• Journal of Information Processing Systems
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• v.10 no.4
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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.

• Journal of the Korea Society of Computer and Information
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• v.12 no.1 s.45
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• pp.89-98
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• 2007

In these days, the use of context in application running on mobile devices such as PDAs and smart Phone has become a crucial requirement for several research areas, including ubiquitous computing. mobile computing. Previous middlewares which support context provisioning uses single strategy. But, this paper proposed middleware integrated multiple strategies for context provisioning, namely internal sensors-based, external infrastructure-based, and distributed provisioning in ad hoc networks. Applications can query needed context items using SQL like context query language and require context information to use different provisioning mechanisms depending on resource availability and presence of external infrastructures.

• The KIPS Transactions:PartC
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• v.16C no.1
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• pp.109-116
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• 2009

Recently, there are many researches to realize context-awareness service that recognizes surrounding environments as context and provide the citizens with pervasive convenience based on ubiquitous computing technology. In the u-City, various sensors collect information as context, and citizens will receive various context-awareness service, making use of their wireless and mobile devices and the infrastructures of the u-City. We designed ontology that is useful to structure information of sensor or device that is linked to networks and use OWL (Web Ontology Language) that can express information of mutual relation and partial situation. And we propose a context-aware reasoning system for personalized u-City services based on collected context information and user's intention.

• Proceedings of the Korea Society of Design Studies Conference
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• 2005.10a
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• pp.16-17
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• 2005

• The Journal of Industrial Distribution & Business
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• v.12 no.10
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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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• v.18 no.4
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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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• v.7 no.3
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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.

• Journal of the Korea Society of Computer and Information
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• v.11 no.3
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• pp.239-248
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• 2006

Current context-aware applications In ubiquitous computing environments make the assumption that the context they are dealing with is correct. However, in reality, both sensed and interpreted context informations are often uncertain or imperfect. In this paper, we propose a probability extension model to ontology-based model for rep resenting uncertain contexts and use Bayesian networks to resolve about uncertainty of context informations. The proposed model can support the development and operation of various context-aware services, which are required in the ubiquitous computing environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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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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• v.8 no.8
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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.