Qatar Foundation Annual Research Forum Volume 2011 Issue 1

While weighted features are known in information retrieval (IR) systems to be used for increasing recall during the document selection step, conceptual methods helped for finding good features. Starting from the features of a sample of Arabic news belonging to k different financial categories, and using the support vector model (SVM), k(k-1) classifiers are generated using one-against-one classification. A new document is submitted to k(k-1) different classifiers then by using the voting heuristic, is assigned to the most selected category. Categorization results obtained for two different methods for feature extraction: one based on the optimal concepts and the other based on isolated labels, proved that isolated labels generate better feature, because of the specificity of the selected features. Therefore, we can say that the quality of the feature, added to weighting methods, using SVM is an important factor for a more accurate classification. The proposed method based on isolated labels gives a good classification rate of Arabic news greater than 80% in the financial domain for five categories. Generalized to English Texts and to more categories, it becomes a good preprocessing filtering preceding automatic annotation step, and therefore helps for more accurate event structuring. Here attached a figure showing the different steps of the new categorization method.

The scarcity of the radio resources and variable channel quality cause many challenges to the resource management for future all-IP wireless communications. One technique to guarantee a certain level of quality of service (QoS) is call admission control (CAC). Briefly, CAC is a mechanism which decides whether a new call could be admitted or rejected depending on its impacts on the current calls’ QoS. Conventional CACs such as guard channel, channel borrowing and queuing priority techniques only consider the instantaneous radio resource availabilities to make a decision on admission problem, thus they are neither able to prevent the network congestion problem nor meet the QoS requirements of different users with multi-service requirements.

In this work, we propose a new CAC technique with a future look into the needed extra resources through a reservation technique to offset the changes of the channel condition due to mobility. We show that during a call session, the needed radio resources may be increased compared with the negotiated resources during call setup. Although such fluctuations are fairly low for a single call, it is not negligible when the network is congested. As a result, some ongoing calls may experience QoS degradation. We show that such a consideration is critical in orthogonal frequency division multiplexing (OFDM) wireless networks such as 3GPP LTE where the radio resources are assigned to the users depending on the channel quality. The study assumes two types of applications denoted by wide-band and narrow-band and the performance of the proposed algorithm is modeled through queuing theory and event-driven simulation approaches. The results show that such a reservation technique improves the call admission performance significantly in terms of call blocking, call drop and call QoS degradation probabilities, and it outperforms the conventional CACs with insignificant loss in network capacity.

Assuming that the IEEE802.11 Wireless Local Area Networks (WLANs) are based on a radio/infrared link, they are more sensitive to the channel variations and connection ruptures. Therefore the support for multimedia applications over WLANs becomes non-convenient due to the compliance failure in term of link rate and transmission delay performance. We studied link adaptation facets and the Quality of Service (QoS) requirements essential for successful multimedia transmissions. In fact, the efficiency of rate control diagrams is linked to the fast response for channel variation. The 802.11 physical layers provide multiple transmission rates (different modulation and coding schemes). The last 802.11g-version maintains 12 physical rates up to 54 Mbps at the 2.4 GHz band. As a result, Mobile Stations (MSs) are able to select the appropriate link rate depending on the required QoS and instantaneous channel conditions to enhance the overall system performance. Hence, the implemented link adaptation algorithm symbolizes a vital fraction to achieve highest transmission capability in WLANs. “When to decrease and when to increase the transmission rate?” Are the two fundamental matters that we will be faced when designing a new physical-rate control mechanism? Many research works focus on tuning channel estimation schemes to better detect when the channel condition was improved enough to accommodate a higher rate, and then adapts its transmission rate accordingly. However, those techniques usually entail modifications on the current 802.11 standard. Another way to perform link control is based on local Acknowledgment (Ack) information for the transmitter station. Consequently, two techniques where accepted by the standard due to their efficiency and implementation simplicity.

