Qatar Foundation Annual Research Conference Proceedings Volume 2014 Issue 1

The next generation sequencing technology creates a huge number of sequences (reads), which constitute the input for genome assemblers. After prefiltering the sequences, it is required to detect exact overlaps between the reads to prepare the necessary ingredients to assemble the genome. The standard method is to the find the maximum exact suffix-prefix match between each pair of reads after executing an error-detection technique. This is applied in most assemblers, however, a few studies worked on finding the approximate overlap. This direction can be useful when error detection and prefiltering techniques are very time consuming and not very reliable. However, there is a huge difference in term of complexity between finding exact and approximate matching techniques. Therefore, any improvement in time could be valuable when approximate overlap is the target. The naive technique to find approximate overlaps applies a modified version of dynamic programming (DP) on every pair of reads, which consumes O(n2) time where n is the total size of all reads. In this work, we take advantage of the fact that many reads share prefixes. Accordingly, it is obvious that some work is continuously repeated. For example, consider the sequences in Figure 1. If dynamic programming is applied on S1 and S2, assuming S2 and S3 share a prefix of length 4, then it is easy to notice that calculation of a portion of DP table of size |S1| X 5 can be avoided when applying the algorithm on S1 and S3 (the shaded area in Figure 1). Figure 1. DP table for S1,S2 alignment. We assume the following: gap = 1, match =0 and mismatch=1. no calculation for the shaded area is required when calculating S1,S3 table since S2,S3 share the prefix AGCC. The modification is based on the above observation: first, the reads are sorted in lexicographical order and the largest common prefix (LCP) between every two consecutive reads is found. Let group G denote the reads after sorting. For every string S, we find the DP table for S and every other string in G. Since the reads are sorted, a portion of DP table can be skipped for every string, depending on the size of LCP, which has already been calculated in the previous step. We implemented the traditional technique to find approximate overlap with and without the proposed modification. The results show that there is an improvement of 10-61% in time. The interpretation for this wide range is that the gain in performance depends on the number of strings. The larger the number of strings is, the better the gain in performance since the sizes of LCPs are typically larger.

Due to its inherent short-term dynamic, the power grid is a critical component of the energy system. When a dangerous event occurs in a section of the grid (i.e. a power line or a plant fails), the overall system is subject to the risk of a blackout. The time available to counteract the risk is very short (only a few milliseconds) and there are no tools to ensure the power to a number of selected critical facilities. A way to tackle the blackout risk and to implement a smart management of the remaining part of the grid is a distributed control system with preemptive commands. It's based on the idea, that in case of dangerous events, there will be definetly no time to inform the control center, to make a decision and to send the commands to the active components of the power grid where, finally, they will be executed. The idea consist in the implementation of an intelligent distributed control system that continuously controls the critical components of the power grid. It monitors the operational conditions and evaluates the ability of single components to work well and their probability of an outage. In parallel, the control system continuously imparts preemptive commands to eventually counteract the outages expected on a probabilistic base. The preemptive commands can be defined taking into account the sensitivity to specific outages by different network elements and of course, on the base of a priority rule that preserve the power for the strategic sites. In case of a dangerous event, the monitoring device directly sends messages to all the actuator devices, where the action will be performed only if a preemptive command was previously delivered. This means that the latency of the traditional control chain will be reduced to the latency of communications between monitoring and actuator devices. The first consequence of this policy is that an event, which is potentially the cause for a complete blackout will affect only a limited portion of the grid. The second consequence is , that the control system will choose the network elements that will be involved in the emergency procedure, preserving the strategic plants. The third consequence is that with this kind of control, the power grid goes from a N-1 stable status to another N-1 stable status. The system loses contributions of generation and load, but it keeps its stability and its standard operations.

