• Journal of Information Processing Systems
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• v.14 no.3
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• pp.709-726
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• 2018

Database classification is an important preprocessing step for the multi-database mining (MDM). In fact, when a multi-branch company needs to explore its distributed data for decision making, it is imperative to classify these multiple databases into similar clusters before analyzing the data. To search for the best classification of a set of n databases, existing algorithms generate from 1 to ($n^2-n$)/2 candidate classifications. Although each candidate classification is included in the next one (i.e., clusters in the current classification are subsets of clusters in the next classification), existing algorithms generate each classification independently, that is, without taking into account the use of clusters from the previous classification. Consequently, existing algorithms are time consuming, especially when the number of candidate classifications increases. To overcome the latter problem, we propose in this paper an efficient approach that represents the problem of classifying the multiple databases as a problem of identifying the connected components of an undirected weighted graph. Theoretical analysis and experiments on public databases confirm the efficiency of our algorithm against existing works and that it overcomes the problem of increase in the execution time.

• 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.

• Korean Journal of Acupuncture
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• v.28 no.3
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• pp.1-12
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• 2011

Objectives : The purpose of this study is to review laser acupuncture studies to find possibility for applying high intensity laser to acupuncture and moxibustion treatment. Methods : Searching papers was performed using search engines of five electronic databases, including Pubmed, Thomson ISI, EMBASE, Sciencedirect, and EBSCO, from inception to May 2011 without language limitation. Inclusion criteria were clinical studies with human, randomized controlled trials (RCTs), case-control studies, and case reports. Selecting papers was performed with titles and abstracts in first step, scrutinize full text in second step, and then the extrated data was analyzed by two authors independently. The methodological quality for RCTs was evaluated using Jadad's scale. Results : Total 8 papers, (3 RCTs, 5 controlld studies, and 1 case reports), were finally selected. The study dealt with surgical laser, argon and $CO_2$ laser was one for each, with GaAs laser was two, and with new semiconductor laser, GaN, were four. The output range was from 110 mW to 15 W. The study diseases were alcohol addiction, knee osteoarthritis, bronchopneumonia and asthma for children, and circulation. All studies reported positive effect. The methodological quality in all RCTs was low because of below 3 points and all studies had few subject numbers. Conclusions : We suggest that high intensity laser can be applied to acupuncture and moxibustion. Further rigorous and well-designed study will be needed for various disease. The oriental medical society needs to take active measures to study and clinical application of acupuncture and moxibustion treatment with high intensity laser.

• Nuclear Engineering and Technology
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• v.47 no.2
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• pp.187-195
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• 2015

Human-induced initiating events, also called Category B actions in human reliability analysis, are operator actions that may lead directly to initiating events. Most conventional probabilistic safety analyses typically assume that the frequency of initiating events also includes the probability of human-induced initiating events. However, some regulatory documents require Category B actions to be specifically analyzed and quantified in probabilistic safety analysis. An explicit modeling of Category B actions could also potentially lead to important insights into human performance in terms of safety. However, there is no standard procedure to identify Category B actions. This paper describes a systematic procedure to identify Category B actions for low power and shutdown conditions. The procedure includes several steps to determine operator actions that may lead to initiating events in the low power and shutdown stages. These steps are the selection of initiating events, the selection of systems or components, the screening of unlikely operating actions, and the quantification of initiating events. The procedure also provides the detailed instruction for each step, such as operator's action, information required, screening rules, and the outputs. Finally, the applicability of the suggested approach is also investigated by application to a plant example.

• Journal of Korean Medicine Rehabilitation
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• v.32 no.3
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• pp.13-27
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• 2022

Objectives This study investigated the trends in domestic and international clinical research in craniosacral therapy, classified as a type of Chuna manual therapy, and suggested further directions in Korean medicine. Methods This scoping review was performed using the Arksey and O'Malley methodological framework and preferred reporting items as per the preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews checklist. Eight electronic databases (PubMed, EMBASE, Cochrane Library, Koreanstudies Information Service System [KISS], KMBASE, Oriental Medicine Advanced Searching Integrated System [OASIS], Research Information Sharing Service [RISS], ScienceON) were searched to identify articles with the search terms "craniosacral therapy" and "cranial osteopathy" until December 2021. Results Forty-five studies were eligible as per our inclusion criteria. Most research studies (n=44) were conducted in the field of medicine and pharmacy, especially in rehabilitation medicine (n=16). As a result of the study design, randomized controlled trials (n=20) were the most common, and chronic pain (n=9) was the most frequently targeted disease, followed by headache (n=7). Thirty-two studies suggested interventions and 20 studies used Upledger's 10-step protocol. The average duration of craniosacral therapy was 41 min per session, administered 1.4 times per week. Outcome measurements were analyzed and categorized with the examination procedure for the patient. Conclusions This is the first scoping review of craniosacral therapy in Korea, and we believe that our findings could support its utility as Chuna. In the future, more studies should be conducted to establish the evidence of clinical efficacy of craniosacral therapy and develop standard techniques in Korean medicine.

