Customer reviews on online platforms have grown to become an important source of insight into a company's performance. Food delivery services (FDS) companies aim to effectively use customers' feedback to identify areas for improvement of customer satisfaction. Although Arabic is becoming one of the most widely used languages on the Internet, only a few studies have focused on Arabic sentiment analysis to date. The present study conducts an extensive emotion mining and sentiment analysis on FDS-related reviews in Arabic, exploiting natural language processing, and machine learning techniques to extract information that reflects customers' subjective viewpoints, recognize their feelings, and determine polarity in the FDS domain. This work begins with collecting the FDS dataset (Talabat), and then extracting and creating a dialects lexicon for Arabic dialects, followed by walking the reader through detailed steps of cleaning and pre-processing a manually annotated dataset. Finally, we examined classification algorithms including Decision Tree (DT), and Support Vector Machine (SVM). We achieved a maximum accuracy of about 82% using the SVM classifier.

The Extensible Markup Language (XML) is widely used to store, retrieve, and share digital documents. Recently, a form of Version Control System has been applied to the language, resulting in Version-Aware XML and allowing for enhanced portability and scalability. While Version Control Systems are able to keep track of changes made to documents, we think that there is untapped potential in the technology. In this paper, we define a set of requirements for visualization tools in a modern version control system. We also present an interactive and scalable visualization framework to represent Version-Aware-related data that helps users visualize and understand version control data, delete specific revisions of a document, and access a comprehensive overview of the entire versioning history.

In this paper, we describe an enhancement of the Automated Policy Enforcement eXchange framework (APEX) called eAPEX. eAPEX uses version-control information as the basis for a more incremental approach to scanning document text to determine if security policies are being followed. Where APEX requires a full or deep scan of the document each time an exposure operation is invoked, eAPEX usually requires only a scan of changed elements (deltas). A new scanning approach was designed and implemented. eAPEX works by combining version-aware document technology with a policy database that functions as a cache of security policy results.

Securing sensitive digital documents (such as health records, legal reports, government documents, and financial assets) is a critical and challenging task. Unreliable Internet connections, viruses, and compromised file storage systems impose a significant risk on such documents and can compromise their integrity especially when shared across domains while they are shared in offline fashion.