[AISWorld] Newly published papers of JCSE (Jun. 2020)

Tue Jun 30 05:34:52 EDT 2020

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 14, Number 2, June 2020

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. 14, no. 2, June 2020

[Paper One]

- Title: Virtual Whole-Hand Grasping Feedback for Object Manipulation with a
Two-Finger Haptic Interface

- Authors: Youngjin An, Hyunyul Cho, and Jinah Park

- Keyword: Haptics; Virtual reality; Human-computer interaction

- Abstract

In many virtual reality (VR) applications, achieving natural interaction
with virtual objects remains a key challenge. In this paper, we present a
virtual grasping feedback method based on a whole-hand model avatar in a
virtual environment using a two-finger haptic device. To provide engaging
visual feedback, we propose a method of rigging a virtual whole hand model
of five fingers from two input positions of the gripper, as well as a
strategy for determining proxy points adapted to various shapes of the
objects in contact. To provide plausible haptic feedback, we investigate
virtual force acting on the user’s fingers by proposing a dynamic model that
includes the main factors influencing grasping force based on the study of
the actual grip. We demonstrate the effectiveness of the proposed method by
conducting experiments where a user grabs three different shapes of a
container to pour the liquid out of the container. Depending on the contact
positions of thumb and index fingers of the user, the five-finger avatar
hand poses an intuitive configuration concerning the shape of the container.
The computed force feedback helps the user to successfully manipulate the
container in lifting and tilting actions.

To obtain a copy of the entire article, click on the link below.
JCSE, vol. 14, no. 2, pp.41-51
<http://jcse.kiise.org/PublishedPaper/year_abstract.asp?idx=353&page_url=Cur
rent_Issues> 

[Paper Two]

- Title: Trajectory Pattern Construction and Next Location Prediction of
Individual Human Mobility with Deep Learning Models

- Authors: Dabin You and Ha Yoon Song

- Keyword: Next location prediction; Mobility model; Deep learning;
Convolution neural network; Recurrent neural network; Trajectory pattern

- Abstract

Many modern portable devices, especially smartphones, are equipped with
positioning functionality. The rapid growth in the use of such devices has
allowed for the accumulation of a vast amount of positioning data. Combined
with deep learning methods, these data may be used for many novel
applications. Herein, a trajectory pattern tree generation method via deep
learning is proposed. The convolutional neural network (CNN) and recurrent
neural network (RNN) model of deep learning were applied for trajectory
generation and prediction. Several volunteers provided their raw positioning
data. The trajectory generation and prediction are for individual mobility
patterns and were performed for every volunteer. We present the results
obtained from seven volunteers. The preciseness of prediction can be
measured both for CNN and RNN. Consequently, we can predict an individual’s
location with 32.98% accuracy, and predict the top-five up to 69.22% for
unit area size of 0.030 km².

To obtain a copy of the entire article, click on the link below.
JCSE, vol. 14, no. 2, pp.52-65
<http://jcse.kiise.org/PublishedPaper/year_abstract.asp?idx=354&page_url=Cur
rent_Issues> 

[Paper Three]

- Title: Enhancement of Security and QoS in Wireless Medical Sensor Networks

- Authors: D. Sathya, K. Pranavi, I. Jeena Jacob, and D. Jagadeesan

- Keyword: Fuzzy logic; Private key; Data encryption; Digital image; Image
feature

- Abstract

Wireless medical sensor networks (WMSN) collect the data of a patient’s
vital body parameters using wearable or nonwearable biosensors. Since WMSN
is wireless in nature there occur numerous issues like a false alarm, lack
of robustness, and low processor speed, which reduce the system’s
effectiveness. One of the major issues is the security and privacy
protection of the collected data and providing a greater Quality of Service
(QoS) for the network in terms of energy efficiency, standardization, etc.
Targeting these problems, we introduced a hybrid secure and fuzzy fusion
system to achieve efficient secure transmission and data fusion in WMSN. The
basic idea of the proposed method is to generate a private key from specific
features of the digital color image; the generated key is encrypted by the
Advanced Encryption Standards (AES) mechanism. The proposed system handles
the vague and imprecise data to reduce energy consumption and increases the
network lifetime. The inference results verify the efficiency of the
proposed method in terms of security and energy consumption of the
network.To obtain a copy of the entire article, click on the link below.
JCSE, vol. 14, no. 2, pp.66-75
<http://jcse.kiise.org/PublishedPaper/year_abstract.asp?idx=355&page_url=Cur
rent_Issues> 

[Paper Four]

- Title: A Local Channel Blacklisting Method Using Adaptive Channel-Quality
Estimation in TSCH-Based Wireless Sensor Networks

- Authors: Kihoon Jeon, Sanghwa Chung, and Donghwa Yoo

- Keyword: IEEE 802.15.4; IEEE 802.15.4e TSCH; Internet of Things; ETSCH;
Wireless channel blacklisting

- Abstract

Owing to the development of the Internet of Things (IoT) paradigm, the
energy consumption of devices and the reliability of communication have
become important issues. Enhanced TSCH technology introduces a technique to
select highquality channels by using energy detection in the TSCH protocol
to improve the reliability of communication in a dynamic environment where
interference changes. However, it is difficult to apply ETSCH technology to
a multi-hop network because the node that performs energy detection consumes
more energy than the node that does not. In this article, we propose an
adaptive channel-quality estimation (ACE), which flexibly adjusts the duty
cycle of energy detection

according to whether interference dynamically changes or not. ACEs are
generally applicable regardless of the degree of change of interference,
which improves energy efficiency. Also, we present ACE-blacklisting based
TSCH (ACEBTSCH) that uses ACE and local channel blacklisting to blacklist
the wireless channel based on energy detection in a multi-hop network.
Experimental results show that ACEB-TSCH has a performance improvement of
15.94% over TSCH and 8.59% over PDR-blacklisting based TSCH.

To obtain a copy of the entire article, click on the link below.
JCSE, vol. 14, no. 2, pp.76-87
<http://jcse.kiise.org/PublishedPaper/year_abstract.asp?idx=356&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).