Qatar Foundation Annual Research Conference Proceedings Volume 2018 Issue 3

One of the main suppliers of the workforce for the engineering industry and the economy in general is the higher education sector. The higher education sector is consistently being challenged by the fast evolving industry and hence it is under constant pressure to fulfill the industry's ever-changing needs. It needs to adapt its academic curricula to supply the industry with students who have update-to-date and relevant competencies. Nevertheless, the gap still exists between what education curricula offer and the skills that are actually needed by the industry. Therefore, it is crucial to find an efficient solution to bridge the gap between the two worlds. Bridging the gap helps the industry to cut the costs of training university graduates and assists in higher education advancement. In response to these issues, competency-based education was developed. It was first developed in the United States, in response to the growing criticisms towards traditional education that was seen as more and more disconnected from the societal evolutions, especially changes within the industry. Despite some criticisms, the competency-based education pedagogical approach has been employed by several western countries to improve their upper-secondary vocational curricula. In recent times, it started to be more and more adapted to the higher education, as a way to update and improve academic courses. In this research work, a semantic ontological model is presented to model the competencies in the domains of education and the industry. It illustrates the use of ontologies for three identified end users: Employers, Educators, and Students. Ontologies are best used to solve problems of interoperability between different domains, provide a shared understating of terms across domains and are helpful in avoiding the wasted effort when translating terminology between domains. They also provide opportunities for domain knowledge reuse in different contexts and applications. Ontologies can also act as unifying framework between software systems and eliminates interoperability and communication issues that are faced when trying to translate concepts between systems. The scope of the research work is to build an ontology representing the domain concepts and validate it by building competency models. The competencies from the domains of education and industry are defined and classified in the ontology. Then by using a set of logical rules and a semantic reasoner, we would be able to analyze the gap between the different education and industry profiles. We will propose different scenarios on how the developed ontology could be used to build competency models. This paper describes how ontologies could be a relevant tool for an initial analysis and focus on the assessment of the competencies needed by the engineering market as a case study. The research questions this work investigates are: 1) Are semantic web ontologies are the best solution to model the domain and analyze the gap between industry needs and higher education? 2) What ontology design approaches are more suitable for representing the competencies model? 3) What are the limitations of ontology modeling? Two main limitations are discussed: The Open World Assumption and the Semantic Reasoner limitations. This work is part of the Qatar Foundation NPRP Pro-Skima research project.

Many statistics show the number of connected devices (Internet of Things; IoT) worldwide to grow drastically in the next few years, to exceed 30 billion just by 2020. All these devices need to be connected to the internet, to establish tow ways communication with the backend applications. The list of applications and services that can be enabled by using IoT can be endless, covering different areas such as smart cities, smart home, connected vehicles, intelligent transport systems, agriculture, air and weather monitoring, industry 4.0, etc. One of the fundamental requirements of an IoT device is to be connected and reachable at any time. But when we look at the different applications that run on top of IoT, many of them do not require a continuous connection to the internet. For example, the air monitoring IoT devices, normally they sense and report data only once every 15 to 60 minutes. Such devices would not require a continuous connection to the Internet, but only every 15 to 60 minutes. For such devices and use-cases, we propose to use a flying IoT gateway, that can come next to the sensor every e.g. 15 minutes, to take the data that has been collected by the sensor and carry it to the backend. In this contribution we present a prototype of a solution that uses unmanned aerial vehicles (UAVs), aka drones, to provide a delay tolerant data routing solution for IoT devices. In this solution, a drone flies over a set of deployed IoT devices, to retrieve the collected and stored data and then deliver it to the backed. Such a solution can be suitable for sensing devices that do not require a real-time communication, like traffic speed cams. Indeed, the speed cams can collect data and store it locally, until a drone comes to carry and transfer it to the backend.This solution helps does not only reduce the overall cost by eliminating the cost of the Internet connectivity at each IoT device, but it also reduce the security vulnerability as the devices are physically not connected to the internet all the time, nor directly.This work has been been conducted under the R&D project NPRP9-257-1-056 which is funded and supported by QNRF.

Extended AbstractPeople increasingly use social media such as Facebook and Twitter during disasters and emergencies. Research studies have demonstrated the usefulness of social media information for a number of humanitarian relief operations ranging from situational awareness to actionable information extraction. Moreover, the use of social media platforms during sudden-onset disasters could potentially bridge the information scarcity issue, especially in the early hours when few other information sources are available. In this work, we analyzed Twitter content (textual messages and images) posted during the recent devastating hurricanes namely Harvey and Maria. We employed state of the art artificial intelligence techniques to process millions of textual messages and images shared on Twitter to understand the types of information available on social media and how emergency response organizations can leverage this information to aid their relief operations. Furthermore, we employed deep neural networks techniques to analyze the imagery content to assess the severity of damage shown in the images. Damage severity assessment is one of the core tasks for many humanitarian organization.To perform data collection and analysis, we employed our Artificial Intelligence for Digital Response (AIDR) technology. AIDR combines human computation and machine learning techniques to train machine learning models specialized to fulfill specific information needs of humanitarian organizations. Many humanitarian organizations such as UN OCHA, UNICEF have used the AIDR technology during many major disasters in the past including the 2015 Nepal earthquake, the 2014 typhoon Hagupit and typhoon Ruby, among others. Next, we provide a brief overview of our analysis during the two aforementioned hurricanes.Hurricane Harvey Case StudyHurricane Harvey was an extremely devastating storm that made landfall to Port Aransas and Port O'Connor, Texas, in the United States on August 24-25, 2017. We collected and analyzed around 4 million Twitter messages to determine how many of these messages are, for example, reporting some kind of infrastructure damage, or reports of injured or dead people, missing or found people, displacements and evacuation, donation and volunteers reports. Furthermore, we also analyzed geotagged tweets to determine the types of information originate from the disaster-hit areas compared to neighboring areas. For instance, we generated maps of different cities in the US in and around the hurricane hit areas. Figure 1 shows the map of geotagged tweets reporting different types of useful information from Florida, USA. According to the results obtained from the AIDR classifiers, both caution and advice and sympathy and support categories are more prominent than other informational categories such as donation and volunteering. In addition to the textual content processing of the collected tweets, we perform automatic image processing to collect and analyze imagery content posted on Twitter during Hurricane Harvey. For this purpose, we employ state-of-the-art deep learning techniques. One of the classifiers deployed in this case was the damage-level assessment. The damage-level assessment task aims to predict the level of damage in one out of three damage levels i.e., SEVERE damage, MILD damage, and NO damage. Our analysis revealed that most of the images (∼86%) do not contain any damage signs or considered irrelevant containing advertisements, cartoons, banners, and other irrelevant content. Of the remaining set, 10% of the images contain MILD damage, and only ∼4% of them show SEVERE damage. However, finding these 10% (MILD) or 4% (SEVERE) useful images is like finding a needle in a giant haystack. Artificial intelligence techniques such as employed by the AIDR platform are hugely useful to overcome such information overload issues and help decision-makers to process large amounts of data in a timely manner.Fig. 1: Geotagged tweets from Florida, USA.Hurricane Maria Case StudyAn even more devastating hurricane than the Harvey that hit Puerto Rico and nearby areas was hurricane Maria. Damaged roofs, uprooted trees, widespread flooding were among the scenes on the path of Hurricane Maria, a Category 5 hurricane that slammed Dominica and Puerto Rico and has caused at least 78 deaths including 30 in Dominica and 34 in Puerto Rico, and many more left without homes, electricity, food, and drinking water.We activated AIDR on September 20, 2017 to collect tweets related to Hurricane Maria. More than 2 million tweets were collected. Figure 2 shows the distribution of the daily tweet counts. To understand what these tweets are about, we applied our tweet text classifier which was originally trained (F1 = 0.64) on more than 30k human-labeled tweets from a number of past disasters. AIDR's image processing pipeline was also activated to identify images that show infrastructure damage due to Hurricane Maria. Around 80k tweets contained images. However, ∼75% of these images were duplicate. The remaining 25% (∼20k) images were automatically classified by the AIDR's damage assessment classifier into three classes as before. Figure 2: Tweets count per dayWe believe that more information can be extracted from image about the devastation caused by the disaster than relying solely on the textual content provided by the users. Even though it is in the testing phase, our image processing pipeline does a decent job in identifying images that show MILD or SEVERE damage. Instead of trying to look at all the images, humanitarian organizations and emergency responders can simply take a look at the retained set of MILD or SEVERE damage images to get a quick sense of the level of destruction incurred by the disaster.