We propose a new dynamic time-based link adaptation mechanism, called MAARF (Modified Adaptive Auto Rate Fallback). Beside the transmission frame results, the new model implements a Round Trip Time (RTT) technique to select adequately an instantaneous link rate. This proposed model is evaluated with most recent techniques adopted by the IEEE 802.11 standard: ARF (Auto Rate Fallback) and AARF (Adaptive ARF) schemes. Simulation results will be given to illustrate the link quality improvement of multimedia transmissions over Wi-Fi networks and to compare its performance with previous published results.

Modern communication systems apply channel-aware adaptive transmission techniques and dynamic resource allocation in order to exploit the peak conditions of the fading wireless links and to enable significant performance gains. However, conveying the channel state information among the users’ mobile terminals into the access points of the network consumes a significant portion of the scarce air-link resources and depletes the battery resources of the mobile terminals rapidly. Despite its evident drawbacks, the channel information feedback cannot be eliminated in modern wireless networks because blind communication technologies cannot support the ever-increasing transmission rates and high quality of experience demands of current ubiquitous services.

Developing new transmission technologies with reduced-feedback requirements is sought. Network operators will benefit from releasing the bandwidth resources reserved for the feedback communications and the clients will enjoy the extended battery life of their mobile devices. The main technical challenge is to preserve the prospected transmission rates over the network despite decreasing the channel information feedback significantly. This is a noteworthy research theme especially that there is no mature theory for feedback communication in the existing literature despite the growing number of publications about the topic in the last few years. More research efforts are needed to characterize the trade-off between the achievable rate and the required channel information and to design new reduced-feedback schemes that can be flexibly controlled based on the operator preferences. Such schemes can be then introduced into the standardization bodies for consideration in next generation broadband systems.

We have recently contributed to this field and published several journal and conference papers. We are the pioneers to propose a novel reduced-feedback opportunistic scheduling scheme that combines many desired features including fairness in resources distribution across the active terminals and distributed processing at the MAC layer level. In addition our scheme operates close to the upper capacity limits of achievable transmission rates over wireless links. We have also proposed another hybrid scheme that enables adjusting the feedback load flexibly based on rates requirements. We are currently investigating other novel ideas to design reduced-feedback communication systems.

Rapid advances in robotic surgical devices have put significant pressure on physicians to learn new procedures using newer and sophisticated instruments. This in turn has increased the demand for effective and practical training methods using these technologies, and has motivated the development of surgical simulators that promise to provide practical, safe, and cost-effective environments for practicing demanding robotic-assisted procedures.

However, despite the significant interest and effort in the development of such simulators, the current state-of-art surgical simulators are lacking. They introduce significant simplifications to obtain real-time performance, and these simplifications often come at the expense of realism and fidelity. There is a need to develop and build the next-generation of surgical simulators that improve haptic and visual realism. The primary challenges for building such high-fidelity simulations for soft-tissue organ simulations come from two computationally demanding tasks that must execute in real time: managing the complexity of the geometric environment that is being dynamically modified during the procedure; and modeling the stresses and deformations of the soft tissue interacting with surgical instruments and subjected to cutting and suturing. The mechanics of soft-tissue behavior are complicated by their anisotropic and nonlinear behavior.

In this presentation, we describe an initial prototype of a robotic-assisted simulator applied to a simplified task in a prostatectomy procedure (anastomosis). The simulator demonstrates new methodologies for modeling nonlinear tissue models, integrated high-resolution geometric contact detection for handling inter- and intra-organ collisions in the dynamically changing geometric environment of the simulation, and suturing with threads. The prototype is deployed on a bi-manual haptic feedback frame and serves as a building block for simulations operating in more complex anatomical structures.

Background: Force plates have been used in sports biomechanics since the 1960's. However, extracting useful information related to performance from curve analysis is a complicated process. It requires a combined knowledge in signal processing (filtering), mechanical equations, testing protocols, biomechanics, and discrete mathematical analysis to properly process the data.

