• Korean System Dynamics Review
• /
• v.12 no.3
• /
• pp.47-66
• /
• 2011

South Korea has achieved remarkable social and economic development together with the process of democratization over the past 20 years. In the democratic process in South Korea, ordinary people have actively participated in conventional political activities such as elections. But recently, one of the salient phenomena is that the public have been showing political apathy associated with a light poll. Especially, the most serious concern in the political environment of South Korea is that young voters (e.g., 20-30s) have serious political apathy leading to low voter turnout. Regarding this concern, many political scientists argued that this political phenomenon is not only the case in South Korea, insisting that many consolidated democratic countries such as European countries and the US have the same problems. However, South Korea has contained different factors (e.g., historical, culture, social, and political differences) leading to political apathy and light poll. Unfortunately, no one has clearly explain the phenomenon. In fact, in order for scholars to understand and explain these concerns, they should carefully look at the phenomenon with diverse perspectives and approaches. The main purpose of this paper is to explain why the digital generation has political apathy and are reluctant to participate in political activities such as voting. Using causal loop analysis which is based on systematic thinking, we not only analyzed the pattern of the digital generation' political participation with regard to diverse perspectives, but also attempted to draw new political implications from the analysis. Based on our analysis, we tried to suggest some implications for political stability and development in South Korea in the future.

• The Journal of the Acoustical Society of Korea
• /
• v.20 no.2E
• /
• pp.17-24
• /
• 2001

In this paper, we propose the bootstrap and aggregating (bagging) vector quantization (VQ) classifier to improve the performance of the text-independent speaker recognition system. This method generates multiple training data sets by resampling the original training data set, constructs the corresponding VQ classifiers, and then integrates the multiple VQ classifiers into a single classifier by voting. The bagging method has been proven to greatly improve the performance of unstable classifiers. Through two different experiments, this paper shows that the VQ classifier is unstable. In one of these experiments, the bias and variance of a VQ classifier are computed with a waveform database. The variance of the VQ classifier is compared with that of the classification and regression tree (CART) classifier[1]. The variance of the VQ classifier is shown to be as large as that of the CART classifier. The other experiment involves speaker recognition. The speaker recognition rates vary significantly by the minor changes in the training data set. The speaker recognition experiments involving a closed set, text-independent and speaker identification are performed with the TIMIT database to compare the performance of the bagging VQ classifier with that of the conventional VQ classifier. The bagging VQ classifier yields improved performance over the conventional VQ classifier. It also outperforms the conventional VQ classifier in small training data set problems.

• Journal of Information Processing Systems
• /
• v.13 no.4
• /
• pp.893-913
• /
• 2017

The handwriting based person identification systems use their designer's perceived structural properties of handwriting as features. In this paper, we present a system that uses those structural properties as features that graphologists and expert handwriting analyzers use for determining the writer's personality traits and for making other assessments. The advantage of these features is that their definition is based on sound historical knowledge (i.e., the knowledge discovered by graphologists, psychiatrists, forensic experts, and experts of other domains in analyzing the relationships between handwritten stroke characteristics and the phenomena that imbeds individuality in stroke). Hence, each stroke characteristic reflects a personality trait. We have measured the effectiveness of these features on a subset of handwritten Devnagari and Latin script datasets from the Center for Pattern Analysis and Recognition (CPAR-2012), which were written by 100 people where each person wrote three samples of the Devnagari and Latin text that we have designed for our experiments. The experiment yielded 100% correct identification on the training set. However, we observed an 88% and 89% correct identification rate when we experimented with 200 training samples and 100 test samples on handwritten Devnagari and Latin text. By introducing the majority voting based rejection criteria, the identification accuracy increased to 97% on both script sets.

• Asia pacific journal of information systems
• /
• v.21 no.2
• /
• pp.89-116
• /
• 2011