According to Energy Technology Perspectives 2014, there are approximately 900 million light duty vehicles (not counting two- and three-wheelers) today, and that number will be doubled by 2050 to reach 2 billion. Such a considerable increase will bring-in further challenges for road safety and traffic efficiency. Motor vehicle crashes are the leading cause of death for children and young adults in the United States, with an annual death toll of 33,000 and over 2.3 million people injured. Those figures are representative of the challenge not only in the US but also in other regions including Qatar and the golf region. Vehicle to Vehicle and Vehicle to Infrastructure (V2X) communication technology, which is heading our roads in few years, is seen as a great solution to reduce number of accidents on the road, and it is considered as an enabler of next generation of road safety and Intelligent Transport System (ITS). V2X communication is not limited to vehicle-to-vehicle and vehicle-to-infrastructure, it is also meant to be used for vehicle-to-bicycle and even vehicle-to-pedestrian communication. Indeed, by enabling real-time and fast communication between vehicles and vulnerable road users such as bicycles and road users, we may make the road much safer for those vulnerable road users. This is one of the use cases we would like to enable and test in the Qatar V2X Field Operational Field (Qatar V2X FOT) which is supported and funded by Qatar National Research Fund (QNRF). Enabling vulnerable road-users such as bicycles and pedestrians with V2X capabilities has many challenges. The main challenge is the energy consumption, as V2X operates in 5.9GHz radio band, which consumes relatively high energy. Therefore, to operate V2X on those vulnerable users, especially the pedestrian, the user needs to hold a battery which requires to be regularly recharged. We came out with a solution to this problem, which reduces the energy consumption of the V2X device to a level it can operate a small battery suitable to be a wearable device. This poster will expose the challenges when using V2X on vulnerable road users, especially pedestrians, and it will present solution and the related prototype that is developed and tested within the NPRP project. The solution and prototype presented in this poster are the outcomes of the research project NPRP 8-2459-1-482 which is supported and funded by QNRF.

Twitter has been developed as an immense information creation and sharing network through which users post information. Information could vary from the world»s breaking news to other topics such as sports, science, religion, and even personal daily updates. Although a user would regularly check her Twitter timeline to stay up-to-date on her topics of interest, it is impossible to cope with manual tracking of those topics while tackling the challenges that emerge from the Twitter timeline nature. Among these challenges are the big volume of posted tweets (about 500M tweets are posted daily), noise (e.g., spam), redundant information (e.g., tweets of similar content), and the rapid development of topics over time. This necessitates the development of real-time summarization systems (RTS) that automatically track a set of predefined interest profiles (representing the users» topics of interest) and summarize the stream while considering the relevance, novelty, and freshness of the selected tweets. For instance, if a user is interested in following the updates on the “GCC crises», the system should efficiently monitor the stream and capture the on-topic tweets including all aspects of the topic (e.g., official statements, interviews and new claims against Qatar) which change over time. Accordingly, real-time summarization approaches should use simple and efficient approaches that can scale to follow multiple interest profiles simultaneously. In this work, we tackle such problem by proposing RTS system that adopts a lightweight and conservative filtering strategy. Given a set of user interest profiles, the system tracks those profiles over Twitter continuous live stream in a scalable manner in a pipeline of multiple phases including pre-qualification, preprocessing, indexing, relevance filtering, novelty filtering, and tweets nomination. In prequalification phase, the system filters out non-English and low-quality tweets (i.e., tweets that are too short or including many hashtags). Once a tweet is qualified, the system preprocesses it in a series of steps (e.g., removing special characters) that aim at preparing the tweet for relevance and novelty filters. The system adopts a vector space model where both interest profiles and incoming tweets are represented as vectors constructed using idf-based term weighting. An incoming tweet is scored for relevance against the interest profiles using the standard cosine similarity. If the relevance score of a tweet exceeds a predefined threshold, the system adds the tweet to the potentially-relevant tweets for the corresponding profile. The system then measures the novelty of the potentially-relevant tweet by computing its lexical overlap with the already-pushed tweets using a modified version of Jaccard similarity. A tweet is considered novel if the overlap does not exceed a predefined threshold. This way the system does not overwhelm the user with redundant notifications. Finally, the list of potentially-relevant and novel tweets of each profile is re-ranked periodically based on both relevance and freshness and the top tweet is then pushed to the user; that ensures the user will not be overwhelmed with excessive notifications while getting fresh updates. The system also allows the expansion of the profiles over time (by automatically adding potentially-relevant terms) and the dynamic change of the thresholds to adapt to the change in the topics over time. We conducted extensive experiments over multiple standard test collections that are specifically developed to evaluate RTS systems. Our live experiments on tracking more than 50 topics over a large stream of tweets lasted for 10 days show both effectiveness and scalability of our system. Indeed, our system exhibited the best performance among 19 international research teams from all over the world in a research track organized by NIST institute (in the United States) last year.

Complex adaptive systems exhibit emergent behavior. This type of behavior occurs in volatile environments involving cyber-physical systems, such as those aimed at smart cities operations. Adaptive systems’ maintenance aims at improving their performance by dealing with the treatment of continuous and frequently changing requirements. Adaptive systems’ behavior requires therefore up-to-date requirements traceability. To this end, we need to understand the requirements and to localize the program parts that should be modified according to the description of the new requirements. This process is known as Requirements Traceability Recovery (RTR). The process of generating requirements traceability, when done by a human (e.g., system maintainer), is time consuming and error-prone. Currently, most of the approaches, in the literature, are time consuming and semi-automatic that always need the intervention of the user. In our work, we are specifically interested in following the link between requirements and the code of the software system with the aim of helping the designer to update the system appropriately by automating the traceability process. To do this, we formulated the RTR problem as a complex combinatorial problem that could be tackled using Heuristic Search (HS) techniques. These techniques can intelligently explore a big search space (space of possible solutions) for a problem and find an acceptable approximate solution. Varieties of HS techniques exist in the literature. In our work, we use the Non-dominated Sorting Genetic Algorithm (NSGA-II), which is an improved version of a classic Genetic Algorithm (GA). NSGA-II is a multi-objective technique that aims at finding the best compromise between objectives (Pareto Front). The application of NSGA-II to a specific problem (i.e., the requirements traceability recovery problem in our context) requires the accomplishment of the following five elements: 1. Representation of the individuals (vector, tree, etc.), 2. Definition of the evaluation function (fitness function), 3. Selection of the (best) individuals to transmit from one generation to another, 4. Creation of new individuals using genetic operators (crossover and mutation) to explore the search space, 5. Generation of a new population using the selected individuals and the newly created individuals. The proposed approach takes as input a software system, a set of requirements, and the maintenance history of the software system and produces as output the trace links, i.e., the artifacts (classes, methods, etc.) related to each requirement. Three objectives are defined to support the NSGA-II: (1) semantic similarity and (2) Recency of Change (RC), and (3) Frequency of Change (FC). We used the cosine of the angle between the vector that represents the requirement and the vector that represents the software element to measure the semantic similarity. To calculate the RC measure, we used the information extracted from the history of change accumulated during the maintenance process of the software system. The intuition behind introducing the RC measure is that artifacts (classes, methods, etc.) that changed more recently than others are more likely to change now, i.e., are related to the new requirements at hand. To calculate the FC measure, we used the information extracted from the history of change accumulated during the maintenance process of the software system. The intuition behind introducing the FC measure is that artifacts (classes, methods, etc.) that change more frequently than others are more likely to change now, i.e., are related to the new requirement at hand. Each solution consists of assigning each requirement to one or many artifacts (classes, methods) of the system. The solution should maximize as much as possible the three objectives mentioned before. Experiments have been conducted on three open source systems in order to evaluate the approach. The obtained results confirm the effectiveness of the approach in correctly generating the traces between the requirements and classes in the source code with a precision of 91% on average and a recall of 89% on average. We also compared our results to those obtained by two recent works. We noticed that our approach outperforms the two other approaches and has higher average for precision and recall for all three projects.

Technology always seeks to improve the little details in our lives for a faster and a more efficient life-pace. One of these little problems we face in our daily lives is finding relevant events. For example you visit a place like Katara with your kids and you spend your time in vain looking for a fun event, and after you leave the venue a friend of yours tells you about this interesting “Henna and face painting workshop organized in building 25.”. To solve this problem we propose QEvents. QEvents is a platform that provides users with real-time recommendations about events happening around their location and that best match their preferences. QEvents renders the events in a map-centric dashboard to allow easy browsing and user-friendly interactions. QEvents continuously listens to online channels that broadcast information about events taking place in Qatar, including specialized websites (e.g. eventsdoha.com), social media (e.g. Twitter), and news (e.g. dohanews.com). The main challenge QEvents strives to solve is how to extract important features such as title, location, and time from free text describing the events. We will show in this paper how one could leverage existing technologies such as Topic modeling, Named Entity Recognition, and advanced text parsing to transform a plain event listing website into a dynamic and alive service capable of recognizing events’ location, title, category, as well as starting and ending time, and nicely rendering them in a map-centric visualization allowing a more natural exploration.