Wireless networking plays a vital role in our daily life style and has tremendous applications in almost all fields of the economy. The wireless medium is a shared medium and, hence, user-scheduling is needed to allow multiple users access the channel jointly. Furthermore, the wireless channel is characterized by its time-based and location-based variations due to physical phenomena such as multi-path propagation and fading, etc. Therefore, unlike the traditional persistent round-robin scheduling schemes, the current standards of telecommunication systems support channel-aware opportunistic scheduling in order to exploit the varying channels of the users when they are at their peak conditions. The advantages of these schemes in enhancing the prospected throughput of the networks are evident and demonstrated. However, these schemes are basically based on selecting a single user to access a certain frequency sub-channel at a given time in order to avoid creating interference if more than one user access the same channel. Nevertheless, allowing multiple users to access the same channel can be feasible by using special coding techniques such as superposition coding with successive interference cancellation at the receivers. The main advantage of this is to improve the spectral efficiency of the precious wireless spectrum and to enhance the overall throughput of the network while maintaining the quality-of-service requirements of all users. Despite their advantages, multiple-users scheduling schemes require the use of proper resource allocation algorithms to process the channel conditions measurements in order to decide which users should be served in a given time slot and frequency sub-channel and the allocated data rate and power of each link in order to maximize the transmission efficiency. Failure to use a suitable resource allocation and scheduling scheme can degrade the performance significantly. We design and analyze the performance of efficient multiple-users scheduling schemes for wireless networks. One scheme is proven theoretically to be the most efficient one. However, the algorithm computation load is significant. The other scheme is a sub-optimal scheme that has low computation load to run the algorithm and it achieves very good performance which is comparable to the optimal scheme. Furthermore, we evaluate the performance gains of multiple-user scheduling over the conventional single-user scheduling under different constraints such as hard fairness and proportional fairness among the users and for fixed merit weights of the users based on their service class. In all of these cases, our proposed schemes can achieve a gain that may exceed 10% in terms of the data rate (bits/sec). This gain is significant taken into consideration that we use the same air-link and power resources of the conventional single-user scheduling schemes.

With the growth of social media and online blogs, people express their opinion and sentiment freely by providing product reviews, as well as comments about celebrities, and political and global events. These texts reflecting opinions are of great interest to companies and individuals who base their decisions and actions upon them. Hence, opinion mining on mobiles is capturing the interest of users and researchers across the world with the growth of available online data. Many techniques and applications have been developed for English while many other languages are still trying to catch up. In particular, there is an increased interest in easy access to Arabic opinion from mobiles. In fact, Arabic presents challenges similar to English for opinion mining, but also presents additional challenges due to its morphological complexity. Mobiles on the other hand present their own challenges due to limited energy, limited storage, and low computational capability. Since some of the state-of-the-art methods for opinion mining in English require the extraction of large numbers of features, and extensive computations, these methods are not feasible for real-time processing on mobile devices. In this work, we provide a solution to address the limitation of the mobile, and the required Arabic resources to derive opinion mining on mobiles. The method is based on matching stemmed tweets to our own developed Arabic sentiment lexicon (ArSenL). While there have been efforts towards building Arabic sentiment lexicons, they suffer from many deficiencies including limited size, unclear usability plan given Arabic's rich morphology, or non-availability publicly. ArSenL is the first publicly available large scale Standard Arabic sentiment lexicon (ArSenL) developed using a combination of English SentiWordnet (ESWN), Arabic WordNet, and the Standard Arabic Morphological Analyzer (SAMA). A public interface to browsing ArSenL is available at http://me-applications.com/test. The scores from the matched stems are then aggregated and processed through a decision tree for determining the polarity. The method was tested on a published set of Arabic tweets, and an average accuracy of 67% was achieved versus a 50% baseline. A mobile application was also developed to demonstrate the usability of the method. The application takes as input a topic of interest and retrieves the latest Arabic tweets related to this topic. It then displays the tweets superimposed with colors representing sentiment labels as positive, negative or neutral. The application also provides visual summaries of searched topics and a history showing how the sentiments for a certain topic has been evolving.

In recent years, we have seen many advances in distributed systems, in the form of cloud computing and distributed embedded mobile devices, drawing more research interest into better ways to harness and coordinate the combined power of distributed computation. While this has made distributed computing resources more readily accessible to main-stream audiences, the fact remains that implementing distributed software and applications that can exploit such resources via traditional distributed programming methodologies is an extremely difficult task. As such, finding effective means of programming distributed systems is more than ever an active and fruitful research and development endeavor. Our work here centres on the development of a programming language known as MSR3e, designed for implementing highly orchestrated communication behaviors of an ensemble of computing nodes. Computing nodes are either traditional main-stream computer architectures or mobile computing devices. This programming language is based on logic programming, and is declarative and concurrent. It is declarative in that it allows the programmer to express the logic of synchronization between computing nodes without describing any form of control flow. It is concurrent in that its operational semantics is based on a concurrent programming model known as multiset rewriting. The result is a highly expressive distributed programming language that provides a programmer with a high-level abstraction to implement highly complex communication behavior between computing nodes. This allows the programmer to focus on specifying what processes need to synchronize between the computing nodes, rather than how to implement the synchronization routines. MSR3e is based on a traditional multiset rewriting model with two important extensions: (1) Explicit localization of predicates, allowing the programmer to explicitly reference the locations of predicates as a first-class construct of the language (2) Comprehension patterns, providing the programmer a concise mean of writing synchronization patterns that matches dynamically sized sets of data. This method of programming often result to more concise codes (relative to main-stream programming methodologies) that are more human readable and easier to debug. Its close foundation to logic programming also suggests the possibilities of effective automated verification of MSR3e programs. We have currently implemented a prototype of MSR3e. This prototype is a trans-compiler that compiles a MSR3e program into two possible outputs: (1) a C++ program that utilizes the MPI libraries, intended for execution on traditional main-stream computer architectures (e.g., ×86, etc..) or (2) a Java program that utilizes WiFi direct libraries of the android SDK, intended for execution on android mobile devices. We have conducted preliminary experimentations on a small set of examples, to show that MSR3e works in practice. In future, we intend to refine our implementation of MSR3e, scaling up the experiment suites, as well as developing more non-trivial applications in MSR3e, as further proof of concept.

