Installation Art In Indonesian Contemporary Art; A Quest For Medium and Social Spaces (인도네시아 현대미술에 있어서의 설치미술 - 미디엄과 사회적 공간을 위한 탐색)
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- The Journal of Art Theory & Practice
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- no.5
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- pp.217-229
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- 2007
Many historical research and facet about modern art in Indonesia which formulating background of contemporary Indonesian Art. Indonesian art critic Sanento Yuliman states that Modern art has been rapidly developing in Indonesia since the Indonesian Independence in 1945. Modern Art is a part of the super culture of the Indonesian metropolitan and is closely related to the contact between the Indonesian and Western Cultures. Its birth was part of the nationalism project, when the Indonesian people consists of various ethnics were determined to become a new nation, the Indonesian nation, and they wished for a new culture, and therefore, a new art. The period 1960s, which was the beginning of the creation and development of the painters and the painters associations, was the first stage of the development of modern art in Indonesia. The second stage showed the important role of the higher education institutes for art. These institutes have developed since the 1950s and in the 1970s they were the main education institutes for painters and other artists. The artists awareness of the medium, forms or the organization of shapes were encouraged more intensely and these encouraged the exploring and experimental attitudes. Meanwhile, the information about the world's modern art, particularly Western Art; was widely and rapidly spread. The 1960s and 1970s were marked by the development of various abstractions and abstract art and the great number of explorations in various new media, like the experiment with collage, assemblage, mixed media. The works of the Neo Art Movement-group in the second half of the 1970s and in the 1980s shows environmental art and installations, influenced by the elements of popular art, from the commercial world and mass media, as well as the involvement of art in the social and environmental affairs. The issues about the environment, frequently launched by the intellectuals in the period of economic development starting in the 1970s, echoed among the artists, and they were widened in the social, art and cultural circles. The Indonesian economic development following the important change in the 1970s has caused a change in the life of the middle and upper class society, as has the change in various aspects of a big city, particularly Jakarta. The new genre emerged in 1975 which indicates contemporary art in Indonesia, when a group of young artists organized a movement, which was widely known as the Indonesian New Art Movement. This movement criticized international style, universalism and the long standing debate on an east-west-dichotomy. As far as the actual practice of the arts was concerned the movement criticized the domination of the art of painting and saw this as a sign of stagnation in Indonesian art development. Based on this criticism 'the movement' introduced ready-mades and installations (Jim Supangkat). Takes almost two decades that the New Art Movement activists were establishing Indonesian Installation art genre as contemporary paradigm and influenced the 1980's gene ration like, FX Harsono, Dadang Christanto, Arahmaiani, Tisna Sanjaya, Diyanto, Andarmanik, entering the 1990's decade as "rebellion period" ; reject towards established aesthetic mainstream i.e. painting, sculpture, graphic art which are insufficient to express "new language" and artistic needs especially to mediate social politic and cultural situation. Installation Art which contains open possibilities of creation become a vehicle for aesthetic establishment rejection and social politics stagnant expression in 1990s. Installation art accommodates two major field; first, the rejection of aesthetic establishment has a consequences an artists quest for medium; deconstruction models and cross disciplines into multi and intermedia i.e. performance, music, video etc. Second aspect is artists' social politic intention for changes, both conclude as characteristics of Indonesian Installation Art and establishing the freedom of expression in contemporary Indonesian Art until today.
