•Paper Title : Extending the Multidimensional Data Model to Handle Complex Data

Dear Colleague:

We are pleased to announce the release of a new issue of Journal of Computing Science and Engineering (JCSE), published by the Korean Institute of Information Scientists and Engineers (KIISE). KIISE is the largest organization for computer scientists in Korea with over 4,000 active members.

Journal of Computing Science and Engineering (JCSE) is a peer-reviewed quarterly journal that publishes high-quality papers on all aspects of computing science and engineering. JCSE aims to foster communication between academia and industry within the rapidly evolving field of Computing Science and Engineering. The journal is intended to promote problem-oriented research that fuses academic and industrial expertise. The journal focuses on emerging computer and information technologies including, but not limited to, embedded computing, ubiquitous computing, convergence computing, green computing, smart and intelligent computing, and human computing. JCSE publishes original research contributions, surveys, and experimental studies with scientific advances.

Please take a look at our new issue posted at http://jcse.kiise.org. All the papers can be downloaded from the Web page.

The contents of the latest issue of Journal of Computing Science and Engineering (JCSE)

Official Publication of the Korean Institute of Information Scientists and Engineers

Volume 15, Number 2, June 2021

pISSN: 1976-4677

eISSN: 2093-8020

* JCSE web page: http://jcse.kiise.org

* e-submission: http://mc.manuscriptcentral.com/jcse

Editor in Chief: Insup Lee (University of Pennsylvania)

Il-Yeol Song (Drexel University)

Jong C. Park (KAIST)

Taewhan Kim (Seoul National University)

JCSE, vol. 15, no. 2, June 2021

[Paper One]

- Title: Compression Techniques for DNA Sequences: A Thematic Review

- Authors: Rosario Gilmary, Akila Venkatesan, and Govindasamy Vaiyapuri

- Keyword: DNA sequences; Lossless compression; Genomic sequence compression; Horizontal compression; Vertical compression

- Abstract

Deoxyribonucleic acid (DNA) is the basic entity that carries genetic instructions. This information is used in the evolution, progression, and improvement of all species. It is estimated that 10 CD-ROMs are required to store the genomic data of an individual being. With the increase in DNA sequencing equipment, an extensive heap of genomic data is created. The increase in DNA data in public databases is surpassing the rate of growth in storage space, thereby raising a significant concern related to data storage, transmission, retrieval, and search. To reduce the data storage and storage expense, lossless compression procedures were applied. Conventional compression methods are not proficient while compressing the biological data. Hence, several unique and contemporary lossless compression mechanisms were used to achieve improved compression ratio in biological sequences. Here, we scrutinize the diverse existing compression procedures that are appropriate for the compression of DNA sequences. The efficiency of algorithms is compared in terms of compression ratio, the ratio of the capacity of the compressed folder, and compression/decompression time. Main challenges and future research directions in DNA compression are also presented. Emphasis has been given to special references related to contemporary techniques.

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JCSE, vol. 15, no. 2, pp.59-71

[Paper Two]

- Title: Impact of Synthetic Task Set Generation Methods on Schedulability Performance

- Authors: Saehwa Kim

- Keyword: Empirical evaluation; Fixed-priority scheduling; Real-time systems and embedded systems; Performance measurement

- Abstract

This paper addresses the various alternative methods of synthesizing task sets even when the continuous uniform distribution of their task utilizations is guaranteed. There are four methods that have been widely used in literature; LinearC, LinearT, LogT, and HarmonicT: C and T represent the worst-case execution times and periods, while linear, log, harmonic represent the spaces for the random generation of C or T. We have demonstrated that the schedulability performances of the task sets generated by those methods are very different. Specifically, the schedulability performance for the fixed priority scheduling is in the decreasing order of LogT, HarmonicT, LinearC, and LinearT. We have introduced notions of C-difference and T-difference, which have been used to demonstrate that the larger the value induced the better schedulability performance.

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JCSE, vol. 15, no. 2, pp.72-77

[Paper Three]

- Title: Minimum-Width Parallelogram Annulus with Given Angles

- Authors: Sang Won Bae

- Keyword: Algorithms design and analysis; Computational geometry; Parallelogram annulus; Arbitrary orientation

- Abstract

In this paper, we study a variant of the problem of computing a minimum-width parallelogram annulus that encloses a given set of n points in the plane. A parallelogram annulus is a closed region between a parallelogram and its inward offset. Specifically, we present the first algorithm that computes a minimum-width parallelogram annulus with inner angles fixed by the input that encloses n input points. The running time is O(n² log n). To the best of our knowledge, there exists no known algorithm in the literature for the stated problem, and our algorithm generalizes the existing O(n² log n)-time algorithm for the rectangular annulus in arbitrary orientation in the same running time-bound.

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JCSE, vol. 15, no. 2, pp.78-83

[Paper Four]

- Title: A Practical Approach to Indoor Path Loss Modeling Based on Deep Learning

- Authors: Shengjie Ma, Hong Cheng, and Hyukjoon Lee

- Keyword: Deep learning; Indoor path loss modeling; Convolutional neural networks

- Abstract

Deep learning has become one of the most powerful prediction approaches, and it can be used to solve classification and regression problems. We present a novel deep learning-based indoor Wi-Fi path loss modeling approach. Specifically, we propose a local area multi-line scanning algorithm that generates input images based on measurement locations and a floor plan. As the input images contain information regarding the propagation environment between the fixed access points (APs) and measurement locations, a convolutional neural network (CNN) model can be trained to learn the features of the indoor environment and approximate the underlying functions of the Wi-Fi signal propagation. The proposed deep learning-based indoor path loss model can achieve superior performance over 3D ray-tracing methods. The average root mean square error (RMSE) between the predicted and measured received signal strength values in the two scenarios is 4.63 dB.

To obtain a copy of the entire article, click on the link below.
JCSE, vol. 15, no. 2, pp.84-95

[Call For Papers]

Journal of Computing Science and Engineering (JCSE), published by the Korean Institute of Information Scientists and Engineers (KIISE) is devoted to the timely dissemination of novel results and discussions on all aspects of computing science and engineering, divided into Foundations, Software & Applications, and Systems & Architecture. Papers are solicited in all areas of computing science and engineering. See JCSE home page at http://jcse.kiise.org for the subareas.

The journal publishes regularly submitted papers, invited papers, selected best papers from reputable conferences and workshops, and thematic issues that address hot research topics. Potential authors are invited to submit their manuscripts electronically, prepared in PDF files, through http://mc.manuscriptcentral.com/jcse, where ScholarOne is used for on-line submission and review. Authors are especially encouraged to submit papers of around 10 but not more than 30 double-spaced pages in twelve point type. The corresponding author's full postal and e-mail addresses, telephone and FAX numbers as well as current affiliation information must be given on the manuscript. Further inquiries are welcome at JCSE Editorial Office, office@kiise.org (phone: +82-2-588-9240; FAX: +82-2-521-1352).