• KIPS Transactions on Software and Data Engineering
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• v.1 no.1
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• pp.19-30
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• 2012

The context aware service is the service to provide useful information to the users by recognizing surroundings around people who receive the service via computer based on computing and communication, and by conducting self-decision. But CAS(Context Awareness System) shows the weak point of small-scale context awareness processing capacity due to restricted mobile function under the current mobile environment, memory space, and inference cost increment. In this paper, we propose a mobile cloud context system with using Google App Engine based on PaaS(Platform as a Service) in order to get context service in various mobile devices without any subordination to any specific platform. Inference design method of the proposed system makes use of knowledge-based framework with semantic inference that is presented by SWRL rule and OWL ontology and Jess with rule-based inference engine. As well as, it is intended to shorten the context service reasoning time with mapping the regular reasoning of SWRL to Jess reasoning engine by connecting the values such as Class, Property and Individual which are regular information in the form of SWRL to Jess reasoning engine via JessTab plug-in in order to overcome the demerit of queries reasoning method of SparQL in semantic search which is a previous reasoning 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.

• Journal of Fisheries and Marine Sciences Education
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• v.13 no.1
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• pp.1-18
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• 2001

The purpose of this research was to develop curricula for the department of marine products marketing in fisheries high schools. The specific objectives were as follows; 1) To investigate the demand of students, teachers in fisheries high schools, and workers in marine products marketing for the educational program. 2) To analyze the jobs of the marine products marketing fields. 3) To develop curricula for the department of marine products marketing on the basis of the theoretical background and the result of the objective 1) and 2). In order to achieve these objectives, domestic and foreign literatures, research reports, and theses were referred to in order to know the academic classification of fisheries economics and curricula of junior colleges and universities were collected and analyzed. To achieve the first objective, the degree of the students' knowledge of marine products marketing through fisheries management textbook was investigated. And the questionnaire survey of the demand was conducted on the subject of professors at the departments of fisheries management, teachers in the charge of the related courses and those who work for marine products marketing-related firms. The questionnaire was composed of 22 items about the knowledge of marine products marketing and 27 items about the job capacity. To achieve the second objective, the occupations were surveyed on the subjects of the works who work for marine products marketing. They were sampled randomly among the marine products buyers, wholesalers, auctioneers and salespersons. The results of this research were as follows; Taking grades and credits at each subject were made out on the consultation of the experts in marine products marketing. The curriculum of the professional subjects related to marine products marketing in fisheries high schools is suggested as follows; General Fisheries(10th grade, 6 credits, curricular discretionary class), General Oceanography(10th grade, 4 credits, curricular discretionary class), Fisheries Marine Transportation Information(11th grade, 8-12 credits), Marine Products Marketing(11th grade, 8-12 credits), Fishery Sale and Management(11th grade, 8-12 credits), General Fisheries Management(11th grade, 6-8 credits), Accounting Principle(11th grade, 4-6 credits), Marine Products Processing(12th grade, 4-8 credits), Commercial Law(12th grade, 4-6 credits), Management Practice(12th grade, 4-6 credits), Computer Practical Business(12th grade, 4-6 credits), Marketing(12th grade, 4-8 credits), General Marketing Management(12th grade, 6-8 credits), Marketing Information Practical Business(12th grade, 4-6 credits) Marketing Management I(12th grade, 4-6 credits), Marketing Management II(12th grade, 4-6 credits). If this curriculum is adopted, it will meet the demands of the educational aims and the industrial society.

• Journal of Intelligence and Information Systems
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• v.12 no.3
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• pp.67-82
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• 2006

We propose the HBEM(Hybrid Behavior Evolution Model) composed of rule classification and evolutionary neural network using rule descriptor and link descriptor for evolutionary behavior of virtual robots. In our model, two levels of the knowledge of behaviors were represented. In the upper level, the representation was improved using rule and link descriptors together. And then in the lower level, behavior knowledge was represented in form of bit string and learned adapting their chromosomes by the genetic operators. A virtual robot was composed by the learned chromosome which had the best fitness. The composed virtual robot perceives the surrounding situations and they were classifying the pattern through rules and processing the result in neural network and behaving. To evaluate our proposed model, we developed HBES(Hybrid Behavior Evolution System) and adapted the problem of gathering food of the virtual robots. In the results of testing our system, the learning time was fewer than the evolution neural network of the condition which was same. And then, to evaluate the effect improving the fitness by the rules we respectively measured the fitness adapted or not about the chromosomes where the learning was completed. In the results of evaluating, if the rules were not adapted the fitness was lowered. It showed that our proposed model was better in the learning performance and more regular than the evolutionary neural network in the behavior evolution of the virtual robots.

