Vol. 44, No. 8,
Aug. 2017
Digital Library
Recovering Network Joining State for Normal/Abnormal Termination of Battlefield Management System
http://doi.org/10.5626/JOK.2017.44.8.749
The weapon system based on voice call can cause delay, error or damage to the message during the exchange of information. Furthermore, since the weapon system has a unique message format, it has limited data distribution. Therefore, a Korea Variable Message Format(KVMF) has been developed in this study to utilize a standard sized data format to guarantee the transmission quality and minimize the transmission amount. The ground tactical data link system quickly and accurately shares tactical information by incorporating a field management system that utilizes the KVMF standard message in the mobile weapon system. In this study, we examine the possibility of performing the mission immediately by recovering the state of network joining when a normal/abnormal termination situation of the battlefield management system occurs.
Parallel Algorithms for Finding δ-approximate Periods and γ-approximate Periods of Strings over Integer Alphabets
http://doi.org/10.5626/JOK.2017.44.8.760
Repetitive strings have been studied in diverse fields such as data compression, bioinformatics and so on. Recently, two problems of approximate periods of strings over integer alphabets were introduced, finding minimum δ-approximate periods and finding minimum γ-approximate periods. Both problems can be solved in O(n²) time when n is the length of the string. In this paper, we present two parallel algorithms for solving the above two problems in O(n²) time using O(n²) threads, respectively. The experimental results show that our parallel algorithms for finding minimum δ-approximate (resp. γ-approximate) periods run approximately 19.7 (resp. 40.08) times faster than the sequential algorithms when n = 10,000.
Garbage Collection Synchronization Technique for Improving Tail Latency of Cloud Databases
Seungwook Han, Sangwook Shane Hahn, Jihong Kim
http://doi.org/10.5626/JOK.2017.44.8.767
In a distributed system environment, such as a cloud database, the tail latency needs to be kept short to ensure uniform quality of service. In this paper, through experiments on a Cassandra database, we show that long tail latency is caused by a lack of memory space because the database cannot receive any request until free space is reclaimed by writing the buffered data to the storage device. We observed that, since the performance of the storage device determines the amount of time required for writing the buffered data, the performance degradation of Solid State Drive (SSD) due to garbage collection results in a longer tail latency. We propose a garbage collection synchronization technique, called SyncGC, that simultaneously performs garbage collection in the java virtual machine and in the garbage collection in SSD concurrently, thus hiding garbage collection overheads in the SSD. Our evaluations on real SSDs show that SyncGC reduces the tail latency of 99.9th and, 99.99th-percentile by 31% and 36%, respectively.
Mention Detection with Pointer Networks
http://doi.org/10.5626/JOK.2017.44.8.774
Mention detection systems use nouns or noun phrases as a head and construct a chunk of text that defines any meaning, including a modifier. The term “mention detection” relates to the extraction of mentions in a document. In the mentions, a coreference resolution pertains to finding out if various mentions have the same meaning to each other. A pointer network is a model based on a recurrent neural network (RNN) encoder-decoder, and outputs a list of elements that correspond to input sequence. In this paper, we propose the use of mention detection using pointer networks. Our proposed model can solve the problem of overlapped mention detection, an issue that could not be solved by sequence labeling when applying the pointer network to the mention detection. As a result of this experiment, performance of the proposed mention detection model showed an F1 of 80.07%, a 7.65%p higher than rule-based mention detection; a co-reference resolution performance using this mention detection model showed a CoNLL F1 of 52.67% (mention boundary), and a CoNLL F1 of 60.11% (head boundary) that is high, 7.68%p, or 1.5%p more than coreference resolution using rule-based mention detection.
‘Hot Search Keyword’ Rank-Change Prediction
Dohyeong Kim, Byeong Ho Kang, Sungyoung Lee
http://doi.org/10.5626/JOK.2017.44.8.782
The service, "Hot Search Keywords", provides a list of the most hot search terms of different web services such as Naver or Daum. The service, bases the changes in rank of a specific search keyword on changes in its users’ interest. This paper introduces a temporal modelling framework for predicting the rank change of hot search keywords using past rank data and machine learning. Past rank data shows that more than 70% of hot search keywords tend to disappear and reappear later. The authors processed missing rank value, using deletion, dummy variables, mean substitution, and expectation maximization. It is however crucial to calculate the optimal window size of the past rank data. We proposed an optimal window size selection approach based on the minimum amount of time a topic within the same or a differing context disappeared. The experiments were conducted with four different machine-learning techniques using the Naver, Daum, and Nate "Hot Search Keywords" datasets, which were collected for 2 years.
