[AISWorld] Newly published papers of JCSE (Sept. 2019)

[office at kiise.org]

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 13, Number 3, September 2019

 

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. 13, no. 3, September 2019

 

[Paper One]

- Title: Deep-Learning Seat Selection on a Tour Bus Based on Scenery and
Sunlight Information

- Authors: Ki Hong Kim and Kwanyong Lee

- Keyword: Deep learning; Transfer learning; Google Street View; Tour

 

- Abstract

When traveling on a tour bus, the seat one chooses for viewing scenery is
one of the main factors affecting one's enjoyment of a trip. However, such
scenery information is not available in advance. Therefore, it is necessary
to predict the scenery for a tour bus route. In previous research, such
predictions have been attempted through machine learning. However, the
prediction result has only informed users about which direction is best, not
about how good that direction is. Moreover, no information was given about
sunlight, which can also affect the viewing of scenery. Therefore, in this
paper, we propose the Beautiful Scenery & Cool Shade system that quantifies
the information about scenery and sunlight in four directions using deep
learning and the azimuth theory. More specifically, we used ResNet-152,
DenseNet-161, and Inception v3 for the prediction, and we used Google Street
View for the input data. After building the system, we tested its
applications to two existing tour bus routes. The results showed that our
system outperformed the previous system. The proposed system allows tourists
to make satisfactory travel plans and allows tour companies to develop more
valuable tour services, ultimately contributing to the development of the
global tourism industry.

 

To obtain a copy of the entire article, click on the link below.
JCSE, vol. 13, no. 3, pp.89-98
<http://jcse.kiise.org/PublishedPaper/year_abstract.asp?idx=336&page_url=Cur
rent_Issues> 

 

[Paper Two]

- Title: Point Cloud Segmentation of Crane Parts Using Dynamic Graph CNN for
Crane Collision Avoidance

- Authors: Hyeonho Jeong, Hyosung Hong, Gyuha Park, Mooncheol Won, Mingyu
Kim and Hoyeong Yu

- Keyword: crane, 3D point cloud, segmentation, DBSCAN, dynamic graph, CNN

 

- Abstract

In this study, we have developed a point cloud segmentation algorithm for a
collision avoidance system between cranes and other objects in construction
yards. We used the Dynamic Graph CNN (DGCNN) algorithm to segment the point
cloud of the entire yard into crane parts and backgrounds. The point cloud
data were obtained from several LIDAR sensors attached to the crane. All
points were grouped into specific core clusters using the DBSCAN algorithm.
The core clusters were used to train the DGCNN after labeling with
corresponding part names. This network classified the point cloud into crane
types and their part names. Experimental results show that the crane part
segmentation performance of the suggested algorithm is accurate enough to be
used for collision avoidance system. It is possible to estimate the pose of
a crane by comparing the segmented point clouds with those of the CAD model.

 

To obtain a copy of the entire article, click on the link below.
JCSE, vol. 13, no. 3, pp.99-106
<http://jcse.kiise.org/PublishedPaper/year_abstract.asp?idx=337&page_url=Cur
rent_Issues> 

 

[Paper Three]

- Title: An Experimental Investigation into Data Flow Annotated-Activity
Diagram-Based Testing

- Authors: Aman Jaffari and Cheol-Jung Yoo

- Keyword: model-based testing, activity diagram-based testing, data
flow-annotated activity diagram, data flow information

 

- Abstract

With the acceptance of Unified Modeling Language (UML) as the de-facto
standard for modeling software systems, many research studies have addressed
the necessity for utilizing models of systems under testing as inputs for
test automation. Recently, activity diagrams have been used as a basis to
derive test cases. Current studies have focused on analyzing the control
flow of activities. However, examining the control flow among activities is
quite simple and such testing on its own is insufficient. This study
proposes technique for test case generation that complements an activity
diagram with data flow information. To investigate the potential benefits of
this technique, we performed an experimental investigation of well-known
systems in testing literature. The experimental results were analyzed and
compared with a state-of-the-art test suite generation tool as an
alternative approach to fault detection effectiveness and efficiency.
Overall, the results indicate that the proposed technique outperforms the
alternative approach by detecting 27.3% more faults on average. In
particular, the proposed technique yielded the best results in detecting
faults related to arithmetic operations or parts used for calculation in our
context.

 

To obtain a copy of the entire article, click on the link below.
JCSE, vol. 13, no. 3, pp.107-123
<http://jcse.kiise.org/PublishedPaper/year_abstract.asp?idx=338&page_url=Cur
rent_Issues> 

 

[Paper Four]

- Title: Fish Species Recognition Using VGG16 Deep Convolutional Neural
Network

- Authors: Praba Hridayami, I Ketut Gede Darma Putra and Kadek Suar Wibawa

- Keyword: Fish Recognition; Deep Convolutional Neural Network; Transfer
Learning; Canny Filter; VGG16

 

- Abstract

Conservation and protection of fish species is very important in aquaculture
and marine biology. A few studies have introduced the concept of fish
recognition; however, it resulted in poor rates of error recognition and
conservation of a small number of species. This study presents a fish
recognition method based on deep convolutional neural networks such as
VGG16, which was pre-trained on ImageNet via transfer learning method. The
fish dataset in this study consists of 50 species, each covered by 15 images
including 10 images for training purpose and 5 images for testing. In this
study, we trained our model on four different types of dataset: RGB color
space image, canny filter image, blending image, and blending image mixed
with RGB image. The results showed that blending image mixed with RGB image
trained model exhibited the best genuine acceptance rate (GAR) value of
96.4%, following by the RGB color space image trained model with a GAR value
of 92.4%, the canny filter image trained model with a GAR value of 80.4%,
and the blending image trained model showed the least GAR value of 75.6%.

 

To obtain a copy of the entire article, click on the link below.
JCSE, vol. 13, no. 3, pp.124-130
<http://jcse.kiise.org/PublishedPaper/year_abstract.asp?idx=339&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).