Qatar Foundation Annual Research Forum Volume 2013 Issue 1

Big data analytics in health is an emerging area due to the urgent need to derive actionable intelligence from the large volumes of healthcare data to efficiently manage the healthcare system and to improve health outcomes. In this paper we present a ‘big’ healthcare data analytics platform—termed as Healthcare Analytics for Intelligence and Learning (HAIL)—that is an end-to-end healthcare data analytics solution to derive data-driven actionable intelligence and situational awareness to inform and transform health decision-making, systems management and policy development. The innovative aspects of HAIL are: (a) the integration of data-driven and knowledge-driven analytics approaches, (b) a sand-box environment for healthcare analysts to develop and test health policy/process models by exploiting a range of data preparation, analytical and visualization methods, (c) the incorporation of specialized healthcare data standards, terminologies and concept maps to support data analytics, and (d) text analytics to analyze unstructured healthcare data. The architecture of HAIL comprises the following four main modules (fig 1): (A) Health Data Integration module that entails a semantics-based metadata manager to synthesize health data originating from a range of healthcare institutions to formulate a rich contextualized data resource. The data integration is achieved through ETL workflows designed by health analysts and researchers, (B) Health Analytics Module provides a range of healthcare analytics capabilities including, (i) Exploratory Analytics using data mining to perform data clustering, classification and association tasks, (ii) Predictive Analytics to predict future trends/outcomes derived from past observations of the healthcare processes, (iii) Text Analytics to analyze unstructured texts (such as clinical notes, discharge summaries, referral notes, clinical guidelines, etc.), (iv) Simulation-based Analytics to simulate what-if questions based on simulation models, (v) Workflow analytics to interact with modeled clinical workflows to understand the affects of various confounding factors, (vi) Semantic Analytics to infer contextualized relationships, anomalies and deviations through reasoning over a semantic health data model, and (vii) Informational Analytics to present summaries, aggregations, charts and reports, (C) Data Visualization Module offers a range of interactive data visualizations, such as geospatial visualizations, causal networks, 2D and 3D graphs, pattern clusters and interactive visualizations to explore high dimensional data, (D) Data Analytics Workbench is an interactive workspace to enable data health analysts to specify and set-up their analytics process in terms of data preparation, selection and set-up of analytical methods and the selection of visualization methods. Using the workbench analysts can design sophisticated data analytics workflows/models using a range of data integration, analytical and visualization methods. The HAIL platform is available via a web portal and the desktop application, and it is deployed on a cloud infrastructure. HAIL can be connected with existing health data sources to provide front-end data analytics. Fig 2 shows the technical architecture. The HAIL platform has been applied to analyze a real-life clinical healthcare situations using actual data from the provincial clinical data warehouse. We will present two case studies of the use of HAIL. In conclusion, the HAIL platform addresses a critical need for healthcare analytics to impact health decision-making, systems management and policy development.

The shortcomings of today's Internet and the high demand for complex and sophisticated applications and services drive a very interesting and novel research area called Future Internet. The area of Future Internet research focuses on developing a new network with similar magnitude as today's Internet but with more demanding and complex design goals and specifications. It strives to solve the issues identified in today's Internet by capitalizing on the advantages of emerging new technologies in the area of computer networking such as Software Defined Networking (SDN), autonomic computing, and cloud computing. SDN represents an extraordinary opportunity to rethink computer networks, enabling the design and deployment of a future Internet. Utilizing these technologies leads to a significant progress in the development of an enhanced, secure, reliable, and scalable Internet. In 2022, Qatar will host the world cup where more than 5 Million people from all over the world are expected to attend. This event is expected to put the host country, Qatar, under massive pressure and huge challenges in terms of providing high quality Internet service especially with the increasing demand for emerging applications such as video streaming service over mobile devices. It is vital to evaluate and deploy state-of-the-art technologies based on a promising future Internet infrastructure in order to provide high-quality Internet services unique to this event and to the continuing rapid growth of the country. The main goal in this paper is to present a new network design with similar magnitude as today's Internet but with more demanding and complex design goals and specifications defined by the knowledge and experience gathered from four decades of using the current Internet. This research focuses on the development of a complete system, designed with the new requirements of the Future Internet in mind, and aims to provide, monitor and enhance the increasing demand for popular video streaming service. The testing environment was built using the Global Environment for Network Innovations (GENI) testbed (see Figure 1). GENI, a National Science Foundation (NSF) funded research and development effort, aims to build a collaborative and exploratory network experimentation platform for the design, implementation and evaluation of future networks. Because GENI is meant to enable experimentation with large-scale distributed systems, experiments will in general always contain multiple communicating software entities. Large experiments may well contain tens of thousands of such communicating entities, spread across continental or transcontinental distances. The conducted experiments illustrate how such a system can function under unstable and changing network conditions, dynamically learn its environment, recognize potential service degradation problems, and react to these challenges in an autonomic manner without the need for human intervention.

Orthogonal Frequency Division Multiplexing (OFDM) is widely adopted as the transmission scheme of choice for almost all broadband wireless standards (including WLAN, WiMAX, LTE, DVB, etc.) due to its multipath resilience and practical implementation complexity. The direct-conversion radio frequency (RF) front-end architecture , where the down-conversion to baseband is accomplished in a single stage, requires fewer analog components compared to the super-heterodyne architecture where the down-conversion is accomplished with one or more intermediate frequency (IF) stages. Fewer analog components result in reduced power consumption and cost. However, direct-conversion OFDM based broadband wireless transceivers suffer from several performance-limiting RF/analog impairments including I/Q imbalance and phase noise (PHN) [1]. I/Q imbalance refers to the amplitude and phase mismatches between the in-phase (I) and quadrature (Q) branches at the transmit and receive sides. In an OFDM system with I/Q imbalance, the transmitted signal at a particular subcarrier is corrupted by interference from the image subcarrier. To compensate for the effect of I/Q imbalance, the received signals from each subcarrier and its image subcarrier are processed jointly. PHN refers to the random unknown phase difference between the carrier signal and the local oscillator. In an OFDM transceiver, PHN rotates the signal constellation on each sub-carrier and causes inter-carrier interference between the sub-carriers resulting in significant performance degradation. In this project, we designed novel algorithms for the efficient estimation and compensation of RF impairments including I/Q imbalance and PHN for beamforming OFDM systems such as 4G LTE cellular systems and 802.11 wireless local area networks (WLAN) [2]. Conventional OFDM transceivers ignore I/Q imbalance effects and each OFDM subcarrier is processed separately which causes an irreducible error floor in the bit error rate performance. However, in our proposed method, each OFDM subcarrier and its image subcarrier are processed jointly to mitigate the effects of I/Q imbalance. This novel method is capable of eliminating the error floor and obtaining performance close to the ideal case where no I/Q imbalance exists. Furthermore, we have developed an experimental OFDM testbed to implement the proposed algorithms. Our testbed uses the Universal Software Radio Peripheral (USRP) N210 RF frontends and it is based on packet-based OFDM similar to the IEEE 802.11.a WLAN standard. The baseband processing is done in MATLAB where the MATLAB driver for USRP is used for stream processing of the transmitted and received signals. The measured experimental results demonstrate that the proposed algorithms improve the performance significantly at low implementation complexity. [1] B. Razavi, RF Microelectronics. Englewood Cliff, NJ: Prentice-Hall, 1998. [2] O. Ozdemir, R. Hamila, and N. Al-Dhahir, “I/Q imbalance in multiple beamforming OFDM transceivers: SINR analysis and digital baseband compensation,” IEEE Trans. on Comunications, vol. 61, no. 5, pp. 1914-1925, May 2013. Acknowledgement: This work is supported by Qatar National Research Fund (QNRF), Grant NPRP 09-062-2-035.

It is widely known that the state of a patient's coronary heart disease can be better assessed using intravascular ultrasound (IVUS) than with more conventional angiography. Recent work has shown that segmentation and 3D reconstruction of IVUS pull-back sequence images can be used for computational fluid dynamic simulation of blood flow through the coronary arteries. This map of shear stress in the blood vessel walls can be used to predict susceptibility of a region of the arteries to future arteriosclerosis and disease. Manual segmentation of images is time consuming as well as cost prohibitive for routine diagnostic use. Current segmentation algorithms do not achieve a high enough accuracy because of the presence of speckle due to blood flow, relatively low resolution of images and presence of various artifacts including guide-wires, stents, vessel branches, and some other growth or inflammations. On the other hand, the image may be induced with additional blur due to movement distortions, as well as resolution-related mixing of closely resembling pixels thus forming a type of out-of-focus blur.. Robust automated segmentation achieving high accuracy of 95% or above has been elusive despite of work by a large community of researchers in the machine vision field. We propose a comprehensive approach where a multitude of algorithms are applied simultaneously to the segmentation problem. In an initial step, pattern recognition methods are used to detect and localize artifacts. We have achieved a high accuracy of 95% or better in detecting frames with stents and location of guide-wire in a large data-set consisting of 15 pull-back sequences with about 1000 image frames each. Our algorithms for lumen segmentation using spatio-temporal texture detection and active contour models have achieved accuracies approaching 70% in the same data-set which is on the high-side of reported accuracies in the literature. Further work is required to combine these methods to increase segmentation accuracy. One approach we are investigating is to combine algorithms using a meta-algorithmic approach. Each segmentation algorithm computes along with the segmentation a measure of confidence in the segmentation which can be biased on prior information about the presence of artifacts. A meta-algorithm then runs a library of algorithms on a sub-sequence of images to be segmented and chooses the segmentation based on computed confidence measures. Machine learning and testing is performed on a large data base. This research is in collaboration with Brigham and Women Hospital in Boston that provides well over 45,000 frames of data for the study.

Introduction: Surgery remains one of the primary methods for terminating cancerous tumours. Minimally-invasive robotic surgery, in particular, provides several benefits, such as filtering of hand tremor, offering more complex and flexible manipulation capabilities that lead to increased dexterity and higher precisions, and more comfortable seating for the surgeon. All in turn lead to reduced blood loss, lower infection and complication rates, less post-operative pain, shorter hospital stays, and better overall surgical outcomes. Pre-operative 3D medical imaging modalities, mainly magnetic resonance imaging (MRI) and computed tomography (CT) are used for surgical planning, in which tumour excision margins are identified for maximal sparing of healthy tissue. However, transferring such plans from the pre-operative frame-of-reference to the dynamic intra-operative scene remains a necessary yet largely unsolved problem. We summarize our team's progress towards addressing this problem focusing on partial nephrectomy (RAPN) performed with a daVinci surgical robot. Method: We perform pre-operative 3D image segmentation of the tumour and surrounding healthy tissue using interactive random walker image segmentation, which provides an uncertainty-encoding segmentation used to construct a 3D model of the segmented patient anatomy. We reconstruct the 3D geometry of the surgical scene from the stereo endoscopic video, regularized by the patient-specific shape prior. We process the endoscopic images to detect tissue boundaries and other features. Then we align, first via rigid then via deformable registration, the pre-operative segmentation to the 3D reconstructed scene and the endoscopic image features. Finally, we present to the surgeon an augmented reality view showing an overlay of the tumour resection targets on top of the endoscopic view, in a way that depicts uncertainty in localizing the tumour boundary. Material: We collected pre-operative and intra-operative patient data in the context of RAPN including stereo endoscopic video at full HD 1080i (da Vinci S HD Surgical System), CT images (Siemens CT Sensation 16 and 64 slices), MR images (Siemens MRI Avanto 1.5T), and US images (Ultrasonix SonixTablet with a flexible laparoscopic linear probe). We also acquired CT images and stereo video from in-silico phantoms and ex-vivo lamb kidneys with artificial tumours for test and validation purposes. Results and Discussion: We successfully developed a novel proof-of-concept framework for prior and uncertainty encoded augmented reality system that fuses pre-operative patient specific information into the intra-operative surgical scene. Preliminary studies and initial surgeons' feedback on the developed augmented reality system are encouraging. Our future work will focus on investigating the use of intra-operative US data in our system to leverage all imaging modalities available during surgeries. Before a full system integration of these components, improving accuracy and speed of aforementioned algorithms, and the intuitiveness of the augmented reality visualization, remain active research projects for our team.