Tremendous interest in visualizing massive datasets has promoted tiled-display wall systems that offer an immersive and collaborative environment with an extremely high-resolution. To achieve an efficient visualization, the rendering process should be parallelized and distributed among multiple nodes. The Data Observatory at Imperial College London has a unique setup consisting of 64 screens powered by 32 machines providing a resolution of over 130 megapixels. Various applications have been developed to achieve a high-performance visualization by implementing parallel rendering techniques and incorporating distributed rendering frameworks. ParaView is one such application that targets the visualization of scientific datasets by taking computing efficiency into consideration. The main objective of this project is to leverage the potential of the Data Observatory and ParaView regarding visualization by fostering data exploration, analysis, and collaboration through a scalable and high-performance approach. The primary concept is to configure ParaView on a distributed clustered network and associate the appropriate view for each screen by controlling ParaView's virtual camera. The interaction events with the application should be broadcasted to all connected nodes in the cluster to update their views accordingly. The major challenges of such implementations are synchronizing the rendering across all screens, maintaining data coherency, and managing data partitioning. Moreover, the project is aimed at evaluating the effectiveness of large display systems compared to typical desktop screens. This has been achieved by conducting two quantitative studies assessing the individual and collaborative task performance. The first task was designed to investigate the mental rotation of individuals by displaying a pair of a 3D model, as proposed by Shepard-Metzler, on the screen with different orientations. Then, the participant was asked if both models were the same or mirrored. This would lead to evaluate the individual task performance by studying the ability to recognize the orientation change in 3D objects. It consisted of two levels: easy and hard. For the easy level, the second model was rotated for a maximum angle of 30 on two axes. In contrast, the hard level had no limitation on the angle of rotation. The second task was developed specifically for ParaView to assess the collaboration aspect. The participants had to use the basic navigational operations to find hidden needles injected in a 3D brain model in 90 seconds. In both tasks, the time taken to complete the task and the correctness were measured in two environments: 1) the Data Observatory, and 2) a simple desktop screen. The average correct responses of the mental rotation task have been calculated for all participants. It has been shown that the number of correct answers in the Data Observatory is significantly higher than on the desktop screen despite the amount of rotation. The participants could better distinguish mirrored objects from similar ones in the Data Observatory with a percentage of 86.7% and 73.3% in easy and hard levels, respectively. However, on the typical desktop screen, participants correctly answered less than half of the hard level questions. This indicates immersive large display environments provides a better representation and depth perception of 3D objects. Thus, improving the task performance of visualizing 3D scenes in fields that require the ability to detect variations in the position or orientation. Overall, the average completion time of both displays in the easy task is relatively the same. In contrast, the participants required a longer time to complete the hard task in the Data Observatory. This could be because of the large display space, which occupies a wide visual field, thus providing an opportunity to the viewers to ponder and think about the right answer. In the collaborative search task, the participants found all the hidden needles within the time limitation in the Data Observatory. The fastest group completed the task in 36 seconds while the longest recorded time was around one minute and 12 seconds. However, on the desktop screen, all participants consumed the full 90 seconds. In the small screen environment, the mean of the correct responses is estimated as 55%. The maximum number of needles found was 3 out of 4, which was achieved by only one group. To evaluate the overall efficiency of the Data Observatory, the one-way ANOVA test was used to find significant effects regarding the correctness of both tasks. The completion time was discarded from this analysis because of the differences in the tasks’ nature. The ANOVA revealed a significant impact of the display type in the number of correct responses, F1,48 = 10.517, p < 0.002. This indicates participants performed better in the Data Observatory in contrast to the simple desktop screen. Therefore, these results support the hypothesis of the effectiveness of large displays in improving the task performance and collaborative activities in terms of accuracy. The integration of both system solutions provides a novel approach to visualize the enormous amount of data generated from complex scientific computing. This adds great value to researchers and scientists to analyze, discuss, and discover the underlying behavior of certain phenomena.

Structural health Monitoring (SHM) is a very critical component for sustainable civil and mechanical structures in modern urban settings. The sky-scrappers and huge bridges in modern metropolis today are essential aspects of the prosperity and development of a country but at the same time they present a great challenge in terms of maintaining and sustaining the structures in a good health. Due to the complex designs of these structures, it is typically very dangerous to do SHM tasks through human personnel. Deployment of a monitoring team with various forms of equipment and scaffolding accompanied with their hoisting machines becomes extremely exorbitant for the maintenance and planning of the structures causing unnecessary cost-spill on other areas of the available budget. For most of the metallic structures, a fast method of scanning an area more closely is the Magnetic Flux Leakage (MFL) based defect detection. The MFL is considered the most economical approach for inspecting the metallic structures. Traditionally a hand-held device is used for performing the MFL inspection. In this paper, an autonomous MFL inspection robot has been presented which is small, flexible and remotely accessible. The robot is constructed with an Aluminum chassis, driven by two servomotors and holds a stack of very powerful Neodymium magnets to produce the required magnetic circuit. As the robot moves on a metallic surface, the magnetic circuit produces a layered magnetic field just under the scanning probe. The probe is composed of several Hall-effect sensors to detect any leakage in the magnetic circuit, which happens due to abnormality in the surface, thus detecting an anomaly. In this paper, a coordinated robotic inspection system has been proposed that utilizes a set of drones with one positioning robotic crawler platform with additional load hoisting capabilities that are utilized in order to position a specific defect-locating probe on the building under scan. Proposed methodology can play a vital role in SHM since it is capable of scanning a specific area and transmit back the results in a shorter time with a very safe mode of operation. This method is more reliable as compared to fixed sensors that focus a particular area of the structure only. Design for SHM robot involves intelligent integration of navigation system comprising of crucial parts that act as its backbone and assist the robot to work autonomously. These parts include GPS module, compass, range sensor, Infrared (IR) sensor along with MFL probe and winch setup and powerful PMDC Servo Motor controller (MC 160) used to drive two (2) powerful motors. The MC160 brushed Motor Controller proves to be a perfect platform for controlling Brushed DC motors. The controller consists of two power drivers in addition to OSMC connector for a third power driver (winch motor control). All these things add extra degrees of freedom to the robotic system for SHM. Novelty of the methodology is that the robot's program logic is not fixed. It is flexible in terms of path following. It has ability to detect an obstacle while it is on its way to scan the building. It not only detects obstacle but also changes its course and automatically adopts new route to the target destination. Such an autonomous robotic system can play a vital role in Structural Health Monitoring (SHM) in contrast to manual inspection eliminating the need of physical presence of human in severe weather conditions. The presented methodology is condition based in contrast to schedule-based approach. Core scan is easily done and robot is reconfigurable in a sense that it automatically changes its course to adopt to rough terrain and avoids obstacles on its way. Easy deployment makes robot an excellent choice for SHM with minimum cost and enhanced flexibility. Proposed robotic system can perform a coarse level of scan of a tall building using drones and the probe deployment robots (PDR). The drones provide a rough estimate of the location of possible defect or abnormality and PDR inspects the anomaly more closely. In addition, the coarse information about a possible defect can also help in deploying other means of inspection in a much lower cost since the whole structure needs not to be inspected.

