• Management & Information Systems Review
• /
• v.39 no.1
• /
• pp.93-114
• /
• 2020

Platform is considered as an alternative strategy to the traditional linear pipeline based business. Moreover, in the 4th industrial revolution period, efficiency driven pipeline business model needs to be changed to platform business. We have such success stories about platform as Apple, Google, Amazon, Uber, and so on. However, for those smaller corporations, it is not easy to find out the transformation strategy. The essence of platform business is to leverage network effect in management. Thus platform based management can be rephrased as network management across the business functions. Research on platform business is popular and related to diverse facets. But few scholars cover what the research trend of the domain is. The main purpose of this paper is to identify the research trend on platform business in Korea. To do that we first propose the analytical model for platform architecture whose components are consumers, suppliers, artifacts, and IT platform system. We conjecture that mapping of the research work on platform to the components of the model will make us understand the hidden domain of platform research. We propose three hypotheses regarding the characteristics of research and one proposition for the transitional path from pipeline to platform business model. The mapping is based on the research articles filtered from the Korea Citation Index, using keyword search. Research papers are searched through the keywords provided by authors using the word of "platform". The filtered articles are summarized in terms of the attributes such as major component of platform considered, platform type, main purpose of the research, and research method. Using the filtered data, we test the hypotheses in exploratory ways. The contribution of our research is as follows: First, based on the findings, scholars can find the areas of research on the domain: areas where research has been matured and territory where future research is actively sought. Second, the proposition provided can give business practitioners the guideline for changing their strategy from pipeline to platform oriented. This research needs to be considered as exploratory not inferential since subjective judgments are involved in data collection, classification, and interpretation of research articles.

• Journal of information and communication convergence engineering
• /
• v.18 no.4
• /
• pp.207-215
• /
• 2020

Part of speech (POS) tagging is an indispensable part of major NLP models. Its progress can be perceived on number of languages around the globe especially with respect to European languages. But considering Indian Languages, it has not got a major breakthrough due lack of supporting tools and resources. Particularly for Odia language it has not marked its dominancy yet. With a motive to make the language Odia fit into different NLP operations, this paper makes an attempt to develop a POS tagger for the said language on a HMM (Hidden Markov Model) platform. The tagger judiciously considers bigram HMM with dynamic Viterbi algorithm to give an output annotated text with maximum accuracy. The model is experimented on a corpus belonging to tourism domain accounting to a size of approximately 0.2 million tokens. With the proportion of training and testing as 3:1, the proposed model exhibits satisfactory result irrespective of limited training size.

• Journal of Korea Game Society
• /
• v.16 no.2
• /
• pp.119-128
• /
• 2016

In computer games, most of GUI methods are keyboards, mouses and touch screens. The total time of processing the sound commands for games is the sum of input time and recognition time. In this paper, we propose a method for taking only the prefixes of the input signals for sound commands, resulting in the reduced the total processing time, instead of taking the whole input signals. In our method, command sounds are recognized using HMM(Hidden Markov Model), where separate HMM's are built for the whole input signals and their prefix signals. We experiment our proposed method with representative commands of platform games. The experiment shows that the total processing time of input command signals reduces without decreasing recognition rate significantly. The study will contribute to enhance the versatility of GUI for computer games.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.29 no.3
• /
• pp.519-529
• /
• 2019

Nowadays, intelligent Android malware applies anti-analysis techniques to hide malicious behaviors and make it difficult for anti-virus vendors to detect its presence. Malware can use background components to hide harmful operations, use activity-alias to get around with automation script, or wipe the logcat to avoid forensics. During our study, several static analysis tools can not extract these hidden components like main activity, and dynamic analysis tools also have problem with code coverage due to partial execution of android malware. In this paper, we design and implement a system to analyze intelligent malware that uses anti-analysis techniques to improve detection rate of evasive malware. It extracts the hidden components of malware, runs background components like service, and generates all the intent events defined in the app. We also implemented a real-time logging system that uses modified logcat to block deleting logs from malware. As a result, we improve detection rate from 70.9% to 89.6% comparing other container based dynamic analysis platform with proposed system.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.33 no.1
• /
• pp.45-50
• /
• 2023

Roblox, the world's No. 1 metaverse platform, has more than 3 billion subscription accounts and more than 150 millionmonthly active users (MAU). Despite such high interest in metaverse, existing studies on analyzing the risk of cyberattacks and security in the metaverse environment is insufficient. Therefore, in this paper, we propose a new steganography-basedcovert communication method in Roblox. In our proposed method, a secret message is hidden into an image by using a function provided in the Roblox Experience environment and then the image is automatically stored in the RobloxExperience participants' devices (PC or Smartphone) so that a malicious software can extract the hidden message fromthe image. By our experiments in the Roblox metaverse environment, we validated our proposed method works and thus want to inform our proposed method can be used in various cyberattacks and crimes such as the spread of secret commands, the establishment of a steganography botnet, and the mass distribution of malicious malware in metaverse platforms.

