Arabic Language Sentiment Analysis on Health Services

The social media network phenomenon leads to a massive amount of valuable data that is available online and easy to access. Many users share images, videos, comments, reviews, news and opinions on different social networks sites, with Twitter being one of the most popular ones. Data collected from Twitter is highly unstructured, and extracting useful information from tweets is a challenging task. Twitter has a huge number of Arabic users who mostly post and write their tweets using the Arabic language. While there has been a lot of research on sentiment analysis in English, the amount of researches and datasets in Arabic language is limited. This paper introduces an Arabic language dataset which is about opinions on health services and has been collected from Twitter. The paper will first detail the process of collecting the data from Twitter and also the process of filtering, pre-processing and annotating the Arabic text in order to build a big sentiment analysis dataset in Arabic. Several Machine Learning algorithms (Naive Bayes, Support Vector Machine and Logistic Regression) alongside Deep and Convolutional Neural Networks were utilized in our experiments of sentiment analysis on our health dataset.Comment: Authors accepted version of submission for ASAR 201

Fault Detection and Isolation in Industrial Processes Using Deep Learning Approaches

Automated fault detection is an important part of a quality control system. It has the potential to increase the overall quality of monitored products and processes. The fault detection of automotive instrument cluster systems in computer- based manufacturing assembly lines is currently limited to simple boundary checking. The analysis of more complex non-linear signals is performed manually by trained operators, whose knowledge is used to supervise quality checking and manual detection of faults. In this paper, a novel approach for automated fault detection and isolation based on deep machine learning techniques is presented. The approach was tested on data generated by computer-based manufacturing systems equipped with local and remote sensing devices. The results show that the proposed approach models the different spatial / temporal patterns found in the data. The approach is also able to successfully diagnose and locate multiple classes of faults under real-time working conditions. The proposed method is shown to outperform other established fault detection and isolation methods

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Local experts finding using user comments in location-based social networks

The opinions of local experts in the location-based social network are of great significance to the collection and dissemination of local information. In this paper, we investigated in-depth how the user comments can be used to identify the local expert over social networks. We first illustrate the existences of potential local experts in a social network using a scored model by considering the personal profiles, comments, friend relationship, and location preferences. Then, a multi-dimensional model is proposed to evaluate the local expert candidates and a local expert discovery algorithm is proposed to identify local experts. Meanwhile, a scoring algorithm is proposed to train the weights in the model. Finally, an expert recommendation list can be given based on the score ranks of the candidates. Experimental results demonstrate that effectiveness of proposed model and algorithm

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

Axo: Detection and Recovery for Delay and Crash Faults in Real-Time Control Systems

Real-time control systems use controllers that compute and issue setpoints within stringent delay constraints. Failure to do so, due to a crash or delay as a result of software and/or hardware faults, can cause failure of the controlled resources. Recently, Axo, a protocol for masking crash and delay faults by replicating the controller, was proposed. Axo provides safety by discarding delayed setpoints, and it relies on the presence of valid setpoints for providing availability. To ensure that enough valid setpoints are issued, faulty controller replicas need to be detected and recovered. We present a mechanism for detection and recovery of delay- and crash-faulty replicas under the Axo framework. These mechanisms were designed to be soft state (i.e., their state can be reconstructed from received messages) to enable seamless additions of new replicas. Besides presenting the design, we analytically characterize the time to detect and recover a faulty replica, and we validate them experimentally. We demonstrate the performance of Axo by using two case studies: the first provides a stability analysis of an inverted pendulum system with Axo, and the second shows the fault-tolerance performance of Axo through a deployment on a real-time control system that controls a CIGRE low-voltage benchmark microgrid

Automated intelligent system for sound signalling device quality assurance

This paper presents a novel approach to the detection and recognition of faulty audio signalling devices as part of an automated industrial manufacturing quality assurance process. The proposed system outperforms other well-established automated systems based on mel-frequency cepstrum coefficients (MFCC) and multi-layer perceptron (MLP). It uses both unlabelled sound data and labelled historical data acquired from human experts in detecting faulty signalling devices. The unlabelled data is used to train a deep neural network generative model to create multiple levels of hierarchical feature extractors which are used to train an MLP classifier, with the intent to model the human reasoning and judging processes in respect to sound classification. This paper presents the results of real world experiments based on data pertaining to the audio signalling quality assurance process for car instrument cluster manufacturing. These results show that the proposed system is able to successfully classify speakers into two groups: "Good" and "No good" depending on the part quality. The proposed system proves to be capable enough to eliminate the need for a manual inspection within the manufacturing process and is shown to be able to diagnose a fault with a high degree of accuracy. This work can be extended to other areas of automotive inspection where there is a need for a robust solution to sound detection and where an output signal is represented by a complex and changing frequency spectrum even with significant environmental noise

Reliable and Robust Cyber-Physical Systems for Real-Time Control of Electric Grids

