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A Label Inference Algorithm Considering Vertex Importance in Semi-Supervised Learning
Byonghwa Oh, Jihoon Yang, Hyun-Jin Lee
Semi-supervised learning is an area in machine learning that employs both labeled and unlabeled data in order to train a model and has the potential to improve prediction performance compared to supervised learning. Graph-based semi-supervised learning has recently come into focus with two phases: graph construction, which converts the input data into a graph, and label inference, which predicts the appropriate labels for unlabeled data using the constructed graph. The inference is based on the smoothness assumption feature of semi-supervised learning. In this study, we propose an enhanced label inference algorithm by incorporating the importance of each vertex. In addition, we prove the convergence of the suggested algorithm and verify its excellence.
Flash Operation Group Scheduling for Supporting QoS of SSD I/O Request Streams
Eungyu Lee, Sun Won, Joonwoo Lee, Kanghee Kim, Eyeehyun Nam
As SSDs are increasingly being used as high-performance storage or caches, attention is increasingly paid to the provision of SSDs with Quality-of-Service for I/O request streams of various applications in server systems. Since most SSDs are using the AHCI controller interface on a SATA bus, it is not possible to provide a differentiated service by distinguishing each I/O stream from others within the SSD. However, since a new SSD interface, the NVME controller interface on a PCI Express bus, has been proposed, it is now possible to recognize each I/O stream and schedule I/O requests within the SSD for differentiated services. This paper proposes Flash Operation Group Scheduling within NVME-based flash storage devices, and demonstrates through QEMU-based simulation that we can achieve a proportional bandwidth share for each I/O stream.
Automated Cell Counting Method for HeLa Cells Image based on Cell Membrane Extraction and Back-tracking Algorithm
Minyoung Kyoung, Jeong-Hoh Park, Myoung gu Kim, Sang-Mo Shin, Hyunbean Yi
Cell counting is extensively used to analyze cell growth in biomedical research, and as a result automated cell counting methods have been developed to provide a more convenient and means to analyze cell growth. However, there are still many challenges to improving the accuracy of the cell counting for cells that proliferate abnormally, divide rapidly, and cluster easily, such as cancer cells. In this paper, we present an automated cell counting method for HeLa cells, which are used as reference for cancer research. We recognize and classify the morphological conditions of the cells by using a cell segmentation algorithm based on cell membrane extraction, and we then apply a cell back-tracking algorithm to improve the cell counting accuracy in cell clusters that have indistinct cell boundary lines. The experimental results indicate that our proposed segmentation method can identify each of the cells more accurately when compared to existing methods and, consequently, can improve the cell counting accuracy.
Improvement of Runtime Intrusion Prevention Evaluator (RIPE)
Hyungyu Lee, Damho Lee, Taehwan Kim, Donghwang Cho, Sanghoon Lee, Hoonkyu Kim, Changwoo Pyo
Runtime Intrusion Prevention Evaluator (RIPE), published in 2011, is a benchmark suite for evaluating mitigation techniques against 850 attack patterns using only buffer overflow. Since RIPE is built as a single process, defense and attack routines cannot help sharing process states and address space layouts when RIPE is tested. As a result, attack routines can access the memory space for defense routines without restriction. We separate RIPE into two independent processes of defense and attacks so that mitigations based on confidentiality such as address space layout randomization are properly evaluated. In addition, we add an execution mode to test robustness against brute force attacks. Finally, we extend RIPE by adding 38 attack forms to perform format string attacks and virtual table (vtable) hijacking attacks. The revised RIPE contributes to the diversification of attack patterns and precise evaluation of the effectiveness of mitigations.
One-Class Classification Model Based on Lexical Information and Syntactic Patterns
Hyeon-gu Lee, Maengsik Choi, Harksoo Kim
Relation extraction is an important information extraction technique that can be widely used in areas such as question-answering and knowledge population. Previous studies on relation extraction have been based on supervised machine learning models that need a large amount of training data manually annotated with relation categories. Recently, to reduce the manual annotation efforts for constructing training data, distant supervision methods have been proposed. However, these methods suffer from a drawback: it is difficult to use these methods for collecting negative training data that are necessary for resolving classification problems. To overcome this drawback, we propose a one-class classification model that can be trained without using negative data. The proposed model determines whether an input data item is included in an inner category by using a similarity measure based on lexical information and syntactic patterns in a vector space. In the experiments conducted in this study, the proposed model showed higher performance (an F1-score of 0.6509 and an accuracy of 0.6833) than a representative one-class classification model, one-class SVM(Support Vector Machine).