• The Journal of the Acoustical Society of Korea
• /
• v.24 no.8
• /
• pp.498-504
• /
• 2005

We introduce a Korean speech recognition platform (ECHOS) developed for education and research Purposes. ECHOS lowers the entry barrier to speech recognition research and can be used as a reference engine by providing elementary speech recognition modules. It has an easy simple object-oriented architecture, implemented in the C++ language with the standard template library. The input of the ECHOS is digital speech data sampled at 8 or 16 kHz. Its output is the 1-best recognition result. N-best recognition results, and a word graph. The recognition engine is composed of MFCC/PLP feature extraction, HMM-based acoustic modeling, n-gram language modeling, finite state network (FSN)- and lexical tree-based search algorithms. It can handle various tasks from isolated word recognition to large vocabulary continuous speech recognition. We compare the performance of ECHOS and hidden Markov model toolkit (HTK) for validation. In an FSN-based task. ECHOS shows similar word accuracy while the recognition time is doubled because of object-oriented implementation. For a 8000-word continuous speech recognition task, using the lexical tree search algorithm different from the algorithm used in HTK, it increases the word error rate by $40\%$ relatively but reduces the recognition time to half.

• Journal of the Korea Convergence Society
• /
• v.9 no.10
• /
• pp.75-80
• /
• 2018

In this paper, we propose heterogeneous lifelog mining model in health big-data platform. It is an ontology-based mining model for collecting user's lifelog in real-time and providing healthcare services. The proposed method distributes heterogeneous lifelog data and processes it in real time in a cloud computing environment. The knowledge base is reconstructed by an upper ontology method suitable for the environment constructed based on the heterogeneous ontology. The restructured knowledge base generates inference rules using Jena 4.0 inference engines, and provides real-time healthcare services by rule-based inference methods. Lifelog mining constructs an analysis of hidden relationships and a predictive model for time-series bio-signal. This enables real-time healthcare services that realize preventive health services to detect changes in the users' bio-signal by exploring negative or positive correlations that are not included in the relationships or inference rules. The performance evaluation shows that the proposed heterogeneous lifelog mining model method is superior to other models with an accuracy of 0.734, a precision of 0.752.

• Biomolecules & Therapeutics
• /
• v.22 no.5
• /
• pp.467-473
• /
• 2014

The purpose of this study is to investigate the memory enhancing effect and underlying molecular mechanism of arabinoxylan (AX), a major component of dietary fiber in wheat against scopolamine (SCO)-induced amnesia in Sprague-Dawley (SD) rats. Diverse behavior tests including Y-maze, Morris water maze, and passive avoidance tests were performed to measure cognitive functions. SCO significantly decreased the spontaneous alterations in Y-maze test and step-through latency in passive avoidance test, whereas increased time spent to find the hidden platform in Morris water maze test compared with the sham control group. In contrast, oral administration of AX (25 mg/kg and 50 mg/kg) effectively reversed the SCO-induced cognitive impairments in SD rats. Furthermore, AX treatment up-regulated the expression of brain-derived neurotrophic factor (BDNF) in the cortex and hippocampus via promoting activation of cAMP response element binding protein (CREB). Therefore, our findings suggest that AX can improve SCO-induced learning and memory impairment possibly through activation of CREB and up-regulation of BDNF levels, thereby exhibiting a cognition-enhancing potential.

• Journal of Public Diplomacy
• /
• v.1 no.1
• /
• pp.80-110
• /
• 2021

Public diplomacy (PD) scholars tend to consider two main principals: the country or agent that conducts PD (Actor A), and target groups in the host country in which PD is conducted (Actor B). The field currently lacks theories of how communications between Actors A and B can be disrupted by a third party, such as a group of motivated trolls, an organised advocacy group, or a hostile country and its agents. The purpose of this article is to outline some theoretical considerations for how the PD research field might move away from a two-actor model of PD to one in which disruption is part of the discussion. The case study explores the activities of an interest group called Media Watchdogs of Sweden (MEWAS). MEWAS was a group of around 200 members who met in a hidden Facebook group to coordinate off-platform activities aimed at influencing perceptions of how the Swedish government handled the COVID-19 pandemic in the eyes of foreign governments, researchers, decision-makers, and media. Much critical news coverage in the international press has been linked to this group. Unpacking some of MEWAS' activities, which can be considered a quite typical mixture of legitimate and illegitimate communication techniques used by activist groups, can help to shed light on some difficult questions regarding disruption in PD.

• Genomics & Informatics
• /
• v.19 no.3
• /
• pp.23.1-23.7
• /
• 2021

Currently, coronavirus disease 2019 (COVID-19) literature has been increasing dramatically, and the increased text amount make it possible to perform large scale text mining and knowledge discovery. Therefore, curation of these texts becomes a crucial issue for Bio-medical Natural Language Processing (BioNLP) community, so as to retrieve the important information about the mechanism of COVID-19. PubAnnotation is an aligned annotation system which provides an efficient platform for biological curators to upload their annotations or merge other external annotations. Inspired by the integration among multiple useful COVID-19 annotations, we merged three annotations resources to LitCovid data set, and constructed a cross-annotated corpus, LitCovid-AGAC. This corpus consists of 12 labels including Mutation, Species, Gene, Disease from PubTator, GO, CHEBI from OGER, Var, MPA, CPA, NegReg, PosReg, Reg from AGAC, upon 50,018 COVID-19 abstracts in LitCovid. Contain sufficient abundant information being possible to unveil the hidden knowledge in the pathological mechanism of COVID-19.