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Lightweight Temporal Segment Network for Video Scene Understanding: Validation in Driver Assault Detection
Juneyong Lee, Joon Kim, Junhui Park, Jongho Jo, Ikbeom Jang
http://doi.org/10.5626/JOK.2024.51.11.987
"The number of driver assaults in transportation such as taxis and buses has been increasing over the past few years. It can be especially difficult to respond quickly to assaults on drivers by drunks late at night. To address this issue, our research team proposed a lightweight CNN-based Temporal Segment Network (TSN) model that could detect driver assaults by passengers in real time. The TSN model efficiently processes videos by sampling a small number of image frames and divides videos into two streams for learning: one for spatial information processing and the other for temporal information processing. Convolutional neural networks are employed in each stream. In this research, we applied a lightweight CNN architecture, MobileOne, significantly reducing the model size while demonstrating improved accuracy even with limited computing resources. The model is expected to contribute to rapid response and prevention of hazardous situations for drivers when it is integrated into vehicular driver monitoring systems."
Optimizing Throughput Prediction Models Based on Feature Category Contribution in 4G/5G Network Environments
http://doi.org/10.5626/JOK.2024.51.11.961
The acceleration in 5G technology adoption due to increased network data consumption and limitations of 4G has led to the establishment of a heterogeneous network environment comprising both 4G and limited 5G. Consequently, this highlights the importance of throughput prediction for network service quality (QoS) and resource optimization. Traditional throughput prediction research mainly relies on the use of single attributes or extraction of attributes through correlation analysis. However, these approaches have limitations, including potential exclusion of variables with nonlinear relationships with arbitrariness and inconsistency of correlation coefficient thresholds. To overcome these limitations, this paper proposed a new approach based on Feature Importance. This method could calculate the relative importance of features used in the network and assign contribution scores to attribute categories. By utilizing these scores, throughput prediction was enhanced. This approach was applied and tested on four open network datasets. Experiments demonstrated that the proposed method successfully derived an optimal category combination for throughput prediction, reduced model complexity, and improved prediction accuracy compared to using all categories.
A Comparative Study on Server Allocation Optimization Algorithms for Accelerating Parallel Training of Large Language Models
Jinkyu Yim, Yerim Choi, Jinho Lee
http://doi.org/10.5626/JOK.2024.51.9.783
As large-scale language models (LLMs) come to be increasingly utilized in various fields, there is an increasing demand to develop models with higher performance. Significant computational power and memory capacity will be needed to train such models. Therefore, researchers have used 3D parallelization methodology for large-scale language model learning on numerous servers equipped with GPUs. However, 3D parallelization requires frequent large-scale data transfers between servers, which bottlenecks the overall training time. To address this, prior studies have proposed a methodology that identifies non-uniform cluster network conditions in advance and arranges servers and GPUs in an optimized parallel configuration. The existing methods of this type use the classical optimization algorithm SA (Simulated Annealing) for mapping. In this paper, we apply genetic algorithms as well as SAT(satisfiability) algorithms to the problem, and compare and analyze the performance of each algorithm under various experimental environments.
Prediction of Cancer Prognosis Using Patient-Specific Cancer Driver Gene Information
http://doi.org/10.5626/JOK.2024.51.6.574
Accurate prediction of cancer prognosis is crucial for effective treatment. Consequently, numerous studies on cancer prognosis have been conducted, with recent research leveraging various machine learning techniques such as deep learning. In this paper, we first constructed patient-specific gene networks for each patient, then selected patient-specific cancer driver genes, considering the heterogeneity of cancer. We propose a deep neural architecture that can predict the prognosis more accurately using patient-specific cancer driver gene information. When our method was applied to gene expression data for 11 types of cancer, it demonstrated a significantly higher prediction accuracy compared to the existing methods.
aRFS+: A New Flow Steering Scheme for High Network Performance
http://doi.org/10.5626/JOK.2024.51.5.397
Recent studies indicate that a significant portion of central processing unit (CPU) usage in network stack processing is attributed to the transfer of data between kernel and user spaces. Direct Cache Access (DCA) has been recognized to enhance data copy efficiency by allowing applications to perform data copy operations utilizing L3 caches. However, current flow steering mechanisms lack awareness of caches; they often employ random selection methods or allocate processing tasks to cores based on the location of corresponding applications subsequently resulting in suboptimal throughput. To address this issue, in this paper, we propose a novel flow steering scheme named aRFS+. The three key ideas of aRFS+ are as follows. First, we dynamically allocated network applications to the DCA-capable NUMA node, enabling them to exploit DCA advantages during data copy operations. Second, we decouple application cores from network processing cores to maximize the benefits of multicore environments. Incoming packets are steered to a CPU distinct from the application core but situated within the same NUMA node. Third, we introduce an optimization technique that significantly mitigates the overhead associated with memory management. Our experimental evaluations demonstrated that aRFS+ substantially improved the overall throughput, with an enhancement of up to 60% compared to existing schemes.
