Qatar Foundation Annual Research Conference Proceedings Volume 2018 Issue 3

Online Social Networks (OSNs), such as Facebook and Twitter, are popular platforms that enable users to interact and socialize through their networked devices. However, the social nature of such applications forces users to share a great amount of personal data with other users and the OSN service providers, including pictures, location check-ins, etc. Even though some OSNs offer configurable privacy controls that limit access to shared data, users might misconfigure these controls due to their complexity or lack of clear instructions. Furthermore, the fact that OSN service providers have full access over the data stored on their servers is an alarming thought, especially for users who are conscious about their privacy. For example, OSNs might share such data with third parties, data mine them for targeted advertisements, collect statistics, etc. As a result, data and communication privacy over OSNs is a popular topic in the data privacy research community. Existing solutions include cryptographic mechanisms [1], trusted third parties [2], external dictionaries [3], and steganographic techniques [4]. Nevertheless, none of the aforementioned approaches offers a comprehensive solution that (i) implements fine-grained access control over encrypted data and (ii) works seamlessly over existing OSN platforms. To this end, we will design and implement a flexible and user-friendly system that leverages encryption-based access control and allows users to assign arbitrary decryption privileges to every data object that is posted on the OSN servers. The decryption privileges can be assigned on the finest granularity level, for example, to a hand-picked group of users. In addition, data decryption is performed automatically at the application layer, thus enhancing the overall experience for the end-user. Our cryptographic-based solution leverages hidden vector encryption (HVE)[5], which is a ciphertext policy-based access control mechanism. Under HVE, each user generates his/her own master key (one-time) that is subsequently used to generate a unique decryption key for every user with whom they share a link in the underlying social graph. Moreover, during the encryption process, the user interactively selects a list of friends and/or groups that will be granted decryption privileges for that particular data object. To distribute the decryption keys, we utilize an untrusted database server where users have to register before using our system. The server stores (i) the social relationships of the registered users, (ii) their public keys, and (iii) the HVE decryption keys assigned to each user. As the database server is untrusted, the decryption keys are stored in encrypted form, i.e., they are encrypted with the public key of the underlying user. Therefore, our solution relies on the existing public key infrastructure (PKI) to ensure the integrity and authenticity of the users’ public keys. To facilitate the deployment of our system over existing OSN platforms, we use steganographic techniques [6] to hide the encrypted data objects within randomly chosen cover images (stego images). The stego images are then uploaded to the OSN servers, and only authorized users (with the correct decryption keys) would be able to extract the embedded data. Unauthorized users will simply see the random cover images. We aim to implement our system as a Chrome-based browser extension where, after installation, the user registers with the un- trusted server and uploads/downloads the necessary decryption keys. The keys are also stored locally, in order to provide a user-friendly interface to share private information. Specifically, our system will offer a seamless decryption process, where all hidden data objects are displayed automatically while surfing the OSN platform, without any user interaction. References [1] S. Jahid, P. Mittal, and N. Borisov, “EASiER: encryption-based access control in social networks with efficient revocation,” in Proc. ACM Symposium on Information, Computer and Communications Security (ASIACCS), pp. 411–415, 2011.[2] A. Tootoonchian, S. Saroiu, Y. Ganjali, and A. Wolman, “Lockr: better privacy for social networks,” in Proceedings of the 2009 ACM Conference on Emerging Networking Exper- iments and Technology, CoNEXT 2009, Rome, Italy, December 1-4, 2009, pp. 169–180, 2009.[3] S. Guha, K. Tang, and P. Francis, “NOYB: privacy in online social networks,” in Proc. Workshop on Online Social Networks (WOSN), pp. 49–54, 2008.[4] J. Ning, I. Singh, H. V. Madhyastha, S. V. Krishnamurthy, G. Cao, and P. Mohapatra, “Secret message sharing using online social media,” in Proc. IEEE Conference on Commu- nications and Network Security (CNS), pp. 319–327, 2014.[5] T. V. X. Phuong, G. Yang, and W. Susilo, “Efficient hidden vector encryption with constant- size ciphertext,” in Proc. European Symposium on Research in Computer Security (ES- ORICS), pp. 472–487, 2014.[6] S. Kaur, S. Bansal, and R. K. Bansal, “Steganography and classification of image steganog- raphy techniques,” in Proc. International Conference on Computing for Sustainable Global Development (INDIACom), 2014.

