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Joint Model of Morphological Analysis and Named Entity Recognition Using Shared Layer
Hongjin Kim, Seongsik Park, Harksoo Kim
http://doi.org/10.5626/JOK.2021.48.2.167
Named entity recognition is a natural language processing technology that finds words with unique meanings such as human names, place names, organization names, dates, and time in sentences and attaches them. Morphological analysis in Korean is generally divided into morphological analysis and part-of-speech tagging. In general, named entity recognition and morphological analysis studies conducted in independently. However, in this architecture, the error of morphological analysis propagates to named entity recognition. To alleviate the error propagation problem, we propose an integrated model using Label Attention Network (LAN). As a result of the experiment, our model shows better performance than the single model of named entity recognition and morphological analysis. Our model also demonstrates better performance than previous integration models.
LEXAI : Legal Document Similarity Analysis Service using Explainable AI
http://doi.org/10.5626/JOK.2020.47.11.1061
Recently, in keeping with the improvement of deep learning, studies on using deep learning a specialized field have diversified. Semantic searching for legal documents is an essential part of the legal field. However, it is difficult to function outside of the service using the expert system because it requires professional knowledge in the relevant field. It is also challenging to establish an automated, semantically similar legal document retrieval environment because the cost of hiring professional human resources is high. While existing retrieval services provide an environment based on expert systems and statistical systems, the proposed method adopts the deep learning method with a classification task. We propose a database system structure that provides searching for legal documents with high semantic similarity using an explainable neural network. The features of these proposed methods show the performance of developing and verifying visual similarity assessment methods for semantic relevance among similar documents.
Binary-Compatible User-Mode Polling-Based Inter-VM Communication Techniques Using Shared Memory
Jihong Min, Juhyung Park, Joonseok Park
http://doi.org/10.5626/JOK.2020.47.11.1015
Virtual machines commonly use TCP/IP protocol to exchange data with the host or other virtual machines, but the protocol is inefficient. Communication using inter-VM shared memory can be used for better efficiency, but a disadvantage forces existing TCP/IP-based programs to be reprogrammed or recompiled. There have been several studies on the binary-compatible inter-VM shared memory-based communication methods to resolve this issue, yet the overheads exist. In this paper, we propose techniques that reduce the overheads of the current binary-compatible inter-VM shared memory-based communication methods. Our scheme bypasses the existing network stack with function hooking to TCP/IP library, and introduces the transmission queue per connection and user-mode polling technique to remove the kernel-mode switching overhead. The experiment results show that the latency can be reduced by 96.96% and throughtput can be increased by 222.24% on average by using the proposed techniques compared to the existing virtual network.
Realtime Video Streaming System over Narrowband Networks
Hyunmin Noh, Seunghwan Lee, Jeung Won Choi, Donghyun Kim, Kyungwoo Kim, Yunsoo Ko, Sangheon Shin, Hyungjun Kim, Hwangjun Song
http://doi.org/10.5626/JOK.2020.47.9.885
In this paper, we propose a real-time video streaming system over narrow networks that provides high-quality video services. The suggested system uses the raptor code, a forward error correction code, to support the reliable and stable data transmission in the narrowband networks. Also, the proposed system adaptively controls the raptor parameters (source symbol size, the number of source symbols, and code rate) according to the narrow network condition and the remaining buffer status. The proposed system is fully implemented on android devices and examined by using a real-time video transmission. Experimental results showed that the proposed system provides high-quality streaming services over the narrowband networks.
Breast Cancer Subtype Classification Using Multi-omics Data Integration Based on Neural Network
Joungmin Choi, Jiyoung Lee, Jieun Kim, Jihyun Kim, Heejoon Chae
http://doi.org/10.5626/JOK.2020.47.9.835
Breast cancer is one of the highly heterogeneous diseases comprising multiple biological factors, causing multiple subtypes. Early diagnosis and accurate subtype prediction of breast cancer play a critical role in the prognosis of cancer and are crucial to providing appropriate treatment for each patient with different subtypes. To identify significant patterns from enormous volumes of genetic and epigenetic data, machine learning-based methods have been adopted to the breast cancer subtype classification. Recently, multi-omics data integration has attracted much attention as a promising approach in recognizing complex molecular mechanisms and providing a comprehensive view of patients. However, because of the characteristics of high dimensionality, multi-omics based approaches are limited in prediction accuracy. In this paper, we propose a neural network-based breast cancer subtype classification model using multi-omics data integration. The gene expression, DNA methylation, and miRNA omics dataset were integrated after preprocessing and the classification model was trained based on the neural network using the dataset. Our performance evaluation results showed that the proposed model outperforms all other methods, providing the highest classification accuracy of 90.45%. We expect this model to be useful in predicting the subtypes of breast cancer and improving patients’ prognosis.
