• The Transactions of the Korea Information Processing Society
• /
• v.6 no.1
• /
• pp.49-56
• /
• 1999

Distributed object Computing, owing to the development of distributed computing, has improved the performance of distributed processing conducted between homogeneous and heterogeneous systems in network. However, it has failed to solve fundamental problems such as network overload and enormous requests demands by servers and clients. In this paper, we propose to design and implement an Object Migration system that uses the java language to tackle the mentioned problems. As the first step of the implementation of the system, we justify the characteristics of t도 mobile object model that keeps codes and states of an object. Implemented Object Migration System would accept objects being migrated to a specific node and support the virtual place in which objects could be executed automatically. Therefore, the Object Migration system we suggest could not only solve problems imposed to traditional distributed computing but also offer transparency of object migration between homogeneous and heterogeneous systems.

• International Journal of Computer Science & Network Security
• /
• v.22 no.4
• /
• pp.245-253
• /
• 2022

Commenced in 1954 by IBM, machine translation has expanded immensely, particularly in this period. Machine translation can be broken into seven main steps namely- token generation, analyzing morphology, lexeme, tagging Part of Speech, chunking, parsing, and disambiguation in words. Morphological analysis plays a major role when translating Indian languages to develop accurate parts of speech taggers and word sense. The paper presents various machine translation methods used by different researchers for Indian languages along with their performance and drawbacks. Further, the paper concentrates on parts of speech (POS) tagging in Marathi dialect using various methods such as rule-based tagging, unigram, bigram, and more. After careful study, it is concluded that for machine translation, parts of speech tagging is a major step. Also, for the Marathi language, the Hidden Markov Model gives the best results for parts of speech tagging with an accuracy of 93% which can be further improved according to the dataset.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.1
• /
• pp.24-29
• /
• 2022

Semantic role labeling task used in various natural language processing fields, such as information extraction and question answering systems, is the task of identifying the arugments for a given sentence and predicate. Predicate used as semantic role labeling input are extracted using lexical analysis results such as POS-tagging, but the problem is that predicate can't extract all linguistic patterns because predicate in korean language has various patterns, depending on the meaning of sentence. In this paper, we propose a korean predicate recognition method using neural network model with pre-trained embedding models and lexical features. The experiments compare the performance on the hyper parameters of models and with or without the use of embedding models and lexical features. As a result, we confirm that the performance of the proposed neural network model was 92.63%.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.21 no.1
• /
• pp.53-63
• /
• 2011

An SNS(Social Network Service) enables people to form a social network on online as in the real world. With the rising popularity of the service, side effects of SNSs were issued. Therefore we propose and implement a policy-based privacy protection module and access control policy language for ensuring the right of control of personal information and sharing data among SNSs. The policy language for protecting privacy is based on an attribute-based access control model which grants an access to personal information based on a user's attributes. The policy language and the privacy protection module proposed to give the right of control of personal information to the owner, they can be adopted to other application domains in which privacy protection is needed as well as secure sharing data among SNSs.

• Journal of the Society of Naval Architects of Korea
• /
• v.51 no.1
• /
• pp.51-57
• /
• 2014

In the KIOST MOERI model ice basin, a lot of experiments to find the material properties of model ice and model ship tests are being performed. And therefore many data information was produced, yet it was saved as excel data format. In order to manage and use the data more effectively and systematically, a proper database management is required. To make realistic engineering DBMS(database management system), understanding of test process, the usage of data, design of data schema and computer language and system management are required. In this paper, basic design process and usage of DBMS based on network and multiple user concept is introduced.

• Proceedings of the Korea Society for Simulation Conference
• /
• 2003.11a
• /
• pp.59-65
• /
• 2003

In order to reduce the cost of software maintenance including software modification, we suggest the object oriented program with checking the version of application program using the Java language and the technique of executing the downloaded application program via network using the application manager. In order to change the traditional scheduler to the application manager we have adopted the Xlet concept in the nuclear fields using the network. In usual Xlet means a Java application that runs on the digital television receiver. The Java TV Application Program Interface(API) defines an application model called the Xlet application lifecycle. Java applications that use this lifecycle model are called Xlets. The Xlet application lifecycle is compatible with the existing application environment and virtual machine technology. The Xlet application lifecycle model defines the dialog(protocol) between an Xlet and its environment

• Journal of Information Technology Services
• /
• v.16 no.3
• /
• pp.167-183
• /
• 2017

