BADR: A Talking Face To Teach Arabic Vocabulary Under COVID-19

We propose an Arabic talking face called Badr to teach the language vocabulary for young students under COVID-19 pandemic. Badr is built on the previous talking face Baldi (e.g., an American English talking face) with many enhancements in interaction and words pronunciation. It can complement the work of instructors who can get tired and bored when teaching online for long hours. It can pronounce standard Arabic vocabulary accurately and interact with learners in natural manner. Listening and observing Badr improve the learners' ability to understand speech in a noisy background. We tested Badr with Qatar University students, and we show its effectiveness and usefulness in introducing smoothly new vocabulary. Badr can act as an independent tutor for different categories of learners including those with learning difficulties, slow learners, and non-Arabic speaking learners. © 2022 IEEE.

An Experience of Using a Handwriting Haptic Device to Fine-Tune the Children Motor Skills

Handwriting can help children to improve their learning of a language and fine-tune their motor skills. Every child needs to develop her/his handwriting skill to grasp new concepts appropriately and learn the language vocabulary. Therefore, in-hand manipulation of the traditional pen is highly important to develop pre-writing and transform the scribbled writings to legible ones at a later stage. In this paper, we evaluate the effectiveness of a customized haptic device in improving the children motor skills and their handwriting quality of Arabic letters. We use the Touch™ device from the 3D-Systems company with a controllable stylus that can be adapted to children needs. Fifteen pupils from the Deutsch International School in Doha, have participated in this experience after obtaining all necessary ethical approvals from concerned stakeholders. We conducted the experiments for a period of two weeks with the assistance of the school instructors and staff. Results show that there is an important increase of children motivation, and a good improvement of their motor skills and handwriting experience. The device can be used at home to learn independently during COVID-19 pandemic that continues to hit severely the whole world and enforces schools to adapt online teaching approach. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Generative Approach to Enrich Arabic Story Text with Visual Aids

Enriching the script of a story with visual aids is an effective approach for promoting language learning and literacy development for young children and learners. In this paper, we propose a new system, that can generate short Arabic stories with generated images that accurately represent the story, scene and context of the given input. We use a text generation technique with a text-to-image synthesis network and minimize the human intervention. We build a corpus of Arabic stories with vocabulary and visualizations. The obtained results with various generative models to create text-image contents show the effectiveness of the proposed approach. The system can be used in education and assist the instructors to build stories on different domains. It can be used in distance learning to deliver online tutorials during COVID-19. © 2021 ACM.

A secure cloud system for maintaining COVID-19 patient's data using image steganography

Background: The COVID-19 pandemic has been life-threatening for many people and as such, a contactless medical system is necessary to prevent the spread of the virus. Smart healthcare systems collect data from patients at one end and process the acquired data at the other end. The cloud is the central point and the communication happens through insecure channels1. The main concern, in this case, is the violation of privacy and security as the channel is untrusted. Traditional methods do not provide enough hiding capacity, security, and robustness2,3. This work proposes an image steganography method using the deep learning method to hide the patient's medical images inside an innocent cover image in such a way that they are not visible to human eyes which reduces the suspicions of the presence of sensitive data. Methods: An auto encoder-decoder-based model is proposed with three components: the pre-processing module, the embedding network, and the extraction network. Features from the cover image and the secret images are extracted and fused to reconstruct the stego image. The stego image is then used to extract the ingrained secret image. Figure 1 shows the overall system workflow. Results: Peak Signal-to-Noise Ratio (PSNR) is the evaluation metrics used. The ImageNet dataset was used for training and testing the proposed model. Figure 2 shows the image results of the proposed method. Conclusion: During a COVID-19 screening test, private patient data such as mobile number and Qatari identity card are collected, transferred, and stored through untrusted channels. It is of paramount importance to preserve the privacy, security, and confidentiality of the collected patient records. A secure deep learning-based image steganography method is proposed to secure the sensitive data transferred through untrusted channels in a cloud-based system.

Pulmonary Fibrosis Progression Prediction Using Image Processing and Machine Learning

The onset of COVID-19 has focused the attention of the research community on lung diseases and conditions. Idiopathic pulmonary fibrosis (IPF), in which internal scarring of the lung takes place, has gone undetected among the various populace. This condition has no known cure. So far, computer vision researchers, along with radiologists, have been successfully able to identify the IPF through lung CT-scans but have had difficulty in identifying the severity of IPF. In this research, we will investigate the use of image processing and machine learning techniques to identify the progression of the disease. For that, we will build two machine learning models and compare them. The first model uses patients’ biological indications and some histogram features of the CT scans. The second model uses the ensemble method of a convolution neural network (CNN) of patients CT scans and quantile regression of the patient’s biological data for predicting the Forced Vital Capacity (FVC, an indicator of IPF severity). The results showed that by using the second model, we got a higher r2 value of 0.93 versus 0.89 using the first model and that the biological data had more importance than the CT scans for predicting the lung declination. © 2021, Springer Nature Switzerland AG.

