[AISWorld] Newly published papers of JCSE (Dec. 2022)

[JCSE]

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
<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 16, Number 4, December 2022

 

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. 16, no. 4, December 2022

 

[Paper One]

- Title: Feeding Longer Frames for Efficient Video Denoising Model

- Authors: Kavita Arjun Bhosale and Sang-hyo Park

- Keyword: Video denoising; Motion compensation; deep learning;
convolutional neural network; noise reduction; signal processing

 

- Abstract

Recently, deep video denoising networks showed substantially high denoising
performance with considerably lower computing times. However, such models
may not be able to denoise long-term frames appropriately due to the various
characteristics of video motion. In this paper, we propose a method that
takes longer input frames and feeds them to the existing architecture. In
particular, the proposed method may extract temporal information effectively
from neighboring frames to address the long-term frame dependency issue. To
demonstrate the performance of the proposed method, we implemented our
method on opt of the state-of-the-art video denoising model. Through
extensive experiments, the proposed method showed better performance in
terms of quality metrics than the existing one, even with higher noise
level, showing considerably lower computing times.

To obtain a copy of the entire article, click on the link below.
JCSE, vol. 16, no. 4, pp.185-193
<http://jcse.kiise.org/PublishedPaper/year_abstract.asp?idx=419&page_url=Cur
rent_Issues> 

 

[Paper Two]

- Title: Utilizing Temporal Locality for Hash Tables with Circular Chaining

- Authors: Changwoo Pyo and Taehwan Kim

- Keyword: Bucket structure; Circular chaining; Hash table; Linear chaining;
Locality; Temporal locality interval

 

- Abstract

A hash table with separate chaining typically adopts linearly linked lists
as buckets to resolve collisions. This study demonstrates that converting
bucket structures from linear to circular chaining enables buckets to
utilize temporal locality and improve search performance. Unlike linear
chaining, circular chaining can track the most recently accessed entry and
preserve the reachability of all bucket entries without complicated data
structures and operations. We defined temporal locality interval (TLI) to
represent the period during which subsequent bucket access repeats itself on
a single entry. We analyzed the average search cost using the TLI length and
load factor. The average search cost converges to the minimum when the TLI
length dominates the load factor. In our experiments using the SPEC CPU 2006
benchmark suite, circular chaining manifested 1.14 comparisons, reducing the
cost of linear chaining by 45.71% when the load factor was 0.99. The
improvement is notable, particularly for tables with a high load factor and
uneven distribution of bucket sizes.

To obtain a copy of the entire article, click on the link below.
JCSE, vol. 16, no. 4, pp.194-198
<http://jcse.kiise.org/PublishedPaper/year_abstract.asp?idx=420&page_url=Cur
rent_Issues> 

 

[Paper Three]

- Title: Recognition and Classification of Human Actions using 2D Pose
Estimation and Machine Learning

- Authors: Monika Dhiman, Akash Sharma, and Sarbjeet Singh

- Keyword: Action Classification; Action Recognition; Openpose; Pose
Estimation; Machine Learning

 

- Abstract

Recognition and classification of human actions is a fundamental but
difficult computer vision task that has been studied by several researchers
throughout the world in recent years. Pose estimation is a widely used
technology to recognize human actions. It has several applications,
especially in the field of computer vision, where it can be used to
recognize basic as well as complex human actions. This research provides a
novel framework for recognition and classification of human actions which
includes five categories - standing, walking, waving, punching and kicking.
The dataset used for the recognition and classification purpose is generated
using the videos that are recorded by using a smart phone and 2D pose
estimation technique has been applied to extract the features from the human
body. The ML classifiers have been trained on a custom-built dataset. While
all algorithms nearly performed well in classification task, LGBM
outperformed the rest in terms of accuracy (98.80 %). 

To obtain a copy of the entire article, click on the link below.
JCSE, vol. 16, no. 4, pp.199-210
<http://jcse.kiise.org/PublishedPaper/year_abstract.asp?idx=421&page_url=Cur
rent_Issues> 

 

[Paper Four]

- Title: Non-Invasive Face Registration for Surgical Navigation

- Authors: Seungwoo Kang, Hyeonjun Kim, Taeyong Park, Jeongjin Lee, and
Hyunjoo Song

- Keyword: rigid registration; face registration; 3D surgical navigation
system; non-invasive registration; computer graphics

 