Our work represents a new starting point for a wireless sensor network implementation of a cooperative algorithm called the binary consensus algorithm. Binary consensus is used to allow a collection of distributed entities to reach consensus regarding the answer to a binary question and the final decision is based on the majority opinion. Binary consensus can play a basic role in increasing the accuracy of detecting event occurrence. Existing work on binary consensus focuses on simulation of the algorithm in a purely theoretical sense. We have adapted the binary consensus algorithm for use in wireless sensor networks. This is achieved by specifying how motes find partners to update state with as well as by adding a heuristic for individual motes to determine convergence. In traditional binary consensus, individual nodes do not have a stop condition, meaning nodes continue to transmit even after convergence has occurred. In WSNs however, this is unacceptable since it will consume power. So in order to save power sensor motes should stop the communication when the whole network converges. For that reason we have designed a tunable heuristic value N that will allow motes to estimate when convergence has occurred. We have evaluated our algorithm successfully in hardware using 139 IRIS sensor motes and have further supported our results using the TOSSIM simulator. We were able to minimize the convergence time reaching optimal results. The results also show that as the network gets denser the convergence time will lower. In addition, convergence speed depends on the number of motes present in the network. During the experiments none of the motes failed and our algorithm converged correctly. The hardware as well as the simulation results demonstrate that the convergence speed depends on the topology type, the number of motes present in the network, and the distribution of the initial states.

The Problem-based Learning Authoring and Transformation Environment (PLATE) project seeks to improve student learning using innovative approaches to problem-based learning (PBL) in a cost-effective, flexible, interoperable, and reusable manner. Traditional subject-based learning that focuses on passively learning facts and reciting them out of context is no longer sufficient to prepare potential engineers and all students to be effective. Within the last two decades, the problem-based learning approach to education has started to make inroads into engineering and science education. This PBL educational approach comprises an authentic, ill-structured problem with multiple possible routes to multiple possible solutions. The PBL approach offers unscripted opportunities for students to identify personal knowledge gaps as starting points for individual learning. Additionally, it requires a facilitator (not a traditional teacher) who guides learning by asking probing questions that model expert cognitive reasoning and problem solving strategies. Bringing real-life context and technologies into the curriculum through a problem based learning approach encourages students to become independent workers, critical thinkers, problem solver, lifelong learners, and team workers. A systematical approach to support online PBL is the use of a pedagogy-generic e-learning platform such as IMS Learning Design (IMS-LD 2003), which is an e-learning technical standard useful to script a wide range of pedagogical strategies as formal models. This PLATE project uses the IMS-DL strategies. It seeks to research and develop a process modeling approach together with software tools to support the development and delivery of face-to-face, online, and hybrid PBL courses or lessons in a cost-effective, flexible, interoperable, and reusable manner. The research team seeks to prove that the PLATE authoring system optimizes learning and that the PLATE system improves learning in PBL activities. For this poster presentation, the research team will demonstrate the progress it has made within the first year of research. This includes the development of a prototype PBL scripting language to represent a wide range of PBL models, the creation of transformation functions to map PBL models represented in the PBL scripting language into the executable models represented in IMS-LD, and the architecture of the PLATE authoring tool. The research team plans to illustrate that the research and development of a PBL scripting language and the associated authoring and execution environment can provide a significant thrust toward further research of PBL by using meta-analysis, designing effective PBL models, and extending or improving a PBL scripting language. The team believes that PBL researchers can use the PBL scripting language and authoring tools to create, analyze, test, improve, and communicate various PBL models. The PLATE project can enable PBL practitioners to develop, understand, customize, and reuse PBL models at a high level by relieving the burdens of handling complex details to implement a PBL course. The research team believes that the project will stimulate the application and use of PBL in curricula with online learning practice by incorporating PBL support into popularly used e-learning platforms and by providing a repository of PBL models and courses.

Unmanned Aerial Vehicles (UAVs) have the potential to provide comprehensive information for traffic monitoring, road conditions and emergency response. However, to enable autonomous UAV operations, video images captured by UAV cameras are processed by using state-of-the art algorithms for vehicle detection, recognition, and tracking. Nevertheless, processing of aerial UAV images is challenging due to the fact that the images are usually captured with low-resolution cameras, from high altitudes, and the UAV is in continuous motion. The latter enforces the need for decoupling the camera and scene motions and most of the techniques for moving vehicle detection perform ego motion compensation to separate camera motion from scene motion. To this end, registration of successive image frames is first carried out to match two or more images of the same scene taken at different times followed by moving vehicle labeling. Detected vehicles of interest are routinely tracked by the UAV. However, vehicle tracking in UAV imagery is challenging due to constantly changing camera vantage points, changes in illumination, and occlusions. The majority of the existing vehicle detection and tracking techniques suffer from reduced accuracy and/or entail intensive processing that prohibits their deployment onboard of UAVs unless intensive computational resources are utilized. This paper presents a novel multiple moving vehicle detection and tracking framework that is suitable for UAV traffic monitoring application. The proposed framework executes in real-time with improved accuracy and is based on image feature processing and projective geometry. FAST image features are first extracted and then outlier features are computed by using least median square estimation. Moving vehicles are subsequently detected with density-based spatial clustering algorithm. Vehicles are tracked by using Kalman filtering while an overlap-rate-based data association mechanism followed by tracking persistency check are used to discriminate between true moving vehicles and false detections. The proposed framework doesn't involve the explicit application of image transformations, i.e. warping, to detect potential moving vehicles which reduces computational time and decreases the probability of having wrongly detected vehicles due to registration errors. Furthermore, the use of data association to correlate detected and tracked vehicles along with the selective target's template update that's based on the data association decision, significantly improves the overall tracking accuracy. For quantitative evaluation, a testbed has been implemented to evaluate the proposed framework on three datasets: The standard DARPA Eglin-1 and the RedTeam datasets, and a home-collected dataset. The proposed framework achieves recall rates of 97.1 and 96.8 (average 96.9%), and precision rates of 99.1% and 95.8% (average 97.4%) for the Eglin-1 and RedTeam datasets, respectively, with overall average precision of (97.1%). And when evaluated on the dataset collected, it achieved 95.6% recall and 96.3% precision. Compared to other moving vehicle detection and tracking techniques found in the literature, the proposed framework achieves higher accuracy on average and is less computationally demanding. The quantitative results thus demonstrate the potential of the proposed framework for autonomous UAV traffic monitoring applications.

Green communications have emerged as one of the most important trends in wireless communications because of its several advantages of interference reduction, battery life increase and electrical bill cut. Its application to handover mechanisms is a crucial operation for its integration in practical systems, as handover is one of the most resource-consuming operations in the system, and it has to be optimized under the objective of green communications. On the other hand, a decrease in the energy consumption should not mean lower performance for the operator and customers. Therefore, this work will present a hybrid handover mechanism where two conflict objectives of load balancing and energy consumption are tackled, where the operator's objective is to balance the data load among its macro and small-cells, while the user equipment objective is to decrease the consumed energy in order to guarantee a larger battery life.

Principal component analysis (PCA) is a commonly used technique for data reduction in general as well as for dimensionality reduction in gas identification systems when a sensor array is being used. A complete PCA IP core for gas application has been designed and implemented on the Zynq programmable system on chip (SoC). The new heterogeneous Zynq platform with its ARM processor and programmable logic (PL) was used because it is becoming an interesting alternative to conventional field programmable gate array (FPGA) platforms. All steps of the learning and testing phases of PCA starting from the mean computation to the projection of data onto the new space, passing by the normalization process, covariance matrix and the eigenvectors computation were developed in C and synthesized using the new Xilinx VIVADO high level synthesis (HLS) tool. The eigenvectors were found using the Jacobi method. The implemented hardware of the presented PCA algorithm for a 16×30 matrix was faster than the software counterpart with a speed up of 1.41 times when executed on a desktop running a 64-bit Intel i7-3770 processor at 3.40GHz. The implemented IP core consumed an average of 23% of all on chip resources. The PCA algorithm used in the learning phase is to be executed first for the system to be trained to a specific data set and then produce the related vectors of means along with the eigenvectors that will be used in the testing part. The PCA algorithm used in the testing phase will also be used in real time identification. For testing purpose, a data set that represents the output of a 16-array gas sensor when exposed to three types of gases (CO, Ethanol and H2) in ten different concentrations (20, 40, 60, 80, 120, 140, 160, 180 and 200ppm) was used. The aim was to reduce the 30 samples of 16 dimensions to 30 vectors of 2 or 3 dimensions data depending on the need. The combination between the Zynq platform and the HLS tool showed many benefits, using Vivado HLS resulted in a considerable gain in terms of time spent on prototyping and this is due to the fact that the design was specified in a high level language such C or C++ and not a hardware description language such as VHDL or Verilog. While using the Zynq platform highlighted some interesting advantages compared with conventional FPGA platforms such us the possibility to split the design, executing the simple part in a software manner on the ARM processor and leaving the complex one for hardware acceleration. It is planned to further optimize the IP core using the Vivado HLS directives, the developed core is to be used in a larger gas identification system for dimensionality reduction purpose. The larger gas identification system will be used to identify a given gas and estimate its concentration and will be part of a Low Power Reconfigurable self-calibrated Multi‐Sensing Platform.

Cloud computing is an emerging paradigm, which is changing the way computing resources are provisioned, accessed, utilized, maintained and managed. The SWOT analysis for the Cloud is depicted in Table 1. Cloud computing is increasingly changing the way software products and services are produced and consumed; thereby implying the need for a change in the ways, methods, tools and concepts by which these products are tested. Software testing is an important quality control activity stage within the Software Development Lifecycle. Software testing involves both function (eg. bugs) and non-functional testing (eg. regression). It verifies and validates the finished product to ensure that development effort meets up with requirements specification. This process often requires consumption of resources over a limited period of time. These resources could be costly, or, not readily available, which in turn, can have an effect on the efficiency of the testing process. Though this process is important, nevertheless it is not a business critical process because it does not contain overly sensitive business data, which makes it an ideal case for migration to the cloud. The increasing complexity and distribution of teams, applications, processes and services, along with the need for adequate testing approaches for cloud-based applications and services creates a convincing case for the need for cloud-based software testing. Cloud-based testing or Software Testing as a Service is a new way of carrying out testing as a service, using the cloud as the underlying platform to provide on-demand software testing services via the internet. Table 2 below shows a SWOT analysis for cloud-based testing, from which a comparison between traditional software testing with cloud-based testing can be made and advantages of the cloud approach can be drawn. A number of major industrial players like IBM, HP, UTest, SOASTA, Sogetti, and SauceLabs, to mention a few, now offer various cloud-based testing services which presents a lot of advantages to customers. Though cloud-based testing presents a lot of advantages and benefits over traditional testing, it cannot overly replace traditional testing because areas of testing and scenarios of testing for synergy and trade-offs exist. For example, some testing areas requiring implicit domain knowledge about the customer's business (like insurance business); or areas where hardware or software is an integral and essential part of the other and directly dependent on each other (like programmable logic controllers), may require the adoption of traditional testing practices over cloud-based testing. This represents an area for further research: developing intelligent/context aware Cloud-based testing services with the ability to recreate or mimic areas/scenarios requiring implicit domain knowledge. Furthermore, there is a lack of adequate support tools cloud-based testing services. These tools include: self-learning test case libraries and tools for measuring cloud-based testing services. This paper will present our research efforts in the area of Cloud based collaborative software development life cycle, with particular focus on the feasibility of provisioning software testing as a cloud service. This research has direct industrial implication and holds huge research and business potentials.

Foundational topics in Computer Science (CS), such as data structures and algorithmic strategies, pose particular challenges to learners and teachers. The difficulty of learning these basic concepts often discourages students from further study, and leads them to lower success. In any discipline, students' interaction with skilled tutors is one of the most effective strategies to address the problem of weak learning. However, human tutors are not always available. Technology can compensate here: Intelligent Tutoring Systems (ITSs) are systems designed to simulate the teaching of human tutors. ITSs use artificial intelligence techniques to guide learners through problem solving exercises using pedagogical strategies similar to those employed by human tutors. ChiQat-Tutor, a novel ITS that we are currently developing, aims at facilitating learning of basic CS data structures (e.g., linked lists, trees, stacks) and algorithmic strategies (e.g., recursion). The system will use a number of effective pedagogical strategies in CS education, including positive and negative feedback, learning from worked-out examples, and learning from analogies. ChiQat will support linked lists, trees, stacks, and recursion. The ChiQat linked list module builds on iList, our previous ITS that provenly helps students learn linked lists. This module provides learners with a simulated environment where linked lists can be seen, constructed, and manipulated. Lists are represented graphically and can be manipulated interactively using programming commands (C++ or Java). The system can currently follow the solution strategy of a student, and provides personalized positive and negative feedback. Our next step is to add support for worked-out examples. The recursion module provides learners with an animated and interactive environment, where they can trace recursion calls of a given recursive problem. This is one of the most challenging tasks students face when learning the concept of recursion. In the near future, students will be aided in breaking down recursive problems into their basic blocks (base case and recursive case) through interactive dialogues. ChiQat employs a flexible, fault-tolerant, distributed plug-in architecture, where each plug-in fulfills a particular role. This configuration allows different system types to be defined, such as all-in-one applications or distributed ones. The system is composed of separate front and back ends. The back-end will house the main logic for heavy computational tasks such as problem knowledge representation and tutor feedback generation. The front-end (user interface) collects users input and sends it to the back-end, while displaying the current state of the problem. Due to this flexible architecture, it will be possible to run the system in two modes; online and offline. Offline mode will run all client and server components on the same machine, allowing the user to use the system in a closed environment. The online mode will allow running the back-end on a server as a web service which can communicate with a front-end running on the client machine. This allows wider and greater reachability for users using lower powered connected devices such as mobile devices, as well as traditional laptops and desktop computers which are connected to the Internet.

