eISSN : 3022-7011
ISSUER : KIPS
ISSUER : KIPS
After the Korea Information Processing Society (KIPS) Transactions journal was founded in 1994, it was reorganized into the KIPS Transactions: Computer and Communication Systems(2287-5891/2734-049X ) and the KIPS Transactions: Software and Data Engi neering(2287-5905/2734-0503) in 2012. Through the KIPS official meeting on January 8th, 2024, the new KIPS Transaction journal was founded by integrating two KIPS Journals, KIPS Transactions: Computer and Communication Systems and KIPS Transactions: Software and Data Engineering. The new journal aims to realize social value and contribute to the development of South Korea’s science and technology with support from the lottery fund of the Ministry of Strategy and Finance and the science/technology promotion fund of the Ministry of Science and ICT. It is indexed in the Korea Science Academic Database, Korea Citation Index (KCI), and EBSCO.
HighlightsMore
A Study on Improving Performance of Software Requirements Classification Models by Handling Imbalanced DataJong-Woo Choi Young-Jun Lee Chae-Gyun Lim Ho-Jin Choi |
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Software requirements written in natural language may have different meanings from the stakeholders’ viewpoint. When designing an architecture based on quality attributes, it is necessary to accurately classify quality attribute requirements because... | |
Extending StarGAN-VC to Unseen Speakers Using RawNet3 Speaker RepresentationBogyung Park Somin Park Hyunki Hong |
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Voice conversion, a technology that allows an individual’s speech data to be regenerated with the acoustic properties(tone, cadence, gender) of another, has countless applications in education, communication, and entertainment. This paper proposes a... | |
A Study on Classification Models for Predicting Bankruptcy Based on XAIJihong Kim Nammee Moon |
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Efficient prediction of corporate bankruptcy is an important part of making appropriate lending decisions for financial institutions and reducing loan default rates. In many studies, classification models using artificial intelligence technology hav... | |
Detecting Common Weakness Enumeration(CWE) Based on the Transfer Learning of CodeBERT ModelChansol Park So Young Moon R. Young Chul Kim |
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Recently the incorporation of artificial intelligence approaches in the field of software engineering has been one of the big topics. In the world, there are actively studying in two directions: 1) software engineering for artificial intelligence an... | |
Latest Publication (Vol. 13, No. 6, Jun. 2024)
Design and Implementation of Multi-HILS based Robot Testbed to Support Software Validation of Biomimetic Robots
Hanjin Kim Kwanhyeok Kim Beomsu Ha Joo Young Kim Sung Jun Shim Jee Hoon Koo Won-Tae Kim
https://doi.org/10.3745/TKIPS.2024.13.6.243
Biomimetic Robot M&S HILS Robot Testbed
Biomimetic robots, which emulate characteristics of biological entities such as birds or insects, have the potential to offer a tactical
advantage in surveillance and reconnaissance in future battlefields. To effectively utilize these robots, it is essential to develop technologies
that emulate the wing flapping of birds or the movements of cockroaches. However, this effort is complicated by the challenges associated
with securing the necessary hardware and the complexities involved in software development and validation processes. In this paper,
we presents the design and implementation of a multi-HILS based biomimic robot software validation testbed using modeling and simulation
(M&S). By employing this testbed, developers can overcome the absence of hardware, simulate future battlefield scenarios, and conduct
software development and testing. However, the multi-HILS based testbed may experience inter-device communication delays as the
number of test robots increases, significantly affecting the reliability of simulation results. To address this issue, we propose the data
distribution service priority (DDSP), a priority-based middleware. DDSP demonstrates an average delay reduction of 1.95 ms compared
to the existing DDS, ensuring the required data transmission quality for the testbed.
The Transactions of the Korea Information Processing Society,
Vol. 13, No. 6, pp. 243-250,
Jun.
2024
![](https://d2kjln74dkk4oj.cloudfront.net/img/doi_icon.png)
Biomimetic Robot M&S HILS Robot Testbed
Development of a Deep Learning-based Midterm PM2.5 Prediction Model Adapting to Trend Changes
Dong Jun Min Hyerim Kim Sangkyun Lee
https://doi.org/10.3745/TKIPS.2024.13.6.251
Particulate Matter PM2.5 Time Series Forecast Deep Learning Ensemble
Fine particulate matter, especially PM2.5 with a diameter of less than 2.5 micrometers, poses significant health and economic risks.
This study focuses on the Seoul region of South Korea, aiming to analyze PM2.5 data and trends from 2017 to 2022 and develop a mid-term
prediction model for PM2.5 concentrations. Utilizing collected and produced air quality and weather data, reanalysis data, and numerical
model prediction data, this research proposes an ensemble evaluation method capable of adapting to trend changes. The ensemble method
proposed in this study demonstrated superior performance in predicting PM2.5 concentrations, outperforming existing models by an
average F1 Score of approximately 42.16% in 2019, 58.92% in 2021, and 34.79% in 2022 for future 3 to 6-day predictions. The model
maintains performance under changing environmental conditions, offering stable predictions and presenting a mid-term prediction model
that extends beyond the capabilities of existing deep learning-based short-term PM2.5 forecasts.
The Transactions of the Korea Information Processing Society,
Vol. 13, No. 6, pp. 251-259,
Jun.
