Investigating Metacognitive Thinking Skills On Problem Solving Related To Social Problems Among Gifted Students In Saudi Arabia

This study examined metacognitive thinking skills on problem solving social problems among gifted students in Saudi Arabia. The results demonstrated the relationships, effects and influence of metacognitive thinking skills for problem solving related to social problems among Saudi Arabian gifted students at different level of study, sub-groups and with respect to gender for the intermediate and secondary schools gifted students. The suggestion and recommendations based on the study findings would benefit the gifted student‘s centers, educational ministry, international and non-governmental organizations in the effort to improve the study learning conditions of gifted students in Saudi Arabia

Overview of Cyberattack on Saudi Organizations

The beginning of Twenty first century saw a new dimension of security, the cybersecurity. Developed countries have started exploiting the vulnerabilities of cybersecurity to gain supremacy and influence over the rival countries. Hence, over the past decade, malware, i.e., malicious software, has become a major security threat in regards to the cybersecurity. The Kingdom of Saudi Arabia (KSA) has become a major target of cyber conflicts due to increased economic activity, digital transformation, high rate of technology adoption between citizen and organizations and rise of the oil and gas industry. However, unfortunately, there is a lack of research or scientific investigation of cyberattacks on KSA. This fact motivated us in conducting this work. This paper presents, a case study of attacks on Saudi Organization by malwares. We concentrate on two particular malwares: Shamoon and Ransomware. The timeline of attacks by these malware, also presented, along with their structures and methodologies in order to shield ourselves against similar attacks in the future

A decade of in-text citation analysis based on natural language processing and machine learning techniques: an overview of empirical studies

In-text citation analysis is one of the most frequently used methods in research evaluation. We are seeing significant growth in citation analysis through bibliometric metadata, primarily due to the availability of citation databases such as the Web of Science, Scopus, Google Scholar, Microsoft Academic, and Dimensions. Due to better access to full-text publication corpora in recent years, information scientists have gone far beyond traditional bibliometrics by tapping into advancements in full-text data processing techniques to measure the impact of scientific publications in contextual terms. This has led to technical developments in citation classifications, citation sentiment analysis, citation summarisation, and citation-based recommendation. This article aims to narratively review the studies on these developments. Its primary focus is on publications that have used natural language processing and machine learning techniques to analyse citations

Exploiting tweet sentiments in altmetrics large-scale data

This article aims to exploit social exchanges on scientific literature, specifically tweets, to analyse social media users’ sentiments towards publications within a research field. First, we employ the SentiStrength tool, extended with newly created lexicon terms, to classify the sentiments of 6,482,260 tweets associated with 1,083,535 publications provided by Altmetric.com. Then, we propose harmonic means-based statistical measures to generate a specialised lexicon, using positive and negative sentiment scores and frequency metrics. Next, we adopt a novel article-level summarisation approach to domain-level sentiment analysis to gauge the opinion of social media users on Twitter about the scientific literature. Last, we propose and employ an aspect-based analytical approach to mine users’ expressions relating to various aspects of the article, such as tweets on its title, abstract, methodology, conclusion or results section. We show that research communities exhibit dissimilar sentiments towards their respective fields. The analysis of the field-wise distribution of article aspects shows that in Medicine, Economics, Business and Decision Sciences, tweet aspects are focused on the results section. In contrast, in Physics and Astronomy, Materials Sciences and Computer Science, these aspects are focused on the methodology section. Overall, the study helps us to understand the sentiments of online social exchanges of the scientific community on scientific literature. Specifically, such a fine-grained analysis may help research communities in improving their social media exchanges about the scientific articles to disseminate their scientific findings effectively and to further increase their societal impact

The effect of the characteristics of the dataset on the selection stability

Download Citation Email Print Request Permissions Feature selection is an effective technique to reduce the dimensionality of a data set and to select relevant features for the domain problem. Recently, stability of feature selection methods has gained increasing attention. In fact, it has become a crucial factor in determining the goodness of a feature selection algorithm besides the learning performance. In this work, we conduct an extensive experimental study using verity of data sets and different well-known feature selection algorithms in order to study the behavior of these algorithms in terms of the stability