Qatar Foundation Annual Research Conference Proceedings Volume 2016 Issue 1

Background

As the number of handheld devices increases, wireless data traffic is expanding exponentially. With the ever increasing demand of higher data rate, it will be very challenging for the system designers to meet the requirement using limited Radio Frequency (RF) communication spectrum. One of the possible remedies to overcome this problem is the use of freely available visible light spectrum [1].

Introduction

This paper proposes an indoor communication system by utilizing Visible Light Communication (VLC). VLC technology utilizes visible light spectrum (750–380 nm) not only for illumination but with an additive advantage of data communication [2]. Visible Light Communication exploits high frequency switching capabilities of Light Emitting Diodes (LEDs) to transmit data. A receiver generally containing a Photo Diode receives signals from optical source and can easily decode the information being transmitted. In practical systems, usage of CMOS imager as a receiver containing an array of Photo Diodes is preferred over a single Photo Diode. Such receiver will enable multi-target detection and multi-channel communication resulting in more robust transceiver architecture [3].

Method

This work demonstrates a real-time transceiver implementation for Visible Light Communication on FPGA. A Pseudo Noise (PN) sequence is generated that will act as input data for the transmitter. Direct Digital Synthesizer (DSS) is implemented for the generation of carrier signal for modulation purpose [4]. Transmitter utilizes On-Off-Keying (OOK) for modulation of incoming data due to its simplicity [5]. The modulated signal is then converted into analog form using Digital-to-Analog (DAC) converter. An analog driver circuit is connected with digital transmitter which is capable of driving an array of Light Emitting Diodes (LEDs) for data transmission. Block level architecture of VLC Transmitter is shown in Fig. 1.

The receiver architecture uses analog circuitry including Photo Diodes for optical detection and Operational Amplifiers for amplification of received signal. Analog-to-Digital (ADC) conversion is performed before transmitting data back to FPGA for demodulation and data reconstruction. Figure 2 demonstrates the architecture of VLC Receiver.

Results and Conclusion

The system is implemented and tested using Xilinx Spartan 3A series FPGA [6]. Basic transceiver implementation utilizes data rate of 1Mbps with a carrier frequency of 5 MHz. However, in VLC, data rate and carrier frequency directly affects the optical characteristics including color and intensity of LEDs. Therefore, different data rates and modulation frequencies are evaluated for optimum data transmission with minimal effects on optical characteristics of LEDs. System complexity in terms of hardware resources and performance analysis including Bit Error Rate (BER) under varying conditions is also compared.

Results demonstrate that it is feasible to establish a low data rate communication link for indoor applications ranging up to 10 m using commercially available LEDs. Integrating a CMOS imager at receiver end will enable a VLC based Multiple-Input-Multiple-Output (MIMO) communication link that can serve multiple channels, maximizing to 1 channel per pixel [3]. Higher data rates are also achievable by utilizing high data rate modulation techniques (OFDM) at the expense of computational complexity and hardware resource utilization [7].

One of the possible implications of this work could be the implementation of VLC based Indoor positioning and navigation system. It can be a potential benefit for large constructions involved in public interactions including but not limited to hospitals, customer support centers, public facilitation offices, shopping malls and libraries. The system will largely utilize existing infrastructure of indoor illumination with added advantage of data communication.

The study also proposes extension of this work for utilization of VLC in outdoor applications. However, more robust algorithms are required for outdoor communication due to the presence of optical noise and interference caused by weather and atmospheric conditions. Robustness of existing algorithm can be increased by integrating Direct Sequence Spread Spectrum (DSSS) together with OOK for modulation. However, further research work is required to evaluate the performance, complexity and robustness of this system under realistic conditions.

References

[1]Cisco Visual Networking Index, “Global Mobile Data Traffic Forecast Update, 2012–2017,” CISCO, White Paper, Feb. 2013.

[2]Terra, D. Inst. de Telecomun., Univ. de Aveiro, Aveiro, Portugal, Kumar, N. Lourenco, N. Alves, L.N.,” Design, development and performance analysis of DSSS-based transceiver for VLC”, EUROCON - International Conference on Computer as a Tool (EUROCON), 2011 IEEE.

[3]“Image Sensor Communication”. VLCC Consortium.

[4]Xilinx DDS Compiler IP Core “http://www.xilinx.com/products/intellectual property/

dds_compiler.html#documentation”

[5]Nuno Lourenço, Domingos Terra, Navin Kumar, Luis Nero Alves, Rui L Aguiar, “Visible Light Communication System for Outdoor Applications”, 8th IEEE, IET International Symposium on Communication Systems, Networks and Digital Signal Processing

[6]Xilinx Spartan-3A Starter Kit “http://www.xilinx.com/products/boards-and-kits/

hw-spar3a-sk-uni-g.html”

[7]Liane Grobe, Anagnostis Paraskevopoulos, Jonas Hilt, Dominic Schulz, Friedrich Lassak, Florian Hartlieb, Christoph Kottke, Volker Jungnickel, and Klaus-Dieter Langer, “High Speed Visible Light Communication Systems”, IEEE Communications Magazine, December 2013.

One of the characteristics of writing in Modern Standard Arabic (MSA) is that the commonly used orthography is mostly consonantal and does not provide full vocalization of the text. It sometimes includes optional diacritical marks (henceforth, diacritics or vowels).