Mega data centers (DCs) are considered as efficient and promising infrastructures for supporting numerous cloud computing services such as online office, online social networking, Web search and IT infrastructure out-sourcing. The scalability of these services is influenced by the performance and dependability characteristics of the DCs. Consequently, the DC networks are constructed with a large number of network devices and links in order to achieve high performance and reliability. As a result, these requirements increase the energy consumption in DCs. In fact, in 2010, the total energy consumed by DCs was estimated to be about 120 billion Kilowatts of electricity in 2012, which is about 2.8% of the total electricity bill in the USA. According to industry estimates, the USA data center market achieved almost US 39 billion in 2009, growing from US 16.2 billion in 2005. One of the primary reasons behind this issue is that all the links and devices are always powered on regardless of the traffic status. The statistics showed that the traffic drastically alternates, especially between mornings and nights, and also between working days and weekends. Thus, the network utilization depends on the actual period, and generally, the peak capacity of the network is reached only in rush times. This non-proportionality between traffic load and energy consumption is caused by the fact that -most of the time- only a subset of the network devices and links can be enough to forward the data packets to their destinations while the remaining idle nodes are just wasting energy. Such observations inspired us to propose a new approach that powers off the unused links by deactivating the end-ports of each one of them to save energy. The deactivation of ports is proposed in many researches. However, these solutions have high computational complexity, network delay and reduced network reliability. In this paper, we propose a new approach to reduce the power consumption in DC. By exploiting the correlation in time of the network traffic, the proposed approach uses the traffic matrix of the current network state, and manages the state of switch ports (on/off) at the beginning of each period, while making sure to keep the data center fully connected. During the rest of each time period, the network must be able to forward its traffic through the active ports. The decision to close or open depends on a predefined threshold value; the port is closed only if the sum of the traffic generated by its connected node is less than the threshold. We also investigate the minimum period of time during which a port should not change its status. This minimum period is necessary given that it takes time and energy to switch a port on and off. Also, one of the major challenges in this work is powering off the idle devices for more energy saving while guaranteeing the connectivity of each server. So, we propose a new traffic aware algorithm that presents a tradeoff between energy saving and reliability satisfaction. For instance, in HyperFlatNet, simulation results show that the proposed approach reduces the energy consumption by 1.8*104 WU (Watt per unit of time) for a correlated network with1000-server (38 % of energy saving). In addition, and thanks to the proposed traffic aware algorithm, the new approach shows a good performance even in case of high failure rate (up to 30%) which means when one third of the links failed, the connection failure rate is only 0.7%. Both theoretical analysis and simulation experiments are conducted to evaluate and verify the performance of the proposed approach compared to the state-of-the-art techniques.

