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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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KIPS Transactions on Software and Data Engineering
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Korea Information Processing Society
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Volume & Issues
Volume 2, Issue 12 - Dec 2013
Volume 2, Issue 11 - Nov 2013
Volume 2, Issue 10 - Oct 2013
Volume 2, Issue 9 - Sep 2013
Volume 2, Issue 8 - Aug 2013
Volume 2, Issue 7 - Jul 2013
Volume 2, Issue 6 - Jun 2013
Volume 2, Issue 5 - May 2013
Volume 2, Issue 4 - Apr 2013
Volume 2, Issue 3 - Mar 2013
Volume 2, Issue 2 - Feb 2013
Volume 2, Issue 1 - Jan 2013
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Predicting Defect-Prone Software Module Using GA-SVM
Kim, Young-Ok ; Kwon, Ki-Tae ;
KIPS Transactions on Software and Data Engineering, volume 2, issue 1, 2013, Pages 1~6
DOI : 10.3745/KTSDE.2013.2.1.001
For predicting defect-prone module in software, SVM classifier showed good performance in a previous research. But there are disadvantages that SVM parameter should be chosen differently for every kernel, and algorithm should be performed iteratively for predict results of changed parameter. Therefore, we find these parameters using Genetic Algorithm and compare with result of classification by Backpropagation Algorithm. As a result, the performance of GA-SVM model is better.
An Integrative Method of Fault Tree Analysis and Fault Modes and Effect Analysis for Security Evaluation of e-Teaching and Learning System
Jin, Eun-Ji ; Kim, Myong-Hee ; Park, Man-Gon ;
KIPS Transactions on Software and Data Engineering, volume 2, issue 1, 2013, Pages 7~18
DOI : 10.3745/KTSDE.2013.2.1.007
These days, the teaching and learning system has been increasing for the rapid advancement of the information technologies. We can access education systems of good quality anytime, anywhere and we can use the individually personalized teaching and learning system depending on developing the wireless communication technology and the multimedia processing technology. The more the various systems develop, the more software security systems become important. There are a lot kind of fault analysis methods to evaluate software security systems. However, the only assessment method to evaluate software security system is not enough to analysis properly on account of the various types and characteristic of software systems by progressing information technology. Therefore, this paper proposes an integrative method of Fault Tree Analysis (FTA) and Fault Modes and Effect Analysis(FMEA) to evaluate the security of e-teaching and learning system as an illustration.
Derivation of State Transition Diagram from Class Using Tree Structure
Choi, Soo Kyung ; Park, Young Bom ;
KIPS Transactions on Software and Data Engineering, volume 2, issue 1, 2013, Pages 19~26
DOI : 10.3745/KTSDE.2013.2.1.019
To improve the reliability and quality of software system, many studies of the testing based on state-transition diagram have been in progress. Existing studies tried to solve the complexity problem of state-transition diagram. But the development of test case demands the better way to derive and manage the state diagram with low complexity. In this paper, the STMT(State-Transition Mapping Tree) is proposed to decrease the complexity of state diagram without changing or loosing the original state or transition information. Comparing with other methods, the proposed method turns out to be less complex.
Background Subtraction Algorithm Based on Multiple Interval Pixel Sampling
Lee, Dongeun ; Choi, Young Kyu ;
KIPS Transactions on Software and Data Engineering, volume 2, issue 1, 2013, Pages 27~34
DOI : 10.3745/KTSDE.2013.2.1.027
Background subtraction is one of the key techniques for automatic video content analysis, especially in the tasks of visual detection and tracking of moving object. In this paper, we present a new sample-based technique for background extraction that provides background image as well as background model. To handle both high-frequency and low-frequency events at the same time, multiple interval background models are adopted. The main innovation concerns the use of a confidence factor to select the best model from the multiple interval background models. To our knowledge, it is the first time that a confidence factor is used for merging several background models in the field of background extraction. Experimental results revealed that our approach based on multiple interval sampling works well in complicated situations containing various speed moving objects with environmental changes.
English-Korean Transfer Dictionary Extension Tool in English-Korean Machine Translation System
Kim, Sung-Dong ;
KIPS Transactions on Software and Data Engineering, volume 2, issue 1, 2013, Pages 35~42
DOI : 10.3745/KTSDE.2013.2.1.035
Developing English-Korean machine translation system requires the construction of information about the languages, and the amount of information in English-Korean transfer dictionary is especially critical to the translation quality. Newly created words are out-of-vocabulary words and they appear as they are in the translated sentence, which decreases the translation quality. Also, compound nouns make lexical and syntactic analysis complex and it is difficult to accurately translate compound nouns due to the lack of information in the transfer dictionary. In order to improve the translation quality of English-Korean machine translation, we must continuously expand the information of the English-Korean transfer dictionary by collecting the out-of-vocabulary words and the compound nouns frequently used. This paper proposes a method for expanding of the transfer dictionary, which consists of constructing corpus from internet newspapers, extracting the words which are not in the existing dictionary and the frequently used compound nouns, attaching meaning to the extracted words, and integrating with the transfer dictionary. We also develop the tool supporting the expansion of the transfer dictionary. The expansion of the dictionary information is critical to improving the machine translation system but requires much human efforts. The developed tool can be useful for continuously expanding the transfer dictionary, and so it is expected to contribute to enhancing the translation quality.