In collaborative tagging systems such as Delicious.com and Flickr.com, users assign keywords or tags to their uploaded resources, such as bookmarks and pictures, for their future use or sharing purposes. The collection of resources and tags generated by a user is called a personomy, and the collection of all personomies constitutes the folksonomy. The most significant need of the folksonomy users Is to efficiently find useful resources or experts on specific topics. An excellent ranking algorithm would assign higher ranking to more useful resources or experts. What resources are considered useful In a folksonomic system? Does a standard superior to frequency or freshness exist? The resource recommended by more users with mere expertise should be worthy of attention. This ranking paradigm can be implemented through a graph-based ranking algorithm. Two well-known representatives of such a paradigm are Page Rank by Google and HITS(Hypertext Induced Topic Selection) by Kleinberg. Both Page Rank and HITS assign a higher evaluation score to pages linked to more higher-scored pages. HITS differs from PageRank in that it utilizes two kinds of scores: authority and hub scores. The ranking objects of these pages are limited to Web pages, whereas the ranking objects of a folksonomic system are somewhat heterogeneous(i.e., users, resources, and tags). Therefore, uniform application of the voting notion of PageRank and HITS based on the links to a folksonomy would be unreasonable, In a folksonomic system, each link corresponding to a property can have an opposite direction, depending on whether the property is an active or a passive voice. The current research stems from the Idea that a graph-based ranking algorithm could be applied to the folksonomic system using the concept of mutual Interactions between entitles, rather than the voting notion of PageRank or HITS. The concept of mutual interactions, proposed for ranking the Semantic Web resources, enables the calculation of importance scores of various resources unaffected by link directions. The weights of a property representing the mutual interaction between classes are assigned depending on the relative significance of the property to the resource importance of each class. This class-oriented approach is based on the fact that, in the Semantic Web, there are many heterogeneous classes; thus, applying a different appraisal standard for each class is more reasonable. This is similar to the evaluation method of humans, where different items are assigned specific weights, which are then summed up to determine the weighted average. We can check for missing properties more easily with this approach than with other predicate-oriented approaches. A user of a tagging system usually assigns more than one tags to the same resource, and there can be more than one tags with the same subjectivity and objectivity. In the case that many users assign similar tags to the same resource, grading the users differently depending on the assignment order becomes necessary. This idea comes from the studies in psychology wherein expertise involves the ability to select the most relevant information for achieving a goal. An expert should be someone who not only has a large collection of documents annotated with a particular tag, but also tends to add documents of high quality to his/her collections. Such documents are identified by the number, as well as the expertise, of users who have the same documents in their collections. In other words, there is a relationship of mutual reinforcement between the expertise of a user and the quality of a document. In addition, there is a need to rank entities related more closely to a certain entity. Considering the property of social media that ensures the popularity of a topic is temporary, recent data should have more weight than old data. We propose a comprehensive folksonomy ranking framework in which all these considerations are dealt with and that can be easily customized to each folksonomy site for ranking purposes. To examine the validity of our ranking algorithm and show the mechanism of adjusting property, time, and expertise weights, we first use a dataset designed for analyzing the effect of each ranking factor independently. We then show the ranking results of a real folksonomy site, with the ranking factors combined. Because the ground truth of a given dataset is not known when it comes to ranking, we inject simulated data whose ranking results can be predicted into the real dataset and compare the ranking results of our algorithm with that of a previous HITS-based algorithm. Our semantic ranking algorithm based on the concept of mutual interaction seems to be preferable to the HITS-based algorithm as a flexible folksonomy ranking framework. Some concrete points of difference are as follows. First, with the time concept applied to the property weights, our algorithm shows superior performance in lowering the scores of older data and raising the scores of newer data. Second, applying the time concept to the expertise weights, as well as to the property weights, our algorithm controls the conflicting influence of expertise weights and enhances overall consistency of time-valued ranking. The expertise weights of the previous study can act as an obstacle to the time-valued ranking because the number of followers increases as time goes on. Third, many new properties and classes can be included in our framework. The previous HITS-based algorithm, based on the voting notion, loses ground in the situation where the domain consists of more than two classes, or where other important properties, such as "sent through twitter" or "registered as a friend," are added to the domain. Forth, there is a big difference in the calculation time and memory use between the two kinds of algorithms. While the matrix multiplication of two matrices, has to be executed twice for the previous HITS-based algorithm, this is unnecessary with our algorithm. In our ranking framework, various folksonomy ranking policies can be expressed with the ranking factors combined and our approach can work, even if the folksonomy site is not implemented with Semantic Web languages. Above all, the time weight proposed in this paper will be applicable to various domains, including social media, where time value is considered important.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.11
• /
• pp.5537-5555
• /
• 2017

