Qatar Foundation Annual Research Forum Volume 2011 Issue 1

Human-annotated data is an important resource for most natural language processing (NLP) systems. Most linguistically annotated text for Arabic NLP is in the news domain, but systems that rely on this data do not generalize well to other domains. We describe ongoing efforts to compile a dataset of 28 Arabic Wikipedia articles spanning four topical domains—sports, history, technology, and science. Each article in the dataset is annotated with three types of linguistic structure: named entities, syntax and lexical semantics. We adapted traditional approaches to linguistic annotation in order to make them accessible to our annotators (undergraduate native speakers of Arabic) and to better represent the important characteristics of the chosen domains.

For the named entity (NE) annotation, we start with the task of marking boundaries of expressions in the traditional Person, Location and Organization classes. However, these categories do not fully capture the important entities discussed in domains like science, technology, and sports. Therefore, where our annotators feel that these three classes are inadequate for a particular article, they are asked to introduce new classes. Our data analysis indicates that both the designation of article-specific entity classes and the token-level annotation are accomplished with a high level of inter-annotator agreement.

Syntax is our most complex linguistic annotation, which includes morphology information, part-of-speech tags, syntactic governance and dependency roles of individual words. While following a standard annotation framework, we perform quality control by evaluating inter-annotator agreement as well as eliciting annotations for sentences that have been previously annotated so as to compare the results.

The lexical semantics annotation consists of supersense tags, coarse-grained representations of noun and verb meanings. The 30 noun classes include person, quantity, and artifact; the 15 verb tags include motion, emotion, and perception. These classes provide a middle-ground abstraction of the large semantic space of the language. We have developed a flexible web-based interface, which allows annotators to review preprocessed text and add the semantic tags.

Ultimately, these linguistic annotations will be publicly released, and we expect that they will facilitate NLP research and applications for an expanded variety of text domains.

Oil and gas companies spend millions of dollars on inspection of pipelines every year. The type of equipment used in carrying out the inspections is very sophisticated and requires very specialized manpower. In this article we present a novel approach to pipeline inspection using a small mobile robot and an omnidirectional vision system. It is a combination of a reflective convex mirror and a perspective camera. Panoramic videos captured by the camera from the mirror are either stored or transmitted to a monitoring station to examine the inner surface of the pipeline. The videos are prepped, unwrapped, and unwarped to get a realistic image of the whole inner surface area of the pipeline. Several mirror shapes and unwarping techchniques are used here to show the efficiency of this inspection technique. The image acquisition is instantaneous with a large field of view, which makes their use in dynamic environment suitable. The cost of such system is relatively low compared to other available inspections systems.

Qatar is a leading natural gas producer and exporter in the world. Most of the natural gas (and oil) of Qatar is extracted from offshore wells, and then it is transferred to onshore for processing. In addition, Qatar is connected to UAE by one of the world's longest underwater pipelines (managed by Dolphin Energy), to transfer processed gas from the offshore north field to the UAE. Security of such critical offshore infrastucture against threats along with the environmental and preventive maintenance monitoring (e.g., pollution, leakage) are of utmost importance. A wireless underwater sensor network can be deployed for the security and safety of underwater pipelines. However, underwater acoustic communication brings its own challenges such as limited transmission range, low data rates and link unreliability.

In this paper, we propose “cooperative communication” as an enabling technology to meet the challenging demands in underwater acoustic communication (UWAC). Specifically, we consider a multi-carrier and multi-relay UWAC system and investigate relay (partner) selection rules in a cooperation scenario. For relay selection, we consider different selection criteria, which rely either on the maximization of signal-to-noise ratio (SNR) or the minimization of probability of error (PoE). These are used in conjunction with so-called per-subcarrier, allsubcarriers, or subcarrier grouping approaches in which one or more relays are selected.

In our simulation study, we choose an offshore area in the North Eastern side of Qatar (which coincides with the North Field) and conduct an extensive Monte Carlo simulation study for the chosen location to demonstrate the performance of the proposed UWAC system. Our channel model builds on an aggregation of both large-scale path loss and small-scale fading. For acoustic path loss modeling, we use the ray-tracing algorithm Bellhop software to precisely reflect the characteristics of the simulation location such as the sound speed profile, sound frequency, bathymetry, type of bottom sediments, depths of nodes, etc (See Fig.1 ). Our simulation results for the error rate performance have demonstrated significant performance improvements over direct transmission schemes and highlighted the enhanced link reliability made possible by cooperative communications (See Fig.2 ).

Over the past few years, advances in the field of molecular biology and genomic technologies have led to an explosive growth of digital biological information. The analysis of this large amount of data is commonly based on the extensive and repeated use of conceptually parallel algorithms, most notably in the context of sequence alignment. Cloud computing provides scientists with a completely new model of utilizing the computing infrastructure. Cloud computing model is excellent in dealing with such bioinformatics applications, which require both management of huge amounts of data and heavy computations.

