In this paper, we describe a multiprocessor system using the HiPi bus with pended protocol and multiple cache memories, and evalute the performance of the multiprocessor system in terms of processor utilization for various cache coherence protocols. The HiPi bus is delveloped as the shared bus of TICOM II which is a main computer system to establish a nation-wide computing network in ETRI. The HiPi bus has high data transfer rate, but it doesn't allow cache-to-cache transfer. In order to evaluate the effect of cache-to-cache transfer upon the performance of system and to choose a best-performed protocol for HiPi bus, we simulate as follows: First, we analyze the performance of multiprocessor system with HiPi bus in terms of processor utilizatIOn through simulation. Each of cache coherence protocol is described by state transition diagram, and then the probability of each state is calculated by Markov steady state. The calculated probability of each state is used as input parameters of simulation, and modeling and simulation are implemented and performed by using SLAM II graphic symbols and language. Second, we propose the HiPi bus which supports cache-to-cache transfer, and analyze the performance of multiprocessor system with proposed HiPi bus in terms of processor utilization through simulation. Considered cache coherence protocols for the simulation are Write-through, Write-once, Berkely, Synapse, Illinois, Firefly, and Dragon.
Female marriage immigrants have increased since the mid-1990s in Korea. Thus, their adaptation to Korean society has been a big social issue in Korea. There are a lot of academic researches on their adaptation to Korean society so far. We cannot sufficiently understand general main factors of their adaptation to Korean society because of methodological problems in the studies on the female marriage immigrants. Particularly, there are very few studies on female marriage immigrants' adaptation to their communities. This study analyzes determinants of female marriage immigrants' adaptation to their communities, using stepwise multi-regression. Data are collected from questionary survey on female marriage immigrants in Seoul, Gyeong-gi, Daegu, Gyeong-buk, Gwangju, Jeon-nam. Dependent variables are community life, spatial cognition and activities, and neighborhood relationship. Each dependent variable is analyzed with 30 independent variables through stepwise multi-regression. As a result, 16 positive determinants and 2 negative ones are selected. Positive determinants are resident identity, age, adaptation to home, number of Korean friends and same nationals' friends in Korea and so on. But, Korean language fluence is not selected as a significant factor. This is different from a general recognition. As exiting researches, the importance of social network and adaptation variables is also identified in this study.
The amount of data is increasing through various IT devices in a hyper-connected society where the 4th revolution is progressing, and new value can be created by analyzing that data. This paper was collected total 1,526 articles from 2017 to 2019 in central magazines, economic magazines, regional associations, and major broadcasting companies with the keyword "(East Coast Tourism or East Coast Travel) and Gangwon-do" through Bigkinds. It was performed the topic modeling using LDA algorithm implemented in the R language to analyze the collected 1,526 articles. It was extracted keywords for each year from 2017 to 2019, and classified and compared keywords with high frequency for each year. It was setted the optimal number of topics to 8 using Log Likelihood and Perplexity, and then inferred 8 topics using the Gibbs Sampling method. The inferred topics were Gangneung and Beach, Goseong and Mt.Geumgang, KTX and Donghae-Bukbu line, weekend sea tour, Sokcho and Unification Observatory, Yangyang and Surfing, experience tour, and transportation network infra. The changes of articles on East coast tourism was was analyzed using the proportion of the inferred eight topics. As the result, the proportion of Unification Observatory and Mt. Geumgang showed no significant change, the proportion of KTX and experience tour increased, and the proportion of other topics decreased in 2018 compared to 2017. In 2019, the proportion of KTX and experience tour decreased, but the proportion of other topics showed no significant change.
Information extraction can facilitate the intensive analysis of documents by providing semantic triples which consist of named entities and their relations recognized in the texts. However, most of the research so far has been carried out separately for named entity recognition and relation extraction as individual studies, and as a result, the effective performance evaluation of the entire information extraction systems was not performed properly. This paper introduces two models of end-to-end information extraction that can extract various entity names in clinical records and their relationships in the form of semantic triples, namely pipeline and joint models and compares their performances in depth. The pipeline model consists of an entity recognition sub-system based on bidirectional GRU-CRFs and a relation extraction module using multiple encoding scheme, whereas the joint model was implemented with a single bidirectional GRU-CRFs equipped with multi-head labeling method. In the experiments using i2b2/VA 2010, the performance of the pipeline model was 5.5% (F-measure) higher. In addition, through a comparative experiment with existing state-of-the-art systems using large-scale neural language models and manually constructed features, the objective performance level of the end-to-end models implemented in this paper could be identified properly.
