Journal of the Korea Society of Computer and Information
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v.27
no.7
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pp.1-7
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2022
Recently, as the use of applications such as big data programs and machine learning programs that are driven while generating large amounts of data in the program itself becomes common, the existing main memory alone lacks memory, making it difficult to execute the program quickly. In particular, the need to derive results more quickly has emerged in a situation where it is necessary to analyze whether the entire sequence is genetically altered due to the outbreak of the coronavirus. As a result of measuring performance by applying large-capacity data to a computing system equipped with a self-developed memory pool MOCA host adapter instead of processing large-capacity data from an existing SSD, performance improved by 16% compared to the existing SSD system. In addition, in various other benchmark tests, IO performance was 92.8%, 80.6%, and 32.8% faster than SSD in computing systems equipped with memory pool MOCA host adapters such as SortSampleBam, ApplyBQSR, and GatherBamFiles by task of workflow. When analyzing large amounts of data, such as electrical dielectric pipeline analysis, it is judged that the measurement delay occurring at runtime can be reduced in the computing system equipped with the memory pool MOCA host adapter developed in this research.
TV advertising with deep analysis of watching pattern of audiences is important to set-top box audience targeting. Applying session-based recommendation model(SBR) to internet commercial, or recommendation based on searching history of user showed its effectiveness in previous studies, but applying SBR to the TV advertising was difficult in South Korea due to data unavailabilities. Also, traditional SBR has limitations for dealing with user preferences, especially in data with user identification information. To tackle with these problems, we first obtain set-top box data from three major broadcasting companies in South Korea(SKB, KT, LGU+) through collaboration with Korea Broadcast Advertising Corporation(KOBACO), and this data contains of watching sequence of 4,847 anonymized users for 6 month respectively. Second, we develop personalized session-based recommendation model to deal with hierarchical data of user-session-item. Experiments conducted on set-top box audience dataset and two other public dataset for validation. In result, our proposed model outperformed baseline model in some criteria.
Comparison shopping is a merchant brokering process that finds the best price for the desired product from several Web-based online stores. To get a scalable comparison shopper, we need an agent that automatically constructs a simple information extraction procedure, called a wrapper, for each semi-structured store. Automatic construction of wrappers for HTML-based Web stores is difficult because HTML only defines how information is to be displayed, not what it means, and different stores employ different ways of manipulating customer queries and different presentation formats for displaying product descriptions. Wrapper induction has been suggested as a promising strategy for overcoming this heterogeneity. However, previous scalable comparison-shoppers such as ShopBot rely on a strong bias in the product descriptions, and as a result, many stores that do not confirm to this bias were unable to be recognized. This paper proposes a more scalable comparison-shopping agent named MORPHEUS. MORPHEUS presents a simple but robust inductive learning algorithm that antomatically constructs wrappers. The main idea of the proposed algorithm is to recognize the position and the structure of a product description unit by finding the most frequent pattern from the sequence of logical line information in output HTML pages. MORPHEUS successfully constructs correct wtappers for most stores by weakening a bias assumed in previous systems. It also tolerates some noises that might be present in production descriptions such as missing attributes. MORPHEUS generates the wrappers rapidly by excluding the pre-processing phase of removing redundant fragments in a page such as a header, a tailer, and advertisements. Eventually, MORPHEUS provides a framework from which a customized comparison-shopping agent can be organized for a user by facilitating the dynamic addition of new stores.
A fully automatic and computationally efficient method is proposed for intelligent display of soccer video on small multimedia mobile devices. The rapid progress of the multimedia signal processing has contributed to the extensive use of multimedia devices with a small LCD panel. With these emerging small mobile devices, the video sequences captured for standard- or HDTV broadcasting may give the small-display-viewers uncomfortable experiences in understanding what is happening in a scene. For instance, in a soccer video sequence taken by a long-shot camera technique, the tiny objects (e.g., soccer ball and players) may not be clearly viewed on the small LCD panel. Thus, an intelligent display technique is needed for small-display-viewers. To this end, one of the key technologies is to determine region of interest (ROI), which is a part of the scene that viewers pay more attention to than other regions. In this paper, the focus is on soccer video display for mobile devices. Instead of taking visual saliency into account, we take domain-specific approach to exploit the characteristics of the soccer video. The proposed scheme includes three modules; ground color learning, shot classification, and ROI determination. The experimental results show the propose scheme is capable of intelligent video display on mobile devices.
Journal of the Korea Academia-Industrial cooperation Society
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v.13
no.1
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pp.169-177
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2012
The purpose of this study was to examine the awareness of dental hygiene students on the components of the dental hygiene curriculum. The subjects in this study were 934 dental hygiene students, on whom a survey was conducted in April and Jun, 2009. The collected data were analyzed by the statistical package PASW 18.0. The findings of the study were as follows: The students from the midsection of the nation put the highest value on educational objectives, selection of what to teach and evaluation, and the students from the metropolitan area set the lowest value on those components. The students who took junior college courses took a better view of those components than those who took college courses. In terms of the organization of what to teach, the students from the midsection of the nation rated it highest, and the students from the Honam area rated it lowest. And those who took college courses took a little better view of it than the students who took junior college courses. Among the components of the curriculum, the educational objective factor was rated higher in terms of vocational education, morality and ethics, and that was rated lower in terms of creativity. The selection of what to teach was rated higher in terms of learning possibility and was rated lower in terms of content validity and relevance. The organization of what to teach was rated highest in terms of continuity, followed by sequence and integration. The educational evaluation factor was rated higher in the area of school evaluation and was rated lowest in the area of national examinations.