Real-time control of electric grids is a novel approach to handling the increasing penetration of distributed and volatile energy generation brought about by renewables. Such control occurs in cyber-physical systems (CPSs), in which software agents maintain safe and optimal grid operation by exchanging messages over a communication network. We focus on CPSs with a centralized controller that receives measurements from the various resources in the grid, performs real-time computations, and issues setpoints. Long-term deployment of such CPSs makes them susceptible to software agent faults, such as crashes and delays of controllers and unresponsiveness of resources, and to communication network faults, such as packet losses, delays, and reordering. CPS controllers must provide correct control in the presence of external non-idealities, i.e., be robust, and in the presence of controller faults, i.e., be reliable. In this thesis, we design, test, and deploy solutions that achieve these goals for real-time CPSs. We begin by abstracting a CPS for electric grids into four layers: the control layer, the network layer, the sensing and actuation layer, and the physical layer. Then, we provide a model for the components in each layer, and for the interactions among them. This enables us to formally define the properties required for reliable and robust CPSs. We propose two mechanisms, Robuster and intentionality clocks, for making a single controller robust to unresponsive resources and non-ideal network conditions. These mechanisms enable the controller to compute and issue setpoints even when some measurements are missing, rather than to have to wait for measurements from all resources. We show that our proposed mechanisms guarantee grid safety and outperform state-of-the-art alternatives. Then, we propose Axo: a framework for crash- and delay-fault tolerance via active replication of the controller. Axo ensures that faults in the controller replicas are masked from the resources, and it provides a mechanism for detecting and recovering faulty replicas. We prove the reliable validity and availability guarantees of Axo and derive the bounds on its detection and recovery time. We showcase the benefits of Axo via a stability analysis of an inverted pendulum system. Solutions based on active replication must guarantee that the replicas issue consistent setpoints. Traditional consensus-based schemes for achieving this are not suitable for real-time CPSs, as they incur high latency and low availability. We propose Quarts, an agreement mechanism that guarantees consistency and a low bounded latency- overhead. We show, via extensive simulations, that Quarts provides an availability at least an order of magnitude higher than state-of-the-art solutions. In order to test the effect of our proposed solutions on electric grids, we developed T-RECS, a virtual commissioning tool for software-based control of electric grids. T-RECS enables us to test the proper functioning of the software agents both in ideal and faulty conditions. This provides insight into the effect of faults on the grid and helps us to evaluate the impact of our reliability solutions. We show how our proposed solutions fit together, and that they can be used to design a reliable and robust CPS for real-time control of electric grids. To this end, we study a CPS with COMMELEC, a real-time control framework for electric grids via explicit power setpoints. We analyze the reliability issues..

Reliability Mechanisms for Controllers in Real-Time Cyber-Physical Systems

Cyber-physical systems (CPSs) are real-world processes that are controlled by computer algorithms. We consider CPSs where a centralized, software-based controller maintains the process in a desired state by exchanging measurements and setpoints with process agents (PAs). As CPSs control processes with low-inertia, e.g., electric grids and autonomous cars, the controller needs to satisfy stringent real-time constraints. However, the controllers are susceptible to delay and crash faults, and the communication network might drop, delay or reorder messages. This degrades the quality of control of the physical process, failure of which can result in damage to life or property. Existing reliability solutions are either not well-suited for real-time CPSs or impose serious restrictions on the controllers. In this thesis, we design, implement and evaluate reliability mechanisms for real-time CPS controllers that require minimal modifications to the controller itself. We begin by abstracting the execution of a CPS using events in the CPS, and the two inherent relations among those events, namely network and computation relations. We use these relations to introduce the intentionality relation that uses these events to capture the state of the physical process. Based on the intentionality relation, we define three correctness properties namely, state safety, optimal selection and consistency, that together provide linearizability (one-copy equivalence) for CPS controllers. We propose intentionality clocks and Quarts, and prove that they provide linearizability. To provide consistency, Quarts ensures agreement among controller replicas, which is typically achieved using consensus. Consensus can add an unbounded-latency overhead. Quarts leverages the properties specific to CPSs to perform agreement using pre-computed priorities among sets of received measurements, resulting in a bounded-latency overhead with high availability. Using simulation, we show that availability of Quarts, with two replicas, is more than an order of magnitude higher than consensus. We also propose Axo, a fault-tolerance protocol that uses active replication to detect and recover faulty replicas, and provide timeliness that requires delayed setpoints be masked from the PAs. We study the effect of delay faults and the impact of fault-tolerance with Axo, by deploying Axo in two real-world CPSs. Then, we realize that the proposed reliability mechanisms also apply to unconventional CPSs such as software defined networking (SDN), where the controlled process is the routing fabric of the network. We show that, in SDN, violating consistency can cause implementation of incorrect routing policies. Thus, we use Quarts and intentionality clocks, to design and implement QCL, a coordination layer for SDN controllers that guarantees control-plane consistency. QCL also drastically reduces the response time of SDN controllers when compared to consensus-based techniques. In the last part of the thesis, we address the problem of reliable communication between the software agents, in a wide-area network that can drop, delay or reorder messages. For this, we propose iPRP, an IP-friendly parallel redundancy protocol for 0 ms repair of packet losses. iPRP requires fail-independent paths for high-reliability. So, we study the fail-independence of Wi-Fi links using real-life measurements, as a first step towards using Wi-Fi for real-time communication in CPSs