Due to the increasing growth in online recruitment, traditional hiring methods are becoming inefficient. This is due to the fact that job portals receive enormous numbers of unstructured resumes - in diverse styles and formats - from applicants with different fields of expertise and specialization. Therefore, the extraction of structured information from applicant resumes is needed not only to support the automatic screening of candidates, but also to efficiently route them to their corresponding occupational categories. This assists in minimizing the effort required by employers to manage and organize resumes, as well as to screen out irrelevant candidates. In this paper, we present JRC - a Job Post and Resume Classification system that exploits an integrated knowledge base for carrying out the classification task. Unlike conventional systems that attempt to search globally in the entire space of resumes and job posts, JRC matches resumes that only fall under their relevant occupational categories. To demonstrate the effectiveness of the proposed system, we have conducted several experiments using a real-world recruitment dataset. Additionally, we have evaluated the efficiency and effectiveness of proposed system against state-of-the-art online recruitment systems.

The rapid development of the Internet has led to introducing new methods for e-recruitment and human resources management. These methods aim to systematically address the limitations of conventional recruitment procedures through incorporating natural language processing tools and semantics-based methods. In this context, for a given job post, applicant resumes (usually uploaded as free-text unstructured documents in different formats such as .pdf, .doc or .rtf) are matched/screened out using the conventional keyword-based model enriched by additional resources such as occupational categories and semantics-based techniques. Employing these techniques has proved to be effective in reducing the cost, time, and efforts required in traditional recruitment and candidate selection methods. However, bridging the skill gap - that is, the propensity to precisely detect and extract relevant skills in applicant resumes and job posts - and highlighting the hidden semantic dimensions encoded in applicant resumes are still challenging issues in the process of devising effective e-recruitment systems. This is due to the fact that resources exploited by current e-recruitment systems are obtained from generic domain-independent sources, therefore resulting in knowledge incompleteness and the lack of domain coverage. In this article, we review state-of-the-art e-recruitment approaches and highlight recent advancements in this domain. An e-recruitment framework addressing current shortcomings through the use of multiple cooperative semantic resources, feature extraction techniques and skill relatedness measures is detailed. An instantiation of the proposed framework is proposed and an experimental validation using a real-world recruitment dataset from two employment portals demonstrates the effectiveness of the proposed approach.

Pedestrian dynamics are affected by several factors including pedestrian compositions. In this article, we examine the movement characteristics of Palestinian pedestrians using the fundamental diagram for single-file movement experiments conducted with an emphasis on gender compositions. Our findings show that the mean velocity of exclusively male pedestrians is approximately the same as exclusively female pedestrians. For instance, when the number of pedestrians is 20, the velocities for male and female are 0.72±0.10ms-1 and 0.71±0.11ms-1, respectively, whereas their velocity decreases gradually if they walk in mixed groups with an average velocity of 0.61±0.11ms-1. We also compare our findings with other culture-based experiments to demonstrate that pedestrian cultures have an effect on their movement characteristics. Moreover, we demonstrate that age is another factor that affects pedestrians’ movement. A comparative analysis is performed between Palestinian and Chinese experiments for this purpose. Our results confirm that for relatively high densities older Chinese pedestrians walk faster than young Palestinians in groups of mixed gender.

Crowded event entrances could threaten the comfort and safety of pedestrians, especially when some pedestrians push others or use gaps in crowds to gain faster access to an event. Studying and understanding pushing dynamics leads to designing and building more comfortable and safe entrances. Researchers—to understand pushing dynamics—observe and analyze recorded videos to manually identify when and where pushing behavior occurs. Despite the accuracy of the manual method, it can still be time-consuming, tedious, and hard to identify pushing behavior in some scenarios. In this article, we propose a hybrid deep learning and visualization framework that aims to assist researchers in automatically identifying pushing behavior in videos. The proposed framework comprises two main components: (i) Deep optical flow and wheel visualization; to generate motion information maps. (ii) A combination of an EfficientNet-B0-based classifier and a false reduction algorithm for detecting pushing behavior at the video patch level. In addition to the framework, we present a new patch-based approach to enlarge the data and alleviate the class imbalance problem in small-scale pushing behavior datasets. Experimental results (using real-world ground truth of pushing behavior videos) demonstrate that the proposed framework achieves an 86% accuracy rate. Moreover, the EfficientNet-B0-based classifier outperforms baseline CNN-based classifiers in terms of accuracy.