• Journal of KIISE:Software and Applications
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• v.31 no.2
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• pp.111-125
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• 2004

In this paper, we applied user-centered context to Smart Home Applications. Current research activities on smart home have just focused on the infrastructure without considering user's contexts and implementation cost. We first realized the user-centered personalized services using ubi-UCAM (a Unified Context-aware Application Model), which exploited contexts from various kinds of smart sensors. We, then, verified its usefulness in the ubiquitous computing-enabled home environment. It can be extended to various application areas since it guarantees independence between sensors and services. Accordingly, it will play a key role in future smart home environment.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2006.07a
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• pp.369-369
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• 2006

• Journal of Digital Convergence
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• v.10 no.1
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• pp.309-315
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• 2012

The dissemination of smartphones is being spread and supplementary services using smartphones are increasing and various as the Mobile network and device are developing rapidly, so smartphones that enables to provide a wide range of services is expected to receive the most attention. It makes users listen to music anytime, anywhere in real-time, use useful applications, and access to Internet to search for information. The service environment is changing on PC into Mobile due to the change of the circumstance mentioned above. these services are done by using just location information rather than other context, and users have to search services and use them. It is essential to have Context-aware technology for personalization recommendation services and the appropriate representation and definition of Context information for context-aware. Ontology is possible to represent knowledge freely and knowledge can be extended by inferring. In addition, design of the ontology model is needed according to the purposes of utilization. This paper used context-aware technologies to implement a user personalization recommendation service. It also defined the context through OWL modeling for user personalization recommendation service and used inference rules and inference engine for context reasoning.

• KIISE Transactions on Computing Practices
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• v.23 no.11
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• pp.641-649
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• 2017

As Internet of Things devices become pervasive, services improve to better assess the context and to alert other users to deal with emergencies. Such services use Internet of Things devices to detect the context around the user and promptly notify public institutions, hospitals or other parental users in emergencies. Most of these systems analyze an event when the value of the device is unchanged for a period of time or if it detects an abnormal value. However, just monitoring sensor values makes it difficult to accurately understand the context surrounding a user. Also if the device is inactive, it can not identify the context or provide services again. However, understanding the user requirements, services provided through other devices, information sent to other users lets, appropriate actions be taken. This paper, proposes a device search method and system based on a context-aware model that includes user demands. The proposed system analyzes the user's context and demands by using data collected from the internet of things devices. If user devices are inactive, they can recognize other devices by searching for other devices and providing services to users again. Through the proposed method, the user-centric services are provided. This method also analyzes and responds to requirements in various emergencies.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.91-100
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• 2023

Pervasive computing is characterized by a key characteristic that affects the operating environment of services and users. It places more emphasis on dynamic environments where available resources continuously vary without prior knowledge of their availability, while in static environments the services provided to users are determined in advance. At the same time, Cloud computing paradigm introduced flexibility of use according to the user's profile and needs. In this paper, we aimed to provide Context-Aware Transactional Service applications with solutions so that it can be integrated and invoked like any service in the digital ecosystem. Being able to compose is not enough, each service and application must be able to offer a well-defined behavior. This behavior must be controlled to meet the dynamicity and adaptability necessary for the new user's requirements. The motivation in this paper is to offer design patterns that will provide a maximum of automatism in order to guarantee short reaction times and minimal human intervention. Our proposal includes a cloud service model by developing a PaaS service that allows CATS adaptation. A new specification for the validation of CATS model has been also introduced using the ACTA formalism.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.538-561
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• 2020

Recommender Systems (RecSys) have a major role in e-commerce for recommending products, which they may like for every user and thus improve their business aspects. Although many types of RecSyss are there in the research field, the state of the art RecSys has focused on finding the user similarity based on sequence (e.g. purchase history, movie-watching history) analyzing and prediction techniques like Recurrent Neural Network in Deep learning. That is RecSys has considered as a sequence prediction problem. However, evaluation of similarities among the customers is challenging while considering temporal aspects, context and multi-component ratings of the item-records in the customer sequences. For addressing this issue, we are proposing a Deep Learning based model which learns customer similarity directly from the sequence to sequence similarity as well as item to item similarity by considering all features of the item, contexts, and rating components using Dynamic Temporal Warping(DTW) distance measure for dynamic temporal matching and 2D-GRU (Two Dimensional-Gated Recurrent Unit) architecture. This will overcome the limitation of non-linearity in the time dimension while measuring the similarity, and the find patterns more accurately and speedily from temporal and spatial contexts. Experiment on the real world movie data set LDOS-CoMoDa demonstrates the efficacy and promising utility of the proposed personalized RecSys architecture.

• Journal of Internet Computing and Services
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• v.15 no.6
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• pp.117-124
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• 2014

Cluttered background is a major obstacle in developing salient object detection and tracking system for mobile device captured natural scene video frames. In this paper we propose a context aware feature vector selection model to provide an efficient noise filtering by machine learning based classifiers. Since the context awareness for feature selection is achieved by searching nearest neighborhoods, known as NP hard problem, we apply a fast approximation method with complexity analysis in details. Separability enhancement in feature vector space by adding the context aware feature subsets is studied rigorously using principal component analysis (PCA). Overall performance enhancement is quantified by the statistical measures in terms of the various machine learning models including MLP, SVM, Naïve Bayesian, CART. Summary of computational costs and performance enhancement is also presented.

• 한국디지털정책학회:학술대회논문집
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• 2006.12a
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• pp.397-407
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• 2006

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.6
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• pp.19-27
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• 2010

Many security issues occur in cloud computing service environment such as authentication, access control, and so on. In this paper, we propose an effective authentication and access control model which provide integrated management and control when we access various resources in cloud computing environment. To address these problems, we suggest a context-aware single sign-on and access control system using context-awareness, integrated authentication, access control, and OSGi service platform in cloud computing environment. And we show design and implementation of context-aware single sign-on and access control system. Also we verified the flexibility and convenience of the proposed system through multi fact based integrated authentication in cloud computing environment. We could provide flexible and secure seamless security service by user context in cloud computing environment.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.9
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• pp.906-910
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• 2008

As the mobile environment becomes widely used, there is a growth on the concern about recognizing and sharing user context. Sharing context makes the interaction between human more plentiful as well as helps to keep a good social relationship. Recently, it has been applied to some messengers or mobile applications with sharing simple contexts, but it is still required to recognize and share more complex and diverse contexts. In this paper, we propose a context-aware messenger that collects various sensory information, recognizes representative user contexts such as emotion, stress, and activity by using dynamic Bayesian networks, and visualizes them. It includes a modular model that is effective to recognize various contexts and displays them in the form of icons. We have verified the proposed method with the scenario evaluation and usability test.