[AISWorld] Newly published papers of JCSE (Sep. 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 3, September 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. 3, September 2022

 

[Paper One]

- Title: Analysis of Theoretical Bounds in Noisy Threshold Group Testing

- Authors: Jin-Taek Seong

- Keyword: Noisy threshold group testing; Lower bound; Defective sample;
Fano's inequality

 

- Abstract

The objective of this study was to describe a noisy threshold group testing
model where positive and negative cases could occur depending on virus
concentration in coronavirus disease 2019 (COVID-19) diagnosis with output
results flipped due to measurement noise. We investigated lower bounds for
successful reconstruction of a small set of defective samples in the noisy
threshold group testing framework. To this end, using Fano's inequality, we
derived the minimum number of tests required to find unknown signals with
defective samples. Our results showed that the minimum number of tests on
probability of error for reconstruction of unknown signals was a function of
the defective rate and noise probability. We obtained lower bounds for on
performance of the noisy threshold group testing framework with respect to
noise intervals. In addition, the relationship between defective rate of
signals and sparsity of group matrices to design optimal noisy threshold
group testing systems is presented.

To obtain a copy of the entire article, click on the link below.
JCSE, vol. 16, no. 3, pp.121-128
<http://jcse.kiise.org/PublishedPaper/topic_abstract.asp?idx=411> 

 

[Paper Two]

- Title: Isolation of Shared Resources for Mixed-Criticality AUTOSAR
Applications

- Authors: Junghwan Lee and Myungjun Kim

- Keyword: Real-time scheduling; Mixed-criticality system; AUTOSAR

 

- Abstract

Temporal isolation without consideration of spatial isolation has been
attained for mixed-criticality systems, while spatial isolation is required
more strictly in the automotive industry. Moreover, tasks with different
criticality levels sharing the same resources are a common requirement for
safety-critical automotive applications. Such tasks are more challenging to
spatially isolate due to context sharing to access the same resources.
Nevertheless, safety certification cannot be received without addressing
spatial isolation. This paper argues that traditional real-time locking
solutions are unsuitable for mixed-criticality applications within the
automotive open system architecture (AUTOSAR). We adopted the server task
named resource server for spatial isolation within AUTOSAR limitations. We
formalized a software component model for reducing design space and proposed
the mapping algorithms. Properties of resource servers within AUTOSAR were
formally analyzed for blocking delays, task priority assignment, and
utilization analysis. Case studies in a powertrain domain of an electric
vehicle were carried out to assess the proposed solutions.

To obtain a copy of the entire article, click on the link below.
JCSE, vol. 16, no. 3, pp.129-142
<http://jcse.kiise.org/PublishedPaper/topic_abstract.asp?idx=412> 

 

[Paper Three]

- Title: Effect of NTT on Performance of AODV in a Grid Topology-Based
Wireless Ad Hoc Network

- Authors: Saurabh Sharma, Alok Singh, and Rajneesh Kumar Srivastava

- Keyword: Ad hoc networks; AODV; NTT; IEEE 802.11b; NS-3; Grid topology

 

- Abstract

Routing in wireless ad hoc networks that enable nodes, acting as routers
also, to find the best path between source and destination nodes, taking
into account cost, is a very challenging task. In the present work, an
investigation of performance of AODV routing protocol in a grid topology
based ad hoc network by varying value of node traversal time (NTT) and
taking into account absence and presence of Hello messages is reported. A
set of metrics, including Average End-to- End Delay, Packet Delivery Ratio,
Throughput, Routing Overhead, Route Error Overhead, Normalized Routing Load,
Average Hop Count, and Total Number of Received Data Packets, has been used
to assess the performance of AODV in the grid network. Performance of AODV
routing protocol varies in the value of NTT. Throughput in grid topology, by
and large, is observed decrease with an increase in NTT. However, explicit
relations between certain metrics with NTT as well as simulation time could
not be traced due to intricacies involved in combination of states of
various links and flows in the grid topology. To have better insights, grid
topologies of two, three, and four rows are planned to be investigated in
the future. 

