Vol. 51, No. 5,
May 2024
Digital Library
Optimized Design of Filesystems for Unikernel
Kyeong Woon Cho, Hyo Kyeong Ban
http://doi.org/10.5626/JOK.2024.51.5.389
Unikernel is a special-purpose kernel optimized for single applications and services. Compared to general-purpose kernels, its advantages include fast boot time, small memory footprint, high performance, and security. Unikernel applications attempt to maintain compatibility with the runtime environment of the general-purpose kernel and are used in binary or source-compatible form with legacy applications. Most existing unikernel projects implemented filesystem APIs with a priority on running applications rather than performance optimizations. Accordingly, it is a common practice to deploy a general-purpose file system naively or to introduce a host filesystem dependent method. In this paper, we discuss the design of unikernel file systems taking into account the purpose of unikernel, i.e., ensuring optimized performance with minimal system resources while maintaining security. Specifically, we analyze the performance and memory requirements for file systems supported by major unikernels through micro-benchmarks and suggest file system design guidelines to provide optimized performance and security.
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.
Prediction of Dehydrogenation Enthalpy Using Graph Isomorphism Network
Kun Young Choi, Woo Hyun Yuk, Jeong Woo Han, Cham Kill Hong
http://doi.org/10.5626/JOK.2024.51.5.406
This paper conducts dehydrogenation enthalpy prediction that could play an important role in selecting optimal liquid organic hydrogen carriers. We employed graph convolutional networks, which produced molecular embeddings for the prediction. Specifically, we adopted Graph Isomorphism Network (GIN) known to be the most expressive graph-based representation learning model. Our proposed approach could build molecular embeddings. Our proposed approach outperformed conventional machine learning solutions and traditional representations based on chemical physics algorithms. In addition, the performance of the proposed model could be improved with small batch sizes and deeper GCN layers using skip connections.
The Development of a Test Environment for Integrated Test of Cyber-Physical Systems with Multiple Instances
Yun Ah Heo, Jin Se Jeong, Beom Joon Yoon
http://doi.org/10.5626/JOK.2024.51.5.414
Cyber-physical systems (CPSs) are closely related to other multiple systems, environments, or users. In some cases, they operate in multiple configuration environments where multiple instances interact during execution time to achieve certain objectives. In such cases, various development artifacts and hazard analysis results that change as the system configuration changes and the test cases due to changes in the connection relationship should be significantly analyzed. To study how to conduct integrated tests for multi-structured CPS with multiple instances, it is necessary to establish a test environment similar to the actual environment. Especially, since CPSs integrate the cyber and physical world, it is necessary to include problems in the physical environments. This paper introduces a test environment that consists of two types of systems and multiple instances of each system. A developed test environment can be utilized for tasks such as generating and verifying integrated test cases.
Formal Specification and Model Checking of TLS Software Security Requirements using Maude
http://doi.org/10.5626/JOK.2024.51.5.426
The Transport Layer Security (TLS) protocol provides encrypted communication on computer networks and is widely used in various network applications such as HTTPS, email, and VolP. Formal verification of security protocols, such as TLS, is very important issue because errors in security protocols cause critical security vulnerabilities. Therefore, several model verification techniques have been studied to verify the security properties at the specification level of the protocols. However, verification at the protocol specification level has a limitation in that it cannot detect errors occurring at the design level. In this paper, we formalize TLS software at the design level using the Maude framework, and through this, design-level vulnerabilities such as FREAK attacks can be formally analyzed using model checking.
Application of OOD Detection Using MSP in EEG-Based Emotion Classification
HyoSeon Choi, Dahoon Choi, Byung Hyung Kim
http://doi.org/10.5626/JOK.2024.51.5.438
Several deep learning approaches have recently improved the performance of emotion classification tasks. However, these successful applications cannot be directly applied to learning EEG signals because of their nonlinear and complex data structure. This limitation leads to inter- and intra-subject variability problems for understanding complex emotion dynamics. To address this limitation, we focus on studying the variability rather than extracting features from high-dimensional neural activities. In the context of deep learning, we propose a framework to detect and remove abnormal pairs of EEG data and labels for enhancing model performance by utilizing the Maximum Softmax Probability approach. Experimental results on public datasets demonstrated the superiority of our method with a maximum improvement of 4% in accuracy.
