• Korea Information Processing Society Review
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• v.11 no.6
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• pp.56-75
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• 2004

In this article we address some system characteristics and challenging issues in developing Context-aware Middleware for Ubiquitous Computing. The functionalities of a Context-aware Middleware includes gathering context data from hardware/software sensors, reasoning and inferring high-level context data, and disseminating/delivering appropriate context data to interested applications/services. The Middleware should facilitate the query, aggregation, and discovery for the contexts, as well as facilities to specify their privacy policy. Following a formal context model using ontology would enable syntactic and semantic interoperability, and knowledge sharing between different domains. Moddleware should also provide different kinds of context classification mechanical as pluggable modules, including rules written in different types of logic (first order logic, description logic, temporal/spatial logic, fuzzy logic, etc.) as well as machine-learning mechanical (supervised and unsupervised classifiers). Different mechanisms have different power, expressiveness and decidability properties, and system developers can choose the appropriate mechanism that best meets the reasoning requirements of each context. And finally, to promote the context-trigger actions in application level, it is important to provide a uniform and platform-independent interface for applications to express their need for different context data without knowing how that data is acquired. The action could involve adapting to the new environment, notifying the user, communicating with another device to exchange information, or performing any other task.

• English Language & Literature Teaching
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• v.11 no.4
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• pp.365-389
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• 2005

The purpose of this study is to present the task model that is the most effective in English language methodology based on the investigation of task-based performance in Korean EFL classroom practice. The subjects were 538 high school students and 126 high school teachers, each of whom had common experiences using the materials of task-based activities for more than one year. To analyze the data, the program SPSS WIN 11.0 including frequency distribution and chi-square analysis was used. The results of the questionnaire analysis showed that both teachers and students had a comparatively high level of satisfaction in task rationale, but that they had some mixed responses in the fields of input data, settings, and activity types. To conclude, a few suggestions are made to provide some meaningful considerations for the EFL teachers and material developers: a) task goals and rationale that encourage the learner's positive motivation; b) authenticity of input data based on the real-world context; c) collaborative learning environment that enhances communicative interaction; d) proportional representation of the creative problem-solving activities related to discussions and decision-making processes; e) systematic introduction of integrated language skills. It also suggests that the multi-lateral task model, which has some positive assets compared to previous task models, be newly introduced and applied to the second language learning classrooms.

• Journal of the Ergonomics Society of Korea
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• v.28 no.3
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• pp.73-79
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• 2009

Driving is a complex psychomotor task often interrupted by secondary activities that increase cognitive workload and divert attention away from the roadway. The risk of inattentive driving is known to vary with age. To assess the characteristics of advancing age on driver's cognitive workload under dual task condition, we evaluate the performance of 96 drivers divided into three age groups: 20's, 40's, and 60's. This study considers driver's cognitive workload in the context of urban and highway driving. Error rate & Dual task cost are used to measure driver's cognitive workload. Results indicate that age impacts cognitive workload during dual task driving conditions.

• Journal of the Ergonomics Society of Korea
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• v.25 no.3
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• pp.7-16
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• 2006

Although several theories and models have been proposed to explain the effects of cultural differences in team decision making, many aspects of team decision-making in multi-cultural contexts such as team performance, team communication, and team cognition still remain unclear. In particular, little attention has paid to the empirical studies on team processes multi-cultural team members use to interact with each other to accomplish the task in different task environments. To investigate the effects of culture and task characteristics on team decision making behavior in multi-cultural contexts, this study compared culturally homogenous and heterogeneous groups in the context of logistics decision making. Results of the study showed that cultural difference and task complexity may affect team performance as well as team interaction process to varying degree.

• Journal of Information Processing Systems
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• v.2 no.1
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• pp.28-33
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• 2006

The initial research of Task Computing in the ubiquitous computing (UbiComp) environment revealed the need for access control of services. Context-awareness of service requests in ubiquitous computing necessitates a well-designed model to enable effective and adaptive invocation. However, nowadays little work is being undertaken on service access control under the UbiComp environment, which makes the exposed service suffer from the problem of ill-use. One of the research focuses is how to handle the access to the resources over the network. Policy-Based Access Control is an access control method. It adopts a security policy to evaluate requests for resources but has a light-weight combination of the resources. Motivated by the problem above, we propose a universal model and an algorithm to enhance service access control in UbiComp. We detail the architecture of the model and present the access control implementation.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.05a
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• pp.303-306
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• 2006