This paper examines the impact on patient experience through the creation of a bespoke patient queuing and communication application using in-house developed technologies. Sidra Medicine hospital's outpatient pharmacy was experiencing mismanaged queue lines, dissatisfied patients, and the lack of data necessary to determine the length of time elapsing in obtaining medication. After analyzing patient surveys through the method of sentiment analysis and generation of word clouds, we validated that there was scope for workflow improvement in the pharmacy department. The Center for Medical Innovation, Software, and Technology (CMIST) department was commissioned to develop the software application necessary to deliver efficiency and improvement in response to the lack of a queuing and communication system. The use of an in-house development team to create an application for queuing and communication as opposed to selecting a popular vendor software resulted in many advantages. Some of the main advantages were that the requirements of pharmacy were delivered through rapid customization and in multiple iterations, which were delivered in response to the ever changing customer demand. By using scrum methodology, the team was able to deliver the application called Saffara, for managing queues in the pharmacy and improving patient experience while obtaining medication. The Saffara application, has a unique feature of being integrated to the hospital EMR (Electronic Medical Record) system while ensuring confidentiality, efficiency and time saving. The application integrates with the hospital's EMR to obtain patient information, appointment times and prescribed medication. This integration allowed for the identification of patients' progress and calculation of patients ‘wait times. Patients are automatically notified when their medication is ready for collection, through system generated SMS texts. The application also utilizes a notification display for communication with patients as part of our business continuity procedure. In addition to notifying the patient, the Saffara application also generates detailed analytical reports for each hour and for each patient, which allows us to analyze the bottlenecks in the clinical workflow. We present these technologies to any stakeholders through a web dashboard and detailed web-based reports in our application. The pharmacy stakeholders, i.e., the pharmacy management team utilize the dashboards and quantitative data in the reports to predict staffing levels to deliver optimization in patient medication delivery. In this paper, we present the methods we use to calculate the useful analytics like patient wait times across different stages in the workflow and hourly breakdown of patients being served. We will also discuss how we reduced patient wait times by adding unique features to a queuing application like automation of steps in the pharmacy workflow through generation of patient identifiers and automatic ticket tracking. This paper will also highlight how we are scaling our application from pharmacy to all clinics of the hospital. The goal of the application is to provide a consistent experience for patients in all clinics as well as a consistent way for staff to gather and analyze data for workflow improvement. Our future work is to explore how we can use machine learning to identify the parameters that play a vital role in wait times as well as patient experience. The objective of this paper is to highlight how our technology converges the patient experience and staff workflow enhancements to deliver improvement in a clinical workflow setting.

In this paper, we propose a novel method for the detection of road accidents by analyzing audio streams for road surveillance application. In the last decade, due to the increase in a number of people and transportation vehicles, traffic accidents have become one of a major public issue worldwide. The vast number of injuries and death due to road traffic accident reveals the story of a global crisis of road safety. A total of 52,160 road traffic accidents (RTA), 1130 injuries and 85 fatalities were registered during the year 2000, in the state of Qatar. An increase in the number of transportation vehicles around cities has risen the need for more security and safety in public environments. The most obvious reason for a person's death during accidents is the absence or prolong response time of the first aid facility, which is due to the delay in the information of the accident being reached to the police, hospital or ambulance team. In the last couple of years, several surveillance systems have been proposed based on image and video processing for automatically detecting road accidents and car crashes to ensure a quick response by emergency teams. However, in some situations such as adverse weather conditions or cluttered environments, the visual information is not sufficient enough, whereas analyzing the audio tracks can significantly improve the overall reliability of surveillance systems. In this paper we propose a novel method that automatically identifies hazardous situations such as tire skidding and car crashes in presence of background noises, by analyzing the audio streams. Previous studies show that methods for the detection, estimation, and classification of nonstationary signals can be enhanced by utilizing the time-frequency (TF) characteristics of such signals. The TF-based techniques have been proved to outperform classical techniques based on either time or frequency domains, in analyzing real-life nonstationary signals. Time-frequency distributions (TFDs) give additional information about signals that cannot be extracted from either the time domain or frequency domain representations, i.e. the instantaneous frequency of the components of a signal. In order to utilize this extra information provided by TF domain representation, the proposed approach extracts TF image features from quadratic time-frequency distributions (QTFDs), for the detection of audio anomalies. The extended modified-B distribution (EMBD) is utilized to transform a 1-dimensional audio signal into 2-dimensional TF representation which is interpreted as an image. The image descriptors based features are then extracted from the TF representation to classify visually the audio signals into background or abnormal activity patterns in the TF domain. The proposed features are based on Haralick's texture features extracted from the TF representation of audio signals considered and processed as a textured image. These features are used to characterize and hence classify audio signals into M classes. This research study demonstrates that a TF image pattern recognition approach offers significant advantages over standard signal classification methods that utilize either t-domain only or f-domain only features. The proposed method has been experimentally validated on a large open source database of sounds, including several kinds of background noise. The events to be recognized are superimposed on different background sounds of roads and traffic jam. The obtained results are compared with a recent study, utilizing the same large and complex data set of audio signals, and the same experimental setup. The overall classification results confirm the superior performance of the proposed approach with accuracy improvement up to 6%.