Energy is becoming a main concern nowadays due to the increasing demands on natural energy resources. Base stations (BS) consume up to 80% of the total energy expenditure in a cellular network. The energy-efficiency of the BSs decreases significantly at off-peak hours since the power amplifiers' energy-efficiency degrades at lower output power. Thus, power savings methods should focus on the access network level by trying to manipulate the BSs power consumption. This could be done by reducing the number of active elements (e.g., BSs) in the network for lower traffic states by switching some BSs off. In this case, network management should allow smooth transition between different network topologies based on the traffic demands. In this work, we evaluate a green radio network planning approach by jointly optimizing the number of active BSs and the BS on/off switching patterns based on the changing traffic conditions in the network in an effort to reduce the total energy consumption of the BSs. Planning is performed based on two approaches: a reactive and a proactive approach. In the proactive approach, planning will be performed starting with the lowest traffic demand until reaching the highest traffic demand whereas in the reactive approach, the reverse way is considered. Performance results are presented for various case studies and evaluated taking into account practical network planning considerations. Moreover, we present real planning results in an urban city environment using the ICS telecom tool from ATDI in order to perform coverage calculations and analysis for LTE networks.

Phosphoproteomic experiments are routinely conducted in laboratories worldwide, and because of the fast development of mass spectrometric techniques and efficient phosphopeptide enrichment methods, life-science researchers frequently end up having lists with tens of thousands of phosphorylation sites for further interrogation. To answer biologically relevant questions from these complex data sets, it becomes essential to apply computational, statistical, and predictive analytical methods. Recently we have provided an advanced bioinformatic platform termed “PhosphoSiteAnalyzer” to the scientific community to explore large phosphoproteomic data sets that have been subjected to kinase prediction using the previously published NetworKIN algorithm. NetworKIN applies sophisticated linear motif analysis and contextual network modeling to obtain kinase-substrate associations with high accuracy and sensitivity. PhosphoSiteAnalyzer provides an algorithm for retrieval of kinase predictions from the public NetworKIN webpage in a semi-automated way and applies hereafter advanced statistics to facilitate a user-tailored in-depth analysis of the phosphoproteomic data sets. The interface of the software provides a high degree of analytical flexibility and is designed to be intuitive for most users. Network biology and in particular kinase-substrate network biology provides an adequate conceptual framework to describe and understand diseases and for designing targeted biomedicine for personalized medicine. Hence network biology and network analysis are absolutely essential to translational medical research. PhosphoSiteAnalyzer is a versatile bioinformatics tool to decipher such complex networks and can be used in the fight against serious diseases such as psychological disorders, cancer and diabetes that arise as a result of dysfunctional cell signalling networks.

The present Qatarian government has a long term vision of introducing intelligent transport, logistics management and road safety services in Qatar. Studies have shown that the public transport system in Qatar, and Doha in particular, is developing, but is not yet as comprehensive as in many renowned world cities (Pernin et al., 2008; Henry et al., 2012). Furthermore, with hosting rights of FIFA 2022 World Cup being granted to Qatar, a seminar paper was recently discussed by 2030 Qatar National Vision aim at world-class transport system for Qatar which meets the requirements of the country's long term goal (Walker, 2013). The introduction of intelligent public transport system involves the incorporation of technology into transportation system so as to improve public safety, conserve resources through seamless transport hub and introduce smartness for maximum utility in public transportation. The aforementioned goals of 2030 Qatar National Vision can be achieved through TVWS technology. TVWS technology was created to make use of sparsely used VHF and UHF spectra bands as indicated in Figure 1. The project focuses on IEEE 802.22 to enhance the Security, Smart, Seamless (SSS) transportation system in Qatar as shown in Figure 2 below. It is sub-divided as: (i) Security- TVWS to provide surveillance camera in public bus and train system. The bus/train system will be fitted with micro-camera. The project will provide the city center management team the ability to monitor and track the public transportation system in-case of accident, terrorism and other social issues. (ii) Seamless-TVWS will be made available for anyone who can purchase a down/up converter terminal to access the internet services for free. The need for up/down converter arises because the current mobile devices operate in ISM bands, whereas TVWS operates in VHF/UHF bands. (iii) Smart-The city center management can seat in their office and take control of eventuality. If there is such a project in the past, it might be using satellite technology. We are all aware of the limitations of satellite technology such as round delay trip. (iv) Novelty- Spectrum sensing by using Grey prediction algorithm is proposed to achieve optimal result. From academic point of view, Grey prediction algorithm has been used in predicting handoff in cellular communication and in stock market prediction, with high prediction accuracy of 95~98% (Sheu and Wu, 2000; Kayacan et al. 2010). The proposal methodology is as shown in Figure 3. Wireless Rural Area Network (WRAN) with cell radius varies from 10 - 100 km leaning towards macro cell architecture. Hence, the roll-out will require less base station infrastructure. In addition, the VHF-UHF bands offer desirable propagation qualities when compared to other frequency bands thereby ensuring wider and reliable radio coverage.

A traditional multi-class classification system assigns each example x a single label l from a set of disjoint labels L. However, in many modern applications such as text classification, image/video categorization, music categorization etc [1, 2], each instance can be assigned to a subset of labels Y ⊆ L. In text classification, news document can cover several topics such as the name of movie, box office ratings, and/or critic reviews. In image/video categorization, multiple objects can appear in the same image/video. This problem is known as multi-label learning. Figures 1 shows some examples of the multi-label images. Collaborative Representation with regularized least square (CRC-RLS) is a state-of-the-art face recognition method that exploits this collaborative representation between classes in representing the query sample [3]. The basic idea is to code the testing sample over a dictionary, and then classify it based on the coding vector. While the benefits of collaborative representation are becoming well established for face recognition or in general multi-class classification, the corresponding use for multi-label classification needs to be investigated. In this research, a kernel collaborative label power set multi-label classifier (ML-KCR) based on regularized least square principle is proposed. ML-KCR directly introduces the discriminative information of the samples using l2-norm\sparsity" and uses the class specified representation residual for classification. Further, in order to capture co-relation among classes, the multi-label problem is transformed using label power set which is based on the concept of handling sets of labels as single labels and thus allowing the classification process to inherently take into account the correlations between labels. The proposed approach is applied to six publicly available multi-label data sets from different domains using 5 different multi-label classification measures. We validate the advocated approach experimentally and demonstrate that it yields significant performance gains when compared with the state-of-the art multi-label methods. In summary, following are our main contributions * A kernel collaborative label powerset classifier (ML-KCR) based on regularized least square principle is proposed for multi-label classification. ML-KCR directly introduces the discriminative information and aim to maximize the margins between the samples of di_erent classes in each local area. * In order to capture correlation among labels, the multi-label problem is transformed using label powerset (LP). The main disadvantage associated with LP is the complexity issue arise due to many distinct label sets. We will show that this complexity issue can be avoided using collaborative representation with regularization. * We applied the proposed approach to publicly available multi-label data sets and compared with state-of-the-art multi-label methods. The proposed EML method is compared with the state-of-the-art multi-label classifiers: RAkEL, ECC, CLR, MLkNN, IBLR [2]. References [1] Tsoumakas, G., Katakis, I., Vlahavas, I., 2009. Data Mining and Knowledge Discovery Handbook. Springer, 2nd Edition, Ch. Mining Multilabel Data. [2] Zhang, M.-L., Zhou, Z.-H., 2013. A review on multi-label learning algorithms. IEEE Transactions on Knowledge and Data Engineering (preprint). [3] Zhang, L., Yang, M., 2011. Sparse representation or collaborative representation: Which helps face recognition? In: IEEE International Conference on Computer Vision (ICCV).

Long Audio Alignment is a known problem in speech processing in which the goal is to align a long audio input with the corresponding text. Accurate alignments help in many speech processing tasks such as audio indexing, speech recognizer's acoustic model training, audio summarizing and retrieving, etc. In this work, we have collected more than 1400 hours of conversational Arabic speech extracted from Al-Jazeerah podcasts besides the corresponding non-aligned text transcriptions. Podcast's length varies from 20-50 minutes each. Five episodes have been manually aligned that meant to be used in evaluating alignment accuracy. For each episode, a split and merge segmentation approach is applied to segment audio file into small segments of average length of 5 sec. having filled pauses on the boundary of each segment. A pre-processing stage in applied on the corresponding raw transcriptions to remove titles, headings, images, speaker's names, etc. A biased language model (LM) is trained on the fly using the processed text. Conversational Arabic speech is mostly spontaneous and influenced by dialectal Arabic. Since phonemic pronunciation modeling is not always possible for non-standard Arabic words, a graphemic pronunciation model (PM) is utilized to generate one pronunciation variant for each word. Unsupervised acoustic model adaptation in applied on a pre-trained Arabic acoustic model using the current podcast audio. The adapted AM along with the biased LM and the graphemic PM are used in a fast speech recognition pass applied on the current podcast's segments. Recognizer's output is aligned with the processed transcriptions using Levenshtein distance algorithm. This way we can ensure error recovery where miss-alignment of a certain segment does not affect alignment of later segments. The proposed approach resulted in an alignment accuracy of 97% on the evaluation set. Most of miss-alignment errors were found to be with segments having significant background noise (music, channel noise, cross-talk, etc.) or significant speech disfluencies (truncated words, repeated words, hesitations, etc.). For some speech processing tasks like acoustic model training, it is required to eliminate miss-aligned segments from the training data. That is why a confidence scoring metric is proposed to accept/reject aligner output. The score is provided for each segment and it is basically the Min-Edit distance between recognizer's output and the aligned text. By using confidence scores, it was possible to reject the majority of miss-aligned segments resulting in 99% alignment accuracy. This work was funded by a grant from the Qatar National Research Fund under its National Priorities Research Program (NPRP) award number NPRP 09-410-1-069. Reported experimental work was performed at Qatar University in collaboration with University of Illinois.

With the rise in mobile device adoption, and growth in mobile application market expected to reach $30 billion by the end of 2013, mobile user expectations for pervasive computation and data access are unbounded. Yet, various applications, such as face recognition, speech and object recognition, and natural language processing, exceed the limits of standalone mobile devices. Such applications resort to exploiting larger resources in the cloud, which sparked researching problems arising from data and computational offloading to the cloud. Research in this area has mainly focused on profiling and offloading tasks to remote cloud resources, automatically transforming mobile applications by provisioning and partitioning its execution into offloadable tasks, and more recently, bringing computational resources (e.g. Cloudlets) closer to task initiators in order to save mobile device energy. In this work, we argue for environments in which computational offloading is performed among mobile devices forming what we call a Mobile Device Cloud (MDC). Our contributions are: (1) Implementing an emulation testbed for quantifying the potential gain, in execution time or energy consumed, of offloading tasks to an MDC. This testbed includes a client offloading application, an offloadee server receiving tasks, and a traffic shaper situated between the client and server emulating different communication technologies (Bluetooth 3.0, Bluetooth 4.0, WiFi Direct, WiFi, and 3G). Our evaluation for offloading tasks with different data and computation characteristics to an MDC registers up to 80% and 90% savings in time or energy respectively, as opposed to offloading to the cloud. (2) Providing an MDC experimental platform to enable future evaluation and assessment of MDC-based solutions. We create a testbed, shown in Figure 1, to measure the energy consumed by a mobile device when running or offloading tasks using different communication technologies. We build an offloading Android-based mobile application and measure the time taken to offload tasks, execute them, and receive the results from other devices within an MDC. Our experimental results show gains in time and energy savings, up to 50% and 26% respectively, by offloading within MDCs, as opposed to locally executing tasks. (3) Providing solutions that address two major MDC challenges. First, due to mobility, offloadee devices leaving an MDC would seriously compromise performance. Therefore, we propose several social-based offloadee selection algorithms that exploit contact history between devices, as well as friendship relationships or common interests between device owners or users. Second, we provide solutions for balancing power consumption by distributing computational load across MDC members to elongate and MDC's life time. This need occurs when users need to maximize the lifetime of an ensemble of devices that belong to the same user or household. We evaluate the algorithms we propose for addressing these two challenges using the real datasets that contain contact mobility traces and social information for conference attendees over the span of three days. Our results show the impact of choosing the suitable offloadee subset, the gain from leveraging social information, and how MDCs can live longer by balancing power consumption across their members.

