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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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KIISE Transactions on Computing Practices
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Journal DOI :
Korean Institute of Information Scientists and Engineers
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Volume & Issues
Volume 22, Issue 8 - Aug 2016
Volume 22, Issue 7 - Jul 2016
Volume 22, Issue 6 - Jun 2016
Volume 22, Issue 5 - May 2016
Volume 22, Issue 4 - Apr 2016
Volume 22, Issue 3 - Mar 2016
Volume 22, Issue 2 - Feb 2016
Volume 22, Issue 1 - Jan 2016
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Performance Enhancement and Evaluation of AES Cryptography using OpenCL on Embedded GPGPU
Lee, Minhak ; Kang, Woochul ;
KIISE Transactions on Computing Practices, volume 22, issue 7, 2016, Pages 303~309
DOI : 10.5626/KTCP.2016.22.7.303
Recently, an increasing number of embedded processors such as ARM Mali begin to support GPGPU programming frameworks, such as OpenCL. Thus, GPGPU technologies that have been used in PC and server environments are beginning to be applied to the embedded systems. However, many embedded systems have different architectural characteristics compare to traditional PCs and low-power consumption and real-time performance are also important performance metrics in these systems. In this paper, we implement a parallel AES cryptographic algorithm for a modern embedded GPU using OpenCL, a standard parallel computing framework, and compare performance against various baselines. Experimental results show that the parallel GPU AES implementation can reduce the response time by about 1/150 and the energy consumption by approximately 1/290 compare to OpenMP implementation when 1000KB input data is applied. Furthermore, an additional 100 % performance improvement of the parallel AES algorithm was achieved by exploiting the characteristics of embedded GPUs such as removing copying data between GPU and host memory. Our results also demonstrate that higher performance improvement can be achieved with larger size of input data.
Segment Scheduling Scheme to Support Seamless DASH-based Live Streaming Service
Yun, Dooyeol ; Chung, Kwangsue ;
KIISE Transactions on Computing Practices, volume 22, issue 7, 2016, Pages 310~314
DOI : 10.5626/KTCP.2016.22.7.310
Currently, several research studies are looking to improve the quality of DASH (Dynamic Adaptive Streaming over HTTP) based live streaming services. However, conventional DASH based streaming schemes cannot provide seamless playback while maintaining the low buffering and it adversely affects the QoE (Quality of Experience). To address this problem, we propose the QoE driven segment scheduling scheme. The proposed scheme adaptively schedules the segment request message according to the time and variation of segment fetching. Simulation results indicate that the proposed scheme improves the QoE of live streaming service by maintaining the low buffering delay and reducing the buffer underflow events.
Class Language Model based on Word Embedding and POS Tagging
Chung, Euisok ; Park, Jeon-Gue ;
KIISE Transactions on Computing Practices, volume 22, issue 7, 2016, Pages 315~319
DOI : 10.5626/KTCP.2016.22.7.315
Recurrent neural network based language models (RNN LM) have shown improved results in language model researches. The RNN LMs are limited to post processing sessions, such as the N-best rescoring step of the wFST based speech recognition. However, it has considerable vocabulary problems that require large computing powers for the LM training. In this paper, we try to find the 1st pass N-gram model using word embedding, which is the simplified deep neural network. The class based language model (LM) can be a way to approach to this issue. We have built class based vocabulary through word embedding, by combining the class LM with word N-gram LM to evaluate the performance of LMs. In addition, we propose that part-of-speech (POS) tagging based LM shows an improvement of perplexity in all types of the LM tests.
Development of an Interface for Data Visualization and Controlling of Classified Objects based on User Conditions
Park, Heesung ; Han, Minseok ; Choi, Yuri ;
KIISE Transactions on Computing Practices, volume 22, issue 7, 2016, Pages 320~325
DOI : 10.5626/KTCP.2016.22.7.320
By developing the IoT(Internet of Things) technology, devices for smart home environment have rapidly increased. With respect to mobiles, these applications are used to control and manage the various smart devices effectively. However, the existing mechanisms only provide simple information, and hence a difficulty to search or control the smart devices persists, since there is no meaningful relationship between them. In this research, we suggest an interface which visualizes the device's data and controls them effectively, based on the user's device using pattern. As a solution for this problem, we classify the user pattern based on a timeline for the associated circumstance, and visualize the device's data to make a group or to control individually in an easier approach. Also, all meaningful information could be confirmed by summarizing all the data of smart devices.
Development and Architecture of Video-to-Images to Enhance User Experience for Video Content Consumption
Jeon, Kyuyeong ; Yang, Jinhong ; Kim, Yongrok ; Park, Hyojin ; Jung, Sungkwan ;
KIISE Transactions on Computing Practices, volume 22, issue 7, 2016, Pages 326~331
DOI : 10.5626/KTCP.2016.22.7.326
The proportion of video content consumption is growing dramatically but some users avoid it. The reasons are initial time to load, lack of the time to watch video content, and particularly a traffic issues on mobile devices. The proposed Video-to-Images(V2I) technology offers a new user experience to end users through converting video into images without content providers' or users' effort. Using the V2I technology, consumption methods of video content with new type of content by users and the advantages of the new user experience will be introduced. Furthermore, the overall architecture of the V2I will be explained.
Windows based PC Log Collection System using Open Source
Song, Jungho ; Kim, Hakmin ; Yoon, Jin ;
KIISE Transactions on Computing Practices, volume 22, issue 7, 2016, Pages 332~337
DOI : 10.5626/KTCP.2016.22.7.332
System administrator or security managers need to collect logs of computing device (desktop or server), which are used for the purpose of cause-analysis of security incident and discover if damage to system was either caused by hacking or computer virus. Furthermore, appropriate log maintenance helps preventing security breech incidents through identification of vulnerability. In addition, it can be utilized for prevention of data leakage through the insider. In the paper, we present log collection system developed using open source supported by commands and basic methods of Windows. Furthermore, we aim to collect log information to enable search and analysis from diverse perspectives and to propose a way to integrate with open source-based search engine system.
A Crowdsourcing-Based Paraphrased Opinion Spam Dataset and Its Implication on Detection Performance
Lee, Seongwoon ; Kim, Seongsoon ; Park, Donghyeon ; Kang, Jaewoo ;
KIISE Transactions on Computing Practices, volume 22, issue 7, 2016, Pages 338~343
DOI : 10.5626/KTCP.2016.22.7.338
Today, opinion reviews on the Web are often used as a means of information exchange. As the importance of opinion reviews continues to grow, the number of issues for opinion spam also increases. Even though many research studies on detecting spam reviews have been conducted, some limitations of gold-standard datasets hinder research. Therefore, we introduce a new dataset called "Paraphrased Opinion Spam (POS)" that contains a new type of review spam that imitates truthful reviews. We have noticed that spammers refer to existing truthful reviews to fabricate spam reviews. To create such a seemingly truthful review spam dataset, we asked task participants to paraphrase truthful reviews to create a new deceptive review. The experiment results show that classifying our POS dataset is more difficult than classifying the existing spam datasets since the reviews in our dataset more linguistically look like truthful reviews. Also, training volume has been found to be an important factor for classification model performance.