• Journal of Korean Society of Archives and Records Management
• /
• v.19 no.3
• /
• pp.223-245
• /
• 2019

As the foundation for the Fourth Industrial Revolution, blockchain is becoming an essential core infrastructure and technology that creates new growth engines in various industries and is rapidly spreading to the environment of businesses and institutions worldwide. In this study, the characteristics and trends of blockchain technology were investigated and arranged, its application to the records management section of public institutions was required, and the procedures and methods of construction in the records management field of public institutions were studied in literature. Finally, blockchain technology was applied to the records management to propose an archive chain model and describe possible expectations. When the transactions that record the records management process of electronic documents are loaded into the blockchain, all the step information can be checked at once in the activity of processing the records management standard tasks that were fragmentarily nonlinked. If a blockchain function is installed in the electronic records management system, the person who produces the document by acquiring and registering the document enters the metadata and information, as well as stores and classifies all contents. This would simplify the process of reporting the production status and provide real-time information through the original text information disclosure service. Archivechain is a model that applies a cloud infrastructure as a backend as a service (BaaS) by applying a hyperledger platform based on the assumption that an electronic document production system and a records management system are integrated. Creating a smart, electronic system of the records management is the solution to bringing scattered information together by placing all life cycles of public records management in a blockchain.

• The Journal of Society for e-Business Studies
• /
• v.27 no.1
• /
• pp.15-28
• /
• 2022

Due to recent expansion of online market such as clothing, utilizing customer review has become a major marketing measure. User review has been used as a tool of analyzing sentiment of customers. Sentiment analysis can be largely classified with machine learning-based and lexicon-based method. Machine learning-based method is a learning classification model referring review and labels. As research of sentiment analysis has been developed, multi-modal models learned by images and video data in reviews has been studied. Characteristics of words in reviews are differentiated depending on products' and customers' categories. In this paper, sentiment is analyzed via considering review data and metadata of products and users. Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Self Attention-based Multi-head Attention models and Bidirectional Encoder Representation from Transformer (BERT) are used in this study. Same Multi-Layer Perceptron (MLP) model is used upon every products information. This paper suggests a multi-modal sentiment analysis model that simultaneously considers user reviews and product meta-information.

• Journal of Intelligence and Information Systems
• /
• v.28 no.1
• /
• pp.89-106
• /
• 2022

Recently, the amount of video data collected from smartphones, CCTVs, black boxes, and high-definition cameras has increased rapidly. According to the increasing video data, the requirements for analysis and utilization are increasing. Due to the lack of skilled manpower to analyze videos in many industries, machine learning and artificial intelligence are actively used to assist manpower. In this situation, the demand for various computer vision technologies such as object detection and tracking, action detection, emotion detection, and Re-ID also increased rapidly. However, the object detection and tracking technology has many difficulties that degrade performance, such as re-appearance after the object's departure from the video recording location, and occlusion. Accordingly, action and emotion detection models based on object detection and tracking models also have difficulties in extracting data for each object. In addition, deep learning architectures consist of various models suffer from performance degradation due to bottlenects and lack of optimization. In this study, we propose an video analysis system consists of YOLOv5 based DeepSORT object tracking model, SlowFast based action recognition model, Torchreid based Re-ID model, and AWS Rekognition which is emotion recognition service. Proposed model uses single-linkage hierarchical clustering based Re-ID and some processing method which maximize hardware throughput. It has higher accuracy than the performance of the re-identification model using simple metrics, near real-time processing performance, and prevents tracking failure due to object departure and re-emergence, occlusion, etc. By continuously linking the action and facial emotion detection results of each object to the same object, it is possible to efficiently analyze videos. The re-identification model extracts a feature vector from the bounding box of object image detected by the object tracking model for each frame, and applies the single-linkage hierarchical clustering from the past frame using the extracted feature vectors to identify the same object that failed to track. Through the above process, it is possible to re-track the same object that has failed to tracking in the case of re-appearance or occlusion after leaving the video location. As a result, action and facial emotion detection results of the newly recognized object due to the tracking fails can be linked to those of the object that appeared in the past. On the other hand, as a way to improve processing performance, we introduce Bounding Box Queue by Object and Feature Queue method that can reduce RAM memory requirements while maximizing GPU memory throughput. Also we introduce the IoF(Intersection over Face) algorithm that allows facial emotion recognized through AWS Rekognition to be linked with object tracking information. The academic significance of this study is that the two-stage re-identification model can have real-time performance even in a high-cost environment that performs action and facial emotion detection according to processing techniques without reducing the accuracy by using simple metrics to achieve real-time performance. The practical implication of this study is that in various industrial fields that require action and facial emotion detection but have many difficulties due to the fails in object tracking can analyze videos effectively through proposed model. Proposed model which has high accuracy of retrace and processing performance can be used in various fields such as intelligent monitoring, observation services and behavioral or psychological analysis services where the integration of tracking information and extracted metadata creates greate industrial and business value. In the future, in order to measure the object tracking performance more precisely, there is a need to conduct an experiment using the MOT Challenge dataset, which is data used by many international conferences. We will investigate the problem that the IoF algorithm cannot solve to develop an additional complementary algorithm. In addition, we plan to conduct additional research to apply this model to various fields' dataset related to intelligent video analysis.