The rapid growth of information technology and mobile service platforms, i.e., internet, google, and facebook, etc. has led the abundance of data. Due to this environment, the world is now facing a revolution in the process that data is searched, collected, stored, and shared. Abundance of data gives us several opportunities to knowledge discovery and data mining techniques. In recent years, data mining methods as a solution to discovery and extraction of available knowledge in database has been more popular in e-commerce service fields such as, in particular, movie recommendation. However, most of the classification approaches for predicting the movie popularity have used only several types of information of the movie such as actor, director, rating score, language and countries etc. In this study, we propose a classification-based support vector machine (SVM) model for predicting the movie popularity based on movie's genre data and social network data. Social network analysis (SNA) is used for improving the classification accuracy. This study builds the movies' network (one mode network) based on initial data which is a two mode network as user-to-movie network. For the proposed method we computed degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality as centrality measures in movie's network. Those four centrality values and movies' genre data were used to classify the movie popularity in this study. The logistic regression, neural network, $na{\ddot{i}}ve$ Bayes classifier, and decision tree as benchmarking models for movie popularity classification were also used for comparison with the performance of our proposed model. To assess the classifier's performance accuracy this study used MovieLens data as an open database. Our empirical results indicate that our proposed model with movie's genre and centrality data has by approximately 0% higher accuracy than other classification models with only movie's genre data. The implications of our results show that our proposed model can be used for improving movie popularity classification accuracy.

• Spatial Information Research
• /
• v.5 no.2
• /
• pp.147-160
• /
• 1997

"The "Map Algebra", beeing recognized as a viable theoretical framework for GIS (Geographica Infonnation System), models map layers as "operands" which are the basic unit of geo-processing, and a variety of GIS commands as "operators." In this paper, we attempt at lifting some limitations of map algebras proposed in GIS literature. First, we model map layer as "function" such that we may employ the notion of meta operator (or, higher-order funtion) available in the functional programming paradigm. This approach provides map algebraic language with "programmability" needed in GIS user language. Second, we extend the semantics of, and improve on the sytactic structure of map algebraic language. Mer the data model and language associated with map algebra are formalized, we proceed to design and implement a prototype of map algebraic processor. The parser of the language in our prototype plays the role of transforming the native and heterogeneous user language of current GISs into a canonical map algebraic language. The prototype, named "MapSee" is a proof-of-concept system for the ideas we propsed in this paper. We believe that the uniform interface based on the map algebraic language will make promising infrastructure to support "Internet GIS." This is because the uniform but powerful interface through the Web clients allow access to both geo-data and geo-processing resources distributed over the network.to both geo-data and geo-processing resources distributed over the network.

• Communications for Statistical Applications and Methods
• /
• v.27 no.3
• /
• pp.313-326
• /
• 2020

Deep Learning is the most important key to the development of Artificial Intelligence (AI). There are several distinguishable architectures of neural networks such as MLP, CNN, and RNN. Among them, we try to understand one of the main architectures called Recurrent Neural Network (RNN) that differs from other networks in handling sequential data, including time series and texts. As one of the main tasks recently in Natural Language Processing (NLP), we consider Neural Machine Translation (NMT) using RNNs. We also summarize fundamental structures of the recurrent networks, and some topics of representing natural words to reasonable numeric vectors. We organize topics to understand estimation procedures from representing input source sequences to predict target translated sequences. In addition, we apply multiple translation models with Gated Recurrent Unites (GRUs) in Keras on English-Korean sentences that contain about 26,000 pairwise sequences in total from two different corpora, colloquialism and news. We verified some crucial factors that influence the quality of training. We found that loss decreases with more recurrent dimensions and using bidirectional RNN in the encoder when dealing with short sequences. We also computed BLEU scores which are the main measures of the translation performance, and compared them with the score from Google Translate using the same test sentences. We sum up some difficulties when training a proper translation model as well as dealing with Korean language. The use of Keras in Python for overall tasks from processing raw texts to evaluating the translation model also allows us to include some useful functions and vocabulary libraries as well.

• Journal of the Korean Institute of Telematics and Electronics C
• /
• v.35C no.11
• /
• pp.63-75
• /
• 1998

Since many co-articulation problems are occurring in continuous spoken Korean language, several researches use words as a basic recognition unit. Though the word unit can solve this problem, it requires much memory and has difficulty fitting an input speech in a word list. In this paper, we propose an hidden Markov model(HMM) based recognition model that is an interconnection network of word HMMs for a syntax of sentences. To match suitably the input sentence into the continuous word list in the network, we use a level building search algorithm. This system represents the large sentence set with a relatively small memory and also has good extensibility. The experimental result of an airplane reservation system shows that it is proper method for a practical recognition system.