• International Journal of Industrial Entomology and Biomaterials
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• v.37 no.2
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• pp.95-99
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• 2018

In insect defense system, antimicrobial peptides (AMPs) are one of important biological molecules to survive in a variety of environments. Insect can synthesize AMPs to protect against invading pathogens in humoral immune response. Taking more advantage of biological antimicrobial molecules, we report antibacterial activity of two cecropin type peptides, cecropin and moricin, isolated from the silkworm against four salmonella species. In this work, we purified antimicrobial candidate peptides (AMCP) from the extracts of immune challenged silkworm larval hemolymph by two-step chromatographic purification procedure, cation exchange and gel permeation chromatography. The molecular weights of purified peptides were estimated to be about 4 ~ 5 kDa by Tricin SDS-PAGE analysis, and identified as silkworm cecropin and moricin by NCBI BLAST homology search with their N-terminal amino acid sequences. As antibacterial activity assay, the purified peptides showed stronger antibacterial activity against Salmonella pathogens with an MIC value of $1{\sim}4{\mu}g/mL$. Therefore two cecropin type peptides purified from the silkworm will be valuable potential materials for development of new natural antibiotics.

• KIISE Transactions on Computing Practices
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• v.21 no.11
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• pp.681-688
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• 2015

The k nearest neighbor (k-NN) graph construction is an important operation with many web-related applications, including collaborative filtering, similarity search, and many others in data mining and machine learning. Despite its many elegant properties, the brute force k-NN graph construction method has a computational complexity of $O(n^2)$, which is prohibitive for large scale data sets. Thus, (Key, Value)-based distributed framework, MapReduce, is gaining increasingly widespread use in Locality Sensitive Hashing which is efficient for high-dimension and sparse data. Based on the two-stage strategy, we engage the locality sensitive hashing technique to divide users into small subsets, and then calculate similarity between pairs in the small subsets using a brute force method on MapReduce. Specifically, generating a candidate group stage is important since brute-force calculation is performed in the following step. However, existing methods do not prevent large candidate groups. In this paper, we proposed an efficient algorithm for approximate k-NN graph construction by regrouping candidate groups. Experimental results show that our approach is more effective than existing methods in terms of graph accuracy and scan rate.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.11
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• pp.2535-2540
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• 2010

In traditional Publish/Subscribe system, the first procedure to deliver event from publisher to subscriber is that publisher publishes publisher's event to broker. Next step is that broker checks simple binary notion of matching : an event either matches a subscription or it does not. Lastly, broker delivers the event matched with subscriptions to the corresponding subscribers. In this system, information delivery has been accomplished in one way only. However, current some applications require two way delivery between subscriber and publisher. Therefore, we initiate an extended Publish/Subscribe system that supports two way delivery. Extended Publish/Subscribe system requires additional functions of delivering subscription to publisher and especially deciding top-n subscriptions using priority because broker might has a number of subscriptions. In this paper, we propose two priority retrieval techniques based on SIF using IS-List with deciding priority among subscriptions and defining SIF(Specific Interval First). The performance measurements show that RSO(resulting set sorting) technique results in better performance in index creation time and ITS&IS(insertion time sorting and inverse search using stack) technique results in better performance in search time.

• Journal of Korean Society of Forest Science
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• v.101 no.3
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• pp.426-433
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• 2012

This study was carried out for preliminary feasibility review to consult forest farmer, to make policy and to search improvement for Ligularia fischeri forest farm. The survey for eight Ligularia fischeri forest farmer in Inje-gun was conducted. And the case study was conducted with computing labor input, gross margin, net margin in each planting stages, which is contented each cultivating stage from creating to harvesting. B/C ratio, Net Present Value and Internal Rate of Return were used for profitability analysis. The results applied 3% of discount rate showed IRR 48.6%, B/C ratio 1.5 and NPV 41 million KRW, which means high profitability. Forest farming is early step in Korean forestry so that standard methods of cultivation for that haven't established yet, and differences among farmers in productivity are being. Establishing organized methods of cultivation in each stages and being political supports are essential for income generation to forest households, supply of safe food and rest place for urbanity.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.137-148
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• 2014

Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used