• Journal of Information Processing Systems
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• 제10권4호
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• pp.581-588
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• 2014

To facilitate the implementation of a wide variety of context-aware applications based on mobile devices, general-purpose context-aware framework that applications can use by calling is needed. The context-aware framework is a middleware that performs the sensing, reasoning, and retrieving based on the knowledge base. The knowledge base must systematically represent the information required on the behavior of the context-aware framework, such as context information and reasoning information. It must also provide functions for storage and retrieval. To date, previous research on the representation of the context information have been carried out, but studies on the unified representation of the knowledge base has seen little progress. This study defines the knowledge base as the unified context information, and proposes the UniOWL, which can do a good job of representing it. UniOWL is based on OWL and represents the information that is necessary for the operation of the context-aware framework. Therefore, UniOWL greatly facilitates the implementation of the knowledge base on a context-aware framework.

• 한국컴퓨터정보학회논문지
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• 제12권1호
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• pp.89-98
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• 2007

근래들어 PDA나 스마트 폰과 같은 모바일 기기에서 동작하는 어플리케이션에서 상황 정보를 이용하게 만드는 것은 유비쿼터스 컴퓨팅, 모바일 컴퓨팅 분야에서 관심을 가지게 되었다. 기존 미들웨어들은 대부분 상황 프로비저닝을 위해 한 가지 전략을 사용하였다. 그러나, 본 논문에서 제안한 미들웨어는 상황 프로비저닝을 위해 내부 센서 기반, 외부 인프라스트럭처 기반, 에드-혹 네트워크에서의 분산 기반 전략을 통합하였다. 어플리케이션은 필요로 하는 상황 아이템을 SQL 형태의 상황 질의어를 사용하여 요청하며 자원의 이용도나 외부 인프라스트럭처의 존재 유무 등이 고려된 상황 프로비저닝 전략을 사용하여 상황 정보를 획득하는 것이 가능하다.

• 정보처리학회논문지C
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• 제16C권1호
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• pp.109-116
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• 2009

유비쿼터스 컴퓨팅 기술을 기반으로 주변 상황을 인식하고 그에 따른 상황인지 서비스를 실현하기 위한 많은 연구가 진행되고 있다. u-City에서는 도시의 곳곳의 센서 등을 통해 상황 정보가 수집되고, 개인들은 자신의 모바일기기와 도시의 정보 통신 인프라를 통하여 상황인지 서비스를 제공 받게 된다. 본 논문에서는 u-City의 네트워크에 연결된 센서나 디바이스에서의 정보를 구조화하는데 유용하고 상호 관계성 및 부분적인 상황의 정보를 표현할 수 있는 OWL(Web Ontology Language)을 사용한 온톨로지를 설계하고, 수집된 상황정보와 사용자의 의도를 기반으로 서비스를 추론하는 맞춤형 u-City 서비스 제공을 위한 상황인지 추론 시스템을 제안한다.

• 한국디자인학회:학술대회논문집
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• 한국디자인학회 2005년도 추계 학술발표대회 논문집
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• pp.16-17
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• 2005

• 산경연구논집
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• 제12권10호
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• pp.43-57
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• 2021

Purpose: This study focuses how motivations influence interactivity in the live streaming commerce context. Live streaming commerce involves the provision of e-commerce activities and transactions via a live streaming platform that offers real-time interaction, entertainment, social activities, and commerce. The purpose of study is to examine effects of motivations on perceived interactivity and the effects of perceived interactivity on attitude and intention to use the live streaming commerce. Research design, data and methodology: The study investigates key questions about consumers' motivation to use live streaming commerce and perceived interactivity by surveying 300 users of live commerce. Participants were asked whether they were live streaming commerce users who had experienced live streaming commerce before participating in the survey. The full survey required live streaming commerce users to respond to all the questions. Results: The study uncovered motivations for using live streaming commerce by finding information, entertainment, pass time, fashion/status and real time and perceived interactivity in the live streaming commerce. The results indicated motivation to use live streaming commerce positively influenced perceived interactivity. Perceived interactivity had positive effects on attitude toward brand. Attitude toward brand had positive effects on intention to use. Conclusions: Live streaming commerce is getting increasing attention from marketers because live streaming commerce has seamlessly integrated commerce, social activities, and hedonic factors. This study clarifies motivations and perceived interactivity in the live streaming commerce context. The study uncovers the relationships between motivations, perceived interactivity, attitude, and intention to use that contributes to the theoretical foundation and practical implications for marketing and management in the live streaming commerce context. Specifically, the study develops the theoretical contributions to perceived interactivity in the in the live streaming commerce context. The results also contribute to the practical implications for new marketing strategies that provides dynamic real-time interaction, exact information, and social and hedonic factors to attract consumers to indulge in the consumption processes. Marketing practitioners will obtain insights that can help them develop and manage brand strategies by understanding the influence of motivation and perceived interactivity in the live commerce context, which offers opportunities for contactless marketing and management.