Obesity is one of the major health risk factors behind the rise of non-communicable conditions. Understanding the factors influencing obesity is very complex since there are many variables that can affect the health behaviors leading to it. Nowadays, multiple data sources can be used to study health behaviors, such as wearable sensors for physical activity and sleep, social media, mobile and health data. In this paper we describe the design of a dashboard for the visualization of actigraphy and biometric data from a childhood obesity camp in Qatar. This dashboard allows quantitative discoveries that can be used to guide patient behavior and orient qualitative research. CONTEXT Childhood obesity is a growing epidemic, and with technological advancements, new tools can be used to monitor and analyze lifestyle factors leading to obesity, which in turn can help in timely health behavior modifications. In this paper we present a tool for visualization of personal health data, which can assist healthcare professionals in designing personalized interventions for improving health. The data used for the tool was collected as part of a research project called «Adaptive Cognitive Behavioral Approach to Addressing Overweight and Obesity among Qatari Youth» (ICAN). The ICAN project was funded by the Qatar National Research Fund (a member of Qatar Foundation) under project number NPRP X- 036- 3–013. The participants in the study were involved in activities aimed at improving their health behavior and losing weight. All participants and their parents/guardians provided informed consent prior to participation. Data from various sources (social media, mobile, wearables and health records) were collected from subjects and linked using a unique subject identifier. These datasets provided what we have defined as a 360-degree Quantified Self (360QS) view of individuals We have focused on the visualization of the biometrics and physical activity data. We proposed different visualization techniques to analyze the activity patterns of participants in the obesity trial. Our dashboard is designed to compare data across time, and among reference individuals and groups. DATA FROM OBESE CHILDREN Biometric data were measured periodically and included height, weight and the derived body-mass index (BMI), body fat percentage, waist circumference and blood pressure for each individual. Physical activity data was collected via accelerometers. The raw signals have been quantized into four activity levels: sedentary, light, moderate and vigorous, using a human activity recognition algorithm. INTERACTIVE ANALYTIC DASHBOARD The objective of the dashboard is to provide an overview of the actigraphy data and enable primary data exploration by an expert user. In a Control Panel, drop down menus enable selecting two subjects or groups of subjects to be compared based on identifiers and gender. During data collection, some devices were not worn at all times; hence they recorded long periods of «sedentary» activity. The user can use check-boxes to select the biometrics she wants to compare (e.g., BMI and Body Fat Percentage). A Visualization Panel shows both selected (groups of) subjects as bar charts indicating the hours of activity with activity level breakdown per day through time. The color legend of the bars is shown in the control panel: reddish colors for moderate (light red) and vigorous (dark red) activity levels, and bluish colors for light (light blue) and sedentary (dark blue) activity levels. The user can select a time window by brushing horizontally on the time range to zoom in or out. Two line chart show the biometrics selected in the control panel, with line colors corresponding to the selected (groups of) subjects. The average activity breakdown by activity levels is also displayed for weekdays and for weekend days. QUANTITATIVE ANALYSIS AND SUPPORT Thanks to our dashboard we can easily identify trends in biometrics, and compare activity level during week days and week-end days to support lifestyle recommendation. CONCLUSION This interface is a tool to give primary overview of the data likely to orient more detailed analysis. For instance, a more in-depth study of the relation between sleep duration and BMI could be conducted. Another outcome related to the experimental setup would consist of recommending biometrics to be measured more often, or to find incentives for subjects to wear the devices more consistently. A health expert could also provide the subject with a target status (e.g. weight) to compare and converge to, along with recommendations about the activities he/she should improve: e.g., go to bed earlier, wake up earlier during weekend, have more vigorous activities during afternoon, etc. Other available tools, such as the Fitbit dashboard (Fitbit Inc, USA), do not give detailed activity levels across time nor comparison with reference individual. Our next steps include performing qualitative evaluation of our dashboard and improvements based on the end users» feedback.

The Internet has become part of almost every organizational culture where security threats increase with the increase use of the Internet. Therefore, security practice has become increasingly important for almost every organization where is the same case of the Qatar Meteorology Department (QMD) in the state of Qatar. The aim of this research is to evaluate the current security level of the QMD by examining the current security practices that present in the organization and the security awareness level among the organization employees. After that, provide the organization with security policy and awareness program recommendations to enhance the organization security practice. Furthermore, the importance of this research is its contribution to enhance the organization security level. In order to achieve the research objectives, a mixture of different methodologies has been used to collect the fundamental data includes: survey questionnaires, interviews, and field observation. For the data collection process to success, a number of strategies have been used in each method to ensure achieving the most benefits of each the used method. These methods satisfied in collecting the essential primary data. Furthermore, a number of literatures were reviewed in order to understand the research subject further. Based on the collected data, a number of analysis methods have been used to draw a conclusion of the organizational security level where the findings illustrate the needs for security policies and awareness programs in order to enhance the organization security level. Thus, a number of security policies and awareness program recommendations have been established. The research findings and the provided recommendations can support the organization to enhance its security level as much as possible since no system is completely secure. Furthermore, this research presents valuable information about the organization current security level and provides recommendations to enhance this security level.

We live in the digital age where most of our activities and services are carried out over the internet. Items such as music, movies, products etc. are being consumed over the web by millions of users. The number of such items is large enough that it is impossible for a user to experience everything. This is where recommender systems come into play. Recommender systems are employed to play a crucial role of filtering and ranking items to each user based on their individual preferences. Recommender systems essentially assist the user in making decisions to overcome the problem of information overload. These systems are responsible for understanding a user's interests and inferring their needs over time. Recommender systems are widely employed across the web and in many cases, are the core aspect of a business. For example, on Quora, a question-answering website, the entire interface relies on the recommender system for deciding what content to display to the user. The content ranges from homepage question ranking, topics recommendation and answer ranking. The goal of a recommender system is to assist users in selecting items based on their personal interest. By doing so, it also increases the number of transactions thereby creating a win-win situation for both the end users and the web service. Recommender systems is a relatively new and exciting field that promises a huge potential in the future. It has originated from the field of information retrieval and search engines where the task was: given a query retrieve the most relevant documents. In the recommender system domain, the user should be able to discover items that he/she would not have been able to search for directly. One main challenge in recommender systems is cold-start. It is defined as the situation when a new user/item joins the system. We are interested in item cold start and in this case the recommender system needs to learn about the new item and decide which users should it recommend to. In this work, we propose a new approach to tackle the cold-start problem in recommender system using word embeddings. Word embeddings are semantic representations of the words in a mathematical form like vectors. Embeddings are very useful since they are able to capture the semantic relationship between words in the vocabulary. There are various methods to generate such a mapping which include: neural networks, dimensionality reduction on word co-occurrence matrix, probabilistic models etc. The underlying concept behind these approaches is that words that share common contexts in the corpus have close proximity in the semantic space. Word2vec is a popular technique by Mikolov et al. that has gained tremendous popularity in the natural language processing domain. They came up with two versions, namely: continuous skip-gram and continuous bag-of-words model (CBOW). They were able to overcome the problem of sparsity in text and demonstrate its effectiveness in a wide range of NLP tasks. Our dataset is based on a popular website called Delicious which allows users to store, share and discover bookmarks on the web. For each bookmark, users are able to generate tags that provide meta information about the page such as the topics discussed, important entities. For example, a website about research might contain tags like science, biology, experiment. The problem now becomes: Given a new bookmark with tags, compute which users to recommend this new bookmark. For item cold start situation, a popular technique is to use content based approaches and find items similar to the new item. The new item can then be recommended to users of the computed similar items. In this paper, we propose a method to compute similar items using word embeddings of the tags present for each bookmark. Our methodology involves representing each bookmark as a vector by combining the word embeddings of its tags. There are various possible aggregation mechanisms and we chose to use the average in our experiments since it is intuitive and easy to compute. The similarity between two bookmarks can be computed by taking the cosine similarity between their corresponding embedding vectors. The total number of bookmarks in the dataset is around 70,000 with around 54,000 tags. The embeddings are obtained from the GloVe project where the training is performed on Wikipedia data based on aggregated global word-word co-occurrence statistics. The vocabulary of these embeddings are fairly large, containing about 400 k words and each word is stored in the form of a 300-dimension vector. The results were evaluated manually and the results look promising. We found that the bookmarks recommended were highly relevant in terms of the topics being discussed. Some example topics being discussed in the bookmarks were: social media analysis, movie reviews, vacation planner, web development etc. The reason that embeddings perform well is that they are to capture the semantic information of bookmarks using tags which is useful in cold start situations. Our future work would involve using other aggregation combinations such as weighting the tags differently based on their importance. A more suitable method of evaluation would be to measure the feedback (ratings/engagement) from users in a live recommender system and compare along with other approaches. In this work, we demonstrate the feasibility of using word embeddings to tackle the item cold start problem in recommender systems. This is an important problem that can deliver a positive impact in improving the performance of recommender systems.

Mental stress occurs when a person perceives abnormal demands or pressures that influence the sense of well-being. These high demands sometimes exceed human capabilities to cope with. Stressors such as workload, inflexible working hours, financial problem, or handling more than one task can cause work-related stress which in turn leads to less productive employees. Lost productivity costs global economy approximately US $ 1 trillion per year [1]. A survey conducted among 7000 workers in U.S. found that 42% had left their job to escape the stressful work environment [2]. Some people can handle stress better than others, therefore the stress symptoms can vary. Stress symptoms can affect the human body and make him down both physically and mentally. Hopelessness, anxiety, and depression are examples of emotional symptoms, while headaches, over-eating, sweaty hands, and dryness of mouth are physical signs of the stress. There are also behavioral cues for stress like aggression, social withdrawal, and loss of concentration [3]. When the thread is perceived, a survival mechanism called «fight or flight response» will be activated to help the human body to adapt the situation quickly. In this mechanism, the central nervous system (CNS) asks adrenal glands to release cortisol and adrenaline hormones, which boost glucose levels in the bloodstream, quicken the heartbeat, and raise blood pressure. If CNS does not succeed to return to normal state, the body reaction will continue which in turn increases the possibility of having heart stroke or attack [4]. There are several techniques used to explore physiological and physical stress measures, for example, electrocardiogram (ECG) measures the heart»s electrical activity, electroencephalography (EEG) records the brain»s electrical activity, electrodermal activity (EDA) or galvanic skin response (GSR) measures the continuous variations in the skin»s electrical characteristics, electromyography (EMG) records electrical activity in muscles, photoplethysmography (PPG) estimates the skin blood flow, and Infrared (IR) tracks eye activities. On the other hand, prolonged ongoing worrying can lead to chronic stress. This type of stress is most harmful and has been linked to cancer, and cardiovascular disease (CVD) [5]. Therefore, several approaches were proposed in an attempt to identify stress triggers and amount of stress. Some of these methods used instruments such as questionnaires to assess affective states, but these techniques usually suffer from memory and response biases. However, stress detection via the analysis of various bio-signals are deemed more valuable and thus have been the focus of modern day research. In particular, various bio-signals are collected from participates. These bio-signals are then subjected to advanced signal processing algorithms in an attempt to extract salient features for classification by machine learning algorithms. In our project, we are interested in exploring new machine learning techniques which wearable devices to record various bio-signals. The goal is the development of an automatic stress detection system based on the analysis of bio-signals through the use of signal processing and machine learning. The outcome of this research will allow users to be notified when their bodies enter a state of unhealthily stress levels so that they may take preventative action to avoid unnecessary consequences.

