• KIPS Transactions on Software and Data Engineering
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• v.11 no.11
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• pp.447-454
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• 2022

In the era of the 4th industrial revolution, we are living in a flood of information. It is very difficult and complicated to find the information people need in such an environment. Therefore, in the flood of information, a recommendation system is essential. Among these recommendation systems, many studies have been conducted on each recommendation system for movies, music, food, and clothes. To date, most personalized recommendation systems have recommended clothes, books, or movies by checking individual tendencies such as age, genre, region, and gender. Future generations will want to be recommended clothes, books, and movies at once by checking age, genre, region, and gender. In this paper, we propose a recommendation system that recommends personalized clothes and food at once according to the user's emotions and weather. We obtained user data from Twitter of social media and analyzed this data as user's basic emotion according to Paul Eckman's theory. The basic emotions obtained in this way were converted into colors by applying Hayashi's Quantification Method III, and these colors were expressed as recommended clothes colors. Also, the type of clothing is recommended using the weather information of the visualcrossing.com API. In addition, various foods are recommended according to the contents of comfort food according to emotions.

• Journal of KIISE:Software and Applications
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• v.36 no.6
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• pp.491-497
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• 2009

This paper proposes a new feature weighting method for document sentiment classification. The proposed method considers the difference of sentiment intensities among sentences in a document. Sentiment features consist of sentiment vocabulary words and the sentiment intensity scores of them are estimated by the chi-square statistics. Sentiment intensity of each sentence can be measured by using the obtained chi-square statistics value of each sentiment feature. The calculated intensity values of each sentence are finally applied to the TF-IDF weighting method for whole features in the document. In this paper, we evaluate the proposed method using support vector machine. Our experimental results show that the proposed method performs about 2.0% better than the baseline which doesn't consider the sentiment intensity of a sentence.

• Journal of Software Assessment and Valuation
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• v.16 no.1
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• pp.89-94
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• 2020

In this paper, we propose a software algorithm that can configure n-key roll-over that detects all keys without limitation on the number of pressed keys in the dynamic scanning detection of a keyboard composed of a matrix. The proposed algorithm uses the timer interrupt of the microcontroller for computer keyboard control, so that a constant and accurate detection interval can be obtained, and an accurate debounce time can be provided. In order to confirm the effectiveness of the proposed algorithm, a microcontroller was connected to a toy keyboard constructed in the form of a clavier and experiments were conducted. As a result of the experiment, it was confirmed that detection of all keys was performed accurately regardless of the number of keys pressed.

• Journal of Software Assessment and Valuation
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• v.17 no.1
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• pp.41-50
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• 2021

Many game and financial apps have emulator detection functionality to defend against dynamic reverse engineering attacks. However, existing Android emulator detection methods have limitations in detecting the latest mobile game emulators that are similar to actual devices. Therefore, in this paper, we propose a method to effectively detect Android emulators for mobile games based on Houdini module and strings of a library. The proposed method detects the two emulators, Nox and LD Player through specific strings included in libc.so of bionic, and an analysis of the system call execution process and memory mapping associated with the Houdini module.

• Journal of Software Assessment and Valuation
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• v.15 no.2
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• pp.73-83
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• 2019

Voice Recognition has recently been improved with AI(Artificial Intelligence) and has greatly improved the false recognition rate and provides an effective and efficient Human Machine Interface (HMI). This trend has also been applied in the defense industry, particularly in the aviation, F-35. However, for the quality evaluation of Voice Recognition, the defense industry, especially the aircraft, requires measurable quantitative models. In this paper, the quantitative evaluation model is proposed for applying Voice Recognition to aircraft. For the proposal, the evaluation items are identified from the Voice Recognition technology and ISO/IEC 25000(SQuaRE) quality attributes. Using these two perspectives, the quantitative evaluation model is proposed under aircraft operation condition and confirms the evaluation results.

• Journal of Software Assessment and Valuation
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• v.15 no.1
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• pp.115-121
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• 2019

I analyzed the structure of a block-chain system and a block-chain-based service system. It is a decentralized book encryption system software technology that does not require a third party to secure trust between the two parties. Block chains are structured in a linked list structure. The block chain manage transaction information by blocking the transaction information, in conjunction with other blocks. As a result, I have discovered structural weaknesses in current block-chain systems and block-chain-based service systems. Once these possible structural problems are resolved, I expect that the block-chain-based service system will make various industrial contributions.

• Journal of Software Assessment and Valuation
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• v.15 no.1
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• pp.1-9
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• 2019

We intend to collect and analyze traffic efficiently in order to detect copyright infringement that illegally share contents on Tor network. We have designed and implemented a Tor traffic collection system using multiple virtual machines. We use a number of virtual machines and Mini PCs as clients to connect to Tor network, and automate both the collection and refinement processes in the traffic collection server through script-based test client software. Through this system, only the necessary field data on Tor network can be stored in the database, and only 95% or more of recognition of Tor traffic is achieved.

• The Journal of the Korea Contents Association
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• v.16 no.2
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• pp.1-13
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• 2016

The purpose of this study is to explore the emotions of Facebook. Various types of emotions are being exchanged on Facebook. The emotional reactions make the Facebook different from previous electronic bulletin board. According to previous researches, computer-mediated communication can deliver visual symbols and non-verbal cues to enhance the abundance of meanings. Data were collected from 205 Facebook users and the number of users' posts were total 10308. The contents analysis was conducted to explore emotions of the 10308 Facebook posts. The results showed that the most frequent emotion was pleasure. The emotional distributions were different according to the contents types; text, video, photo, and link. For the text content type, emotion of curiosity was apparent and for the photo content type, emotion of love was more frequent than others, and for the video content type, emotion of surprise was salient. The results of the analysis for the shared contents also revealed that pleasure and hope were more frequent emotions than other emotions.

• The KIPS Transactions:PartB
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• v.8B no.5
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• pp.485-492
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• 2001

Recently, character agents are used as a user-friendly interface. In this paper, we have studied a generic framework for emotional character agents which are designed to infer emotions from diverse personalities, situations, user behaviors and to express them. The method of emotion inference is based on blackboard systems which are used to solve the problems in AI. Because it keeps independence between knowledge sources which are rules of emotions, a blackboard-based inference engine is easy to manage knowledge sources, Blackboard-based systems gave the system flexibility. So we can adapt the engine to various application systems. Each emotional agent monitors user behavior, learns user profile and infers user behavior. And it generates characters emotions according to the user profile. So, in case of same situations, the agent can generate different emotions according to users. We have studied to build an personalized emotional character agent which according to situations and user modeling.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.289-294
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• 2024

Traditional poll-based preference analysis techniques are time-consuming, expensive, and limited in the domains they can survey. To solve this problem, this paper proposes a preference analysis system based on sentiment analysis. After collecting web documents using the keywords entered by the user, the polarity is calculated using the N-gram technique. To reduce the analysis time when analyzing a large amount of web documents, we designed and implemented a container-based system using worker services. Comparing the analyzed results of the proposed system with existing polls shows a difference of 1% to 8%.