Stock market investors are generally split into foreign investors, institutional investors, and individual investors. Compared to individual investor groups, professional investor groups such as foreign investors have an advantage in information and financial power and, as a result, foreign investors are known to show good investment performance among market participants. The purpose of this study is to propose an investment strategy that combines investor-specific transaction information and machine learning, and to analyze the portfolio investment performance of the proposed model using actual stock price and investor-specific transaction data. The Korea Exchange offers daily information on the volume of purchase and sale of each investor to securities firms. We developed a data collection program in C# programming language using an API provided by Daishin Securities Cybosplus, and collected 151 out of 200 KOSPI stocks with daily opening price, closing price and investor-specific net purchase data from January 2, 2007 to July 31, 2017. The self-organizing map model is an artificial neural network that performs clustering by unsupervised learning and has been introduced by Teuvo Kohonen since 1984. We implement competition among intra-surface artificial neurons, and all connections are non-recursive artificial neural networks that go from bottom to top. It can also be expanded to multiple layers, although many fault layers are commonly used. Linear functions are used by active functions of artificial nerve cells, and learning rules use Instar rules as well as general competitive learning. The core of the backpropagation model is the model that performs classification by supervised learning as an artificial neural network. We grouped and transformed investor-specific transaction volume data to learn backpropagation models through the self-organizing map model of artificial neural networks. As a result of the estimation of verification data through training, the portfolios were rebalanced monthly. For performance analysis, a passive portfolio was designated and the KOSPI 200 and KOSPI index returns for proxies on market returns were also obtained. Performance analysis was conducted using the equally-weighted portfolio return, compound interest rate, annual return, Maximum Draw Down, standard deviation, and Sharpe Ratio. Buy and hold returns of the top 10 market capitalization stocks are designated as a benchmark. Buy and hold strategy is the best strategy under the efficient market hypothesis. The prediction rate of learning data using backpropagation model was significantly high at 96.61%, while the prediction rate of verification data was also relatively high in the results of the 57.1% verification data. The performance evaluation of self-organizing map grouping can be determined as a result of a backpropagation model. This is because if the grouping results of the self-organizing map model had been poor, the learning results of the backpropagation model would have been poor. In this way, the performance assessment of machine learning is judged to be better learned than previous studies. Our portfolio doubled the return on the benchmark and performed better than the market returns on the KOSPI and KOSPI 200 indexes. In contrast to the benchmark, the MDD and standard deviation for portfolio risk indicators also showed better results. The Sharpe Ratio performed higher than benchmarks and stock market indexes. Through this, we presented the direction of portfolio composition program using machine learning and investor-specific transaction information and showed that it can be used to develop programs for real stock investment. The return is the result of monthly portfolio composition and asset rebalancing to the same proportion. Better outcomes are predicted when forming a monthly portfolio if the system is enforced by rebalancing the suggested stocks continuously without selling and re-buying it. Therefore, real transactions appear to be relevant.
This study was conducted to examine the quantitative trend of domestic studies in invention gifted education, identify the intrinsic meaning and connection attributes in these research analysis, and provide basic data to explore future development plans. To this end, 97 domestic academic papers were finally selected as "Invention Gifted Education" by the Korea Research and Information Service (RISS), technical statistical analysis was conducted with SPSS on publication year, author composition, researcher's affiliation and location area, and published journal. The trend, which had been on the rise since 2007, confirmed by academic papers on gifted education in invention, peaked at the time of the 3rd comprehensive plan for gifted education and has since declined again. As a result of technical statistical analysis of the author's characteristics, half of the papers were jointly published, followed by a number of independent authors. The papers published alone were identified as belonging to universities, research institutes, elementary schools, and middle schools, and the cooperative papers were many studies cooperated with young researchers and professional researchers, and only one collaborative study was conducted between young researchers. When looking at the regions and journals in which the Invention Gifted Education thesis was published, it was concentrated in some regions or journals, and the deviation was very large. As a result of language network analysis using academic paper keywords, creativity and programs were identified as meaningful keywords that showed top appearance, and the keyword pair with high co-appearance was invention gifted-creativity. The keyword of connection-centeredness at the top served as an intermediary for creativity, problem-solving, development, and company to expand to other research topics, and served as a research topic that could be expanded to various topics. In the case of mediation-centeredness, creativity, programs, and effects showed high mediation-centeredness, indicating that it is an important keyword that plays a role in mediating or mediating other keywords. Through these research results, national policy measures need to be prepared for the development of gifted education, and the need to create an invention ecological culture that can enhance teachers' expertise while increasing social responsibility for gifted education.