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.
Data Consistency-Control Scheme Using a Rollback-Recovery Mechanism for Storage Class Memory
Hyun Ku Lee, Junghoon Kim, Dong Hyun Kang, Young Ik Eom
Storage Class Memory(SCM) has been considered as a next-generation storage device because it has positive advantages to be used both as a memory and storage. However, there are significant problems of data consistency in recently proposed file systems for SCM such as insufficient data consistency or excessive data consistency-control overhead. This paper proposes a novel data consistency-control scheme, which changes the write mode for log data depending on the modified data ratio in a block, using a rollback-recovery scheme instead of the Write Ahead Logging (WAL) scheme. The proposed scheme reduces the log data size and the synchronization cost for data consistency. In order to evaluate the proposed scheme, we implemented our scheme on a Linux 3.10.2- based system and measured its performance. The experimental results show that our scheme enhances the write throughput by 9 times on average when compared to the legacy data consistency control scheme.
Improvement of XRCE-DDS Communication System for Swarm Environment of Unmanned Vehicles Based on PX4-ROS2
Hyeongyu Lee, Doyoon Kim, Dongoo Lee, Sungtae Moon
http://doi.org/10.5626/JOK.2025.52.3.227
Recently, swarm vehicles are being used in various fields due to the development of swarm operation technology. Among various systems that constitute a swarm vehicle, PX4-ROS2 connects the PX4, an unmanned vehicle control computer, and ROS2 for mission execution through XRCE-DDS (eXtremely Resource Constrained Environments-Data Distribution Service), an open-source-based software that supports real-time communication between devices. However, the operation of swarm unmanned vehicles based on a wireless network using a distributed service of XRCE-DDS is not optimized. It requires communication optimization work for stable operation. In this paper, we analyzed the XRCE-DDS communication structure operating in PX4-ROS2 and proposed a new Discovery mechanism to solve the problem of increased communication volume due to increased nodes during swarm operation. We present a method to enhance the stability and scalability of communication and verified it through simulation.
IDFusion: Joint Angle Measurement Method through Fusion of Inertial Measurement Sensor and Depth Camera
Juyeon Park, Mingyu Park, Gyumin Park, Hyun Lee
http://doi.org/10.5626/JOK.2025.52.3.208
Recent advancements in human and object recognition technologies are increasingly applied across various fields, particularly in motion detection research utilizing inertial measurement sensors and depth cameras in areas such as gaming, healthcare, and security. However, challenges such as cumulative errors and variable measurement accuracies depending on the environment persist. This study proposed IDFusion, a method that could integrate inertial measurement sensors and depth cameras for joint angle measurement, distinguishing itself through data transformation and joint angle conversion stages before fusion. Comparative analysis against using inertial measurement sensors and depth cameras individually demonstrated a superior performance of IDFusion. This technique holds promise for applications in healthcare, sports science, and human-computer interaction.
An Efficient Continuous Subgraph Matching Technique for Graph Stream Processing in a Memory-constrained Environment
Somin Lee, Sanghyeuk Kim, Hyeonbyeong Lee, Dojin Choi, Jongtae Lim, Kyoungsoo Bok, Jaesoo Yoo
http://doi.org/10.5626/JOK.2022.49.12.1154
Recently, with the proliferation of social network services, the size of graph data has been becoming increasingly vast and graph data are changed in real-time. Therefore, it is necessary to perform continuous query processing on real-time graph streams. Moreover, it is difficult keep the entire large graph data in the main memory since its size is constrained in real-world application environments. Consequently, continuous subgraph matching techniques are required by considering memory-constrained environments. In this paper, we propose a continuous subgraph matching technique for graph streams in a memory-constrained environment. The proposed technique consists of modules such as index manager, query processor, and cache manager for efficient continuous subgraph matching. We conduct performance evaluations to demonstrate the superiority of the proposed technique.
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