• Journal of Korea Spatial Information System Society
• /
• v.1 no.2 s.2
• /
• pp.99-113
• /
• 1999

The role of a GIS as a tool for a national information infrastructure can best be fulfilled once GIS standards are implemented. In this paper, we have identified what the contents of GIS standards in other countries are, and what should be the future direction for implementing a nation's GIS standards. Based on a detailed review on GIS standards in the USA, Australia, Japan and the United Kingdom, we derived the following: (1) A nations's GIS standards should include both geographic information content standards and geographic information service standards: (2) A nation's GIS standards should be a profile of ISO GIS standards: (3) Each GIS standards should be developed on the bassis of the Entity-Relationship Model using Unified Modeling Language: and (4) Experts in GIS should pay much more attention on studies on GIS service standardization. As for building the national GIS Standards for Korea, we recommend both GIS Content Standards and GIS Service Standards be simultaneously developed. GIS Content Standards include geographic feature content standard, feature classification standard, portrayal standard, rules for application standards, spatial reference model and terminology. GIS Service Standards include standards for data sharing such as metadata standard and transfer standard, quality standard, quality principle and portrayal standards.

• Journal of Korea Multimedia Society
• /
• v.7 no.8
• /
• pp.1131-1144
• /
• 2004

In this paper, we investigate a problem with an e-learning system for e-business environments and introduce the solving method of the problem. To be more accurate, existing Web-hosted and ASP (Application Service Provider)-oriented service model is difficult to cooperate and integrate among the different kinds of systems. So we have produced sharable and reusable learning object, they have extracted a principle from pedagogical designs for units of reuse. We call LIO (Learning Item Object). This modeling makes use of a constructing for XML Web Services. So to speak, units of reuse from pedagogical designs are test tutorial, resource, case example, simulation, problem, test, discovery and discussion and then map introduction, fact, try, quiz, test, link-more, tell-more LIO learning object. These typed LIOs are stored in metadata along with the information for a content location. Each one of LIOs is designed with components and exposed in an interface for XML Web services. These services are module applications, which are used a standard SOAP (Simple Object Access Protocol) and locate any computer over Internet and publish, find and bind to services. This guarantees the interoperation and integration of the different kinds of systems. As a result, the problem of e-learning systems for e-business environments was resolved and then the power of understanding about learning objects based on pedagogical design was increased for learner and instruction designers. And organizations of education hope for particular decreased costs in constructing e-learning systems.

