Qatar Foundation Annual Research Forum Volume 2012 Issue 1

With expansion in the fields of urbanization, technical development and industrialization, Qatar faces the challenge of advancing urban and industrial development while ensuring effective safety and environmental management. Whether assessing environmental quality, the impact of a metro system, or emergency response planning, decision making relies on the integration of disparate sources of data. Geospatial referencing provides a unifying framework from which civil engineering, public policy and research decisions can be made within a web-based environment. The foundation of a geospatial reference system should be the surface geology of the country and its marine habitats. In addition to bedrock geology, the database should include features such as lineaments, drainages, land movement studies, karst features and soil types. Marine data include biota, sediment types, water quality, hydrodynamic data, and ecological sensitivity. In an arid climate, where water resources are key considerations, it is important to have a consistent geological framework for both surface and near surface geology. Accordingly, the near surface, aquifers should have a sequence stratigraphic analysis. Within a web-based infrastructure, geospatial data can be accessible to many users with access controls set by data owners and can be appropriated to user needs. Such a geodatabase mapping tool may combine 3 functions: 1) database/resource-a query tool for government, industry and the public, 2) an evaluation tool allowing the end user forward modeling/impact assessment and cause/effect analysis capability, 3) a communication tool for decision making--where results from a query may be circulated to a linked forum of specialists for consideration. The emergence of web-based, spatial referencing GIS tools shows great promise in many key aspects of public policy and research, including data storage, data analysis, and decision making. The vision is the implementation of integrated diverse multi-scale, multi-disciplinary spatial data from geological rock samples to cutting edge multi-spectral satellite imagery for use in solving complex geological, geophysical, geotechnical and environmental problems.

Rendering of computer-generated imagery (CGI) is a very compute-intensive process. Rendering time of individual frames may vary from a few seconds to several hours depending on the scene complexity, output resolution and quality. For example, a short animation project may be about two minutes in length. It comprises 3600 frames at 30 frames per second (fps). An average rendering time for a fairly simple frame can be approximately 2 minutes, resulting in a total of 120 hours to render a simple 2-minute animation. Fortunately, animation rendering is a highly parallelizable activity as frames can be computed independently of each other. A typical animation studio has a render farm, a sophisticated cluster of special computers (nodes) used to render 3D graphics. By spreading the rendering of individual frames across hundreds of machines, the overall render time is reduced significantly. Researchers and students in a university do not usually have a render farm available. Some universities have general-purpose compute clusters but these are used mainly for running complex numerical simulations. Although rendering on these clusters is doable but it usually involves using generic queue manager (e.g. Condor, Portable Batch System PBS) rather than specialized render queue manager (e.g. DrQueue, Qube!), due to which rendering workflow becomes tedious. This paper presents a solution to create a render frame-like environment using compute-cluster resources and most importantly by using the existing render queue manager. This way, researchers and students can be presented with a rendering environment similar to any animation studio. Several benchmarking results will be presented to prove the potential benefit of this method in terms of execution time, simplicity and portability.

One of the radio resource management (RRM) functionalities in LTE systems, call admission control (CAC) is employed to control the number of LTE bearer requests in order to maintain the quality of service (QoS) of the admitted bearers. However, no quality guarantee can be provided due to the inherently dynamic nature of wireless communication. For example, during congestion periods when several communications experience poor channel quality or high mobility, it is highly possible that the network cannot maintain its bearers' QoS requirements. Thus, preemption schemes may be employed to alleviate the situation. As a result, resource preemption mechanism and its fairness are prominent issues as they may directly affect applications' QoS in the higher layers, as well as other network attributes such as generated revenue. In general, preemption is unavoidable in two circumstances, namely, to manage the resources among bearers when the network is overloaded as a congestion control mechanism, or, to allocate a high-priority bearer request while sufficient resources are not available. In this study, we propose a preemption technique by which each an established bearer may be preempted according to its priority as well as the amount of extra allocated resources compared to its basic data rate. We define the contribution of each bearer in the preemption through a contribution metric with tuning parameters which is presented in the form of Cobb-Douglas production function. We compare the fairness of the proposed scheme with a traditional preemption scheme by means of two well-known fairness indices, i.e. Jain's index and min-max index. We also discuss its effect on bearers' blocking and dropping probability and the total generated revenue. Through a simulation approach, we conclude substantial improvements in preemption fairness when compared to the traditional approach. We discuss that the proposed scheme does not affect the main performance measurements of the network, i.e. the bearers' blocking and dropping probabilities due to congestion. We also show that the preemption contribution metric can be effectively used by the service providers to vary the total generated revenue.

