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A Network Topology Scaling Method for Improving Network Comparison Using Colon Cancer Transcriptome Data
http://doi.org/10.5626/JOK.2022.49.8.646
Various research methods have been proposed based on gene expression information in the disease analysis model. In cancer transcriptome data analysis, methods of discovering hidden characteristics based on pathways are useful for the interpretation of results. In this study, the gene correlation network in the pathway unit was compared and analyzed based on the gene co-expression data. If there is a difference in the size of the two networks to be compared, the bias of the amount of information results in biased network information on a larger scale. To resolve this bias, the network of patients from different backgrounds was adjusted using the same amount of information in the network configuration. Normalized networks applied comparative analysis of important gene groups using the characteristics of biological networks, normalized 202 pathways networks using data of subtypes of total 4 types of colon cancer, and identified 5 pathways with specific results among subspecies.
Knowledge Graph Embedding for Link Prediction using Node-Link Interaction-based Graph Attention Networks
http://doi.org/10.5626/JOK.2022.49.7.555
Knowledge graphs are structures that express knowledge in the real world in the form of nodes and links-based triple form. These knowledge graphs are incomplete and many embedding techniques have been studied to effectively represent nodes and links in low-dimensional vector spaces to find other missing relationships. Recently, many neural network-based knowledge graph link prediction methods have been studied. However existing models consider nodes and links independently when determining the importance of a triple to a node which makes it difficult to reflect the interaction between nodes and links. In this paper, we propose an embedding method that will be used to analyze the importance of triple units by simultaneously considering nodes and links using composition operators, and at the same time prove that the model outperforms other methods in knowledge graph link prediction.
Information Collection of COVID-19 Pandemic Using Wikipedia Template Network
Danu Kim, Damin Lee, Jaehyeon Myung, Changwook Jung, Inho Hong, Diego Sáez-Trumper, Jinhyuk Yun, Woo-Sung Jung, Meeyoung Cha
http://doi.org/10.5626/JOK.2022.49.5.347
Access to accurate information is essential to reduce the social damage caused by the Coronavirus Disease 2019 (COVID-19) pandemic. Information about ongoing events, such as COVID-19, is quickly updated on Wikipedia, an accessible internet encyclopedia that allows users to edit it themselves. However, the existing Wikipedia information retrieval method has a limitation in collecting information, including relationships between documents. The template format of Wikipedia reflects the structure of information as a link that is selectively applied to documents with high relevance. This study collected information on COVID-19 in 10 languages on Wikipedia using a template and reorganized it into networks. Among the 10 networks with 130,662 nodes and 202,258 edges, languages with a large number of active users had a template network with a large size and depth, and documents highly related to COVID-19 existed within a 3-hop connection structure. This research proposed a new information retrieval method applicable to multiple languages and contributes to the construction of document lists related to specific topics.
Learning-based QoS Path Prediction Method in SDN Environment
Seunghoon Jeong, Seondong Heo, Hosang Yun
http://doi.org/10.5626/JOK.2021.48.11.1241
When Quality of Service (QoS) is supported by flow path control in Software-Defined Networking (SDN) environment, the current simple least cost path finding method can cause inefficient rerouting problems. The measured performance of the flow path derived based on the link quality may differ from the predicted performance. In particular, in the case of sequential QoS condition search for candidate paths, the effectiveness of path-based QoS support may decrease due to repeatedly searching for the same path previously identified as the final path. In this paper, we propose a learning-based QoS path search model. The model learns the path that finally satisfies the QoS conditions according to the network state, and predicts the QoS path for the network state when rerouting is required. The experiment shows that this learning model can reduce unnecessary path iteration search costs given the similar network conditions, and is more effective than other learning-based models in a service environment that requires rapid QoS quality restoration.
Instagram User Embedding and Fashion Photo Recommendation Using "likes" of Fashion Photos
http://doi.org/10.5626/JOK.2021.48.11.1235
As individual preference of fashion styles diversifies, demands for research recommending personalized fashion are increasing. Recently, with the development of deep learning technology, many studies have been conducted using deep learning to extract features from fashion photos and use them for recommendations. In this work, we exploit social network data to consider users and fashion styles in recommending fashion photos. Since social network users tend to post fashion photos in their preferred style and tag them with “Like“, social network data are very important for understanding relationship between users and fashion photos. We propose a technique to map users and fashion photos into the same vector space using social network data structure which consists of users and fashion photos. Especially, it is possible to use our method to recommend fashion photos that a user might prefer by mapping users and fashion photos not used for learning into a vector space without additional learning.