Development of technologies in artificial intelligence has been rapidly increasing with the Fourth Industrial Revolution, and researches related to AI have been actively conducted in a variety of fields such as autonomous vehicles, natural language processing, and robotics. These researches have been focused on solving cognitive problems such as learning and problem solving related to human intelligence from the 1950s. The field of artificial intelligence has achieved more technological advance than ever, due to recent interest in technology and research on various algorithms. The knowledge-based system is a sub-domain of artificial intelligence, and it aims to enable artificial intelligence agents to make decisions by using machine-readable and processible knowledge constructed from complex and informal human knowledge and rules in various fields. A knowledge base is used to optimize information collection, organization, and retrieval, and recently it is used with statistical artificial intelligence such as machine learning. Recently, the purpose of the knowledge base is to express, publish, and share knowledge on the web by describing and connecting web resources such as pages and data. These knowledge bases are used for intelligent processing in various fields of artificial intelligence such as question answering system of the smart speaker. However, building a useful knowledge base is a time-consuming task and still requires a lot of effort of the experts. In recent years, many kinds of research and technologies of knowledge based artificial intelligence use DBpedia that is one of the biggest knowledge base aiming to extract structured content from the various information of Wikipedia. DBpedia contains various information extracted from Wikipedia such as a title, categories, and links, but the most useful knowledge is from infobox of Wikipedia that presents a summary of some unifying aspect created by users. These knowledge are created by the mapping rule between infobox structures and DBpedia ontology schema defined in DBpedia Extraction Framework. In this way, DBpedia can expect high reliability in terms of accuracy of knowledge by using the method of generating knowledge from semi-structured infobox data created by users. However, since only about 50% of all wiki pages contain infobox in Korean Wikipedia, DBpedia has limitations in term of knowledge scalability. This paper proposes a method to extract knowledge from text documents according to the ontology schema using machine learning. In order to demonstrate the appropriateness of this method, we explain a knowledge extraction model according to the DBpedia ontology schema by learning Wikipedia infoboxes. Our knowledge extraction model consists of three steps, document classification as ontology classes, proper sentence classification to extract triples, and value selection and transformation into RDF triple structure. The structure of Wikipedia infobox are defined as infobox templates that provide standardized information across related articles, and DBpedia ontology schema can be mapped these infobox templates. Based on these mapping relations, we classify the input document according to infobox categories which means ontology classes. After determining the classification of the input document, we classify the appropriate sentence according to attributes belonging to the classification. Finally, we extract knowledge from sentences that are classified as appropriate, and we convert knowledge into a form of triples. In order to train models, we generated training data set from Wikipedia dump using a method to add BIO tags to sentences, so we trained about 200 classes and about 2,500 relations for extracting knowledge. Furthermore, we evaluated comparative experiments of CRF and Bi-LSTM-CRF for the knowledge extraction process. Through this proposed process, it is possible to utilize structured knowledge by extracting knowledge according to the ontology schema from text documents. In addition, this methodology can significantly reduce the effort of the experts to construct instances according to the ontology schema.
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.
Insomnia is a chronic disease in modern society, with the number of new patients increasing by more than 20% in the last 5 years. Insomnia is a serious disease that requires diagnosis and treatment because the individual and social problems that occur when there is a lack of sleep are serious and the triggers of insomnia are complex. This study collected 5,699 data from 'insomnia', a community on 'Reddit', a social media that freely expresses opinions. Based on the International Classification of Sleep Disorders ICSD-3 standard and the guidelines with the help of experts, the insomnia corpus was constructed by tagging them as insomnia tendency documents and non-insomnia tendency documents. Five deep learning language models (BERT, RoBERTa, ALBERT, ELECTRA, XLNet) were trained using the constructed insomnia corpus as training data. As a result of performance evaluation, RoBERTa showed the highest performance with an accuracy of 81.33%. In order to in-depth analysis of insomnia social data, topic modeling was performed using the newly emerged BERTopic method by supplementing the weaknesses of LDA, which is widely used in the past. As a result of the analysis, 8 subject groups ('Negative emotions', 'Advice and help and gratitude', 'Insomnia-related diseases', 'Sleeping pills', 'Exercise and eating habits', 'Physical characteristics', 'Activity characteristics', 'Environmental characteristics') could be confirmed. Users expressed negative emotions and sought help and advice from the Reddit insomnia community. In addition, they mentioned diseases related to insomnia, shared discourse on the use of sleeping pills, and expressed interest in exercise and eating habits. As insomnia-related characteristics, we found physical characteristics such as breathing, pregnancy, and heart, active characteristics such as zombies, hypnic jerk, and groggy, and environmental characteristics such as sunlight, blankets, temperature, and naps.
Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.
In this paper, we study the performance improvement of the answer extraction in Question-Answering system by using sentence dependency parsing result. The Question-Answering (QA) system consists of query analysis, which is a method of analyzing the user's query, and answer extraction, which is a method to extract appropriate answers in the document. And various studies have been conducted on two methods. In order to improve the performance of answer extraction, it is necessary to accurately reflect the grammatical information of sentences. In Korean, because word order structure is free and omission of sentence components is frequent, dependency parsing is a good way to analyze Korean syntax. Therefore, in this study, we improved the performance of the answer extraction by adding the features generated by dependency parsing analysis to the inputs of the answer extraction model (Bidirectional LSTM-CRF). The process of generating the dependency graph embedding consists of the steps of generating the dependency graph from the dependency parsing result and learning the embedding of the graph. In this study, we compared the performance of the answer extraction model when inputting basic word features generated without the dependency parsing and the performance of the model when inputting the addition of the Eojeol tag feature and dependency graph embedding feature. Since dependency parsing is performed on a basic unit of an Eojeol, which is a component of sentences separated by a space, the tag information of the Eojeol can be obtained as a result of the dependency parsing. The Eojeol tag feature means the tag information of the Eojeol. The process of generating the dependency graph embedding consists of the steps of generating the dependency graph from the dependency parsing result and learning the embedding of the graph. From the dependency parsing result, a graph is generated from the Eojeol to the node, the dependency between the Eojeol to the edge, and the Eojeol tag to the node label. In this process, an undirected graph is generated or a directed graph is generated according to whether or not the dependency relation direction is considered. To obtain the embedding of the graph, we used Graph2Vec, which is a method of finding the embedding of the graph by the subgraphs constituting a graph. We can specify the maximum path length between nodes in the process of finding subgraphs of a graph. If the maximum path length between nodes is 1, graph embedding is generated only by direct dependency between Eojeol, and graph embedding is generated including indirect dependencies as the maximum path length between nodes becomes larger. In the experiment, the maximum path length between nodes is adjusted differently from 1 to 3 depending on whether direction of dependency is considered or not, and the performance of answer extraction is measured. Experimental results show that both Eojeol tag feature and dependency graph embedding feature improve the performance of answer extraction. In particular, considering the direction of the dependency relation and extracting the dependency graph generated with the maximum path length of 1 in the subgraph extraction process in Graph2Vec as the input of the model, the highest answer extraction performance was shown. As a result of these experiments, we concluded that it is better to take into account the direction of dependence and to consider only the direct connection rather than the indirect dependence between the words. The significance of this study is as follows. First, we improved the performance of answer extraction by adding features using dependency parsing results, taking into account the characteristics of Korean, which is free of word order structure and omission of sentence components. Second, we generated feature of dependency parsing result by learning - based graph embedding method without defining the pattern of dependency between Eojeol. Future research directions are as follows. In this study, the features generated as a result of the dependency parsing are applied only to the answer extraction model in order to grasp the meaning. However, in the future, if the performance is confirmed by applying the features to various natural language processing models such as sentiment analysis or name entity recognition, the validity of the features can be verified more accurately.