In this research work, we propose a framework of experiential learning to enhance student work skills and experience. This research main to contribute to the development and expansion of local industry, through the conduct of long-term fundamental research that contributes to the science base and understanding needs of national economy through industrial by providing an adapted method, enhance the teaching contents and pedagogical organization to be more accurate and adapted to the competency requirements of local employers.

Wikipedia is the most influential popular information source on the Internet, and is ranked as the fifth most visited website [1] (Alexa, 2017). The English-language Wikipedia is a prominent source of online health information compared to other providers such as MedlinePlus and NHS Direct (Laurent and Vickers, 2009). Wikipedia has challenged the way that traditional medical encyclopaedia knowledge is built by creating an open sociotechnical environment that allows non-domain experts to contribute to its articles. Also, this sociotechnical environment allows bots – computer scripts that automatically handle repetitive and mundane tasks – to work with humans to develop, improve, maintain and contest information in Wikipedia articles. The contestation in Wikipedia is unavoidable as a consequence of its open nature, which means that it accepts contradictory views on a topic and involves controversies. The objective of this research is to understand the impact of controversy on the relationship between humans and bots in environments that are managed by the crowd. This study analyses all the articles under the WikiProject Medicine, and includes 36,850 Wikipedia articles. Medical articles and their editing history have been harvested from the Wikipedia API covering all edits from 2001 till 2016. The data includes the revisions ID, username, timestamp, and comment. The articles under the WikiProject Medicine contain 6,220,413 edits and around 1,285,936 human and bot editors. To measure controversies, we studied reverted and undone edits. A revert on Wikipedia occurs when an editor, whether human or bot, restores the article to an earlier version after another editor's contribution. Undone edits are reverted single edits from the history of a page, without simultaneously undoing all constructive changes that have been made since the previous edit. Reverted and undone edits that occur systematically indicate controversy and conflict (Tsvetkova et al., 2017). To measure the relationship between humans and bots, we focused on both positive and negative relationships. A positive relationship is when an editor, such as a human, endorses another editor, such as a bot, by reverting or undoing a recent edit to the other editor's contribution. A negative relationship is when an editor, such as human, discards another editor, such as a bot, by reverting or undoing the other editor's contribution. Our results show that there is a relationship between controversial articles and the development of a positive relationship between humans and bots. The results demonstrate that bots and humans could behave differently in controversial environments. The study highlights some of the important features of building health-related knowledge on Wikipedia. The contribution of this work is to build on previous theories that consider web-based systems as social machines. These theories recognise the joint contribution of humans and machines to activities on the web, but assume a very static type of relationships that is not sensitive to the environment in which humans and machines operate in. Understanding the interactions between humans and bots is crucial for designing crowdsourced environments that are integrative to their human and non-human population. We discuss how our findings can help set up future research directions and outline important implications for research on crowd. References: Laurent, M. R &amp; Vickers, T. J (2009) ‘Seeking Health Information Online: Does Wikipedia Matter.?’ J Am Med Inform Assoc, 16(4), 471-479. Tsvetkova, M, García-Gavilanes, R, Floridi, L and Yasseri, T. (2017) ‘Even good bots fight: The case of Wikipedia.’ PLoS One, 12(2): e0171774. [1] https://www.alexa.com/topsites

As urban data keeps getting bigger, deep learning is coming to play a key role in providing big data predictive analytics solutions. We are interested in developing a new generation of deep learning based computational technologies that predict traffic congestion and crowd management. In this work, we are mainly interested in efficiently predicting future traffic with high accuracy. The proposed deep learning solution allows the revealing of the latent (hidden) structure common to different cities in terms of dynamics. The data driven insights of traffic analytics will help shareholders, e.g., security forces, stadium management teams, and travel agencies, to take fast and reliable decisions to deliver the best possible experience for visitors. Current traffic data sources in Qatar are incomplete as sensors are not yet permanently deployed for data collection.The following topics are being addressed:Predictive Crowd and Vehicles Traffic Analytics: Forecasting the flow of crowds and vehicles is of great importance to traffic management, risk assessment and public safety. It is affected by many complex factors, including spatial and temporal dependencies, infrastructure constraints and external conditions (e.g. weather and events). If one can predict the flow of crowds and vehicles in a region, tragedies can be mitigated or prevented by utilizing emergency mechanisms, such as conducting traffic control, sending out warnings, signaling diversion routes or evacuating people, in advance. We propose a deep-learning-based approach to collectively forecast the flow of crowds and vehicles. Deep models, such as Deep-Neural-Networks, are currently the best data-driven techniques to handle heterogeneous data and to discover and predict complex data patterns such as traffic congestion and crowd movements. We will focus in particular on predicting inflow and outflow of crowds or vehicles to and from important areas, tracking the transitions between these regions. We will study different deep architectures to increase the accuracy of the predictive model, and explore ways on how to integrate spatio-temporal information into these models. We will also study how deep models can be re-used without retraining to handle new data and better scale to large data sets. What-If Scenarios Modeling: Understanding how congestion or overcrowd at one location can cause ripples throughout a transportation network is vital to pinpoint traffic bottlenecks for congestion mitigation or emergency response preparation. We will use predictive modeling to simulate different states of the transportation network enabling the stakeholder to test different hypotheses in advance. We will use the theory of multi-layer networks to model and then simulate the complex relationship between different but coexisting types of flows (crowd, vehicles) and infrastructures (roads, railways, crossings, passageways, squares…). We will propose a visual analytic platform that will provide necessary visual handles to generate different cases, navigate through different scenarios, and identify potential bottleneck, weak points and resilient routes. This visualization platform connected to the real-time predictive analytic platform will allow supporting stakeholder decision by automatically matching the current situation to the already explored scenarios and possible emergency plans. Safety and Evacuation Planning based on Resilience Analytics: Determining the best route to clear congestion or overcrowded areas, or new routes to divert traffic and people from such areas is crucial to maintain high security and safety levels. The visual analytic platform and the predictive model will enable the test and set up of safety and evacuation plans to be applied in case of upcoming emergency as detected by the predictive analytic platform. Overall, the proposed approach is independent of the type of flows, i.e., vehicles or people, or infrastructures, as long as proper sensors (magnetic loops, video camera, GPS tracking, etc.) provide relevant data about these flows (number of people or vehicles per time unit along a route of some layer of the transportation network). The proposed data-driven learning models are efficient, and they adapt to the specificities of the type of flows, by updating the relevant parameters during the training phase.

