Since the start of the coronavirus 2019 (COVID-19) outbreak, governments across the world have mobilized to inform citizens on the virus spread details, nation-level processes, and best health measures and practices to be taken. A large percentage of the media posted through the COVID-19 crisis has been graphical, which raised the question of whether Arabic-speaking blind and deaf persons were able to independently access reliable information. This article presents the results of two studies. The first study involves a content analysis of official social media posts about COVID-19 during critical phases of the outbreak via heuristic evaluation of WCAG2.1 on an iOS smartphone and an iPad. The second study explores the experiences of native Arabic-speaking blind and deaf persons on social media during the pandemic and curfew or lockdown periods in the State of Kuwait using a semi-structured interview (11 people who are blind/low vision and 7 people who are deaf). Overall, our findings highlight the accessibility gaps in the current government social media information content and its dissemination practices and barriers in providing information and services. Also, it gives insights into how people who are blind and people who are deaf are able to manage their lifestyle within and beyond the COVID-19 pandemic.

COVID-19 is the disease evoked by a new breed of coronavirus called the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Recently, COVID-19 has become a pandemic by infecting more than 152 million people in over 216 countries and territories. The exponential increase in the number of infections has rendered traditional diagnosis techniques inefficient. Therefore, many researchers have developed several intelligent techniques, such as deep learning (DL) and machine learning (ML), which can assist the healthcare sector in providing quick and precise COVID-19 diagnosis. Therefore, this paper provides a comprehensive review of the most recent DL and ML techniques for COVID-19 diagnosis. The studies are published from December 2019 until April 2021. In general, this paper includes more than 200 studies that have been carefully selected from several publishers, such as IEEE, Springer and Elsevier. We classify the research tracks into two categories: DL and ML and present COVID-19 public datasets established and extracted from different countries. The measures used to evaluate diagnosis methods are comparatively analysed and proper discussion is provided. In conclusion, for COVID-19 diagnosing and outbreak prediction, SVM is the most widely used machine learning mechanism, and CNN is the most widely used deep learning mechanism. Accuracy, sensitivity, and specificity are the most widely used measurements in previous studies. Finally, this review paper will guide the research community on the upcoming development of machine learning for COVID-19 and inspire their works for future development. This review paper will guide the research community on the upcoming development of ML and DL for COVID-19 and inspire their works for future development.

University web portals are considered one of the main access gateways for universities. Accessibility of university online services is a major issue for undergraduate and graduate students with disabilities. Online registration makes people with disabilities more independent to register courses, add, drop courses, or attend courses independently. Yet, many people with disabilities in Palestine face major challenges when using university websites. In order to understand the issues that face people with disabilities when they use websites of Palestinian universities, this study evaluates the accessibility of the home pages of these universities during COVID-19. In order to evaluate partially the accessibility of Palestinian universities during COVID-19, we apply automatic evaluation on all homepages of eighteen Palestinian universities. The most violated guideline is empty link which is related to success criterion 2.4.4 Link Purpose. The second highest violated error is linked image missing alternative text which is related to the success criterion 1.1.1 Non-text Content. The obtained results show that all the universities websites are not conforming to Web Content Accessibility Guidelines (WCAG) 2.0 level A. Copyright © 2020 by SCITEPRESS-Science and Technology Publications, Lda. All rights reserved.

Generally, indoor navigation is considered as a challenging task, especially when people navigate through an unfamiliar place (e.g. a university or a mall). It is even a more challenging endeavor for the visually impaired and blind community. This paper presents an innovative approach to the precise indoor navigation challenge for the blind individuals using a multi-tier solution with the help of an intuitive smartphone interface. We utilize a set of different communication technologies (WiFi, Bluetooth and radio-frequency identification) to help users reach an object with high accuracy. As a proof of concept, we deploy a fully functional testbed and we evaluate our entire solution inside our university library by helping blind users find a specific book. Our results demonstrate the high accuracy of the proposed system to reach an object with accuracy up to 10 cm. The intuitive smartphone interface provides step-by-step navigation voice instructions of the least hazardous path for the blind users while minimizing the cognitive load on their short-term memory. In addition, we show that our iterative improvements on our smartphone's interface has improved the system's efficiency and its accuracy in reaching specific objects successfully.

