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Automatic Generation of Custom Advertisement Messages based on Literacy Styles of Classified Personality Types
Jimin Seong, Yunjong Choi, Doyeon Kwak, Hansaem Kim
http://doi.org/10.5626/JOK.2024.51.1.23
This study introduces a novel framework that defines marketing styles based on the MBTI personality types, and presents a machine learning technique to generate customized advertising messages aligned to these types. We use the BART algorithm to synthesize customized advertising content by training on the advertisement texts incorporating personality type prefixes. Our experiments confirm the model’s efficacy in transforming generic advertising copy into custom messages that embody the distinct style characteristics of each personality type, via prefix manipulation. Theoretically, our research establishes the relationship between style characteristics and personality types; practically, it provides the technique to fine-tune a language model to generate advertising messages that align with specific personality types. Moreover, this research serves as a foundational work for systematizing and replicating stylistic differences across various languages and regions.
Hierarchical Latent Representation-based Framework for Automatic Detection of Cybercrime Slang
http://doi.org/10.5626/JOK.2023.50.12.1121
Cybercriminals constantly produce and use slang by adding criminal meanings to existing words or replacing them with similar words for communication. Continuous monitoring and manual work are required to respond to this, and a large amount of labeled training data is required when using deep learning. However, the ability to collect a large amount of training data is limited because direct labeling by a person requires a lot of time and money and proceeds secretly due to the nature of cybercrime. Thus, we develop a framework based on an autoencoder and propose a method to effectively detect contextual cybercrime slang and neologisms through hierarchical latent vector similarity comparisons to address these limitations. Experiments using a cybercrime post dataset showed that the framework had an accuracy of up to 99.1% at a similarity threshold of 0.5.
Open-source-based 5G Access Network Security Vulnerability Automated Verification Framework
Jewon Jung, Jaemin Shin, Sugi Lee, Yusung Kim
http://doi.org/10.5626/JOK.2023.50.6.531
Recently, various open sources based on 5G standards have emerged, and are widely used in research to find 5G control plane security vulnerabilities. However, leveraging those open sources requires extensive knowledge of complex source code, wireless communication devices, and massive 5G security standards. Therefore, in this paper, we propose a framework for the automatic verification of security vulnerabilities in the 5G control plane. This framework builds a 5G network using commercial Software Defined Radio (SDR) equipment and open-source software and implements a Man-in-the-Middle (MitM) attacker to deploy a control plane attack test bed. It also implements control plane message decoding and correction modules to execute message spoofing attacks and automatically classifies security vulnerabilities in 5G networks. In addition, a GUI-based web user interface is implemented so that users can create MitM attack scenarios and check the verification results themselves.
An Automatic Framework for Nested Normalization and Table Migration of Large-Scale Hierarchical Data
Dasol Kim, Myeong-Seon Gil, Heesun Won, Yang-Sae Moon
http://doi.org/10.5626/JOK.2023.50.6.521
In the open data portal, a lot of data is distributed in the hierarchical structure of JSON and XML formats, and the scale is very large. Such hierarchical data includes several nestings because of its structural characteristics. As a result, nested table normalization and scale limitation problems can occur, which limits the utilization of large-scale open data. In this paper, we adopt Airbyte, an open-source ELT platform, for table migration of hierarchical files, and propose a new framework for automating table migration. This is the first study to report Airbyte’s nested JSON handling issue and contribute to solving the issue. Through extensive evaluation of the proposed framework for actual US data portals, we show that it operates normally even for structures that include multiple nestings, and it can process large-scale migration of 1.6K or more by providing automated processing logic. These results mean that the proposed framework is a very practical one that supports the nested normalization of hierarchical data and provides a reliable large-scale migration function.
C++ based Deep Learning Open Source Framework WICWIU.v3 that Supports Natural Language and Time-series Data Processing
Junseok Oh, Chanhyo Lee, Okkyun Koo, Injung Kim
http://doi.org/10.5626/JOK.2023.50.4.313
WICWIU is the first open-source deep learning framework developed by Korean university. In this work, we developed WICWIU.v3 that includes features for natural language and time-series data processing. WICWIU was designed for C++ environment, and supports GPU-based parallel processing, and has excellent readability and extensibility, allowing users to easily add new features. In addition to WICWIU.v1 and v2 that focus on image processing models, such as convolutional neural networks (CNN) and general adversarial networks (GAN), WICWIU.v3 provides classes and functions for natural language and time-series data processing, such as recurrent neural networks (RNN), including LSTM (Long Short-Term Memory Networks) and GRU (Gated Recurrent Units), attention modules, and Transformers. We validated the newly added functions for natural language and time-series data by implementing a machine translator and a text generator with WICWIU.v3.