It is widely accepted at present that the electronics based information-processing technology has fundamental limitations. A promising alternative to electronic excitations are the spin waves on magnetically ordered systems, which usher a potentially powerful solution towards fabricating devices that transmit and process information (Khitun and Wang 2006). This approach to information-processing technology, known as magnonics, is rapidly growing (Kruglyak and Hicken 2006, Choi et al. 2007), and key magnonic components such as wave guides, emitters, nanojunctions and filters (Khater et al. 2011) are currently explored as basic elements of magnonic circuitry. This paper deals with the theory for ballistic spin wave transport across ultrathin iron-gadolinium nanojunctions, ..-Fe] [Gd]nML [Fe-.. , which are known to present structural interfacial disorder; n is the number of gadolinium monoatomic planes between the iron leads. It is shown that our PFMT-DNCLCPA theory gives a detailed and complete analysis for the properties of the ballistic transmission, and the corresponding reflection and absorption spectra across the structurally disordered nanojunction. We have developed the dynamic non-local coherent phase approximation (DNLCPA), and the phase field matching theory (PFMT) methods (Ghader and Khater 2013), and fully integrate them to study the ballistic spin wave transport across such nanojunctions. The DNCLPA method yields a full description of the dynamics of the spin wave excitations localized on the nanojunction, and their corresponding life-times and local density of states. These are excitations propagating laterally in the nanojunction atomic planes with finite life-times, but their fields are localized along the direction normal to the nanojunction. Moreover, the calculations determine the reflection, transmission, and absorption spectra for the spin waves incident at any arbitrary angle from the iron leads onto the nanojunction. The PFMT-DNCLCPA calculated results vary with nanojunction thickness. In particular, the normal incidence transmission spectra present no absorption effects and resonance assisted maxima are identified, notably at low frequencies at microscopic and submicroscopic wavelengths, which shift to lower frequencies with increasing nanojunction thickness. The results render these systems interesting for potential applications in magnonic circuitry. Fig.1 Calculated DNLCPA-PFMT reflection and transmission spectra for spin waves at normal incidence from the iron leads onto the magnetic ..-Fe] [Gd]3ML [Fe-.. nanojunction, as a function of the spin wave energies in units J(Fe-Fe)S(Fe) of the iron exchange and its spin. Note the transmission assisted maxima. Fig.2 Calculated absorption spectra for obliquely incident spin waves at the nanojunction cited in Fig.1, due to its structural interfacial disorder. Acknowledgements: The authors acknowledge QNRF financial support for the NPRP 4-184-1-035 project. References - S. Choi, K.S. Lee, K.Y. Guslienko, S.K. Kim, Phys. Rev. Lett. 98, 087205 (2007) - D. Ghader and A. Khater, to be published (2013) - A. Khater, B. Bourahla, M. Abou Ghantous, R. Tigrine, R. Chadli, Eur. Phys. J. B: Cond. Matter 82, 53 (2011) - A. Khitun and K. L. Wang, Proceedings of the Third International Conference on Information Technology, New Generations ITNG, 747 (2006) - V.V. Kruglyak and R.J. Hicken, J. Magn. Magn. Mater. 306, 191 (2006)

It is widely believed in the semiconductor community that the progress of the electronics based information technology is coming to an end (ITRS 2007), owing to fundamental electronic limitations. A promising alternative to electrons is the use of spin wave excitations. This has ushered a potentially powerful solution towards fabricating devices that use these excitations to transmit and process information (Khitun and Wang 2006). This new approach to information-processing technology, known as magnonics, is rapidly growing (Kruglyak and Hicken 2006), and key magnonic components such as spin wave guides, emitters, and filters are currently explored (Choi et al. 2007). The first working spin wave based logic device has been experimentally demonstrated by Kostylev et al (2005). In the present paper we develop and apply a model to analyze the spin dynamics for iron-gadolinium films of a few Gd(0001) atomic planes between two Fe(110) atomic planes. These ultrathin systems may be deposited layer by layer on a nonmagnetic substrate using techniques like dc-sputtering or pulsed laser deposition. They constitute prototypes for iron-gadolinium nanojunctions between iron leads in magnonics. In this system the Fe/Gd interfaces present structural disorder due to the mismatch between the Fe_bcc and Gd_hcp lattices. This engenders a quasi-infinite ensemble of Fe-Gd cluster configurations at the interface. In the absence of DFT or ab initio results for the magnetic Fe-Gd exchange, we have developed an integration based analytic approach to determine the spatial dependence of this exchange using available experimental data from the corresponding multilayer systems. A dynamic non-local CPA method is also developed to analyze the spin dynamics for the disordered systems. This DNLCPA introduces the idea of a scattering potential built up from the phase matching of the spin dynamics on structurally disordered Fe-Gd interface clusters with the spin dynamics of the virtual crystal. This method accounts properly for the quasi-infinite ensemble of interfacial structural configurations, and yields the configurationally averaged Green's function for the disordered system. The computations yield the spin wave eigenmodes, their energies, life-times, and local densities of states, for any given thickness of the ultrathin magnetic Fe-Gd film. Fig.1 DNLCPA calculated dispersion branches for the spin waves propagating in the ultrathin magnetic 1Fe-5Gd-1Fe film (7 atomic planes) presenting structural interfacial disorder. The normalized energies are in units of iron exchange and spin J(Fe-Fe)S(Fe). The curves are plotted as a function of the y-component of the wave-vector (inverse angstroms), the z-component = 0, in both figures. Fig.2 DNLCPA calculated life-times in picoseconds of the spin waves propagating in the ultrathin magnetic 1Fe-5Gd-1Fe film. Acknowledgements: The authors acknowledge QNRF financial support for the NPRP 4-184-1-035 project. References - S. Choi, K.S. Lee, K.Y. Guslienko, S.K. Kim, Phys. Rev. Lett. 98, 087205 (2007) - A. Khitun and K. L. Wang, Proceedings of the Third International Conference on Information Technology, New Generations ITNG, 747 (2006) - M.P. Kostylev, A.A. Serga, T. Schneider, B. Leven, B. Hillebrands, Appl. Phys. Lett. 87 153501 (2005) - V.V. Kruglyak and R.J. Hicken, J. Magn. Magn. Mater. 306, 191 (2006)

Using the effective field theory (EFT) and mean field theory (MFT), we investigate the magnetic properties of the nanojunction system...Co][Co_(1-c)Gd_c]_l ][Co]_l [Co_(1-c)Gd_c ]_l [Co... between Co leads. The amorphous ][Co_(1-c)Gd_c]_l ] composite nanomaterial is modeled as a homogeneous random alloy of concentrations "c" on an hcp crystal lattice, and "l" is the number of its corresponding hcp (0001) atomic planes. In particular, EFT determines the appropriate exchange constants for Co and Gd by computing their single-atom spin correlations and magnetizations, in good agreement with experimental data in the ordered phase. The EFT results for the Co magnetization in the leads serve to seed the MFT calculations for the nanojunction from the interfaces inward. This combined analysis yields the sublattice magnetizations for Co and Gd sites, and compensation effects, on the individual nanojunction atomic planes, as a function of the alloy concentration, temperature and nanojunction thicknesses. We observe that the magnetic variables are different for the first few atomic planes near the nanojunction interfaces, but tend to limiting solutions in the core planes. The EFT and MFT calculated exchange constants and sublattice magnetizations are necessary elements for the computation of the spin dynamics for this nanojunction system, using a quasi-classical treatment over the spin precession amplitudes at temperatures distant from the critical temperatures of $Co_{1-c}Gd_c$ alloy. The full analysis in the virtual crystal approximation (VCA) over the magnetic ground state of the system yields both the spin waves (SWs) localized on the nanojunction, and also the ballistic scattering and transport across the nanojunction for SWs incident from the leads by applying the phase field matching theory (PFMT). The model results demonstrate the possibility of resonance assisted maxima for the SW transmission spectra owing to interactions between the incident SWs and the localized spin resonances on the nanojunction. The spectral transmission results for low frequency spin waves are of specific interest for experimentalist, because these lower frequency SWs correspond to submicroscopic wavelengths which are of present interest in experimental magnonics research and the VCA is increasingly valid as a model approximation for such frequencies. Fig.1: Calculated spin variables sigma_Co and sigma_Gd, in the first layer of [Co_(1-c}Gd_(c)]_2[Co]_2[Co_(1-c}Gd_(c)]_2 layer nanojunction as a function of kT in meV. Fig.2: The total reflection R and transmission T cross sections associated with the scattering at the nanojunction for the cobalt leads SW modes 1, 2, with the selected choices of incident angle (phi_z, phi_y). Acknowledgements: The authors acknowledge QNRF financial support for the NPRP 4-184-1-035 project.

In modern enterprise and Internet-based application environments, a separate middlebox infrastructure for providing application delivery services such as security (e.g., firewalls, intrusion detection), performance (e.g., SSL off loaders), and scaling (e.g., load balancers) is deployed. However, there is no explicit support for middleboxes in the original Internet design; forcing datacenter administrators to accommodate middleboxes through ad-hoc and error-prone network configuration techniques. Given their importance and yet the ad-hoc nature of their deployment, we plan to study application delivery (in general) in the context of cloud-based application deployment environments. To fully leverage these opportunities, ASPs need to deploy a globally distributed application-level routing infrastructure to intelligently route application traffic to the right instance. But, since such an infrastructure would be extremely hard to own and mange, it is best to design a shared solution where application-level routing could be provided as a service by a third party provider having a globally distributed presence, such as an ISP. Although these requirements seem separate, they can be converged into a single abstraction for supporting application delivery in the cloud context.

Smart cities could be defined as developed urban areas, creating sustainable economic development and high quality of life by excelling in multiple key areas such as transportation, environment, economy, living, and government. This excellence could be reached through efficiency based on the intelligent management and integrated Information and Communication Technologies (ICT). Motivations. In the near future (2030), two thirds of the world population will reside on a city, thus increasing drastically demands on city infrastructures. As a result, urbanization is becoming a crucial issue. The Internet of Things (IoT) vision foresees billions of devices to form a worldwide network of interconnected objects including computers, mobile phones, RFID tags and wireless sensors. In this study, we focus on Wireless Sensor Networks (WSNs). The WSNs are a specific technology suitable to create Smart Cities. A distributed network of intelligent sensor nodes could measure numerous parameters and communicate them wirelessly and in real-time and makes possible a more efficient management of the city. For example, the pollution concentration in each street can be monitored, water leaks can be detected or noise maps of the city obtained. The number of applications with WSNs available for smart cities is only bounded by imagination: environmental care, sustainable development, healthcare, efficient traffic management, energy supply, water management, green buildings, etc. In short, WSN could improve the quality of life in a city. Scope. However, such urban applications often use multi-hop wireless networks with high density to obtain sufficient area coverage. As a result, they need networking stacks and routing protocols that can scale with network size and density, while remaining energy-efficient and lightweight. To this end, the IETF RoLL working group has designed the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL). This paper presents experimental results on the RPL protocol. The RPL properties in terms of delivery ratio, control packet overhead, dynamics and robustness are studied. The results are obtained by several experimentations conducted on two large WSNs testbeds composed of more than 100 sensor nodes each. In this real-life scenario (high density and convergecast traffic), several intrinsic characteristics of RPL are underlined: path length stability but reduced delivery ratio and important overhead (Fig. 1). However, the routing metrics, as defined by default, favor the creation of "hubs", aggregating most of 2-hops nodes (Fig. 2). To investigate the RPL robustness, we observe its behavior when facing a sudden death of several sensors and when several sensors are redeployed. RPL shows good abilities to maintain the routing process despite such events. However, the paper highlights that this ability can be reduced if only few critical nodes fail. To the best of our knowledge, it is the first study of RPL on such large platform.