Different attack and defence techniques have been evolved over time as actions and reactions between black-hat and white-hat communities. Encryption, polymorphism, metamorphism and obfuscation are among the techniques used by the attackers to bypass security controls. On the other hand, pattern matching, algorithmic scanning, emulation and heuristic are used by the defence team. The Antivirus (AV) is a vital security control that is used against a variety of threats. The AV mainly scans data against its database of virus signatures. Basically, it claims a virus if a match is found. This paper seeks to find the minimal possible changes that can be made on the virus so that it will appear normal when scanned by the AV. Brute-force search through all possible changes can be a computationally expensive task. Alternatively, this paper tries to apply a Genetic Algorithm in solving such a problem. Our proposed algorithm is tested on seven different malware instances. The results show that in all the tested malware instances only a small change in each instance was good enough to bypass the AV.

This paper develops a method for presenting the standard six-point Braille characters on mobile devices that feature tactile feedback. The proposed method views each Braille character as a two-column code, each column consisting of three points. The eight various combination of raised and lowered points of the three-point column are encoded with a single pattern of vibration, with the two columns of a Braille character are separated by a period of silence. The encoding scheme for the different point-combinations in a column is inspired by Morse code (a series of dots and dashes). Dots are represented by a small-duration vibration whereas dashes are represented by a longer-duration vibration. The encoding scheme is optimized to reduce the average reading time per character and to reduce the average power consumption per character. These optimizations target increasing the speed of reading and the mobile device's battery life respectively. As a proof-of-concept, the proposed method is implemented and tested with twelve blind users and three non-blind users. The different parameters of the proposed method were tuned based on the testing results. The overall results suggest that the proposed method significantly reduces the average reading time per character and reduces the average power consumption per character when compared to existing methods that utilize the mobile device's tactile feedback for presenting Braille characters.

Accessibility of e-government services is a key issue for people with disabilities. E-government services can significantly save lot of their effort and provide them with lot of easy to reach services. Yet, accessibility of e-government websites is still under-explored topic in Jordan. In order to understand the accessibility of e-government websites and its problems, this study evaluates a set of e-government websites using 20 blind and visually impaired volunteers and at the same time conducts a survey on e-government websites developers. The results from e-government websites accessibility evaluation are compared with expert's review. For both the evaluation and the survey we used a set of accessibility guidelines developed by W3C [i.e. Web Content Accessibility Guidelines (WCAG 2.0)], Section 508 of the US Rehabilitation Act Amendments of 1998, and other literature review. In order to evaluate a reasonable number of e-government Web sites, a set of common e-government websites visited by the blind community were identified and a set of specific common tasks to test were defined. The analysis of the research results revealed a serious weakness in understanding, adopting and implementing Web accessibility guidelines throughout nearly all Jordanian e-government websites. Improving awareness, training developers and users, and developing formal guidelines of Web accessibility are needed to enable visually impaired and blind users in accessing e-government Web sites and their services. Further research analysis discusses and identifies key areas in which e-government accessibility can be enhanced.

Detecting tables in a spreadsheet is the first step needed to make spreadsheet documents accessible to individuals with visual disabilities. Techniques to enable aural presentation and navigation of tables have been proposed, but they assume a thorough knowledge of the structure of the table; on the other hand, boundaries and structure of tables in a spreadsheet are not evident without a visual exploration. This paper presents an algorithm for table recognition in spreadsheets. The algorithm uses three types of cells as its basis: title cell, header cell, and data cell. Different attributes of the cells are used to identify the cell type within a spreadsheet. Hierarchical clustering is used to aggregate cells to compose the functional components of a table. The algorithm has been evaluated on a diverse set of benchmarks with very encouraging results.

Several solutions, based on aural and haptic feedback, have been developed to enable access to complex on-line information for people with visual impairments. Nevertheless, there are several components of widely used software applications that are still beyond the reach of screen readers and Braille displays.