• ETRI Journal
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• v.34 no.2
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• pp.292-295
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• 2012

Beyond the simple keyword matching methods in contextual advertising, we propose a rich contextual matching (CM) model adopting a classification method for topic targeting and a query expansion method for semantic ad matching. This letter reports on an investigation into the empirical effects of the CM model by comparing the click-through rates (CTRs) of two practical online news advertising systems. Based on the evaluation results from over 100 million impressions, we prove that the average CTR of our proposed model outperforms that of a traditional model.

• 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.

• Proceedings of the KIEE Conference
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• 1990.07a
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• pp.535-538
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• 1990

The main problem in speech recognition is the enormous variability in acoustic signals due to complex but predictable contextual effects. Especially in plosive consonants it is very difficult to find invariant cue due to various contextual effects, but humans use these contextual effects as helpful information in plosive consonant recognition. In this paper we experimented on three artificial neural net models for the recognition of plosive consonants. Neural Net Model I used "Multi-layer Perceptron ". Model II used a variation of the "Self-organizing Feature Map Model". And Model III used "Interactive and Competitive Model" to experiment contextual effects. The recognition experiment was performed on 9 Korean plosive consonants. We used VCV speech chains for the experiment on contextual effects. The speech chain consists of Korean plosive consonants /g, d, b, K, T, P, k, t, p/ (/ㄱ, ㄷ, ㅂ, ㄲ, ㄸ, ㅃ, ㅋ, ㅌ, ㅍ/) and eight Korean monothongs. The inputs to Neural Net Models were several temporal cues - duration of the silence, transition and vot -, and the extent of the VC formant transitions to the presence of voicing energy during closure, burst intensity, presence of asperation, amount of low frequency energy present at voicing onset, and CV formant transition extent from the acoustic signals. Model I showed about 55 - 67 %, Model II showed about 60%, and Model III showed about 67% recognition rate.

• The Research Journal of the Costume Culture
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• v.25 no.3
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• pp.327-339
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• 2017

A consumer is an individual entity with various motivations. This study is intended to incorporate a hierarchical structure of motivation to understand self-determined motivation for purchasing secondhand merchandise at thrift stores. A conceptual model adopted from Cadwallader et al. (2010)'s comprehensive model of motivation used in a marketing context was developed to investigate motivational process in secondhand merchandise shopping. The conceptual model includes the three levels of motivational structure-the global, contextual (environmental concern and frugality), and situational motivation. A series of the causal relationships among the three levels of self-determined motivations and buying intention to shop at thrift stores were hypothesized. A total of 219 respondents from two different northeastern state universities in the U.S. completed a self-administered survey. The results indicated that secondhand merchandise shopping is well explained in the hierarchical structure of self-determined motivation where the global motivation had a positive impact on the contextual motivations regarding environmental concern and frugality. Of the two contextual motivations, only environmental concern had a positive impact on situational motivation for shopping at thrift stores. Finally, the situational motivation positively influenced the intention to shop at thrift stores. The results of this model suggest that the hierarchical structure of self-determined motivation would be a very useful framework to understand consumer behavior for apparel shopping. Also, further research can be done to identify other contextual motivational factors to understand consumer motivation for shopping at thrift stores.

• Asia pacific journal of information systems
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• v.15 no.3
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• pp.41-64
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• 2005

The effects of information quality and the importance of information have been reported in the information Systems(IS) literature. However, little has been learned about the impact of data quality(DQ) on decision performance. Recognizing with this problem, this study explores the effects of contextual DQ on decision performance. To examine them, a laboratory experiment was conducted. Based on two levels of contextual DQ and two levels of task complexity, this study had a $2{\times}2$ factorial design. The dependent variables used to measure the outcomes of decision performance were problem-solving accuracy and time. The results demonstrated that the effects of contextual DQ on decision performance were significant. The findings suggest that decision makers can expect to improve their decision performance by enhancing contextual DQ. This research not only extends a body of research examining the effects of factors that can be tied to human decision-making performance, but also provides empirical evidence to validate and extend DeLone and McLean's IS success model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3168-3186
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• 2020