Since I studied chemical education chemistry in a very unique way at Oklahoma State University, Oklahoma , United States Of America. teaching chemistry became a hobby not a job for me. which motivated me to apply the teaching with educational technology to every chemistry aspect that I teach as I applied my unique expertise through the smart digital flipped classroom technique in teaching the different chemistry laboratories, on the blackboard and in the different chemistry tutorial classes that I taught offered by the chemistry Department, college of arts and sciences , Qatar University. Through building An educational web site with the theme chemistry flipped for teaching chemistry. The blended learning chemistry web site provides students with explanatory material that is augmented by audio visual simulations with technology immersed education. The general idea that students work at their own pace, receiving lectures at home via online videos or podcasts which I record and post for them so they got prepared before coming to the class. the students are able to use class time to practice what they've learned with traditional schoolwork—but with my freed up for additional one-on-one time. Students can review lessons anytime, anywhere on their personal computers and smart phones, reserving class time for in-depth discussions or doing the actual experiment and most importantly they know and understand what they are doing. Through unlocking knowledge and empowering minds. The video lecture is often seen as the key ingredient in the flipped approach. My Talk will be on the use of educational technology in teaching chemistry as my chem Demos go behind the magic as I used new techniques for helping students visualize the concept which helped them understand the topic more. I converted the writing discussion into a conversation into the cloud system. The web site includes online interactive pre/post-classroom activities assessment pages, live podcast capture for the experiments done by the students, post-classroom activities, dynamic quizzes and practice exams pages, honor pages for recognizing the hard working students, in class lecture live podcasting, linked experiments and many more. Probabilistic system analysis is used for keeping track of the students' progress, their access and their learn and I used statistic to relate the students results before and after the use of my blended learning audiovisual simulation flipped classroom. During the study the students showed an extraordinary passion to chemistry , they study it on iTunes, YouTube, on Facebook, on Twitter, they learned it very well, chemistry with them everywhere even I their free time. demonstrate the advantages associated with using web based learning with the flipped chemistry teaching methods provide support for students in a large lecture and laboratory classes. The two biggest changes resulting from the method are the manner in which content is delivered (outside of class) and the way students spend time in the classroom (putting principles into practice). Feedback from students has conveyed that the style is more dynamic and motivational than traditional, passive teaching. Which help keep open courseware going and growing.

We overview the Arabic Ontology, an ongoing project at Sina Institute, at Birzeit University, Palestine. The Arabic Ontology is a linguistic ontology that represents the meanings (i.e., concepts) of Arabic terms using formal semantic relationships, such as SubtypeOf and PartOf. In this way, the ontology becomes a tree (i.e., classification) of meanings of the Arabic terms. To build this ontology (see Fig.1), the set of all Arabic terms are collected; then for each term, the set of its concepts (polysemy) are identified using unique numbers and described using glosses. Terms referring to same meaning (called synsets) are given the same concept identifier. These concepts are then classified using Subsumption and Parenthood relationships. The Arabic Ontology follows the same design as WordNet (i.e., network of synsets), thus it can be used as an Arabic WordNet. However, unlike WordNet, the Arabic Ontology is logically and philosophically well-founded, following strict ontological principles. The Subsumption relation is a formal subset relation. The ontological correctness of a relation (e.g., whether "PeriodicTable SubtypeOf Table" is true in reality) in WordNet is based on whether native speakers accept such a claim. However, the ontological correctness of the Arabic Ontology is based on what scientists accept; but if it can't be determined by science, then what philosophers accept; and if philosophy doesn't have an answer then we refer to what linguistics accept. Our classification also follows the OntoClean methodology when dealing with, instances, concepts, types, roles, and parts. As described in the next section, the top levels of the Arabic Ontology are derived from philosophical notions, which further govern the ontological correctness of its lower levels. Moreover, glosses are formulated using strict ontological rules focusing on intrinsic properties. Figure 1. Illustration of terms' concepts and its conceptual relations Why the Arabic Ontology It can be used in many application scenarios such as: (1) information search and retrieval, to enrich queries and improve the results' quality, i.e., meaningful search rather than string-matching search; (2) Machine translation and term disambiguation, by finding the exact mapping of concepts across languages, as the Arabic Ontology is also mapped to the English WordNet; (3) Data Integration and interoperability in which the Arabic Ontology can be used as a semantic reference to several autonomous information systems; (4) Semantic web and web 3.0, by using the Arabic Ontology as a semantic reference to disambiguate meanings used in web sites; (5) Conceptual dictionary, allowing people to easily browse and find meanings and the differences between meanings. The Arabic Ontology Top Levels Figure 2 presents the top levels of the Arabic Ontology, which is a classification of the most abstract concepts (i.e., meanings) of the Arabic terms. Only three levels are presented below for the sake of brevity. All concepts in the Arabic Ontology are classified under these top-levels. We designed these concepts after a deep investigation of the philosophy literature and based on well-recognized upper level ontologies like BFO, DOLCE, SUMO, and KYOTO. Figure 2. The Top three levels of the Arabic Ontology (Alpha Version)

Increasing attention has recently been drawn in the human-computer interaction community towards the design and development of accessible computer applications for children and youth with developmental or cognitive impairments. Due to better healthcare and assistive technology, the quality of life for children with intellectual disability (ID) has evidently been improved. Many children with ID often have cognitive disabilities, along with overweight problems due to lack of physical activity. This paper introduces an edutainment system specifically designed to help these children have an enhanced and enjoyable learning process, and addresses the need for integrating physical activity into their daily lives. These games are developed with the following pedagogical model in mind: A combination of Mayer's Cognitive Theory of Multimedia Learning with a mild implementation of Skinner's Operant Condition, incorporated with physical activity as part of the learning process. The system proposed consists of a padded floor mat that consists of sixteen square tiles supported by sensors, which are used to interact with a number of software games specifically designed to suit the mental needs of children with ID. The games consist of multimedia technology with a tangible user interface. The edutainment system consists of three games, each with three difficulty levels meant to suit specific needs of different children. The system aims at enhancing the learning, coordination and memorization skills of the children with ID while involving them into physical activities, thus offering both mental and physical benefits. The edutainment system was tested on 100 children with different IDs, half of which have Down syndrome (DS. The children pertain to three disability levels; mildly, moderately and severely disabled. The obtained results depict a high increase in the learning process as the children became more proactive in the classrooms. Assessment methodology took into account the following constraints; disability type, disability level, gender, scoring, timing, motivation, coordination, acceptance levels and relative performance. The following groups, when compared with other groups, achieved best results in terms of scores and coordination: children with DS, mildly disabled children and females. In contrast to children with other IDs, moderately and severely disabled children, and males performed with lower scores and coordination levels, but all the above mentioned groups exhibited high motivation levels. Rejection rate was found to be very low, at 3% of children refusing to participate. When children repeated the games, 92% were noted to achieve significantly higher results. The edutainment is developed with the following aims: helping children with ID have an enhanced cognitive experience, allowing them a learning environment where they can interact with the game and exert physical activities, ensuring a proactive role for all in the classroom, boosting motivation, coordination and memory levels. Results proved that the system had very positive effects on the children, in terms of cognition and motivational levels. Instructors also expressed willingness to incorporate the edutainment system into the classroom on a daily basis, as a complementary tool to conventional learning.

With the decline in physical activities among young people, it is essential to monitor their physical activity and ensure their calorie expenditure is within the range necessary to lead a healthy life style. For many children and young adults, video gaming is one favorable venue for physical activities. A new flavor of video games on popular platforms, such as Wii and Xbox, aim to improve the health of young adults through competing in games which require players to perform various physical activities. These popular platforms detect the user movements, and through an avatar in the game, players can be part of the game activities, such as boxing, playing tennis, dancing, avoiding obstacles, etc. Early studies used self-administered questionnaires or interviews to collect data about the patient's activities. These self-reporting methods ask participants to report their activities on hourly, daily, or weekly basis. But self-reporting techniques suffer from a number of limitations, such as inconvenience in entering data, poor compliance, and inaccuracy due to bias or poor memory. Reporting activities is a sensitive task for overweight/obese individuals with research evidence showing that they tend overestimate the calories they burn. Having a tool to help estimate calories consumption is therefore becoming essential to manage obesity and over-weight issues. We propose providing a calories expenditure estimation service. This service would augment the treatment provided by an obesity clinic or a personal trainer for obese children. Our energy expenditure estimation system consists of two main components: activity recognition and calories estimation. Activity recognition systems have three main components: low-level sensing module to gather sensor data, feature selection module to process raw sensor data and select the features necessary to recognize activities, and a classification module to infer the activity from the captured features. Using the activity type, we can estimate the calories consumption using existing models on energy expenditure developed on the gold standard of respiratory gases measurements. We choose Kinetic as our test platform. The natural user interface in Kinect is the low-level sensing module providing skeleton tracking data. The skeleton positions will be the raw input to our activity recognition module. From this raw data, we define the features that help the classifier, such as the speed of the hands and legs, body orientation, and rate of change in the vertical and horizontal position. These are some features that can be quantified and passed periodically (e.g., every 5 seconds) to the classifier to distinguish between different activities. Other important features might need more processing, such as the standard deviation, the difference between peaks (in periodic activities), and the distribution of skeleton positions. We also plan to build an index for the calories expenditure for game activities using the medical gold standard of oxygen consumption, CO2. Game activities, such as playing tennis, running, and boxing are different from the same real world activities in terms of enegy concumption and it would be useful to quantify the difference in order to answer the question of whether these “health” games are useful for weight loss.

We describe an assistive technology for improved literacy among the Deaf and Hard of Hearing, that is cost-wise and accessible to deaf individuals and their families/service providers (e.g., educators), businesses which employ them or list them as customers and healthcare professionals. The technology functions as (1) A real-time translation system between Moroccan Sign Language (a visual-gestural language) and standard written Arabic. Moroccan Sign Language (MSL) is a visual/gestural language that is distinct from spoken Moroccan Arabic and Modern Standrad Arabic (SA) and has no text representation. In this context, we describe some challenges in SA-to-MSL machine translation. In Arabic, word structure is not built linearly as is the case in concatenative morphological systems, which results in a large space of of morphological variation. The language has a large degree of ambiguity in word senses, and further ambiguity attributable to a writing system that omits diacritics. (e.g. short vowels, consonant doubling, inflection marks). The lack of diacritics coupled with word order flexibility are causes of ambiguity in the syntactic structure of Arabic. The problem is compounded when translating into a visual/gestural language that has far fewer signs than words of the source language. In this presentation, we show how Natural language processing tools are integrated into the system, the architecture of the system and provide a demo of several input examples with different levels of complexity. Our Moroccan Sign Language database has currently 2000 Graphic signs and their corresponding video clips. The database extension is an ongoing process task that is done in collaboration with MSL interpreters, deaf signers and educators in Deaf schools in different regions in Morocco. (2) Instructional tool: Deaf school children, in general, have poor reading skills. It is easier for them to understand text represented in sign language than in print. Several works have demonstrated that a combination of sign language and spoken/written language can significantly improve literacy and comprehension (Singleton, Supalla, Litchfield, & Schley, 1998; Prinz, Kuntz, & Strong, 1997; Ramsey & Padden, 1998). While many assistive technologies have been created for the blind, such as hand-held scanners and screen readers, there are only a few products targeting poor readers who are deaf. An example of such technology is the iCommunicator™ which translates in real time: speech to text, speech/typed text to videos of signs, and speech/typed text to computer generated voice. This tool, however, does not generate text from scans and display them with sign graphic supports that a teacher can print, edit, and use to support reading. It also does not capture screen text. We show a set of tools aiming at improving literacy among the Deaf and Hard-of-Hearing. Our tools offer a variety of input and ouput options, including scanning, screen text transfer, sign graphics and video clips. The technology we have developed is useful to teachers, educators, Health care professionals, speech/language pathologists, etc. who have a need to support understanding of Arabic text with Moroccan Sign Language signs for purposes of literacy improvement, curriculum enhancement, or communication in emergency situations.

The study of complex multi-faceted scientific questions demand innovative computing solutions—solutions that transcend beyond the management of big data to dedicated semantics-enabled, services-driven infrastructures that can effectively aggregate, filter, process, analyze, visualize and share the cumulative scientific efforts and insights of the research community. From a technical standpoint, E-Science purports technology-enabled collaborative research platforms to (i) collect, store and share multi-modal data collected from different geographic sites, (ii) perform complex simulations and experiments using sophisticated simulation models; (iii) design complex experiments by integrating data and models and executing them as per the experiment workflow; (iv) visualize high-dimensional simulation results; and (v) aggregate and share the scientific results (Fig 1). Taking a knowledge management approach, we have developed an innovative E-Science platform— termed Platform for Ocean Knowledge Management (POKM)—that is built using innovative web-enabled services, services-oriented architecture, semantic web, workflow management and data visualization technologies. POKM offers a suite of E-Science services that allow oceanographic researchers to (a) handle large volumes of ocean and marine life data; (b) access, share, integrate and operationalize the data and simulation models; (c) visualization of data and simulation results; (d) multi-site collaborations in joint scientific research experiments; and (e) form a broad, virtual community of national and international researchers, marine resource managers, policy makers and climate change specialists. (Fig 2) The functional objective of our E-Science infrastructure is to establish an online scientific experimentation platform that supports an assortment of data/knowledge access and processing tools to allow a group of scientists to collaborate and conduct complex experiments by sharing data, models, knowledge, computing resources and expertise. Our E-Science approach is to complement data-driven approaches with domain-specific knowledge-centric models in order to establish causal, associative and taxonomic relations between (a) raw data and modeled observations; (b) observations and their causes; and (c) causes and theoretical models. This is achieved by taking a unique knowledge management approach, whereby we have exploited semantic web technologies to semantically describe the data, scientific models, knowledge artifacts and web services. The use of semantic web technologies provides a mechanism for the selection and integration of problem-specific data from large repositories. To define the functional aspects of the e-science services we have developed a services ontology that provides a semantic description of knowledge-centric e-science services. POKM is modeled along a services-oriented architecture that exposes a range of task-specific web services accessible through a web portal. The POKM architecture features five layers—Presentation Layer, Collaboration Layer, Service Composition Layer, Service Layer and Ontology Layer (Fig 3). POKM is applied to the domain of oceanography to understand our changing eco-system and its impact on marine life. POKM helps researchers investigate (a) changes in marine animal movement on time scales of days to decades; (b) coastal flooding due to changes in certain ocean parameters; (c) density of fish colonies and stocks; and (d) time-varying physical characteristics of the oceans (Fig 4 & 5). In this paper, we present the technical architecture and functional description of POKM, highlighting the various technical innovations and their applications to E-Science.