The study aims at transforming a recently developed bioinformatics sequence alignment tool, named BFAST, to the cloud environment. The MapReduce version of the BFAST tool will be used to demonstrate the effectiveness of the MapReduce framework and the cloud-computing model in handling the intensive computations and management of the huge bioinformatics data.

A number of existing tools and technologies are utilized in this study to achieve an efficient transformation of the BFAST tool into the cloud environment. The implementation is mainly based on two core components; BFAST and MapReduce. BFAST is a software package for aligning next generation genomic reads against a target genome with a very high accuracy and reasonable speed. MapReduce general-purpose parallelization technology [in its open source implementation, Hadoop] appears to be particularly well adapted to the intensive computations and huge data storage tasks involved in the BFAST sequence alignment tool.

The MapReduce version of the BFAST tool is expected to offer better results than the original one in terms of maintaining good computational efficiency, accuracy, scalability, deployment and management efforts.

The study demonstrates how a general-purpose parallelization technology, i.e. MapReduce running on the cloud, can be tailored to tackle the class of bioinformatics problems with good performance and scalability, and, more importantly, how this technology could be the basis of a computational parallel platform for several problems in the context of bioinformatics. Although the effort of transforming existing bioinformatics algorithms from local compute infrastructure is not trivial, the speed and flexibility of cloud computing environments provide a substantial boost with manageable cost.

Background: Reliable high-speed data communication that supports multimedia application for both indoor and outdoor mobile users is a fundamental requirement for next generation wireless networks and requires a dense deployment of physically coexisting network architectures. Due to the limited spectrum availability, a novel interference-aware spectrum-sharing concept is introduced where networks that suffer from congested spectrums (secondary-networks) are allowed to share the spectrum with other networks with available spectrum (primary-networks) under the condition that limited interference occurs to primary networks.

Objective: Multiple-antenna and adaptive rate can be utilized as a power-efficient technique for improving the data rate of the secondary link while satisfying the interference constraint of the primary link by allowing the secondary user to adapt its transmitting antenna, power, and rate according to the channel state information.

Methods: Two adaptive schemes are proposed using multiple-antenna transmit diversity and adaptive modulation in order to increase the spectral-efficiency of the secondary link while maintaining minimum interference with the primary. Both the switching efficient scheme (SES) and bandwidth efficient scheme (BES) use the scan-and-wait combining antenna technique (SWC) where there is a secondary transmission only when a branch with an acceptable performance is found; else the data is buffered.

Results: In both these schemes the constellation size and selected transmit branch are determined to minimized the average number of switches and achieve the highest spectral efficiency given a minimum bit-error-rate (BER), fading conditions, and peak interference constraint. For delayed sensitive applications, two schemes using power control are used: SES-PC and BES-PC. In these schemes the secondary transmitter sends data using a nominal power level, which is optimized to minimize the average delay. Several numerical examples show that the BES scheme increases the capacity of the secondary link.

Conclusion: The SES and BES schemes reach high spectral efficiency and BER performance at the expense of an increased delay. The SES-PC and BESPC minimize the average delay, satisfy the BER, and maintain a high spectral efficiency. The proposed power optimization and power control processes minimize the delay and the dropping probability especially if we extend the presented work to a multiuser scenario.

Many data-intensive applications on the Web require integrating data from multiple sources (Web databases) at query time. Online sources may refer to the same real world entity in different ways and some may provide outdated or erroneous data. An important task is to recognize and merge the various references that refer to the same entity at query time. Almost all existing duplicate detection and fusion techniques work in the offline setting and, thus, do not meet the online constraint. There are at least two aspects that differentiate online duplicate detection and fusion from its offline counterpart. First, the latter assumes that the entire data is available, while the former cannot make such a hard assumption. Second, several iterations (query submissions) may be required to compute the “ideal” representation of an entity in the online setting.

We propose a general framework to address this problem: an interactive caching solution. A set of frequently requested records is cleaned off-line and cached for future references. Newly arriving records in response to a stream of queries are cleaned jointly with the records in the cache, presented to users and appended to the cache.

We introduce two online record linkage and fusion approaches: (i) a record-based and (ii) a graph-based. They chiefly differ in the way they organize data in the cache as well as computationally. We conduct a comprehensive empirical study of the two techniques with real data from the Web. We couple their analysis with commonly used cache settings: static/dynamic, cache size and eviction policies.

The ability to efficiently extract useful information from volumes of data distributed across myriad networks is hindered by the latencies inherent to magnetic storage devices and computer networks. We propose overcoming these limitations by leveraging solid-state drive (SSD) and field-programmable gate array (FPGA) technologies to process large streams of data directly at the storage sites.

