Human Rights in the Middle East: An Overview of the Last Decade

First presented at the 1994 Cairo Papers Annual Symposiumhttps://fount.aucegypt.edu/faculty_book_chapters/1928/thumbnail.jp

Students preferences for teaching and exam delivery modes in accounting education post COVID 19 pandemic

The COVID-19 pandemic has sparked a revolution in the delivery of modules in higher education. This paper aims to answer the research question: What are the preferences of undergraduate accounting and finance students regarding teaching delivery and exam modes following the COVID-19 pandemic lockdown? Specifically, we focus on campus, online synchronous, and hybrid synchronous teaching delivery and exam modes. To address this research question, we conducted an online questionnaire surveying students at a U. K. university. Our data show that modules involving calculations, such as financial accounting, are preferred to be taught on campus, whereas theoretical modules like business law are preferred to be taught online. Additionally, the data reveal reasons for these preferences, including community learning, isolation, concentration, and access to recordings. This research contributes valuable insights into optimising accounting education. We advocate for flexibility in both teaching and exam delivery within accounting education, recognising the diverse needs of students

Information extraction from social media for route planning

Micro-blogging is an emerging form of communication and became very popular in recent years. Micro-blogging services allow users to publish updates as short text messages that are broadcast to the followers of users in real-time. Twitter is currently the most popular micro-blogging service. It is a rich and real-time information source and a good way to discover interesting content or to follow recent developments. Additionally, the updates published on Twitter public timeline can be retrieved through their API. A significant amount of traffic information exists on Twitter platform. Twitter users tweet when they are in traffic about accidents, road closures or road construction. With this in mind, this paper presents a system that extracts traffic information from Twitter to be used in route planning. Route planning is of increasing importance as societies try to reduce their energy consumption. Furthermore, route planning is concerned with two types of constraints: stable, such as distance between two points and temporary such as weather conditions, traffic jams or road construction. Our system attempt to extract these temporary constraints from Twitter. We train Naive bayes, Maxent and SVM classifiers to filter non relevant traffic. We then apply NER on traffic tweets to extract locations, highwaysand directions. These extracted locations are then geocoded and used in route planning to avoid routes with traffic jams