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Driver fatigue and inattention have long been recognized as one of the main contributing factors in traffic accidents. Therefore, the development of intelligent driver assistance systems, which provides automatic monitoring of driver's vigilance, is an urgent and challenging task. This paper presents a novel system for video-based driving behavior recognition. The fundamental idea is to monitor driver's hand movements and to use these as predictors for safe/unsafe driving behavior. In comparison to previous work, the proposed method utilizes hierarchical classification and treats driving behavior in terms of a spatio-temporal reference framework as opposed to a static image. The approach was verified using the Southeast University Driving-Posture Dataset, a dataset comprised of video clips covering aspects of driving such as: normal driving, responding to a cell phone call, eating and smoking. After pre-processing for illumination variations and motion sequence segmentation, eight classes of behavior were identified. The overall prediction accuracy obtained using the proposed approach was $89.62\%$ when using a hierarchical classification approach. The proposed approach was able to clearly identify two dangerous driving behaviors, Responding to a cellphone call and Eating, with recognition rates of 92.39% and 92.29% respectively.

Automatic categorization is a viable method to deal with the scaling problem on the World Wide Web. In this paper, we consider how to apply general text categorization techniques to Web site classification tasks. Two relevant issues concern, first, the case where the classifying object is not one document, as with a home page, but a Web site, which is a collection of Web pages. Second, real world Web directories have a complex hierarchical structure, in which leaf and non-leaf categories are directly assigned to Web sites, unlike the hierarchical structure treated in most previous research. On the first issue, this paper proposes the use of Web pages linked by a home page in addition to the home page itself. To accomplish this, we propose a Web site classification method based on connectivity analysis, as well as content analysis of Web sites. On the second issue, the hierarchical structure of classifiers is transformed into a flattened structure, but the classifier for each category uses features of its next-of-kin categories to take advantage of the hierarchical relationship. In experiments on a Korean commercial Web directory, the proposed classification method achieved an amazing improvement of microaveraging breakeven point by 36.6%, compared with an ordinary classifier.

In the last two years, we have seen a huge number of debates and discussions on COVID-19 in social media. Many authors have analyzed these debates on Facebook and Twitter, while very few ones have considered Reddit. In this paper, we focus on this social network and propose three approaches to extract information from posts on COVID-19 published in it. The first performs a semi-automatic and dynamic classification of Reddit posts. The second automatically constructs virtual subreddits, each characterized by homogeneous themes. The third automatically identifies virtual communities of users with homogeneous themes. The three approaches represent an advance over the past literature. In fact, the latter lacks studies regarding classification algorithms capable of outlining the differences among the thousands of posts on COVID-19 in Reddit. Analogously, it lacks approaches able to build virtual subreddits with homogeneous topics or virtual communities of users with common interests.

Hierarchical classification (HC) assigns each object with multiple labels organized into a hierarchical structure. The existing deep learning-based HC methods usually predict an instance starting from the root node until a leaf node is reached. However, in the real world, images impaired by noise, occlusion, blur, or low resolution may not provide sufficient information for the classification at subordinate levels. To address this issue, we propose a novel Semantic Guided level-category Hybrid Prediction Network (SGHPN) that can jointly perform the level and category prediction in an end-to-end manner. SGHPN comprises two modules: a visual transformer that extracts feature vectors from the input images, and a semantic guided cross-attention module that uses categories word embeddings as queries to guide learning category-specific representations. In order to evaluate the proposed method, we construct two new datasets in which images are at a broad range of quality and thus are labeled to different levels (depths) in the hierarchy according to their individual quality. Experimental results demonstrate the effectiveness of our proposed HC method.

The single-class verification framework is gaining increasing attention for problems involving authentication and retrieval. In this paper, nonlinear features are extracted using the kernel trick. The class of interest is modeled by using all the available samples rather than a single representative sample. Kernel selection is used to enhance the class specific feature set. A tunable objective function is used to select the kernel which enables the adjustment of the false acceptance and false rejection rates. The errors caused due to the presence of highly similar classes are reduced by using a two-stage hierarchical authentication framework. The performance of the resulting verification system is demonstrated on the hand-geometry based authentication problem with encouraging results.