Our proposed reconfigurable, active, solid-state drive (RASSD) platform consists of distributed nodes that couple SSDs with FPGAs. While SSDs store data, FPGAs implement processing elements that couple soft-core RISC processors with dynamically reconfigurable logic resources. The processors execute data-processing software drivelets, and the logic resources implement hardware for accelerating performance-critical operations. Executing appropriate drivelets and using matching hardware accelerators enables us to efficiently process streams of data stored across SSDs.

To manage the configuration of RASSD nodes and provide a transparent interface to applications, our platform also consists of distributed middleware software. Client local middleware (CLM) resides on client host machines to interpret application data processing requests, locate storage sites, and exchange data-processing requests and results with middleware servers (MWS). MWS connect to clusters of RASSD nodes and contain libraries of drivelets and accelerator configuration bit streams. An MWS loads appropriate drivelet and accelerator bit streams onto a RASSD node's FPGA, aggregates processed data, and returns it to a CLM.

To evaluate our platform, we implemented a simple system consisting of a host computer connected to a RASSD node over a peer-to-peer network. We ran a keyword search application on the host computer, which also provided middleware functionality. We then evaluated this platform under three configurations. In configuration C1, the RASSD node was only used to store data while all data was processed by the MWS running on the host computer. In configuration C2, the data was processed by a drivelet running on the RASSD node. Finally, in configuration C3, the data was processed both by a drivelet and a hardware accelerator.

Our experimental results show that C3 is 2x faster than C2, and 6x faster than C1. This demonstrates our platform's potential for enhancing the performance of data-intensive applications over current systems.

Cooperative communication relaying systems are gaining much interest because they can improve average link signal to nose ratio by replacing longer hops with multiple shorter hops. The method of relaying has been introduced to enable a source (i.e. mobile terminal) communicate with a target destination via a relaying (i.e. mobile terminal). Furthermore, multiple-input multiple output (MIMO) communication systems have been considered as powerful candidates for the fourth generation of wireless communication standards because they can achieve further performance improvements including an increase in the achievable spectral efficiency and the peak data rates (Multiplexing), and robustness against severe effects of fading (transmit beamforming). In this work, we consider hop-by-hop beamforming relaying system over Rayleigh fading channels. In wireless communication environments, it is well known understood that the performance of wireless networks can be limited by both fading and co-channel interference (CCI). In this work, the multiple antennas at each node of relaying systems can be used to adaptively modify the radiation pattern of the array to reduce the interference by placing nulls in the direction of the dominant interferers. In this paper, we investigate the effect of CCI on the performance of hop-by-hop beamforming relaying system. First, we derive exact closed-form expressions for the outage probability and average symbol error rates. Moreover, we look into the high signal to noise ratio (SNR) regime and study the diversity order and coding gain achieved by the system.

Machine learning methods can be used to train automatic speech recognizers (ASR). When porting ASR to a new language, however, or to a new dialect of spoken Arabic, we often have too few labeled training data to allow learning of a high-precision ASR. It seems reasonable to think that unlabeled data, e.g., untranscribed television broadcasts, should be useful to train the ASR; human infants, for example, are able to learn the distinction between phonologically similar words after just one labeled training utterance. Unlabeled data tell us the marginal distribution of speech sounds, p(x), but do not tell us the association between labels and sounds, p(y|x). We propose that knowing the marginal is sufficient to rank-order all possible phoneme classification functions, before the learner has heard any labeled training examples at all. Knowing the marginal, the learner is able to compute the expected complexity (e.g., derivative of the expected log covering number) of every possible classifier function, and based on measures of complexity, it is possible to compute the expected mean-squared probable difference between training-corpus error and test-corpus error. Upon presentation of the first few labeled training examples, then, the learner simply chooses, from the rank-ordered list of possible phoneme classifiers, the first one that is reasonably compatible with the labeled examples. This talk will present formal proofs, experimental tests using stripped-down toy problems, and experimental results from English-language ASR; future work will test larger-scale implementations for ASR in the spoken dialects of Arabic.

Background: With the world's population projected to grow from the current 6.8 billion to around 9 billion by 2050, the resultant increase of megacities and the associated demands on public transport, there is an urgent imperative to understand the dynamics of crowded environments. Very dense crowds that exceed 3 people per square metre present many challenges for efficiently measuring quantities such as density and pedestrian trajectories. The Hajj and the associated minor Muslim pilgrimage of Umrah, present some of the most densely crowded human environments in the world, and thus present an excellent observational laboratory for the study of dense crowd dynamics. An accurate characterisation of such dense crowds cannot only improve existing models, but can also help to develop better intervention strategies for mitigating crowd disasters such as the Hajj 2006 Jamarat stampede that killed over 300 pilgrims. With Qatar set to be one of the cultural centres in the region, e.g. FIFA World Cup 2022, the proper control and management of large singular events is important for not only our safety but also our standing in the international stage.