Automatic topic essay generation (TEG) is a controllable text generation task that aims to generate informative, diverse, and topic-consistent essays based on multiple topics. To make the generated essays of high quality, a reasonable method should consider both diversity and topic-consistency. Another essential issue is the intrinsic link of the topics, which contributes to making the essays closely surround the semantics of provided topics. However, it remains challenging for TEG to fill the semantic gap between source topic words and target output, and a more powerful model is needed to capture the semantics of given topics. To this end, we propose a pretraining-based contextual self-attention (PC-SAN) model that is built upon the seq2seq framework. For the encoder of our model, we employ a dynamic weight sum of layers from BERT to fully utilize the semantics of topics, which is of great help to fill the gap and improve the quality of the generated essays. In the decoding phase, we also transform the target-side contextual history information into the query layers to alleviate the lack of context in typical self-attention networks (SANs). Experimental results on large-scale paragraph-level Chinese corpora verify that our model is capable of generating diverse, topic-consistent text and essentially makes improvements as compare to strong baselines. Furthermore, extensive analysis validates the effectiveness of contextual embeddings from BERT and contextual history information in SANs.

• 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.

• The Journal of Industrial Distribution & Business
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• v.13 no.5
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• pp.27-40
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• 2022

Purpose: Research on job crafting has thus far focused on how alter job demand and resources behaviors relate to employee task performance. However, job crafting behaviors do not necessarily have an impact only on task performance, but also on employees' contextual performance, a phenomenon that has little research in job crafting research. Therefore, this study aims to investigate the effect of job crafting on task performance or contextual performance and the mediating effect of work engagement between them in the Chinese context. Research design, data and methodology: In order to achieve the above research goals and test the proposed hypotheses, we used a cross-sectional design and a self-administered questionnaire to collect quantitative data from September 8, 2021 to September 27, 2021 among knowledge workers in Shandong Province various financial companies and finally analyzed 211 questionnaires. Descriptive statistics and research model analysis were performed using SPSS 25.0 Version and AMOS 27.0 Version to test the developed hypotheses. Results: The results are as follows; firstly, the study showed that job crafting of employees had a significant positive impact on task performance and contextual performance. Secondly, the higher job crafting of employees, the higher their work engagement. Thirdly, this study showed that work engagement of employees had a positive impact on task performance and contextual performance. Fourthly, we predicted and found that work engagement of employees had a positive mediating effect between job crafting and task performance and a positive mediating effect between job crafting and contextual performance. Overall, this study showed that the proactive job crafting behaviors of employees enhance their engagement for their work, which in turn improves task performance and contextual performance. Conclusions: This paper develops job crafting research by exploring the positive impact of job crafting on employees' task performance or contextual performance through their work engagement. It also proposes that both job crafting behaviors and work engagement are important approaches to improve employees' task performance or contextual performance. Practical implications for organizations, such as increasing employee' work engagement, as well as the limitations and suggestions are concluded for the future research directions.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.1052-1057
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• 1994

Dynamically localized self-organizing map model (DLSMM) is a new speech recognition model based on the well-known self-organizing map algorithm and dynamic programming technique. The DLSMM can efficiently normalize the temporal and spatial characteristics of speech signal at the same time. Especially, the proposed can use contextual information of speech. As experimental results on ten Korean digits recognition task, the DLSMM with contextual information has shown higher recognition rate than predictive neural network models.

• Korean Journal of Health Education and Promotion
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• v.28 no.4
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• pp.1-14
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• 2011

Objectives: The aim of the present study is to elucidate the relationship of community capacity to health in a metropolitan area in Korea. To do so, a multi-level model to verify the contextual effects of community capacity is presented. Methods: The study materials are the "The 4th Seoul Citizens Health Indicators Surveys" on 404 dong in Seoul. The community capacity indicators were developed in two strata: individual-level indicators with community identity domain; and community-level indicators with participation in community organizations, number of non-profit organizations, degree of organizing of community-based organizations, and volunteer activities. Results: Higher unhealthy probability occurs among those with lower community capacity at the community level, lower individual income, and lower community satisfaction at the individual level. It contributed to explaining self-rated health status and showed that there were contextual effects of the community going beyond the compositional effects of the individual. Conclusions: In the process of building community capacity, a community autonomously finds pending issues and solves related problems, and in so doing, raises the social quality and establishes the conditions for health promotion. Thus, the significance of neighborhood needs to be discovered and created in a new way through the development of community capacity.