• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.5
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• pp.2122-2140
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• 2020

Proper and precise embedding of commercial ads within Webpages requires Ad-hoc analysis and understanding of their content. By the successful implementation of this step, both publishers and advertisers gain mutual benefits through increasing their revenues on the one hand, and improving user experience on the other. In this research work, we propose a novel multi-level context-based ads serving approach through which ads will be served at generic publisher websites based on their contextual relevance. In the proposed approach, knowledge encoded in domain-specific and generic semantic repositories is exploited in order to analyze and segment Webpages into sets of contextually-relevant segments. Semantically-enhanced indexes are also constructed to index ads based on their textual descriptions provided by advertisers. A modified cosine similarity matching algorithm is employed to embed each ad from the Ads repository into one or more contextually-relevant segments. In order to validate our proposal, we have implemented a prototype of an ad serving system with two datasets that consist of (11429 ads and 93 documents) and (11000 documents and 15 ads), respectively. To demonstrate the effectiveness of the proposed techniques, we experimentally tested the proposed method and compared the produced results against five baseline metrics that can be used in the context of ad serving systems. In addition, we compared the results produced by our system with other state-of-the-art models. Findings demonstrate that the accuracy of conventional ad matching techniques has improved by exploiting the proposed semantically-enhanced context-based ad serving model.

• Safety and Health at Work
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• v.14 no.2
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• pp.163-173
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• 2023

In many industrial sectors, workers are exposed to manufactured or unintentionally emitted airborne nanoparticles (NPs). To develop prevention and enhance knowledge surrounding exposure, it has become crucial to achieve a consensus on how to assess exposure to airborne NPs by inhalation in the workplace. Here, we review the literature presenting recommendations on assessing occupational exposure to NPs. The 23 distinct strategies retained were analyzed in terms of the following points: target NPs, objectives, steps, "measurement strategy" (instruments, physicochemical analysis, and data processing), "contextual information" presented, and "work activity" analysis. The robustness (consistency of information) and practical aspects (detailed methodology) of each strategy were estimated. The objectives and methodological steps varied, as did the measurement techniques. Strategies were essentially based on NPs measurement, but improvements could be made to better account for "contextual information" and "work activity". Based on this review, recommendations for an operational strategy were formulated, integrating the work activity with the measurement to provide a more complete assessment of situations leading to airborne NP exposure. These recommendations can be used with the objective of producing homogeneous exposure data for epidemiological purposes and to help improve prevention strategies.

• Proceedings of the Korea Society of Design Studies Conference
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• 2005.10a
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• pp.16-17
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• 2005

• The Journal of Information Systems
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• v.26 no.4
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• pp.163-188
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• 2017

Purpose The purpose of this study is to empirically analyze the factors affecting the intention to use the IPA focusing on functional characteristics. Based on the research result, this research has significance in that it not only suggested strategic guidelines for the related business operators, it also helped identify the factors that will influence the intention to use an intelligent personal assistant centering on the functional characteristics of the IPA. Design/methodology/approach Accordingly, in an attempt to identify factors that will influence the intention to use the intelligent personal assistant, we proposed a research model, together with a corresponding hypothesis, which incorporates the functional characteristics (personalization, anthropomorphism, autonomy, communication ability, contextual offer) and perceived enjoyment of the intelligent personal assistant into a technology acceptance model. To verify the research hypothesis of this research, we have conducted a questionnaire survey with individuals who have used an intelligent personal assistant as target. And with the data collected from 215 copies of the questionnaire survey, we have carried out a path analysis using the PLS structural equation. Findings As a result, it turned out that, of the IPA functional characteristics, personalization had a positive effect on perceived usefulness, autonomy had a positive effect on perceived usefulness and perceived ease of use. Also, communication ability had a positive effect on perceived ease of use and perceived enjoyment, and anthropomorphism and contextual offer had a positive effect on perceived ease of use and perceived enjoyment and turned out to be major factors that increased the use intention of intelligent personal assistant.

• Journal of The Korean Association For Science Education
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• v.33 no.4
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• pp.763-777
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• 2013

This is a case study on two schools practising integrated science education (hereafter ISE). The purposes of this study are to investigate the types and features of ISE in the schools actively practising ISE, to identify the contextual factors of the schools, and to give implications for implementing ISE in schools. This study investigated the contextual factors in practicing ISE with a focus on the two schools, a middle school in Gyeonggi-do and a high school in Busan. They were breaking down the boundaries among teaching subjects and providing student-oriented instruction with problems in the real world. The data were collected by observing classes, by interviewing teachers, and by reviewing school documents and students' reports. The research findings are as follows: first, the two schools took part in ISE actively. They teach science to students providing integrated experiences mainly by using interdisciplinary knowledge and/or by solving the problems pertaining to the real world. While the former integrated subjects centering on topics, the latter focused on a project-based learning driven by students. They have differences in regard to the role of teachers and students, the level of integration and the type of integration. Second, the contextual factors that enabled ISE to be implemented there were found. The previous studies revealed six contextual factors in practising ISE: small and stable learning environment, leadership, team activities, in-school planning time, flexible timetable and community links. This study also found similar factors. However, the cases of this study provided ISE on a large scale and in a short period of time, instead of a small and stable learning environment. Teachers viewed the process of ISE not only as a tool to overcome the conservative culture of teachers but also as a pursuit of innovation.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.3
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• pp.76-88
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• 2015

