• Journal of Smart Tourism
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• v.3 no.3
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• pp.29-40
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• 2023

This research aims to understand the role of online reviews in the local search context by examining the effects of reviews on the representation of tourism businesses on local search platforms (LSPs). By simulating tourists' local searches for restaurants on three LSPs, namely Google, Bing, and Yelp, this study examines how different ranking results are generated across the platforms and how online reviews contribute to the differences. The findings suggest that online reviews are incorporated into LSPs as ranking factors and, thus, affect tourists' decision-making by influencing the information search results in the local search context. As one of the earliest studies on local search, this study discusses how the existing knowledge about the role of online reviews in tourists' decision-making needs to be reevaluated in mobile and more dynamic environments, and offers practical implications for tourism businesses' search engine marketing.

• Journal of Information Processing Systems
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• v.16 no.3
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• pp.612-628
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• 2020

This paper presents a new approach for detecting abnormal behaviors in complex surveillance scenes where anomalies are subtle and difficult to distinguish due to the intricate correlations among multiple objects' behaviors. Specifically, a cascaded probabilistic topic model was put forward for learning the spatial context of local behavior and the temporal context of global behavior in two different stages. In the first stage of topic modeling, unlike the existing approaches using either optical flows or complete trajectories, spatio-temporal correlations between the trajectory fragments in video clips were modeled by the latent Dirichlet allocation (LDA) topic model based on Markov random fields to obtain the spatial context of local behavior in each video clip. The local behavior topic categories were then obtained by exploiting the spectral clustering algorithm. Based on the construction of a dictionary through the process of local behavior topic clustering, the second phase of the LDA topic model learns the correlations of global behaviors and temporal context. In particular, an abnormal behavior recognition method was developed based on the learned spatio-temporal context of behaviors. The specific identification method adopts a top-down strategy and consists of two stages: anomaly recognition of video clip and anomalous behavior recognition within each video clip. Evaluation was performed using the validity of spatio-temporal context learning for local behavior topics and abnormal behavior recognition. Furthermore, the performance of the proposed approach in abnormal behavior recognition improved effectively and significantly in complex surveillance scenes.

• Journal of KIISE:Software and Applications
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• v.27 no.7
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• pp.769-783
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• 2000

In this paper, in order to disambiguate Korean noun word sense, we define a local context and explain how to extract it from a raw corpus. Following the intuition that two different nouns are likely to have similar meanings if they occur in the same local context, we use, as a clue, the word that occurs in the same local context where the target noun occurs. This method increases the usability of extracted knowledge and makes it possible to disambiguate the sense of infrequent words. And we can overcome the data sparseness problem by extending the verbs in a local context. The sense of a target noun is decided by the maximum similarity to the clues learned previously. The similarity between two words is computed by their concept distance in the sense hierarchy borrowed from WordNet. By reducing the multiplicity of clues gradually in the process of computing maximum similarity, we can speed up for next time calculation. When a target noun has more than two local contexts, we assign a weight according to the type of each local context to implement the differences according to the strength of semantic restriction of local contexts. As another knowledge source, we get a co-occurrence information from dictionary definitions and example sentences about the target noun. This is used to support local contexts and helps to select the most appropriate sense of the target noun. Through experiments using the proposed method, we discovered that the applicability of local contexts is very high and the co-occurrence information can supplement the local context for the precision. In spite of the high multiplicity of the target nouns used in our experiments, we can achieve higher performance (89.8%) than the supervised methods which use a sense-tagged corpus.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.4
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• pp.590-597
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• 1990

To represent shape characteristics of digital closed curve, many algorithms, mainly based on local properties, have been proposed. In this paper, we propose a new algorithm for detecting local curvature maxima which reflects context, i.e., structural or surrounding regional characteristics. The algorithm does not require the value of k as an input parameter which is the major problem in k-curvature method in digital curve, but calculates it at each point depening on the context. The algorithm has been applied to two dimensional image boundaries. The efficiency of the algorithm is addressed by comparing the result of existing contest dependent algorithm.

• Korean Journal of Cognitive Science
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• v.2 no.2
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• pp.279-307
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• 1990

The present research inverstigates the nature of contextual effects on pronoun reference resolution during text comprehesion.Through three experiments,this research examines how various contextuall informations influence on reference resolution and interact with syntactic variables.In experiment 1,the local context was controlled by biasing the pronoun-sentence context towards a certain preceding referent.The lexical decision time and the forced choice time for the correct referent were measured.The results showed that the local contexts have clear effect on reference resolution.The effects of syntactic ambiguity were also observed though the local context was biased towards a certain referent noun.In experiment 2,the global context effect was examined by introducing the text-thematic context in a preceding sentence while keeping the following pronoun-sentence context neutral.The results showed that the global thematic context bias towards a subject or object in a preceding sentence entails a faster response time than the thematically neutral context.In experiment 3,another aspects of context effects were inverstigated by manipulating the consistency of the preceding thematic context with the following pronoun-sentence context.The results showed that the lexical decision responses and forced referent choice responses were faster when the prethematic context and the post-anaphoric context match than when they mismatch.In sum,the overall results of three experiments of this research indicates that context has a clear effect on pronoun reference resolution during text comprehension.

