Qatar Foundation Annual Research Forum Volume 2013 Issue 1

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)