This paper presents a classification method based on the combination of active learning with temporal contextual information extracted from past land-cover maps for the classification of crop cultivation areas. Iterative classification based on active learning is designed to extract reliable training data and cultivation rules from past land-cover maps are quantified as temporal contextual information to be used for not only assignment of training data but also relaxation of spectral ambiguity. To evaluate the applicability of the classification method proposed in this paper, a case study with MODIS time-series vegetation index data sets and past cropland data layers(CDLs) is carried out for the classification of corn and soybean in Illinois state, USA. Iterative classification based on active learning could reduce misclassification both between corn and soybean and between other crops and non crops. The combination of temporal contextual information also reduced the over-estimation results in major crops and led to the best classification accuracy. Thus, these case study results confirm that the proposed classification method can be effectively applied for crop cultivation areas where it is not easy to collect the sufficient number of reliable training data.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.545-559
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• 2006

The function of product design has been an important part for success on new product development We deeply studied 'Chocolate-Phone' case which is considered as a representative of adopting design-oriented new product development strategy. According to this study, we found three contextual factors and nine key success factors for design-oriented new product development. The contextual factors consist of the strong needs for innovative product development, customer's needs for the emotional value, competitive situation for the new product launching. The key success factors consist of design, development marketing, overall sides. The key success factors of design side are to select talented designers and take an insight for market and communication skill. The key success factors of development side are to possess high technological abilities, to do divergence with removing or giving up some function management's strong support. The key success factors of marketing side are to bring core marketers from outside, capacity to gather ideas from outside. The key success factors of overall side are to share design-oriented principle with other parts and to change member's mind from engineer-oriented to market-oriented.

• Journal of Electrical Engineering and information Science
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• v.3 no.2
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• pp.211-221
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• 1998

It is becoming apparent that stereo matching algorithms need much information from high level cognitive processes. Otherwise, conventional algorithms based on bottom-up control alone are susceptible to local minima. We introduce a system that consists of two levels. A lower level, using a usual matching method, is based upon the local neighborhood and a second level, that can integrate the partial information, is aimed at contextual matching. Conceptually, the introduction of bottom-up and top-down feedback loop to the usual matching algorithm improves the overall performance. For this purpose, we model the image attributes using a Markov random field (MRF) and thereupon derive a maximum a posteriori (MAP) estimate. The energy equation, corresponding to the estimate, efficiently represents the natural constraints such as occlusion and the partial informations from the other levels. In addition to recognition, we derive a training method that can determine the system informations from the other levels. In addition to recognition, we derive a training method that can determine the system parameters automatically. As an experiment, we test the algorithms using random dot stereograms (RDS) as well as natural scenes. It is proven that the overall recognition error is drastically reduced by the introduction of contextual matching.

• English Language & Literature Teaching
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• v.13 no.4
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• pp.87-108
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• 2007

Given that various classroom contextual factors influence the nature of writing instructional practices, it would be worthwhile to explore these factors to generate better environment for learning to write. Among many factors, this study examined teachers' beliefs and knowledge, which would operate as a very influential contextual factor in that changes in principles and methods of teaching writing would be the results of their underlying beliefs and knowledge related to teaching writing. Three professional teachers who teach second- and third-grade English language learners (ELLs) were interviewed, and the analysis of teacher interviews was conducted. The research findings indicated that basically all of the teachers perceived the role of writing in second language learning as very important, sharing the belief that the ultimate goal of teaching writing is to have their students gain fluency in writing and that some of instructional methods such as integration of writing and other language aspects, content-based writing, and providing scaffolding are important. In addition, some beliefs that two ESL teachers shared included the importance of ample and continuous opportunities to write, vocabulary knowledge, and explicit instruction about writing. Other beliefs, including the importance of creating a comfortable writing environment and opportunities for writing for varied purposes and genres were represented.

• ETRI Journal
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• v.34 no.5
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• pp.743-752
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• 2012

Semantic interpretation of the relationship between noun compound (NC) elements has been a challenging issue due to the lack of contextual information, the unbounded number of combinations, and the absence of a universally accepted system for the categorization. The current models require a huge corpus of data to extract contextual information, which limits their usage in many situations. In this paper, a new semantic relations interpreter for NCs based on novel lightweight binary features is proposed. Some of the binary features used are novel. In addition, the interpreter uses a new feature selection method. By developing these new features and techniques, the proposed method removes the need for any huge corpuses. Implementing this method using a modular and plugin-based framework, and by training it using the largest and the most current fine-grained data set, shows that the accuracy is better than that of previously reported upon methods that utilize large corpuses. This improvement in accuracy and the provision of superior efficiency is achieved not only by improving the old features with such techniques as semantic scattering and sense collocation, but also by using various novel features and classifier max entropy. That the accuracy of the max entropy classifier is higher compared to that of other classifiers, such as a support vector machine, a Na$\ddot{i}$ve Bayes, and a decision tree, is also shown.