• Journal of the Korea Society of Computer and Information
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• v.17 no.2
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• pp.187-195
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• 2012

Smart phone allows advertisers to estimate customers behavior by selecting user context awareness information and gives users instant feed back about their behavior. Electronic equipments such as smart phone enable advertisers to advertise interesting product for each customers at the point of purchase. In this paper, we deal with the trends of Smart phone and internet based TV in the spotlight as the upcoming advertising media and propose the effective way of advertising, Smart Advertising model, which can give users advertising contents of their interesting product by collecting user context information from a variety of devices including N-screen in smart space. This model will induce modern people who live in flood of advertisements to buy products by providing interesting advertising contents.

• ETRI Journal
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• v.44 no.4
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• pp.599-612
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• 2022

This study focuses on improving a word embedding model to enhance the performance of downstream tasks, such as those of dialog systems. To improve traditional word embedding models, such as skip-gram, it is critical to refine the word features and expand the context model. In this paper, we approach the word model from the perspective of subword embedding and attempt to extend the context model by integrating various sentence models. Our proposed sentence model is a subword-based skip-thought model that integrates self-attention and relative position encoding techniques. We also propose a clustering-based dialog model for downstream task verification and evaluate its relationship with the sentence-model-based subword embedding technique. The proposed subword embedding method produces better results than previous methods in evaluating word and sentence similarity. In addition, the downstream task verification, a clustering-based dialog system, demonstrates an improvement of up to 4.86% over the results of FastText in previous research.

• Journal of Korean Society of Archives and Records Management
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• v.20 no.1
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• pp.115-137
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• 2020

This study aims to examine the limitations of status that describe archives based on the Archival rules, and to propose a new method using the Records in Context - Conceptual model (RiC-CM) as a solution. Given this, the study conducted literature reviews and case studies. The solutions based on RiC-CM and its effects on the limitations of the existing environment are as follows. First, RiC-CM can describe multiple provenances about archives. This can be solved by defining individual records and provenances as "entity" and expressing their associations as relationships. The interrelation of entities alone can more accurately represent the information of provenances associated with a particular archive, making it easier to identify the overall context that makes records. Second, RiC-CM can link related files. Those that belong to a specific records group (fonds) can be resolved by assigning them to individual entities and making interrelation according to the context that makes records. This method makes it possible to serve information about the context that makes records. From the user's point of view, more options are available for searching records. Third, RiC-CM can link all relevant producer-made records related to a specific production organization. If organizations are related to each other, they can be defined as "entity," and their relationship can be expressed as "associated with." It helps to comprehensively examine the context of provenances. The findings of this study are expected to be used as a basis for future research on RiC-CM, in response to the paradigm shift for electronic records management systems.

• Journal of Intelligence and Information Systems
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• v.15 no.2
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• pp.97-111
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• 2009

Context-aware computing, as a core of smart space development, has been widely regarded as useful in realizing individual service provision. However, most of context-aware services so fat are in its early stage to be dispatched for actual usage in the real world, caused mainly by user's privacy concerns. Moreover, since legacy context-aware services have focused on acquiring in an automatic manner the extra-personal context such as location, weather and objects near by, the services are very limited in terms of quality and variety if the service should identify intra-personal context such as attitudes and privacy concern, which are in fact very useful to select the relevant and timely services to a user. Hence, the purpose of this paper is to propose a novel methodology to infer the user's privacy concern as intra-personal context in an intelligent manner. The proposed methodology includes a variety of stimuli from outside the person and then performs model-based reasoning with social theory models from model base to predict the user's level of privacy concern semi-automatically. To show the feasibility of the proposed methodology, a survey has been performed to examine the performance of the proposed methodology.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.9
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• pp.407-418
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• 2023

The Multi-Object Goal Visual Navigation(MultiOn) is a visual navigation task in which an agent must visit to multiple object goals in an unknown indoor environment in a given order. Existing models for the MultiOn task suffer from the limitation that they cannot utilize an integrated view of multimodal context because use only a unimodal context map. To overcome this limitation, in this paper, we propose a novel deep neural network-based agent model for MultiOn task. The proposed model, MCFMO, uses a multimodal context map, containing visual appearance features, semantic features of environmental objects, and goal object features. Moreover, the proposed model effectively fuses these three heterogeneous features into a global multimodal context map by using a point-wise convolutional neural network module. Lastly, the proposed model adopts an auxiliary task learning module to predict the observation status, goal direction and the goal distance, which can guide to learn the navigational policy efficiently. Conducting various quantitative and qualitative experiments using the Habitat-Matterport3D simulation environment and scene dataset, we demonstrate the superiority of the proposed model.