Secure Multi-Party Computation (SMPC) enables parties to compute a pub- lic function over private inputs. A classical example is the millionaires problem, where two millionaires want to figure out who is wealthier without revealing their actual wealth to each other. The insight gained from the secure computa- tion is nothing more than what is revealed by the output (in this case, who was wealthier but not the actual value of the wealth). Other applications of secure computation include secure voting, on-line bidding and privacy-preserving cloud computations, to name a few. Technological advancements are making secure computations practical, and recent optimizations have made dramatic improve- ments on their performance. However, there is still a need for effective tools that facilitate the development of SMPC applications using standard and famil- iar programming languages and techniques, without requiring the involvement of security experts with special training and background. This work addresses the latter problem by enabling SMPC application de- velopment through programs (or repurposing existing code) written in a stan- dard programming language such as ANSI C. Several high-level language (HLL) platforms have been proposed to enable secure computation such as Obliv-C [1], ObliVM [2] and Frigate [3] These platforms utilize a variation of Yao's garbled circuits [4] in order to evaluate the program securely. The source code written for these frameworks is then converted into a lower-level intermediate language that utilizes garbled circuits for program evaluation. Garbled Circuits have one party (garbler) who compiles the program that the other party (evaluator) runs, and the communication between the two parties happens through oblivi- ous transfer. Garbled circuits allow two parties to do this evaluation without a need for a trusted third party. These frameworks have two common characteristics: either define a new language [2] or make a restricted extension of a current language [1]. This is somewhat prohibitive as it requires the programmer to have a sufficient under- standing of SMPCs related constructs and semantics. This process is error-prone and time-consuming for the programmer. The other characteristic is that they use combinational circuits, which often require creating and materializing the entire circuit (circuit size may be huge) before evaluation. This introduces a restriction on the program being written. TinyGarble [5], however, is a secure two-party computation framework that is based on sequential circuits. Compared with the frameworks mentioned earlier, TinyGarble outperforms them by orders of magnitude. We are developing a framework that can automatically convert a HLL pro- gram (in this case ANSI C) into an hardware definition language, which is then evaluated securely. The benefit of having such transformation is that it does not require knowledge of unfamiliar SMPC constructs and semantics, and per- forms the computation in a much more efficient manner. We are combining the efficiency of sequential circuits for computation as well as the expressiveness of a HLL like ANSI C to be able to develop a secure computation framework that is expected to be effective and efficient. Our proposed approach is two-fold: first, it offers a separation of concern between the function of computation, written in C, and a secure computation policy to be enforced. This leaves the original source code unchanged, and the programmer is only required to specify a policy file where he/she specifies the function/variables which need secure computations. Secondly, it leverages the current state-of-the-art framework to generate sequential circuits. The idea is to covert the original source code to Verilog (a Hardware Definition Language) as this can then be transformed into standard circuit description which TinyGarble [5] would run. This will enable us to leverage TinyGarbles efficient sequential circuits. The result would be having the best of both worlds where we have HLL that would be converted and evaluated as a sequential circuit. References [1] S. Zahur and D. Evans, “Obliv-c: A language for extensible data-oblivious computation,” IACR Cryptology ePrint Archive, vol. 2015, p. 1153, 2015. [2] C. Liu, X. S. Wang, K. Nayak, Y. Huang, and E. Shi, “Oblivm: A pro- gramming framework for secure computation,” in 2015 IEEE Symposium on Security and Privacy, SP 2015, San Jose, CA, USA, May 17-21, 2015, pp. 359–376, 2015. [3] B. Mood, D. Gupta, H. Carter, K. R. B. Butler, and P. Traynor, “Frigate: A validated, extensible, and efficient compiler and interpreter for secure com- putation,” in IEEE European Symposium on Security and Privacy, EuroS&amp;P 2016, Saarbrúcken, Germany, March 21-24, 2016, pp. 112–127, 2016. [4] A. C. Yao, “Protocols for secure computations (extended abstract),” in 23rd Annual Symposium on Foundations of Computer Science, Chicago, Illinois, USA, 3-5 November 1982, pp. 160–164, 1982. [5] E. M. Songhori, S. U. Hussain, A. Sadeghi, T. Schneider, and F. Koushanfar, “Tinygarble: Highly compressed and scalable sequential garbled circuits,” in 2015 IEEE Symposium on Security and Privacy, SP 2015, San Jose, CA, USA, May 17-21, 2015, pp. 411–428, 2015.

One of the most prominent areas of energy consumption in residential units is for heating, ventilation and air-conditioning (HVAC) systems. The conventional systems for HVAC depend on the wired thermostats that are deployed at fixed locations and hence, are not convenient and do not respond to the dynamic nature of the thermal envelope of the buildings. Moreover, it is important to note that the distribution of the spatial temperature is not uniform. The current environment control strategies are based on the maximum occupancy numbers for the building. But there are always certain areas of a building which are used less frequently and are cooled needlessly. Having the real-time occupancy data and mining on it to predict the occupancy patterns of the building will help in energy effective strategy development for the regulation of HVAC systems through a central controller. In this work, we have deployed a network of multiple wireless sensors (humidity, temperature, CO2 sensors etc.), computational elements (in our case, a raspberry pi, to make it cost effective) and camera network with an aim to integrate the data from the multiple sensors in a large multifunction building. The sensors are deployed at multiple locations in such a way that the non-uniform spatial temperature distribution is overcome, and these sensors capture the various environmental conditions at a temporal and much finer spatial granularity. The pi camera is connected to a raspberry pi which is fixed at an elevation. The detection is performed using the OpenCV library and the python programming. This system can detect the occupancy with an accuracy of up to 90%. For occupancy detection and counter, a linear SVM is trained sampling on positive and negative images and the evaluation on test images or video feed makes use of non-maximum suppression (NMS Algorithm) to ignore redundant, overlapping HOG (Histogram Oriented Gradient) boxes. The data collected by the sensors is sent to the central controller on which the video processing algorithm is also running. Using the multiple environmental factors data available to us, models are developed to predict the usage in the building. These models help us to define the control parameters for the HVAC systems in adaptive manner in such a way that these parameters not only help in reducing the energy used in a building, but also help to maintain the thermal comfort. The control parameters are then sent as IR signals to AC systems that are controlled by IR remotes or as wireless signals to AC systems controlled by wireless thermostats. In comparison to the conventional temperature controller, our system will avoid overcooling of areas to save energy and predict the occupancy in the buildings so that the temperature is brought within the comfort zone of humans before over-occupancy takes place. Our system also has benefits of using wireless sensors that operate on low power, but the tradeoff between the power and the communication frequency should be well maintained. Our system additionally has two features: firstly, it can provide the live video streaming for remote monitoring using a web browser for the user interface and secondly, sending automatic notifications as messages in case of anomalies like abnormally high temperatures or high carbon dioxide concentration in a room. These two features can be used as cost-effective replacement for traditional systems in the applications of CCTV, burglary systems respectively. Keywords: wireless sensors, Air conditioning, opencv, NMS Algorithm, Histogram Oriented Gradient, thermal comfort.

E-learning is the use of Internet technologies or computer-assisted instruction, to enhance and improve knowledge and performance, because knowledge is the basic right for a human right that should be accessible by everyone regardless of the status of they disabilities. E-learning technologies offer learners control over content, learning sequence, pace of learning, time, and often media, allowing them to tailor their experiences to meet their personal learning objectives. this paper explores how adaptive e-learning for person with disabilities focusing intellectual disabilities in the Higher Education (HE) can show the important of making the technology with digital content have accessible to student with disabilities instead of face-to-face education with respect to ‘electronic’ vs. ‘traditional’ learning methods, this way of adapting E-Learning can be considered as its competent substitute complement, and examine the current situation progress in Qatar HE, because ubiquitous technologies have become a positive force of transformation and a crucial element of any personal development/empowerment and institutional framework for inclusive development. Keywords: e-learning, person with disabilities, intellectual disabilities, and learning methods.