Performance Comparison between Haskell Eval Monad and Cloud Haskell
Yeoneo Kim, Hyungjun An, Sugwoo Byun, Gyun Woo
http://doi.org/10.5626/JOK.2017.44.8.791
Competition in the modern CPU market has shifted from speeding up the clock speed of a single core to increasing the number of cores. As such, there is a growing interest in parallel programming to maximize the use of resources of many core processors. In this paper, we propose parallel programming models in Haskell to find an advisable parallel programming model for many-core environments. Specifically, we used Eval monad and Cloud Haskell to develop two versions of parallel programs: plagiarism detection and K-means. Then, we evaluated the performance of the developed programs in 32-core and 120-core environments. The results of our experiment show that the Eval monad is highly efficient in an environment with a small number of cores. On the other hand, the Cloud Haskell runtime shows 37% improvement over Eval monad and the scalability shows a 134% improvement over Eval monad as the number of cores increases.
T-Commerce Sale Prediction Using Deep Learning and Statistical Model
Injung Kim, Kihyun Na, Sohee Yang, Jaemin Jang, Yunjong Kim, Wonyoung Shin, Deokjung Kim
http://doi.org/10.5626/JOK.2017.44.8.803
T-commerce is technology-fusion service on which the user can purchase using data broadcasting technology based on bi-directional digital TVs. To achieve the best revenue under a limited environment in regard to the channel number and the variety of sales goods, organizing broadcast programs to maximize the expected sales considering the selling power of each product at each time slot. For this, this paper proposes a method to predict the sales of goods when it is assigned to each time slot. The proposed method predicts the sales of product at a time slot given the week-in-year and weather of the target day. Additionally, it combines a statistical predict model applying SVD (Singular Value Decomposition) to mitigate the sparsity problem caused by the bias in sales record. In experiments on the sales data of W-shopping, a T-commerce company, the proposed method showed NMAE (Normalized Mean Absolute Error) of 0.12 between the prediction and the actual sales, which confirms the effectiveness of the proposed method. The proposed method is practically applied to the T-commerce system of W-shopping and used for broadcasting organization.
A Method to Solve the Entity Linking Ambiguity and NIL Entity Recognition for efficient Entity Linking based on Wikipedia
Hokyung Lee, Jaehyun An, Jeongmin Yoon, Kyoungman Bae, Youngjoong Ko
http://doi.org/10.5626/JOK.2017.44.8.813
Entity Linking find the meaning of an entity mention, which indicate the entity using different expressions, in a user’s query by linking the entity mention and the entity in the knowledge base. This task has four challenges, including the difficult knowledge base construction problem, multiple presentation of the entity mention, ambiguity of entity linking, and NIL entity recognition. In this paper, we first construct the entity name dictionary based on Wikipedia to build a knowledge base and solve the multiple presentation problem. We then propose various methods for NIL entity recognition and solve the ambiguity of entity linking by training the support vector machine based on several features, including the similarity of the context, semantic relevance, clue word score, named entity type similarity of the mansion, entity name matching score, and object popularity score. We sequentially use the proposed two methods based on the constructed knowledge base, to obtain the good performance in the entity linking. In the result of the experiment, our system achieved 83.66% and 90.81% F1 score, which is the performance of the NIL entity recognition to solve the ambiguity of the entity linking.
Korean Dependency Parsing using Pointer Networks
http://doi.org/10.5626/JOK.2017.44.8.822
In this paper, we propose a Korean dependency parsing model using multi-task learning based pointer networks. Multi-task learning is a method that can be used to improve the performance by learning two or more problems at the same time. In this paper, we perform dependency parsing by using pointer networks based on this method and simultaneously obtaining the dependency relation and dependency label information of the words. We define five input criteria to perform pointer networks based on multi-task learning of morpheme in dependency parsing of a word. We apply a fine-tuning method to further improve the performance of the dependency parsing proposed in this paper. The results of our experiment show that the proposed model has better UAS 91.79% and LAS 89.48% than conventional Korean dependency parsing.