Objectives: To use the data gathered from the Hajj to assess the dynamics of large dense crowds with a particular focus on crowd instabilities and pattern formation.

Methods: We will make use of advanced image processing and pattern recognition techniques (mathematical morphology, feature selection etc.) in assessing some of the bulk properties of crowds such as density and flow, as well as the finer details such as the ensemble of pedestrian trajectories. We are currently in the process of taking multiple wide-angle stereo videos at this year's Hajj, with our collaborators in Umm Al-Qurra University in Mecca. Multiple video capture of the same scene from different angles allows one to overcome the problem of occlusion in dense crowds.

Results: We will present our field study in the Hajj this year, where we took extensive high quality multiple camera video data. We will also present some of the techniques, which we will be using over the coming year in analyzing this large data set that we have now successfully collated.

Recent advances in imaging and communication have lead to the development of smart cameras that can operate autonomously and collaboratively to meet various application requirements. Networks of such cameras, called Smart Camera Networks (SCNs), have a range of applications in areas such as monitoring and surveillance, traffic management and health care. The self-configuring nature of cameras, by adjusting their pan, tilt and zoom (PTZ) settings, coupled with wireless connectivity differentiate them substantially from classical multi-camera surveillance networks.

One of the important problems in SCNs is: “How to configure cameras to obtain the best possible coverage of events happening within the area of interest?” As the scale of cameras grows from tens to hundreds of cameras, it is impractical to rely on humans to configure cameras to best track areas of interest. Thus, supporting autonomous configuration of cameras to maximize their collective coverage is a critical requirement in SCNs.

Our research first focuses on a simplified version of the problem, where the field-of-view (FoV) of a camera can be adjusted only by adjusting its pan in discrete manner. Even with such simplifications, solving the problem optimally is NP-hard. Thus, we propose centralized, distributed and semi-centralized heuristics that outperform the state-of-the-art approaches. Furthermore, the semi-centralized approach provides coverage accuracy close to the optimal, while reducing the communication latency by 97% and 74% compared to the centralized and distributed approaches, respectively.

Next, we consider the problem without FoV constraints; we allow FoVs to be adjusted in PTZ dimensions in continuous manner. While, PTZ configurations significantly increase the coverable area, the continuous adjustment nature eliminates any sub-optimality resulting from the discrete settings. However, supporting these features typically results in generating extremely large number of feasible FoVs per camera, out of which only one optimal FoV will be selected. We show that the problem of finding minimum feasible FoVs per camera is NP-hard. However, due to the geometric constraints introduced by the camera's FoV, the problem can be solved in polynomial time. Our proposed algorithm has polynomial-time worst-case complexity of O(n3).

User-localization and positioning systems have been a core challenge in the domain of context-aware pervasive systems and applications. GPS has been the de-facto standard for outdoor localization; however, geo-satellite signals upon which GPS rely, are inaccurate in indoor environments. Therefore, various indoor localization techniques based on triangulation, scene analysis, or proximity, have been introduced. The most prominent technologies over which these techniques are applied include WiFi, Bluetooth, RFID, Infrared, and UWB. Due to the ubiquitous deployment of access points, WiFi-based localization via triangulation has emerged to be among the most prominent indoor positioning solutions. A major deployment obstacle for such systems, however, is the high-energy consumption rates of Wifi adapters in mobile devices where energy is the most valuable resource.

We propose GreenLoc, an indoor green localization system that exploits sensors prevalent in today's smart-phones in order to dynamically adapt the frequency of location updates required. Significant energy gains can, therefore, be acquired when users are not mobile. For example, accelerometers can aid in detecting different user states such as walking, running or stopping. Based on these states, mobile devices can dynamically decide upon the appropriate update frequency. We accommodate various motion speeds by estimating the velocity of the device using the latest two location coordinates, and the time interval between these two-recorded locations. We have taken the first steps towards implementing GreenLoc, based on the infamous Ekahau system. We have also conducted preliminary tests utilizing the accelerometer, gravity, gyroscope, and light sensors residing on the HTC Nexus One and IPhone4 smart-phones.

To further save energy in typical indoor environments, such as malls, schools, and airports, GreenLoc exploits people's proximity when moving in groups. Devices within short-range of each other do not necessarily require that they each be individually tracked. Therefore, GreenLoc detects and clusters users moving together and elects a reference node (RN) based on device energy levels and needs. The elected RN will then be tracked via triangulation while other nodes in the group will be tracked based on the RN's location using Bluetooth. Our initial analysis demonstrates very promising results with this system.

