• The Journal of the Acoustical Society of Korea
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• v.19 no.3
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• pp.51-56
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• 2000

Is this paper, we studied on the automatic speech control system in real-time windows environment using voice recognition. The applied reference pattern is the variable DMS model which is proposed to fasten execution speed and the one-stage DP algorithm using this model is used for recognition algorithm. The recognition vocabulary set is composed of control command words which are frequently used in windows environment. In this paper, an automatic speech period detection algorithm which is for on-line voice processing in windows environment is implemented. The variable DMS model which applies variable number of section in consideration of duration of the input signal is proposed. Sometimes, unnecessary recognition target word are generated. therefore model is reconstructed in on-line to handle this efficiently. The Perceptual Linear Predictive analysis method which generate feature vector from extracted feature of voice is applied. According to the experiment result, but recognition speech is fastened in the proposed model because of small loud of calculation. The multi-speaker-independent recognition rate and the multi-speaker-dependent recognition rate is 99.08% and 99.39% respectively. In the noisy environment the recognition rate is 96.25%.

• Informatization Policy
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• v.19 no.2
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• pp.21-38
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• 2012

The proliferation of the Internet and the advent of e-commerce have amplified public concerns about privacy. Accordingly, much research effort has been made on the issue. While existing research on online information privacy has usually focused on the examination of antecedents of personal information disclosure, the literature has not paid attention to the potential changes of the antecedents' effects depending on the user's experience of the service. The current study aims to investigate the moderating role of site usage experience in Internet users' decision on personal information disclosure. Specifically, this study considers two types of antecedents of personal information disclosure on a site - the attributes of personal information requested (sensitivity and relevance of information) and the value of the service provided by the site; and examines how the effects of the antecedents on the disclosure intention are affected by the users'experience of the site. Our analysis of the data gathered through a web-based experiment reveals that site usage experience moderates the relationship between the attributes of personal information and disclosure intention. While usage experience attenuates the negative effect of information sensitivity on disclosure intention, it intensifies the positive impact that relevance of information has on disclosure intention. Based on the analysis results, we provide implications for the mitigation of the Internet users' privacy concerns as well as theoretical implications.

• The KIPS Transactions:PartC
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• v.12C no.2 s.98
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• pp.301-308
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• 2005

According to banking online transaction grows very rapidly, guarantee validity about business transaction has more meaning. To offer guarantee validity about banking online transaction efficiently, certificate status verification system is required that can an ieai-time offer identity certification, data integrity, guarantee confidentiality, non-repudiation. Existing real-time certificate status verification system is structural concentration problem generated that one node handling all transactions. And every time status verification is requested, network overload and communication bottleneck are occurred because ail useless informations are transmitted. it does not fit to banking transaction which make much account of real response time because of these problem. To improve problem by unnecessary information and structural concentration when existing real-time certificate status protocol requested , this paper handle status verification that break up inspection server by domain. This paper propose the method of real~time certificate status verification that solves network overload and communication bottleneck by requesting certification using really necessary Reduction information to certification status verification. And we confirm speed of certificate status verification $15\%$ faster than existing OCSP(Online Certificate Status Protocol) method by test.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.4
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• pp.289-297
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• 2010

In this paper, A method to prevent the external acoustic noise from being misrecognized as the speech recognition object is presented in the speech activity detection process for the speech recognition. Also this paper confirmed besides the acoustic energy to the lip movement image signals. First of all, the successive images are obtained through the image camera for personal computer and the lip movement whether or not is discriminated. The next, the lip movement image signal data is stored in the shared memory and shares with the speech recognition process. In the mean time, the acoustic energy whether or not by the utterance of a speaker is verified by confirming data stored in the shared memory in the speech activity detection process which is the preprocess phase of the speech recognition. Finally, as a experimental result of linking the speech recognition processor and the image processor, it is confirmed to be normal progression to the output of the speech recognition result if face to the image camera and speak. On the other hand, it is confirmed not to the output the result of the speech recognition if does not face to the image camera and speak. Also, the initial feature values under off-line are replaced by them. Similarly, the initial template image captured while off-line is replaced with a template image captured under on-line, so the discrimination of the lip movement image tracking is raised. An image processing test bed was implemented to confirm the lip movement image tracking process visually and to analyze the related parameters on a real-time basis. As a result of linking the speech and image processing system, the interworking rate shows 99.3% in the various illumination environments.

