Background: The effectiveness of online learning in higher education during the COVID-19 pandemic period is a debated topic but a systematic review on this topic is absent.Methods: The present study implemented a systematic review of 25 selected articles to comprehensively evaluate online learning effectiveness during the pandemic period and identify factors that influence such effectiveness.Results: It was concluded that past studies failed to achieve a consensus over online learning effectiveness and research results are largely by how learning effectiveness was assessed, e.g., self-reported online learning effectiveness, longitudinal comparison, and RCT. Meanwhile, a set of factors that positively or negatively influence the effectiveness of online learning were identified, including infrastructure factors, instructional factors, the lack of social interaction, negative emotions, flexibility, and convenience.Discussion: Although it is debated over the effectiveness of online learning during the pandemic period, it is generally believed that the pandemic brings a lot of challenges and difficulties to higher education and these challenges and difficulties are more prominent in developing countries. In addition, this review critically assesses limitations in past research, develops pedagogical implications, and proposes recommendations for future research.

Face morphing attacks seek to deceive a Face Recognition (FR) system by presenting a morphed image consisting of the biometric qualities from two different identities with the aim of triggering a false acceptance with one of the two identities, thereby presenting a significant threat to biometric systems. The success of a morphing attack is dependent on the ability of the morphed image to represent the biometric characteristics of both identities that were used to create the image. We present a novel morphing attack that uses a Diffusion-based architecture to improve the visual fidelity of the image and the ability of the morphing attack to represent characteristics from both identities. We demonstrate the effectiveness of the proposed attack by evaluating its visual fidelity via Fréchet Inception Distance (FID). Also, extensive experiments are conducted to measure the vulnerability of FR systems to the proposed attack. The ability of a morphing attack detector to detect the proposed attack is measured and compared against two state-of-the-art GAN-based morphing attacks along with two Landmark-based attacks. Additionally, a novel metric to measure the relative strength between different morphing attacks is introduced and evaluated.

Data prefetching has been a successful technique in high-performance computing platforms. However, the conventional wisdom is that they significantly increase energy consumption, and thus not suitable for embedded mobile systems. On the other hand, as modern mobile applications pose an increasing demand for high performance, it becomes essential to implement high-performance techniques, such as prefetching, in these systems. In this paper, we study the impact of prefetching on the performance and energy consumption of embedded mobile systems. Contrary to the conventional wisdom, our findings demonstrate that as technology advances, prefetching can be energy-efficient while improving performance. Furthermore, we have developed a simple but effective analytical model to help system designers to identify the conditions for energy efficiency.

Extensible Markup Language (XML) has become a widely adopted standard for data representation and exchange. However, its features also introduce significant overhead threatening the performance of modern applications. In this paper, we present a study of XML parsing and determine that memory-side data loading in the parsing stage incurs a significant performance overhead, as much as the computation does. Hence, we propose memory-side acceleration which incorporates of data prefetching techniques, and can be applied on top of computation-side acceleration to speed up the XML data parsing. To this end, we study here the impact of our proposed scheme on the performance and energy consumption and demonstrated how it is capable of improving performance by up to 20 percent as well as produce up to 12.77 percent of energy saving when implemented in 32-nm technology. In addition, we implement a prefetcher on an platform in an effort to evaluate its implementation feasibility in terms of area and energy overhead.