[AISWorld] Newly published papers of JCSE (Mar. 2023)

Thu Mar 30 04:22:58 EDT 2023

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 17, Number 1, March 2023

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. 17, no. 1, March 2023

[Paper One]

- Title: Pharos: A Transparent and Steerable Visualization Recommendation
System

- Authors: Youli Chang, Sehi L'Yi, Young Taek Kim, Hyunjoo Song, Bohyoung
Kim, and Jinwook Seo

- Keyword: Human computer interaction; Visualization; Visual analytics;
Visualization recommendation system

- Abstract

We propose Pharos, a novel visualization recommendation system that makes
use of several provenance data sources and multi-perspective overviews to
boost the transparency and steerability of the recommendation engine. Pharos
helps a user understand recommendation contexts along with the user's
analysis progress in three complementary overviews. Pharos also serves
categorized and scalable recommendations, either expanding or narrowing a
user's analysis scope. Based on provenance data and explicit user
annotations (i.e., bookmarked or excluded visualizations), Pharos
dynamically updates a recommendation list. According to the provided
context, a user can steer the recommendation direction by filtering
recommended candidates and rearranging them via the weight controller of the
similarity measure on the recommendation engine. We showed how Pharos helped
users understand and steer visualization recommendations through two
comparative user studies.

To obtain a copy of the entire article, click on the link below.
JCSE, vol. 17, no. 1, pp.1-12
<http://jcse.kiise.org/PublishedPaper/year_abstract.asp?idx=426&page_url=Cur
rent_Issues> 

[Paper Two]

- Title: Knowledge Distillation for Optical Flow-Based Video
Super-resolution

- Authors: Jungwon Lee and Sang-hyo Park

- Keyword: Video super-resolution; Optical flow; Knowledge distillation;
Deep learning; Super-resolution

- Abstract

Recently, deep learning-based super-resolution (SR) models have been used to
improve SR performance by equipping preprocessing networks with baseline SR
networks. In particular, in video SR, which creates a high-resolution (HR)
image with multiple frames, optical flow extraction is accompanied by a
preprocessing process. These preprocessing networks work effectively in
terms of quality, but at the cost of increased network parameters, which
increase the computational complexity and memory consumption for SR tasks
with restricted resources. One well-known approach is the knowledge
distillation (KD) method, which can transfer the original model's knowledge
to a lightweight model with less performance degradation. Moreover, KD may
improve SR quality with reduced model parameters. In this study, we propose
an effective KD method that can effectively reduce the original SR model
parameters and even improve network performance. The experimental results
demonstrated that our method achieved a better PSNR than the original
state-ofthe-art SR network despite having fewer parameters.

To obtain a copy of the entire article, click on the link below.
JCSE, vol. 17, no. 1, pp.13-19
<http://jcse.kiise.org/PublishedPaper/year_abstract.asp?idx=428&page_url=Cur
rent_Issues> 

[Paper Three]

- Title: A Study on the VLSI Implementation of Fingerprint Thinning
Processors Using Hybrid GDI Technique

- Authors: Seungmin Jung

- Keyword: Hybrid GDI circuit; Single-well; Standard cell library; RTL; SoC;
VLSI

- Abstract

Although the gate-diffusion input (GDI) technique supports low power and
small area compared to conventional CMOS standard cell, it is limited to
design larger integrated circuits for several reasons. It is difficult to
apply the general RTL design flow of building a GDI standard cell library
and designing a chip through logic circuit synthesis. In this paper, we
proposed the hybrid GDI technique with new structure. We analyzed the
problems of the GDI technique, and the analysis extracted the circuit
characteristics of previous CMOS and GDI cells and found the cause of the
problems. We proposed the synthesis algorithm for the hybrid GDI design. The
performance of hybrid GDI was compared and analyzed using synthesized
thinning image processor on 1.8 V, 180n CMOS process. The proposed hybrid
GDI technique proved that it could be applied to system-on-a-chip (SoC)
design with low power and small cell area. 

To obtain a copy of the entire article, click on the link below.
JCSE, vol. 17, no. 1, pp.20-29
<http://jcse.kiise.org/PublishedPaper/year_abstract.asp?idx=429&page_url=Cur
rent_Issues> 

[Paper Four]

- Title: A New Profiling-based Side-Channel Attack on Graphics Processing
Units

- Authors: Xin Wang and Wei Zhang

- Keyword: Security; Correlation-based side-channel attack; Performance
profiling; Graphics processing units (GPU)

- Abstract

Graphics processing units (GPUs) have been increasingly used to solve a
range of compute-intensive and data-parallel scientific computing problems
that can be perfectly parallelized for performance speedups. Particularly,
GPUs have recently become popular to host the encryption/decryption
algorithms due to its high-throughput computing capability. However, the
security issues of moving the cryptographic algorithms onto GPUs have not
been studied adequately. Consequently, with absence of any protection
strategy, the potential vulnerabilities of GPUs to side-channel attacks
(SCAs) may expose the confidential information with high risk. In this
paper, we proposed a new profiling-assisted correlationbased side-channel
attack (pacSCA) to demonstrate that ignoring security issues and naively
moving security services onto GPUs can offer adversaries fatal
vulnerabilities to thieve critical information. The results showed that the
proposed SCA can rebuild the secure key of the AES-128 algorithm in less
than 6 seconds, revealing the urgency of protecting GPUs against
side-channel threats. 

To obtain a copy of the entire article, click on the link below.
JCSE, vol. 17, no. 1, pp.30-40
<http://jcse.kiise.org/PublishedPaper/year_abstract.asp?idx=430&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).