Cloud computing revolutionizes the way large amounts of data are processed and offers a compelling paradigm to organizations. An increasing number of data-intensive scientific applications are being ported to cloud environments such as virtualized clusters, in order to take advantage of increased cost-efficiency, flexibility, scalability, improved hardware utilization and reduced carbon footprint, among others.

However, due to the complexity of the application execution environment, routine tasks such as monitoring, performance analysis and debugging of applications deployed on the cloud become cumbersome and complex. These tasks often require close interaction and inspection of multiple layers in the application and system software stack. For example, when analyzing a distributed application that has been provisioned on a cluster of virtual machines, a researcher might need to monitor the execution of his program on the VMs, or the availability of physical resources to the VMs. This would require the researcher to use different sets of tools to collect and analyze performance data from each level.

Otus is a tool that enables resource attribution in clusters and currently reports only the virtual resource utilization and not the physical resource utilization on virtualized clusters. This is insufficient to fully understand application behavior on a cloud platform; it would fail to account for the state of the physical infrastructure, its availability or the variation in load by other VMs on the same physical host, for example.

We are extending Otus to collect metrics from multiple layers, starting with the Hypervisor. Otus can now collect information from both the VM level as well as the Hypervisor level; and this information is collected in an OpenTSDB database, which is scalable to large clusters. A web-based application allows the researcher to selectively visualize these metrics in real-time or for a particular time range in the past.

We have tested our multi-layered monitoring technique on several Hadoop Mapreduce applications and clearly identified the causes of several performance problems that would otherwise not be clear using existing methods. Apart from helping researchers understand application needs, our technique could also help accelerate the development and testing of new platforms for cloud researchers.

SmartReader is a general-purpose “reading appliance” being implemented at Carnegie Mellon University (Qatar and Pittsburgh) - building upon an earlier prototype version. It is an artificial intelligence system that employs advanced language processing technologies and can interact with the reader and respond to queries about the content, words and sentences in a text. We expect it to be used by students in Qatar and elsewhere to help improve their comprehension of English text. SmartReader is motivated by the observation that text is still the predominant medium for learning especially at the advanced level and that text, being ``bland’’, is hardly a conducive and motivating medium for learning, especially when one does not have access to tools that enable one get over language roadblocks, ranging from unknown words to unrecognized and forgotten names, to hard-to-understand sentences. SmartReader strives to make reading (English) textual material, an “active” and an “interactive” process with the user interacting with the text using anytime-anywhere contextually-guided query mechanism based-on contextual user intent recognition. With SmartReader, a user can -inquire about the contextually correct meaning or synonyms of a word or idiomatic and multi-word constructions, -select a person's name, and then get an immediate ``flashback’’ to the first (or the last) time the person was encountered in text to remind herself the details of the person, -extract a summary of a section to remember important aspects of the content at the point she left off, and continue reading with a significantly refreshed context, -select a sentence that she may not be able to understand fully and ask SmartReader to break it down, simplify or paraphrase to comprehend it better. -test her comprehension of the text in a page or a chapter, by asking SmartReader to dynamically generate quizzes and answering them. -ask questions about the content of the text and get answers in addition to many other functions. SmartReader is being implemented as a multi-platform (tablet/PC) client-server system using HTML5 technology, with Unstructured Information Management Architecture -UIMA technology (used recently in IBM's Watson Q/A system in the Jeopardy Challenge) as the underlying language processing framework.

In general, scientific applications require different types of computing resources based on the application's behavior and needs. For example, page indexing in an Arabic search engine requires sufficient network bandwidth to process millions of web pages while seismic modeling is CPU and graphics intensive for real-time fluid analysis and 3D visualization. As a potential solution, cloud computing, with its elastic, on-demand and pay-as-you-go model, can offer a variety of virtualized compute resources to satisfy the demands of various scientific applications. Currently, deploying scientific applications onto large-scale virtualized cloud computing platforms is based on a random mapping or some rule-of-thumb developed through past experience. Such provisioning and scheduling techniques cause overload or inefficient use of the shared underlying computing resources, while delivering little to no satisfactory performance guarantees. Virtualization, a core enabling technology in cloud computing, enables the coveted flexibility and elasticity yet it introduces several difficulties with resource mapping for scientific applications.

In order to enable informed provisioning, scheduling and perform optimizations on cloud infrastructures while running scientific workloads, we propose the utilization of a profiling technique to characterize the resource need and behavior of such applications. Our approach provides a framework to characterize scientific applications based on their resource capacity needs, communication patterns, bandwidth needs, sensitivity to latency, and degree of parallelism. Although the programming model could significantly affect these parameters, we focus this initial work on characterizing applications developed using the MapReduce and Dryad programming models. We profile several applications, while varying the cloud configurations and scale of resources in order to study the particular resource needs, behavior and identify potential resources that limit performance. A manual and iterative process using a variety of representative input data sets is necessary to reach informative conclusions about the major characteristics of an application's resource needs and behavior. Using this information, we provision and configure a cloud infrastructure, given the available resources, to best target the given application. In this preliminary work, we show experimental results across a variety of applications and highlight the merit in precise application characterization in order to efficiently utilize the resources available across different applications.

