Chest X-ray (CXR) imaging is a standard and crucial examination method used for suspected cases of coronavirus disease (COVID-19). In profoundly affected or limited resource areas, CXR imaging is preferable owing to its availability, low cost, and rapid results. However, given the rapidly spreading nature of COVID-19, such tests could limit the efficiency of pandemic control and prevention. In response to this issue, artificial intelligence methods such as deep learning are promising options for automatic diagnosis because they have achieved state-of-the-art performance in the analysis of visual information and a wide range of medical images. This paper reviews and critically assesses the preprint and published reports between March and May 2020 for the diagnosis of COVID-19 via CXR images using convolutional neural networks and other deep learning architectures. Despite the encouraging results, there is an urgent need for public, comprehensive, and diverse datasets. Further investigations in terms of explainable and justifiable decisions are also required for more robust, transparent, and accurate predictions.

Sentiment analysis can be used to derive knowledge that is connected to emotions and opinions from textual data generated by people. As computer power has grown, and the availability of benchmark datasets has increased, deep learning models based on deep neural networks have emerged as the dominant approach for sentiment analysis. While these models offer significant advantages, their lack of interpretability poses a major challenge in comprehending the rationale behind their reasoning and prediction processes, leading to complications in the models' explainability. Further, only limited research has been carried out into developing deep learning models that describe their internal functionality and behaviors. In this timely study, we carry out a first of its kind overview of key sentiment analysis techniques and eXplainable artificial intelligence (XAI) methodologies that are currently in use. Furthermore, we provide a comprehensive review of sentiment analysis explainability.

Over the past 30 years, the Kingdom of Saudi Arabia (KSA) has recorded rapid economic growth and development rates matched by corresponding levels of consumption. This has led to an exponential increase in the volume of municipal solid waste. Even if the bulk share of this waste is recyclable, the propensity of households to engage in the recycling of waste has so far produced a negligible effect. Arguably, in the context of the pervasive digitalization of public and private spaces, accompanied by the developments in information and communication technology (ICT), best conceptualized by reference to the concepts of smart cities and circular economy, it is natural to expect ICT and policy objectives to converge in order to encourage and enable greater engagement with waste segregation and recycling amongst households. Against this backdrop, this paper explores the feasibility and ways of increasing households’ participation in recyclable waste collection, segregation, and disposal in a context devoid of a top-down city-level waste management policy. To this end, the design thinking approach was employed to identify factors inhibiting/facilitating households’ engaging in recycling. A variety of ideation techniques were adopted in subsequent stages of the design thinking process, including interviews with stakeholders from the government and industry sectors, surveys with Jeddah households (N = 446), an eight week-long recyclable waste collection experiment, and feedback surveys (N = 46). The results suggest that applying relatively simple and resource-savvy tools aimed at promoting household recyclable waste collection has substantial potential. This, in turn, not only feeds into the debate on smart cities but also into the research of public policy.

Progressing digitalization of business, economy, and the society places higher education institutions (HEIs) in the center of the debate on how to effectively respond to challenges and opportunities that are thus triggered. Several facets of this process and corresponding challenges exist, including the complex question of how to match students’ skills and competencies with the demands and expectations of the industry. From a different angle, considering the changing nature of work, HEIs are responsible for equipping future employees with skills necessary to work in virtual, distributed, culturally diverse, and frequently global, teams. In the domain of software development, i.e., the backbone of the digital world, the challenge HEIs need to face is paramount. For this reason, the way software development is taught at HEIs is crucial for the industry, for the economy, for the students, and for the HEIs. As there is a tendency in the industry to embrace the scrum method and seek employees equipped with skills necessary for the scrum methodology use, it is necessary to ensure that HEIs offer the students the opportunity to get exposed to scrum. By querying the challenges of switching to agile software development methodologies in senior capstone projects, this paper makes a case that software development and software development methodology form the thrust of a multi-stakeholder ecosystem that defines today’s digital economy and society. In this context, the added value of this paper rests in the elaboration of a method enabling HEIs to move toward scrum in senior projects.

With the advancement of technology, academics and curriculum developers are always under pressure to provide students with skills that match the market’s requirements. A systematic and continuous examination of the market is needed, to stay up to date with the required skills, and then to update the curriculum to train the students with required market skills. In this article, we present a framework referred to as Align My Curriculum (AMC). The AMC framework aims to facilitate alignment between acquired university curriculum outcomes and required market skills. It can be used to classify, compare and visualize the data of a university curriculum and job vacancies in the market. The presented framework benefits academics and curriculum developers by improving the courses and therefore bridging the skills gap. Stakeholders from both academia and industry can gain insights into the predominant required and acquired skills. In addition, it may be useful for analysts, students, and job applicants. This article describes the architecture, implementation and experimental results, with visual analysis to help decision and policy-makers.