Information dynamics of different quantum systems under influence of both a phonon bath in contact with the resonator and irreversible decay of the qubits is considered. The focus of our analysis is devoted to multilevel atoms and the effects arising from the coupling to the reservoir. Even in the presence of the reservoirs, the inherent entanglement is found to be rather robust. Due to this fact, together with control of system parameters, the system may therefore be especially suited for quantum information processing. Information entropy, entropy squeezing and wehrl entropy are discussed as indicators of entanglement. Our findings also shed light on the evolution of open quantum many-body systems.

This work considers the deployment of the binary consensus algorithm in wireless sensor networks (WSN). This algorithm is applied for the evaluation of the average measurement with the presence of a faulty/attacked node. As this algorithm has been tested theoretically, we deploy it in real life, including its distributed and routing features. In this paper, we propose the development, under simulation environment, of a distributed binary consensus algorithm. We formulate the algorithm into nesC derived from C language and running over the tiny operating system (TinyOS). The implementation was tested on sensor nodes using the TinyOSSimulator (Tossim) for a large number of nodes N and a testbed with a limited number of nodes. In performance evaluation, we considered the analysis of the average convergence time for node states to a consensus value. As in analytical results, in the simulations we applied the distributed algorithm for fully connected, paths, cycles Erdos Reny random, and starr-shaped graph topologies. Our achieved results of simulation and hardware implementations are consistent with theory.

Background and Objectives: Mada, Qatar's Center for Assistive Technology, is a non-profit organization that strives to empower and enable people with disabilities through ICT. During late 2011 and early 2012, Mada conducted research to identify barriers to accessibility in Qatar Methods: In the survey, the key groups of respondents were identified including disabled people themselves. A combination of quantitative and qualitative methods were applied. The results were validated through the Assistive Technology Research Forum formed by Mada, which analyzed the survey results and gave additional input. These were further validated in discussions with disabled people's organizations as part of the input to the Mada future strategy. Results: The survey indicated the following major results: *People with visual impairment had both a high awareness and usage rate. *People with physical and hearing disabilities were very aware of available assistive technology but the usage rate was still low. *People with learning disabilities were not aware of available assistive technology and hence the usage rate was unsurprisingly low. Based on these findings, the AT Research Forum identified the following priority areas, which could potentially have a very high impact on barriers to accessibility in Qatar: *Arabic crowd sourced free symbol set *Improved statistical source on needs and a registry for easy user communication *Development of a free Arabic text to speech. *Wayfinding technologies for the blind incorporating enhanced technologies in emergency situations *Research into enhanced literacy amongst the deaf community when provided with text based communication solutions In addition, it was possible to highlight some crucial issues that which impeded uptake of technology, including: *The lack of accessible Arabic digital content *The limitations of current OCR technologies *The lack of useful Arabic continuous speech *Arabic word prediction and the lack of a significant available corpus Conclusions: The areas identified are fundamental projects with a very high impact in Qatar. They would best be addressed through collaboration and funding. Such collaborations bridge the private and academic sectors with specialist input from an organization supporting disabled people such as Mada.

One of the most ambitious goals of Qatar in the next few years is to become a country based on scientific and technical researches instead of being dependent on hydrocarbons. To this end, Qatar Foundation has established a number of high caliber universities and institutes. In particular, Qatar Computing Research Institue (QCRI) is forming a scientific computing multidisciplinary group with a focus on data mining machine learning, statistical modeling and bioinformatics. We now are able to satisfy the computational statistics needs of a variety of fields, especially of biomedical researchers in Qatar. Functional magnetic resonance imaging (fMRI) is a noninvasive neuroimaging method that is widely used in cognitive neuroscience. It relies on the measurement of changes in the blood oxygenation level resulting from neural activity. The technique is widely used in cognitive neuroscience. fMRI is known to be contaminated by artifacts. Artifacts are known to have fat tailed distributions and are often skewed therefore modeling the error using a Gaussian distribution is not enough. In this paper we introduce RAFNI, an extension of AFNI, which is an fMRI open source software for the analysis of functional neuroimages. We are model the error introduced by artifacts using alpha-stable distribution. We demonstrate the applicability and efficiency of stable distributions on real fMRI. We show that the alpha-stable estimator gives better results than the OLS-based estimators.

