• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.1-13
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• 2023

As federated learning brings a large paradigm to modern artificial intelligence research, efforts are being made to incorporate federated learning into research in various fields. However, researchers who apply federated learning face the problem of choosing a federated learning framework and benchmark tool suitable for their situation and purpose. This study aims to present guidelines for selection of federated learning frameworks and benchmark tools considering the circumstances of researchers who apply federated learning in practice. In particular, there are three main contributions in this study. First, it generalizes the situation of the researcher applying federated learning by combining it with the goal of federated learning and proposes guidelines for selecting a federated learning framework suitable for each situation. Second, it shows the suitability of selection by comparing the characteristics and performance of each federated learning framework to the researcher. Finally, the limitations of the existing federated learning framework and a plan for real-world federated learning operation are proposed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.272-275
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• 2022

Large-scale online platforms such as MOOCs-Massive Open Online Courses, which provide a variety of educational contents, have provided a learning environment that allows students to freely access and learn anytime and anywhere. Currently, the proportion of online lectures and home-based learning is increasing, and portfolio or experience-based learning such as bootcamp, field activities, and team project-based group learning are also being actively carried out for educational outcomes. At present, interest in nano or microdegree focused on core technology in units of hours or credits is increasing significantly because such strategic intensive education enables effective learning in terms of continuity and efficiency of education. In an era of large changes in job market due to the reorganization of the industrial structure by new technologies, intensive education in specialized new technology fields such as smart mobility, big data, and artificial intelligence is much more conducive to finding a job. With this reason it is attracting attention as an alternative to lifelong learning are receiving In this paper we propose an educational platform that can efficiently and effectively support the purpose learning for the personalized microdegree education in the online learning era.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.11
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• pp.319-328
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• 2023

Unlike cloud computing, edge computing technology analyzes and judges data close to devices and users, providing advantages such as real-time service, sensitive data protection, and reduced network traffic. EdgeX Foundry, a representative open source of edge computing platforms, is an open source-based edge middleware platform that provides services between various devices and IT systems in the real world. EdgeX Foundry provides a service for handling camera devices, along with a service for handling existing sensed data, which only supports simple streaming and camera device management and does not store or process image data obtained from the device inside EdgeX. This paper presents a technique that can store and process image data inside EdgeX by applying some of the services provided by EdgeX Foundry. Based on the proposed technique, a service pipeline for object detection services used core in the field of autonomous driving was created for experiments and performance evaluation, and then compared and analyzed with existing methods.

• Information Systems Review
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• v.12 no.3
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• pp.75-88
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• 2010

Business models in open market systems targeting smart phone users are determined by several important factors. First, by providing developers efficient technical platforms, it contains a setting for developers to learn, apply and improve the skills relating to the product category easily while they stay beyond a corporate boundary. Second, by the first condition, a huge population of talented developers becomes to join a specific open market where will invite more customers to use their applications. Hence it will attract more and more developer participants who will finally give a rise to a persistent market growth. Third, the evaluation system between platform providers and application producers, and one between application producers and application users may underlie the trust relationships between them. The research conducted a multiple embedded case study to test the success factors of open market based business models. It focused on smart phone game communities that have installed user evaluation, and feedback systems. The user innovation empowerment model within the social game networks has highlighted the theories on the roles and characteristics of lead users, and lead user network behaviors for future NPD participations.

• Journal of the Korea Society for Simulation
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• v.22 no.1
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• pp.9-20
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• 2013

Defense M&S for weapons effectiveness is a realistic way to support virtual warfare similar to real warfare. As the war paradigm becomes platform-centric to network-centric, people try to utilize smartphones as the source of sensor, and command/control data in the simulation-based weapons effectiveness analysis. However, there have been limited researches on integrating smartphones into the weapon simulators, partly due to high modeling cost - modeling cost to accomodate client-server architecture, and re-engineering cost to adapt the simulator on various devices and platforms -, lack of efficient mechanisms to exchange large amount of simulation data, and low-level of security. In this paper, we design and implement Smartphone Adaptor to utilize smartphones for the simulationbased weapons effectiveness analysis. Smartphone Adaptor automatically sends sensor information, GPS and motion data of a client's smartphone to a simulator and receives simulation results from the simulator on the server. Also, we make it possible for data to be transferred safely and quickly through JSON and SEED. Smartphone Adaptor is applied to OpenSIM (Open simulation engine for Interoperable Models) which is an integrated simulation environment for weapons effectiveness analysis, under development of our research team. In this paper, we will show Smartphone Adaptor can be used effectively in constructing a Live simulation, with an example of a chemical simulator.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.69-78
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• 2022

