• KIPS Transactions on Software and Data Engineering
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• v.12 no.5
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• pp.217-228
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• 2023

Video instance segmentation is an intelligent visual task with high complexity because it not only requires object instance segmentation for each image frame constituting a video, but also requires accurate tracking of instances throughout the frame sequence of the video. In special, human instance segmentation in drama videos has an unique characteristic that requires accurate tracking of several main characters interacting in various places and times. Also, it is also characterized by a kind of the class imbalance problem because there is a significant difference between the frequency of main characters and that of supporting or auxiliary characters in drama videos. In this paper, we introduce a new human instance datatset called MHIS, which is built upon drama videos, Miseang, and then propose a novel video data augmentation method, CDVA, in order to overcome the data imbalance problem between character classes. Different from the previous video data augmentation methods, the proposed CDVA generates more realistic augmented videos by deciding the optimal location within the background clip for a target human instance to be inserted with taking rich spatio-temporal context embedded in videos into account. Therefore, the proposed augmentation method, CDVA, can improve the performance of a deep neural network model for video instance segmentation. Conducting both quantitative and qualitative experiments using the MHIS dataset, we prove the usefulness and effectiveness of the proposed video data augmentation method.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.1-13
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• 2015

As opinion mining in big data applications has been highlighted, a lot of research on unstructured data has made. Lots of social media on the Internet generate unstructured or semi-structured data every second and they are often made by natural or human languages we use in daily life. Many words in human languages have multiple meanings or senses. In this result, it is very difficult for computers to extract useful information from these datasets. Traditional web search engines are usually based on keyword search, resulting in incorrect search results which are far from users' intentions. Even though a lot of progress in enhancing the performance of search engines has made over the last years in order to provide users with appropriate results, there is still so much to improve it. Word sense disambiguation can play a very important role in dealing with natural language processing and is considered as one of the most difficult problems in this area. Major approaches to word sense disambiguation can be classified as knowledge-base, supervised corpus-based, and unsupervised corpus-based approaches. This paper presents a method which automatically generates a corpus for word sense disambiguation by taking advantage of examples in existing dictionaries and avoids expensive sense tagging processes. It experiments the effectiveness of the method based on Naïve Bayes Model, which is one of supervised learning algorithms, by using Korean standard unabridged dictionary and Sejong Corpus. Korean standard unabridged dictionary has approximately 57,000 sentences. Sejong Corpus has about 790,000 sentences tagged with part-of-speech and senses all together. For the experiment of this study, Korean standard unabridged dictionary and Sejong Corpus were experimented as a combination and separate entities using cross validation. Only nouns, target subjects in word sense disambiguation, were selected. 93,522 word senses among 265,655 nouns and 56,914 sentences from related proverbs and examples were additionally combined in the corpus. Sejong Corpus was easily merged with Korean standard unabridged dictionary because Sejong Corpus was tagged based on sense indices defined by Korean standard unabridged dictionary. Sense vectors were formed after the merged corpus was created. Terms used in creating sense vectors were added in the named entity dictionary of Korean morphological analyzer. By using the extended named entity dictionary, term vectors were extracted from the input sentences and then term vectors for the sentences were created. Given the extracted term vector and the sense vector model made during the pre-processing stage, the sense-tagged terms were determined by the vector space model based word sense disambiguation. In addition, this study shows the effectiveness of merged corpus from examples in Korean standard unabridged dictionary and Sejong Corpus. The experiment shows the better results in precision and recall are found with the merged corpus. This study suggests it can practically enhance the performance of internet search engines and help us to understand more accurate meaning of a sentence in natural language processing pertinent to search engines, opinion mining, and text mining. Naïve Bayes classifier used in this study represents a supervised learning algorithm and uses Bayes theorem. Naïve Bayes classifier has an assumption that all senses are independent. Even though the assumption of Naïve Bayes classifier is not realistic and ignores the correlation between attributes, Naïve Bayes classifier is widely used because of its simplicity and in practice it is known to be very effective in many applications such as text classification and medical diagnosis. However, further research need to be carried out to consider all possible combinations and/or partial combinations of all senses in a sentence. Also, the effectiveness of word sense disambiguation may be improved if rhetorical structures or morphological dependencies between words are analyzed through syntactic analysis.

• KDI Journal of Economic Policy
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• v.13 no.1
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• pp.115-134
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• 1991

The role of pension plans in the macroeconomy has been a subject of much interest for some years. It has come to be recognized that pension plans may alter basic macroeconomic behavior patterns. The net effects on both savings and labor supply are thus matters for speculation. The aim of the present paper is to provide quantitative results which may be helpful in attaching orders of magnitude to some of the possible effects. We are not concerned with the providing empirical evidence relating to actual behavior, but rather with deriving the macroeconomic implications for a alternative possibilities. The pension plan interacts with the economy and the population in a number of ways. Demographic variables may thus affect both the economic burden of a national pension plan and the ability of the economy to sustain the burden. The tax transfer process associated with the pension plan may have implications for national patterns of saving and consumption. The existence of a pension plan may have implications also for the size of the labor force, inasmuch as labor force participation rates may be affected. Changes in technology and the associated changes in average productivity levels bear directly on the size of the national income, and hence on the pension contribution base. The vehicle for the analysis is a hypothetical but broadly realistic simulation model of an economic- demographic system into which is inserted a national pension plan. All income, expenditure, and related aggregates are in real terms. The economy is basically neoclassical; full employment is assumed, output is generated by a Cobb-Douglas production process, and factors receive their marginal products. The model was designed for use in computer simulation experiments. The simulation results suggest a number of general conclusions. These may be summarized as follows; - The introduction of a national pension plan (funded system) tends to increase the rate of economic growth until cost exceeds revenue. - A scheme with full wage indexing is more expensive than one in which pensions are merely price indexed. - The rate of technical progress is not a critical element in determining the economic burden of the pension scheme. - Raising the rate of benefits affects its economic burden, and raising the age of eligibility may decrease the burden substantially. - The level of fertility is an element in determining the long-run burden. A sustained low fertility rate increases the proportion of the aged in total population and increases the burden of the pension plan. High fertility has inverse effects.