With the advancements in computing and sensing technologies, large amounts of data are collected from various biological systems. These data are a rich source of information about the biological systems they represent. For example, time-series metabolic data can be used to construct dynamic genetic regulatory network models, which can be used not only to better understand the interactions among different genes inside a cell, but also to design intervention strategies that can cure or manage major diseases. Also, copy number data can be used to determine the locations and extent of aberrations in chromosome sequences which are associated with many diseases such as cancer. Unfortunately, measured biological data are usually contaminated with errors that mask the important features in the data. Therefore, noisy biological measurements need to be filtered to enhance their usefulness in practice. Conventional linear low-pass filtering techniques are widely used because they are computationally efficient and can be implemented online. However, they are not effective because they operate on a single scale, meaning that they define a specific frequency, above which all features are considered noise. Real biological data possesses multiscale characteristics, i.e., may contain important features having high frequencies (such as sharp changes) or noise occurring at low frequencies (such as correlated or colored noise). Filtering such data requires multiscale filtering techniques that can account for the multiscale nature of the data. In this work, different batch as well as online multiscale filtering techniques are used to denoise biological data. These techniques include standard multiscale (SMS) filtering, online multiscale (OLMS) filtering, translation invariant (TI) filtering, and boundary corrected TI (BCTI) filtering. The performances of these techniques are demonstrated and compared to those of some conventional low-pass filters (such as the mean filter and the exponentially weighted moving average filter) using two case studies. The first case study uses simulated dynamic metabolic data, while the second case study uses real copy number data. Simulation results show that significant improvement can be achieved using multiscale filtering over conventional filtering techniques.

Writer identification consists of identifying the writer of a certain handwritten document and is of high importance in forensic document examination. Indeed, numerous cases over the years have dealt with evidence provided by handwritten documents such as wills and ransom notes. Automatic methods for writer identification can be divided into codebook-based and feature-based approaches. In codebook-based approaches, the writer is assumed to act as a stochastic generator of graphemes. The probability distribution of grapheme usage is used to distinguish between writers. Feature-based approaches compare the handwritings according to some geometrical, structural or textural features. Feature-based approaches prove to be efficient and are generally preferred when there is a limited amount of available handwriting. Therefore, we are more interested in this study in this category of approaches. Writer identification is performed by comparing a query document to a set of known documents and assigning it to the closest document in term of similarity of handwriting. This is done by extracting characterizing features from the documents including: directions, curvatures, tortuosities (or smoothness), chain codes distributions and edge based directional features. These features correspond to probability distribution functions (PDF) extracted from the handwriting images to characterize writer individuality. When matching a query document against any other document, the ¬χ2 distance between their corresponding features is computed. The smaller the distance, the more likely the two documents are written by the same writer. Therefore, the identified writer is the one of the document with the smallest distance to the query document. The writer is said to be correctly identified when the identified writer corresponds to the actual writer of the query document. The performance has been evaluated on the IAM handwriting dataset, with chain code based features generally outperforming the other features reaching 71% correct identification rate. The combination of all the features lead to 76% correct identification rate. The proposed system also won the music scores writer identification contest reaching 77% identification rate. The proposed method automatically extracts features used by forensic experts in order to identify writers of handwritten document. The results show that the method is efficient and language-independent.

Online signatures are acquired using a digital tablet which provides all the trajectories of the signature, as well as the variation in pressure with respect to time. Therefore, online signature verification achieves higher recognition rates than offline signature verification. Nowadays, forensic document examiners distinguish between forgeries, in which an impostor tries to imitate a given signature of another person and disguised signatures, in which the authentic author deliberately tries to hide his/her identity with the purpose of denial at a later stage. The disguised signatures play an important role in real forensic cases but are not considered in recent literature. In this study, we used online signatures acquired using a WACOM Intuos4 digitizing tablet with a sampling rate of 200Hz, a resolution of 2000 lines/cm and a precision of 0.25mm. The pressure information is available in 1024 levels. Online signatures contain a set of samples, each sample corresponds to the point coordinates on the digitizing tablet along with the corresponding pressure (Xt; Yt; Pt) where t corresponds to time. From those three basic signals, four other are extracted: distances, angles, speeds and angular speed. In order to compare the questioned signature with the reference signature, the differences between their corresponding features are computed at both the signal level and the histogram level. This study has been evaluated on ICDAR2009 signature verification competition dataset and a new dataset of online signatures collected at Qatar University (QU-dataset). This dataset contains signatures of genuine signatures, forgeries and disguised of 194 persons. To the extent of our knowledge, this dataset is the only one that contains disguised online signatures. The best individual performing feature for the ICDAR2009 dataset is the pressure histogram difference which reaches 8% equal error rate (EER). The pressure signal difference is the best individual performing feature for the QU-dataset (29% EER). The combination of features led to 7% EER on the ICDAR2009 dataset and 22% EER on the QU-dataset. This online signature verification system deals with both forgeries and disguised signatures. Several features have been proposed and combined using different classifiers reaching promising performance for disguised signatures detection.