In the context of web Islamic data analysis and authentication an important task is to be able to authenticate the holy book if published in the net. For that purpose, in order to detect texts contained in the holy book, it seems obvious to first characterize words which are specific to existing chapters (i.e. "Sourat") and words characterizing each sentence in any chapter (i.e. "Aya"). In this current research, we have first mapped the text of the Quran to a binary context R linking each chapter to all words contained in it, and by calculating the fringe relation F of R, we have been able to discover in a very short time all specific words in each chapter of the holy book. By applying the same approach we have found all specific words of each sentence (i.e. "Aya") in the same chapter whenever it is possible. We have found that almost all sentences in the same chapter have one or many specific words. Only sentences repeated in the same chapter or those sentences included in each other might not have specific words. Observation of words simultaneously specific to a chapter in the holy book and to the sentence in the same chapter gave us the idea for characterizing all specific sentences in each chapter with respect to the whole Quran. We found that for 42 chapters all specific words of a chapter are also specific of some sentence in the same chapter. Such specific words might be used to detect in a shorter time website containing some part of the Quran and therefore should help for checking their authenticity. As a matter of fact by goggling only two or three specific words of a chapter, we observed that search results are directly related to the corresponding chapter in the Quran. Al results have been obtained for Arabic texts with or without vowels. Utilization of adequate data structures and threads enabled us to have efficient software written in Java language. The present tool is directly useful for the recognition of different texts in any domain. In the context of our current project, we project to use the same methods to characterize Islamic books in general. ACKNOWLEDGMENT: This publication was made possible by a grant from the Qatar National Research Fund through National Priority Research Program (NPRP) No. 06-1220-1-233. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the Qatar National Research Fund or Qatar University.

Traffic congestion is a major problem in many big cities around the world. According to a study performed by the world bank in Egypt in 2010 and concluded in 2012, the traffic congestion was estimated to 14 Billion EGP in the Cairo metropolitan area and to 50 Billion EGP (4\% of the GDP) in the entire Egypt. Few of the reasons of the high monetary cost of the traffic congestion are: (1) travel time delay, (2) travel time unreliability, and (3) excess fuel consumption. Smart traffic management addresses some of the causes and consequences of traffic congestion. It can predict congested routes, take preventive decisions to reduce congestion, disseminate information about accidents and work zones, and identify the alternate routes that can be taken. In this project, we develop a real-time and scalable data storage and analysis framework for traffic prediction and management. The input to this system is a stream of GPS and/or cellular data that has been cleaned and mapped to the road network. Our proposed framework allows us to (1) predict the roads that will suffer from traffic congestion in the near future, and traffic management decisions that can relieve this congestion; and (2) a what-if traffic system that is used to simulate what will happen if a traffic management or planning decision is taken. For example, it answers questions, such as: "What will happen if an additional ring road is built to surround Cairo?" or "What will happen if point of interest X is moved away from the downtown to the outskirts of the city. This framework has the following three characteristics. First, it predicts the flow of the vehicles in the road based on historical data. This is done by tracking vehicles every day trajectories and using them in a statistical model to predict the vehicles movement on the road. It then predicts the congested traffic zones based on the current vehicles in the road and their predicted paths. Second, historical traffic data are heavily exploited in the approach we use to predict traffic flow and traffic congestion. Therefore, we develop new techniques to efficiently store traffic data in the form of graphs for fast retrieval. Third, it is required to update the traffic flow of vehicles and predict congested areas in real-time, therefore we deploy our framework in the cloud and employ optimization technique to speedup the execution of our algorithms.

Some lecturers gave reports and showed instances of moving part or all of their electronic course support from the Virtual Learning Environment (VLE) to social networking systems like Youtube, MySpace and Facebook because of greater student engagement with these kinds of social networking tools. Recent student interviews in Aspire Academy in Qatar have revealed that students are not concerned with what they are taught (e.g. through lectures, seminars, distance learning sessions, or through a blended learning approach) so long as the instruction was good. The latter reason opens great opportunities for delivering courses through SC media over the VLE, but also raises the question: To what extent can VLE and Social Media be leveraged as a good practice in learning and teaching in different modalities? In this research, the new experience of enriching the VLE with SC tools in Aspire Academy is presented through developing a new system prototype as a more effective solution. The prototyping process included usability testing with Aspire student-athletes and lecturers, plus heuristic evaluation by Human Computer Interaction (HCI) experts. Implementing the prototype system in academic institutions is expected to develop better learning levels and consequently better educational outcomes.