Having the characteristics of unlinkability, anonymity, and unforgeability, blind signatures are widely used for privacy-related applications such as electronic cash, electronic voting and electronic auction systems where to maintain the anonymity of the participants. Among these applications, the blinded message is needed for a certain purpose by which users delegate signing operation and communicate with each other in a trusted manner. This application leads to the need of proxy blind signature schemes. Proxy blind signature is an important type of cryptographic primitive to realize the properties of both blind signature and proxy signature. Over the past years, many proxy blind signature algorithms have been adopted to fulfill such task based on the discrete logarithm problem (DLP) and the elliptic curve discrete log problem (ECDLP), and most of the existing studies mainly aim to provide effective models to satisfy the security requirements concerning a single blinded message. Unlike many previous works, the proposed scheme applies the signcryption paradigm to the proxy blind signature technology for handling multiple blinded messages at a time based on elliptic curve cryptography (ECC). This innovative method thus has a higher level of security to achieve the security goals of both blind signature and proxy signature. Moreover, the evaluation results show that this proposed protocol is more efficient, consuming low communication overhead while increasing the volume of digital messages compared to the performance from other solutions. Due to these features, this design is able to be implemented in small low-power intelligent devices and very suitable and easily adoptable for e-system applications in pervasive mobile computing environment.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.13 no.6
• /
• pp.149-161
• /
• 2003

Partially blind signature scheme allows the signer to insert non-removable common information into his blind signature. Blind signatures providing with both users privacy and data authenticity are one of key parts of information systems, such anonymous electronic cash and electronic voting as typical examples. Partially blind signature, with which all expired e-cash but for still-alive can be removed from the banks database, copes well with the problem of unlimited growth of the banks' database in an electronic cash system. In this paper we propose an efficient ID-based partially blind signature scheme using the Weil-pairing on Gap Diffie-Hellman group. The security of our scheme relies on the hardness of Computational Diffie-Hellman Problem. The proposed scheme provides higher efficiency than existing partially blind signature schemes by using three-pass protocol between two participants, the signer and requesters also by reducing the computation load. Thus it can be efficiently used in wireless environment.

• Journal of Intelligence and Information Systems
• /
• v.22 no.3
• /
• pp.129-142
• /
• 2016

Customer product reviews have become one of the important factors for purchase decision makings. Customers believe that reviews written by others who have already had an experience with the product offer more reliable information than that provided by sellers. However, there are too many products and reviews, the advantage of e-commerce can be overwhelmed by increasing search costs. Reading all of the reviews to find out the pros and cons of a certain product can be exhausting. To help users find the most useful information about products without much difficulty, e-commerce companies try to provide various ways for customers to write and rate product reviews. To assist potential customers, online stores have devised various ways to provide useful customer reviews. Different methods have been developed to classify and recommend useful reviews to customers, primarily using feedback provided by customers about the helpfulness of reviews. Most shopping websites provide customer reviews and offer the following information: the average preference of a product, the number of customers who have participated in preference voting, and preference distribution. Most information on the helpfulness of product reviews is collected through a voting system. Amazon.com asks customers whether a review on a certain product is helpful, and it places the most helpful favorable and the most helpful critical review at the top of the list of product reviews. Some companies also predict the usefulness of a review based on certain attributes including length, author(s), and the words used, publishing only reviews that are likely to be useful. Text mining approaches have been used for classifying useful reviews in advance. To apply a text mining approach based on all reviews for a product, we need to build a term-document matrix. We have to extract all words from reviews and build a matrix with the number of occurrences of a term in a review. Since there are many reviews, the size of term-document matrix is so large. It caused difficulties to apply text mining algorithms with the large term-document matrix. Thus, researchers need to delete some terms in terms of sparsity since sparse words have little effects on classifications or predictions. The purpose of this study is to suggest a better way of building term-document matrix by deleting useless terms for review classification. In this study, we propose neutrality index to select words to be deleted. Many words still appear in both classifications - useful and not useful - and these words have little or negative effects on classification performances. Thus, we defined these words as neutral terms and deleted neutral terms which are appeared in both classifications similarly. After deleting sparse words, we selected words to be deleted in terms of neutrality. We tested our approach with Amazon.com's review data from five different product categories: Cellphones & Accessories, Movies & TV program, Automotive, CDs & Vinyl, Clothing, Shoes & Jewelry. We used reviews which got greater than four votes by users and 60% of the ratio of useful votes among total votes is the threshold to classify useful and not-useful reviews. We randomly selected 1,500 useful reviews and 1,500 not-useful reviews for each product category. And then we applied Information Gain and Support Vector Machine algorithms to classify the reviews and compared the classification performances in terms of precision, recall, and F-measure. Though the performances vary according to product categories and data sets, deleting terms with sparsity and neutrality showed the best performances in terms of F-measure for the two classification algorithms. However, deleting terms with sparsity only showed the best performances in terms of Recall for Information Gain and using all terms showed the best performances in terms of precision for SVM. Thus, it needs to be careful for selecting term deleting methods and classification algorithms based on data sets.

