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

• Proceedings of the Korean Information Science Society Conference
• /
• 2003.10b
• /
• pp.253-255
• /
• 2003

We address an integrated multimedia framework based on a software agent platform for context-aware multimedia computing. We adopt the FIPA (Foundation for Intelligent Physical Agents) platform which provides agent communications and management mechanisms. In order to express context information, we use MPEG-21 metadata for describing user characteristics and usage environment. We encapsulate such context information as a FIPA message to be delivered between agents. Based on context information, appropriate multimedia content delivery becomes possible. We present our system implementation with a use case scenario and show that our proposed framework is effective for context-aware multimedia computing so that personalization of multimedia consumption can be possible.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.7 no.3
• /
• pp.165-170
• /
• 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
• /
• v.11 no.3
• /
• pp.239-248
• /
• 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.

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

• Journal of the Korean Data and Information Science Society
• /
• v.18 no.4
• /
• pp.1157-1170
• /
• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.5
• /
• pp.1396-1412
• /
• 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.

• Korean Institute of Interior Design Journal
• /
• v.21 no.6
• /
• pp.252-259
• /
• 2012

The aim of this study is in emphasizing the importance of the use of material and the tectonic and their influence on the spatial quality by analyzing Peter Zumthor's projects as case studies. In the research, Zumthor's choice of material, construction method (detail and structure) and the relationship with the context were analyzed in order to illustrate Zumthor's approach in making a strong tie with the locality. Three of Zumthor's projects of different sizes and programs were chosen to heighten the sensitivity of Zumthor's unique working method for each project. Peter Zumthor's architecture is neither superficial nor visually complex. On the contrary, he aims to express through the use of material and construction method, a unique relationship with the physical context, social context and most importantly the actual experience of the space itself. This study aims to contribute towards increasing the awareness and the interest in the materiality and the tectonics which are the fundamentals in spatial experience and spatial identity.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.8 no.8
• /
• pp.2881-2894
• /
• 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.

• The Journal of Information Systems
• /
• v.5
• /
• pp.205-248
• /
• 1996

Historically, the en-users' acceptance of the expert systems(ES) have generally been used as a proxy for the ES' implementation success by both practitioners and academicians. However, with regard to bank loan decisions, most loan officers approach the acquisition of an ES with apprehension. In order to overcome this skepticism, more research should focus on the behavioral aspects relate to systems acquisition and usage. This research applied Vroom's(1964) expectancy theory in an effort to predict end-users' motivation to use an ES in a bank loan decision context. Because human behaviors and judgements are nonlinear rather than linear functions, accurately predicting human behavior is very difficult. To increase the prediction power for end-users' motivation to use an ES in a bank loan decision context, this research used an artificial neural network (ANN) model. In this research, an attempt was made to evaluate adequacy of the surrogates by analyzing differences between real bank loan officers and student surrogates in applying expectancy theory to estimate bank loan officers' motivation to use ES in a bank loan decision context.