Earticle

본 논문의 목적은 한국과 미국의 안보환경, 정보 조직 및 정보활동의 유사성과 차이점을 비교분석하는데 있다. 이 같은 비교는 정보 전반에 대한 통찰력과 폭넓은 이해를 제공함으 로써 정보연구의 방법론적 발전은 물론 한국 및 여타 국가정보기관에 대한 통찰력을 제공 해줄 것이다. 한미 양국의 역사와 문화, 그리고 국력이 상이한 만큼 정보기관의 조직과 활동 역시 다 를 수 밖에 없다. 우선 환경면에서 보면 미국은 북미 대륙국가들은 물론 남미와 중동, 아시 아 그리고 아시아지역까지 광범위한 영역에서 국익과 안보를 위해 정보활동을 수행하고 있는 반면, 한국의 정보활동은 주로 북한과 한반도 주변 국가들을 대상으로 이루어지고 있다. 정보조직적인 측면에서 보면 미국의 정보기관들은 국내외 정보 및 수사기관이 분리 된 분리형 정보기관인 데 비해, 한국의 정보기관은 미국과 달리 정보와 수사가 결합된 통 합형 정보기관의 유형에 해당된다. 또한 미국의 경우 정보공동체(Intelligence Community) 로 운영되면서 계층 조직이외 센터와 같은 유연한 조직들이 많이 있는 점도 한국과 상이하 다. 미국 정보기관의 정보활동은 분석과 해외공작활동에 주안을 두고 있는데 비해 한국의 정보기관은 여전히 국내 정보활동이 많은 비중을 차지하고 있다. 이 같은 차이에도 한국의 정보기관이 미국 정보기관을 모방하여 창설한 만큼 안보위협 의 평가, 조직과 활동면에서 유사한 측면도 있다. 그러나 이와 같은 유사성은 모든 정보기 관이 공유하고 있기 때문에 이 글에서는 차이점을 위주로 분석할 것이다. 마지막으로 한국 의 정보기관의 발전을 위해 정보공동체의 설립과 국회의 효율적인 통제 등의 방안을 제시 할 것이다.

The purpose of this paper is to compare and analyze the similarities and differences between the security environment, information organization and information activities of Korea and the United States. The comparison will provide insight into Korea and other national intelligence agencies, as well as methodological advances in information research, by providing insight into the overall information and a broad understanding As the history, culture and national power of Korea and the U.S. are different, the organization and activities of intelligence agencies are also different. First of all, in terms of environment, the U.S. carries out intelligence activities for national interest and security in a wide range of areas ranging from North American continental countries to South America, the Middle East, Asia and Asia, while South Korea's intelligence activities are mainly aimed at North Korea and neighboring countries around the Korean Peninsula. In terms of information organization, U.S. intelligence agencies are separate, whereas domestic and foreign intelligence agencies are separate, whereas Korean intelligence agencies are a type of integrated intelligence agency that combines information and investigation, unlike the U.S. In the U.S., the U.S. also operates as an intelligence community, and there are many flexible organizations such as non-tier organizations and centers. Intelligence activities by U.S. intelligence agencies are mainly focused on analysis and overseas processing activities, while Korean intelligence agencies still account for a large portion of domestic information activities. Despite these differences, Korea's intelligence agency was created by imitating U.S. intelligence agencies, and thus has similar aspects in terms of evaluation of security, organization and activities. However, this similarity is shared by all intelligence agencies, so the article will focus on analyzing differences. Finally, for the development of Korean intelligence agencies, the establishment of an intelligence community and efficient control of the National Assembly will be proposed.

The goal of Ambient Intelligence (AmI) is to build a smart environment for users where they are supported in some of their activities by many interaction mechanisms. The diversity of AmI characteristics requires special support from Operating Systems (OSes). In this paper, in order to support a conscious choice of an operating system for any specific AmI application, features requested by AmI systems were characterized and defined considering various applications. Then, characteristics of existing Operating Systems have been investigated in the context of AmI application support to relate their key characteristics to the typical requirements of AmI systems. Qualitative mapping table between AmI characteristics and as features has been proposed with an illustration of how to use it. As no as completely covers the range of characteristics required by AmI systems, challenging issues are summarized for the development of a new as and a product line of OSes.

Background An artificial intelligence-assisted motion-analysis system without markers, OpenPose, is used to calculate joint angles in sports and medical analyses. Measuring the angles of the hip and knee joint in the frontal plane during the performance of weight-bearing activities is valuable in patients with patellofemoral pain syndrome (PFPS). Purpose The purpose of this study was to assess the validity of OpenPose using a pre-trained human motion-tracking algorithm for measuring the angles of the hip and knee joint in the frontal plane during standing hip abduction, semi-squat movements, and forward step-down movements compared with marker-based three-dimensional motion analysis. Study design Cross-sectional study Methods Eight individuals with PFPS participated in the current study. To investigate the validity of OpenPose, the angles of the hip and knee in the frontal plane were measured simultaneously with a smartphone camera using the OpenPose library and Vicon as the gold-standard motion-analysis system while performing three weight-bearing activities. Pearson and Spearman correlation analysis was used to assess the validity of the OpenPose-based motion-analysis system. Results Correlation coefficients ranged from 0.04 to 0.61 on the more symptomatic side and from 0.02 to 0.88 on the less symptomatic side for the three weight-bearing activities. When performing standing hip abduction and step-down movements, the validity of the measurements of hip abduction was fair or good. When performing semi-squat movements, the validity of the knee abduction measurements was fair. Conclusions The OpenPose-based motion-analysis system can provide fair or good level of validity of measurements of frontal hip and knee angles during weight-bearing activities of individuals with PFPS in real environments and for remote rehabilitation.

