Contextual advertising seeks to place relevant textual ads within the content of generic webpages. In this article, we explore a novel semantic approach to contextual advertising. This consists of three tasks: (1) building a well-organized hierarchical taxonomy of topics, (2) developing a robust classifier for effectively finding the topics of pages and ads, and (3) ranking ads based on the topical relevance to pages. First, we heuristically build our own taxonomy of topics from the Open Directory Project (ODP). Second, we investigate how to increase classification accuracy by taking the unique characteristics of the ODP into account. Last, we measure the topical relevance of ads by applying a link analysis technique to the similarity graph carefully derived from our taxonomy. Experiments show that our classification method improves the performance of Ma-
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by as much as 25.7% over the baseline classifier. In addition, our ranking method enhances the relevance of ads substantially, up to 10% in terms of precision at
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, compared to a representative strategy.
For the successful operation of pavement management system, it is necessary to automate the detection, classification, and severity assessment of road cracks, which are bottlenecks in the entire process. Although good results for the detection and classification of road cracks have been published in many related studies, the number of crack types detected is still insufficient for actual field use, and studies on crack severity assessment are difficult to find. In this study, the number of crack types are expanded to five types (alligator crack, longitudinal crack, transverse crack, pothole, and patching) to meet the needs of fieldwork, and the assessment of crack severity is also included in the proposed model. In this system constructed using SqueezeNet, U-Net, and Mobilenet-SSD models together, an accuracy of 91.2% has been achieved for both crack type and severity assessment. The authors have performed segmentation of the input images using separately trained U-Nets for linear cracking and area cracking to improve object detection performance and automate crack severity assessment. With reference to the system presented in this study, it is expected that an automated pavement management system that better reflects each country’s requirements for various crack types and severity standards is possible.
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