Searching for Authoritative Documents in Knowledge-Base Communities

Knowledge-based communities are popular Web-based tools that allow members to share and seek knowledge globally. However, research on how to search effectively within such knowledge repositories is scant. In this paper we study the problem of finding authoritative documents for user queries within a knowledge-based community. Unlike prior research on the ranking function design which considers only content or hyperlink information, we leverage the social network information embedded in the rich social media, in addition to content, to design novel ranking strategies. Using the Knowledge Adoption Model as the guiding theoretical framework, we design features that gauge the two major factors affecting users’ knowledge adoption decisions: argument quality (AQ) and source credibility (SC). We design two ranking strategies that blend these two sources of evidence with the content-based relevance judgment. A preliminary study using a real world knowledge-based community showed that both AQ and SC features improved search effectiveness

Sunspot tilt angles revisited: Dependence on the solar cycle strength

The tilt angle of sunspot groups is crucial in the BL type dynamo. Some studies have shown that the tilt coefficient is anti-correlated with the cycle strength. If the anti-correlation exists, it will be shown to act as an effective nonlinearity of the BL-type dynamo to modulate the solar cycle. However, some studies have shown that the anti-correlation has no statistical significance. We aim to investigate the causes behind the controversial results of tilt angle studies and to establish whether the tilt coefficient is indeed anti-correlated with the cycle strength. We first analyzed the tilt angles from DPD. Based on the methods applied in previous studies, we took two criteria to select the data, along with the linear and square-root functions to describe Joy's law, and three methods to derive the tilt coefficients for cycles 21-24. This allowed us to evaluate different methods based on comparisons of the differences among the tilt coefficients and the tilt coefficient uncertainties. Then we utilized Monte Carlo experiments to verify the results. Finally, we extended these methods to analyze the separate hemispheric DPD data and the tilt angle data from Kodaikanal and Mount Wilson. The tilt angles exhibit an extremely wide scatter due to both the intrinsic mechanism for its generation and measurement errors, for instance, the unipolar regions included in data sets. Different methods to deal with the uncertainties are mainly responsible for the controversial character of the previous results. The linear fit to the tilt-latitude relation of sunspot groups with $\Delta s>2.5$ of a cycle carried out without binning the data can minimize the effect of the tilt scatter on the uncertainty of the tilt coefficient. Based on this method the tilt angle coefficient is anti-correlated with the cycle strength with strong statistical significance.Comment: 14 pages, 7 figures, 8 Tables, Accepted for publication in A&

Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding

Crack is one of the most common road distresses which may pose road safety hazards. Generally, crack detection is performed by either certified inspectors or structural engineers. This task is, however, time-consuming, subjective and labor-intensive. In this paper, we propose a novel road crack detection algorithm based on deep learning and adaptive image segmentation. Firstly, a deep convolutional neural network is trained to determine whether an image contains cracks or not. The images containing cracks are then smoothed using bilateral filtering, which greatly minimizes the number of noisy pixels. Finally, we utilize an adaptive thresholding method to extract the cracks from road surface. The experimental results illustrate that our network can classify images with an accuracy of 99.92%, and the cracks can be successfully extracted from the images using our proposed thresholding algorithm.Comment: 6 pages, 8 figures, 2019 IEEE Intelligent Vehicles Symposiu