• Journal of Music and Human Behavior
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• v.19 no.1
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• pp.93-112
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• 2022

The purpose of this study was to compare participants' perceived emotion following harmonic changes in music. In this study, 144 participants, aged 19 to 29 years, listened to music online that included low to high harmonic progression in tonal music (major-minor). After listening to each piece of music, participants were asked to rate 4 items using a 7-point Likert scale: emotional potency, arousal, degree to which the harmony impacted the listener's emotions, and listener's preference for the music. There were significant differences between each of the four items upon the level of harmonic progression. When the participants were divided into two groups (i.e., those with a background in music and those with no background in music), there was a significant difference between the groups in terms of emotional potency, but there was no significant interaction effect. This study confirmed that various emotional responses in listeners can be induced by controlling the exogenous variables in musical excerpts. Based on this, it is expected that the harmonic progression level can be provided to the client to be used as an effective therapeutic tool in music therapy intervention.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.155-160
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• 2022

This paper intends to focus on the practical activities and historical significance of network art, that is, 'net art' as an artistic form that depends on network technology. This is because the appearance of net art, which constructs a new interactive art that cooperates and exchanges with each other beyond the boundaries of time and space, can be a contemporary alternative in overcoming the limitations of traditional art. Another important research area to be considered in this paper is net art curating, as well as considering the significance of net art in art history. As new media art that is defined as a 'process' rather than a complete object, net art is a digital art that requires functions such as aesthetic appropriation, dissemination, and mediation performed online, unlike physical presentations with high perfection in exhibition halls. It is accompanied by a new social and cultural curatorial. This appearance requires both artists and curators to reorganize the strategy that net art curating in technoculture should have. Therefore, this paper attempts to question the creative strategies of net art in the wave of globalization in the 21st century, and to examine the artistic meaning and critical value of the experimental net art curatorial method that emerged through the realm of new technology and media. For this purpose, this paper demonstrated key examples of net art works and exhibition projects that started with Fluxus in the 1960s and are spreading all over the world in the 2000s.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.6
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• pp.63-72
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• 2017

This study made of centrally actual application in general arts classes based on movie in university. Especially, I analyzed the activities of the class with 'Untouchable: 1% friendship' among 6 films. The objects of this study are 44 students of D university in Busan Metropolitan City who take 'creative fusion from movie' general arts class which opened first semester in 2016. In this study, students were able to watch movies through the creative class, which was out of the traditional classroom method, and after 15 hours of learning the quiz online, they conducted 15 weeks as a teaching method to perform tasks, presentations, experiments, and experiences in regular class time. The results of this study are as follows. 'It is a general arts class that makes movements live,' 'It is a general arts class that shows movies from various perspectives,' and 'It is a general arts class that makes me know.' This suggests that the educational medium, which is easily accessible in everyday life, and the general arts class, which is active in the space outside the framework, are perceived as stimulating curiosity and adding fun to college students.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.39-70
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• 2017