I. Background In power grid, circuit breakers in switchgears are often the last line of defense when big systems must be protected from faults [1], sudden switchgear failures could cause long outages, huge economic losses and even present threats to the public safety. Based on field experience, the major causes of switchgear failure are loose or corroded metal connections, degraded cable insulation and external agents (e.g. dust, water) [2]. Due to ohmic loss at these weak points, the causes of switchgear failure are always accompanied with an increased thermal signature over time. With continuous thermal monitoring inside the switchgear, adequate data can be collected to make timely failure predication and prevention, especially for equipment deployed in harsh environment. II. Objective This paper presents the design of a passive radio frequency identification (RFID) sense tag, which measures temperature at critical spots of the switchgear and wirelessly (EPC C1G2 standard) transmits the data to the base station for real-time analysis. Compared with infrared imaging [2], surface acoustic wave (SAW) sensing system or fiber bragg grating (FBG) sensing system [1][3], no cables for power and communication are necessary, which avoids potential side effects like arcing in the grid after the addition of the sensor. The use of standard CMOS process results in a cost effective solution and the proposed passive wireless sensor can be easily retrofitted to existing switchgears with simple bolted connections. III. Passive Tag Design and Measurement Results Fig. 1 shows the proposed passive tag with temperature sensing capability. The power management unit in the chip harvests the incoming RF wave (860 ∼ 960 MHz) and sustains all the other on-chip building blocks (sensor, clock, memory, baseband, modem). The energy harvesting efficiency is in the range of 15%∼25% based on the operating mode of the tag. With 10 uW system power, the effective reading distance of this tag is 4.5 m ∼ 6 m. The on-chip temperature sensor adopts PNP bipolar as the sensing device, which has a temperature sensitivity of ∼2 mV/oC [4]. By using a triple-slope analog-to-digital converter (A/D), temperature-sensitive voltages are digitized and transmitted back to the reader after modulation by the modulator. Because there's no battery or other energy sources on the device, the power consumption of the tag, especially the sensor should be in the order of sub-mA to maintain the tag sensitivity. In this work, a passive integrator instead of an active one is used in the A/D, where its caused nonlinearity error is compensated by adding an additional nonlinear current into the temperature signal. The overall power consumption of the sensor is 0.75 uW and achieves 10 bit sensing resolution (0.12oC/LSB) within 10 ms conversion time, corresponding to a resolution FoM of 1.08x102 pJ√K2, which is among the most energy-efficient embedded sensor designs. Fig.2(a) shows the micro-photo of the designed passive RFID sense tag. Fig.2(b) shows its ceramic package, which can be readily installed on the spot locations with bolted connection. By designing the antenna with an additional ground layer, this tag is able to work in switchgear with full metal enclosure [5]. The measured tag sensitivity is -12 dBm. By measuring and calibrating multiple samples, the overall sensing precision of the tag is +/-1.5oC, which is enough for switchgear thermal monitoring, as shown in Fig.3(a). Thanks to the designed on-chip supply protection circuit, the sensor performance does not degrade much with the reading power or reading distance (received power ∝ or ∝ 1/distance2), as shown in Fig.3(a).IV. Conclusion The combination of passive RFID tags with sensors enables a lot of new applications and it help to bring embedded intelligence to the legacy power grid. The designed passive sense tag is of low-cost and with robust sensing performance after optimizing the tag at the system level and the use of low-power circuit design techniques. By re-designing the tag package, it can also be used in other applications like cold supply chain or highly flammable goods monitoring. Acknowledgement This work is in collaboration with Land Semiconductor Ltd., Hangzhou China and thanks Mr. Qibin Zhu and Mr. Shengzhou Lin for helping the measurement. Reference [1] G.-M. Ma et al., “A Wireless and Passive Online Temperature Monitoring System for GIS Based on Surface-Acoustic-Wave Sensor,” IEEE Trans. on Power Delivery, vol.31, no.3, pp. 1270 - 1280, June 2016. [2] Top Five Switchgear Failure Causes, Netaworld. [Online]. Available: http://www.netaworld.org/sites /default/files/public/neta-journals/NWsu10-NoOutage-Genutis.pdf, accessed Oct. 2017. [3] Fundamentals of Fiber Bragg Grating (FBG) Optical Sensing, NI White Papers. [Online]. Available: http://www.ni.com/white-paper/11821/en/, accessed Oct. 2017. [4] B. Wang, M.-K. Law and A. Bermak, “A Precision CMOS Voltage Reference Exploiting Silicon Bandgap Narrowing Effect,” IEEE Trans. on Electron Devices, vol. 62, no.7, pp.2128-2135, July 2015. [5] Chong Ryol Park and Ki Hwan Eom, “RFID Label Tag Design for Metallic Surface Environments,” Sensors, vol. 11, no. 1, pp. 938 - 948, Jan. 2011.

Online Social Networks (OSNs), such as Facebook and Twitter, are popular platforms that enable users to interact and socialize through their networked devices. However, the social nature of such applications forces users to share a great amount of personal data with other users and the OSN service providers, including pictures, location check-ins, etc. Even though some OSNs offer configurable privacy controls that limit access to shared data, users might misconfigure these controls due to their complexity or lack of clear instructions. Furthermore, the fact that OSN service providers have full access over the data stored on their servers is an alarming thought, especially for users who are conscious about their privacy. For example, OSNs might share such data with third parties, data mine them for targeted advertisements, collect statistics, etc. As a result, data and communication privacy over OSNs is a popular topic in the data privacy research community. Existing solutions include cryptographic mechanisms [1], trusted third parties [2], external dictionaries [3], and steganographic techniques [4]. Nevertheless, none of the aforementioned approaches offers a comprehensive solution that (i) implements fine-grained access control over encrypted data and (ii) works seamlessly over existing OSN platforms. To this end, we will design and implement a flexible and user-friendly system that leverages encryption-based access control and allows users to assign arbitrary decryption privileges to every data object that is posted on the OSN servers. The decryption privileges can be assigned on the finest granularity level, for example, to a hand-picked group of users. In addition, data decryption is performed automatically at the application layer, thus enhancing the overall experience for the end-user. Our cryptographic-based solution leverages hidden vector encryption (HVE)[5], which is a ciphertext policy-based access control mechanism. Under HVE, each user generates his/her own master key (one-time) that is subsequently used to generate a unique decryption key for every user with whom they share a link in the underlying social graph. Moreover, during the encryption process, the user interactively selects a list of friends and/or groups that will be granted decryption privileges for that particular data object. To distribute the decryption keys, we utilize an untrusted database server where users have to register before using our system. The server stores (i) the social relationships of the registered users, (ii) their public keys, and (iii) the HVE decryption keys assigned to each user. As the database server is untrusted, the decryption keys are stored in encrypted form, i.e., they are encrypted with the public key of the underlying user. Therefore, our solution relies on the existing public key infrastructure (PKI) to ensure the integrity and authenticity of the users’ public keys. To facilitate the deployment of our system over existing OSN platforms, we use steganographic techniques [6] to hide the encrypted data objects within randomly chosen cover images (stego images). The stego images are then uploaded to the OSN servers, and only authorized users (with the correct decryption keys) would be able to extract the embedded data. Unauthorized users will simply see the random cover images. We aim to implement our system as a Chrome-based browser extension where, after installation, the user registers with the un- trusted server and uploads/downloads the necessary decryption keys. The keys are also stored locally, in order to provide a user-friendly interface to share private information. Specifically, our system will offer a seamless decryption process, where all hidden data objects are displayed automatically while surfing the OSN platform, without any user interaction. References [1] S. Jahid, P. Mittal, and N. Borisov, “EASiER: encryption-based access control in social networks with efficient revocation,” in Proc. ACM Symposium on Information, Computer and Communications Security (ASIACCS), pp. 411–415, 2011.[2] A. Tootoonchian, S. Saroiu, Y. Ganjali, and A. Wolman, “Lockr: better privacy for social networks,” in Proceedings of the 2009 ACM Conference on Emerging Networking Exper- iments and Technology, CoNEXT 2009, Rome, Italy, December 1-4, 2009, pp. 169–180, 2009.[3] S. Guha, K. Tang, and P. Francis, “NOYB: privacy in online social networks,” in Proc. Workshop on Online Social Networks (WOSN), pp. 49–54, 2008.[4] J. Ning, I. Singh, H. V. Madhyastha, S. V. Krishnamurthy, G. Cao, and P. Mohapatra, “Secret message sharing using online social media,” in Proc. IEEE Conference on Commu- nications and Network Security (CNS), pp. 319–327, 2014.[5] T. V. X. Phuong, G. Yang, and W. Susilo, “Efficient hidden vector encryption with constant- size ciphertext,” in Proc. European Symposium on Research in Computer Security (ES- ORICS), pp. 472–487, 2014.[6] S. Kaur, S. Bansal, and R. K. Bansal, “Steganography and classification of image steganog- raphy techniques,” in Proc. International Conference on Computing for Sustainable Global Development (INDIACom), 2014.

Mega data centers (DCs) are considered as efficient and promising infrastructures for supporting numerous cloud computing services such as online office, online social networking, Web search and IT infrastructure out-sourcing. The scalability of these services is influenced by the performance and dependability characteristics of the DCs. Consequently, the DC networks are constructed with a large number of network devices and links in order to achieve high performance and reliability. As a result, these requirements increase the energy consumption in DCs. In fact, in 2010, the total energy consumed by DCs was estimated to be about 120 billion Kilowatts of electricity in 2012, which is about 2.8% of the total electricity bill in the USA. According to industry estimates, the USA data center market achieved almost US 39 billion in 2009, growing from US 16.2 billion in 2005. One of the primary reasons behind this issue is that all the links and devices are always powered on regardless of the traffic status. The statistics showed that the traffic drastically alternates, especially between mornings and nights, and also between working days and weekends. Thus, the network utilization depends on the actual period, and generally, the peak capacity of the network is reached only in rush times. This non-proportionality between traffic load and energy consumption is caused by the fact that -most of the time- only a subset of the network devices and links can be enough to forward the data packets to their destinations while the remaining idle nodes are just wasting energy. Such observations inspired us to propose a new approach that powers off the unused links by deactivating the end-ports of each one of them to save energy. The deactivation of ports is proposed in many researches. However, these solutions have high computational complexity, network delay and reduced network reliability. In this paper, we propose a new approach to reduce the power consumption in DC. By exploiting the correlation in time of the network traffic, the proposed approach uses the traffic matrix of the current network state, and manages the state of switch ports (on/off) at the beginning of each period, while making sure to keep the data center fully connected. During the rest of each time period, the network must be able to forward its traffic through the active ports. The decision to close or open depends on a predefined threshold value; the port is closed only if the sum of the traffic generated by its connected node is less than the threshold. We also investigate the minimum period of time during which a port should not change its status. This minimum period is necessary given that it takes time and energy to switch a port on and off. Also, one of the major challenges in this work is powering off the idle devices for more energy saving while guaranteeing the connectivity of each server. So, we propose a new traffic aware algorithm that presents a tradeoff between energy saving and reliability satisfaction. For instance, in HyperFlatNet, simulation results show that the proposed approach reduces the energy consumption by 1.8*104 WU (Watt per unit of time) for a correlated network with1000-server (38 % of energy saving). In addition, and thanks to the proposed traffic aware algorithm, the new approach shows a good performance even in case of high failure rate (up to 30%) which means when one third of the links failed, the connection failure rate is only 0.7%. Both theoretical analysis and simulation experiments are conducted to evaluate and verify the performance of the proposed approach compared to the state-of-the-art techniques.

