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
Smart City Framework Based on Geospatial Information StandardsEunbi Ko Guk Sik Jeong Kyoung Cheol Koo |
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Modern cities are actively adopting smart city services to address various urban challenges. Geospatial information acts as the foundational infrastructure of smart cities, promoting the sustainable development of urban areas. Consequently, as the st... | |
Performance Evaluation and Analysis on Single and Multi-Network Virtualization Systems with Virtio and SR-IOVJaehak Lee Jongbeom Lim Heonchang Yu |
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As functions that support virtualization on their own in hardware are developed, user applications having various workloads are operating efficiently in the virtualization system. SR-IOV is a virtualization support function that takes direct access t... | |
Technique to Reduce Container Restart for Improving Execution Time of Container Workflow in Kubernetes EnvironmentsTaeshin Kang Heonchang Yu |
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The utilization of container virtualization technology ensures the consistency and portability of data-intensive and memory volatile workflows. Kubernetes serves as the de facto standard for orchestrating these container applications. Cloud users of... | |
Model-Based Intelligent Framework Interface for UAV Autonomous MissionSon Gun Joon Lee Jaeho |
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Recently, thanks to the development of artificial intelligence technologies such as image recognition, research on unmanned aerial vehicles is being actively conducted. In particular, related research is increasing in the field of military drones, w... | |
Latest Publication (Vol. 13, No. 9, Sep. 2024)
Detection of Abnormal CAN Messages Using Periodicity and Time Series Analysis
Se-Rin Kim Ji-Hyun Sung Beom-Heon Youn Harksu Cho
https://doi.org/10.3745/TKIPS.2024.13.9.395
CAN GRU Anomaly Detection Time Series Machine Learning
Recently, with the advancement of technology, the automotive industry has seen an increase in network connectivity. CAN (Controller
Area Network) bus technology enables fast and efficient data communication between various electronic devices and systems within a
vehicle, providing a platform that integrates and manages a wide range of functions, from core systems to auxiliary features. However,
this increased connectivity raises concerns about network security, as external attackers could potentially gain access to the automotive
network, taking control of the vehicle or stealing personal information. This paper analyzed abnormal messages occurring in CAN and
confirmed that message occurrence periodicity, frequency, and data changes are important factors in the detection of abnormal messages.
Through DBC decoding, the specific meanings of CAN messages were interpreted. Based on this, a model for classifying abnormalities
was proposed using the GRU model to analyze the periodicity and trend of message occurrences by measuring the difference (residual)
between the predicted and actual messages occurring within a certain period as an abnormality metric. Additionally, for multi-class
classification of attack techniques on abnormal messages, a Random Forest model was introduced as a multi-classifier using message
occurrence frequency, periodicity, and residuals, achieving improved performance. This model achieved a high accuracy of over 99%
in detecting abnormal messages and demonstrated superior performance compared to other existing models.
The Transactions of the Korea Information Processing Society,
Vol. 13, No. 9, pp. 395-403,
Sep.
2024
https://doi.org/10.3745/TKIPS.2024.13.9.395
CAN GRU Anomaly Detection Time Series Machine Learning
eBPF-based Container Activity Analysis System
Jisu Kim Jaehyun Nam
https://doi.org/10.3745/TKIPS.2024.13.9.404
Container eBPF monitoring Observability
The adoption of cloud environments has revolutionized application deployment and management, with microservices architecture and
container technology serving as key enablers of this transformation. However, these advancements have introduced new challenges,
particularly the necessity to precisely understand service interactions and conduct detailed analyses of internal processes within complex
service environments such as microservices. Traditional monitoring techniques have proven inadequate in effectively analyzing these
complex environments, leading to increased interest in eBPF (extended Berkeley Packet Filter) technology as a solution. eBPF is a powerful
tool capable of real-time event collection and analysis within the Linux kernel, enabling the monitoring of various events, including file
system activities within the kernel space. This paper proposes a container activity analysis system based on eBPF, which monitors events
occurring in the kernel space of both containers and host systems in real-time and analyzes the collected data. Furthermore, this paper
conducts a comparative analysis of prominent eBPF-based container monitoring systems (Tetragon, Falco, and Tracee), focusing on aspects
such as event detection methods, default policy application, event type identification, and system call blocking and alert generation.