Objectives: The aim is to provide a practical and accurate tool to analyze force curves from select standard biomechanical performance tests (e.g., counter movement jump, drop jump).

Methods The software is a tool built using Microsoft.Net framework. Key features of the software include:

* Real-time data acquisition module able to acquire data from third-party 8 channel 3D force plates with real-time results for immediate feedback during tests.

* Digital filtering module where the signal is treated for best fit for the analysis.

* Analysis module able to calculate Force, Power, Velocities, Trajectories, Mechanical Impulse and Timing during the different phases of the tests using discrete analysis algorithms.

* Reporting module for plotting and exporting selected variables.

Results The software has been used by ASPIRE Academy Sport Scientists in performance assessments of professional and semi-professional athletes from Qatar and other countries.

Currently, the software can analyze Counter Movement Jump, Drop Jump, Isometric Pulls and Squat Jump.

It contains automatic algorithms to detect specific points for each test type, but allows the user to change these suggestions when needed. Feedback is immediate, in both graphical and numerical form.

Conclusions: This novel software has proven to be a useful tool for immediate and accurate analysis and reporting of select field and lab based biomechanical tests. Going forward, further feedback from the applied users can lead to more features added. Considering the architecture of the software, adding more analysis modules is relatively simple. For example work is currently underway on a sprint running analysis module.

Data outsourcing is a growing business. Cloud computing developments such as Amazon Relational Database Service promise further reduced cost. However, use of such a service can be constrained by privacy laws, requiring specialized service agreements and data protection that could reduce economies of scale and dramatically increase costs.

We propose a private data outsourcing approach where the link between identifying information and sensitive (protected) information is encrypted, with the ability to decrypt this link residing only with the client. As the server no longer has access to individually identifiable protected information, it is not subject to privacy laws, and can offer a service that does not need to be customized to the needs of each country- or sector-specific requirements; any risk of violating privacy through releasing sensitive information tied to an individual remains with the client. The data model used in this work is shown with an example in Figure 1 .

This work presents a relational query processor operating within this model. The goal is to minimize communication and client-side computation, while ensuring that the privacy constraints captured in the anatomization are maintained. At first glance, this is straightforward: standard relational query processing at the server, except that any joins involving the encrypted key must be done at the client; an appropriate distributed query optimizer should do a reasonably good job of this. However, two issues arise that confound this simple approach:

1. By making use of the anatomy groups, and the knowledge that there is a one-to-one mapping (unknown to the server) between tuples in such groups, we can perform portions of the join between identifying and sensitive information at the server without violating privacy constraints, and

2. Performing joins at the client and sending results back to the server for further processing can violate privacy constraints.

Inspired by the success and the increasing prevalence of MapReduce, this work proposes a novel MapReduce task scheduler. MapReduce is by far one of the most successful realizations of large-scale, data-intensive, cloud computing platforms. As compared to traditional programming models, MapReduce automatically and efficiently parallelizes computation by running multiple Map and/or Reduce tasks over distributed data across multiple machines. Hadoop, an open source implementation of MapReduce, schedules Map tasks in the vicinity of their input splits seeking diminished network traffic. However, when Hadoop schedules Reduce tasks, it neither exploits data locality nor addresses data partitioning skew inherent in many MapReduce applications. Consequently, MapReduce experiences a performance penalty and network congestion as observed in our experimental results.

