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. INDEX TERMS Chest x-ray, coronavirus, COVID-19, deep learning, radiological imaging.

Service-oriented computing promises to create flexible business processes and applications on demand by dynamically assembling loosely coupled services within and across organizations. Quality requirements play a central role in service sourcing and, together with Service Level Agreements, facilitate service selection and measurement of service delivery effectiveness. This empowers customers to make better decisions when faced with multiple service offerings and varying service costs. However, existing business process modeling languages provide little support for quality requirements annotation and specification. This paper argues that quality requirements are a central aspect of business process modeling specification, and thus proposes to incorporate time, cost and reliability quality requirements as extensions to the Business Process Modeling Notation (BPMN). These quality requirements are evaluated based on analytical model using reduction rules. An example of online purchasing business process is illustrated to demonstrate the applicability of the proposed approach.

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.

Since the early days of the coronavirus (COVID-19) outbreak in Wuhan, China, Saudi Arabia started to implement several preventative measures starting with the imposition of travel restrictions to and from China. Due to the rapid spread of COVID-19, and with the first confirmed case in Saudi Arabia in March 2019, more strict measures, such as international travel restriction, and suspension or cancellation of major events, social gatherings, prayers at mosques, and sports competitions, were employed. These non-pharmaceutical interventions aim to reduce the extent of the epidemic due to the implications of international travel and mass gatherings on the increase in the number of new cases locally and globally. Since this ongoing outbreak is the first of its kind in the modern world, the impact of suspending mass gatherings on the outbreak is unknown and difficult to measure. We use a stratified SEIR epidemic model to evaluate the impact of Umrah, a global Muslim pilgrimage to Mecca, on the spread of the COVID-19 pandemic during the month of Ramadan, the peak of the Umrah season. The analyses shown in the paper provide insights into the effects of global mass gatherings such as Hajj and Umrah on the progression of the COVID-19 pandemic locally and globally.

Social Media (SM) platforms, particularly Twitter, have become useful tools for startup companies (henceforth startups) which use the latter to support most of their business activities. As a result, there is a need to gauge the performance of specific business initiatives vis-à-vis public sentiment, or more specifically the spread of such initiatives based on Twitter user-generated content. Previous research which makes use of Twitter analysis to analyze the business activities of startups is minimal, especially for Twitter user content in the Arabic language. Consequently, this paper proposes an analytics-based framework called Startup Initiatives Response Analysis (SIRA) designed to assess the performance of initiatives launched by startups via text classification, sentiment analysis, and statistical analysis techniques. To provide empirical evidence for the viability of the proposed research framework, this paper examined the case of an Arab transportation network startup, carrying out a SIRA analysis of an initiative undertaken by Careem to empower women by encouraging them to work for the company. The results confirm the effectiveness of the proposed framework for statistically measuring the initiative spread and the public feedback based on the user-generated content on the Twitter social platform.