Arabic script consists of two classes of symbols: letters and diacritics. Letters comprise long vowels such as A, y, w as well as consonants. Diacritics on the other hand comprise short vowels, gemination markers, nunation markers, as well as other markers (such as hamza, the glottal stop which appears in conjunction with a small number of letters, dots on letters, elongation and emphatic markers) which in all, if present, render a more or less exact precise reading of a word. In this study, we are mostly addressing three types of diacritical marks: short vowels, nunation, and shadda (gemination).

Diacritics are extremely useful for text readability and understanding. Their absence in Arabic text adds another layer of lexical and morphological ambiguity. Naturally occurring Arabic text has some percentage of these diacritics present depending on genre and domain. For instance, religious text such as the Quran is fully diacritized to minimize chances of reciting it incorrectly. So are children's educational texts. Classical poetry tends to be diacritized as well. However, news text and other genre are sparsely diacritized (e.g., around 1.5% of tokens in the United Nations Arabic corpus bear at least one diacritic (Diab et al., 2007)).

In general, building models to assign diacritics to each letter in a word requires a large amount of annotated training corpora covering different topics and domains to overcome the sparseness problem. The currently available diacritized MSA corpora are generally limited to the newswire genres (those distributed by the LDC) or religion related texts such as Quran or the Tashkeela corpus. In this paper we present a pilot study where we annotate a sample of non-diacritized text extracted from five different text genres. We explore different annotation strategies where we present the data to the annotator in three modes: basic (only forms with no diacritics), intermediate (basic forms–POS tags), and advanced (a list of forms that is automatically diacritized). We show the impact of the annotation strategy on the annotation quality.

It has been noted in the literature that complete diacritization is not necessary for readability Hermena et al. (2015) as well as for NLP applications, in fact, (Diab et al., 2007) show that full diacritization has a detrimental effect on SMT. Hence, we are interested in discovering the optimal level of diacritization. Accordingly, we explore different levels of diacritization. In this work, we limit our study to two diacritization schemes: FULL and MIN. For FULL, all diacritics are explicitly specified for every word. For MIN, we explore what is a minimum and optimal number of diacritics that needs to be added in order to disambiguate a given word in context and make a sentence easily readable and unambiguous for any NLP application.

We conducted several experiments on a set of sentences that we extracted from five corpora covering different genres. We selected three corpora from the currently available Arabic Treebanks from the Linguistic Data Consortium (LDC). These corpora were chosen because they are fully diacritized and had undergone significant quality control, which will allow us to evaluate the anno tation accuracy as well as our annotators understanding of the task. We select a total of 16,770 words from these corpora for annotation. Three native Arabic annotators with good linguistic background annotated the corpora samples. Diab et al. (2007), define six different diacritization schemes that are inspired by the observation of the relevant naturally occurring diacritics in different texts. We adopt the FULL diacritization scheme, in which all the diacritics should be specified in a word. Annotators were asked to fully diacritize each word.

The text genres were annotated following the different strategies:

- Basic: In this mode, we ask for annotation of words where all diacritics are absent, including the naturally occurring ones. The words are presented in a raw tokenized format to the annotators in context.

- Intermediate: In this mode, we provide the annotator with words along with their POS information. The intuition behind adding POS is to help the annotator disambiguate a word by narrowing down on the diacritization possibilities.

- Advanced: In this mode, the annotation task is formulated as a selection task instead of an editng task. Annotators are provided with a list of automatically diacritized candidates and are asked to choose the correct one, if it appears in the list. Otherwise, if they are not satisfied with the given candidates, they can manually edit the word and add the correct diacritics. This technique is designed in order to reduce annotation time and especially reduce annotator workload. For each word, we generate a list of vowelized candidates using MADAMIRA (Pasha et al., 2014). MADAMIRA is able to achieve a lemmatization accuracy 99.2% and a diacritization accuracy of 86.3%. We present the annotator with the top three candidates suggested by MADAMIRA, when possible. Otherwise, only the available candidates are provided.

We also provided annotators with detailed guidelines, describing our diacritization scheme and specifying how to add diacritics for each annotation strategy. We described the annotation procedure and specified how to deal with borderline cases. We also provided in the guidelines many annotated examples to illustrate the various rules and exceptions.

In order to determine the most optimized annotation setup for the annotators, in terms of speed and efficiency, we test the results obtained following the three annotation strategies. These annotations are all conducted for the FULL scheme. We first calculated the number of words annotated per hour, for each annotator and in each mode. As expected, following the Advanced mode, our three annotators could annotate an average of 618.93 words per hour which is double those annotated in the Basic mode (only 302.14 words). Adding POS tags to the Basic forms, as in the Intermediate mode, does not accelerate the process much. Only − 90 more words are diacritized per hour compared to the basic mode.

Then, we evaluated the Inter-Annotator Agreement (IAA) to quantify the extent to which independent annotators agree on the diacritics chosen for each word. For every text genre, two annotators were asked to annotate independently a sample of 100 words.

We measured the IAA between two annotators by averaging WER (Word Error Rate) over all pairs of words. The higher the WER between two annotations, the lower their agreement. The results obtained show clearly that the Advanced mode is the best strategy to adopt for this diacritization task. It is the less confusing method on all text genres (with WER between 1.56 and 5.58).

We also conducted a preliminary study for a minimum diacritization scheme. This is a diacritization scheme that encodes the most relevant differentiating diacritics to reduce confusability among words that look the same (homographs) when undiacritized but have different readings. Our hypothesis in MIN is that there is an optimal level of diacritization to render a text unambiguous for processing and enhance its readability. We showed the difficulty in defining such a scheme and how subjective this task can be.