As the use of Internet of Things (IoT) devices has expanded, digital forensics coverage of the National Police Agency has expanded to smart home areas. Accordingly, most of the existing studies conducted to acquire smart home platform data were mainly conducted to analyze local data of mobile devices and analyze network perspectives. However, meaningful data for evidence analysis is mainly stored on cloud storage on smart home platforms. Therefore, in this paper, we study how to acquire stored in the cloud in a Hey Home Air environment by extracting accessToken of user accounts through a cookie database of browsers such as Microsoft Edge, Google Chrome, Mozilia Firefox, and Opera, which are recorded on a PC when users use the Hey Home app-based "Hey Home Square" service. In this paper, the it was configured with smart temperature and humidity sensors, smart door sensors, and smart motion sensors, and artifacts such as temperature and humidity data by date and place, device list used, and motion detection records were collected. Information such as temperature and humidity at the time of the incident can be seen from the results of the artifact analysis and can be used in the forensic investigation process. In addition, the cloud data acquisition method using OpenAPI proposed in this paper excludes the possibility of modulation during the data collection process and uses the API method, so it follows the principle of integrity and reproducibility, which are the principles of digital forensics.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.1-19
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• 2019

Artificial intelligence (AI) is one of the main driving forces leading the Fourth Industrial Revolution. The technologies associated with AI have already shown superior abilities that are equal to or better than people in many fields including image and speech recognition. Particularly, many efforts have been actively given to identify the current technology trends and analyze development directions of it, because AI technologies can be utilized in a wide range of fields including medical, financial, manufacturing, service, and education fields. Major platforms that can develop complex AI algorithms for learning, reasoning, and recognition have been open to the public as open source projects. As a result, technologies and services that utilize them have increased rapidly. It has been confirmed as one of the major reasons for the fast development of AI technologies. Additionally, the spread of the technology is greatly in debt to open source software, developed by major global companies, supporting natural language recognition, speech recognition, and image recognition. Therefore, this study aimed to identify the practical trend of AI technology development by analyzing OSS projects associated with AI, which have been developed by the online collaboration of many parties. This study searched and collected a list of major projects related to AI, which were generated from 2000 to July 2018 on Github. This study confirmed the development trends of major technologies in detail by applying text mining technique targeting topic information, which indicates the characteristics of the collected projects and technical fields. The results of the analysis showed that the number of software development projects by year was less than 100 projects per year until 2013. However, it increased to 229 projects in 2014 and 597 projects in 2015. Particularly, the number of open source projects related to AI increased rapidly in 2016 (2,559 OSS projects). It was confirmed that the number of projects initiated in 2017 was 14,213, which is almost four-folds of the number of total projects generated from 2009 to 2016 (3,555 projects). The number of projects initiated from Jan to Jul 2018 was 8,737. The development trend of AI-related technologies was evaluated by dividing the study period into three phases. The appearance frequency of topics indicate the technology trends of AI-related OSS projects. The results showed that the natural language processing technology has continued to be at the top in all years. It implied that OSS had been developed continuously. Until 2015, Python, C ++, and Java, programming languages, were listed as the top ten frequently appeared topics. However, after 2016, programming languages other than Python disappeared from the top ten topics. Instead of them, platforms supporting the development of AI algorithms, such as TensorFlow and Keras, are showing high appearance frequency. Additionally, reinforcement learning algorithms and convolutional neural networks, which have been used in various fields, were frequently appeared topics. The results of topic network analysis showed that the most important topics of degree centrality were similar to those of appearance frequency. The main difference was that visualization and medical imaging topics were found at the top of the list, although they were not in the top of the list from 2009 to 2012. The results indicated that OSS was developed in the medical field in order to utilize the AI technology. Moreover, although the computer vision was in the top 10 of the appearance frequency list from 2013 to 2015, they were not in the top 10 of the degree centrality. The topics at the top of the degree centrality list were similar to those at the top of the appearance frequency list. It was found that the ranks of the composite neural network and reinforcement learning were changed slightly. The trend of technology development was examined using the appearance frequency of topics and degree centrality. The results showed that machine learning revealed the highest frequency and the highest degree centrality in all years. Moreover, it is noteworthy that, although the deep learning topic showed a low frequency and a low degree centrality between 2009 and 2012, their ranks abruptly increased between 2013 and 2015. It was confirmed that in recent years both technologies had high appearance frequency and degree centrality. TensorFlow first appeared during the phase of 2013-2015, and the appearance frequency and degree centrality of it soared between 2016 and 2018 to be at the top of the lists after deep learning, python. Computer vision and reinforcement learning did not show an abrupt increase or decrease, and they had relatively low appearance frequency and degree centrality compared with the above-mentioned topics. Based on these analysis results, it is possible to identify the fields in which AI technologies are actively developed. The results of this study can be used as a baseline dataset for more empirical analysis on future technology trends that can be converged.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1936-1939
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• 2004