Recently there has been some work concerned with leveraging data locality in Reduce task scheduling. For instance, one study suggests a locality-aware mechanism that inspects Map inputs and predicts corresponding consuming reducers. The input splits are subsequently assigned to Map tasks near the future reducers. While such a scheme addresses the problem, it targets mainly public-resource grids and doesn't fully substantiate the accuracy of the suggested prediction process. In this study, we propose Locality-Aware Skew-Aware Reduce Task Scheduler (LASAR), a practical strategy for improving MapReduce performance in clouds. LASAR attempts to schedule each reducer at its center-of-gravity node. It controllably avoids scheduling skew, a situation where some nodes receive more reducers than others, and promotes effective pseudo-asynchronous Map and Reduce phases resulting in earlier completion of submitted jobs, diminished network traffic, and better cluster utilization.

vWe implemented LASAR in Hadoop-0.20.2 and conducted extensive experimentations to evaluate its potential. We found that it outperforms current Hadoop by 11%, and by up to 26% for the utilized benchmarks. We believe LASAR is applicable to several cloud computing environments and multiple essential applications, including but not limited to shared environments and scientific applications. In fact, a large body of work observed partitioning skew in many of critical scientific applications. LASAR paves the way for these applications, and others, to get effectively ported to various clouds.

Modern societies rely on software applications for performing many critical tasks. As this trend is increasing, so it is the necessity to develop cost-effective methods of writing software that ensure that essential safety and security requirements are met. In this context, dependent type theories are recently gaining adoption as a valid tool for performing formal verification of software.

The focus of this work is Coq, a proof assistant based on a dependent type theory called the Calculus of Inductive Constructions. Developed at INRIA (France) for over 20 years, it is arguably one of the most successful proof assistant to this date. It has been used in several real-world large-scale projects such as the formalization of a verification framework for the Java Virtual Machine, a proof of the Four Color Theorem, and a formally verified compiler for the C programming language (project Compcert).

Coq is both a proof assistant and a programming language. To ensure soundness of the formal verification approach, Coq imposes several conditions on the source programs. In particular, all programs written in Coq must be terminating. The current implementation of the termination checker uses syntactic criteria that are too restrictive and limiting in practice, hindering the usability of the system.

In previous work we have proposed an extension of Coq using a termination checker based on the theory of sized types and we have shown that the soundness of the approach. Furthermore, compared to syntactic criteria currently used, our approach is more powerful, easier to understand, and easier to implement, as evidenced by a prototype implementation we developed.

Our objective is to turn our prototype into an implementation of a new core theory and termination checker for Coq. We expect that the resulting system will be more efficient and easier to understand for users. Furthermore it will increase the expressive power and usability of Coq, permitting the use of formal verification on a wider range of applications.

Usability and usefulness have made the spreadsheet one of the most successful computing applications of all times: millions rely on it every day for anything from typing grocery lists to developing multimillion dollar budgets. One thing spreadsheets are not very good at is manipulating symbolic data and helping users make decisions based on them. By tapping into recent research in logic programming, databases and cognitive psychology, we propose a deductive extension to the spreadsheet paradigm which addresses precisely this issue. The accompanying tool, which we call NEXCEL, is intended as an automated assistant for the daily reasoning and decision-making needs of computer users, in the same way as a spreadsheet application such as Microsoft Excel assists them every day with calculations simple and complex. Users without formal training in logic or computer science can interactively define logical rules in the same simple way as they define formulas in Excel. NEXCEL immediately evaluates these rules thereby returning lists of values that satisfy them, again just like with numerical formulas. The deductive component is seamlessly integrated into the traditional spreadsheet so that a user not only still has access to the usual functionalities, but is able to use them as part of the logical inference and, additionally, to embed deductive steps in a numerical calculation.

Under the hood, NEXCEL uses a small logic programming language inspired by Datalog to define derived relations: the target region of the spreadsheet contains a set of logical clauses in the same way that calculated cells contain a numerical formula in a traditional spreadsheet. Therefore, logical reasoning reduces to computing tables of data by evaluating Datalog-like definitions, a process that parallels the calculation of numerical formulas. Each row in the calculated relation is a tuple of values satisfying the definition for this relation, so that the evaluated table lists all such solutions, without repetitions. This linguistic extension significantly enriches the expressive power of the spreadsheet paradigm. Yet, it is provided to the user through a natural extension of the mostly excellent interfaces of modern spreadsheet applications.