Acknowledgement

This publication was made possible by grant NPRP-6-1020-1-199 from the Qatar National Research Fund (a member of the Qatar Foundation).

Introduction

This research aims to enhance the culture of data in education which is in the middle of a major transformation by technology and Big Data Analytics. The core purpose of schools is providing an excellent education to every learner; data can be the leverage of that mission. Big data analytics is the process of examining large sets containing a variety of data types to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. Valuable lessons can be learnt from other industries when considered in terms of their practicality for public education. Hence, Big Data Analytics, also known as Education Data Mining and Learning Analytics, develop capacity for quantitative research in response to the growing need for evidence-based analysis related to education policy and practice. However, education has been slow to follow the Data Analytics evolution due to difficulties surrounding what data to collect, how to collect those data and what they might mean. Our research works identify, quantify, and measure the qualitative teaching practices, the learning performances, and track the learners' academic progress. Teaching and learning databases are accumulated from quantitative “measures” done through indoors classroom visits within academic institutions, online web access learners' questionnaires answers, paper written statements' analysis of academic exams in mathematics, science, and literacy disciplines, and online elementary grades seizure from written traces of learners' performances within mathematics, science, and literacy exams. The project's data mining strategy will support and develop teachers' expertise, enhance and scaffold students' learning, improve and raise education system's performance. The supervisor expertise will mentor the researcher for information and educational knowledge extraction from collected data. As consequence, the researcher will acquire the wisdom of knowledge use to translate it into more effective training sessions on educational concrete policies.

State-of-the-art

Anne Jorro says: “evaluate is necessarily considering how we will support, advise, exchange, to give recognition to encourage the involvement of the actor giving him the means to act”. PISA report states that many of the world's best-performing education systems have moved from bureaucratic “command and control” environments towards school systems in which the people at the frontline have much more control. Making teaching and learning data available leads to information then knowledge extraction. As advised by PISA report, the effective use of extracted knowledge drives decision making to wisdom. Linda Darling-Hammond and Charles E. Ducommun underscore the important assumption that, undoubtedly, teachers are the fulcrum that has the biggest impact and makes any school initiative leads toward success or failure. Rivkin and al ensure that a teacher's classroom instructional practice is perhaps one of the most important yet least understood factors contributing to teacher effectiveness. As consequence, many classroom observation tools are designed to demystify effective teaching practices. The Classroom Assessment Scoring System is a well-respected classroom climate observational system. The CLASS examines three domains of behaviour including, firstly, emotional support (positive classroom climate, teacher sensitivity, and regard for student perspectives). Secondly, it includes classroom organization (effective behaviour management, productivity, and instructional learning formats). Thirdly, it contains instructional supports (concept development, quality of feedback, and language modelling). The Framework for Teaching Method for Evaluation Classroom Observation is lastly releasing as 2013 edition. It divides the complex activity of teaching into 22 components clustered into four domains of teaching responsibility. This last tool's edition was conceived to respond to the instructional implications of the American Common Core State Standards. Those standards envision, for literacy and mathematics initially, deep engagement by students with important concepts, skills, and perspectives. They emphasize active, rather than passive, learning by students. In all areas, they place a premium on deep conceptual understanding, thinking and reasoning, and the skill of argumentation. Heather Hill from Harvard University, and Deborah Loewenberg Ball from university of Michigan, had developed “Mathematical Quality of Instruction (MQI)”. Irving Hamer is an education consultant and a former deputy superintendent for academics, technology, and innovation for school system.

Objectives

Our project's wider objective is to improve teaching and learning effectiveness within K12 classes by exploiting data mining methods for educational knowledge extraction. The researcher realizes three daily visits to mathematics, science, and literacy courses. Using his interactive educational grid, an average of 250 numerical data were stored as quantified teaching and learning practices for one classroom visit for every teacher. At the same time through, and in parallel with on-field activities, distance interactivity via website is processed. At the beginning and for once, each learner from planned classes to be visited fills an individual questionnaire form for learning style identification. He seizes, on another website form, every elementary grade on each question from his maths, science and literacy exams' answer sheets. Those exams statements were previously analysed and saved by the researcher on website. Averages of 150 numerical data were stored as quantified learning performances for every learner. Meetings at partner University for data analytics and educational knowledge extraction were done followed by meetings at inspectorate headquarters for in-depth data. Then, in partner schools, training sessions were the theatres of constructive reflections and feedbacks on major findings about teaching and learning effectiveness. Those actions were reiterated for months. Each year, averagely the performance of 1000 students and the educational practices of 120 teachers will be specifically and tracked. Within summer's months, workshops, seminars, and an international conference will be organised for stakeholders from educational fields. Thus, among project's actions three specific objectives shall be achieved. First, sufficient data on students' profiles and performances related to educational weaknesses and strengths will be provided. Second, teachers' practices inside classrooms at each partner school will be statistically recorded. Third, a complete data mining centre for educational research will be conceived and cognitively interpreted by researchers' teams then findings are exposed for teachers' reflexive thoughts, and discussions within meetings and training sessions

Research methodology and approach

1.Introduction

Social media gives us instant access to a continuous stream of information generated by users around the world. This enables real-time monitoring of users’ behavior (Abbar et al., 2015), events’ life-cycles (Weng and Lee. 2010), and large-scale analysis of human interactions in general. Social media platforms are also used to propagate influence, spread content, and share information about events happening in real-time. Detecting the location of events directly from user-generated text can be useful in different contexts, such as humanitarian response, detecting the spread of diseases, or monitoring traffic. In this abstract, we define a system that can be used for any of the purposes described above, and illustrate its usefulness with an application for locating traffic-related events (e.g., traffic jams) in Doha.

