A Large-scale Empirical Analysis of Browser Fingerprints Properties for Web Authentication

Modern browsers give access to several attributes that can be collected to form a browser fingerprint. Although browser fingerprints have primarily been studied as a web tracking tool, they can contribute to improve the current state of web security by augmenting web authentication mechanisms. In this article, we investigate the adequacy of browser fingerprints for web authentication. We make the link between the digital fingerprints that distinguish browsers, and the biological fingerprints that distinguish Humans, to evaluate browser fingerprints according to properties inspired by biometric authentication factors. These properties include their distinctiveness, their stability through time, their collection time, their size, and the accuracy of a simple verification mechanism. We assess these properties on a large-scale dataset of 4,145,408 fingerprints composed of 216 attributes and collected from 1,989,365 browsers. We show that, by time-partitioning our dataset, more than 81.3% of our fingerprints are shared by a single browser. Although browser fingerprints are known to evolve, an average of 91% of the attributes of our fingerprints stay identical between two observations, even when separated by nearly six months. About their performance, we show that our fingerprints weigh a dozen of kilobytes and take a few seconds to collect. Finally, by processing a simple verification mechanism, we show that it achieves an equal error rate of 0.61%. We enrich our results with the analysis of the correlation between the attributes and their contribution to the evaluated properties. We conclude that our browser fingerprints carry the promise to strengthen web authentication mechanisms.

(How) Does Religiousness Impact on Job Satisfaction? Results for Germany

Although research has established a positive link between spirituality or religiousness and job satisfaction, this influence’s pathways remain a ‘black box’. Whether it is an effect of a trait- relationship or of a need-satisfaction-relationship remains an open question. Additionally, data and results for West European countries are largely missing. Following King and Williamson (2005), and with a large-scale dataset for Germany (N = 2,551), we empirically assess the link between religiousness and job satisfaction, considering individual employees’ desire to express religiousness and actual expression at work in a serial mediation model, scrutinizing also the influences of discrimination experiences and perceived employers’ stances on religiousness at work. Results strongly support the needs-satisfaction perspective, implying high relevance of workplace spirituality for human resource management (HRM) but also of the research field of management, spirituality and religion in general. Contrary to our expectations, experiences of religious-based discrimination and the perception of a negative employer stance influence the desire to express religiousness at work and de facto expressions positively.

Boosting Career and Employability Outcomes Through Multiple Learning Abroad Experiences

While participation in learning abroad has increased rapidly over the last decade, short-term programs played an important role in boosting participation and widening access to learning abroad. The current study takes advantage of a new pattern of participation in learning abroad to examine self-reported career outcomes and employability development benefits based on program duration and the number of programs undertaken. Using a large-scale dataset of graduates of Australian universities, the study challenges conventional wisdom that a longer experience is better and explores the impact of multiple short-term program participation as a new intervention in graduate career outcomes. Although this study is based on the Australian higher education context, the results may be informative to educators and policy-makers from countries with comparable learning abroad programs in considering how short-term programs can be used more purposefully to foster positive careers and employability outcomes.