• Land and Housing Review
• /
• v.2 no.3
• /
• pp.249-258
• /
• 2011

In order to achieve an efficient urban regeneration of the nation, it is required to determine the extent of decline nation-wide and the declined areas for each district and also to evaluate the potentials of the concerned areas. For this task to be accomplished, a construction of a comprehensive diagnostic system based on spatial information considering diversity and complexity is required. In this study, a total information system architecture for urban regeneration is designed as part of the construction of such a diagnostic system. In order to develop the system, a city and district level unit decline diagnostic indicators has been constructed and a decline diagnostic system has been developed. Also, a scheme to promote the advancement of the system is proposed. The DB construction is based on the city and district level nation-wide and metadata for the concerned level is constructed as well. The system is based on the Open API and designed to be flexible for extension. Also, an RIA-based intuitive UI has been implemented. Main features of the system consist of the management of the indicators, diagnostic analysis (city and district level decline diagnosis), related information, etc. As for methods for the advancement, an information model in consideration of the spation relations of the urban regeneration DB has been designed and application methods of semantic webs. Also, for improvement methods for district unit analytical model, district level analysis models, GIS based spatial analysis platforms and linked utiliation of KOPSS analysis modules are suggested. A use of a total information system for urban regeneration is anticipated to facilitate concerned policy making through the identification of the status of city declines to identify and the understanding of the demands for regeneration.

• KIPS Transactions on Software and Data Engineering
• /
• v.10 no.3
• /
• pp.91-98
• /
• 2021

Today's information and communication technology is rapidly developing, the security of IT infrastructure is becoming more important, and at the same time, cyber attacks of various forms are becoming more advanced and sophisticated like intelligent persistent attacks (Advanced Persistent Threat). Early defense or prediction of increasingly sophisticated cyber attacks is extremely important, and in many cases, the analysis of network-based intrusion detection systems (NIDS) related data alone cannot prevent rapidly changing cyber attacks. Therefore, we are currently using data generated by intrusion detection systems to protect against cyber attacks described above through Host-based Intrusion Detection System (HIDS) data analysis. In this paper, we conducted a comparative study on machine learning algorithms using LID-DS (Leipzig Intrusion Detection-Data Set) host-based intrusion detection data including thread information, metadata, and buffer data missing from previously used data sets. The algorithms used were Decision Tree, Naive Bayes, MLP (Multi-Layer Perceptron), Logistic Regression, LSTM (Long Short-Term Memory model), and RNN (Recurrent Neural Network). Accuracy, accuracy, recall, F1-Score indicators and error rates were measured for evaluation. As a result, the LSTM algorithm had the highest accuracy.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_3
• /
• pp.1789-1803
• /
• 2022

With the increase of data and the development of AI technology, the strategies and policies related to integrated data are being actively established to increase the usability of data all over the world. Recently, in the research field, infrastructure projects and management systems are being prepared to utilize research data at the initiative of the government. Also, in Korea, platforms for searching and sharing research data are being actively developed. The National Disaster Management Research Institute (NDMI) has been conducting extensive research on disaster & safety as a national institute, but data-oriented management and utilization are insufficient. Because it still lacks consistent data management systems, metadata for outcomes of research, experts on data and policies for utilization of data to research. In order to move to the data-based research paradigm, we defined the master plans and verified a target model for the integrated management and utilization of disaster & safety research data. In this study, we found out the need to establish differentiated data governance, such as data standardization and unification of the data management system, and dedicated organization for managing data, based on the necessity and actual demands of NDMI. In order to verify the effectiveness of the target model reflecting the derived implications, we intend to establish a pilot mode. In the future, major improvement measures to establish a disaster & safety research data management system will be implement.

• Journal of KIISE:Computing Practices and Letters
• /
• v.15 no.9
• /
• pp.637-648
• /
• 2009