The goal of this project is to design a system that, given a social-media post describing an event, predicts whether or not the event belongs to a specific category (e.g., traffic accidents) within a specific location (e.g. Doha). If the post is found to belong to the target category, the system proceeds with the detection of all possible mentions of locations (e.g. “Corniche”, “Sports R/A”, “Al Luqta Street.”, etc.), landmarks (“City Center”, “New Al-Rayyan gas station”, etc.), and location expressions (e.g. “On the Corniche between the MIA park and the Souq)”. Finally, the system geo-localizes (i.e. assigns latitude and longitude coordinates) to every location expression used in the description of the event. This makes it useful for placing the different events onto a map; a downstream application will use these coordinates to monitor real-time traffic, and geo-localize traffic-related incidents.

2.System Architecture

In this section we present an overview of our system. We first describe its general “modular” architecture, and then proceed with the description of each module.

2.1. General view

The general view of the system is depicted in Figure 1. The journey starts by listening to some social media platforms (e.g., Twitter, Instagram) to catch relevant social posts (e.g., tweets, check-ins) using a list of handcrafted keywords related to the context of the system (e.g., road traffic). Relevant posts are then pushed through a three-steps pipeline in which we double-check the relevance of the post using an advanced binary classifier (Content Filter). We then extract location names mentioned in the posts if any. Next, we geo-locate the identified locations to their accurate placement on the map. This process allow to filter undesirable posts, and augment the relevant once with precise geo-location coordinates which are finally exposed for consumption via a restful API. We provide below details on each of the aforementioned modules.

Figure 1: Data processing pipeline.

2.2. Content filter

The Content Filter consists of a binary classifier that, given a tweet deemed to be about Doha, decides whether the tweet is a real-time report about traffic in Doha or not. The classifier receives as input tweets that have been tweeted from a location enclosed in a geographic rectangle (or bounding box) that roughly corresponds to Doha, and that contain one or more keywords expected to refer to traffic-related events (e.g., “accident”, “traffic”, “jam”, etc.). The classifier is expected to filter out those tweets that are not real-time reports about traffic (e.g., tweets that mention “jam”’ as a type of food, tweets that complain about the traffic in general, etc.). We build the classifier using supervised learning technology; in other words, a generic learning process learns, from a set of tweets that have been manually marked as being either real-time reports about traffic or not, the characteristics that a new tweet should have in order to be considered a real-time report about traffic. For our project, 1000 tweets have been manually marked for training purposes. When deciding about a new tweet, the classifier looks for “cues” that, in the training phase, have been found to be “discriminative”, i.e., helpful in taking the classification decision. In our project, we used the Stanford Maximum Entropy Classifier (Manning and Klein, 2003) to perform the discriminative training. In order to generate candidate cues, the tweet is preprocessed via a pipeline of natural language analysis tools, including a social-media-specific tokenizer (O'Connor et al., 2010) which splits words, and a rule-based Named-Entity Simplifier which substitutes mentions of local entities by their corresponding meta-categories (for example, it substitutes “@moi_qatar” or “@ashghal” for “government_entity”).

2.3.NLP components

The Location Expression Extractor is a module that identifies (or extracts) location expressions, i.e., natural language expressions that denote locations (e.g., “@ the Slope roundabout”, “right in front of the Lulu Hypermarket”, “on Khalifa”, “at the crossroads of Khalifa and Majlis Al Taawon”, etc.). A location expression can be a complex linguistic object, e.g., “on the Corniche between the MIA and the underpass to the airport”. A key component of the Location Expression Extractor is the Location Named Entity Extractor, i.e., a module that identifies named entities of Location type (e.g. “the Slope roundabout”) or Landmark type (e.g., “the MIA”). For our purposes, a location is any proper name in the Doha street system (e.g., “Corniche”, “TV roundabout”, “Khalifa”, “Khalifa Street”); landmarks are different from locations, since the locations are only functional to the Doha street system, while landmarks have a different purpose (e.g., the MIA is primarily a museum, although its whereabouts may be used as a proxy of a specific location in the Doha street system – i.e., the portion of the Corniche that is right in front of it).

The Location Named Entity Extractor receives as input the set of tweets that have been deemed to be about some traffic-related event in Doha, and returns the same tweet where named entities of type Location or of type Landmark have been marked as such. We generate a Location Named Entity Extractor via (again) supervised learning technology. In our system, we used the Stanford CRF-based Named Entity Recognizer (Finkel et al., 2005) to recognize named entities of type Location or of type Landmark using a set of tweets where such named entities have been manually marked. From these “training” tweets the learning system automatically recognizes the characteristics that a natural language expression should have in order to be considered a named entity of type Location or of type Landmark. Again, the learning system looks for “discriminative” cues, i.e., features in the text that may indicate the presence of one of the sought named entities. To improve the accuracy over tweets, we used a tweet-specific tokenizer (O'Connor et al., 2010), a tweet-specific Part-of-Speech tagger (Owoputi et al., 2013) and an in-house gazetteer of locations related to Qatar.