Pedestrian-Aware Panoramic Video Stitching Based on a Structured Camera Array

The panorama stitching system is an indispensable module in surveillance or space exploration. Such a system enables the viewer to understand the surroundings instantly by aligning the surrounding images on a plane and fusing them naturally. The bottleneck of existing systems mainly lies in alignment and naturalness of the transition of adjacent images. When facing dynamic foregrounds, they may produce outputs with misaligned semantic objects, which is evident and sensitive to human perception. We solve three key issues in the existing workflow that can affect its efficiency and the quality of the obtained panoramic video and present Pedestrian360, a panoramic video system based on a structured camera array (a spatial surround-view camera system). First, to get a geometrically aligned 360○ view in the horizontal direction, we build a unified multi-camera coordinate system via a novel refinement approach that jointly optimizes camera poses. Second, to eliminate the brightness and color difference of images taken by different cameras, we design a photometric alignment approach by introducing a bias to the baseline linear adjustment model and solving it with two-step least-squares. Third, considering that the human visual system is more sensitive to high-level semantic objects, such as pedestrians and vehicles, we integrate the results of instance segmentation into the framework of dynamic programming in the seam-cutting step. To our knowledge, we are the first to introduce instance segmentation to the seam-cutting problem, which can ensure the integrity of the salient objects in a panorama. Specifically, in our surveillance oriented system, we choose the most significant target, pedestrians, as the seam avoidance target, and this accounts for the name Pedestrian360 . To validate the effectiveness and efficiency of Pedestrian360, a large-scale dataset composed of videos with pedestrians in five scenes is established. The test results on this dataset demonstrate the superiority of Pedestrian360 compared to its competitors. Experimental results show that Pedestrian360 can stitch videos at a speed of 12 to 26 fps, which depends on the number of objects in the shooting scene and their frequencies of movements. To make our reported results reproducible, the relevant code and collected data are publicly available at https://cslinzhang.github.io/Pedestrian360-Homepage/ .

White Blood Cells (WBC) Images Classification Using CNN-Based Networks

Background: Computer-aided methods for analyzing white blood cells (WBC) are popular due to the complexity of the manual alternatives. Recent works have shown highly accurate segmentation and detection of white blood cells from microscopic blood images. However, the classification of the observed cells is still a challenge, in part due to the distribution of the five types that affect the condition of the immune system.Methods: (i) This work proposes W-Net, a CNN-based method for WBC classification. We evaluate W-Net on a real-world large-scale dataset that includes 6,562 real images of the five WBC types. (ii) For further benefits, we generate synthetic WBC images using Generative Adversarial Network to be used for education and research purposes through sharing.Results: (i) W-Net achieves an average accuracy of 97%. In comparison to state-of-the-art methods in the field of WBC classification, we show that W-Net outperforms other CNN- and RNN-based model architectures. Moreover, we show the benefits of using pre-trained W-Net in a transfer learning context when fine-tuned to specific task or accommodating another dataset. (ii) The synthetic WBC images are confirmed by experiments and a domain expert to have a high degree of similarity to the original images. The pre-trained W-Net and the generated WBC dataset are available for the community to facilitate reproducibility and follow up research work.Conclusion: This work proposed W-Net, a CNN-based architecture with a small number of layers, to accurately classify the five WBC types. We evaluated W-Net on a real-world large-scale dataset and addressed several challenges such as the transfer learning property and the class imbalance. W-Net achieved an average classification accuracy of 97%. We synthesized a dataset of new WBC image samples using DCGAN, which we released to the public for education and research purposes.

The Link Between Income, Income Inequality, and Prosocial Behavior Around the World

The questions of whether high-income individuals are more prosocial than low-income individuals and whether income inequality moderates this effect have received extensive attention. We shed new light on this topic by analyzing a large-scale dataset with a representative sample of respondents from 133 countries ( N = 948,837). We conduct a multiverse analysis with 30 statistical models: 15 models predicting the likelihood of donating money to charity and 15 models predicting the likelihood of volunteering time to an organization. Across all model specifications, high-income individuals were more likely to donate their money and volunteer their time than low-income individuals. High-income individuals were more likely to engage in prosocial behavior under high (vs. low) income inequality. Avenues for future research and potential mechanisms are discussed.

Social Interactions for Detecting Stress Based Issues

Mental stress is showing harmfulness to human health leads abnormal stress in chronology with this may lose our mental health for proactive care. With recognizable pieces of proof of web-based media, individuals cannot share their everyday exercises and collaborate with companions via web-based media stages, making it happing to use online informal community information for stress identification. We find that users stress state is closely associated with thereupon of his/her friends in social media, which we employ a large-scale dataset from real-world social platforms to systematically study the relationship between users’ stress states and social interactions. We first define a gaggle of stress-related comments, images, and social attributes from various aspects, then proposed a plot. Research results saying that the proposed model can improve the detection performance. With the help of enumeration, we build an internet site for the users to spot their stress rate level and may check other related activities.