Data-drive analytics techniques have been recently applied to public surveys. Instead of simply gathering survey results or expert opinions to research the preference for a recently launched product, enterprises need a way to collect and analyze various types of online data and then accurately figure out customer preferences. In the main concept of existing data-based survey methods, the sentiment lexicon for a particular domain is first constructed by domain experts who usually judge the positive, neutral, or negative meanings of the frequently used words from the collected text documents. In order to research the preference for a particular product, the existing approach collects (1) review posts, which are related to the product, from several product review web sites; (2) extracts sentences (or phrases) in the collection after the pre-processing step such as stemming and removal of stop words is performed; (3) classifies the polarity (either positive or negative sense) of each sentence (or phrase) based on the sentiment lexicon; and (4) estimates the positive and negative ratios of the product by dividing the total numbers of the positive and negative sentences (or phrases) by the total number of the sentences (or phrases) in the collection. Furthermore, the existing approach automatically finds important sentences (or phrases) including the positive and negative meaning to/against the product. As a motivated example, given a product like Sonata made by Hyundai Motors, customers often want to see the summary note including what positive points are in the 'car design' aspect as well as what negative points are in thesame aspect. They also want to gain more useful information regarding other aspects such as 'car quality', 'car performance', and 'car service.' Such an information will enable customers to make good choice when they attempt to purchase brand-new vehicles. In addition, automobile makers will be able to figure out the preference and positive/negative points for new models on market. In the near future, the weak points of the models will be improved by the sentiment analysis. For this, the existing approach computes the sentiment score of each sentence (or phrase) and then selects top-k sentences (or phrases) with the highest positive and negative scores. However, the existing approach has several shortcomings and is limited to apply to real applications. The main disadvantages of the existing approach is as follows: (1) The main aspects (e.g., car design, quality, performance, and service) to a product (e.g., Hyundai Sonata) are not considered. Through the sentiment analysis without considering aspects, as a result, the summary note including the positive and negative ratios of the product and top-k sentences (or phrases) with the highest sentiment scores in the entire corpus is just reported to customers and car makers. This approach is not enough and main aspects of the target product need to be considered in the sentiment analysis. (2) In general, since the same word has different meanings across different domains, the sentiment lexicon which is proper to each domain needs to be constructed. The efficient way to construct the sentiment lexicon per domain is required because the sentiment lexicon construction is labor intensive and time consuming. To address the above problems, in this article, we propose a novel product reputation mining algorithm that (1) extracts topics hidden in review documents written by customers; (2) mines main aspects based on the extracted topics; (3) measures the positive and negative ratios of the product using the aspects; and (4) presents the digest in which a few important sentences with the positive and negative meanings are listed in each aspect. Unlike the existing approach, using hidden topics makes experts construct the sentimental lexicon easily and quickly. Furthermore, reinforcing topic semantics, we can improve the accuracy of the product reputation mining algorithms more largely than that of the existing approach. In the experiments, we collected large review documents to the domestic vehicles such as K5, SM5, and Avante; measured the positive and negative ratios of the three cars; showed top-k positive and negative summaries per aspect; and conducted statistical analysis. Our experimental results clearly show the effectiveness of the proposed method, compared with the existing method.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.117-127
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• 2012

Due to the recent expansion of the Web 2.0 -based services, along with the widespread of smartphones, online social network services are being popularized among users. Online social network services are the online community services which enable users to communicate each other, share information and expand human relationships. In the social network services, each relation between users is represented by a graph consisting of nodes and links. As the users of online social network services are increasing rapidly, the SNS are actively utilized in enterprise marketing, analysis of social phenomenon and so on. Social Network Analysis (SNA) is the systematic way to analyze social relationships among the members of the social network using the network theory. In general social network theory consists of nodes and arcs, and it is often depicted in a social network diagram. In a social network diagram, nodes represent individual actors within the network and arcs represent relationships between the nodes. With SNA, we can measure relationships among the people such as degree of intimacy, intensity of connection and classification of the groups. Ever since Social Networking Services (SNS) have drawn increasing attention from millions of users, numerous researches have made to analyze their user relationships and messages. There are typical representative SNA methods: degree centrality, betweenness centrality and closeness centrality. In the degree of centrality analysis, the shortest path between nodes is not considered. However, it is used as a crucial factor in betweenness centrality, closeness centrality and other SNA methods. In previous researches in SNA, the computation time was not too expensive since the size of social network was small. Unfortunately, most SNA methods require significant time to process relevant data, and it makes difficult to apply the ever increasing SNS data in social network studies. For instance, if the number of nodes in online social network is n, the maximum number of link in social network is n(n-1)/2. It means that it is too expensive to analyze the social network, for example, if the number of nodes is 10,000 the number of links is 49,995,000. Therefore, we propose a heuristic-based method for finding the shortest path among users in the SNS user graph. Through the shortest path finding method, we will show how efficient our proposed approach may be by conducting betweenness centrality analysis and closeness centrality analysis, both of which are widely used in social network studies. Moreover, we devised an enhanced method with addition of best-first-search method and preprocessing step for the reduction of computation time and rapid search of the shortest paths in a huge size of online social network. Best-first-search method finds the shortest path heuristically, which generalizes human experiences. As large number of links is shared by only a few nodes in online social networks, most nods have relatively few connections. As a result, a node with multiple connections functions as a hub node. When searching for a particular node, looking for users with numerous links instead of searching all users indiscriminately has a better chance of finding the desired node more quickly. In this paper, we employ the degree of user node vn as heuristic evaluation function in a graph G = (N, E), where N is a set of vertices, and E is a set of links between two different nodes. As the heuristic evaluation function is used, the worst case could happen when the target node is situated in the bottom of skewed tree. In order to remove such a target node, the preprocessing step is conducted. Next, we find the shortest path between two nodes in social network efficiently and then analyze the social network. For the verification of the proposed method, we crawled 160,000 people from online and then constructed social network. Then we compared with previous methods, which are best-first-search and breath-first-search, in time for searching and analyzing. The suggested method takes 240 seconds to search nodes where breath-first-search based method takes 1,781 seconds (7.4 times faster). Moreover, for social network analysis, the suggested method is 6.8 times and 1.8 times faster than betweenness centrality analysis and closeness centrality analysis, respectively. The proposed method in this paper shows the possibility to analyze a large size of social network with the better performance in time. As a result, our method would improve the efficiency of social network analysis, making it particularly useful in studying social trends or phenomena.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.85-109
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• 2018