Although Computerized Numerical Controlled (CNC) machines are currently regarded as the heart of machining workshops they are still suffering from machine blindness, hence, they cannot automatically judge the performance of applied machining parameters or monitor tool wear. Therefore, parts produced on these machines may not be as precise as expected. In this research an innovative system is developed and successfully tested to improve the performance of CNC machines. The system utilizes twin-camera computer vision system. This vision system is integrated with the CNC machine controller to facilitate on-line monitoring and assessment of machined surfaces. Outcome from the monitoring and assessment task of is used to real-time control of the applied machining parameters by a developed decision making subsystem which automatically decides whether to keep or alter the employed machining parameters or to apply tool change. To facilitate the integration of computer vision with CNC machines a comprehensive system is developed to tackle a number of pinpointed issues that obstruct such integration including scene visibility issue (e.g. effects of coolant and cut chips as well as camera mounting and lighting), effects of machine vibration on the quality of obtained roughness measurement, selection of a the most proper roughness parameter to be employed, and assessment of machining parameters effects on acquired roughness measurement. Two system rigs employing different models of CNC machines are practically developed and employed in the conducted tests to beneficially generalize the findings. Two cameras are mounted on the machine spindle of each of the two employed CNC machines to provide valid image data according to the cutting direction. Proper selection and activation of relative camera is achieved automatically by the developed system which analyze the most recent conducted tool path movement to decide on which camera is to be activated. In order to assess the machining surface quality and cutting tool status, image data are processed to evaluate resulting tool imprints on the machined surface. An indicating parameter to assess resulting tool imprints is proposed and used. The overall results show the validity of the approach and encourage further development to realize wider scale applications of vision-based-CNC machines.

Cooperative communications has gained a lot of attention in the research community recently. This was possible due to the fact that the broadcast nature of wireless networks, which was earlier considered a drawback, can now be used to provide spatial diversity to increase throughput, reduce energy consumption and provide network resilience. The main drawback of cooperative communication is that it requires more bandwidth compared to traditional communication networks. Decode and Forward (DF) is one of the cooperative communications forwarding strategy where the relay nodes first decodes the data and then retransmit it to the destination. DF requires advanced techniques, which can be used at the intermediate relay nodes to improve spectrum utilization.

Some well-known techniques for spectrum efficiency are Network Coding (NC) and Hierarchical Modulation (HM). In NC technique, nodes in a network are capable of combining packets for a transmission, thus reducing number of transmissions. HM is a technique which allows transmission of multiple data streams simultaneously. Both HM and NC are useful techniques for spectral efficiency.

In this work, we evaluate the performance of a joint HM and NC scheme for two-way relay networks. The relaying is based on the signal-to-noise (SNR) threshold at relay. In particular, a two way cooperative network with two sources and one relay is considered as shown in fig 1. Two different protection classes are modulated by a hierarchical 4/16 - Quadrature Amplitude Modulation (QAM) constellation at the source. Based on the instantaneous received SNR at the relay, the relay decides to retransmit both classes by using a hierarchical 4/16 - QAM constellation, or the more-protection class by using a Quadrature Phase Shift Keying (QPSK) constellation, or remains silent. These thresholds at the relay give rise to multiple transmission scenarios in a two-way cooperative network. Simulation results are provided to verify the analysis.

A service software is a self-contained, modular application deployed over standard computing platforms, and readily accessible by users within or across organizational boundaries using Internet. For businesses to open up their applications for the interaction with other service software, a fundamental requirement is that there has to be sufficient choices for security provisions allowing service consumers to select and verify the actual security assurances of services. In this context, the specific research challenge is how we could design service software focusing on the consumer's specific security requirements, and provide assurances to those security needs. Clearly, the security requirements vary from consumers to consumers. This work outlines a framework focusing on the selection of service software consistent with the security requirements of consumers, and compatibility checking of the assurances provided by services. We use profile-based compatibility analysis techniques to form an essential building block towards assuring security of service software.

In our research, we envision a security profile based compatibility checking that focuses more on automatic analysis of security compatibility using formal analysis techniques of security properties of software services. Our approach is based on three main building blocks: reflection of security assurances; selection of preferred assurances; and checking of security compatibility. Clearly, our vision and research for service security based on profile based compatibility analysis will form an essential building block towards realizing the full potential of service oriented computing. We foresee that the provision of the proposed scheme for service security profiling and compatibility analysis will significantly advance the state of practice in service oriented computing. At the same time, its development represents a new and highly challenging research target in the area.