This paper examines the impact on patient experience through the creation of a bespoke patient queuing and communication application using in-house developed technologies. Sidra Medicine hospital's outpatient pharmacy was experiencing mismanaged queue lines, dissatisfied patients, and the lack of data necessary to determine the length of time elapsing in obtaining medication. After analyzing patient surveys through the method of sentiment analysis and generation of word clouds, we validated that there was scope for workflow improvement in the pharmacy department. The Center for Medical Innovation, Software, and Technology (CMIST) department was commissioned to develop the software application necessary to deliver efficiency and improvement in response to the lack of a queuing and communication system. The use of an in-house development team to create an application for queuing and communication as opposed to selecting a popular vendor software resulted in many advantages. Some of the main advantages were that the requirements of pharmacy were delivered through rapid customization and in multiple iterations, which were delivered in response to the ever changing customer demand. By using scrum methodology, the team was able to deliver the application called Saffara, for managing queues in the pharmacy and improving patient experience while obtaining medication. The Saffara application, has a unique feature of being integrated to the hospital EMR (Electronic Medical Record) system while ensuring confidentiality, efficiency and time saving. The application integrates with the hospital's EMR to obtain patient information, appointment times and prescribed medication. This integration allowed for the identification of patients' progress and calculation of patients ‘wait times. Patients are automatically notified when their medication is ready for collection, through system generated SMS texts. The application also utilizes a notification display for communication with patients as part of our business continuity procedure. In addition to notifying the patient, the Saffara application also generates detailed analytical reports for each hour and for each patient, which allows us to analyze the bottlenecks in the clinical workflow. We present these technologies to any stakeholders through a web dashboard and detailed web-based reports in our application. The pharmacy stakeholders, i.e., the pharmacy management team utilize the dashboards and quantitative data in the reports to predict staffing levels to deliver optimization in patient medication delivery. In this paper, we present the methods we use to calculate the useful analytics like patient wait times across different stages in the workflow and hourly breakdown of patients being served. We will also discuss how we reduced patient wait times by adding unique features to a queuing application like automation of steps in the pharmacy workflow through generation of patient identifiers and automatic ticket tracking. This paper will also highlight how we are scaling our application from pharmacy to all clinics of the hospital. The goal of the application is to provide a consistent experience for patients in all clinics as well as a consistent way for staff to gather and analyze data for workflow improvement. Our future work is to explore how we can use machine learning to identify the parameters that play a vital role in wait times as well as patient experience. The objective of this paper is to highlight how our technology converges the patient experience and staff workflow enhancements to deliver improvement in a clinical workflow setting.

In this paper, we propose a novel method for the detection of road accidents by analyzing audio streams for road surveillance application. In the last decade, due to the increase in a number of people and transportation vehicles, traffic accidents have become one of a major public issue worldwide. The vast number of injuries and death due to road traffic accident reveals the story of a global crisis of road safety. A total of 52,160 road traffic accidents (RTA), 1130 injuries and 85 fatalities were registered during the year 2000, in the state of Qatar. An increase in the number of transportation vehicles around cities has risen the need for more security and safety in public environments. The most obvious reason for a person's death during accidents is the absence or prolong response time of the first aid facility, which is due to the delay in the information of the accident being reached to the police, hospital or ambulance team. In the last couple of years, several surveillance systems have been proposed based on image and video processing for automatically detecting road accidents and car crashes to ensure a quick response by emergency teams. However, in some situations such as adverse weather conditions or cluttered environments, the visual information is not sufficient enough, whereas analyzing the audio tracks can significantly improve the overall reliability of surveillance systems. In this paper we propose a novel method that automatically identifies hazardous situations such as tire skidding and car crashes in presence of background noises, by analyzing the audio streams. Previous studies show that methods for the detection, estimation, and classification of nonstationary signals can be enhanced by utilizing the time-frequency (TF) characteristics of such signals. The TF-based techniques have been proved to outperform classical techniques based on either time or frequency domains, in analyzing real-life nonstationary signals. Time-frequency distributions (TFDs) give additional information about signals that cannot be extracted from either the time domain or frequency domain representations, i.e. the instantaneous frequency of the components of a signal. In order to utilize this extra information provided by TF domain representation, the proposed approach extracts TF image features from quadratic time-frequency distributions (QTFDs), for the detection of audio anomalies. The extended modified-B distribution (EMBD) is utilized to transform a 1-dimensional audio signal into 2-dimensional TF representation which is interpreted as an image. The image descriptors based features are then extracted from the TF representation to classify visually the audio signals into background or abnormal activity patterns in the TF domain. The proposed features are based on Haralick's texture features extracted from the TF representation of audio signals considered and processed as a textured image. These features are used to characterize and hence classify audio signals into M classes. This research study demonstrates that a TF image pattern recognition approach offers significant advantages over standard signal classification methods that utilize either t-domain only or f-domain only features. The proposed method has been experimentally validated on a large open source database of sounds, including several kinds of background noise. The events to be recognized are superimposed on different background sounds of roads and traffic jam. The obtained results are compared with a recent study, utilizing the same large and complex data set of audio signals, and the same experimental setup. The overall classification results confirm the superior performance of the proposed approach with accuracy improvement up to 6%.

We present MATQAM (Multilingual Answer Triggering Question Answering Machine) a multilingual answer triggering open-domain QA system, focusing on answering questions whose answers might be in free texts in multiple languages within Wikipedia.Obtaining relevant information from the Web has become more challenging, since online communities and social media tend to confine people to bounded trends and ways of thinking. Due to the large amount of data available, getting the relevant information has become a more challenging task. Unlike in standard Information Retrieval (IR), Question Answering (QA) systems aim at retrieving the relevant answer(s) to a question expressed in natural language, instead of returning a list of documents. On the one hand, information is dispersed in different languages and needs to be gathered to get more knowledge. On the other hand, extracting answers from multilingual documents is a complicated task because natural languages follow diverse linguistic syntaxes and rules, especially for Semitic languages, such as Arabic. This project tackles open-domain QA using Wikipedia as source of knowledge by building a multilingual —Arabic, French, English— QA system. In order to obtain a collection of Wikipedia articles as well as questions in multiple languages, we extended an existing English dataset: WikiQA (Yang et al., 2015). We used the WikiTailor toolkit (Barrón-Cedeño et al., 2015) to build a comparable corpus form Wikipedia articles and to extract the corresponding articles in Arabic, French, and English. We used neural machine translation to generate the questions in the three languages as well. Our QA system consists of the three following modules. (i) Question processing consists of transforming a natural language question into a query and determining the expected type of the answer in order to define the retrieval mechanism for the extraction function. (ii) The document retrieval module consists of retrieving the most relevant documents from the search engines —in multiple languages— given the produced query. The purpose of this module is to identify the documents that may contain an answer to the question. It requires cross-language representations as well as machine translation technology to do that, as the question could be asked in Arabic, French or English and the answer could be in either of these languages. (iii) The answer identification module ranks specific text fragments that are plausible answers to the question. It first ranks the candidate text fragments in the different languages and, if they are found, they are combined into one consolidated answer. This is a variation of the cross-language QA scenario enabling answer triggering, where no concrete answer has to be provided, if it does not exist. In order to build our QA system, we extend an existing framework (Rücklé and Gurevych, 2017) integrating neural networks for answer selection. References Alberto Barrón-Cedeño, Cristina España Bonet, Josu Boldoba Trapote, and Luís Márquez Villodre. A Factory of Comparable Corpora from Wikipedia. In Proceedings of the Eighth Workshop on Building and Using Comparable Corpora, pages 3–13, Beijing, China, 2015. Association for Computational Linguistics. Andreas Rücklé and Iryna Gurevych. End-to-End Non-Factoid Question Answering with an Interactive Visualization of Neural Attention Weights. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics-System Demonstrations (ACL 2017), pages 19–24, Vancouver, Canada, August 2017. Association for Computational Linguistics. doi:10.18653/v1/P17-4004. URL http://aclweb.org/anthology/P17-4004. Yi Yang, Wen-tau Yih, and Christopher Meek. WikiQA: A Challenge Dataset for Open-Domain Question Answering. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 2013–2018, Lisbon, Portugal, 2015.