We present MATQAM (Multilingual Answer Triggering Question Answering Machine) a multilingual answer triggering open-domain QA system, focusing on answering questions whose answers might be in free texts in multiple languages within Wikipedia.Obtaining relevant information from the Web has become more challenging, since online communities and social media tend to confine people to bounded trends and ways of thinking. Due to the large amount of data available, getting the relevant information has become a more challenging task. Unlike in standard Information Retrieval (IR), Question Answering (QA) systems aim at retrieving the relevant answer(s) to a question expressed in natural language, instead of returning a list of documents. On the one hand, information is dispersed in different languages and needs to be gathered to get more knowledge. On the other hand, extracting answers from multilingual documents is a complicated task because natural languages follow diverse linguistic syntaxes and rules, especially for Semitic languages, such as Arabic. This project tackles open-domain QA using Wikipedia as source of knowledge by building a multilingual —Arabic, French, English— QA system. In order to obtain a collection of Wikipedia articles as well as questions in multiple languages, we extended an existing English dataset: WikiQA (Yang et al., 2015). We used the WikiTailor toolkit (Barrón-Cedeño et al., 2015) to build a comparable corpus form Wikipedia articles and to extract the corresponding articles in Arabic, French, and English. We used neural machine translation to generate the questions in the three languages as well. Our QA system consists of the three following modules. (i) Question processing consists of transforming a natural language question into a query and determining the expected type of the answer in order to define the retrieval mechanism for the extraction function. (ii) The document retrieval module consists of retrieving the most relevant documents from the search engines —in multiple languages— given the produced query. The purpose of this module is to identify the documents that may contain an answer to the question. It requires cross-language representations as well as machine translation technology to do that, as the question could be asked in Arabic, French or English and the answer could be in either of these languages. (iii) The answer identification module ranks specific text fragments that are plausible answers to the question. It first ranks the candidate text fragments in the different languages and, if they are found, they are combined into one consolidated answer. This is a variation of the cross-language QA scenario enabling answer triggering, where no concrete answer has to be provided, if it does not exist. In order to build our QA system, we extend an existing framework (Rücklé and Gurevych, 2017) integrating neural networks for answer selection. References Alberto Barrón-Cedeño, Cristina España Bonet, Josu Boldoba Trapote, and Luís Márquez Villodre. A Factory of Comparable Corpora from Wikipedia. In Proceedings of the Eighth Workshop on Building and Using Comparable Corpora, pages 3–13, Beijing, China, 2015. Association for Computational Linguistics. Andreas Rücklé and Iryna Gurevych. End-to-End Non-Factoid Question Answering with an Interactive Visualization of Neural Attention Weights. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics-System Demonstrations (ACL 2017), pages 19–24, Vancouver, Canada, August 2017. Association for Computational Linguistics. doi:10.18653/v1/P17-4004. URL http://aclweb.org/anthology/P17-4004. Yi Yang, Wen-tau Yih, and Christopher Meek. WikiQA: A Challenge Dataset for Open-Domain Question Answering. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 2013–2018, Lisbon, Portugal, 2015.