To obtain a copy of the entire article, click on the link below.
JCSE, vol. 16, no. 3, pp.143-152
<http://jcse.kiise.org/PublishedPaper/topic_abstract.asp?idx=413> 

 

[Paper Four]

- Title: Fish Freshness Identification Using Machine Learning: Performance
Comparison of k-NN and Naive Bayes Classifier

- Authors: Anton Yudhana, Rusydi Umar and Sabarudin Saputra

- Keyword: Fish freshness identification; Machine learning; Fisheye images;
Computer science; k-NN; Naive Bayes

 

- Abstract

Fish is one of the food sources that should be examined for freshness before
being consumed. The consumption of rotten fish can cause various diseases.
The rotten fish have changed color on the gills, skin, flesh, and eyes and
have a pungent odour. Fish freshness can be assessed using a variety of
conventional methods, but these methods have limitations, such as requiring
relatively expensive equipment, trained personnel and being destructive. The
machine learning method is used because it is non-destructive, reduces
costs, and is easy to use. This study aims to identify the freshness of fish
using k-nearest neighbor (k-NN) and Naive Bayes (NB) classification methods
based on the fish-eye image. The features used in the classification process
are RGB and GLCM. The research stages consist of the fish collection
process, image acquisition and class division, preprocessing and ROI
detection, feature extraction and dataset split, and the classification
process. Based on these results, it can be stated that the k-NN method has
better performance than NB with average accuracy, precision, recall,
specificity, and AUC of 0.97, 0.97, 0.97, 0.97, and 0.97. 

To obtain a copy of the entire article, click on the link below.
JCSE, vol. 16, no. 3, pp.153-164
<http://jcse.kiise.org/PublishedPaper/topic_abstract.asp?idx=414> 

 

[Paper Five]

- Title: Low-Dimensional Vector Representation Learning for Text
Visualization Using Task-Oriented Dialogue Dataset

- Authors: Taewook Hwang, Sangkeun Jung and Yoon-Hyung Roh

- Keyword: Natural language processing; Natural language understanding;
Vector representation learning; Text visualization; Task-oriented dialogue
dataset

 

- Abstract

Text visualization is a complex technique that helps in data understanding
and insight, and may lead to loss of information. Through the proposed
low-dimensional vector representation learning method, deep learning and
visualization through low-dimensional vector space construction were
simultaneously performed. This method can transform a taskoriented dialogue
dataset into low-dimensional coordinates, and based on this, a deep learning
vector space can be constructed. The low-dimensional vector representation
deep learning model found the intent of a sentence within a dataset and
predicted the sentence components well in 3 out of 5 datasets. In addition,
by checking the prediction results in the low-dimensional vector space, it
was possible to improve the understanding of the data, such as identifying
the structure or errors in the data. 

To obtain a copy of the entire article, click on the link below.
JCSE, vol. 16, no. 3, pp.165-177
<http://jcse.kiise.org/PublishedPaper/topic_abstract.asp?idx=415> 

 

[Paper Six]

- Title: Switching DNN for Autonomous Driving System

- Authors: Yu-Seung Ma, Hojae Han and Seung-won Hwang

- Keyword: Deep neural network; Object-detection; Autonomous driving;
Software engineering

 

- Abstract

In autonomous driving system, building a rigorous object detection model
unaffected by conditions, such as weather or time-of-day, is essential for
safety. However, as deep learning models are often limited in
generalizability, training over the entire data collection can be
suboptimal, e.g., daytime training instances hinder the training for
nighttime prediction. We call this curse of multitasking (CoM), which was
first observed in multilingual training, where training a multilingual model
can be suboptimal, compared to multiple monolingual models. Our contribution
is observing CoM in autonomous driving, overcoming the problem by building
multiple mono-task models, or specialized experts for each task, then
switching models according to the input condition, enhancing the overall
effectiveness of the detection model. We show the effectiveness of using the
proposed strategy in both YOLOv3 and RetinaNet models on BDD dataset. 

To obtain a copy of the entire article, click on the link below.
JCSE, vol. 16, no. 3, pp.178-184
<http://jcse.kiise.org/PublishedPaper/topic_abstract.asp?idx=416> 

 

[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).