This work is of great significance to the development of future software systems that facilitate security-aware cross-organizational business activities. The envisioned capability to integrate service software across-organizational boundaries that meets security requirements of all parties involved represents a significant technological advance in enabling practical business-to-business computing, leading to new business opportunities. At the same time, the approach will make significant scientific advancement in understanding the problem of application-level system security in a service oriented computing context.

The objective of this paper is to present the edutainment learning games that we are developing for Qatari children with moderate intellectual disability. These games will help them to learn effectively in funny and enjoyable ways. We use multimedia technology merged with intelligent algorithms to guide the children in play. As the number of children with intellectual disability is increasing, an early intervention to teach them properly using information technology is very important. Few research projects on disability are being conducted in the Arab world however, and these projects are still not enough to respond to the real needs of the disabled and achieve satisfactory outcomes. Developing edutainment games for children with intellectual disability is a very challenging task. First, it requires content developed by specialized instructors. Second, the interface design of the games must be presented clearly and be easy to interact with. Third, the games must run slowly, in order to give the children some time to think and interact. Fourth, regardless of the results, the game must allow a minimum level of general satisfaction, to avoid depressing the children. Fifth, the game must make maximum use of multimedia elements to draw the attention of the children. We show some multimedia applications for children with different disabilities, which were developed in Qatar University (enhancing mathematics skills with symbolic gift reward in case of guessing the answer). This feature motivated the children to play the game several times in the day. The applications also used some videos to illustrate the game before they play it. The purpose of the second multimedia application is to test the children's memory. The application uses different multimedia elements to present different games, which requires deep concentration in order to guess the answer. These games helped the children develop a strong sense of self-confidence. Learning puzzles that we have developed were based on intelligent algorithms to avoid cycling and which allow the children to reach a solution. Two different approaches were used: Simulated Annealing and Tabu Search.

With the emergence of cloud computing, providing safe data outsourcing has become an active topic. Several regulations have been issued to foresee that individual and corporate information would be kept private in a cloud computing environment. To guarantee that these regulations are fully maintained, the research community proposed new privacy constraints such as k-anonymity, l-diversity and t-closeness. These constraints are based on generalization which, transforms identifying attribute values into a general form and partitions to eliminate possible linking attacks. Despite their efficiency, generalization techniques affect severely the quality of outsourced data and their correlation. To cope with such defects, Anatomy has been proposed. Anatomy releases quasi-identifiers values and sensitive values into separate tables which, essentially preserve privacy and at the same time capture large amount of data correlation. However, there are situations where data correlation could lead to an unintended leak of information. In this example, if an adversary knows that patient Roan (P1) takes a regular drug, the join of Prescription (QIT) and Prescription (SNT) on the attribute GID leads to the association of RetinoicAcid to patient P1 due to correlation.

In this paper, we present a study to counter privacy violation due to data correlation and at the same time improve aggregate analysis. We show that privacy requirements affect table decomposition based on what we call correlation dependencies. We propose a safe grouping principle to ensure that correlated values are grouped together in unique partitions that obey to l-diversity and at the same time preserve the correlation. An optimization strategy is designed as well to reduce the number of anonymized tuples. Finally, we extended the UTD Anonymization Toolbox to implement the proposed algorithm and demonstrate its efficiency.

The community with Hearing or Speaking Disabilities represents a significant component of the society that needs to be well integrated in order to foster great advancements through leveraging all contributions of every member in the society. When those people cannot read lips they usually need interpreters to help them communicate with people who do not know sign language, and they also need an interpreter when they use phones, because the communication will not be done easily if they are not using special aiding devices, like a Relay Service or Instant Messaging (IM). As the number of people with hearing and speaking disabilities is increasing significantly; building bridges of communications between deaf and hearing community is essential, to deepen the mutual cooperation in all aspects of life. The problem could be summarized in one question: How to construct this bridge to allow people with hearing and speaking difficulties communicate?

This project suggests an innovative framework that contributes to the efficient integration of people with hearing disabilities with the society by using wireless communication and mobile technology. This project is completely operator independent unlike the existing solutions (Captel and Relay Service), it depends on an extremely powerful Automatic Speech Recognition and Processing Server (ASRPS) that can process speech and transform it into text. Independent means, it recognizes the voice regardless of the speaker and the characteristics of his/ her voice. On the other side there will be a Text To Speech (TTS) engine, which will take the text sent to the application server and transmit it as speech. The second aim of the project is to develop an iPhone/iPad application for the hearing impaired. The application facilitates the reading of the received text by converting it into sign language animations, which are constructed from a database; we are currently using American Sign Language for its simplicity. Nevertheless, the application can be further developed in other languages like Arabic sign language and British sign language. The application also assists the writing process by developing a customized user interface for the deaf to communicate efficiently with others that includes a customized keyboard.