• Journal of the Korean Data and Information Science Society
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• 제18권4호
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• pp.1157-1170
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• 2007

LBS must manage various context data and make the best use of this data for application service in ubiquitous environment. Conventional mobile object data management architecture did not consider process of context data. Therefore a new mobile data management framework is needed to process location context data. In this paper, we design a new context management framework for a location based application service. A suggestion framework is consisted of context collector, context manager, rule base, inference engine, and mobile object context database. It describes a form of rule base and a movement process of inference engine that are based on location based application scenario. It also presents an embodiment instance of interface which suggested framework is applied to location context interference of mobile object.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제7권3호
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• pp.165-170
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• 2007

Recommendation systems provide users with proper services using context information being input from many sensors occasionally under ubiquitous computing environment. But in case there isn't sufficient context information for service recommendation in spite of much context information, there can be problems of resulting in inexact result. In addition, in the quantification step to use context information, there are problems of classifying context information inexactly because of using an absolute classification course. In this paper, we solved the problem of lack of necessary context information for service recommendation by using dynamic profile information. We also improved the problem of absolute classification by using a relative classification of context information in quantification step. As the result of experiments, expectation preference degree was improved by 7.5% as compared with collaborative filtering methods using an absolute quantification method where context information of P2P mobile agent is used.

• 한국컴퓨터정보학회논문지
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• 제11권3호
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• pp.239-248
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• 2006

유비쿼터스 컴퓨팅 환경에서 현재의 상황 인식 어플리케이션은 다루고 있는 상황 정보가 정확하다고 가정하지만, 실제로 센서로 입력되고 해석된 상황 정보들은 종종 모호하거나 불확실하다. 본 논문에서는 상황 정보의 모호성을 해결하기 위하여 베이지안 네트워크를 사용하고 상황 정보를 표현하기 위해 온톨로지 기반 모델을 확장한 확률 모델을 제안한다. 이 논문에서 제시한 확률 확장 온톨로지 기반 상황 인식 미들웨어는 유비쿼터스 환경에서 요구되는 다양한 상황 인식 서비스의 개발 및 운용을 효과적으로 지원 할 수 있다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권5호
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• pp.1396-1412
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• 2023

Conversation modeling is an important and challenging task in the field of natural language processing because it is a key component promoting the development of automated humanmachine conversation. Most recent research concerning conversation modeling focuses only on the current utterance (considered as the current question) to generate a response, and thus fails to capture the conversation's logic from its beginning. Some studies concatenate the current question with previous conversation sentences and use it as input for response generation. Another approach is to use an encoder to store all previous utterances. Each time a new question is encountered, the encoder is updated and used to generate the response. Our approach in this paper differs from previous studies in that we explicitly separate the encoding of the question from the encoding of its context. This results in different encoding models for the question and the context, capturing the specificity of each. In this way, we have access to the entire context when generating the response. To this end, we propose a deep neural network-based model, called the Context Model, to encode previous utterances' information and combine it with the current question. This approach satisfies the need for context information while keeping the different roles of the current question and its context separate while generating a response. We investigate two approaches for representing the context: Long short-term memory and Convolutional neural network. Experiments show that our Context Model outperforms a baseline model on both ConvAI2 Dataset and a collected dataset of conversational English.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권8호
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• pp.2881-2894
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• 2014

In complicated environment, context information plays an important role in image segmentation/labeling. The recently proposed auto-context algorithm is one of the effective context-based methods. However, the standard auto-context approach samples the context locations utilizing a fixed radius sequence, which is sensitive to large scale-change of objects. In this paper, we present a scale invariant auto-context (SIAC) algorithm which is an improved version of the auto-context algorithm. In order to achieve scale-invariance, we try to approximate the optimal scale for the image in an iterative way and adopt the corresponding optimal radius sequence for context location sampling, both in training and testing. In each iteration of the proposed SIAC algorithm, we use the current classification map to estimate the image scale, and the corresponding radius sequence is then used for choosing context locations. The algorithm iteratively updates the classification maps, as well as the image scales, until convergence. We demonstrate the SIAC algorithm on several image segmentation/labeling tasks. The results demonstrate improvement over the standard auto-context algorithm when large scale-change of objects exists.