Through this evaluation, the paper identifies the strengths and weaknesses of each system and determines the necessary features for
effective container process monitoring and restriction. In addition, the proposed system is evaluated in terms of container metadata
collection, internal activity monitoring, and system metadata integration, and the effectiveness and future potential of eBPF-based
monitoring systems.
The Transactions of the Korea Information Processing Society,
Vol. 13, No. 9, pp. 404-412,
Sep.
2024
https://doi.org/10.3745/TKIPS.2024.13.9.404
Container eBPF monitoring Observability
Performance Evaluation and Consideration of Shadow Stack on RISC-V Architecture
Kang Ha Young Han Go Won Park Sung Hwan Kwon Dong Hyun
https://doi.org/10.3745/TKIPS.2024.13.9.413
RISC-V Shadow Stack Security
RISC-V is an open-source instruction set architecture, used in various hardware implementations, and can be flexibly expanded to
meet system requirements through the RV64I base instruction set and 16 standard extensions. Currently, the RISC-V architecture employs
the shadow stack technique to protect return addresses. This paper compares the performance of the compact shadow stack mechanism
and the parallel shadow stack mechanism in the RISC-V architecture using the SPEC CPU 2017 and beebs benchmarks. Experimental
results show that the parallel shadow stack mechanism exhibits higher overhead than the compact shadow stack mechanism. This suggests
that the efficiency of the parallel mechanism is reduced due to the limitations of the RISC-V architecture, making the compact shadow
stack more suitable for RISC-V. Additionally, this paper identifies the security limitations of the existing RISC-V shadow stack and proposes
directions for enhancing the performance and security of shadow stack mechanisms to ensure a secure execution environment for RISC-V.
The Transactions of the Korea Information Processing Society,
Vol. 13, No. 9, pp. 413-420,
Sep.
2024
https://doi.org/10.3745/TKIPS.2024.13.9.413
RISC-V Shadow Stack Security
A Study on the Evaluation Methods for Assessing the Understanding of Korean Culture by Generative AI Models
Son Ki Jun Kim Seung Hyun
https://doi.org/10.3745/TKIPS.2024.13.9.421
LLM Korean culture Culture Understanding Evaluation Dataset
Recently, services utilizing large-scale language models (LLMs) such as GPT-4 and LLaMA have been released, garnering significant
attention. These models can respond fluently to various user queries, but their insufficient training on Korean data raises concerns about
the potential to provide inaccurate information regarding Korean culture and language. In this study, we selected eight major publicly
available models that have been trained on Korean data and evaluated their understanding of Korean culture using a dataset composed
of five domains (Korean language comprehension and cultural aspects). The results showed that the commercial model HyperClovaX
exhibited the best performance across all domains. Among the publicly available models, Bookworm demonstrated superior Korean
language proficiency. Additionally, the LDCC-SOLAR model excelled in areas related to understanding Korean culture and language.
The Transactions of the Korea Information Processing Society,
Vol. 13, No. 9, pp. 421-428,
Sep.
2024
https://doi.org/10.3745/TKIPS.2024.13.9.421
LLM Korean culture Culture Understanding Evaluation Dataset
A Survey on the Latest Research Trends in Retrieval-Augmented Generation
Eunbin Lee Ho Bae
https://doi.org/10.3745/TKIPS.2024.13.9.429
LLM Retrieval-Augmented Generation Hallucination
As Large Language Models (LLMs) continue to advance, effectively harnessing their potential has become increasingly important. LLMs,
trained on vast datasets, are capable of generating text across a wide range of topics, making them useful in applications such as content
creation, machine translation, and chatbots. However, they often face challenges in generalization due to gaps in specific or specialized
knowledge, and updating these models with the latest information post-training remains a significant hurdle. To address these issues,
Retrieval-Augmented Generation (RAG) models have been introduced. These models enhance response generation by retrieving information
from continuously updated external databases, thereby reducing the hallucination phenomenon often seen in LLMs while improving
efficiency and accuracy. This paper presents the foundational architecture of RAG, reviews recent research trends aimed at enhancing
the retrieval capabilities of LLMs through RAG, and discusses evaluation techniques. Additionally, it explores performance optimization
and real-world applications of RAG in various industries. Through this analysis, the paper aims to propose future research directions
for the continued development of RAG models.