Background & Objectives Semantic Web technologies are used extensively in the health domain to enable expressive, standards-based reasoning. Deploying Semantic Web reasoning processes directly on mobile devices has a number of advantages, including robustness to connectivity loss and more timely results. By leveraging local reasoning processes, Clinical Decision Support Systems (CDSS) can thus present timely alerts given dangerous health issues, even when connectivity is lacking. However, a number of challenges arise as well, related to mobile platform heterogeneity and limited computing resources. To tackle these challenges, developers should be empowered to benchmark mobile reasoning performance across different mobile platforms, with rule- and datasets of varying scale and complexity, and under typical CDSS reasoning process flows. To deal with the current heterogeneity of rule formats, a uniform interface on top of mobile reasoning engines also needs to be provided. System We present a mobile, cross-platform benchmark framework, comprising two main components: 1) a generic Semantic Web layer, supplying a uniform, standards-based rule- and dataset interface to mobile reasoning engines; and 2) a Benchmark Engine, to investigate mobile reasoning performance. This framework was implemented using the PhoneGap cross-platform development tool, allowing it to be deployed on a range of mobile platforms. During benchmark execution, the benchmark rule- and dataset (encoded using the SPARQL Inferencing Notation (SPIN) and Resource Description Framework (RDF)) are first passed to the generic Semantic Web layer. In this layer, the local Proxy component contacts an external Conversion Web Service, where converters perform conversion into the different rule engine formats. Developers may develop new converters to support other engines. The results are then communicated back to the Proxy and passed on to the local Benchmark Engine. In the Benchmark Engine, reasoning can be conducted using different process flows, to better align the benchmarks with real-world CDSS. To plugin new reasoning engines (JavaScript or native), developers need to implement a plugin realizing a uniform interface (e.g., load data, execute rules). New process flows can also be supplied. In the benchmarks, data and rule loading times, as well as reasoning times, are measured. From our work in clinical decision support, we identified two useful reasoning process flows: * Frequent Reasoning: To infer new facts, the reasoning engine is loaded with the entire datastore each time a certain timespan has elapsed, and the relevant ruleset is executed. * Incremental Reasoning: In this case, the datastore is kept in-memory, whereby reasoning is applied each time a new fact has been added. Currently, 4 reasoning engines (and their custom formats) are supported, including RDFQuery (https://code.google.com/p/rdfquery/wiki/RdfPlugin), RDFStore-JS (http://github.com/antoniogarrote/rdfstore-js), Nools (https://github.com/C2FO/nools) and AndroJena (http://code.google.com/p/androjena/). Conclusion In this paper, we introduced a mobile, cross-platform and extensible benchmark framework for comparing mobile Semantic Web reasoning performance. Future work consists of investigating techniques to optimize mobile reasoning processes.

Shannon's theory of information was developed to address the fundamental problems of communication, such as the reliable data transmission over a noisy channel and the optimal data compression. During the years, it has expanded to find wide range of applications in many areas ranging from cryptography and cyber security to economics and genetics. Recent technological advances designate information theory as a promising and elegant tool to analyze and model information structures within living organisms. The key characteristics of data transmission within organisms are that they consider sources and channels with memory and feedback, they handle their information in a fascinating Shannon-optimum way, while the transmission of the data is delayless. Despite the extensive literature on memoryless sources and channels, the literature regarding sources and channels with memory is limited. Moreover, the optimality of communication schemes for these general sources and channels is completely unexplored. Optimality is often addressed via Joint Source Channel Coding (JSCC) and it is achieved if there exists an encoder-decoder scheme such that the Rate Distortion Function (RDF) of the source is equal to the capacity of the channel. This work is motivated by neurobiological data transmission and aims to design and analyze optimal communication systems consisting of channels and sources with memory, within a delay-sensitive environment. To this aim, we calculate the capacity of the given channel with memory and match it to a Markovian source via an encoder-decoder scheme, utilizing concepts from information theory and stochastic control theory. The most striking result to emerge from this research is that optimal and delayless communication for sources and channels with memory is not only feasible, but also it is achieved with the minimum complexity and computational cost. Though the current research is stimulated by a neurobiological application, the proposed approach and methodology as well as the provided results deliver several noteworthy contributions to a plethora of applications. These, among others, include delay sensitive and real time communication systems, control-communication applications and sensor networks. It addresses issues such as causality, power efficiency, complexity and security, extends the current knowledge of channels and source with memory, while it contributes to the inconclusive debates of real time communication and uncoded data transmission.