2.4.Resolving location expression onto the map

Once location entities are extracted using the NLP components, we use the APIs of Google, Bing and Nominatim to request the geographic coordinates of the map location entities into geographic coordinates. Each location entity is geo-coded by the Google Geolocation API, Bing Maps REST API and Nomination gazetteer individually. We use multiple geo-coding sources to increase the robustness of our application, as a single API might fail to retrieve geo-coding data. Given a location entity, the result of the geo-coding retrieval is formatted as a JSON object containing the name of the location entity, its address, and the corresponding geo-coding results from Bing, Google or Nominatim. The geo-coding process is validated by comparing the results of the different services used. We first make sure that the location returned falls within Qatar's bounding box. We then compute the pairwise distance between the different geographic coordinates to ensure their consistency.

2.5.Description of the Restful API

In order to ease the consumption of the relevant geo-located posts and make it possible to integrate these posts in a comprehensive way with other platforms, we have built a Restful API. In the context of our system, this refers to using HTTP verbs (GET, POST, PUT) to retrieve relevant social posts stored by our back-end processing.

Our API exposes two endpoints: Recent and Search. The former endpoint provides an interface to request the latest posts identified by our system. It supports two parameters: Count (maximum number of posts to return) and Language (the language of posts to return i.e., English or Arabic.) The later endpoint enables querying the posts for specific keywords and return only posts matching them. This endpoint supports three parameters: Query (list of keywords), Since (date-time of the oldest post to retrieve), From-To (two date-time parameters to express the time interval of interest.) In the case of a road traffic application, one could request tweets about “accidents” that occurred in West-Bay since the 10th of October.

3.Target: single architecture for multiple applications

Our proposed platform is highly modular (see Figure 1). This guarantees that relatively simple changes in some modules can make the platform relevant to any applicative context where locating user messages on a map is required. For instance, the content classifier – the first filtering element in the pipeline – can be oriented to mobility problems in a city: accident or congestion reporting, road blocking or construction sites, etc. With the suitable classifier, our platform will collect traffic and mobility tweets, and geo-locate them when possible. However, there are many other contexts in which precise location is needed. For instance, in natural disaster management, it is well admitted that people involved in catastrophic events (floods, typhoons, etc.) use social media as a means to create awareness, demand help or medical attention (Imran et al., 2013). Quite often, these messages may contain critical information for relief forces, who may not have enough knowledge of the affected place and/or accurate information of the level of damage in buildings or roads. Often, the task to read, locate on a map and mark is crowd-sourced to volunteers; we foresee that, in such time-constrained situations, our proposed technology would represent an advancement. Likewise, the system may be oriented towards other applications: weather conditions, leisure, etc.

4.System Instantiation

We have instantiated the proposed platform to the problem of road traffic in Doha. Our objective is to sense in real-time the traffic status in the city using social media posts only. Figure 2 shows three widgets of the implemented system. First, the Geo-mapped Tweets Widget shows a Doha map with different markers: the yellow markers symbolize the tweets geo-located by the users, the red markers represent the tweets geo-located by our system; the large markers come from tweets that have an attached photo, while the small markers represent the text-only tweets. Second, the Popular Hashtags Widget illustrates hashtags mentioned by the users, where the large font size shows the most frequent one. Third, the Tweets Widget lists the traffic-related tweets which are collected by our system.

Figure 2: Snapshot of some System's frontend widgets.

5.References

This paper addresses an important problem in the aircraft traffic management caused by the rapid growth of air traffic. The air route traffic control center has to deal with different plans of airlines in which they specify a requested entry time of their aircraft to the airspace. Each flight has to be assigned to a track and a level in order to ensure the Federal Aviation Administration (FAA) safety standards. When two flights are assigned to the same track and level, a minimum separation time has to be ensured. If this condition could not be satisfied, one of the flight will be delayed. This solution is undesirable for many reasons such as missing the connecting flight, decrease in the passengers' satisfaction, etc.

The problem of track-level scheduling can be defined as follows. Given a set of flights, each flight has to be assigned to one track and one level. To ensure the separation time between two flights assigned to the same track and level, it is possible to delay the requested departure time of a flight. The objective is to minimize the overall flight delay.

To deal with this problem, we propose a mixed integer programming formulation to find a flight plan that minimizes the objective function, while ensuring the FAA safety standards. In particular, this model considers an aircraft-dependent separation time: the separation time depends on the type of the aircraft assigned to the same track and level. However, some problems are too large to be solved in a reasonable time with the proposed model using a commercial solver. In this study, we developed a scatter search (SS) to deal with larger instances. SS is an evolutionary heuristic and the feature to be a problem-independent structure. This metaheuristic has been efficiently applied to a variety of optimization problems. Initially, SS starts with a set of solutions (reference set) that is constantly updated through two procedures (solution generation and combination) in the aim to produce high-quality solutions.

In order to assess the quality of the exact method and the scatter search, we carried out an experimental study on a set of instances that are generated from a real case data. This includes small (80 to 120 flights), medium (200 to 220 flights), and large (400 to 420 flights) instances. The mathematical model has been solved using CPLEX 12.6 and the scatter search has been coded using C language under Microsoft Visual Studio v12 environment. The tests were conducted under a Windows 7 machine with an Intel Core i7 and 8 GB of RAM. The model was tested on each instance with 1 hour time limit. The results show that no instances have been solved to optimality. For small instances, the model and the scatter search provide comparable results; however, for medium and large instances, scatter search gives the best results.