Design and Implementation of Framework Based Emulator Considering Expansion of MIDS LVT Platform
Sangtae Lee, Jongseo Kim, Sounghyouk Wi, Taegwon Lee, Seungbae Jee, Seungchan Lee
http://doi.org/10.5626/JOK.2021.48.1.61
MIDS LVT is communication equipment mounted with Link-16-based weapon systems to provide the Link-16 operating environment between weapon systems. Currently, the military operates the MIDS LVT BU1, but it will be changed to the BU2 and JTRS according to the performance improvement. The communication interface, message data format, and message composition of the MIDS LVT are different depending on the platform family (BU1/BU2/JTRS) and type (A,D,J). In this paper, we propose a framework based emulator design and implementation method that considers the MIDS LVT platform extension to improve these problems. In consideration of the quality attributes of the sw architecture, we designed a common based framework for modifiability, reusability, and extensibility. The MIDS LVT emulator comprises the MIDS LVT emulator processing, link interface processing, and monitoring tool. The MIDS LVT emulator was implemented by deriving and improving functions through the analysis of the functions of the previously developed overseas tools. Through the development of the MIDS LVT emulator, it can be used to develop and verify the developed Link-16 host system.
The Design of a Multi-Function Radar Simulator for the Identification of Friend or Foe(IFF) in the Mode-5 Product Improvement Program
Younghwan Jeong, Chansu Kim, Jungin Oh, Wonsik Lee, Sounghyouk Wi
http://doi.org/10.5626/JOK.2020.47.6.622
Identification of Friend or Foe(IFF) is a function of military surveillance system used to identify whether the monitored object is an ally or an enemy, these system are installed in fighters, ships, and interceptors among others. The United States plans to suspend the Mode-4 identification system and apply Mode-5 from July 2020 forward. The transition to Mode-5 is inevitable as it ensures the interoperability of peer identification systems used in the Republic of Korea’s military operations with the US as well as other NATO member states. If the IFF function found in control centers and multi-function radars is changed in regional air defense weapons systems, revalidation of these weapons systems is required to ensure stability and correct function. Therefore, a multi-function radar simulator is vital use in interface verification, unit tests, and integrated tests before evaluation of the new systems can be completed. This paper presents the design of a simulator for mode-5 performance testing and improvement.
C++ based General-purpose Open Source Deep Learning Framework, WICWIU
Chunmyong Park, Jeewoong Kim, Yunho Kee, Jihyeon Kim, Seonggyeol Yoon, Eunseo Choi, Injung Kim
http://doi.org/10.5626/JOK.2019.46.3.253
In this paper, we introduce WICWIU, the first open source deep learning framework among Korean universities. WICWIU provides a variety of operators and modules together with a network structure that can represent an arbitrary general computational graph. The WICWIU features are sufficient to compose widely used deep learning models such as Inception, ResNet, and DenseNet. WICWIU also supports GPU-based massive parallel computing which significantly accelerates the training of neural networks. It is also easily accessible for C++ developers because the whole API is provided in C++. WICWIU has an advantage over Python-based frameworks in memory and performance optimization based on the C++ environment. This eases the customizability of WICWIU for environments with limited resources. WICWIU is readable and extensible because it is composed of C++ codes coupled with consistent APIs. With Korean documentation, it is particularly suitable for Korean developers. WICWIU applies the Apache 2.0 license which is available for any research or commercial purposes for free.
A Prediction-based Dynamic Component Offloading Framework for Mobile Cloud Computing
http://doi.org/10.5626/JOK.2018.45.2.141
Nowadays, mobile computing has become a common computing paradigm that provides convenience to people’s daily life. More and more useful mobile applications’ appearance makes it possible for a user to manage personal schedule, enjoy entertainment, and do many useful activities. However, there are some inherent defects in a mobile device that battery constraints and bandwidth limitations. These drawbacks get a user into troubles when to run computationally intensive applications. As a remedy scheme, component offloading makes room for handling mentioned issues via migrating computationally intensive component to the cloud server. In this paper, we will present the predictive offloading method for efficient mobile cloud computing. At last, we will present experiment result for validating applicability and practicability of our proposal.
SWAT: A Study on the Efficient Integration of SWRL and ATMS based on a Distributed In-Memory System
Myung-Joong Jeon, Wan-Gon Lee, Batselem Jagvaral, Hyun-Kyu Park, Young-Tack Park
http://doi.org/10.5626/JOK.2018.45.2.113
Recently, with the advent of the Big Data era, we have gained the capability of acquiring vast amounts of knowledge from various fields. The collected knowledge is expressed by well-formed formula and in particular, OWL, a standard language of ontology, is a typical form of well-formed formula. The symbolic reasoning is actively being studied using large amounts of ontology data for extracting intrinsic information. However, most studies of this reasoning support the restricted rule expression based on Description Logic and they have limited applicability to the real world. Moreover, knowledge management for inaccurate information is required, since knowledge inferred from the wrong information will also generate more incorrect information based on the dependencies between the inference rules. Therefore, this paper suggests that the SWAT, knowledge management system should be combined with the SWRL (Semantic Web Rule Language) reasoning based on ATMS (Assumption-based Truth Maintenance System). Moreover, this system was constructed by combining with SWRL reasoning and ATMS for managing large ontology data based on the distributed In-memory framework. Based on this, the ATMS monitoring system allows users to easily detect and correct wrong knowledge. We used the LUBM (Lehigh University Benchmark) dataset for evaluating the suggested method which is managing the knowledge through the retraction of the wrong SWRL inference data on large data.
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