Disassortative Network Distribution Techniques Using Hub Grouping Based On Local Differential Privacy
http://doi.org/10.5626/JOK.2020.47.6.603
With the development of the wireless Internet and popularization of smartphones, many people are using social network services that connect with others in online. Personal data generated by social network services have high value, but comprise sensitive personal information that could potentially result in serious privacy breaches. The existing studies have presented techniques for generating synthetic data similar to the original network data, or anonymous user information. However, the existing techniques have inherent weaknesses in privacy and data utility because such techniques have not considered the characteristics of network graphs formed by relationships with users. In this paper, we propose the privacy-protected social network data distribution techniques by applying local differential privacy techniques that reflect the characteristics on the social network graph. Through experiments with real data, we have shown that the proposed techniques perform better than the existing differentially private social network data distribution techniques.
Pattern Extraction from Lifelog Based on Semantic Network Structure Using Petri-Net
http://doi.org/10.5626/JOK.2020.47.6.553
Recently, with the spread of smart devices, the user’s lifelog data is automatically stored through various types of sensors. But the lifelog collected from smart devices records heterogeneous information from different sensors. In addition, since the user"s life patterns are determined by different judgment cycles, it is difficult to express them in a simple rule-based system. Therefore, in order to extract and provide useful life patterns for users from the lifelog, it is necessary to express the relationship of numerous dynamic elements. In this paper, we propose a method to automatically extract user life patterns using Petri-nets from the lifelog represented by the semantic network. Petri-net reduces the uncertainty in smart device sensor data and increases the diversity of life patterns. The proposed life pattern extraction method is structured by the semantic network to represent the semantic relationship of heterogeneously collected user lifelog. Also, the Petri-net graph automatically determines the lifelog and then extracts individual sleep and eating patterns.
SDN-based Task Allocation for IoT-Fog Network
Dzaky Zakiyal Fawwaz, Sang-Hwa Chung
http://doi.org/10.5626/JOK.2020.47.5.535
The Internet of Things requires that resources are allocated for it to execute tasks. Utilizing fog computing may have many benefits in these circumstances since it offers distributed resources that could give lower latency, lower bandwidth, and various other advantages compared to cloud computing. If we wish to use fog computing, we need to consider how to perform task allocation over multiple fog nodes. The kind of networks used are likely to have not only continuous incoming IoT tasks but also other dynamic network conditions. Hence, we introduce dynamic task allocation that utilizes a Software-Defined Network. Our system handles each incoming task by considering network and fog node statistics. The task allocation method must select the optimal pair of fog nodes and also the path because there are multiple fog nodes and many feasible related paths to deal with. Thus, we define the problem to be one of finding the multi-source, single-target, shortest path on a network graph, to help solve the problem we formulate the joint fog node-link utilization cost. We also propose a Many-to-One Shortest Path algorithm to solve such a problem. The experiments we performed to evaluate our system show that it outperforms the previous state-of-the-art work. Averaging over all the experiment"s topologies, our method achieves higher tasks per second completion rate, a lower response time and lower fog node/link utilization with scores of 37 tasks/sec, 676ms and 65%/24% utilization, respectively.
A New Light-Weight and Efficient Convolutional Neural Network Using Fast Discrete Cosine Transform
http://doi.org/10.5626/JOK.2020.47.3.276
Recently proposed light-weight neural networks maintain high accuracy in some degree with a small amount of weight parameters and low computation cost. Nevertheless, existing convolutional neural networks commonly have a lot of weight parameters from the Pointwise Convolution (1x1 convolution), which also induces a high computational cost. In this paper, we propose a new Pointwise Convolution operation with one dimensional Fast Discrete Cosine Transform (FDCT), resulting in dramatically reducing the number of learnable weight parameters and speeding up the process of computation. We propose light-weight convolutional neural networks in two specific aspects: 1) Application of DCT on the block structure and 2) Application of DCT on the hierarchy level in the CNN models. Experimental results show that our proposed method achieved the similar classification accuracy compared to the MobileNet v1 model, reducing 79.1% of the number of learnable weight parameters and 48.3% of the number of FLOPs while achieving 0.8% increase in top-1 accuracy.
A Product Review Summarization Considering Additional Information
Jaeyeun Yoon, Ig-hoon Lee, Sang-goo Lee
http://doi.org/10.5626/JOK.2020.47.2.180
Automatic document summarization is a task that generates the document in a suitable form from an existing document for a certain user or occasion. As use of the Internet increases, the various data including texts are exploding and the value of document summarization technology is growing. While the latest deep learning-based models show reliable performance in document summarization, the problem is that performance depends on the quantity and quality of the training data. For example, it is difficult to generate reliable summarization with existing models from the product review text of online shopping malls because of typing errors and grammatically wrong sentences. Online malls and portal web services are struggling to solve this problem. Thus, to generate an appropriate document summary in poor condition relative to quality and quantity of the product review learning data, this study proposes a model that generates product review summaries with additional information. We found through experiments that this model showed improved performances in terms of relevance and readability than the existing model for product review summaries.
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