This conference was made possible by the UREP award [UREP 13 - 025 - 2 - 010] from the Qatar National Research Fund (a member of The Qatar Foundation).

Simultaneous wireless information and power transfer (SWIPT) is a promising solution to increase the lifetime of wireless nodes and hence alleviate the energy bottleneck of energy constrained wireless networks. To recent days, there are three different designs of SWIPT system which includes integrated SWIPT, closed-loop SWIPT, decoupled SWIPT. Integrated SWIPT is the simplest design where power and information are extracted by the mobile from the same modulated microwave transmitted by a base station (BS). For this scheme, the information transfer (IT) and power transfer (PT) distances are equal. For the closed loop scenario, it splits IT and PT between uplink and downlink wherein PT is in downlink and IT is for dedicated for uplink. The last one is to add additional special base station (BS) called a power beacon PB in which PT and IT are orthogonalized by using different frequency bands or time slots to avoid interference. Therefore, powering a cognitive radio networks through RF energy harvesting can be efficient in terms of spectrum usage and energy limits for wireless networking. The RF energy harvesting technique also is applicable in cooperative networks wherein an energy constrained relay with limited battery depends on external charging mechanism to assist the transmission of source information to the destination. In an effort to further improve spectrum sharing network performance, a number of works has suggested the idea of incorporating the multiple antenna technique into cognitive relaying. In particular, transmit antenna selection with receive maximal ratio combining (TAS/MRC) is adopted as a low complexity and power efficient approach which achieves full transmit/receive diversity.

Since the SUs and PUs share the same frequency band, there will be inevitably interference between the SUs and PUs. Therefore, reducing the effect of PU interference on the performance of secondary receiver is of significance important. Consequently, smart antennas can be employed to mitigate the PU interference. With knowledge of the direction of arrival (DoA), the receive radiation pattern can be shaped to place deep nulls in the directions of some of interfering signals. By doing so, two null-steering algorithms were proposed in the literature i.e., dominant interference reduction algorithm, and adaptive arbitrary interference reduction algorithm. The first algorithm requires perfect predication and statistical ordering of the interference signals instantaneous power, and the later algorithm does not need prior knowledge of the statistical properties of interfering signals. In this work, we limit our analysis to the dominant interference reduction algorithm.

In this work, we consider a dual-hop relaying with amplify-and-forward (AF) scheme where the source, relay, and the destination are equipped with multiple antennas. The relay node is experiencing co-channel interference. The purpose of array processing at the relay is to provide interference cancellation. Therefore, the energy constrained relay collects energy from ambient RF signals, cancel CCI, and then forward the information to the destination. In particular, we provide a comprehensive analysis for the system assuming selection at the source, and the destination. We derive the end-to-end exact and asymptotic outage probability for the proposed system model. A key parameters are also obtained featuring the diversity and coding gains.

The field of plasmonics is continuously attracting research in the area of integrated photonics development, to highly integrate photonic components, devices and detectors in a single photonic chip, just as electronic chips containing many electronic components. The interesting properties that plasmonics offer include the electromagnetic fields enhancement and the confinement of the propagating surface plasmon polaritons to sub-100 nm size features at metal dielectric interfaces. Thereby, the field of plasmonics is very promising for minimizing the photonic components to smaller sizes that cannot be experienced using conventional optics and in particular the Silicon photonics industry. Many applications based on plasmonics are being increasingly developed and studied such as electromagnetic field enhancement for surface enhanced spectroscopy, wave guiding, sensing, modulation, switching, and photovoltaic applications.

We hereby propose a novel compact plasmonic resonator that can be utilized for different applications that depend on optical resonance phenomena in the Near Infrared spectral range, a very interesting range for a variety of applications, including sensing and modulation. The resonator structure consists of a gold layer which is etched to form a metal-insulator-metal wave guide and a rectangular cavity. The rectangular cavity and the wave guide are initially treated as a dielectric material. The strong reflectivity of gold at frequencies higher than the plasma frequency is the origin of the Fabry Perot resonator behavior of the rectangular cavity. The fano resonance was produced successfully and controlled by varying the rectangular cavity dimensions. The fano profile is generated in as the result of the redistribution of the electromagnetic field in the rectangular cavity as depicted by the plasmonic mode distribution in the resonator. The fano resonance is characterized by its sharp spectral line which attracts applications requiring sharp spectral line shapes such as sensing and modulation applications.

Optical modulators are key components in the modern communication technology. The research trend on optical modulators aims at achieving compact designs, low power consumption and large bandwidth operation. Plasmonic modulators emerge as promising devices since they can have high modulation speeds and very compact designs.

The operation mechanism of our introduced plasmonic modulator is as follows: instead of a constant refractive index dielectric, an electro-optic polymer that has its refractive index is dependent on some controlled phenomena, is filled in the metal insulator metal waveguide and the rectangular cavity. Efficient modulation was achieved by changing the applied voltage (DC Signal) on the metal contacts which then changes the refractive index of the polymer, thereby shifting the resonant wavelength position in the resonator, leading to signal modulation. Our modulator is operational at the telecom wavelength 1.55 μm, thereby suitable for the modern communication technology.

Finite Difference Time Domain (FDTD) simulations were conducted to design the modulator structure and run the simulations experiments, and to study the resonance effects of the structure and optimize its response to the desired results, the most important results however are the efficient modulation of the optical energy at the wavelengths required in the modern communication technology, around 1.5 μm, all results were carried on using the commercially available Lumerical FDTD software.