Bug Report Quality Prediction for Enhancing Performance of Information Retrieval-based Bug Localization
Misoo Kim, June Ahn, Eunseok Lee
http://doi.org/10.5626/JOK.2017.44.8.832
Bug reports are essential documents for developers to localize and fix bugs. These reports contain information regarding software bugs or failures that occur during software operation and maintenance phase. Information Retrieval-based Bug Localization (IR-BL) techniques have been proposed to reduce the time and cost it takes for developers to resolve bug reports. However, if a low-quality bug report is submitted, the performance of such techniques can be significantly degraded. To address this problem, we propose a quality prediction method that selects low-quality bug reports. This process; defines a Quality property of a Bug report as a Query (Q4BaQ) and predicts the quality of the bug reports using machine learning. We evaluated the proposed method with 3 open source projects. The results of the experiment show that the proposed method achieved an average F-measure of 87.31% and outperformed previous prediction techniques by up to 6.62% in the F-measure. Finally, a combination of the proposed method and traditional automatic query reformulation method improved the MRR and MAP by 0.9% and 1.3%, respectively.
A Dynamic Ensemble Method using Adaptive Weight Adjustment for Concept Drifting Streaming Data
Young-Deok Kim, Cheong Hee Park
http://doi.org/10.5626/JOK.2017.44.8.842
Streaming data is a sequence of data samples that are consistently generated over time. The data distribution or concept can change over time, and this change becomes a factor to reduce the performance of a classification model. Adaptive incremental learning can maintain the classification performance by updating the current classification model with the weight adjusted according to the degree of concept drift. However, selecting the proper weight value depending on the degree of concept drift is difficult. In this paper, we propose a dynamic ensemble method based on adaptive weight adjustment according to the degree of concept drift. Experimental results demonstrate that the proposed method shows higher performance than the other compared methods.
Game Theoretic Cache Allocation Scheme in Wireless Networks
Tra Huong Thi Le, Do Hyeon Kim, Choong Seon Hong
http://doi.org/10.5626/JOK.2017.44.8.854
Caching popular videos in the storage of base stations is an efficient method to reduce the transmission latency. This paper proposes an incentive proactive cache mechanism in the wireless network to motivate the content providers (CPs) to participate in the caching procedure. The system consists of one/many Infrastructure Provider (InP) and many CPs. The InP aims to define the price it charges the CPs to maximize its revenue while the CPs compete to determine the number of files they cache at the InP’s base stations (BSs). We conceive this system within the framework of Stackelberg game where InP is considered as the leader and CPs are the followers. By using backward induction, we show closed form of the amount of cache space that each CP renting on each base station and then solve the optimization problem to calculate the price that InP leases each CP. This is different from the existing works in that we consider the non-uniform pricing scheme. The numerical results show that InP’s profit in the proposed scheme is higher than in the uniform pricing.
Secure Format-Preserving Encryption for Message Recovery Attack
Sooyong Jeong, Dowon Hong, Changho Seo
http://doi.org/10.5626/JOK.2017.44.8.860
Recently, due to the personal information security act, the encryption of personal information has attracted attention. However, if the conventional encryption scheme is used directly, the database schema must be changed because the conventional encryption scheme does not preserve the format of the data, which can yield a large cost. Therefore, the Format-Preserving Encryption(FPE) has emerged as an important technique that ensures the confidentiality of the data and maintains the database schema naturally. Accordingly, National Institute of Standards and Technology(NIST) recently published the FF1 and FF3 as standards for FPE, although problems have been found in the security of FF1 and FF3 against message recovery attacks. In this paper, we study and analyze FF1 and FF3 as the standards of FPE, as well as the message recovery attack on these schemes. We also study a secure FPE against message recovery attack and verify the efficiency by implementing standardized FF1 and FF3.
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