An Image Harmonization Method with Improved Visual Uniformity of Composite Images in Various Lighting Colors
Doyeon Kim, Jonghwa Shim, Hyeonwoo Kim, Changsu Kim, Eenjun Hwang
http://doi.org/10.5626/JOK.2024.51.4.345
Image composition is a technique that creates a composite image by arranging foreground objects extracted from other images onto a background image. To improve the visual uniformity of the composite images, deep learning-based image harmonization techniques that adjust the lighting and color of foreground objects to match the background image have been actively proposed recently. However, existing techniques have limited performance in visual uniformity because they adjust colors only for the lighting color distribution of the dataset used for training. To address this problem, we propose a novel image harmonization scheme that has robust performance for various lighting colors. First, iHColor, a new dataset composed of various lighting color distributions, is built through data preprocessing. Then, a pre-trained GAN-based Harmonization model is fine-tuned using the iHColor dataset. Through experiments, we demonstrate that the proposed scheme can generate harmonized images with better visual uniformity than existing models for various lighting colors.
Open-source-based 5G Access Network Security Vulnerability Automated Verification Framework
Jewon Jung, Jaemin Shin, Sugi Lee, Yusung Kim
http://doi.org/10.5626/JOK.2023.50.6.531
Recently, various open sources based on 5G standards have emerged, and are widely used in research to find 5G control plane security vulnerabilities. However, leveraging those open sources requires extensive knowledge of complex source code, wireless communication devices, and massive 5G security standards. Therefore, in this paper, we propose a framework for the automatic verification of security vulnerabilities in the 5G control plane. This framework builds a 5G network using commercial Software Defined Radio (SDR) equipment and open-source software and implements a Man-in-the-Middle (MitM) attacker to deploy a control plane attack test bed. It also implements control plane message decoding and correction modules to execute message spoofing attacks and automatically classifies security vulnerabilities in 5G networks. In addition, a GUI-based web user interface is implemented so that users can create MitM attack scenarios and check the verification results themselves.
Integrating Domain Knowledge with Graph Convolution based on a Semantic Network for Elderly Depression Prediction
Seok-Jun Bu, Kyoung-Won Park, Sung-Bae Cho
http://doi.org/10.5626/JOK.2023.50.3.243
Depression in the elderly is a global problem that causes 300 million patients and 800,000 suicides every year, so it is critical to detect early daily activity patterns closely related to mobility. Although a graph-convolution neural network based on sensing logs has been promising, it is required to represent high-level behaviors extracted from complex sensing information sequences. In this paper, a semantic network that structuralizes the daily activity patterns of the elderly was constructed using additional domain knowledge, and a graph convolution model was proposed for complementary uses of low-level sensing log graphs. Cross-validation with 800 hours of data from 69 senior citizens provided by DNX, Inc. revealed improved prediction performance for the suggested strategy compared to the most recent deep learning model. In particular, the inference of a semantic network was justified by a graph convolution model by showing a performance improvement of 28.86% compared with the conventional model.
A Sensing Node Selection Scheme for Energy-Efficient Cooperative Spectrum Sensing in Cognitive Radio Sensor Networks
Fanhua Kong, Zilong Jin, Jinsung Cho
Cognitive radio technology can allow secondary users (SUs) to access unused licensed spectrums in an opportunistic manner without interfering with primary users (PUs). Spectrum sensing is a key technology for cognitive radio (CR). However, few studies have examined energy-efficient spectrum sensing in cognitive radio sensor networks (CRSNs). In this paper, we propose an energy-efficient cooperative spectrum sensing nodes selection scheme for cluster-based cognitive radio sensor networks. In our proposed scheme, false alarm probability and energy consumption are considered to minimize the number of spectrum sensing nodes in a cluster. Simulation results show that by applying the proposed scheme, spectrum sensing efficiency is improved with a decreased number of spectrum sensing nodes. Furthermore, network energy efficiency is guaranteed and network lifetime is substantially prolonged.
Energy-aware Selective Compression Scheme for Solar-powered Wireless Sensor Networks
Min Jae Kang, Semi Jeong, Dong Kun Noh
Data compression involves a trade-off between delay time and data size. Greater delay times require smaller data sizes and vice versa. There have been many studies performed in the field of wireless sensor networks on increasing network life cycle durations by reducing data size to minimize energy consumption; however, reductions in data size result in increases of delay time due to the added processing time required for data compression. Meanwhile, as energy generation occurs periodically in solar energy-based wireless sensor networks, redundant energy is often generated in amounts sufficient to run a node. In this study, this excess energy is used to reduce the delay time between nodes in a sensor network consisting of solar energy-based nodes. The energy threshold value is determined by a formula based on the residual energy and charging speed. Nodes with residual energy below the threshold transfer data compressed to reduce energy consumption, and nodes with residual energy above the threshold transfer data without compression to reduce the delay time between nodes. Simulation based performance verifications show that the technique proposed in this study exhibits optimal performance in terms of both energy and delay time compared with traditional methods.
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