Nowadays, inexpensive smart devices with multiple heterogeneous on-board sensors, networked through wired or wireless links and deployable in large numbers, are distributed throughout a physical process or in a physical environment, providing real-time, and dense spatio-temporal measurements and enabling surveillance and monitoring capability that could not be imagined a decade ago. Such a system-wide deployment of sensing devices is known as distributed sensing, and is considered one of the top ten emerging technologies that will change the world. Oil and gas pipeline systems, electrical grid systems, transportation systems, environmental and ecological monitoring systems, security systems, and advanced manufacturing systems are just a few examples among many others. Malfunction of any of the large-scale systems typically results in enormous economic loss and sometimes even endangers critical infrastructure and human lives. In any of these systems, the system state variables, whose values trigger various actions, are estimated based on the measurements gathered by the sensor system that monitors and controls the system of interest. Consequently, the reliability of these estimations is of utmost importance in economic and safe operation of these large-scale systems. In a linear sensor system, the sensor measurements are combined linear responses of the system states that need to be estimated. In the engineering literature, a linear model is often used to establish connection between sensor measurements in a system and the system's state variables through the sensor system's design matrix. In such systems, the sensor outputs y and the system states x are linked by the set of linear equations represented as y =  Ax+e, where y and e are n by 1 vectors, and x is a p by 1 vector. A is an n by p design matrix (n &gt;&gt; p) that models the linear measurement process. The matrix A is assumed to be of full column rank i.e., r(A) = p, where r(A) denotes the rank of A. The last term e is a random noise vector, which is assumed to be normally distributed with mean 0. In the context of estimation reliability, the redundancy degree in a sensor system is the minimum number of sensor failures (or measurement outliers) which can happen before the identifiability of any state is compromised. This number, called the degree of redundancy of the matrix A and denoted by d(A), is formally defined as d(A) =  {d-1: there esists A[-d] s.t. r(A[-d]) < r(A)}, where A[ − d] is the reduced matrix after deleting some d rows from the original matrix. The degree of redundancy of linear sensor systems is a measure of robustness of the system against sensor failures and hence the reliability of a linear sensor system. Finding the degree of redundancy for structured linear systems is proven to be NP-hard. Bound and decompose, mixed integer programming, l1-minimization methods have all been studied and compared in the literature. But none of these methods are suitable for finding the degree of redundancy in large scale sensor systems. We propose a decomposition approach which effectively disintegrates the problem into a reasonable number of smaller subproblems utilizing the structure inherent in such linear systems using concepts of duality and connectivity from matroid theory. We propose two different but related algorithms, both of which solves the same redundancy degree problem. While the former algorithm applies the decomposition technique over the vector matroid (the design matrix), the latter uses its corresponding dual matroid. These subproblems are then solved using mixed integer programming to evaluate the degree of redundancy for the whole sensor system. We report substantial computational gains (up to 10 times) for both these algorithms as compared to even the best known existing algorithms.

Traditionally, in cellular networks, users communicate with the base station that serves the particular cell under coverage. The main functions of a base station can be divided into two, which are the baseband unit (BBU) functionalities and the remote radio head (RRH) functionalities. The RRH module is responsible for digital processing, frequency filtering and power amplification. The main sub-functions of the baseband processing module are coding, modulation, Fast Fourier Transform (FFT) and others. Data generally flows from RRH to BBU for further processing. Such BBU functionalities may be shifted to the cloud based resource pool, called as the Cloud-Radio Access Network (C-RAN) to be shared by multiple RRHs. Advancements in the field of cloud computing, software defined networking and virtualization technology may be leveraged by operators for the deployment of their BBU services, reducing the total cost of deployment. Recently, there has been a trend to collocate the baseband unit (BBU) functionalities and services from multiple cellular base stations into a centralized BBU pool for the statistical multiplexing gains. The technology is known as Cloud Radio Access Network (C-RAN). C-RAN is a novel mobile network architecture that can address a number of challenges the mobile operators face while trying to support the growing end users’ needs. The idea is to virtualize the BBU pools, which can are shared by different cellular network operators, allowing them to rent radio