The Transactions of the Korea Information Processing Society,
Vol. 13, No. 9, pp. 429-436,
Sep.
2024
https://doi.org/10.3745/TKIPS.2024.13.9.429
LLM Retrieval-Augmented Generation Hallucination
Implementation of a Coding Style Checking System in an Online Judge System
Yeonghun Kim Junseok Cheon Gyun Woo
https://doi.org/10.3745/TKIPS.2024.13.9.437
Online Judge Auto Judge System Coding Style Programming Education
Adhering to coding style guidelines is crucial for both companies and developers as it improves code readability and reduces the
costs associated with testing and maintenance. However, teaching coding style in programming courses poses challenges. Setting up an
environment for learning coding styles is hard, and there are no predefined coding style rules for beginners. From the learners' perspective,
since adherence to coding styles does not affect their grades, they do not feel a strong need to learn them. This paper introduces a
coding style checking system for an online evaluation system. The proposed system is implemented to check and evaluate coding styles
in C, Java, and Python. Additionally, we applied 234 out of the 1,023 rules provided by the language-specific tools, which is 23.08%,
allowing for the application of coding style rules according to the course progression. Moreover, we motivated learners to improve their
coding style by adding quality scores to their basic scores. After introducing the coding style education system, the number of students
scoring over 25 points on their initial submissions increased by 149.47%, from 18 students in the first week to 44 students in the sixth
week. Learners used the coding style checking system to learn how to apply coding style rules and subsequently implemented their code
in adherence to the specified coding styles.
The Transactions of the Korea Information Processing Society,
Vol. 13, No. 9, pp. 437-443,
Sep.
2024
https://doi.org/10.3745/TKIPS.2024.13.9.437
Online Judge Auto Judge System Coding Style Programming Education
A Study on the Optimization of Fire Awareness Model Based on Convolutional Neural Network: Layer Importance Evaluation-Based Approach
Won Jin Mi-Hwa Song
https://doi.org/10.3745/TKIPS.2024.13.9.444
Layer Importance Evaluation transfer learning model CNN Optimization Real-Time Fire Detection Contribution
This study proposes a deep learning architecture optimized for fire detection derived through Layer Importance Evaluation. In order
to solve the problem of unnecessary complexity and operation of the existing Convolutional Neural Network (CNN)-based fire detection
system, the operation of the inner layer of the model based on the weight and activation values was analyzed through the Layer Importance
Evaluation technique, the layer with a high contribution to fire detection was identified, and the model was reconstructed only with
the identified layer, and the performance indicators were compared and analyzed with the existing model. After learning the fire data
using four transfer learning models: Xception, VGG19, ResNet, and EfficientNetB5, the Layer Importance Evaluation technique was applied
to analyze the weight and activation value of each layer, and then a new model was constructed by selecting the top rank layers with
the highest contribution. As a result of the study, it was confirmed that the implemented architecture maintains the same performance
with parameters that are about 80% lighter than the existing model, and can contribute to increasing the efficiency of fire monitoring
equipment by outputting the same performance in accuracy, loss, and confusion matrix indicators compared to conventional complex
transfer learning models while having a learning speed of about 3 to 5 times faster.
The Transactions of the Korea Information Processing Society,
Vol. 13, No. 9, pp. 444-452,
Sep.