Automatic Segmentation of Lung Cancer in Chest CT Images through Capsule Network-based Dual-Window Ensemble Learning
Jumin Lee, Julip Jung, Helen Hong, Bong-Seog Kim
http://doi.org/10.5626/JOK.2021.48.8.905
It is difficult to accurately segment lung cancer in chest CT images when it has an irregular shape or nearby structures have a similar intensity as lung cancer. In this study, we proposed a dual window ensemble network that uses a capsule network to learn the relationship between lung cancer and nearby structures and additionally considers the mediastinal window image with the lung window image to distinguish lung cancer from the nearby structures. First, intensity and spacing normalization was performed on the input images of the lung window setting and mediastinal window setting. Second, two types of 2D capsule network were performed with the lung and mediastinal setting images. Third, the final segmentation mask was generated by ensemble the probability maps of the lung and mediastinal window images through average voting by reflecting the weight based on the characteristics of each image. The proposed method showed a Dice similarity coefficient(DSC) of 75.98% which was 0.53% higher than the method not considering the weight of each window setting. Furthermore, segmentation accuracy was improved even when lung cancer was surrounded by nearby structures.
Alpha-Integration Pooling for Convolutional Neural Networks
http://doi.org/10.5626/JOK.2021.48.7.774
Convolutional neural networks (CNNs) have achieved remarkable performance in many applications, especially in image recognition tasks. As a crucial component of CNNs, sub-sampling plays an important role for efficient training or invariance property, and max-pooling and arithmetic average-pooling are commonly used sub-sampling methods. In addition to the two pooling methods, however, there are many other pooling types, such as geometric average, harmonic average, among others. Since it is not easy for algorithms to find the best pooling method, usually the pooling types are predefined, which might not be optimal for different tasks. As other parameters in deep learning, however, the type of pooling can be driven by data for a given task. In this paper, we propose α-integration pooling (αI-pooling), which has a trainable parameter α to find the type of pooling. αI-pooling is a general pooling method including max-pooling and arithmetic average-pooling as a special case, depending on the parameter α. Experiments show that αI-pooling outperforms other pooling methods, in image recognition tasks. Also, it turns out that each layer has a different optimal pooling type.
Analysis of Limits in Applying AP-QoS-based Wi-Fi Slicing for Real-Time Systems
Jin Hyun Kim, Hyonyoung Choi, Gangjin Kim, Yundo Choi, Tae-Won Ban, Se-Hoon Kim
http://doi.org/10.5626/JOK.2021.48.6.723
Network slicing is a new network technology that guarantees the quality of network services according to application services or user’s types. Wi-Fi, IEEE 802.11-based LAN, is the mostly popularly used short-range wireless network and has been continually attracting more and more from users. Recently, the use of Wi-Fi by safety critical IoT devices, such as medical devices, has been drastically increasing. Moreover, enterprises require network slicing of Wi-Fi to introduce the provision of prioritized QoS of Wi-Fi depending on the service type of customer. This paper presents the analysis of the limits and difficulties in applying AP-QoS-based network slicing for hard real-time systems that demand temporal deterministic streaming services. In this paper, we have defined a formal framework to analyze QoS-providing IEEE 802.11e Enhanced Distributed Coordination Access and provide the worst-case streaming scenarios and thereby demonstrated why the temporal determinism of network streaming is broken. In addition, simulation results of AP-QoS-based network slicing using NS-3 are presented to show the limits and difficulties of the network slicing. Moreover, we present Wi-Fi network slicing techniques based on EDCA of AP-QoS for real-time systems through our technical report referenced in this paper.
Improvement in Network Intrusion Detection based on LSTM and Feature Embedding
Hyeokmin Gwon, Chungjun Lee, Rakun Keum, Heeyoul Choi
http://doi.org/10.5626/JOK.2021.48.4.418
Network Intrusion Detection System (NIDS) is an essential tool for network perimeter security. NIDS inspects network traffic packets to detect network intrusions. Most of the existing works have used machine learning techniques for building the system. While the reported works demonstrated the effectiveness of various artificial intelligence algorithms, only a few of them have utilized the time-series information of network traffic data. Also, categorical information of network traffic data has not been included in neural network-based approaches. In this paper, we propose network intrusion detection models based on sequential information using the long short-term memory (LSTM) network and categorical information using the embedding technique. We have conducted experiments using models with UNSW-NB15, which is a comprehensive network traffic dataset. The experiment results confirm that the proposed method improves the performance, with a binary classification accuracy rate of 99.72%.
Ensemble Modeling with Convolutional Neural Networks for Application in Visual Object Tracking
Minji Kim, Ilchae Jung, Bohyung Han
http://doi.org/10.5626/JOK.2021.48.2.211
In the area of computer vision, visual object tracking aims to estimate the status of a target object from an input video stream, which can be broadly applicable to industries such as surveillance and the military. Recently, deep learning-based tracking algorithms have gone through significant improvements by using tracking-by-detection or template-based approach. However, these approaches are still suffering from inherent limitations caused by each strategy. In this paper, we propose a novel method to model ensemble trackers by fusing the two strategies, tracking-by-detection and template-based approach. We report significantly enhanced performance on widely adopted visual object tracking benchmarks, OTB100, UAV123, and LaSOT.
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