Purpose: The peer review process is essential for maintaining the quality of scientific publications. However, identifying reviewers who possess the necessary expertise can be challenging. In Open Journal Systems (OJS), which is commonly utilized by journals, the most effective method of inviting reviewers is when they are already registered in the system. This study seeks to improve the efficiency and accuracy of the reviewer selection process to ensure high-quality peer reviews. Methods: We introduced a process innovation to analyze users within OJS and obtain recommendations for potential reviewers possessing the relevant expertise for the manuscript under review. This study collected user data from OJS as potential reviewers and utilized information from the Scopus search application programming interface (API). We extracted authors’ data from the Scopus API to obtain their Scopus IDs, which were then used to scrape publication data of potential reviewers. The system matched the previous works of reviewers with the title and abstract of the manuscript using term frequency-inverse document frequency and cosine similarity algorithms. Results: The system was evaluated by comparing its recommendations with the assessments made by the editorial team. This evaluation yielded precision, mean average precision, and mean reciprocal rank values of 0.47, 0.77, and 0.87, respectively. Conclusion: The results clearly demonstrate the system’s ability to provide relevant reviewer recommendations. This system offers significant benefits by assisting editors in identifying suitable reviewer candidates from the existing user database in OJS, particularly for the evaluation of manuscripts.

In this paper, we propose an intelligent research fund management system that applies artificial intelligence technology to an integrated research fund management system. By defining research fund management rules as work rules, a detection model learned using deep learning is designed, through which the disapproval status is presented for each research fund usage history. The disapproval detection system of the RCMS implemented in this study predicts whether the newly registered usage details are recognized or disapproved using an artificial intelligence model designed based on the use of an 8.87 million research fund registered in the RCMS. In addition, the item-detail recommendation system described herein presents the usage details according to the usage history item newly registered by the artificial intelligence model through a correlation between the research cost usage details and the item itself. The accuracy of the recommendation was shown to be 97.21%.

Our paramount task is to examine and detect network attacks, is one of the daunting tasks because the variety of attacks are day by day existing in colossal number. The program proposed detects botnet attacks using the newest CSE-CIC-IDS2018 cyber dataset published by the Canadian Cybersecurity Establishment (CIC). The cyber dataset can be accessed on AWS (Amazon Web Services). The realistic network dataset consists of all the modern and existing attacks such as Brute-force attacks and password cracking, Heartbleed, Botnet, DoS (Denial of Service), DDoS also known as Distributed Denial of Service, Web attacks i.e. vulnerable web app attacks, and infiltration of the network from inside. The objective of the proposed research is to identify a classification of Botnet attacks. Botnet attack is a Trojan Horse malware attack that poses a serious security threat to the banking and financial sectors. Since a specific classifier could possibly work for such datasets it is crucial to finish a comparative examination of classifiers in order to achieve the most noteworthy execution in such basic detection of network attacks. The proposed framework is to incorporate different classifier methods such as KNearset Neighbor classifier, Naive Bayes, Adaboost with Decision Tree, Support Vector Machine classifier, Random Forest classifier, and Artificial Intelligence to distinguish a portrayal of botnet attacks on the recent and realistic cyber dataset CSE-CIC-IDS2018. The results of the classification are given as precise precision for the specific classifiers. And furthermore, the proposed framework uses the Calibration curve as a standard approach in analytical methods which generates reliability diagrams to check the predicted probabilities of various classifiers are well-calibrated or not. Finally, the displayed graph proves how well the artificial intelligence technique outperforms all other classifiers which generates reliability diagrams to check the predicted probabilities of various classifiers are well-calibrated or not.

This paper proposes an algorithm that automatically converts images into Pipo, painting images using OpenCV-based image processing technology. The existing "purity," "palm," "puzzling," and "painting," or Pipo, painting image production method relies on manual work, so customized production has the disadvantage of coming with a high price and a long production period. To resolve this problem, using the OpenCV library, we developed a technique that automatically converts an image into a Pipo painting image by designing a module that changes an image, like a picture; draws a line based on a sector boundary; and writes sector numbers inside the line. Through this, it is expected that the production cost of customized Pipo painting images will be lowered and that the production period will be shortened.

Ubiquitous learning has aroused great interest and is becoming a new way for foreign language education in today’s society. However, how to increase the learners’ initiative and their community cohesion is still an issue that deserves more profound research and studies. Emotional intelligence can help to detect the learner’s emotional reactions online, and therefore stimulate his interest and the willingness to participate by adjusting teaching skills and creating fun experiences in learning. This is, actually the new concept of smart education. Based on the previous research, this paper concluded a neural mechanism model for analyzing the learners’ emotional characteristics in ubiquitous environment, and discussed the intelligent monitoring and automatic recognition of emotions from the learners’ speech signals as well as their behavior data by multi-agent system. Finally, a framework of emotional intelligence system was proposed concerning the smart foreign language education in ubiquitous learning.

