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The COVID-19 pandemic has disrupted the ‘real’ world and substantially impacted the virtual world and thus the Internet ecosystem. It has caused a significant exogenous shock that offers a wealth of natural experiments and produced new data about broadband, clouds, and the Internet in times of crisis. In this chapter, we characterise and evaluate the evolving impact of the global COVID-19 crisis on traffic patterns and loads and the impact of those on Internet performance from multiple perspectives. While we place a particular focus on deriving insights into how we can better respond to crises and better plan for the post-COVID-19 ‘new normal’, we analyse the impact on and the responses by different actors of the Internet ecosystem across different jurisdictions. With a focus on the USA and Europe, we examine the responses of both public and private actors, with the latter including content and cloud providers, content delivery networks, and Internet service providers (ISPs). This chapter makes two contributions: first, we derive lessons learned for a future post-COVID-19 world to inform non-networking spheres and policy-making; second, the insights gained assist the networking community in better planning for the future.

Although student-centered learning (SCL) has been encouraged for decades in higher education, to what level instructors are practicing SCL strategies remains in question. The purpose of this paper is to investigate a university faculty’s understanding and perceptions of SCL, along with current instructional practices in Qatar.

A mixed-method research design was employed including quantitative data from a survey of faculty reporting their current instructional practices and qualitative data on how these instructors define SCL and perceive their current practices via interviews with 12 instructors. Participants of the study are mainly from science, technology, engineering and mathematics (STEM) field.

Study results show that these instructors have rather inclusive definitions of SCL, which range from lectures to student interactions via problem-based teamwork. However, a gap between the instructors’ perceptions and their actual practices was identified. Although student activities are generally perceived as effective teaching strategies, the interactions observed were mainly in the form of student–content or student-teacher, while student–student interactions were limited. Prevailing assessment methods are summative, while formative assessment is rarely practiced. Faculty attributed this lack of alignment between how SCL could and should be practiced and the reality to external factors, including students’ lack of maturity and motivation due to the Middle Eastern culture, and institutional constraints such as class time and size.

The study is limited in a few ways. First regarding methodological justification the data methods chosen in this study were mainly focused on the faculty’s self-reporting. Second the limited number of participants restricts this study’s generalizability because the survey was administered in a volunteer-based manner and the limited number of interview participants makes it difficult to establish clear patterns. Third, researching faculty members raises concerns in the given context wherein extensive faculty assessments are regularly conducted.

A list of recommendations is provided here as inspiration for institutional support and faculty development activities. First, faculty need deep understanding of SCL through experiences as learners so that they can become true believers and implementers. Second, autonomy is needed for faculty to adopt appropriate assessment methods that are aligned with their pedagogical objectives and delivery methods. Input on how faculty can adapt instructional innovation to tailor it to the local context is very important for its long-term effectiveness (Hora and Ferrare, 2014). Third, an inclusive approach to faculty evaluation by encouraging faculty from STEM backgrounds to be engaged in research on their instructional practice will not only sustain the practice of innovative pedagogy but will also enrich the research profiles of STEM faculty and their institutes.

The faculty’s understanding and perceptions of implementing student-centered approaches were closely linked to their prior experiences – experiencing SCL as a learner may better shape the understanding and guide the practice of SCL as an instructor.

SCL is not a new topic; however, the reality of its practice is constrained to certain social and cultural contexts. This study contributes with original and valuable insights into the gap between ideology and reality in implementation of SCL in a Middle Eastern context.

Diagnosing pain in neonates is difficult but critical. Although approximately thirty manual pain instruments have been developed for neonatal pain diagnosis, most are complex, multifactorial, and geared toward research. The goals of this work are twofold: 1) to develop a new video dataset for automatic neonatal pain detection called iCOPEvid (infant Classification Of Pain Expressions videos), and 2) to present a classification system that sets a challenging comparison performance on this dataset. The iCOPEvid dataset contains 234 videos of 49 neonates experiencing a set of noxious stimuli, a period of rest, and an acute pain stimulus. From these videos 20 s segments are extracted and grouped into two classes: pain (49) and nopain (185), with the nopain video segments handpicked to produce a highly challenging dataset. An ensemble of twelve global and local descriptors with a Bag-of-Features approach is utilized to improve the performance of some new descriptors based on Gaussian of Local Descriptors (GOLD). The basic classifier used in the ensembles is the Support Vector Machine, and decisions are combined by sum rule. These results are compared with standard methods, some deep learning approaches, and 185 human assessments. Our best machine learning methods are shown to outperform the human judges.