access network (RAN) as a cloud service. However, the manual configuration of the BBU services over the virtualized infrastructure may be inefficient and error-prone with the increasing mobile traffic. Similarly, in centralized BBU pools, non-optimal placement of the Virtual Functions (VFs) might result in a high deployment cost as well as long delays to the end-users. This may mitigate the advantages of this novel technology platform. Hence, the optimized placement of these VFs is necessary to reduce the total delays as well as minimize the overall cost to operate the C-RANs. Despite great advantages provided by the C-RAN architecture, there is no explicit support for the mobile operators to deploy their BBU services over the virtualized infrastructure, which may lead to the ad-hoc and error-prone service deployment in the BBU pools. Given the importance of C-RANs and yet the ad-hoc nature of their deployment, there is a need of automated and optimal application delivery in the context of cloud-based radio access networks to fully leverage the cloud computing opportunities in the Internet. In this work, we propose development of a novel automated service deployment platform, which will help to automate the instantiation of virtual machines at the cloud as user demands vary to achieve end-to-end automation in service delivery for C-RANs. Also, we consider the problem of optimal VF placement over distributed virtual resources spread across multiple clouds, creating a centralized BBU cloud. The aim is to minimize the total response time to the base stations in the network, as well as to satisfy the cost and capacity constraints. In this work, we implement an enhanced version of the two common approaches in the literature, which are: (1) branch-and-bound (BnB) and (2) Simulated Annealing (SA). The enhancement reduces the execution complexity of the BnB heuristic so that the allocation is faster. The proposed enhancements also improve the quality of the solution significantly. We compare the results of the standard BnB and SA schemes with the enhanced approaches to demonstrate these claims. Our aim was to develop a faster solution which can meet the latency requirements of the C-RANs, while the performance (here, in terms of cost and latency) is not far from the optimal. The proposed work contributes to “Information &amp; Computing Technology” pillar of ARC’18. Also, it contributes to Qatar National Vision 2030 that encourages ICT initiatives. This vision, envisages Qatar at the forefront of the latest revolutions in computing, networking, Internet, and Mobility. Mobile applications form the majority of business applications on the Internet. This research proposal addresses the latest research issues in proliferation of the novel technology such as 5G. This project is timely since there is limited research, in Qatar (as well as globally) on supporting application delivery in general in the context of multiple heterogeneous cloud-based application deployment environments.

I am a current student researching the findings from the QNRF NPRP grant, “Media Use in the Middle East” (NPRP 7-1757-5-261), a seven-nation survey by Northwestern University in Qatar. I am particularly interested in the potential of the Internet to increase feelings of political efficacy among citizens in the Arab region. The Internet has been shown to create feelings of political efficacy in many instances around the world, like how social media accelerated Egypt's 2011 (Gustin, S, 2011), but it can also create feelings of disempowerment (Bibri, S, E, 2015). I am interested in this topic specifically because, with the lack of freedom of expression in the Gulf region, many are turning to the internet to share their opinion on their country's political matter. Although there are consequences to those who criticize Gulf governments, the growing significance of the internet has become a communication tool between the country and its people. In my research, I look only at nationals in the Gulf countries of Qatar, Saudi Arabia, and the United Arab Emirates. Although the survey covers expatriate residents as well, I choose to use data on nationals only due to the personal nature of the question. Expatriates living in one of these Gulf countries might answer the question with their own country in mind or they might answer with their residence country in mind. How can we analyze the meaning of their responses without knowing exactly what they were thinking about? However, we know that nationals of specific countries will be thinking of whether they have increased political efficacy in their own countries, making the analysis clearer. Also, nationals on the Internet would most likely have a bigger political influence on their country than expatriates, and officials would probably prioritize their opinions over expatriates, which also justifies the look at only nationals. I investigate feelings of political efficacy through the Internet by focusing on a set of two questions. The question begins with, “Do you think by using the Internet…” and then asks the respondents two different statements: “…People like you can have more political influence?” and “…Public officials will care more what people like you think?” The response options were a scale of 1 to 5 where 1 means strongly disagree