• Korean Journal of Cognitive Science
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• v.22 no.2
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• pp.173-192
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• 2011

The visual system applies previously learned contextual knowledge to facilitate the perception and encoding of scenes. When extrapolation following the employment of contextual information occurs, it sometimes leads to scene memory distortion: people report as if they saw more than they actually had seen. This phenomenon is called the "boundary extension" effect (Intraub & Richardson, 1989). The present study aimed to clarify the effects of contextual information on boundary extension in a more systematic way. Based on the assumption that it is harder to extract contextual information from inverted scenes compared to intact scenes, we presented inverted scenes either during encoding or retrieval to manipulate the level of contextual information and compared the magnitude of boundary extension effect for upright versus inverted scenes. In a series of experiments, we found that scene inversion during encoding, but not during retrieval, significantly reduced boundary extension. Showing reduced memory distortion for inverted scenes, the current study directly demonstrated that access to contextual information is a critical component of scene extrapolation process.

• Journal of Korea Society of Industrial Information Systems
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• v.19 no.2
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• pp.85-91
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• 2014

The objective of the research is to automatically recognize railway objects from MLS data in which 9 key objects including terrain, track, bed, vegetation, platform, barrier, posts, attachments, powerlines are targeted. The proposed method can be divided into two main sub-steps. First, multi-scale contextual features are extracted to take the advantage of characterizing objects of interest from different geometric levels such as point, line, volumetric and vertical profile. Second, by considering contextual interactions amongst object labels, a contextual classifier is utilized to make a prediction with local coherence. In here, the Conditional Random Field (CRF) is used to incorporate the object context. By maximizing the object label agreement in the local neighborhood, CRF model could compensate the local inconsistency prediction resulting from other local classifiers. The performance of proposed method was evaluated based on the analysis of commission and omission error and shows promising results for the practical use.

• Journal of Korean Society of Archives and Records Management
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• v.9 no.1
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• pp.151-173
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• 2009

The classification in the Records and Archives Sciences focuses on the contextual information in producing and utilizing records rather than their contents. This study aimed at designing an automatic records classification system to enable an automatic classification focusing on the aggregation of the context of records rather than the contents of individual record in the classification scheme, structured on the basis of business activities analyses for records reflecting the business activities. The automatic records classification system was designed to have mutual supplements by constructing the classification scheme and thesaurus together as the classification reference, as well as the aggregation of records that have been already classified. Additionally included are plans to apply the classified contextual information of records to the classification reference on the real-time base right after the category assignment of records to be classified. Although there are limitations as the designed system depends on the quality of the contextual information, it is considered that the system could lead to ensure that the contextual information of records should be more substantial.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.675-681
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• 2024

The purpose of this study is to investigate the mediating effects of job characteristics and organizational contextual performance of care workers. This is an empirical analysis of the dual mediating effect of job attitude and organizational climate. A total of 274 caregivers were recruited. SASS process macro was used for data analysis. The results of the study were as follows. First, there was a significant correlation between job characteristics, job attitude, organizational climate, and contextual performance. Second, job attitude and organizational climate had a double mediating effect between job characteristics and contextual performance. Third, job attitude and organizational climate had a significant mediating effect between job characteristics and contextual performance. Based on this, psychological voluntary job competency improvement methods and follow-up studies were suggested to improve the job performance of care workers.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.15-28
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• 2015

As personal devices and pervasive technologies for interacting with networked objects continue to proliferate, there is an unprecedented world of scattered pieces of contextualized information available. However, the explosive growth and variety of information ironically lead users and service providers to make poor decision. In this situation, recommender systems may be a valuable alternative for dealing with these information overload. But they failed to utilize various types of contextual information. In this study, we suggest a methodology for context-aware recommender systems based on the concept of contextual boundary. First, as we suggest contextual boundary-based profiling which reflects contextual data with proper interpretation and structure, we attempt to solve complexity problem in context-aware recommender systems. Second, in neighbor formation with contextual information, our methodology can be expected to solve sparsity and cold-start problem in traditional recommender systems. Finally, we suggest a methodology about context support score-based recommendation generation. Consequently, our methodology can be first step for expanding application of researches on recommender systems. Moreover, as we suggest a flexible model with consideration of new technological development, it will show high performance regardless of their domains. Therefore, we expect that marketers or service providers can easily adopt according to their technical support.

