Social media networks such as Twitter, Facebook, YouTube, blogs, and discussion forums are becoming powerful tools that extremist groups use to disseminate radical ideologies and propaganda, and to recruit people to their cause. Identifying extremist social media content and profiles is a top priority for counter-terrorist agencies, technology companies, and governments. The main objective of this paper is to provide a better understanding of the definition of extremism, and a detailed review of the current research regarding online extremism in text. To identify gaps in the literature, a systematic literature review (SLR) of 45 studies published between 2015 and 2020 was undertaken, which revealed challenges, technical pitfalls in previous studies, and opportunities for extending and improving prior results in meaningful ways. The systematic review indicates the need for better understanding of the landscape and directions of the online extremism. This study offers a critical analysis of the new area of research.

DevOps (development and operations) is a set of collaborative practices that automate delivery of new software updates with the aim to reduce the development life cycle and produce quality software products. Software organizations face several barriers while adopting DevOps practices as the integration of development and operation teams requires merger of different processes, tools, and skill sets. This study aims to develop a prioritization-based framework of the DevOps best practices based on evidence collected from industry experts. To attain the study aims, firstly, a systematic literature review was conducted to identify DevOps best practices reported in the literature. Next, a questionnaire survey study was conducted to receive insight from industry practitioners for the identified best practice. Finally, the fuzzy-AHP technique was applied to prioritize the best practices concerning to the significance for DevOps process. We believe that the identified best practices, their categorization and fuzzy-AHP based framework will help industry experts to revise and improve their strategies to make the DevOps process sustainable.

State-of-the-art software technologies have enabled Higher Education Institutions to record and store large amounts of student data. Analyzing this large amount of data can facilitate the decision-making process. Decisions made in higher education institutions affect policies, strategies and actions that improve education quality. It can be argued that machine learning algorithms demonstrate a remarkable ability to recognize models and predict results based on data. However, machine learning outcomes were subject to bias and erroneous labelling. Consequently, to mitigate the impact of such errors in decision making, it is necessary to put in place mechanisms to correct decision rules. This article presents an approach to learn decision rules through supervised machine learning and proves the correctness of these rules with hierarchical Coloured Petri Nets. The use of formalism in the proposed methodology ensures that the decision rules are correct and comprehensible. Empirical results show that we improved correctness with 98.68% accuracy on decision rules. This research work contributes to the improvement of the decision-making process for the academic administration of Higher Education Institutes.

Recently, there has been enormous development due to advancements in technology. Industries and enterprises are moving towards a digital system, and the oil and gas industries are no exception. There are several threats and risks in digital systems, which are controlled through cyber-security. For the first time in the theory of fuzzy sets, this research analyzes the relationships between cyber-security and cyber-crimes in the oil and gas sectors. The novel concepts of complex intuitionistic fuzzy relations (CIFRs) are introduced. Moreover, the types of CIFRs are defined and their properties are discussed. In addition, an application is presented that uses the Hasse diagram to make a decision regarding the most suitable cyber-security techniques to implement in an industry. Furthermore, the omnipotence of the proposed methods is explained by a comparative study.

The dissemination of extremist ideas and causes online has intensified over the last decade. Extremist organizations use social media to gain publicity and new recruits, often with little interference from network providers. New techniques are being developed to identify extremist content, ensuring it can be promptly removed and its authors blocked from network access. However, most techniques are only compatible with the English language, despite the fact that extremist propaganda is frequently shared in other languages, including Arabic. Since the most effective methods for automated linguistic analysis use deep learning and require large, high-quality datasets, creating specialised data samples containing examples of extremist communication is an essential step toward a practical solution. In this paper, we present a dataset compiled for this purpose and discuss the classification methods that can be used for extremism detection. The manually annotated Arabic Twitter dataset consists of 89,816 tweets published between 2011 and 2021. Using guidelines, three expert annotators labelled the tweets as extremist or non-extremist. Exploratory data analysis was performed to understand the dataset’s features. Classification algorithms were used with the dataset, including logistic regression, support vector machine, multinominal naïve Bayes, random forest, and BERT. Among the traditional machine learning models, support vector machine with term frequency-inverse document frequency features achieved the highest accuracy (0.9729). However, BERT outperformed the traditional models with an accuracy of 0.9749. This dataset is expected to enhance the accuracy of Arabic online extremism classification in future research, and so we have made it publicly available.

The Arabic language is a complex language with little resources; therefore, its limitations create a challenge to produce accurate text classification tasks such as sentiment analysis. The main goal of sentiment analysis is to determine the overall orientation of a given text in terms of whether it is positive, negative, or neutral. Recently, language models have shown great results in promoting the accuracy of text classification in English. The models are pre-trained on a large dataset and then fine-tuned on the downstream tasks. Particularly, XLNet has achieved state-of-the-art results for diverse natural language processing (NLP) tasks in English. In this paper, we hypothesize that such parallel success can be achieved in Arabic. The paper aims to support this hypothesis by producing the first XLNet-based language model in Arabic called AraXLNet, demonstrating its use in Arabic sentiment analysis in order to improve the prediction accuracy of such tasks. The results showed that the proposed model, AraXLNet, with Farasa segmenter achieved an accuracy results of 94.78%, 93.01%, and 85.77% in sentiment analysis task for Arabic using multiple benchmark datasets. This result outperformed AraBERT that obtained 84.65%, 92.13%, and 85.05% on the same datasets, respectively. The improved accuracy of the proposed model was evident using multiple benchmark datasets, thus offering promising advancement in the Arabic text classification tasks.