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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
> Journal Vol & Issue
Journal of Computing Science and Engineering
Journal Basic Information
Journal DOI :
Korean Institute of Information Scientists and Engineers
Editor in Chief :
In-Sup Lee / Il-Yeol Song / Jong C. Park / Tae-Whan Kim
Volume & Issues
Volume 5, Issue 4 - Dec 2011
Volume 5, Issue 3 - Sep 2011
Volume 5, Issue 2 - Jun 2011
Volume 5, Issue 1 - Mar 2011
Selecting the target year
A Technique for Fast Process Creation Based on Creation Location
Kim, Byung-Jin ; Ahn, Young-Ho ; Chung, Ki-Seok ;
Journal of Computing Science and Engineering, volume 5, issue 4, 2011, Pages 283~287
DOI : 10.5626/JCSE.2011.5.4.283
Due to the proliferation of software parallelization on multi-core CPUs, the number of concurrently executing processes is rapidly increasing. Unlike processes running in a server environment, those executing in a multi-core desktop or a multi-core mobile platform have various correlations. Therefore, it is crucial to consider correlations among concurrently running processes. In this paper, we exploit the property that for a given created location in the binary image of the parent process, the average running time of child processes residing in the run-queue differs. We claim that this property can be exploited to improve the overall system performance by running processes that have a relatively short running time before those with a longer running time. Experimental results verified that the running time was actually improved by 11%.
Review of Korean Speech Act Classification: Machine Learning Methods
Kim, Hark-Soo ; Seon, Choong-Nyoung ; Seo, Jung-Yun ;
Journal of Computing Science and Engineering, volume 5, issue 4, 2011, Pages 288~293
DOI : 10.5626/JCSE.2011.5.4.288
To resolve ambiguities in speech act classification, various machine learning models have been proposed over the past 10 years. In this paper, we review these machine learning models and present the results of experimental comparison of three representative models, namely the decision tree, the support vector machine (SVM), and the maximum entropy model (MEM). In experiments with a goal-oriented dialogue corpus in the schedule management domain, we found that the MEM has lighter hardware requirements, whereas the SVM has better performance characteristics.
Load Shedding for Temporal Queries over Data Streams
Al-Kateb, Mohammed ; Lee, Byung-Suk ;
Journal of Computing Science and Engineering, volume 5, issue 4, 2011, Pages 294~304
DOI : 10.5626/JCSE.2011.5.4.294
Enhancing continuous queries over data streams with temporal functions and predicates enriches the expressive power of those queries. While traditional continuous queries retrieve only the values of attributes, temporal continuous queries retrieve the valid time intervals of those values as well. Correctly evaluating such queries requires the coalescing of adjacent timestamps for value-equivalent tuples prior to evaluating temporal functions and predicates. For many stream applications, the available computing resources may be too limited to produce exact query results. These limitations are commonly addressed through load shedding and produce approximated query results. There have been many load shedding mechanisms proposed so far, but for temporal continuous queries, the presence of coalescing makes theses existing methods unsuitable. In this paper, we propose a new accuracy metric and load shedding algorithm that are suitable for temporal query processing when memory is insufficient. The accuracy metric uses a combination of the Jaccard coefficient to measure the accuracy of attribute values and
interval orders to measure the accuracy of the valid time intervals in the approximate query result. The algorithm employs a greedy strategy combining two objectives reflecting the two accuracy metrics (i.e., value and interval). In the performance study, the proposed greedy algorithm outperforms a conventional random load shedding algorithm by up to an order of magnitude in its achieved accuracy.
Intrusion Detection: Supervised Machine Learning
Fares, Ahmed H. ; Sharawy, Mohamed I. ; Zayed, Hala H. ;
Journal of Computing Science and Engineering, volume 5, issue 4, 2011, Pages 305~313
DOI : 10.5626/JCSE.2011.5.4.305
Due to the expansion of high-speed Internet access, the need for secure and reliable networks has become more critical. The sophistication of network attacks, as well as their severity, has also increased recently. As such, more and more organizations are becoming vulnerable to attack. The aim of this research is to classify network attacks using neural networks (NN), which leads to a higher detection rate and a lower false alarm rate in a shorter time. This paper focuses on two classification types: a single class (normal, or attack), and a multi class (normal, DoS, PRB, R2L, U2R), where the category of attack is also detected by the NN. Extensive analysis is conducted in order to assess the translation of symbolic data, partitioning of the training data and the complexity of the architecture. This paper investigates two engines; the first engine is the back-propagation neural network intrusion detection system (BPNNIDS) and the second engine is the radial basis function neural network intrusion detection system (BPNNIDS). The two engines proposed in this paper are tested against traditional and other machine learning algorithms using a common dataset: the DARPA 98 KDD99 benchmark dataset from International Knowledge Discovery and Data Mining Tools. BPNNIDS shows a superior response compared to the other techniques reported in literature especially in terms of response time, detection rate and false positive rate.