• Journal of Information Technology Applications and Management
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• v.22 no.1
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• pp.131-152
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• 2015

Environmental differences across countries such as socio-cultural, political, economic, and technological differences require business strategies of multinational corporations to vary business practices across regions. Despite the keen awareness of the necessity for strategic adaptation to local context, IS management and strategy tend to remain similar across countries. One of the reasons is to maintain the stability and compatibility of information technology infrastructure. After a careful observation of retail business practice, this study finds IS strategy should also be highly responsive to the local context. This study shows how information resources including systems architecture, processes, human resources, and national context are interlinked together. Despite global excellence in general systems management, failure in such alignment can be a serious problem in extending competitive advantages across regions. This study aims to reveal issues to be taken care of in order to accomplish global technological alignment. Results of this study provide senior management with guidelines and a framework for aligning IT with regional strategic thrust that can improve local responsiveness of multinational companies.

• Korean Institute of Interior Design Journal
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• v.21 no.1
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• pp.211-219
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• 2012

I.M. Pei, 2nd generation Chinese immigrant to America, has been one of the most recognized architects who designs museum architecture through out the world for completing significant projects such as the East Building of National Gallery of Art(1068-78) as well as Le Grand Louvre Renovation(1983-89) to name a few. His museum architecture, in particular, reveals his detailed consideration on viewer's artistic experience by providing well balanced architecture design to support exhibiting objects while showing his sensitivity on overall functionality of space. In recent projects such as Miho Museum in Japan, Suzhou Museum of China, and Museum of Islamic Art of Qatar, reveals his growing interests in considering "local context" in museum architecture. Therefore, the purpose of this study is focused on analyzing I.M. Pei's three museum projects having its concept focused on local context. Through out the paper, above mentioned museums were analyzed and compared to summarize his design characters and concept including site plan, spatial organization and architectural form. As a result, it is evident that I.M. Pei had put full effort to apply oriental context with modernism through out his museums. In particular, his site plan, spatial organization, and architectural form shows visible connection to comply with nature which is fundamental idea in oriental philosophy. While his basic design philosophy has been borrowed from the tradition, his ultimate design concept shows nature friendliness as well as theoretical system of thoughts and emotion and most of all, his design excellency in representing local context.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.6
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• pp.1230-1237
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• 2009

In this paper, we develop a design methodology of Granular-based Radial Basis Function Neural Networks(GRBFNN) by context-based clustering. In contrast with the plethora of existing approaches, here we promote a development strategy in which a topology of the network is predominantly based upon a collection of information granules formed on a basis of available experimental data. The output space is granulated making use of the K-Means clustering while the input space is clustered with the aid of a so-called context-based fuzzy clustering. The number of information granules produced for each context is adjusted so that we satisfy a certain reconstructability criterion that helps us minimize an error between the original data and the ones resulting from their reconstruction involving prototypes of the clusters and the corresponding membership values. In contrast to "standard" Radial Basis Function neural networks, the output neuron of the network exhibits a certain functional nature as its connections are realized as local linear whose location is determined by the values of the context and the prototypes in the input space. The other parameters of these local functions are subject to further parametric optimization. Numeric examples involve some low dimensional synthetic data and selected data coming from the Machine Learning repository.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.34 no.11
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• pp.57-65
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• 2018

The aim of this study is to understand the direction of planning for regeneration based on six representative cases of western part of Seoul that were regenerated from the unpleasant facilities since the 20th century. In particular, the similarities and differences were derived by analyzing the characteristics of the history and the location in terms of the 1st establishment and the regeneration in each cases. As a result, looking the tendency to regenerate the unpleasant facility, it can be seen that there is a correlation between the historical and the locational context when establishing and regenerating. Considering the distinct characteristic of the Location and the history of establishment & regeneration, we could classify the characteristics of public facilities as extroverted tendency and intrinsic tendency analysed by a geographic and an architectural, a programmatic aspects. Therefore, when planning the project regenerated from unpleasant facilities, it should be set considering the locational context and the historical context of establishment & regeneration.

• 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.