and 5 means strongly agree. These two statements probe different, but important areas of political efficacy. They probe the freedom of expression in the Gulf and whether these expressions are being heard and acted upon by the government, both in terms of citizen perceptions of political influence and emotional connection. My initial research demonstrates differences in levels of political efficacy across the three countries, including over time, and also within different demographics. The overall results show Saudi Arabia (58%) with the most belief in the political efficacy of the Internet, followed by Qatar (36%), then the UAE (18%). For Saudi, the majority of the sample believe they have a political influence on the internet, whereas in Qatar and the UAE, less than half believe so. This demonstrates a significant difference between the nations, which will need further investigating. We also see some interesting changes over time. Overall, more people in 2017 believe that officials care about what's being said on social media than in 2015. In Saudi, it moved up 15%, whereas in the UAE, there was only a 4% increase. Since this particular question was asked differently in Qatar in 2015, the comparison data wasn't given. However, the overall showings implicate that Gulf governments have begun to respond to the public's political input on the Internet. This will further be explored through interviews with nationals from the three countries. I've also found that there are similarities among the three countries as well. By looking at the results by gender, I find that more men (for example, in Saudi, 62%) than women (54%) believe they have more political influence by using the Internet. Also, when looking at the results by cultural conservativism or cultural progressivism, more progressives than conservatives believe they have political influence (Saudi: 65% vs. 53%) and that the government cares more about what people like them think when using the Internet (Saudi: 61% vs. 59%). My next stage of research is to investigate the number of issues about free speech and political efficacy in the Middle East that I have begun to uncover through analysis of the survey data. The main story I want to explain is the belief that the use of the Internet increases political influence in this region. I am particularly interested in investigating questions about gender inequality on social media, the extent to which the public is able to talk about important issues facing their countries on the Internet and if they feel they are being heard. I am also interested in learning more about the perceptions of conservative and progressive people on free speech and politics and how these perceptions have changed over the years. My analysis will be based on data from the survey as well as interviews conducted by myself of citizens from the three countries, which will give context to the survey data. Work cited: Bibri, S. E. (2015) The Shaping of Ambient Intellience and the Internet of Things. Norway: Atlantis Press. Dennis, E. E., Martin, J. D., &amp; Wood, R. (2017). Media use in the Middle East, 2017: A seven-nation survey. Northwestern University in Qatar. Retrieved from www.mideastmedia.org/survey/2017. Gustin, S. Social Media Sparked, Accelerated Egypt's Revolutionary Fire. (2011, Nov 2), Wired. Retrieved from https://www.wired.com/2011/02/egypts-revolutionary-fire/

The impact and validity of driving simulators as an educational tool in driving schools in the licensing process remains questioned in literature. Many driving schools utilize driving simulators as a tool to assist students to learn required skills faster. Few studies showed conflicting results whether utilizing driving simulator is effective in improving the quality of driving education process. The applications of driving simulators are not limited to the driver training and education. It can assist in identifying risky drivers for further training to improve risk perception. Driver training has two key aspects: vehicle control and safety knowledge. However, it is common for training courses to focus on vehicle control and priority rules while giving lower attention to safety and risk identification skills. In this regard, driving simulators can play an important role by providing an artificial environment in which students can experience potential risks while driving. In Qatar, the training process to get licensed typically covers basics of vehicle control and driving laws (road signs etc.). Advanced training courses such as defensive driving are also available for those who completed the normal training process and successfully received their license. Such advanced courses are usually limited to companies who require such training to their employees. This paper aims to investigate the effectiveness of driving simulators in driving education. A driving school in the State of Qatar utilizes advanced simulators in its training programme. This study looks at students who go through both simulator and non-simulator training tracks in the driving school. Novice students begin with a 10-hour theory course which mainly focuses on road signs and markings. Following their theory course, the students are required to complete a sign test. This is followed by 5 simulator training sessions of 20-minutes each for