With the increase in the number of employees working remotely from home following the COVID-19 pandemic, cyberattacks have grown in quantity and strength. While companies invest tremendous resources in technical defense practices and protection tools, the main weak link is still the human factor. The current study aims to provide theoretical and empirical evidence of the antecedents that contribute to active cyber defense as well as cyber risk behaviors. Based on a sample of 338 employees who worked from home or on-site during and after COVID-19, we examined the effects of organizational training on employee defense behaviors. The results of the current study suggest that the workplace and the amount of confidence in the defense measures play an important role in contributing to cyber risk behavior. Therefore, it is crucial that managers raise employee awareness of the hazards and educate them on the different defense methods that they can apply.

Data science is considered to be a complex domain. It involves the skillful implementation of a multitude of data analysis and interpretation methods to reach managerial and operational decisions. For this reason, it poses a number of challenges to students in all disciplines, especially in the social sciences. The present study highlights these challenges through the analysis of 4 complementary research works that are based on 3 instruments: a scientific corpus, surveys, and data science job offers posted on LinkedIn-Israel. The study’s findings indicate that although data science is perceived to be an important domain that exerts a high influence on our society, social sciences’ students still do not have sufficient skills to cope with the challenges it poses. The study suggests equipping students with the skills and qualifications necessary for big data analysis through the design of adequate academic programs. The current article discusses these findings and their implications.

Cyber-attacks represent a potential threat to information security. As rates of data usage and internet consumption continue to increase, cyber awareness turned to be increasingly urgent. This study focuses on the relationships between cyber security awareness, knowledge and behavior with protection tools among individuals in general and across four countries: Israel, Slovenia, Poland and Turkey in particular. Results show that internet users possess adequate cyber threat awareness but apply only minimal protective measures usually relatively common and simple ones. The study findings also show that higher cyber knowledge is connected to the level of cyber awareness, beyond the differences in respondent country or gender. In addition, awareness is also connected to protection tools, but not to information they were willing to disclose. Lastly, findings exhibit differences between the explored countries that affect the interaction between awareness, knowledge, and behaviors. Results, implications, and recommendations for effective based cyber security training programs are presented and discussed.

Detecting and recognizing texts in real world images such as sign boards and advertisements is an important part of computer vision applications. The complexity of the problem comes out of many factors such as nonuniform background, different languages and fonts, and non consistent text alignment and orientation. In this paper, we present a novel approach to detect characters and words in real-world images. The presented approach decompose the gray level image into sequence of images, each one includes pixels with gray level values from different disjoint ranges. This decomposition enables extracting connected components representing characters or other non textual objects separated from their neighborhood background. An interpolation of two classes of features translated to histograms is used by a support vector machine to classify and collect the textual objects generating the textual zones. The Shape Context Descriptor [1], is used by the Earth Movers Distance(EMD) method to recognize the characters within the image. The recognized characters are fed to heuristic rule based system to determine words and give final results. To optimize the speed of the system, we follow the embedding of the EMD metric presented in [22] to a normed space to enable fast approximation of the k-Nearest Neighbors using Local Sensitivity Hashing functions(LSH). Experiments show that our algorithm can detect and recognize text regions from the ICDAR 2005 datasets [17] with high rates.

SUMMARY: The genomic abundance and pharmacological importance of membrane proteins have fueled efforts to identify them based solely on sequence information. Previous methods based on the physicochemical principle of a sliding window of hydrophobicity (hydropathy analysis) have been replaced by approaches based on hidden Markov models or neural networks which prevail due to their probabilistic orientation. In the current study, an optimization of the hydrophobicity tables used in hydropathy analysis is performed using a genetic algorithm. As such, the approach can be viewed as a synthesis between the physicochemically and statistically based methods. The resulting hydrophobicity tables lead to significant improvement in the prediction accuracy of hydropathy analysis. Furthermore, since hydropathy analysis is less dependent on the basis set of membrane proteins is used to hone the statistically based methods, as well as being faster, it may be valuable in the analysis of new genomes. Finally, the values obtained for each of the amino acids in the new hydrophobicity tables are discussed.