Neural Network Learning Method using Weight Mirroring and Direct Feedback Error
Soha Lee, Heesung Yang, Hyeyoung Park
http://doi.org/10.5626/JOK.2024.51.5.445
Error backpropagation algorithm is a core learning algorithm of neural networks and, until recently, has been used in various deep learning models. However, the weight update rule of error backpropagation, in which the error signal of the upper layer is sequentially transmitted to the lower layer and the weight values of the upper layer that are used to update the lower layer weights, has a problem of biological implausibility and computational inefficiency. To address these issues, learning methods using separate backward weights have been proposed, but they are still at an early stage and require further analysis and improvement from various perspectives. In this paper, we proposed a new learning method by combining the direct feedback alignment method, which directly projects the errors of the last layer into each hidden layer, and a weight mirror method with a separate step for updating backward weights. The proposed method overcomes the limitations of learning methods to implement a weight update method that is biologically plausible and allows for more efficient parallel learning. We confirmed the potential of the proposed method through experiments on various benchmark datasets.
ConTL: Improving the Performance of EEG-based Emotion Recognition via the Incorporation of CNN, Transformer and LSTM
Hyunwook Kang, Byung Hyung Kim
http://doi.org/10.5626/JOK.2024.51.5.454
This paper proposes a hybrid-network called ConTL, which is composed of a convolutional neural network (CNN), Transformer, and long short-term memory (LSTM) for EEG-based emotion recognition. Firstly, CNN is exploited to learn local features from the input EEG signals. Then, the Transformer learns global temporal dependencies from the output features. To further learn sequential dependencies of the time domain, the output features from the Transformer are fed to the bi-directional LSTM. To verify the effects of the proposed model, we compared the classification accuracies with five state-of-the-art models. There was an 0.73% improvement on SEED-IV compared to CCNN, and improvements of 0.97% and 0.63% were observed compared to DGCNN for valence and arousal of DEAP, respectively.
Pseudo-label Correction using Large Vision-Language Models for Enhanced Domain-adaptive Semantic Segmentation
http://doi.org/10.5626/JOK.2024.51.5.464
It is very expensive to make semantic segmentation labels for real-world images. To solve this problem in unsupervised domain adaptation, the model is trained by using data generated in a virtual environment that can easily collect labels or data is already collected and real-world images without labels. One of the common problems in unsupervised domain adaptation is that thing classes with similar appearance are easily confused. In this paper, we propose a method of calibrating the label of the number of target data using large vision-language models. Making the number of labels generated for the target image more accurate can reduce confusion among thing classes. The proposed method improves the performance of DAFormer by +1.1 mIoU in adaptation from game to reality and +1.1 mIoU in adaptation from day to night. For thing classes, the proposed method improved the performance of the MIC by +0.6 mIoU in adaptation from game to reality and +0.7 mIoU in adaptation from day to night.
Graph Structure Learning-Based Neural Network for ETF Price Movement Prediction
Hyeonsoo Jo, Jin-gee Kim, Taehun Kim, Kijung Shin
http://doi.org/10.5626/JOK.2024.51.5.473
Exchange-Traded Funds (ETFs) are index funds that mirror particular market indices, usually associated with their low risk and expense ratio to individual investors. Various methods have emerged for accurately predicting ETF price movements, and recently, AI-based technologies have been developed. One representative method involves using time-series-based neural networks to predict the price movement of ETFs. This approach effectively incorporates past price information of ETFs, allowing the prediction of their movement. However, it has a limitation as it only utilizes historical information of individual ETFs and does not account for the relationships and interactions between different ETFs. To address this issue, we propose a model that can capture relationships between ETFs. The proposed model uses graph structure learning to infer a graph representing relationships between ETFs. Based on this, a graph neural network predicts the ETF price movement. The proposed model demonstrates superior performance compared to time-series-based deep-learning models that only use individual ETF information.
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