Understand my world: An interactive app for children learning arabic vocabulary

COVID-19 imposed a new paradigm in education especially in elementary schools. Children are no longer able to go regularly to schools as normal. They should then rely mainly on themselves to learn and acquire knowledge. The Understand My World app is a new technological solution that allows children to learn new Arabic vocabularies interactively and independently using their smart phones or tablets (i.e., iPad, iPhone). They can use the devices' camera and microphone to explore the world, understand spoken words, and read written language properly. Images captured by the camera are recognized, labeled, and defined in both speech and writing. Children can also speak into their device to record their speech and listen to it. They can address questions and receive answers. The app then presents the dictated words in both spoken and written form. Using the required Internet connection, the interface automatically provides exceedingly accurate image and speech recognition, all while preserving the participants' anonymity. We use two AI platforms namely Clarifai and Houndify. The application is designed to be intuitive and seamless to use, which makes it very attractive to children to learn through multimedia. © 2021 IEEE.

MOALLEMCorpus: A large-scale multimedia corpus for children education of Arabic vocabularies

The education of children with learning difficulties is a challenging task especially during COVID-19 pandemic. In fact, these children need to go regularly to specialized school, receive focused education, and interact with teachers to learn. Instructors allocate important time to teach them and use different approaches including, attractive stories, tangible photos, physical plays, sites visit, awards and gifts. However, these modes of education become nowadays very hard to achieve as instructors are teaching from homes or offices through the Internet. They cannot have face-to-face meetings with children in classrooms. The major issue is how to find the necessary materials to teach them the new Arabic vocabularies and explain their meanings in an effective manner. Instructors can use textbooks, online libraries and search engines looking for Arabic educational resources while most of them are in English or in other western languages. The process is very long, time consuming and does not fill-in the gap. We propose to build a new educational large-scale multimedia Arabic corpus that provides thousands of vocabularies and chunks associated with best representative images. The instructors can use the bimodal corpus directly during the learning sessions to explain new Arabic words through images. It currently covers the animals' domain and contains thousands of well-structured and interconnected entities. Instructors can collaborate in enhancing the corpus by adding new materials through a web-based platform and build then rich source of learning materials. © 2021 IEEE.

Covid -19 lockdown-challenges to higher education in qatar

Covid 19 is a pandemic disease and it has spread all over the world. Due to the concern for the health of students, colleges and school authorities all over the world have decided to close the education institution and universities. It has interrupted the face to face education, assessments, final exams, different educational activities, graduation day, and other events. To overcome these crises, the education sectors have taken on the challenge of online education. Most of the countries had started education online. The sudden transform of education platforms physically and mentally had affected teachers and students. Most of them have it is the new platform and they have struggled to adapt to the new environment. To assess the effectiveness of online education during the pandemic time in Qatar, we conducted a survey in the Computer Science Department of Qatar University. Also compared different strategies adopted by universities in Qatar and other universities in the world for online education. The survey indicated that 50% of students are dissatisfied with online education due to network problems and technical issues. The main issues that students highlighted in the survey were lack of face-to-face interaction with the instructor, mental stress due to the absence of campus environment, and connectivity problems. © 2021 IEEE.

An evidence base for school health policy during the covid-19 pandemic

Objective: Children represent a small fraction of confirmed COVID-19 cases, with a low case fatality rate (CFR). In this paper, we lay out an evidence-based policy for reopening schools. Methods: We gathered age-specific COVID-19 case counts and identified mortality data for 14 countries. Dose-response meta-analysis was used to examine the relationship of the incremental case fatality rate (CFR) to age. In addition, an evidence-to-decision framework (EtD) was used to correlate the dose-response data with other epidemiological characteristics of COVID-19 in childhood. Results: In the dose-response analysis, we found that there was an almost negligible fatality below age 18. CFR rose little between ages 5 to 50 years. The confidence intervals were narrow, suggesting relative homogeneity across countries. Further data suggested decreased childhood transmission from respiratory droplets and a low viral load among children. Conclusions: Opening up schools and kindergartens is unlikely to impact COVID-19 case or mortality rates in both the child and adult populations. We outline a robust plan for schools that recommends that general principles not be micromanaged, with authority left to schools and monitored by public health authorities. © 2021, Paris Scholar Publishing. All rights reserved.