Learning Algorithm for Multiple Distribution Data using Haar-like Feature and Decision Tree
Kwak, Ju-Hyun ; Woen, Il-Young ; Lee, Chang-Hoon ;
KIPS Transactions on Software and Data Engineering, volume 2, issue 1, 2013, Pages 43~48
DOI : 10.3745/KTSDE.2013.2.1.043
Adaboost is widely used for Haar-like feature boosting algorithm in Face Detection. It shows very effective performance on single distribution model. But when detecting front and side face images at same time, Adaboost shows it`s limitation on multiple distribution data because it uses linear combination of basic classifier. This paper suggest the HDCT, modified decision tree algorithm for Haar-like features. We still tested the performance of HDCT compared with Adaboost on multiple distributed image recognition.
Design of an Activity Recognition System using Smartphone Accelerometer
Kim, Joo-Hee ; Nam, Sang-Ha ; Heo, Se-Kyeong ; Kim, In-Cheol ;
KIPS Transactions on Software and Data Engineering, volume 2, issue 1, 2013, Pages 49~54
DOI : 10.3745/KTSDE.2013.2.1.049
Activity recognition using smartphone accelerometer suffers from the user dependency problem that acceleration patterns of one user differ from those of others for the same activity. Moreover, it also suffers from the position dependency problem since a smartphone may be placed in any pockets or hands. In order to overcome these problems, this paper proposes an effective activity recognition method which is less dependent with both specific users and specific positions of the smartphone. Based on the proposed method, we implement a real-time activity recognition system working on an Android smartphone. Throughout some experiments with 6642 examples collected from different users and different positions, we investigate the performance of our activity recognition system.
Counterfeit Money Detection Algorithm using Non-Local Mean Value and Support Vector Machine Classifier
Ji, Sang-Keun ; Lee, Hae-Yeoun ;
KIPS Transactions on Software and Data Engineering, volume 2, issue 1, 2013, Pages 55~64
DOI : 10.3745/KTSDE.2013.2.1.055
Due to the popularization of digital high-performance capturing equipments and the emergence of powerful image-editing softwares, it is easy for anyone to make a high-quality counterfeit money. However, the probability of detecting a counterfeit money to the general public is extremely low. In this paper, we propose a counterfeit money detection algorithm using a general purpose scanner. This algorithm determines counterfeit money based on the different features in the printing process. After the non-local mean value is used to analyze the noises from each money, we extract statistical features from these noises by calculating a gray level co-occurrence matrix. Then, these features are applied to train and test the support vector machine classifier for identifying either original or counterfeit money. In the experiment, we use total 324 images of original money and counterfeit money. Also, we compare with noise features from previous researches using wiener filter and discrete wavelet transform. The accuracy of the algorithm for identifying counterfeit money was over 94%. Also, the accuracy for identifying the printing source was over 93%. The presented algorithm performs better than previous researches.
Efficient Image Stitching Using Fast Feature Descriptor Extraction and Matching
Rhee, Sang-Burm ;
KIPS Transactions on Software and Data Engineering, volume 2, issue 1, 2013, Pages 65~70
DOI : 10.3745/KTSDE.2013.2.1.065
Recently, the field of computer vision has been actively researched through digital image which can be easily generated as the development and expansion of digital camera technology. Especially, research that extracts and utilizes the feature in image has been actively carried out. The image stitching is a method that creates the high resolution image using features extract and match. Image stitching can be widely used in military and medical purposes as well as in variety fields of real life. In this paper, we have proposed efficient image stitching method using fast feature descriptor extraction and matching based on SURF algorithm. It can be accurately, and quickly found matching point by reduction of dimension of feature descriptor. The feature descriptor is generated by classifying of unnecessary minutiae in extracted features. To reduce the computational time and efficient match feature, we have reduced dimension of the descriptor and expanded orientation window. In our results, the processing time of feature matching and image stitching are faster than previous algorithms, and also that method can make natural-looking stitched image.
Implementation of Interface to Support Mobile Accessibility Using Speech I/O APIs
Oh, Seungchur ; Yun, Young-Sun ;
KIPS Transactions on Software and Data Engineering, volume 2, issue 1, 2013, Pages 71~80
DOI : 10.3745/KTSDE.2013.2.1.071
Due to the increased use of mobile devices, there is a lot of discussion on mobile accessibility. Mobile accessibility means that everyone, who includes the disabled, the elderly people, can easily use the functions of mobile devices. In this paper, we presented and implemented a mobile interface using a speech I/O APIs to improve the accessibility. The proposed interfaces are implemented on Android platforms and they used speech recognition and text-to-speech APIs supported as built-in services. In addition, to facilitate the internet access for visually impaired or blind people, we also implemented the web browsing application (web reader).