• Journal of Digital Convergence
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• v.14 no.7
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• pp.373-383
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• 2016

With mobile-epoch and emerging of Fin-tech, Bio-recognition technology utilizing bio-information in secure method has spread. Specially, In order to change convenient payment services and transportation cards, the combination of biometrics and mobile services are being expanded. The basic concept of authentication such as access control, IA&A, OpenID, OAuth 1.0a, SSO, and Biometrics techniques are investigated, and the protocol stack for security API platform, FIDO, SCIM, OAuth 2.0, JSON Identity Suite, Keystone of OpenStack, Cloud-based SSO, and AIM Agent are described detailed in aspect of application of AIM. The authentication technology in domestic and foreign will accelerate technology development and research of standardization centered in the federated FIDO Universal Authentication Framework(UAF) and Universal 2 Factor Framework(U2F). To accommodate the changing needs of the social computing paradigm recently in this paper, the trends of various authentication technology, and design and function of AIM framework was defined.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2771-2773
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• 2005

In network human-robot interaction, human can access services of a robot system through the network The communication is done by interacting with the distributed sensors via voice, gestures or by using user network access device such as computer, PDA. The service organization and exploration is very important for this distributed system. In this paper we propose a new agent-based framework to integrate partners of this distributed system together and help users to explore the service effectively without complicated configuration. Our system consists of several robots. users and distributed sensors. These partners are connected in a decentralized but centralized control system using agent-based technology. Several experiments are conducted successfully using our framework The experiments show that this framework is good in term of increasing the availability of the system, reducing the time users and robots needs to connect to the network at the same time. The framework also provides some coordination methods for the human robot interaction system.

• Proceedings of the CALSEC Conference
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• 2001.08a
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• pp.379-391
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• 2001

In this paper, we presented function catalogs that Internet content transcoding system for heterogeneous client devices must offer, and, we proposed content transcoding framework architecture that is good in extensibility. This transcoding framework can accommodate each transcoder in efficient way using device capability and user preference information based on W3C's CC/PP and Wap forum's UAProf specification. This architecture offers advantages that can add developed transcoder dynamically in Plug-In form later.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.8
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• pp.780-785
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• 2010

The OPRoS (Open Platform for Robotic Services) framework is proposed as an application runtime environment for service robot systems. For the successful deployment of the OPRoS framework, fault tolerance support is crucial on top of its basic functionalities of lifecycle, thread and connection management. In the previous work [1] on OPRoS fault tolerance supports, we presented a framework-based fault tolerance architecture. In this paper, we extend the architecture with component-based fault tolerance techniques, which can provide more simplicity and efficiency than the pure framework-based approach. This argument is especially true for fault detection, since most faults and failure can be defined when the system cannot meet the requirement of the application functions. Specifically, the paper applies two widely-used fault detection techniques to the OPRoS framework: 'bridge component' and 'process model' component techniques for fault detection. The application details and performance of the proposed techniques are demonstrated by the same application scenario in [1]. The combination of component-based techniques with the framework-based architecture would improve the reliability of robot systems using the OPRoS framework.

• Journal of Information Processing Systems
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• v.13 no.6
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• pp.1613-1627
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• 2017

The concept of the Internet of Things (IoT) enables physical objects or things to be virtually accessible for both consuming and providing services. Undue access from irresponsible activities becomes an interesting issue to address. Maintenance of data integrity and privacy of objects is important from the perspective of security. Privacy can be achieved through various techniques: password authentication, cryptography, and the use of mathematical models to assess the level of security of other objects. Individual methods like these are less effective in increasing the security aspect. Comprehensive security schemes such as the use of frameworks are considered better, regardless of the framework model used, whether centralized, semi-centralized, or distributed ones. In this paper, we propose a new semi-centralized security framework that aims to improve privacy in IoT using the parameters of trust and reputation. A new algorithm to elect a reputation coordinator, i.e., ConTrust Manager is proposed in this framework. This framework allows each object to determine other objects that are considered trusted before the communication process is implemented. Evaluation of the proposed framework was done through simulation, which shows that the framework can be used as an alternative solution for improving security in the IoT.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.37-45
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• 2023

Adaptive has gained significant attention in Education Technology (EdTech), with personalized learning experiences becoming increasingly important. Next-generation chatbots, including models like ChatGPT, are emerging in the field of education. These advanced tools show great potential for delivering personalized and adaptive learning experiences. This paper reviews previous research on adaptive learning and the role of chatbots in education. Based on this, the paper explores current and future chatbot technologies to propose a framework for using ChatGPT or similar chatbots in adaptive learning. The framework includes personalized design, targeted resources and feedback, multi-turn dialogue models, reinforcement learning, and fine-tuning. The proposed framework also considers learning attributes such as age, gender, cognitive ability, prior knowledge, pacing, level of questions, interaction strategies, and learner control. However, the proposed framework has yet to be evaluated for its usability or effectiveness in practice, and the applicability of the framework may vary depending on the specific field of study. Through proposing this framework, we hope to encourage learners to more actively leverage current technologies, and likewise, inspire educators to integrate these technologies more proactively into their curricula. Future research should evaluate the proposed framework through actual implementation and explore how it can be adapted to different domains of study to provide a more comprehensive understanding of its potential applications in adaptive learning.

• International Journal of Advanced Culture Technology
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• v.11 no.2
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• pp.315-322
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• 2023

Intelligent IoT environments that accommodate a very large number of clients require technologies that provide secure information service regardless of the number of clients. Wireless data broadcast is an information service technique that ensures scalability to deliver data to all clients simultaneously regardless of the number of clients. In wireless data broadcasting, clients access the wireless channel linearly to explore the data, so the access time of clients is greatly affected by the broadcast cycle. Data compression-based data broadcasting can reduce the broadcast cycle and thus reduce client access time. Therefore, a simulation framework that can evaluate the performance of data broadcasting by applying different data compression algorithms is essential and important. In this paper, we propose a simulation framework to evaluate the performance of data broadcasting that can adopt data compression. We design the framework that enables to apply different data compression algorithms according to the data characteristics. In addition to evaluating the performance according to the data, the proposed framework can also evaluate the performance according to the data scheduling technique and the kind of queries the client wants to process. We implement the proposed framework and evaluate the performance of data broadcasting using the framework applying data compression algorithms to demonstrate the performances of data compression broadcasting.

• Journal of Information Technology Services
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• v.5 no.3
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• pp.165-172
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• 2006

In this paper, we designed a new type of X-internet framework by using Flash component and server service API and analyzed its performance. Proposed X-internet framework uses Flash MX Professional 2004, Java, and software which opened middleware and database. Since proposed framework use the server service API that we developed in this paper and Flash player, this framework does not need server module. Proposed framework enables to obtain design with dynamic user interface compare to Web application and enables to reduce development time. In analytical results, it has been shown that proposed x-internet framework have efficient characteristics such as network traffic, low development cost and dynamic user interface implementation. Since proposed X-internet framework can operate in environment of current developer friendly, it is useful for development of various new application programs and we confirm it through Flash web mail implementation by using proposed x-internet framework.

• Korean Management Science Review
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• v.26 no.3
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• pp.101-115
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• 2009

In this paper, we propose a technology mining framework for mobile communication industry. We develop a two phase approach of new technology identification and service enhancement. The new technology identification process consists of R&D issues analysis, technology theme design, and emerging technology sampling. On the other hand, existing service enhancement process has technology landscaping, keyword based search, and technological growth analysis. By implementing these two phase frameworks, we develop a technology portfolio for mobile communication industry.