As the cost of disks decreases, PCs are soon expected to be equipped with a disk of 1TB or more. Assuming that a single person generates 1GB of data per month, 1TB is enough to store data for the entire lifetime of a person. This has lead to the growth of researches on lifelog management, which manages what people see and listen to in everyday life. Although many different lifelog management systems have been proposed, including those based on the relational data model, based on ontology, and based on file systems, they have all advantages and disadvantages: Those based on the relational data model provide good query processing performance but they do not support complex queries properly; Those based on ontology handle more complex queries but their performances are not satisfactory: Those based on file systems support only keyword queries. Moreover, these systems are lack of support for lifelog group management and do not provide a convenient user interface for modifying and adding tags (metadata) to lifelogs for effective lifelog search. To address these problems, we propose a lifelog management system based on the relational data model. The proposed system models lifelogs by using the relational data model and transforms queries on lifelogs into SQL statements, which results in good query processing performance. It also supports a simplified relationship query that finds a lifelog based on other lifelogs directly related to it, to overcome the disadvantage of not supporting complex queries properly. In addition, the proposed system supports for the management of lifelog groups by providing ways to create, edit, search, play, and share them. Finally, it is equipped with a tagging tool that helps the user to modify and add tags conveniently through the ion of various tags. This paper describes the design and implementation of the proposed system and its various applications.

• Journal of Intelligence and Information Systems
• /
• v.27 no.3
• /
• pp.95-112
• /
• 2021

In university education, the choice of major class plays an important role in students' careers. However, in line with the changes in the industry, the fields of major subjects by department are diversifying and increasing in number in university education. As a result, students have difficulty to choose and take classes according to their career paths. In general, students choose classes based on experiences such as choices of peers or advice from seniors. This has the advantage of being able to take into account the general situation, but it does not reflect individual tendencies and considerations of existing courses, and has a problem that leads to information inequality that is shared only among specific students. In addition, as non-face-to-face classes have recently been conducted and exchanges between students have decreased, even experience-based decisions have not been made as well. Therefore, this study proposes a recommendation system model that can recommend college major classes suitable for individual characteristics based on data rather than experience. The recommendation system recommends information and content (music, movies, books, images, etc.) that a specific user may be interested in. It is already widely used in services where it is important to consider individual tendencies such as YouTube and Facebook, and you can experience it familiarly in providing personalized services in content services such as over-the-top media services (OTT). Classes are also a kind of content consumption in terms of selecting classes suitable for individuals from a set content list. However, unlike other content consumption, it is characterized by a large influence of selection results. For example, in the case of music and movies, it is usually consumed once and the time required to consume content is short. Therefore, the importance of each item is relatively low, and there is no deep concern in selecting. Major classes usually have a long consumption time because they have to be taken for one semester, and each item has a high importance and requires greater caution in choice because it affects many things such as career and graduation requirements depending on the composition of the selected classes. Depending on the unique characteristics of these major classes, the recommendation system in the education field supports decision-making that reflects individual characteristics that are meaningful and cannot be reflected in experience-based decision-making, even though it has a relatively small number of item ranges. This study aims to realize personalized education and enhance students' educational satisfaction by presenting a recommendation model for university major class. In the model study, class history data of undergraduate students at University from 2015 to 2017 were used, and students and their major names were used as metadata. The class history data is implicit feedback data that only indicates whether content is consumed, not reflecting preferences for classes. Therefore, when we derive embedding vectors that characterize students and classes, their expressive power is low. With these issues in mind, this study proposes a Net-NeuMF model that generates vectors of students, classes through network analysis and utilizes them as input values of the model. The model was based on the structure of NeuMF using one-hot vectors, a representative model using data with implicit feedback. The input vectors of the model are generated to represent the characteristic of students and classes through network analysis. To generate a vector representing a student, each student is set to a node and the edge is designed to connect with a weight if the two students take the same class. Similarly, to generate a vector representing the class, each class was set as a node, and the edge connected if any students had taken the classes in common. Thus, we utilize Node2Vec, a representation learning methodology that quantifies the characteristics of each node. For the evaluation of the model, we used four indicators that are mainly utilized by recommendation systems, and experiments were conducted on three different dimensions to analyze the impact of embedding dimensions on the model. The results show better performance on evaluation metrics regardless of dimension than when using one-hot vectors in existing NeuMF structures. Thus, this work contributes to a network of students (users) and classes (items) to increase expressiveness over existing one-hot embeddings, to match the characteristics of each structure that constitutes the model, and to show better performance on various kinds of evaluation metrics compared to existing methodologies.