강 데이터만을 활용하여 기존보다 우수한 성능의 AI 모델을 학습하는 준지도학습 기법을 제안한다.

• Journal of Information Processing Systems
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• 제16권1호
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• pp.96-112
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• 2020

Solving the disclosure problem of sensitive information with the k-nearest neighbor query, location dummy technique, or interfering data in location-based services (LBSs) is a new research topic. Although they reduced security threats, previous studies will be ineffective in the case of sparse users or K-successive privacy, and additional calculations will deteriorate the performance of LBS application systems. Therefore, a model is proposed herein, which is based on geohash-encoding technology instead of latitude and longitude, memcached server cluster, encryption and decryption, and authentication. Simulation results based on PHP and MySQL show that the model offers approximately 10× speedup over the conventional approach. Two problems are solved using the model: sensitive information in LBS application is not disclosed, and the relationship between an individual and a track is not leaked.

• Journal of Multimedia Information System
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• 제7권3호
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• pp.205-214
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• 2020

Crime has become one of the major problems in modern society. Even though visual surveillances through closed-circuit television (CCTV) is extensively used for solving crime, the number of crimes has not decreased. This is because there is insufficient workforce for performing 24-hour surveillance. In addition, CCTV surveillance by humans is not efficient for detecting dangerous situations owing to accuracy issues. In this paper, we propose the autonomous detection of dangerous situations in CCTV scenes using a deep learning model with relational inference. The main feature of the proposed method is that it can simultaneously perform object detection and relational inference to determine the danger of the situations captured by CCTV. This enables us to efficiently classify dangerous situations by inferring the relationship between detected objects (i.e., distance and position). Experimental results demonstrate that the proposed method outperforms existing methods in terms of the accuracy of image classification and the false alarm rate even when object detection accuracy is low.

• Journal of Computing Science and Engineering
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• 제10권4호
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• pp.103-110
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• 2016

Emotion recognition is an important component of affective computing, and is significant in the implementation of natural and friendly human-computer interaction. An effective approach to recognizing emotion from text is based on a machine learning technique, which deals with emotion recognition as a classification problem. However, in emotion recognition, the texts involved are usually very short, leaving a very large, sparse feature space, which decreases the performance of emotion classification. This paper proposes to resolve the problem of feature sparseness, and largely improve the emotion recognition performance from short texts by doing the following: representing short texts with word cluster features, offering a novel word clustering algorithm, and using a new feature weighting scheme. Emotion classification experiments were performed with different features and weighting schemes on a publicly available dataset. The experimental results suggest that the word cluster features and the proposed weighting scheme can partly resolve problems with feature sparseness and emotion recognition performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권7호
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• pp.2304-2320
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• 2021

Intelligently detecting anomalies in health sensor data streams (e.g., Electrocardiogram, ECG) can improve the development of E-health industry. The physiological signals of patients are collected through sensors. Timely diagnosis and treatment save medical resources, promote physical health, and reduce complications. However, it is difficult to automatically classify the ECG data, as the features of ECGs are difficult to extract. And the volume of labeled ECG data is limited, which affects the classification performance. In this paper, we propose a Generative Adversarial Network (GAN)-based deep learning framework (called CAB) for heart arrhythmia classification. CAB focuses on improving the detection accuracy based on a small number of labeled samples. It is trained based on the class-imbalance ECG data. Augmenting ECG data by a GAN model eliminates the impact of data scarcity. After data augmentation, CAB classifies the ECG data by using a Bidirectional Long Short Term Memory Recurrent Neural Network (Bi-LSTM). Experiment results show a better performance of CAB compared with state-of-the-art methods. The overall classification accuracy of CAB is 99.71%. The F1-scores of classifying Normal beats (N), Supraventricular ectopic beats (S), Ventricular ectopic beats (V), Fusion beats (F) and Unclassifiable beats (Q) heartbeats are 99.86%, 97.66%, 99.05%, 98.57% and 99.88%, respectively. Unclassifiable beats (Q) heartbeats are 99.86%, 97.66%, 99.05%, 98.57% and 99.88%, respectively.