- Abstract

This study aims to propose a precise rigid-body face registration method
that does not require an invasive marker attachment for surgical navigation
devices. Non-invasive face registration involves the following steps: (1)
Anatomical feature points such as the eyes and nose are found from the
computed tomography (CT) image and the location tracking device attached to
the patient's forehead just prior to the procedure. (2) An initial
registration is attempted on the two previously extracted feature points.
(3) Secondary registration is performed using a coherent point drift (CPD)
algorithm. (4) Using the Powell method, precise registration is performed to
minimize the closest point registration error (CPRE). The CPRE
co-registration accuracy was measured for the original and sampled phantom
data, and the error was found to be 1.07 mm on average for skin coordinate
data. The method introduced in this study enables precise rigid-body face
registration in a non-invasive manner using only skin coordinates. 

To obtain a copy of the entire article, click on the link below.
JCSE, vol. 16, no. 4, pp.211-221
<http://jcse.kiise.org/PublishedPaper/year_abstract.asp?idx=422&page_url=Cur
rent_Issues> 

 

[Paper Five]

- Title: A Study of Job Failure Prediction on Supercomputers with
Application Semantic Enhancement

- Authors: Haotong Zhang, Gang Xian, Wenxiang Yang, and Jie Yu

- Keyword: Execution Efficiency; Job Failure Prediction; Application
Semantic Enhancement; Machine Learning

 

- Abstract

The powerful computing capabilities of supercomputers play an important role
in today's scientific computing. A large number of high performance
computing jobs are submitted and executed concurrently in the system. Job
failure will cause a waste of system resources and impact the efficiency of
the system and user jobs. Job failure prediction can support fault-tolerant
technology to alleviate this phenomenon in supercomputers. At present, the
related work mainly predicts job failure by collecting the real-time
performance attributes of jobs, but it is difficult to be applied in the
real environment because of the high cost of collecting job attributes. In
addition to analyzing the time and resource attributes in the job logs, this
study also explores the semantic information of jobs. We mine job
application semantic information from job names and job paths, where job
path is collected by additional monitoring of the job submitting process. A
prediction method based on job application semantic enhancement is proposed,
and the prediction results of the non-ensemble learning algorithm and the
ensemble learning algorithm are compared under each evaluation indicator.
This prediction method requires more miniature feature collection and
computation overhead and is easy to apply. The experimental results show
that the prediction effect is promisingly improved with job application
semantic enhancement, and the final evaluation indicator S score is improved
by 5%-6%, of which is 88.16% accuracy with 95.23% specificity and 88.24%
sensitivity. 

To obtain a copy of the entire article, click on the link below.
JCSE, vol. 16, no. 4, pp.222-232
<http://jcse.kiise.org/PublishedPaper/year_abstract.asp?idx=423&page_url=Cur
rent_Issues> 

 

[Paper Six]

- Title: Power System Connectivity Visualization Using an Orthogonal Graph
Layout Algorithm Based on the Space-Filling Technique

- Authors: San Hong, Sangjun Park, Chanil Kim, and Hyunjoo Song

- Keyword: Graph layout algorithm; Orthogonal layout; Space-filling
technique; Power system visualization; Visualization; Human-computer
interaction

 

- Abstract

In this study, a novel orthogonal graph layout algorithm was proposed to
efficiently represent the connection relationship between each node in power
system data. The proposed layout algorithm is a four-stage algorithm. First,
clustering is performed based on the connection relationship between nodes,
and the obtained clusters are placed close to squares by using the
squarified treemap technique to fully utilize the given space. Next,
adjacent nodes were arranged in a snakelike order in each cluster according
to the characteristics of the IEEE test system data. The links were then
arranged according to the positional relationship of the pairs of connected
nodes. Each node had several ports so that links could be distributed evenly
according to the direction of the links. A case in which all nodes were
arranged orthogonally in an arbitrary manner without performing clustering;
a case in which adjacent nodes within each cluster were arranged in an
arbitrary order after only performing clustering; and a case where adjacent
nodes within each cluster were arranged in row-major order after only
performing clustering were compared. The results verified that the proposed
method improved edge crossing, edge bending, and edge length considerably. 

To obtain a copy of the entire article, click on the link below.
JCSE, vol. 16, no. 4, pp.233-243
<http://jcse.kiise.org/PublishedPaper/year_abstract.asp?idx=424&page_url=Cur
rent_Issues> 

 

[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 <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> 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,  <mailto:office at kiise.org>
office at kiise.org (phone: +82-2-588-9240; FAX: +82-2-521-1352).