• The Journal of Industrial Distribution & Business
• /
• v.9 no.6
• /
• pp.77-85
• /
• 2018

Purpose - The purpose of this study is to subdivide trade transaction-centered structure in a logistics/distribution industry system to apply blockchain, to establish and resolve with which types of technology, and to provide policy direction of government institution and technology to apply blockchain in this kind of industry. Research design, data, and methodology - This study was conducted with previous researches centered on cases applied in various industry sectors on the basis of blockchain technology. Results - General fields of blockchain application include digital contents distribution, IoT platform, e-Commerce, real-estate transaction, decentralized app. development(storage), certification service, smart contract, P2P network infrastructure, publication/storage of public documents, smart voting, money exchange, payment/settlement, banking security platform, actual asset storage, stock transaction and crowd funding. Blockchain is being applied in various fields home and abroad and its application cases can be explained in the banking industry, public sector, e-Commerce, medical industry, distribution and supply chain management, copyright protection. As examined in the blockchain application cases, it is expected to establish blockchain that can secure safety through distributed ledger in trade transaction because blockchain is established and applied in various sectors of industries home and abroad. Parties concerned of trade transaction can secure visibility even in interrupted specific section when they provide it as a base for distributed ledger application in trade and establish trade transaction model by applying blockchain. In case of interrupted specific section by using distributed ledger, blockchain model of trade transaction needs to be formed to make it possible for parties concerned involved in trade transaction to secure visibility and real-time tracking. Additionally, management should be possible from the time of contract until payment, freight transfer to buyers through land, air and maritime transportation. Conclusions - In order to boost blockchain-based logistics/distribution industry, the government, institutionally, needs to back up adding legal plan of shipping, logistics and distribution, reviewing standardization of electronic switching system and coming up with blockchain-based industrial road maps. In addition, the government, technologically, has to support R&D for integration with other high technology, standardization of distribution industry's blockchain technology and manpower training to expand technology development.

• The Journal of Society for e-Business Studies
• /
• v.22 no.1
• /
• pp.51-64
• /
• 2017

After Bitcoin was proposed by Satoshi Nakamoto in 2009, studies have been carried out to apply the Block Chain technology in various environment, which was applied as a distributed transaction of Bitcoin. Smart contracts, voting and proof of ownership of digital contents are typical applications of Block Chain. They used the feature that it is impossible to modify or delete once recorded facts. They also applied to prove relevant facts and to provide data integrity. The applied cases are mainly made in an environment where the data should or could be open to the public, and they have been proposed as solutions to solve the problems occurred in relations. This fact has led to the attention that Block Chain can be applied as a good alternative in similar circumstances. In this study, real estate market service was selected to expand the application range of Block Chain. Although there are about 250 applications and web services in total, the satisfaction is not high due to false offerings. Thus we propose a countermeasure against the problem by applying the Block Chain to the real estate market service, and investigate the research direction of the Block Chain in the future market.

• Korean Journal of Legislative Studies
• /
• v.24 no.1
• /
• pp.135-170
• /
• 2018

The recent success of populist parties and candidates in the US and European countries leads to a massive amount of empirical research on populism, a deviant form of representative democracy. Much ink has been spilled to define populism and to identify the causes of its rise and continued success in democratic political system. However, little is known about populist attitudes of individual voters. Using a large-scale online survey fielded in the context of the South Korean presidential election in 2017, this study examines (1) what determines populist attitudes of South Korean voters and (2) how populist attitudes are associated with evaluations of political parties, candidates, and political issues. Statistical analysis reveals that people high on populism are more likely to support an underdog left-wing political party and its presidential candidate, and are less likely to support policies implemented or proposed under the auspices of the Park Geun-hye administration. These findings do not necessarily suggest the inherent affinity between populism and left-wing ideology; rather, it implies populist attitudes happened to appear in 2017, in reactions to lack of confidence in the previous government.