• Korean Journal of Cognitive Science
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• v.33 no.2
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• pp.109-133
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• 2022

Episodic memory consists of a core event and the associated contexts. Although the role of the hippocampus and its neighboring regions in contextual representations during encoding has become increasingly evident, it remains unclear how these regions handle various context-specific information other than spatio-temporal contexts. Using high-resolution functional MRI, we explored the patterns of the medial temporal lobe (MTL) and cortical regions' involvement during the encoding of various types of contextual information (i.e., journalism principle 5W1H): "Who did it?," "Why did it happen?," "What happened?," "When did it happen?," "Where did it happen?," and "How did it happen?" Participants answered six different contextual questions while looking at simple experimental events consisting of two faces with one object on the screen. The MTL was divided to sub-regions by hierarchical clustering from resting-state data. General linear model analyses revealed a stronger activation of MTL sub-regions, the prefrontal lobe (PFC), and the inferior parietal lobule (IPL) during social (Who), behavioral (How), and intentional (Why) contextual processing when compared with spatio-temporal (Where/When) contextual processing. To further investigate the functional networks involved in contextual encoding dissociation, a multivariate pattern analysis was conducted with features selected as the task-based connectivity links between the hippocampal subfields and PFC/IPL. Each social, behavioral, and intentional contextual processing was individually and successfully classified from spatio-temporal contextual processing, respectively. Thus, specific contexts in episodic memory, namely social, behavior, and intention, involve distinct functional connectivity patterns that are distinct from those for spatio-temporal contextual memory.

• Proceedings of the KOSOMBE Conference
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• v.1995 no.11
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• pp.269-272
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• 1995

• Investigative Magnetic Resonance Imaging
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• v.3 no.2
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• pp.179-187
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• 1999

Purpose : This paper introduces a computationally inexpensive context-dependent classification of multi-echo MRI with Bayes compound decision model. In order to produce accurate region segmentation especially in homogeneous area and along boundaries of the regions, we propose a classification method that uses contextual information of local enighborhood system in the image. Material and Methods : The performance of the context free classifier over a statistically heterogeneous image can be improved if the local stationary regions in the image are disassociated from each other through the mechanism of the interaction parameters defined at he local neighborhood level. In order to improve the classification accuracy, we use the contextual information which resolves ambiguities in the class assignment of a pattern based on the labels of the neighboring patterns in classifying the image. Since the data immediately surrounding a given pixel is intimately associated with this given pixel., then if the true nature of the surrounding pixel is known this can be used to extract the true nature of the given pixel. The proposed context-dependent compound decision model uses the compound Bayes decision rule with the contextual information. As for the contextual information in the model, the directional transition probabilities estimated from the local neighborhood system are used for the interaction parameters. Results : The context-dependent classification paradigm with compound Bayesian model for multi-echo MR images is developed. Compared to context free classification which does not consider contextual information, context-dependent classifier show improved classification results especially in homogeneous and along boundaries of regions since contextual information is used during the classification. Conclusion : We introduce a new paradigm to classify multi-echo MRI using clustering analysis and Bayesian compound decision model to improve the classification results.

• 한국HCI학회:학술대회논문집
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• 2008.02b
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• pp.158-164
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• 2008

This study was conducted for raising the considering the dynamical context of mobile phone use environment in the mobile-phone research. For this I identified the characteristic of the mobile phone use. The first characteristic is that the mobile phone is the context sensitive device. Also, it reflects the user's life pattern because it is the very personal device. I defined the context of mobile phone use with the basis on this identification of those characteristics. I referenced the definition, 'situational context', defining the mobile phone use context. Also, I set up the research scope within the user task that is influential from the situational context, I named this kind of task as the 'contextual task'. I developed the Contextual Task Model in this study. I named the model as the 'ESR Model'. The reason that I developed this contextual task model is that this model can help novice designers and designers unfamiliar with an application domain understand the user behavior and user centered design. And also this model can be effective to communicate each other, I identified the user's contextual tasks three kinds of model. First, the Extend Model includes user tasks that related to extending from user physical working space to the virtual level. Second model is Synchronize Model, which includes issues that lesson the blocking when use several functions at a time or sequentially. Third model is Replace Model, which is come from the characteristic of user life pattern to use the mobile phone. Finally, I proposed an application of this model, CIQ. Through the process to make CIQ I proved the effectiveness of this ESR Model.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.12 no.3
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• pp.163-176
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• 2017

The fourth industrial revolution and creative economy are in the center of today's economic environments. In this context, venture companies make great efforts in achieving innovative capability for sustainable growth. Especially, in order to create the innovative capability or innovation performance, they are very much interested in obtaining such characteristics of organizational ambidexterity as exploration and exploitation. Thus, this study aims to study the organizational contextual ambidexterity as a key factor to create SMEs' performance and to conduct an empirical research with the data collected from domestic SMEs in order to find the antecedent and consequence of this ambidexterity. In so doing, we selected as an antecedent of contextual ambidexterity quality management practice, which is an innovative method of enterprise innovation and enterprise level, innovation performance (radical and incremental Innovation performance) as a consequence and ran a series of regression analyses. As a result, quality management practices such as quality circle activities have had positive effect on organizational ambidexterity, that is, exploration and exploration, and contextual ambidexterity has affected positively on innovation performance. It is also found that the contextual ambidexterity plays a full mediating role in the relationship between quality management practices and innovation performance. The results of this study suggest that contextual ambidexterity is essential to achieving organizational innovation performance and quality management activities such as quality circle should be preceded in order to stimulate or stimulate contextual ambidexterity.