• Journal of Digital Convergence
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• v.10 no.2
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• pp.293-297
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• 2012

In order to provide with personalized recommendation service in context-awareness environment, the collected context data should be analyzed fast and the objective of user should be able to inferred effectively. But, the context collected from the mobile devices is not suitable for applying the existing inference algorithms as they are due to the omission or uncertainty of information and the efficient algorithms are required for mobile environment. In this paper, the behavior pattern was classified using naive bayes classification for minimize the loss caused by the omission or error of information. And pattern matching was used to effectively learn of the users inclination and infer the behavior purpose. The accuracy of the suggested inference model was evaluated by applying to the application recommendation service in the smart phones.

• Journal of IKEEE
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• v.25 no.4
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• pp.598-606
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• 2021

In this paper, we presents a new service binding and resource management model for context based services in mobile edge computing (MEC) networks. The proposed control is composed of two layers: MEC service bindng control layer (MCL) and user context control layer (UCL). The MCL manages service binding construction, resource allocation, and service policy construction from a system point of view; and the UCL manages real-time service adaptation using meta-objects. Through simulations, we confirmed that the proposed control offers enhanced throughput and content transfer time when it is compared to the legacy computing and control models. The proposed control model can be employed as a key component for the context based various internet-of-things (IoT) services in MEC environments.

• Journal of Information Processing Systems
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• v.18 no.5
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• pp.614-627
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• 2022

Aspect-based sentiment analysis is to discover the sentiment polarity towards an aspect from user-generated natural language. So far, most of the methods only use the implicit position information of the aspect in the context, instead of directly utilizing the position relationship between the aspect and the sentiment terms. In fact, neighboring words of the aspect terms should be given more attention than other words in the context. This paper studies the influence of different position embedding methods on the sentimental polarities of given aspects, and proposes a position embedding interactive attention network based on a long short-term memory network. Firstly, it uses the position information of the context simultaneously in the input layer and the attention layer. Secondly, it mines the importance of different context words for the aspect with the interactive attention mechanism. Finally, it generates a valid representation of the aspect and the context for sentiment classification. The model which has been posed was evaluated on the datasets of the Semantic Evaluation 2014. Compared with other baseline models, the accuracy of our model increases by about 2% on the restaurant dataset and 1% on the laptop dataset.

• Journal of the Korea Society of Computer and Information
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• v.14 no.9
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• pp.205-210
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• 2009

This paper proposes an ontology-based context aware system model for the purpose of storing and managing agricultural products using ubiquitous sensors to share and distribute information. In these days, according to penetrating ubiquitous technologies into our way of life, the importance of information is increasing gradually. The importance of ontology in a domain is getting as well. Therefore, this paper designs and build an ontology-based agricultural products warehouse model using context aware state information obtained by using wireless sensors. Also, it shows the result described by graphical ontology results to share common understanding on the structure of context information among users, devices and services to enable semantic interoperability owing to the information of the context aware state of the warehouse.

• Journal of KIISE:Software and Applications
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• v.36 no.4
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• pp.283-290
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• 2009

Context-awareness is one of the key features of ubiquitous paradigm. A methodology that is specifying the relationships between the contexts and services needs to be developed to intelligently and sensitively deal with dynamic environment. The existing models on context-aware modeling are difficult to verify the correctness of models with respect to timeliness. In this paper we propose an approach which includes timing constraint in the relations of the context model, and verify its effectiveness using colored Petri-Net. Moreover, a context-modeling toolkit including context-awareness engine and simulator is developed to support agent-based context-aware service. The effectiveness of the proposed methodology is demonstrated using an example of Usilvercare.