In this work, we develop a spontaneous large vocabulary speech recognition system for Qatari Arabic (QA). A major problem with dialectal Arabic speech recognition is due to the sparsity of speech resources. So, we propose an Automatic Speech Recognition (ASR) framework to jointly use Modern Standard Arabic (MSA) data and QA data to improve acoustic and language modeling by orthographic normalization, cross-dialectal phone mapping, data sharing, and acoustic model adaptation. A wide-band speech corpus has been developed for QA. The corpus consists of 15 hours speech data collected from different TV series and talk-show programs. The corpus was manually segmented and transcribed. A QA tri-gram language model (LM) was linearly interpolated with a large MSA LM in order to decrease Out-Of-Vocabulary (OOV) rate and to improve perplexity. The vocabulary consists of 21K words extracted from the QA training set with additional 256K MSA vocabulary. The acoustic model (AM) was trained with a data pool of QA data and additional 60 hours of MSA data. In order to boost the contribution of QA data, Maximum-A-Posteriori (MAP) adaptation was applied on the resulted AM using only the QA data, effectively increasing the weight of dialectal acoustic features in the final cross-lingual model. All trainings were performed with Maximum Mutual Information Estimation (MMIE) and with Speaker Adaptive Training (SAT) applied on top of MMIE. Our proposed approach achieves more than 16% relative reduction in WER on QA testing set compared to a baseline system trained with only QA data. This work was funded by a grant from the Qatar National Research Fund under its National Priorities Research Program (NPRP) award number NPRP 09-410-1-069. Reported experimental work was performed at Qatar University in collaboration with University of Illinois.

In embedded real-time communication and multimedia processing applications, the manipulation of large amounts of data has a major effect on both power consumption and performance of the system. Due to the significant amount of data transfers between the processing units and the large and energy consuming off-chip memories, these applications are often called data-dominated or data-intensive. Providing sufficient bandwidth to sustain fast and energy-efficient program execution is a challenge for system designers: due to the growing gap of speed between processors and memories, the performance of the whole VLSI system will mainly depend on the memory subsystem, when memory is unable to provide data and instructions at the pace required by the processor. This effect is, sometimes, referred in the literature as the memory wall problem. At system level, the power cost can be reduced by introducing an optimized custom memory hierarchy that exploits the temporal data locality. Hierarchical memory organizations reduce energy consumption by exploiting the non-uniformity of memory accesses: the reduction can be achieved by assigning the frequently-accessed data to low hierarchy levels, a problem being how to optimally assign the data to the memory layers. This hierarchical assignment diminishes the dynamic energy consumption of the memory subsystem - which expands due to memory accesses. Moreover, it diminishes the static energy consumption as well, since this decreases monotonically with the memory size. Moreover, within a given memory hierarchy level, power can be reduced by memory banking - whose principle is to divide the address space in several smaller blocks, and to map these blocks to physical memory banks that can be independently enabled and disabled. Memory partitioning is also a performance-oriented optimization strategy, because of the reduced latency due to accessing smaller memory blocks. Arbitrarily fine partitioning is prevented since an excessively large number of small banks is area inefficient, imposing a severe wiring overhead -- which increases communication power and decreases performance. This presentation will introduce an electronic design automation (EDA) methodology for the design of hierarchical memory architectures in embedded data-intensive applications, mainly in the area of multidimensional signal processing. The input of this memory management framework is the behavioral specifications of the applications, that are assumed to be procedural and affine. Figure 1 shows an illustrative example of behavioral specification with 6 nested loops. This framework employs a formal model operating with integral polyhedra, using techniques specific to the data-dependence analysis employed in modern compilers. Different from previous works, three optimization problems - the data assignment to the memory layers (on-chip scratch-pad memory and off-chip DRAM), the mapping of multidimensional signals to the physical memories, and the banking of the on-chip memory (see Figure 2) - are addressed in a consistent way, based on the same formal model. The main design target is the reduction of the static and dynamic energy consumption in the memory subsystem, but the same formal model and algorithmic principles can be applied for the reduction of the overall time of access to memories, or combinations of these design goals.

Line and word segmentation is a key-step in any document image analysis system. It can be used for instance in handwriting recognition when separating words before their recognition. Line segmentation can also serve as a prior step before extracting the geometric characteristics of lines which are unique to each writer. Text line and word segmentation is not an easy task because of the following problems: 1) text lines do not all have the same direction in the handwritten text; 2) text lines are not always horizontal which makes their separation more difficult; 3) characters may overlap between successive text lines; 4) it is often confusing to distinguish between inter and intra word distances. In our method, line segmentation is done by using a smoothed version of the handwritten document which makes it possible to detect the main line components using a subsequent thresholding algorithm. The connected components of the resulting image are then assigned to a separate label which represents a line component. Then, each text region which intersects only with one line component is assigned to the same label of that line component. The Voronoi diagram of the image thus obtained is then computed in order to label the remaining text pixels. Word segmentation is performed by computing a generalized Chamfer distance in which the horizontal distance is slightly favored. This distance is subsequently smoothed in order to reflect the distances between word components and neglect the distance to dots and diacritics. Word segmentation is then performed by thresholding the distance thus obtained. The threshold depends on the characteristics of the handwriting. We have therefore computed several features in order to predict it, including: the sum of maximum distances within each line component, the number of connected components within the document and the average width and height of lines. The optimal threshold is then obtained by training a linear regression of those features on a training set of about 100 documents. This method achieved the best performance on the ICFHR Handwriting Segmentation Contest dataset reaching a matching score of 97.4% on line segmentation and 91% on word segmentation. The method has also been tested on the QUWI Arabic dataset reaching 97.1% on line segmentation and 49.6% on word segmentation. The relatively low performance of word segmentation in Arabic script is due to the fact that words are very close to each other with respect to English script. The proposed method tackles most of the problems of line and word segmentation and achieves high segmentation results. It can however be improved by combining it with a handwriting recognizer which will eliminates words which are not recognized.

Attenuators are extensively employed as an amplitude control circuit in communication and radar systems. Flatness, attenuation range, and bandwidth are typical specifications for attenuators. Most attenuators in previous studies rely on the basic topologies of the Pi-, T-, and distributed attenuators. The performance of the Pi- and T-attenuators, however, is affected substantially by the switch performance of transistors, and it is hard to obtain optimum flatness, attenuation range, and bandwidth with these attenuators. The conventional distributed attenuator also demands a large chip area for large attenuation ranges. We report the design of a new microwave/millimeter-wave CMOS attenuator. A design method is proposed and implemented in the attenuator to improve its flatness, attenuation range, and bandwidth. It is recognized that the Pi- and T-attenuators at a certain attenuation state inherently has the insertion-loss slope increasing as the frequency is increased. This response is owing to the off-capacitance of the series transistors. On the other hand, the distributed attenuators can be designed to have the opposite insertion-loss slope by shortening the transmission lines. The reason is that short transmission lines causes the center frequency be shifted higher. The proposed design method utilizes the two opposite insertion-loss slopes and is implemented for the Pi-, T-and distributed attenuators in a cascade connection. Over 10-67 GHz, the measured results exhibit attenuation flatness of 6.8 dB and attenuation range of 32-42 dB.

We propose a multimedia-based learning system to teach children with intellectual disabilities (ID) basic concepts in Science, Math and daily living tasks. The tutorials' pedagogical development is based on Mayer's Cognitive Learning Model combined with Skinner's Behaviorist Operant Conditioning Model. Two types of Arabic tutorials are proposed: (1) Statically generated tutorials, which are pre-designed by special needs instructors, and developed by animation experts. (2) Dynamically generated tutorials, which are developed using natural language processing and ontology building. Dynamic tutorials are generated by processing Arabic text, and using machine learning to query the Google engine and generate multimedia elements, which are automatically updated into an ontology system and are hence used to construct a customized tutorial. Both types of tutorials have shown considerable improvements in the learning process and allowed children with ID to enhance their cognitive skills and become more motivated and proactive in the classroom.

This paper presents a robust real-time vision framework that detects and tracks vehicles from stationary traffic cameras with certain regions of interest. The framework enables intelligent transportation and road safety applications such as road-occupancy characterization, congestion detection, traffic flow computation, and pedestrian tracking. It consists of three main modules:1) detection, 2) tracking, and 3) data association. To this end, vehicles are first detected using Haar-like features. In the second phase, a light-weight appearance-based model is built using random projections to keep track of the detected vehicles. The data association module fuses new detections and existing targets for accurate tracking. The practical value of the proposed framework is demonstrated with evaluation involving several real-world experiments and variety of challenges.

We will present an overview and in-progress report of a QNRF-sponsored project for research in the effects of using program visualization in teaching and learning computational thinking. Computers and computing have made possible incredible leaps of innovation and imagination in solving critical problems such as finding a cure for diseases, predicting a hurricane's path, or landing a spaceship on the moon. The modern economic conditions of countries around the world are increasingly related to their ability to adapt to the digital revolution. This, in turn, drives the need for educated individuals who can bring the power of computing-supported problem solving to an increasingly expanded field of career paths. It is no longer sufficient to wait until students are in college to introduce computational thinking. All of today's students will go on to live in a world that is heavily influenced by computing, and many of these students will work in fields that involve or are influenced by computing. They must begin to work with algorithmic problem solving and computational methods and tools in K-12. Many courses taught in K-12 (for example, math and science) teach problem solving and logical thinking skills. Computational thinking embodies these skills and brings them to bear on problems in other fields and on problems that lie at the intersection of these fields. In the same way that learning to read opens a gateway to learning a multitude of knowledge, learning to program opens a gateway to learning all things computational. Alice is a programming environment designed to enable novice programmers in creating 3-D virtual worlds, including animations and games. In Alice, 3-D models of objects (e.g., people, animals and vehicles) populate a virtual world and students use a drag and drop editor to manipulate the movement and activities of these objects. Alice for the Middle East - "Alice ME" is a research project funded by Qatar National Research Fund (QNRF) under their National Priorities Research Program (NPRP) that aims to modify the well-known Alice introductory programming software to be internationally and culturally relevant for the Middle East. In this project, we are working on developing new 3D models that provide animation objects encountered in daily lives (animals, buildings, clothing, vehicles, etc.) as well as artifacts that reflect and respect the heritage of Qatari culture. The new models will provide characters and images that enable students to create animations that tell the stories of local culture, thereby supporting the QNV 2030 aspiration for maintaining a balance between modernizations and preserving traditions. We are also working with local schools to develop an ICT curriculum centered on using Alice to help students learn computational thinking. We are also designing workshops to train teachers in using Alice and delivering this curriculum.