2024
![](https://d2kjln74dkk4oj.cloudfront.net/img/doi_icon.png)
Particulate Matter PM2.5 Time Series Forecast Deep Learning Ensemble
Cox Model Improvement Using Residual Blocks in Neural Networks: A Study on the Predictive Model of Cervical Cancer Mortality
Nang Kyeong Lee Joo Young Kim Ji Soo Tak Hyeong Rok Lee Hyun Ji Jeon Jee Myung Yang Seung Won Lee
https://doi.org/10.3745/TKIPS.2024.13.6.260
Cervical cancer Survival Prediction Model Cox Proportional Hazards Machine Learning Deep Neural Networks ResNet
Cervical cancer is the fourth most common cancer in women worldwide, and more than 604,000 new cases were reported in 2020
alone, resulting in approximately 341,831 deaths. The Cox regression model is a major model widely adopted in cancer research, but
considering the existence of nonlinear associations, it faces limitations due to linear assumptions. To address this problem, this paper
proposes ResSurvNet, a new model that improves the accuracy of cervical cancer mortality prediction using ResNet's residual learning
framework. This model showed accuracy that outperforms the DNN, CPH, CoxLasso, Cox Gradient Boost, and RSF models compared
in this study. As this model showed accuracy that outperformed the DNN, CPH, CoxLasso, Cox Gradient Boost, and RSF models compared
in this study, this excellent predictive performance demonstrates great value in early diagnosis and treatment strategy establishment in
the management of cervical cancer patients and represents significant progress in the field of survival analysis.
The Transactions of the Korea Information Processing Society,
Vol. 13, No. 6, pp. 260-268,
Jun.
2024
![](https://d2kjln74dkk4oj.cloudfront.net/img/doi_icon.png)
Cervical cancer Survival Prediction Model Cox Proportional Hazards Machine Learning Deep Neural Networks ResNet
Characteristic Analysis on Urban Road Networks Using Various Path Models
Bee Geum Hwan-Gue Cho
https://doi.org/10.3745/TKIPS.2024.13.6.269
Shortest Path Quickest Path Simple Path Road Network
With the advancement of modern IT technologies, the operation of autonomous vehicles is becoming a reality, and route planning
is essential for this. Generally, route planning involves proposing the shortest path to minimize travel distance and the quickest path
to minimize travel time. However, the quality of these routes depends on the topological characteristics of the road network graph. If
the connectivity structure of the road network is not rational, there are limits to the performance improvement that routing algorithms
can achieve. Real drivers consider psychological factors such as the number of turns, surrounding environment, traffic congestion, and
road quality when choosing routes, and they particularly prefer routes with fewer turns. This paper introduces a simple path algorithm
that seeks routes with the fewest turns, in addition to the traditional shortest distance and quickest time routes, to evaluate the
characteristics of road networks. Using this simple path algorithm, we compare and evaluate the connectivity characteristics of road
networks in 20 major cities worldwide. By analyzing these road network characteristics, we can identify the strengths and weaknesses
of urban road networks and develop more efficient and safer route planning algorithms. This paper comprehensively examines the quality
of road networks and the efficiency of route planning by analyzing and comparing the road network characteristics of each city using
the proposed simple path algorithm.
The Transactions of the Korea Information Processing Society,
Vol. 13, No. 6, pp. 269-277,
Jun.
2024
![](https://d2kjln74dkk4oj.cloudfront.net/img/doi_icon.png)
Shortest Path Quickest Path Simple Path Road Network
A Study on the Analysis of Internal and External Factors of Software Threat Elements
Lee Eun Ser
https://doi.org/10.3745/TKIPS.2024.13.6.278
Software Test Reliability Software Safety
When implementing software, there can be side effects that pose a threat to human life. Therefore, it is necessary to measure the
impact of software on safety and create alternatives to mitigate and prevent threats. To conduct a software safety assessment to measure
the impact of threat factors, the following components are necessary. This paper aims to classify the threat factors of software into internal
and external factors and quantitatively demonstrate the impact of these threat factors.
The Transactions of the Korea Information Processing Society,
Vol. 13, No. 6, pp. 278-283,
Jun.
2024
![](https://d2kjln74dkk4oj.cloudfront.net/img/doi_icon.png)
Software Test Reliability Software Safety
Spontaneous Speech Emotion Recognition Based On Spectrogram With Convolutional Neural Network
Guiyoung Son Soonil Kwon
https://doi.org/10.3745/TKIPS.2024.13.6.284
Spontaneous Speech Speech Emotion Recognition spectrogram Convolutional Neural Network
Speech emotion recognition (SER) is a technique that is used to analyze the speaker's voice patterns, including vibration, intensity,
and tone, to determine their emotional state. There has been an increase in interest in artificial intelligence (AI) techniques, which are
now widely used in medicine, education, industry, and the military. Nevertheless, existing researchers have attained impressive results
by utilizing acted-out speech from skilled actors in a controlled environment for various scenarios. In particular, there is a mismatch
between acted and spontaneous speech since acted speech includes more explicit emotional expressions than spontaneous speech. For
this reason, spontaneous speech-emotion recognition remains a challenging task. This paper aims to conduct emotion recognition and
improve performance using spontaneous speech data. To this end, we implement deep learning-based speech emotion recognition using
the VGG (Visual Geometry Group) after converting 1-dimensional audio signals into a 2-dimensional spectrogram image. The experimental
evaluations are performed on the Korean spontaneous emotional speech database from AI-Hub, consisting of 7 emotions, i.e., joy, love,
anger, fear, sadness, surprise, and neutral. As a result, we achieved an average accuracy of 83.5% and 73.0% for adults and young people
using a time-frequency 2-dimension spectrogram, respectively. In conclusion, our findings demonstrated that the suggested framework
outperformed current state-of-the-art techniques for spontaneous speech and showed a promising performance despite the difficulty in
quantifying spontaneous speech emotional expression.
The Transactions of the Korea Information Processing Society,
Vol. 13, No. 6, pp. 284-290,
Jun.
2024
![](https://d2kjln74dkk4oj.cloudfront.net/img/doi_icon.png)
Spontaneous Speech Speech Emotion Recognition spectrogram Convolutional Neural Network