2024
https://doi.org/10.3745/TKIPS.2024.13.9.444
Layer Importance Evaluation transfer learning model CNN Optimization Real-Time Fire Detection Contribution
3D Object Extraction Mechanism from Informal Natural Language Based Requirement Specifications
Hyuntae Kim Janghwan Kim Jihoon Kong Kidu Kim R. Young Chul Kim
https://doi.org/10.3745/TKIPS.2024.13.9.453
Natural Language Sequence Diagram 3D image requirement engineering
Recent advances in generative AI technologies using natural language processing have critically impacted text, image, and video
production. Despite these innovations, we still need to improve the consistency and reusability of AI-generated outputs. These issues
are critical in cartoon creation, where the inability to consistently replicate characters and specific objects can degrade the work's quality.
We propose an integrated adaption of language analysis-based requirement engineering and cartoon engineering to solve this. The
proposed method applies the linguistic frameworks of Chomsky and Fillmore to analyze natural language and utilizes UML sequence models
for generating consistent 3D representations of object interactions. It systematically interprets the creator's intentions from textual inputs,
ensuring that each character or object, once conceptualized, is accurately replicated across various panels and episodes to preserve visual
and contextual integrity. This technique enhances the accuracy and consistency of character portrayals in animated contexts, aligning
closely with the initial specifications. Consequently, this method holds potential applicability in other domains requiring the translation
of complex textual descriptions into visual representations.
The Transactions of the Korea Information Processing Society,
Vol. 13, No. 9, pp. 453-459,
Sep.
2024
https://doi.org/10.3745/TKIPS.2024.13.9.453
Natural Language Sequence Diagram 3D image requirement engineering
Enhancing Career Development Utilizing LLM for Targeted Learning Pathway
Mahisha Patel Vishakha Tyagi Isabel Hyo Jung Song
https://doi.org/10.3745/TKIPS.2024.13.9.460
Career Development Job Matching Skill Gap Identification Customized Learning Paths LLM
Targeted career development is critical for student success but is often lacking for underrepresented students at many public
higher-education institutions due to insufficient career counseling resources. We propose an innovative career development tool leveraging
Large Language Models (LLMs) to enhance student career prospects through three steps: (1) identifying relevant jobs by analyzing resumes,
(2) pinpointing skill gaps using external resources such as classroom assignments, in addition to resumes, and (3) suggesting customized
learning paths. Our tool accurately matches jobs in real-world settings, identifies true skill gaps while reducing false positives, and provides
learning paths that receive high satisfaction scores from faculty. Future research will enhance the solution's capabilities by incorporating
diverse external resources and leveraging advancements in LLM technology to better support early-stage career seekers.
The Transactions of the Korea Information Processing Society,
Vol. 13, No. 9, pp. 460-467,
Sep.
2024
https://doi.org/10.3745/TKIPS.2024.13.9.460
Career Development Job Matching Skill Gap Identification Customized Learning Paths LLM
Robust and Efficient Measurement Using a 3D Laser Line Sensor on UGVs
Jiwoo Shin Jun-Yong Park Seoyeon Kim Taesik Kim Jinman Jung
https://doi.org/10.3745/TKIPS.2024.13.9.468
3D Laser Line Sensor 3D point cloud Measurement on UGV Paving Blocks Monitoring Ground Deformation
Excavation work in urban areas can induce ground deformation, which may damage nearby infrastructure. Such ground deformation
can result in displacement of paving blocks near the construction site. Accurate measurement of these displacements can serve as an
indicator for assessing the potential risks associated with ground deformation. This paper proposes a robust and efficient method for
paving block displacement measurement using a 3D laser line sensor mounted on an Unmanned Ground Vehicle (UGV). The proposed
method consists of two stages: 2D projection based object detection and measurement through the CPLF algorithm. Experimental results
demonstrate that the CPLF algorithm is more efficient compared to the PLF algorithm, achieving an error of 1.36 mm and a processing
time of 10.76 ms, confirming that the proposed method ensures robust online measurements with high accuracy in real-world environments
with various types of paving blocks and environmental factors using a 3D laser line sensor on a UGV.
The Transactions of the Korea Information Processing Society,
Vol. 13, No. 9, pp. 468-473,
Sep.
2024
https://doi.org/10.3745/TKIPS.2024.13.9.468
3D Laser Line Sensor 3D point cloud Measurement on UGV Paving Blocks Monitoring Ground Deformation