This paper proposes streaming layer for personal robot's middleware. Under assumption that robot has open architecture, i.e. consists of modules created by different vendors and intercommunication between these modules is necessary, we have to consider that there are many different network interfaces. To make communication between modules possible it is necessary to develop new type of middleware. Such middleware has to support different platforms, i.e. OS, network interface, hardware, etc. In addition, it is necessary to implement effective interface between network and application in order to manage inter application communications and use network resources more effectively. Streaming layer is such interface that implements necessary functionality together with simplicity and portability. Streaming layer provides high level of abstraction and makes communication between distributed applications transparent as if are located in same module. With possibility of extension by user defined application interfaces it is suitable for distributed environments, i.e. module based architecture including small-embedded systems like as DSP board. To verify the proposed streaming layer structure it is implemented using C and tested.

• IEMEK Journal of Embedded Systems and Applications
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• v.3 no.4
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• pp.218-228
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• 2008

The convergence of broadcasting and telecommunication services is being accelerated by broadband networks, digital broadcasting and Web2.0. This paper describes the definition and service characteristics of IPTV as a representative of broadcasting and telecommunication convergence services. Especially, the changes of infrastructure and technology for IPTV2.0 are addressed in terms of the service features of mobility, intelligence and participation. IPTV2.0 shall be characterized by the open IPTV service based on Web2.0 and the mobile IPTV service over the heterogeneous networks employing various wireless/wired access technologies. The IP Multimedia Subsystem (IMS) and Service Delivery Platform (SDP) technologies are increasingly considered to support the personalization and openness. The mobility management technology is being evolved to provide QoS-guaranteed mobile communication services to users at anytime and anywhere. IPTV2.0 services and platforms are also anticipated to be core components to achieve knowledge-based ubiquitous society. IPTV2.0 contents are required to be integrated with the enhanced metadata to efficiently support search, selection, convergence and delivery of the contents. Moreover, those contents shall be enhanced to provide the scalable services which is adaptable to the network status and user preferences. Therefore, the networking technologies for IPTV2.0 should tightly cooperate with application services and adaptive contents. Those technologies will be developed to construct the ubiquitous content service platform considering the evolution of networks and various converged services.

• Journal of Information Technology Services
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• v.17 no.1
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• pp.47-58
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• 2018

In this study, we define Fintech services as review previous literatures and identify the traditional Fintech service market for analysing the economic effects of the Fintech Industry by using the 2014 Input-Output Table. We can identify the current market of Fintech industry which consists of VAN, PG, financial SW, mobile banking and Fintech R&D and we conduct Input-Output analysis by using non-competitive import model. The Input-Output analysis results show that production inducement effect and front/rear chain effect of the Fintech Industry are below average of other industries. This is because the Fintech technology and industry were emerging in Korea at that time (2014), and thus the ripple effects are not significant. Especially, due to the existing white risk financial regulation, new business opportunities have not been open to adapt new ICT-financial technologies. Therefore, when the business ecosystem is build through deregulation and platforms of the financial sector, it is expected that the Fintech Industry will have a high ripple effect. In this study, we identify the current market of Fintech industry from ICT indusries and conduct Input-Output analysis. The economic effects of the Fintech industry are not remarkable, but it is significant to identify the emerging market and present the basic analysis of issued research field.