유비쿼터스 컴퓨팅의 핵심은 네트워크 환경에 대한 고 가용성이라 할 수 있다. 이러한 사실은 사용자 컨텍스트(Context)가 반영된 서비스를 제공하기 위한 필수조건이 이미 갖추어져 있다는 것을 시사한다. 지금까지 상황인지(Context-Aware) 서비스를 위한 여러 응용들이 제시되어 왔지만, 동적으로 변화하는, 즉 예측하기 어려운 환경을 충분히 반영할 만큼의 유연성을 제공하지 못했다. 왜냐하면, 응용 태스크 시나리오가 시작단계부터 이미 정해져 있었기 때문이다. 여기에, 본 고는 평생동안 개인화된 태스크를 동적으로 생성, 제공할 수 있는 멀티 에이전트 시스템 구조를 제안하고자 한다. 평생 개인화 태스크(Life Long Personalized Task)는 끊임없이 변화하는 사용자의 행동패턴을 반영할 수 있도록, 동적으로 생성, 제공되는 태스크를 의미한다. 이는 태스크 시나리오가 컴파일 타임에 이미 결정되지 않고, 실행 시간 중에 자동으로 생성된다는 것을 의미한다. 이러한 유연성은 평생학습 엔진(Life Long Learning Engine)을 활용함으로써 가능하다. 이 엔진은 사용자의 행동패턴을 학습하며, 결과적으로 사용자 행동패턴 규칙들을 생성한다.

• Speech Sciences
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• v.10 no.1
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• pp.71-84
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• 2003

In the acoustic modeling for large vocabulary speech recognition, a sparse data problem caused by a huge number of context-dependent (CD) models usually leads the estimated models to being unreliable. In this paper, we develop a new clustering method based on the C45 decision-tree learning algorithm that effectively encapsulates the CD modeling. The proposed scheme essentially constructs a supervised decision rule and applies over the pre-clustered triphones using the C45 algorithm, which is known to effectively search through the attributes of the training instances and extract the attribute that best separates the given examples. In particular, the data driven method is used as a clustering algorithm while its result is used as the learning target of the C45 algorithm. This scheme has been shown to be effective particularly over the database of low unknown-context ratio in terms of recognition performance. For speaker-independent, task-independent continuous speech recognition task, the proposed method reduced the percent accuracy WER by 3.93% compared to the existing rule-based methods.

• 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.

• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.308-315
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• 2007

상황 인지 컴퓨팅에서 상황정보 추론 기능은 상황정보 관리를 위해 중요한 기능 중의 하나이다. 상황정보 추론 기능은 하위 레벨의 상황정보들로부터 사용자의 상황을 나타내는 상위 레벨의 상황정보를 제공한다. 인프라 기반 지능형 공간에서 중앙 집중 형의 상황정보 관리 시스템은 상황정보 추론을 위한 자원 소모를 고려할 필요가 없었다. 하지만 자원이 제약된 장치들로만 구성된 개인 지능형 공간에서는 공간 내 전체의 자원 소모뿐만 아니라 상황정보 관리자 역할을 하는 장치 (coordinator)들의 자원 소모가 최소화 되어야 한다. 본 논문에서는 중앙 집중적인 상황정보 추론 작업을 분배하여 개인 지능형 공간 내의 다른 장치들에게 작업을 분산시키는 상황정보 추론 작업 분배 기법을 제안한다. 제안된 분배 기법은 건강정보, 환경정보, 지리정보 같이 상황정보가 자주 발생하는 환경에서 더 효율적이다. 상황정보 추론작업을 분배 함으로써 상황정보 추론을 위한 개인 지능형 공간의 전체의 처리량을 크게 증가시키지 않으면서 코디네이터의 처리량을 줄일 수 있다. 본 논문의 작업분배 기법은 상황정보 추론의 역할을 하는 코디네이터와 분산된 로컬 상황정보 추론기능을 제안한다. 본 논문에서는 제안된 상황정보 추론 기능을 개인 지능형 공간을 구성하는 장치들에 각각 구현하고 상황정보 추론을 위한 처리부하를 측정하여 제안된 기법의 실행 가능성을 보였다.

• Science of Emotion and Sensibility
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• v.25 no.3
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• pp.107-116
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• 2022

Purpose: Researchers have shown that aesthetic judgments of artworks depend on contexts, such as the authenticity of an artwork (Newman & Bloom, 2011) and an artwork's location of display (Kirk et al., 2009; Silveira et al., 2015). The present study aims to examine whether contextual information related to the creator, such as whether an artwork was created by a human or artificial intelligence (AI), influences viewers' preference judgments of an artwork. Methods: Images of Impressionist landscape paintings were selected as human-made artworks. AI-made artwork stimuli were created using Google's Deep Dream Generator by mimicking the Impressionist style via deep learning algorithms. Participants performed a preference rating task on each of the 108 artwork stimuli accompanied by one of the two creator labels. After this task, an art experience questionnaire (AEQ) was given to participants to examine whether individual differences in art experience influence their preference judgments. Results: Setting AEQ scores as a covariate in a two-way ANCOVA analysis, the stimuli with the human-made context were preferred over the stimuli with the AI-made context. Regarding the types of stimuli, the viewers preferred AI-made stimuli to human-made stimuli. There was no interaction effect between the two factors. Conclusion: These results suggest that preferences for visual artworks are influenced by the contextual information of the creator when the individual differences in art experience are controlled.