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Spatio-Temporal Modeling via Adaptive Frequency Filtering for Video Action Recognition
Minji Kim, Taehoon Kim, Jonghyeon Seon, Bohyung Han
http://doi.org/10.5626/JOK.2024.51.12.1078
Modeling long-term spatio-temporal dependencies in video data is challenging, as CNNs often struggle to capture global context through their local receptive fields. To address this problem, we propose an efficient global spatio-temporal modeling method that integrates easily with existing CNN models. Our approach utilizes Discrete Cosine Transform (DCT) to shift information into the frequency domain, where two adaptive filtering paths operate complementarily: one removes redundant frequencies while preserving essential information, and the other enhances important frequencies for spatio-temporal modeling. We introduce DynamicMNIST, a lightweight dataset featuring various digit behaviors like shifting, rotating, and scaling. Our evaluations on three public benchmarks and DynamicMNIST demonstrate that the proposed module enhances activity recognition performance across different CNN models with minimal additional parameters and computational costs.
A Software-based Secure Disaggregated Memory System on Commodity Servers
Yewon Yong, Taehoon Kim, Sungho Lee, Changdae Kim
http://doi.org/10.5626/JOK.2024.51.9.757
A disaggregated memory system is a technology that consolidates memory from multiple servers. While this technique provides large amounts of memory for applications, it also poses serious security threats due to sensitive data transmission between servers. Several studies have addressed this issue by relying on specialized hardware. However, the use of such hardware introduces not only additional costs but also challenges in adopting it on commercial servers because of compatibility issues. In this paper, we propose a software-based mechanism to ensure the security of disaggregated memory systems. Our approach aims to prevent security threats by performing encryption and integrity verification on data transmitted between servers within a disaggregated memory system. To minimize the performance overhead associated with software implementation, our approach overlaps data transmission and decryption, and encrypts only private data. In addition, we optimize the size of encryption metadata to reduce memory overhead. Through empirical evaluations, we demonstrate that our proposed software-based security mechanism incurs negligible additional performance overhead, particularly when the performance overhead from the disaggregated memory system is already minimal.
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.
SVD-based Cross-Domain Recommendation Using K-means Clustering
http://doi.org/10.5626/JOK.2022.49.5.360
Cross-domain recommendation is a method that shares related user information data and item data in different domains. It is mainly used in online shopping malls with many users or multimedia service contents, such as YouTube or Netflix. Through K-means Clustering, embeddings are created by performing clustering based on user data and ratings. After learning the result through a multi-layer neural network, user satisfaction is predicted. Then, items suitable for the user are recommended using matrix factorization, which is a collaborative filtering technique. Through this study, it was shown through experiments that recommendations can predict cold-start problems at a lesser time cost and increase the user satisfaction.
QEMU/KVM Based In-Memory Block Cache Module for Virtualization Environment
TaeHoon Kim, KwangHyeok Song, JaeChun No, SungSoon Park
http://doi.org/10.5626/JOK.2017.44.10.1005
Recently, virtualization has become an essential component of cloud computing due to its various strengths, including maximizing server resource utilization, easy-to-maintain software, and enhanced data protection. However, since virtualization allows sharing physical resources among the VMs, the system performance can be deteriorated due to device contentions. In this paper, we first investigate the I/O overhead based on the number of VMs on the same server platform and analyze the block I/O process of the KVM hypervisor. We also propose an in-memory block cache mechanism, called QBic, to overcome I/O virtualization latency. QBic is capable of monitoring the block I/O process of the hypervisor and stores the data with a high access frequency in the cache. As a result, QBic provides a fast response for VMs and reduces the I/O contention to physical devices. Finally, we present a performance measurement of QBic to verify its effectiveness.
A MapReduce-based kNN Join Query Processing Algorithm for Analyzing Large-scale Data
HyunJo Lee, TaeHoon Kim, JaeWoo Chang
Recently, the amount of data is rapidly increasing with the popularity of the SNS and the development of mobile technology. So, it has been actively studied for the effective data analysis schemes of the large amounts of data. One of the typical schemes is a Voronoi diagram based on kNN join algorithm (VkNN-join) using MapReduce. For two datasets R and S, VkNN-join can reduce the time of the join query processing involving big data because it selects the corresponding subset Sj for each Ri and processes the query with them. However, VkNN-join requires a high computational cost for constructing the Voronoi diagram. Moreover, the computational overhead of the VkNN-join is high because the number of the candidate cells increases as the value of the k increases. In order to solve these problems, we propose a MapReduce-based kNN-join query processing algorithm for analyzing the large amounts of data. Using the seed-based dynamic partitioning, our algorithm can reduce the overhead for constructing the index structure. Also, it can reduce the computational overhead to find the candidate partitions by selecting corresponding partitions with the average distance between two seeds. We show that our algorithm has better performance than the existing scheme in terms of the query processing time.
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