The purpose of this study was to propose a model which is suitable for the actual delivery system by designing a fetal delivery hospital operation management and fetal health classification model. The number of deaths during childbirth is similar to the number of maternal mortality rate of 295,000 as of 2017. Among those numbers, 94% of deaths are preventable in most cases. Therefore, in this paper, we proposed a model that predicts the health condition of the fetus using data like heart rate of fetuses, fetal movements, uterine contractions, etc. that are extracted from the Cardiotocograms(CTG) test using a random forest. If the redundancy of the data is unbalanced, This proposed model guarantees a stable management of the fetal delivery health management system. To secure the accuracy of the fetal delivery health management system, we remove the outlier which embedded in the system, by setting thresholds for the upper and lower standard deviations. In addition, as the proportion of the sequence class uses the health status of fetus, a small number of classes were replicated by data-resampling to balance the classes. We had the 4~5% improvement and as the result we reached the accuracy of 97.75%. It is expected that the developed model will contribute to prevent death and effective fetal health management, also disease prevention by predicting and managing the fetus'deaths and diseases accurately in advance.
This study examined the content diversity of seven authorized science textbooks by comparing the characteristics of the science concept description and the contents of inquiry activities in the "weight of objects" unit. For each textbook, the flow of concept description content and the uniqueness of the concept description process were analyzed, and the number of nodes and links and words with high connections were determined using language network analysis. In addition, for the inquiry activities described in each textbook, the inquiry subject, inquiry type, science process skill, and uniqueness were investigated. Results showed that the authorized textbooks displayed no more diversity than expected in their scientific concept description method or their inquiry activity composition. The learning elements, inclusion of subconcepts, and central words were similar for each textbook. The comparison of inquiry activities showed similarities in their contents, inquiry types, and scientific process skills. Specifically, these textbooks did not introduce any research topics or experimental methods that were absent in previous textbooks. However, despite the fact that the authorized textbook system was developed based on the same curriculum, some efforts were made to make use of its strengths. Since the sequence of subconcepts to explain the core contents differed across textbooks, this explanation process was divided into several types, and although the contents of inquiry activities were the same, the materials for inquiry activities were shown differently for each textbook to improve and overcome the difficulties in the existing experiments. These findings necessitate the continuation of efforts to utilize the strengths of certified textbooks.
The purpose of this study was to explore the factors that are critical for successful scientific inquiry activity in the classroom and to analyze the students' abilities of 'Doing' scientific inquiry. Two hundred and forty gifted science students in grades $7^{th}$ and $8^{th}$ participated in this study and demonstrated their abilities of framing questions and designing investigation through a survey questionnaire. The survey was developed for measuring factors in terms of personal and interactive variables that are needed for 'Doing' a successful scientific. Additionally, two other questionnaires were developed to measure students' abilities of framing testable questions and designing the investigation in a sequence. The results were as follows: Students' learning motivation factors as personal variable (self-confidence about group and inquiry activity, views about inquiry value) also considered as influential for students' group inquiry activity. Other four components of interactive variable (grouping, kinds of task, physical context, and teachers' role) were found to be influential in successful students' 'Doing' group inquiry activity. In students' evaluation of group inquiry activity, the grouping factor was the most critical one for a successful 'Doing' inquiry activity. Participating students showed some level of inability of in the process of framing inquiry question and designing investigation.
KIPS Transactions on Software and Data Engineering
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v.10
no.11
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pp.449-456
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2021
An intrusion detection system is a technology that detects abnormal behaviors that violate security, and detects abnormal operations and prevents system attacks. Existing intrusion detection systems have been designed using statistical analysis or anomaly detection techniques for traffic patterns, but modern systems generate a variety of traffic different from existing systems due to rapidly growing technologies, so the existing methods have limitations. In order to overcome this limitation, study on intrusion detection methods applying various machine learning techniques is being actively conducted. In this study, a comparative study was conducted on data preprocessing techniques that can improve the accuracy of anomaly detection using NGIDS-DS (Next Generation IDS Database) generated by simulation equipment for traffic in various network environments. Padding and sliding window were used as data preprocessing, and an oversampling technique with Adversarial Auto-Encoder (AAE) was applied to solve the problem of imbalance between the normal data rate and the abnormal data rate. In addition, the performance improvement of detection accuracy was confirmed by using Skip-gram among the Word2Vec techniques that can extract feature vectors of preprocessed sequence data. PCA-SVM and GRU were used as models for comparative experiments, and the experimental results showed better performance when sliding window, skip-gram, AAE, and GRU were applied.
We study on the concept and reason of seasonal change that 164 university students have. Subsequently the concept types on the seasonal change are classified according to the characteristics and conceptual change after teaching on astronomy. All of the students were simply checked by the questionnaire of multiple choice and essay method before learning on the subjects. And then they answered to questionnaires of similar type after one semester. By the analyzed results, we classify it to three steps of hierarchical concept structure. The first step is the cosmic perspective that is related to the Earth's condition and motion. The second step is the influence of the Earth that is directly affected by the first step. The third step is observer's perspective on the Earth depending on the second step. Among the answers, the first step is prominent and second step is rare. The answers on the reason of seasonal change show some kinds of type which are 1st, 1-2nd, 1-3rd, and 1-2-3rd step. By the result, it is arranged in sequence like as 1-3rd>1st>1-2nd>1-2-3rd type. The lowest number of students was 2nd step of the Sun's altitude and duration of daytime in pre-test. However the students of 2nd step obtained more correct scientific concept on the seasonal change after learning on the subjects, and got the higher score in the post-test than in the pre-test. We found how much important the hierarchical structure on the reason of seasonal change is. As the results, second step on the learning of the Sun's altitude and duration of daytime essentially have to teach after first step. And then third step have to teach. At last, it is sure that the students can obtain the concept of seasonal change.
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