Recommender system recommends the items expected to be purchased by a customer in the future according to his or her previous purchase behaviors. It has been served as a tool for realizing one-to-one personalization for an e-commerce service company. Traditional recommender systems, especially the recommender systems based on collaborative filtering (CF), which is the most popular recommendation algorithm in both academy and industry, are designed to generate the items list for recommendation by using 'overall rating' - a single criterion. However, it has critical limitations in understanding the customers' preferences in detail. Recently, to mitigate these limitations, some leading e-commerce companies have begun to get feedback from their customers in a form of 'multicritera ratings'. Multicriteria ratings enable the companies to understand their customers' preferences from the multidimensional viewpoints. Moreover, it is easy to handle and analyze the multidimensional ratings because they are quantitative. But, the recommendation using multicritera ratings also has limitation that it may omit detail information on a user's preference because it only considers three-to-five predetermined criteria in most cases. Under this background, this study proposes a novel hybrid recommendation system, which selectively uses the results from 'traditional CF' and 'CF using multicriteria ratings'. Our proposed system is based on the premise that some people have holistic preference scheme, whereas others have composite preference scheme. Thus, our system is designed to use traditional CF using overall rating for the users with holistic preference, and to use CF using multicriteria ratings for the users with composite preference. To validate the usefulness of the proposed system, we applied it to a real-world dataset regarding the recommendation for POI (point-of-interests). Providing personalized POI recommendation is getting more attentions as the popularity of the location-based services such as Yelp and Foursquare increases. The dataset was collected from university students via a Web-based online survey system. Using the survey system, we collected the overall ratings as well as the ratings for each criterion for 48 POIs that are located near K university in Seoul, South Korea. The criteria include 'food or taste', 'price' and 'service or mood'. As a result, we obtain 2,878 valid ratings from 112 users. Among 48 items, 38 items (80%) are used as training dataset, and the remaining 10 items (20%) are used as validation dataset. To examine the effectiveness of the proposed system (i.e. hybrid selective model), we compared its performance to the performances of two comparison models - the traditional CF and the CF with multicriteria ratings. The performances of recommender systems were evaluated by using two metrics - average MAE(mean absolute error) and precision-in-top-N. Precision-in-top-N represents the percentage of truly high overall ratings among those that the model predicted would be the N most relevant items for each user. The experimental system was developed using Microsoft Visual Basic for Applications (VBA). The experimental results showed that our proposed system (avg. MAE = 0.584) outperformed traditional CF (avg. MAE = 0.591) as well as multicriteria CF (avg. AVE = 0.608). We also found that multicriteria CF showed worse performance compared to traditional CF in our data set, which is contradictory to the results in the most previous studies. This result supports the premise of our study that people have two different types of preference schemes - holistic and composite. Besides MAE, the proposed system outperformed all the comparison models in precision-in-top-3, precision-in-top-5, and precision-in-top-7. The results from the paired samples t-test presented that our proposed system outperformed traditional CF with 10% statistical significance level, and multicriteria CF with 1% statistical significance level from the perspective of average MAE. The proposed system sheds light on how to understand and utilize user's preference schemes in recommender systems domain.