those registered in simulator track. The first session is for simulator adaptation and familiarization with the vehicle's controls. The student is required to drive slowly around a simple oval road; a few cars are added at the end of the session. The second session uses a more complex road network with intersections and roundabouts. The last three sessions use a virtual replica of a section of Doha. The third session is conducted with no cars on the road. Traffic is added in the fourth session. In the fifth simulation session, surrounding vehicles are designed to run with unexpected or even aggressive behaviors such as sudden lane changes, speeding and not giving right of way. At the end of the fifth session, the student is issued a performance report. After the simulator sessions, students start with the 40 hours of on the road training. The student is then required to do a parking test followed by a road test. The students are permitted to take their road tests after 20 hours of on the road training. Each student is allowed to fail up to 2 road tests before they are required to sign up for further courses. A random sample of student data was collected from both simulator training and non-simulator training tracks. All the students were first time learners with no previous license and who passed their road tests. Data collected include gender, age, nationality and number of road tests undertaken before they passed. The study aims to determine whether any of the collected variables have significant effect on the number of road tests attempted and passing driving test on first attempt. The factors tested are gender, ethnicity, age and whether on simulator or non-simulator lesson track. Furthermore, the study attempted to formulate a model that can predict the likelihood of passing the driving test on first attempt. This pilot study is expected to clarify the effectiveness of driving simulators as an educational tool and whether their utilization is justifiable.Acknowledgment: This publication was made possible by the NPRP award [NPRP 9-360-2-150] from the Qatar National Research Fund (a member of The Qatar Foundation). The statements made herein are solely the responsibility of the author[s].

Internet of things (IoT) is shifting the healthcare delivery paradigm from in-person encounters between patients and providers to an «anytime, anywhere» model delivery. Connected health has become more profound than ever due to the availability of wireless wearable sensors, reliable communication protocols and storage infrastructures. Wearable sensors would offer various insights regarding the patient's health (electrocardiogram (ECG), electroencephalography (EEG), blood pressure, etc.) and their daily activities (hours slept, step counts, stress maps,) which can be used to provide a thorough diagnosis and alert healthcare providers to medical emergencies. Remote elderly monitoring system (REMS) is the most popular sector of connected health, due to the spread of chronic diseases amongst the older generation. Current REMS use low power sensors to continuously collect patient's records and feed them to a local computing unit in order to perform real-time processing and analysis. Afterward, the local processing unit, which acts as a gateway, feeds the data and the analysis report to a cloud server for further analysis. Finally, healthcare providers can then access the data, visualize it and provide the proper medical assistant if necessary. Nevertheless, the state-of-the-art IoT-based REMS still face some limitations in terms of high energy consumption due to raw data streaming. The high energy consumption decreases the sensor's lifespan immensely, hence, a severe degradation in the overall performance of the REMS platform. Therefore, sophisticated signal acquisition and analysis methods, such as compressed sensing (CS), should be incorporated. CS is an emerging sampling/compression theory, which guarantees that an N-length sparse signals can be recovered from M-length measurement vector (M&lt;&lt;N) using efficient algorithms such as convex relaxation approaches and greedy algorithms. This work aims to enable two different scenarios for REMS by leveraging the concept of CS in order to reduce the number of samples transmitted form the sensors while maintaining a high quality of service. The first one is dedicated to abnormal heart beat detection, in which, ECG data from different patients is collected, transmitted and analysed to identify any type of arrhythmia or irregular abnormalities in the ECG. The second one aims to develop an automatic fall detection platform in order to detect falls occurrence, their strength, their direction in order to raise alert and provide prompt assistance and adequate medical treatment. In both applications, CS is explored to reduce the number of transmitted samples form the sensors, hence, increase the sensors lifespan. In addition, the identification and the detection is enabled by means of machine learning and pattern recognition algorithms. In order to quantify the performance of the system, subspace pursuit (SP) has been adopted as recovery algorithm. Whereas for data identification and classification, K-nearest neighbour (KNN), E-nearest neighbour (ENN), decision tree (BDT) and committee machine (CM) have been adopted.