Translation of named entities (NEs) is important for NLP applications such as Machine Translation (MT) and Cross-lingual Information Retrieval. For MT, named entities are major subset of the out-of-vocabulary terms. Due to their diversity, they cannot always be found in parallel corpora, dictionaries or gazetteers. Thus, state-of-the-art MT systems need to handle NEs in specific ways: (i) direct translation which results in missing many out of vocabulary terms and (ii) blind transliteration of out of vocabulary terms which does not necessarily contribute to translation adequacy and may actually create noisy contexts for the language model and the decoder. For example, in the sentence "Dudley North visits North London", the MT system is expected to transliterate "North" in the former case, and translate "North" in the latter. In this work, we present a classification-based framework, that enables MT system to automate the decision of translation vs. transliteration for different categories of NEs. We model the decision as a binary classification at the token level: each token within a named-entity gets a decision label to be translated or transliterated. Training the classifier requires a set of NEs with token-level decision labels. For this purpose, we automatically construct a set of bilingual lexicon of NEs paired with the translation/transliteration decisions from two different domains: We heuristically extract and label parallel NEs from a large word aligned news parallel corpus and we use a lexicon of bilingual NEs collected from Arabic and Wikipedia titles. Then, we designed a procedure to clean up the noisy Arabic NE spans by part-of-speech verification, and heuristically filtering impossible items (e.g. verbs). For training, the data is automatically annotated using a variant of edit distance measuring the similarity between an English word and its Arabic transliteration. For test set, we manually reviewed the labels and fixed the incorrect ones. As part of our project, this bilingual corpus of named entities has been released to the research community. Using Support Vector Machines, we trained the classifier using a set of token-based, contextual and semantic features of the NEs. We evaluated our classifier both in the limited news and diverse Wikipedia domains, and achieved promising accuracy of 89.1%. To study the utility of using our classifier on an English to Arabic statistical MT system, we deployed it as a pre-translation component to the MT system. We automatically located the NEs in the source language sentences and used the classifier to find those which should be transliterated. For such terms, we offer the transliterated form as an option to the decoder. The impact of adding the classifier to the SMT pipeline resulted in a major reduction of out of vocabulary terms and a modest improvement of the BLEU score. This research is supported by the Qatar National Research Fund (a member of the Qatar Foundation) through grants NPRP-09-1140-1-177 and YSREP-1-018-1-004. The statements made herein are solely the responsibility of the authors.

There are many cities where urbanization occurs at a faster rate than that of communication infrastructure deployment. Mobile users with sophisticated devices are often dissatisfied with this lag in infrastructure deployment; their Internet connection is via opportunistic open Access Points for short durations, or via weak, unreliable, and costly 3G connections. With increased demands on network infrastructure, we believe that opportunistic networking, where user mobility is exploited to increase capacity and augment Internet reachability, can play an active role as a complimentary technology to improve user experience, particularly with delay insensitive data. Opportunistic forwarding solutions were mainly designed using a set of assumptions that have grown in complexity, rendering them unusable outside their intended environment. Figure 1 categorizes sample state-of-the-art opportunistic forwarding solutions based on their assumption complexity. Most of these solutions however are not designed for large scale urban environments. In this work, we believe to be the first to exploit the space syntax paradigm to better guide forwarding decisions in large scale urban environments. Space syntax, initially proposed in the field of architecture to model natural mobility patterns by analyzing spacial configurations, offers a set of measurable metrics that quantify the effect of road maps and architectural configurations on natural movement. By interacting with the pre-built static environment, space syntax predicts natural movement patterns in a given area. Our goal is to leverage space syntax concepts to create efficient opportunistic forwarding distributed solutions for large scale urban environments. We address two communication themes: (1) Mobile-to-Infrastructure: We propose a set of space syntax based algorithms that adapt to a spectrum of simplistic assumptions in urban environments. As depicted in Figure 1, our goal is to gain performance improvement across the spectrum, within each assumption category, when compared to other state-of-the-art solutions. We adopt a data driven approach to evaluate the space syntax based forwarding algorithms we propose, within each of three assumption categories, based on large scale mobility traces capturing vehicle mobility. Overall, our results show that our space syntax based algorithms perform more efficiently within each assumption category. (2) Infrastructure-to-Mobile: We propose a new algorithm, Select&Spray, which leverages space syntax, and enables data transfers to mobile destinations reached directly through the infrastructure or opportunistically via other nodes. This architecture consists of: (i) a select engine that identifies a subset of directly connected nodes with a higher probability to forward messages to destinations, and (ii) a spray engine residing on mobile devices that guide the opportunistic dissemination of messages towards destination devices. We evaluate our algorithm using several mobility traces. Our results show that Select&Spray is more efficient in guiding messages towards their destinations. It helps extend the reach in data dissemination to more than 20% of the interested destinations within very short delays, and successfully reaches almost 90% of the destinations in less than 5 minutes.

The use of more effective processing tools such as advanced video codecs in wireless sensor networks (WSNs) has enabled widespread adoption of video-based WSNs for monitoring and surveillance applications. Considering that in video-based WSN applications large amounts of energy resources are required for both compression and transmission of video content, optimizing the energy consumption is of paramount importance. There is a trade-off between the encoding complexity and compression performance in the sense that high compression efficiency comes at the expense of increased encoding complexity. On the other hand, there is a direct relationship between coding complexity and energy consumption. Since the nodes in a video sensor network (VSN) share the same wireless medium, there is also an issue with fairness of bandwidth allocation per each node. Nevertheless, the fairness of resource allocation (encoding and transmission energy) for nodes placed at different locations in VSNs has a significant effect on energy consumption. In our study, our objective is to maximize the lifetime of the network by reducing the consumption of the node with the maximum energy usage. Our research focuses on VSNs with linear topology where the nth node relays its data through the nth-1 node, and the node closest to the sink relays information from all the other nodes. In our approach, we analyze the relation between the fairness of nodes' resource allocation, video quality and VSNs' energy consumption to propose an algorithm for adjusting the coding parameters and fairness ratio of each node such that energy consumption is balanced. Our results show that by allocating higher fairness ratios to the closest nodes to the sink, we reduce the maximum energy consumption and achieve a more balanced energy usuge. For instance, in the case of a VSN with six nodes, by allocating the fairness ratios between 0.17 to 0.3 to the closer nodes to the sink, the maximum energy consumption is reduced by 11.28%, with standard deviation of nodes' energy consumption (STDen) of 0.09W compared to 0.25W achieved by the maximum fairness scheme.

Quantifying the Cybersecurity of Cloud Computing as a Mean Failure Cost

This work focuses on developing a contextual spellchecker to improve the correctness of input queries to a multi-lingual, cross-cultural robot receptionist system. Queries that have fewer misspellings will improve the robot's ability to answer them and in turn improve the effectiveness of the human-robot interaction. We focus on developing an n-gram model based contextual spell-checker to correct misspellings and increase the query-hit rate of the robot. Our test bed is a bi-lingual, cross-cultural robot receptionist, Hala, deployed at the Carnegie Mellon University in Qatar's reception area. Hala can accept typed input queries in Arabic and English and speak responses in both languages as she interacts with users. All input queries to Hala are logged. These logs allow the study of multi-lingual aspects, the influence of socio-cultural norms and the nature of human-robot interaction within a multicultural, yet primarily ethnic Arab, setting. A recent statistical analysis has shown that 26.3% of Hala's queries are missed. The missed queries are due to either Hala not having the required answer in the knowledge base or due to misspellings. We have measured that 50% are due to misspellings. We designed, developed and assessed a custom spellchecker based on an n-gram model. We focused our efforts on a spellchecker for the English mode of Hala. We trained our system on our existing language corpus consisting of valid input queries making the spellchecker more specific to Hala. Finally, we adjusted the n in the n-gram model and evaluated the correctness of the spellchecker in the context of Hala. Our system makes use of the Hunspell, which is an engine that uses algorithm based on n-gram similarity, rule and dictionary based pronunciation data and morphological analysis. Misspelled words are passed through the Hunspell spellchecker and the output is a list of possible words. Utilizing the list of words, we apply our n-gram model algorithm to find which word is best suited in a particular context. The model calculates the conditional probability P(w|s) of a word w given the previous sequence of words s, that is, predicting the next word based on the preceding n-1 words. To assess the effectiveness of our system, we evaluate it using 5 different cases of misspelled word location. The table below lists our results, correct indicates when the sentence is correctly spellchecked and incorrect when the sentences did not change after passing through the spellchecker, or the sentences included transliterated Arabic, or were incorrectly spellchecked which resulted in loss of semantics. Refer to table. We observed that context makes the spellchecking of a sentence more sensible resulting in a higher hit rate in Hala's knowledge base. For case 5, despite having more context than the previous cases, the hit rate is lower. This is because other sources of errors were introduced such as user making use of SMS languages or mixture of English and Arabic. In our future work we would like to tackle the above-mentioned problems and also work on a Part-of-Speech tagging system that would help in correcting real-word mistakes.

Abstract This study began focused on the use of traditional knowledge in promoting sustainable development in crisis conditions. Presented the question: How have successful community- level sustainable development efforts undertaken under crisis conditions drawn upon indigenous knowledge to achieve positive outcomes? The study is a cross case analysis. The three cases addressed in this study have explained some of the ways that indigenous knowledge has played significant positive roles in promoting sustainable development for communities living under crisis conditions in Palestine. Indigenous knowledge community based patterns indicated significant focus on strengths of local culture, social cohesion, the integration process, and special advantages for policy implementation from the community level as key components of sustainable development in crisis conditions. This study especially focuses on efforts to implement sustainable development in crisis conditions. As the World Commission on Environment and Development, better known as the Brundtland Commission, explained in its seminal report (1987), the core problem for sustainable development is the need to integrate social development, economic development, and environmental protection to ensure “development that meet the needs of the present without compromising the ability of the future generations to meet their own needs” (World Commission on Environment and Development 1987, p. 8). And as that report and subsequent studies indicate, too often the social development dimension of the living triangle has been ignored or dramatically undervalued as those involved in development have concentrated on economic development and to some extent environmental protection (World Commission on Environment and Development 1987). In addition to the important ongoing problem of lack of focus on social aspects, sustainable development is particularly important and especially challenging in crisis conditions that include war, terrorism, and civil disorder and their aftermath. This research specifically considers the challenges of sustainable development in the Palestinian context with sensitivity to the need for the integration of all three elements of the living triangle and with concern for the special challenges presented by efforts to achieve sustainable development in crisis conditions. The study contributes to theory by analyzing common elements from the case studies and providing a set of testable propositions, grounded in those successful experiences that can be a starting point for building theory. Practically, the study has generated lessons that sustainable development policy implementers and decision makers can learn from when addressing sustainable development in different crisis conditions contexts such as the aftermath of what is called the “Arab Spring” contexts.

Qatar's rich Arabic heritage is not only captured in its buildings, artworks and stories, but also in the living memory of its people. Historians and ethnographers work to capture these stories by interviewing people in “oral history” projects, typically one by one. This is, naturally, a slow process, which can only record selected highlights of a people's rich memory. We here introduce a digital infrastructure that enables crowd-sourcing and distribution of Qatar's oral history. The goal is to inspire people to actively participate in shaping their country's historic record. People in Qatar as well as those living overseas will be stimulated to participate, thus weaving a rich heritage tapestry available electronically to Qataris and tourists alike. Each user will have our software installed as an app on their smartphone. While they are moving though their environs, users are prompted to create audio recording using our app. As in an interview with an ethnographer, they are guided through a set of questions, one at a time. The person acting as the Oral Historian creates the dynamically configurable script of questions and further defines time limits for each answer. If a response is brief, the app prompts for more detail, if a response is overly lengthy, the app prompts for closure on that question. Our software automatically captures some basic metadata: the time, GPS location, and length of the recording. It can further prompt for semi-structured metadata from its user, such as descriptions of their surroundings and the period to which the recording refers. Users are also prompted to upload any photos, documents, or videos that might relate to their audio contribution. At the end of a recording, a user is asked if they wish to provide personal data, e.g., name and age. The system does not automatically use registration account details as a user might be accompanied by another person who contributes to the oral history recording. These captured oral histories are grouped into collections and curated by an Oral Historian to prevent misuse and ensure quality. They may additionally decide to periodically publish a new set of questions to the registered users, for example to enrich particular topic areas in the collection. The end user software is available as a smartphone app, whereas the interface for the historian is server-based for ease of use. The underlying infrastructure uses the Open Source digital library system Greenstone, which has a pedigree of two decades (www.greenstone.org). It is sponsored by UNESCO as part of its "Information for All" programme, and its user interface has been translated into over 50 languages. Recent developments enable mobile phone operation and multimedia content. Greenstone therefore provides an ideal platform to deliver this integrated, mobile infrastructure for crowd-sourcing oral history information. The captured oral histories and any accompanying multimedia artefacts are sent to a central library where Greenstone's content management tools are available to the curating Oral Historian. The items are thus integrated into an evolving, public collection that are available to mobile and web users alike.