Handled devices and software applications are susceptible to ameliorate learning strength, awareness, and career development. Many mobile-based learning applications are obtainable from the market but Arabic learning shortage is not taken in consideration. We conduct an Arabic Text-to-Picture (TTP) mobile educational application which performs knowledge extraction and concept analysis to generate pictures that represent the content of the Arabic text. The knowledge extraction is based on Arabic semantic models cover important scopes for young children and new Arabic learners (i.e., grammar, nature, animals). The concept analysis uses semantic reasoning, semantic rules, and Arabic natural text processing (NLP) tool to identify word-to-word relationships. The retrieval of images is done spontaneously from local repository and online search engine (i.e., Google or Bing). The instructor can select the Arabic educational content, get semi-automatic generated pictures, and use them for explanation. Preliminary results show improvement in Arabic learning strength and memorization.

Keywords

Mobile Learning, Natural Language Processing, Ontology, Multimedia Learning, Engineering Education.

I. Introduction

Nowadays, mobile learning environment has been excessively used in diverse fields and has become a common matter in educational movement. In such an environment, learners are able to reach online educational materials from any location. Learners of Arabic language suffer from the lack of adequate resources. In fact, most of the educational software, tools, and web sites use classical techniques of introducing the concepts and explaining the vocabulary. We present in this paper a text-to-picture (TTP) educational mobile system that promotes Arabic children stories through semi-automatic generated pictures to illustrate their contents in an attractive manner. Preliminary results show that the system enhances the Arabic learners' comprehension, deduction and realization.

II. Background

Natural language processing (NLP) stresses the extraction of useful information and mining natural text. These information can be used to identify the scope of the text in order to generate summaries, classify contents and teach vocabulary. Diverse NLP-based systems that illustrate the text to images have been developed recently [1, 2, 3]. In general, these systems divide the text into segments and single words, access local multimedia resources, or explore the web to get pictures and images to illustrate the content.

All the proposed systems and techniques do not include the Arabic language. In this paper, we propose an Arabic TTP educational system using multimedia technology to teach children in an attractive way. Our proposal generates the multimedia tutorials dynamically by using Arabic text processing, entities relationship extraction, multimedia ontology, and online extraction of multimedia contents fetched from Google search engine.

III. Methodology:

In order to develop our system, we have created first the general system artwork, set the end user graphical user interface, design the semantic model that will store all semantic information about terms, and collect educational stories and analyze them. We have gathered 30 educational stories, annotated terms, and associated illustrations manually. Illustrations were gathered from the Internet and educational repository. The semantic model is developed using “Protégé editor”, a free open source ontology editor developed by Stanford [4]. The semantic model is composed from many classes that are referred to as concepts.

IV. The proposed system

The proposed system is a client-server application. When the server launched, it loads its packages and components, it loads the defined ontology, text parser components, and finally it opens a connection to listen for users' requests. Upon an effective connection trial, the user will be eligible to enter or open existing Arabic stories and process them. On the client side, the processing request and response of the story is done in a different thread, to keep the user able to continue his work without any interruption. Finally, server reply will be displayed for the user on his mobile device which consists from the processed Arabic story, related images, and different questions about an animal.

V. Conclusion

This study presents a complete system that automatically generates illustrations for Arabic stories through text processing, Arabic ontology, relationship extraction, and illustration generation. The proposed system belongs to learning technology which can be on mobile devices to teach children in an attractive and non-traditional style. Preliminary results demonstrate that the system improved learners' comprehension and realization.

References

[1] Bui, Duy, Carlos Nakamura, Bruce E Bray, and Qing Zeng-Treitler, “Automated illustration of patients instructions,” in AMIA Annual Symposium Proceedings, vol. 2012, pp. 1158, 2012.

[2] Li, Cheng-Te, Chieh-Jen Huang, and Man-Kwan Shan, “Automatic generation of visual story for fairy tales with digital narrative,” in Web Intelligence, vol. 13, pp. 115–122, 2015.

[3] Ustalov, Dmitry and R Kudryavtsev, “An Ontology Based Approach to Text to Picture Synthesis Systems,” in Proceedings of the 2nd International Workshop on Concept Discovery in Unstructured Data (CDUD 2012), 2012.

[4] Protégé. Ontology Editor Software. Available from: http://protege.stanford.edu, Accessed: September 2015.

