Global livelihoods are impacted by the novel coronavirus (COVID-19) disease, which mostly affects the respiratory system and spreads via airborne transmission. The disease has spread to almost every nation and is still widespread worldwide. Early and reliable diagnosis is essential to prevent the development of this highly risky disease. The computer-aided diagnostic model facilitates medical practitioners in obtaining a quick and accurate diagnosis. To address these limitations, this study develops an optimized Xception convolutional neural network, called "XCovNet," for recognizing COVID-19 from point-of-care ultrasound (POCUS) images. This model employs a stack of modules, each of which has a slew of feature extractors that enable it to learn richer representations with fewer parameters. The model identifies the presence of COVID-19 by classifying POCUS images containing Coronavirus samples, viral pneumonia samples, and healthy ultrasound images. We compare and evaluate the proposed network with state-of-the-art (SOTA) deep learning models such as VGG, DenseNet, Inception-V3, ResNet, and Xception Networks. By using the XCovNet model, the previous study's problems are cautiously addressed and overhauled by achieving 99.76% accuracy, 99.89% specificity, 99.87% sensitivity, and 99.75% F1-score. To understand the underlying behavior of the proposed network, different tests are performed on different shuffle patterns. Thus, the proposed "XCovNet" can, in regions where test kits are limited, be used to help radiologists detect COVID-19 patients through ultrasound images in the current COVID-19 situation.

Any problem is a problem until a solution is designed and implemented. This paper reports on a workshop that highlights preliminary work done by the working group on Gamification in the scope of European Network for Academic Integrity (ENAI), which aims to explore the possibility of developing and testing a gamified learning module on academic integrity values. In this paper, the group aims to look at proposing steps we are currently using to develop storyboards of scenarios for the first phase of the project, which were presented at the 6th International Conference Plagiarism Across Europe and Beyond 2020 held virtually in Dubai as a workshop. The study also presents updated findings and scenarios drawn from the workshop conducted and audience feedback, in the following sections that pave the way for the future stages of the gamification process. This serves as a guide to academics and researchers in academic integrity who may wish to study gamification and apply it to develop their own modules for their learning modules.

This paper proposes a design of a complete system to identify weak grip strength that is caused by multiple factors like ageing, diseases, or accidents. This paper presents a grip measurement system that comprises of force sensing resistor and flex sensor to evaluate the condition of the hand. The system is tested by gripping a pencil and a cylindrical object using the glove, to determine the condition of the hand. Force sensitive resistor (FSR) evaluates the force applied by the different parts of the palm on the object being grasped. Flex sensor evaluates the bending of the fingers and thumb. The data from the sensors is then compared with existing data to evaluate the state of the hand. The data from the sensors is stored on the personal computer (PC) through serial communication. A model is trained using the data from the sensors, which determine if the grip strength of the user is weak or strong. The model is also trained to differentiate between two modes that are pen mode and object mode. The model achieved an accuracy of 90.8 percent using support vector machine (SVM) algorithm. This glove can be deployed in medical centers to assist in grip strength measurement.

Learning the fundamentals of programming languages has always been a difficult task for students. It is equally challenging for lecturers to teach these concepts. A number of methods have been deployed by teachers to teach these concepts. This article analyses the result of a class test to identify fundamental programming concepts that students have difficulty in understanding. On the basis of this result, an open educational resource, ‘Alice’, was introduced in the class along with C++ in the following semester. This article also provides a sample lecture that was designed using Alice and that corresponded to the objectives of the subject. The objectives of the subject were taken into consideration while designing the lectures, labs and assignment tasks using Alice. Students enrolled in Applied Programming in Summer 2010 were tested on the concepts after the introduction to Alice, and the results were compared with those of Summer 2009, where the traditional style of teaching programming was followed. Analysis of the results shows that Alice is very effective for teaching students programming concepts.