Language and culture are bound together. In Arabic, courtesy expressions play an important role. Consequently, a learner should be aware of them in order to fully master the Arabic language. The current research studies the compliment responses in Colloquial Arabic and their use when teaching Arabic as a foreign language. The speech act of complimenting was chosen because of the important role it plays in human communication. Compliments strengthen solidarity between the speakers and are an explicit reflection of cultural values. The first part of the study was a comparative ethnographic study on compliment responses in peninsular Spanish and in Lebanese Arabic. 72 selected members of a Lebanese and a Spanish social network participated in the research. The independent variables were: origin, age and gender. In both social networks, parallel communicative situations were created. The participants were linked by kinship or friendship and paid a compliment on the same topic. Secret recordings were used in order to register these communicative interactions and create a corpus formed with natural conversations. Compliment response sequences were analysed following a taxonomy created by the researcher for the specific study of the Spanish and Lebanese corpora. In the Lebanese corpus, formulaic expressions and invocations against the 'evil eye' were used. In Arabic and Islamic societies, it is believed that a compliment could attract the 'evil eye' if it is not accompanied by expressions invoking God's protection. In the Spanish corpus, both long and detailed explanations were frequently used. In the second part of the research, a corpus of courtesy expressions in colloquial Arabic is being built. The corpus of the current research could serve for future studies in the field of Arabic dialectology and sociolinguistics as it is the first one to include all the different Arabic dialects. The researcher will study the relationship between language and culture in Arabic societies. Participants of the second part of research are female Arab University students with an advance proficiency level of English and French. The independent variable is origin. Muted videos are the instrument to collect the data for this study. Three different videos for compliments about physical appearance, belongings and skills were recorded in Beirut and Bahrain. Students are requested to recreate the dialogue between the characters in Colloquial Arabic. The compliment response sequence is collected through this instrument because it enhances the students' creative freedom. The objectives of the study are: - Building a corpus of courtesy expressions in colloquial Arabic and conducting a comparative analysis of the formulaic responses to compliments in all Arabic dialects. - Studying if courtesy expressions are included in textbooks for teaching Arabic as a foreign language and if they are currently taught in Institutions and Universities. The results of the present research have some pedagogical implications. Courtesy expressions in spoken Arabic are essential and therefore should be introduced in the language classroom through real language examples. Developing the pragmatic competence plays an important role in teaching foreign languages and it helps Arabic learners to become intercultural speakers.

This presentation provides systematic evidence on an emergent debate about a Muslim woman's dress and her perceived credibility as a witness in a court context. The objective of this research is to understand how Muslim women's dress impacts their perceived credibility. The issue of testifying in western courts while wearing either a head veil (hijab) or a face veil (niqab) has been strongly contested on the grounds that physically seeing the witness's face helps observers judge her credibility (e.g., R. v. N.S., 2010). Canada's Supreme Court (N.S. v. Her Majesty the Queen, et al., 2012) ruled that judges will disallow the niqab "whenever the effect of wearing it would impede an evaluation of the witness's credibility." In 2010, an Australian judge ruled that a Muslim woman must remove her full veil while giving evidence before a jury. In 2007, the UK guidance on victims also indicated that the niqab may affect the quality of evidence given in the court room. All of those decisions and opinions run contrary to systematic research. Psychological studies suggest that nonverbal cues are not only poor indicators of veracity, but that they are the least useful indicators of deception (e.g., DePaulo et al., 2003, Vrij, 2008). This presentation discusses results of a research project that examined Muslim women's credibility. Using a quasi-experimental design, three groups of Muslim women lied or told the truth while testifying about a mock crime: 1) women without any coverings (safirat), 2) women wearing hijab (muhjabat) and 3) women wearing niqab (munaqabat). Videos of these women were then shown to audiences who assessed the credibilityof the witnesses. The research further explored the witness's general perceptions of being an eyewitness within the court system, and to what extent (if any) their dress impacted their perceptions. The presentation fits within the Social Sciences, Arts and Humanities thematic pillar of Qatar's National Research Strategy. It more particularly fits within two of the grand challenges: Managing Transition to a Diversified Knowledge- based Society: Build a knowledge-based society by emphasizing a robust research culture, and Holistic and Systematic Assessment of the Rapidly Changing Environment: Foster motivation, scholarship, and prosperity among Qatari nationals and expatriates along with cultural accommodations that are in-sync with modern practice.

Geolocated video represents an opportunity for innovation in journalism and media. Reported here are the results of a proof-of-concept research project demonstrating and assessing the process of creating geolocated video in journalism and media. Geolocated refers to tagging video or other media content with geographic location information, usually obtained from GPS data. Geolocation is a growing feature of news and media content. It is being used increasingly in photographs and social media, including Twitter posts. Geolocation in video is a relatively new application. Employing a proof-of-concept method, this project demonstrates how geolocated video (using Kinomap technology) serves several purposes in news and media (see Figure 1). First, geolocation allows the content to be automatically uploaded to Google Earth or other mapping software available online. This enables others anywhere to access that content by location. It is an aspect of Big Data, in that it permits mapping or other analysis of geolocated content. Such analysis can reveal a variety of insights about the production of media content. Second, geolocation, in concert with other digital watermarking, provides a useful tool to authenticate video. Geolocation in a digital watermark is a valuable tool to help establish the veracity of video or other content. Geolocation can help document when and where video produced by users or freelancers or even professionally employed reporters covering a sensitive story was captured. Reporters (or lay-citizens) providing smartphone video of an event can use geolocation to help establish time, date and location. Third, geolocation supports freelance media practitioners in protecting copyright or intellectual property rights by helping provide a strong digital watermark that includes their identity and the precise time, date and location the video was captured. Fourth, geolocated video represents an opportunity for a new approach to storytelling including in digital maps. Geolocated videos have been produced and made available on Google Earth. Viewing can occur immersively with mobile or wearable devices (e.g., augmented reality, Google Glass). This project aligns with three of the Qatar Research Grand Challenges. First, it supports Culture, Arts, Heritage, Media and Language within the Arabic Context, providing a medium to foster investment in the nation's legacy in Arabic arts, design, architecture, and cultural programs. Second, it supports Holistic and Systematic Assessment of the Rapidly Changing Environment, exploring the roles of communication (e.g., education, journalism, traditional and social media channels) in fostering awareness of social issues. Third, it supports Sustainable Urbanization-Doha as a smart city, demonstrating a state-of-the-art communications technology especially effective and efficient in facilitating location-based communications challenges and needs.

This presentation draws on data from a four-year longitudinal study of undergraduate literacy development at an English-medium university in Qatar. While previous studies have documented literacy development at the primary and secondary school levels (Christie & Derewianka, 2010, Coffin, 2006) and much has been researched about the nature of writing genres at the graduate and professional levels (Hyland, 2009, Swales, 1990, 2004), there is a limited body of research on writing at the undergraduate level (Ravelli & Ellis, 2005). The limited work at this level has been either largely qualitative (Leki, 2007, Sternglass, 1997) or primarily text-based (Byrnes, 2010, Colombi, 2002, Nessi, 2009, 2011, North, 2005, Woodward-Kron, 2002, 2005, 2008). Recently, drawing on systemic functional linguistics (SFL) and genre pedagogy, the work of Dreyfus (2013), and Humphrey and Hao (2013) has begun to shed light on the nature of disciplinary writing and writing development at the undergraduate level. However, there is much to learn about the nature of undergraduate writing. This study aims to contribute to this area by examining faculty expectations, student trajectories, and development from a text-based and ethnographic approach. This presentation reports on different trajectories of academic literacy development by presenting four case studies of multilingual students at an English-medium university in the Middle East. While there have been several studies that have looked at the developmental literacy trajectories of undergraduate students (Leki, 2007, Sternglass, 1997, Zamel, 2004) using case studies, they have not closely and systematically examined writing development from a text-based approach. This paper aims to contribute to the growing interest in understanding the nature of undergraduate writing, especially among multilingual students, by using detailed case studies and analysis of student writing longitudinally. The presenters will describe the college experiences of four students and present longitudinal analysis of their writing over four years in the disciplines of business administration and information systems. The findings suggest that students enter the university with differing pre-college experiences that shape their college experiences and impact their rate of development. While personal, social, and academic development is documented in all cases, there are differences between those who came in with a strong academic background in English and those that had limited experiences with English academic reading and writing. Using the tools of Systemic Functional Linguistics (Halliday, 1984), the text analysis of student writing shows development as their writing progressively becomes more academic (with increasing use of nominalizations and abstractions), analytical (with increasing use of evaluations), and better organized, with differences among the four case studies. Overall, the findings suggest that while weaker students do improve their literacy skills while in college, many still graduate with inconsistencies and infelicities in their writing. Documenting the literacy development of university students in Qatar is pivotal as Qatar continues to invest in English-medium education to build its human capital. This project aims to generate insights for curricular planning and assessment in Qatar and a basis for research on academic literacy development that will be of interest to scholars internationally.

Universal surveys—such as the World Values Survey—seek to promote generalizability across contexts. But what if two different cultures interpret and respond to a general question in two different ways (e.g., King et al. 2004)? Using this methodological conundrum as a starting point, over the past year I have a led a research team of four faculty and twelve students—drawn from Northwestern University in Qatar, Qatar University, and Georgetown University in Qatar—in creating, translating, and analyzing a context-sensitive survey of the Qatari population, funded through a Qatar National Research Fund UREP (Undergraduate Research Experience Program) grant. The survey was conducted by Qatar University's Social and Economic Survey Research Institute (SESRI) from January 15 to February 3 for a total of 798 Qatari respondents, making it a professional and valuable addition to the literature. Further, we were able to insert many questions that had never previously been asked of the population, including ones spanning the relative importance of tradition versus modern symbolism, specific opinions on the national education reforms, personal versus state priorities, satisfaction with particular welfare benefits offered by the state, and measures of religiosity. Both the process of creating a contextualized survey for the Qatari population—including what could and could not be asked, and how sensitive concepts were translated—as well as the fascinating results, which have opened up new avenues of research into the sociopolitical transformations of the Qatari people, are well worth presenting to the community and receiving feedback. Even more importantly, the insights gained from how we contextualized the survey can be applied to improve the current state of social science survey research in Qatar. The explosion of survey research in Qatar—pioneered by the Qatar Statistics Authority and Qatar University's SESRI, and recently joined by the multinational surveys of the World Values Survey, Harris Polling, Zogby's, and the Arab Barometer—demonstrates the need for questions that are contextually and culturally sensitive and ensure full understanding of the Qatari population. The ability to present my work to the Qatar Foundation Annual Research Conference will provide valuable feedback and networking opportunities with likeminded professionals, researchers, and community members in my quest to continue this collaborate and important research effort. Citation: King, Gary, Christopher Murray, Joshua Salomon, and Ajay Tandon. 2004. “Enhancing the Validity and Cross-Cultural Comparability of Measurement in Survey Research.” American Political Science Review 98 (1): 191-207.

The Qatar National Vision aims at “transforming Qatar into an advanced country by 2030, capable of sustaining its own development and providing for a high standard of living for all of its people for generations to come”. The grand challenge of Human Capacity Development aims to develop sustainable talent for Qatar's knowledge economy in order to meet the needs for a high-quality workforce. In order for Qatar to achieve its 2030 National Vision and become an advanced country by 2030, it has to train its citizens to function in a globalized and competitive world. Important skills for Qatari to function in the 21st century are communication and use of emerging technologies skills. This presentation will propose a training model to develop the Qatar workforce for the 21st century using emerging learning technologies. The training model was based on a mobile learning research project funded by the Qatar Foundation through the Qatar National Research Fund. The project is a collaborative research project with Qatar University, Qatar Petroleum, Qatar Wireless Innovation Centre, and Athabasca University, Canada. The project developed and implemented training lessons on Communication Skills for the oil and gas industry using mobile technology to deliver the training. The workers were employed at Qatar Petroleum and completed the training as part of their professional development to improve their English communication skills. Results from the project showed that workers performance improved after they completed the training and they reported that use of mobile technology to deliver the training provides flexibility for learning on the job. They suggested that the training should be more interactive and game-like. This is important since today's young workers are comfortable using mobile technologies and they need to be motivated to learn using the mobile technologies. The proposed Qatar National Training Model (QNTM) (Figure 1) is based on the mobile learning research project funded by the Qatar Foundation through the Qatar National Research Fund. In the QNTM, the learner/trainee/worker is at the center of the learning since the goal of training is to provide the knowledge and skills to improve workers' performance on the job. The design of the training must follow good learning design principles including preparing the learner for the training, providing activities for the learners to complete to improve their knowledge and skills, allowing learners to practice to improve their performance, certifying learners based on their performance, and providing opportunities for learners to transfer what they learn to the job environment. The delivery of the training should be flexible using a blended approach that includes face-to-face, hands-on, E-learning, mobile learning, and online learning. A variety of learning strategies such as practice with feedback, tutorials, simulations, games, and problem solving can be used depending on the learning outcomes to be achieved. The proposed Qatar National Training Model will allow for learner-centered training, lifelong learning, just-in-time learning, learning in context, developing skills required for 21st century learning, and interaction between learners and between learners and the trainer using social media.