Information Systems (IS) is a discipline that has its roots in the field of organizational development. Information Technology (IT) is a primary tool that has been used by IS to support the aim of developing organisations. However, IT has focused on supporting two main areas in organisations to help them become better at achieving their goals.These two areas are: operational and strategic. Little research has been devised to support the Human (workforce) for the aim of improving organization’s abilities to achieve their goals. In IS the Socio-Technical Theory is one theory that researches approaches to improve employees' satisfaction for the sake of better work productivity and business value. This theory is based on the idea of achieving harmonious integration of different subsystems in an organization (social, technical and environmental subsystems).

The aim of this research is to find out if IT can be used to improve the capability and motivation of the workforce in organisations so that these organizations can better achieve their goals.

This research is an applied study with reference to the Qatar National Vision 2030(QNV2030) Human Pillar. Using grounded theory (GTH) research methodology, the research characterized the main factors that affect capability and motivation of the Qatari workforce. These findings were used to develop the theoretical model. This model identifies core factors, gives a description of each factor and explains interactions between them. The theoretical model was then transformed into an IT system consisting of a number of IT tools, which was then tested in different organizations in Qatar. The test was to evaluate its effectiveness in improving the capabilities and motivation of Qatari workforce and to explore its effectiveness in helping these organizations better achieve their goals.

The research concluded that the developed IT system based on the theoretical model can help in improving motivation and capability of a workforce providing a defined set of organizational and leadership qualities exist within the organisation.

Plastic/organic /polymer photovoltaic solar cells are fourth generation cells however the efficiency, thermal stability and cost of fourth generation solar cells are still not sufficient to replace conventional solar cells. Hybrid solar cells have been one of the alternate technologies to harness solar power into electrical power to overcome the high cost of conventional solar cells. This review paper has focused on the concept of hybrid solar cells with the combination of organic/polymer materials, blended with inorganic semiconducting materials. The paper presents the importance of nanoscale materials and its shape and size, nanotubes, nanowire, nanocrystal, which can increase the efficiency of the solar cells. The study shows that nanomaterials have immense potential and application of nanomaterials (inorganic/organic/polymer) can improve the performance of photovoltaic solar cells. Tuning of nanomaterials increase the functionality, band gap, optical absorption and shape of the materials, in multiple orders compared to micro scale materials. Hybrid solar cells have unique properties of inorganic semiconductors along with the film forming properties of conjugated polymers. Hybrid materials have great potential because of their unique properties and are showing great results at the preliminary stages of research. The advantage of organic/polymer is easy processing; roll to roll production, lighter weight, flexible shape and size of the solar cells. Application of nanotechnology in hybrid solar cells has opened the door to manufacturing of a new class of high performance devices.

This research work is in line with the State of Qatar's aspiration on becoming the “gas capital of the world”, as it focuses on developing cost effective Gas-to- Liquid (GTL) technologies via Fischer-Tropsch synthesis (FTS). The objective of our present research activities is developing a novel approach to the FTS reactor design through controlling the thermo-physical characteristics of the reaction media by the introduction of a supercritical solvent.

The research is facilitated by QNRF through the flagship National Priorities Research Program, highlighting the importance of the subject matter to Qatar. It is a multidisciplinary consortium comprising of highly specialized teams of foremost scientists in their fields from four universities.

FTS is the focal process in which natural gas is converted to ultra-clean liquid based fuels; it is a highly complex chemical reaction where synthesis gas (a mixture of Hydrogen & Carbon Monoxide) enters the reactor and propagates to various hydrocarbons over a metallic based catalyst. Many factors impede the current commercial FTS technologies, chiefly transport and thermal limitations due to the nature of the phase of operation (either liquid or gas phase classically). Interestingly, the most advanced FTS technologies that employ either the liquid phase or gas phase are currently in operation in Qatar (Shell's Pearl project and Sasol's Oryx GTL plant).

This project is concerned with the design of an FTS reactor to be operated under supercritical fluid conditions in order to leverage certain advantages over the aforementioned commercial technologies. The conception of designing this novel reactor is based on phase behavior and kinetic studies of the non-ideal SCF media in addition to a series of process integration and optimization studies coupled with the development of sophisticated dynamic control systems. These results are currently under use at TAMUQ to build a bench-scale reactor to verify simulation studies.

To date, our collective research has yielded 8 peer-reviewed publications, more than 8 conference papers and proceedings, as well as numerous presentations in international conferences. It is noteworthy to mention that an advisory board composed of experts from the world leading energy companies follows the progress of this project toward its ultimate goal.