Our research is focused on expanding the reach and impact of Arabic language news articles by attracting more readers. In pursuit of this research goal, we analyze attributes that result in certain news articles becoming viral, relative to other news articles that do not become viral or so viral. Specifically, we focus on Arabic language news articles, as Arabic language articles have unique linguistic, cultural, and social constrains relative to most Western languages news stories. In order to understand virality, we take two approaches, a time series and linguistical, in an Arabic language data of more than 1,000 news articles with associated temporal traffic data. For data collection, we select (Kasra, “a breaking”) (http://kasra.co/) is an Arabic language online news site that targets Arabic language speakers worldwide, but particularly in the Middle East North Africa (MENA) region. We gathered more than 3,000 articles, originally, then gathered traffic data for this set of articles, reducing the set to more than 1,000 with complete traffic data. We focus first on the temporal attributes in order to categorize clusters of virality with this set of articles. Then, with topical analysis, we seek to identify linguistical aspects common to articles within each virality cluster identified by time series. Based on results from the time series analysis, we cluster articles based on common temporal characteristics of traffic access. Once clustered by time series, we analyze each cluster for content attributes, topical and linguistical, in order to identify specific attributes that may be causing the virality of articles within each times-series cluster. To compute dissimilarity for time-series, we utilize and evaluate the performance of several state-of-the-art time series dissimilarity-based clustering approaches, such as dynamic time warping, discrete wavelet transformation, and others. To identify the dissimilarity algorithm with the most discriminating power, we conduct a principal component analysis (PCA), which is a statistical technique used to highlight variations and patterns in a dataset. Based on findings from our PCA, we select discrete wavelet transformation-based dissimilarity as the best times-series algorithm for our research because the resulting principal axes explain more proportion of variability (75.43 percent) relative to the other time-series algorithms that we had employed. We identify five virality clusters using times series. For topic modeling, we employ Latent Dirichlet allocation (LDA) for this portion of the research. LDA is a generative probabilistic model for collections of discrete data, such as text, LDA explains similarities among groups of observations within a data set. For text modeling, the topic probabilities of LDA provide an explicit representation of a document. For the topical classification analysis, we use Linguistic Inquiry and Word Count (LIWC), which is a sentiment analysis tool. LIWC is a text processing program based on occurrences of words in several categories covering writing style and psychological meaning. Prior empirical work shows the value of a LIWC linguistic analysis for detecting meanings in various experimental settings, including attention focus, thinking style, and social relationships. In terms of results, surprising, the article topic is not predictive of virality of Arabic language news articles. Instead we find that linguistical aspects and style of the news article is the most predictive attribute for predicting virality for Arabic news articles. In analyzing the attributes of virality in Arabic language news articles, our research finds that, perhaps counter intuitively, the topic of the article does not impact the virality. Instead, we find that style of the article is the most impactful attribute for predicting virality for Arabic news articles. Building on these findings, we will leverage aspects of the news articles with other factors to develop tools to assist content creators to more effectively reach their user segment. Our research results will assist in understanding the virality of Arabic news and ultimately improve readership and dissemination of Arabic language news articles.

Education of deaf children in the developing world is very dire and there is a dearth of sign language interpreters to assist them with translation for sign language-dependent students in the classrooms. Illiteracy within the deaf population is rampant. Over the past several years, a unique team of Moroccan and American deaf and hearing researchers have united to enhance the literacy of deaf students by creating tools that incorporate Moroccan Sign Language (MSL) and American Sign Language (ASL) under funding grants from USAID and the National Science Foundation (NSF). MSL is a gestural language distinct from both the spoken languages and written language of Morocco and has no text representation. Accordingly, translation is quite challenging and requires representation of MSL in graphics and video.

Many deaf and hard of hearing people do not have good facility with their native spoken language because they have no physiological access to it. Because oral languages depend, to a great extent, upon phonology, reading achievement of deaf children usually falls far short of that of hearing children of comparable abilities. And, by extension, reading instructional techniques that rely on phonological awareness, letter/sound relationships, and decoding, all skills proven essential for reading achievement, have no sensory relevance. Even in the USA, where statistics are available and education of the deaf is well advanced, on average, deaf high school graduates have a fourth grade reading level; only 7–10% of deaf students read beyond a seventh to eighth grade reading level; and approximately 20% of deaf students leave school with a second grade or below reading level (Gallaudet University's national achievement testing programs (1974, 1983, 1990, and 1996); Durnford, 2001; Braden, 1992; King & Quigley, 1985; Luckner, Sebald, Cooney, Young III, & Muir, 2006; Strong, & Prinz, 1997).

Because of spoken language inaccessibility, many deaf people rely on a sign language. Sign language is a visual/gestural language that is distinct from spoken Moroccan Arabic and Modern Standard/written Arabic and has no text representation. It can only be depicted via graphics, video, and animation.

In this presentation, we present an innovative technology Clip and Create, a tool for automatic creation of sign language supported instructional material. The technology has two tools– Custom Publishing and Instructional Activities Templates, and the following capabilities:

(1)Automatically constructs customizable publishing formats;

(2)Allows users to import Sign Language clip art and other graphics;

(3)Allows users to draw free-hand orusere-sizable shapes;

(4)Allows users to incorporate text, numbers, and scientific symbols in various sizes, fonts, and colors;

(5)Saves and prints published products;

(6)Focuses on core vocabulary, idioms, and STEM content;

(7)Incorporates interpretation of STEM symbols into ASL/MSL;

(8)Generates customizable and printable Instructional Activities that reinforce vocabulary and concepts found in instructional content using Templates:

a. Sign language BINGO cards,

b. Crossword puzzles,

c. Finger spelling/spelling scrambles,

d. Word searches (in finger spelling and text),

e. Flashcards (with sign, text, and concept graphic options), and

f. Matching games (i.e., Standard Arabic-to-MSL and English-to-ASL).

(cf. Figure 1: Screenshots from Clip and Create)

The ability of this tool to efficiently create bilingual (i.e., MSL and written Arabic and ASL and English) educational materials will have a profound positive impact on the quantity and quality of sign-supported curricular materials teachers and parents are able to create for young deaf students. And, as a consequence, deaf children will show improved vocabulary recognition, reading fluency, and comprehension.

A unique aspect of this software is that written Arabic is used by many Arab countries even though the spoken language varies. Though there are variations in signs as well, there is enough consistency to make this product useful in other Arab-speaking nations as is. Any signing differences can easily be adjusted by swapping sign graphic images.