이 글은 인공지능과 인간이 함께 사회적 가치를 만들어 가는 작업이 중요하게 대두되는 현재의 시점에서 생활과 연관된 문화와 제도에 대한 패러다임의 전환을 모색해 보는 것을 목적으로 한다. AI와 관련된 현상들이 현대 사회에서 어떻게 작동하는가에 주목하는 접근 방법은 이 연구의 기초를 이룬다. 이에 “AI 현상”을 사회 문화의 일부로 수렴하면 서 그 의미를 밝히기 위해 다양한 문헌 자료를 활용, 윤리나 기술평등 같은 가치를 연계시켜 AI의 사회 제도적 면을 짚어 보고자 하였다. 사회의 구성원들이 함께 할 수 있는 AI와 접목된 기술 문화를 추론하는 일도 기능적인 면에서의 기술적 이해 못지않게 중요한 일이다. 따라서 이 연구가 문화적 상상력과 사회적 시스템이 어우러져 만들어내는 새로운 문화, 즉 “인공지능 문화”의 가능성을 제시할 수 있기를 바란다. 그렇기에 이 글은 하나의 시론적인 성격도 더불어 갖는다.

The aim of this study is to explore the paradigm shifts in culture and system related to life in terms of AI and the present point of view in which creating human values together are important. An approach that focuses on how AI-related phenomena work in modern society forms the basis of this research. Therefore, to clarify the meaning of “AI phenomenon” converging it as a part of social culture, this study was intended to find out the value incorporated in the social system such as ethics and equality together with the literature review. Inferring the technical culture that are combined with the AI that the members of society can do together is as important as technical understanding in the functional aspect. Therefore, this study was intended to suggest new culture that the cultural imagination and the social system create harmonizing each other, that is, the possibility of "AI culture". So, this article has a characteristic of a preliminary study, too.

The purpose of this study is to incorporate telemarketing processes to improve telemarketing performance. For this application, we have attempted to mix the model of machine learning to extract potential customers with personalisation techniques to derive recommended products from actual contact. Most of traditional recommendation systems were mainly in ways such as collaborative filtering, which predicts items with a high likelihood of future purchase, based on existing purchase transactions or preferences for products. But, under these systems, new users or items added to the system do not have sufficient information, and generally cause problems such as a cold start that can not obtain satisfactory recommendation items. Also, indiscriminate telemarketing attempts can backfire as they increase the dissatisfaction and fatigue of customers who do not want to be contacted. To this purpose, this study presented a multi-purpose hybrid recommendation algorithm to achieve two goals: to select customers with high possibility of contact, and to recommend products to selected customers. In addition, we used subscription data from telemarketing agency that handles insurance products to derive realistic applicability of the proposed recommendation system. Our proposed recommendation system would certainly solve the cold start and scarcity problem of existing recommendation algorithm by using contents information such as customer master information and telemarketing history. Also. the model could show excellent performance not only in terms of overall performance but also in terms of the recommendation success rate of the unpopular product.

This research proposes the development of an online psychological counseling platform utilizing an Artificial Intelligence (AI)-based emotion analysis system. The platform, leveraging facial video, voice, and text data, aims to real-time identify and analyze the emotional states of counselees in a non-face-to-face counseling environment, providing counselors with the necessary information to facilitate appropriate counseling. The outcomes of this research are expected to enhance communication effectiveness between counselors and counselees and contribute to the psychological well-being of counselees in online counseling scenarios.

To better predict and classify failures of information system development projects (ISDPs), this study proposes ISDPs failure prediction models using the traditional statistical methods and artificial intelligence methods, namely multiple discriminant analysis (MDA), logistic regression (LR), multi-layer perceptron (MLP), classification and regression tree (CART), commercial version 5.0 (C5.0), and support vector machine (SVM). We performed the analysis on the audit report data of 446 projects that were conducted by a global information technology (IT) company, to build the IT service systems and relevant service infrastructures needed for a project with South Korea’s mobile telecommunication companies. The research variables, which were confirmed by project performance management system (PPMS) of the company, are composed of thirteen variables. Empirical results indicated that SVM outperforms other models such as MDA, LR, MLP, CART, and C5.0.

In order to solve the safety hazards caused by the operation failure of elevators, pump rooms, fire control facilities, access control and other equipment and facilities in colleges, this paper uses the Internet of Things technology to realize the ubiquitous access of equipment and facilities in colleges, and collect the operating status data of the equipment and facilities in real time. Establish an equipment and facility operation data center, use big data and deep learning technology to realize online monitoring and real-time early warning of equipment and facilities, and send the monitoring and early warning information to managers and equipment maintenance personnel for processing, and realize the monitoring, early warning, and processing of equipment and facilities Automated process management to build an intelligent monitoring system for college equipment and facilities.