• The KIPS Transactions:PartC
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• v.10C no.5
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• pp.541-548
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• 2003

A fair exchange protocol enable two parties to exchange secrets with fairness, so that neither can gain any information advantage by quitting prematurely or otherwise misbehaving. Therefore a fair exchange is the most important for electronic transactions between untrusted parties. To design new fair exchange, after describing basic concepts, definitions and existing protocols and designing a non-interactive OT protocol using ELGamal's public key system, I will design new protocol to support fair exchange. In my designed new protocol, untrusted parties exchange secrets obliviously and verify that their received secrets are true by using transformed Zero Knowledge Interactive Proof extended to duplex. At this time, concerned two parties can't decrypt the other's ciphertext. .After all of the steps, two parties can do it. It is the most important to provide perfect fairness and anonymity to untrusted parties in this protocol.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.384-389
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• 2008

Recently, the necessity of systems which effectively store and retrieve video data has increased. Conventional video retrieval systems retrieve data using menus or text based keywords. Due to the lack of information, many video clips are simultaneously searched, and the user must have a certain level of knowledge to utilize the system. In this paper, we suggest a natural language based conversational video retrieval system that reflects users' intentions and includes more information than keyword based queries. This system can also retrieve from events or people to their movements. First, an event database is constructed based on meta-data which are generated by domain analysis for collected video in an office environment. Then, a script database is also constructed based on the query pre-processing and analysis. From that, a method to retrieve a video through a matching technique between natural language queries and answers is suggested and validated through performance and process evaluation for 10 users The natural language based retrieval system has shown its better efficiency in performance and user satisfaction than the menu based retrieval system.

• Journal of Internet Computing and Services
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• v.13 no.1
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• pp.65-74
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• 2012

Cloud services are used for improving business. Moreover, customer relationship management(CRM) approaches use social networking as tools to enhance services to customers. However, most cloud systems do not support the semantic structures, and because of this, vital information from social network sites is still hard to process and use for business strategy. This paper proposes a collaboration framework based on social semantic web for cloud system. The proposed framework consists of components to support social semantic web to provide an efficient collaboration system for cloud consumers and service providers. The knowledge acquisition module extracts rules from data gathered by social agents and these rules are used for collaboration and business strategy. This paper showed the implementations of processing of social network site data in the proposed semantic model and pattern extraction which was used for the virtual grouping of cloud service providers for efficient collaboration.

• The KIPS Transactions:PartD
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• v.14D no.1 s.111
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• pp.45-56
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• 2007

As the knowledge-based society has been constructed, the size of work process that has to be done grows bigger and the amount of the information that has to be analyzed increases. So each company is trying to construct more conformable process models in business model. This study helps to understand the interaction between Process data and structure which are collected on the profess by appling six Sigma as a process improving methodology. On Six Sigma, businesses tn practice Best Practice Process with the view of Value Chain To measure data for the process performing action, introducing Schedule management method makes it possible to collect and reflect accurate data of workers' ability. By this method, efficiency in production will be improved because workers we able to perform the process correctly and preliminary management for defects.

• The KIPS Transactions:PartC
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• v.15C no.6
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• pp.493-506
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• 2008

In this paper we suggest dynamic access control model based on context satisfied with requirement of u-healthcare environment through researching the role based access control model. For the dynamic security domain management, we used a distributed object group framework and context information for dynamic access control used the constructed database. We defined decision rule by knowledge reduction in decision making table, and applied this rule in our model as a rough set theory. We showed the executed results of context based dynamic security service through u-healthcare application which is based on distributed object group framework. As a result, our dynamic access control model provides an appropriate security service according to security domain, more flexible access control in u-healthcare environment.

• BMB Reports
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• v.39 no.4
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• pp.441-447
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• 2006

Abuse of drugs can elicit compulsive drug seeking behaviors upon repeated administration, and ultimately leads to the phenomenon of addiction. We developed a procedure for the standardization of microarray gene expression data of rat brain in drug addiction and stored them in a single integrated database system, focusing on more effective data processing and interpretation. Another characteristic of the present database is that it has a systematic flexibility for statistical analysis and linking with other databases. Basically, we adopt an intelligent SQL querying system, as the foundation of our DB, in order to set up an interactive module which can automatically read the raw gene expression data in the standardized format. We maximize the usability of this DB, helping users study significant gene expression and identify biological function of the genes through integrated up-to-date gene information such as GO annotation and metabolic pathway. For collecting the latest information of selected gene from the database, we also set up the local BLAST search engine and non-redundant sequence database updated by NCBI server on a daily basis. We find that the present database is a useful query interface and data-mining tool, specifically for finding out the genes related to drug addiction. We apply this system to the identification and characterization of methamphetamine-induced genes' behavior in rat brain.