Multicast Extension to Proxy Mobile IPv6 for Mobile Multicast Services
Kim, Dae-Hyeok ; Lim, Wan-Seon ; Suh, Young-Joo ;
Journal of Computing Science and Engineering, volume 5, issue 4, 2011, Pages 316~323
DOI : 10.5626/JCSE.2011.5.4.316
Recently, Proxy Mobile IPv6 (PMIPv6) has received much attention as a mobility management protocol in next-generation all-IP mobile networks. While the current research related to PMIPv6 mainly focuses on providing efficient handovers for unicast-based applications, there has been relatively little interest in supporting multicast services with PMIPv6. To provide support for multicast services with PMIPv6, there are two alternative approaches called Mobile Access Gateway (MAG)-based subscription and Local Mobility Anchor (LMA)-based subscription. However, MAG-based subscription causes a large overhead for multicast joining and LMA-based subscription provides non-optimal multicast routing paths. The two approaches may also cause a high packet loss rate. In this paper, we propose an efficient PMIPv6-based multicast protocol that aims to provide an optimal delivery path for multicast data and to reduce handover delay and packet loss rate. Through simulation studies, we found that the proposed protocol outperforms existing multicast solutions for PMIPv6 in terms of end-to-end delay, service disruption period, and the number of lost packets during handovers.
Fast Retransmission Scheme for Overcoming Hidden Node Problem in IEEE 802.11 Networks
Jeon, Jung-Hwi ; Kim, Chul-Min ; Lee, Ki-Seok ; Kim, Chee-Ha ;
Journal of Computing Science and Engineering, volume 5, issue 4, 2011, Pages 324~330
DOI : 10.5626/JCSE.2011.5.4.324
To avoid collisions, IEEE 802.11 medium access control (MAC) uses predetermined inter-frame spaces and the random back-off process. However, the retransmission strategy of IEEE 802.11 MAC results in considerable time wastage. The hidden node problem is well known in wireless networks; it aggravates the consequences of time wastage for retransmission. Many collision prevention and recovery approaches have been proposed to solve the hidden node problem, but all of them have complex control overhead. In this paper, we propose a fast retransmission scheme as a recovery approach. The proposed scheme identifies collisions caused by hidden nodes and then allows retransmission without collision. Analysis and simulations show that the proposed scheme has greater throughput than request-to-send and clear-to-send (RTS/CTS) and a shorter average waiting time.
Data Firewall: A TPM-based Security Framework for Protecting Data in Thick Client Mobile Environment
Park, Woo-Ram ; Park, Chan-Ik ;
Journal of Computing Science and Engineering, volume 5, issue 4, 2011, Pages 331~337
DOI : 10.5626/JCSE.2011.5.4.331
Recently, Virtual Desktop Infrastructure (VDI) has been widely adopted to ensure secure protection of enterprise data and provide users with a centrally managed execution environment. However, user experiences may be restricted due to the limited functionalities of thin clients in VDI. If thick client devices like laptops are used, then data leakage may be possible due to malicious software installed in thick client mobile devices. In this paper, we present Data Firewall, a security framework to manage and protect security-sensitive data in thick client mobile devices. Data Firewall consists of three components: Virtual Machine (VM) image management, client VM integrity attestation, and key management for Protected Storage. There are two types of execution VMs managed by Data Firewall: Normal VM and Secure VM. In Normal VM, a user can execute any applications installed in the laptop in the same manner as before. A user can access security-sensitive data only in the Secure VM, for which the integrity should be checked prior to access being granted. All the security-sensitive data are stored in the space called Protected Storage for which the access keys are managed by Data Firewall. Key management and exchange between client and server are handled via Trusted Platform Module (TPM) in the framework. We have analyzed the security characteristics and built a prototype to show the performance overhead of the proposed framework.
Personalized Battery Lifetime Prediction for Mobile Devices based on Usage Patterns
Kang, Joon-Myung ; Seo, Sin-Seok ; Hong, James Won-Ki ;
Journal of Computing Science and Engineering, volume 5, issue 4, 2011, Pages 338~345
DOI : 10.5626/JCSE.2011.5.4.338
Nowadays mobile devices are used for various applications such as making voice/video calls, browsing the Internet, listening to music etc. The average battery consumption of each of these activities and the length of time a user spends on each one determines the battery lifetime of a mobile device. Previous methods have provided predictions of battery lifetime using a static battery consumption rate that does not consider user characteristics. This paper proposes an approach to predict a mobile device's available battery lifetime based on usage patterns. Because every user has a different pattern of voice calls, data communication, and video call usage, we can use such usage patterns for personalized prediction of battery lifetime. Firstly, we define one or more states that affect battery consumption. Then, we record time-series log data related to battery consumption and the use time of each state. We calculate the average battery consumption rate for each state and determine the usage pattern based on the time-series data. Finally, we predict the available battery time based on the average battery consumption rate for each state and the usage pattern. We also present the experimental trials used to validate our approach in the real world.
Online Clustering Algorithms for Semantic-Rich Network Trajectories
Roh, Gook-Pil ; Hwang, Seung-Won ;
Journal of Computing Science and Engineering, volume 5, issue 4, 2011, Pages 346~353
DOI : 10.5626/JCSE.2011.5.4.346
With the advent of ubiquitous computing, a massive amount of trajectory data has been published and shared in many websites. This type of computing also provides motivation for online mining of trajectory data, to fit user-specific preferences or context (e.g., time of the day). While many trajectory clustering algorithms have been proposed, they have typically focused on offline mining and do not consider the restrictions of the underlying road network and selection conditions representing user contexts. In clear contrast, we study an efficient clustering algorithm for Boolean + Clustering queries using a pre-materialized and summarized data structure. Our experimental results demonstrate the efficiency and effectiveness of our proposed method using real-life trajectory data.