The objectives of the 1963 Tokyo Convention cover a variety of subjects, with the intention of providing safety in aircraft, protection of life and property on board, and promoting the security of civil aviation. These objectives will be treated as follows: first, the unification of rules on jurisdiction; second, the question of filling the gap in jurisdiction; third, the scheme of maintaining law and order on board aircraft; fourth, the protection of persons acting in accordance with the Convention; fifth, the protection of the interests of disembarked persons; sixth, the question of hijacking of aircraft; and finally some general remarks on the objectives of the Convention. The Tokyo Convention mainly deals with general crimes such as murder, violence, robbery on board aircraft rather than aviation terrorism. The Article 11 of the Convention deals with hijacking in a simple way. As far as aviation terrorism is concerned 1970 Hague Convention and 1971 Montreal Convention cover the hijacking and sabotage respectively. The Problem of national jurisdiction over the offence and the offender was as tangled at the Hague and Montreal Convention, as under the Tokyo Convention. Under the Tokyo Convention the prime base of jurisdiction is the law of the flag (Article 3), but concurrent jurisdiction is also allowed on grounds of: territorial principle, active nationality and passive personality principle, security of the state, breach of flight rules, and exercise of jurisdiction necessary for the performance of obligations under multilateral agreements (Article 4). No Criminal jurisdiction exercised in accordance with national law is excluded [Article 3(2)]. However, Article 4 of the Hague Convention(hereafter Hague Article 4) and Article 5 of the Montreal Convention(hereafter Montreal Article 5), dealing with jurisdiction have moved a step further, inasmuch as the opening part of both paragraphs 1 and 2 of the Hague Article 4 and the Montreal Article 5 impose an obligation on all contracting states to take measures to establish jurisdiction over the offence (i.e., to ensure that their law is such that their courts will have jurisdiction to try offender in all the circumstances covered by Hague Article 4 and Montreal Article 5). The state of registration and the state where the aircraft lands with the hijacker still on board will have the most interest, and would be in the best position to prosecute him; the paragraphs 1(a) and (b) of the Hague Article 4 and paragraphs 1(b) and (c) of the Montreal Article 5 deal with it, respectively. However, paragraph 1(b) of the Hague Article 4 and paragraph 1(c) of the Montreal Article 5 do not specify if the aircraft is still under the control of the hijacker or if the hijacker has been overpowered by the aircraft commander, or if the offence has at all occurred in the airspace of the state of landing. The language of the paragraph would probably cover all these cases. The weaknesses of Hague Article 4 and Montreal Article 5 are however, patent. The Jurisdictions of the state of registration, the state of landing, the state of the lessee and the state where the offender is present, are concurrent. No priorities have been fixed despite a proposal to this effect in the Legal Committee and the Diplomatic Conference, and despite the fact that it was pointed out that the difficulty in accepting the Tokyo Convention has been the question of multiple jurisdiction, for the reason that it would be too difficult to determine the priorities. Disputes over the exercise of jurisdiction can be endemic, more so when Article 8(4) of the Hague Convention and the Montreal Convention give every state mentioned in Hague Article 4(1) and Montreal Article 5(1) the right to seek extradition of the offender. A solution to the problem should not have been given up only because it was difficult. Hague Article 4(3) and Montreal Article 5(3) provide that they do not exclude any criminal jurisdiction exercised in accordance with national law. Thus the provisions of the two Conventions create additional obligations on the state, and do not exclude those already existing under national laws. Although the two Conventions do not require a state to establish jurisdiction over, for example, hijacking or sabotage committed by its own nationals in a foreign aircraft anywhere in the world, they do not preclude any contracting state from doing so. However, it has be noted that any jurisdiction established merely under the national law would not make the offence an extraditable one under Article 8 of the Hague and Montreal Convention. As far as international aviation terrorism is concerned 1988 Montreal Protocol and 1991 Convention on Marking of Plastic Explosives for the Purpose of Detention are added. The former deals with airport terrorism and the latter plastic explosives. Compared to the other International Terrorism Conventions, the International Aviation Terrorism Conventions do not have clauses of the passive personality principle. If the International Aviation Terrorism Conventions need to be revised in the future, those clauses containing the passive personality principle have to be inserted for the suppression of the international aviation terrorism more effectively. Article 3 of the 1973 Convention on the Prevention and Punishment of Crimes Against Internationally Protected Persons, Including Diplomatic Agents, Article 5 of the 1979 International Convention against the Taking of Hostages and Article 6 of the 1988 Convention for the Suppression of Unlawful Acts Against the Safety of Maritime Navigation would be models that the revised International Aviation Terrorism Conventions could follow in the future.
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