The game industry has evolved from mobile games to PC online games after the smart-phone industry was opened up. In this environment, the game industry has rather been negatively developing its commercials means than the sufficient fundamental entertainment to the users. Especially, many games were released with better graphic qualities yet poor originality, continuing to be popular without enhancing the market itself. Moreover, the user's recognition level has improved. The users share their online gaming experience easily with the development of network environment. They receive the feedbacks on the quality of the game through the online channels and media by sharing them together. The high margin of the game industry will lead to the negative feedbacks of the users, effecting them to critique the content although the market looks good for now. The game industry's evolution has to be reviewed in the perspective of users, to look back at the successful cases of the past before the mobile era by analyzing and indicating the quality of the games and content's direction. This research is focused on the success factors of
Dimensionality reduction is one of the methods to handle big data in text mining. For dimensionality reduction, we should consider the density of data, which has a significant influence on the performance of sentence classification. It requires lots of computations for data of higher dimensions. Eventually, it can cause lots of computational cost and overfitting in the model. Thus, the dimension reduction process is necessary to improve the performance of the model. Diverse methods have been proposed from only lessening the noise of data like misspelling or informal text to including semantic and syntactic information. On top of it, the expression and selection of the text features have impacts on the performance of the classifier for sentence classification, which is one of the fields of Natural Language Processing. The common goal of dimension reduction is to find latent space that is representative of raw data from observation space. Existing methods utilize various algorithms for dimensionality reduction, such as feature extraction and feature selection. In addition to these algorithms, word embeddings, learning low-dimensional vector space representations of words, that can capture semantic and syntactic information from data are also utilized. For improving performance, recent studies have suggested methods that the word dictionary is modified according to the positive and negative score of pre-defined words. The basic idea of this study is that similar words have similar vector representations. Once the feature selection algorithm selects the words that are not important, we thought the words that are similar to the selected words also have no impacts on sentence classification. This study proposes two ways to achieve more accurate classification that conduct selective word elimination under specific regulations and construct word embedding based on Word2Vec embedding. To select words having low importance from the text, we use information gain algorithm to measure the importance and cosine similarity to search for similar words. First, we eliminate words that have comparatively low information gain values from the raw text and form word embedding. Second, we select words additionally that are similar to the words that have a low level of information gain values and make word embedding. In the end, these filtered text and word embedding apply to the deep learning models; Convolutional Neural Network and Attention-Based Bidirectional LSTM. This study uses customer reviews on Kindle in Amazon.com, IMDB, and Yelp as datasets, and classify each data using the deep learning models. The reviews got more than five helpful votes, and the ratio of helpful votes was over 70% classified as helpful reviews. Also, Yelp only shows the number of helpful votes. We extracted 100,000 reviews which got more than five helpful votes using a random sampling method among 750,000 reviews. The minimal preprocessing was executed to each dataset, such as removing numbers and special characters from text data. To evaluate the proposed methods, we compared the performances of Word2Vec and GloVe word embeddings, which used all the words. We showed that one of the proposed methods is better than the embeddings with all the words. By removing unimportant words, we can get better performance. However, if we removed too many words, it showed that the performance was lowered. For future research, it is required to consider diverse ways of preprocessing and the in-depth analysis for the co-occurrence of words to measure similarity values among words. Also, we only applied the proposed method with Word2Vec. Other embedding methods such as GloVe, fastText, ELMo can be applied with the proposed methods, and it is possible to identify the possible combinations between word embedding methods and elimination methods.
The wall shear stress in the vicinity of end-to end anastomoses under steady flow conditions was measured using a flush-mounted hot-film anemometer(FMHFA) probe. The experimental measurements were in good agreement with numerical results except in flow with low Reynolds numbers. The wall shear stress increased proximal to the anastomosis in flow from the Penrose tubing (simulating an artery) to the PTFE: graft. In flow from the PTFE graft to the Penrose tubing, low wall shear stress was observed distal to the anastomosis. Abnormal distributions of wall shear stress in the vicinity of the anastomosis, resulting from the compliance mismatch between the graft and the host artery, might be an important factor of ANFH formation and the graft failure. The present study suggests a correlation between regions of the low wall shear stress and the development of anastomotic neointimal fibrous hyperplasia(ANPH) in end-to-end anastomoses. 30523 T00401030523 ^x Air pressure decay(APD) rate and ultrafiltration rate(UFR) tests were performed on new and saline rinsed dialyzers as well as those roused in patients several times. C-DAK 4000 (Cordis Dow) and CF IS-11 (Baxter Travenol) reused dialyzers obtained from the dialysis clinic were used in the present study. The new dialyzers exhibited a relatively flat APD, whereas saline rinsed and reused dialyzers showed considerable amount of decay. C-DAH dialyzers had a larger APD(11.70
The wall shear stress in the vicinity of end-to end anastomoses under steady flow conditions was measured using a flush-mounted hot-film anemometer(FMHFA) probe. The experimental measurements were in good agreement with numerical results except in flow with low Reynolds numbers. The wall shear stress increased proximal to the anastomosis in flow from the Penrose tubing (simulating an artery) to the PTFE: graft. In flow from the PTFE graft to the Penrose tubing, low wall shear stress was observed distal to the anastomosis. Abnormal distributions of wall shear stress in the vicinity of the anastomosis, resulting from the compliance mismatch between the graft and the host artery, might be an important factor of ANFH formation and the graft failure. The present study suggests a correlation between regions of the low wall shear stress and the development of anastomotic neointimal fibrous hyperplasia(ANPH) in end-to-end anastomoses. 30523 T00401030523 ^x Air pressure decay(APD) rate and ultrafiltration rate(UFR) tests were performed on new and saline rinsed dialyzers as well as those roused in patients several times. C-DAK 4000 (Cordis Dow) and CF IS-11 (Baxter Travenol) reused dialyzers obtained from the dialysis clinic were used in the present study. The new dialyzers exhibited a relatively flat APD, whereas saline rinsed and reused dialyzers showed considerable amount of decay. C-DAH dialyzers had a larger APD(11.70