Social media analytics: a survey of techniques, tools and platforms

This paper is written for (social science) researchers seeking to analyze the wealth of social media now available. It presents a comprehensive review of software tools for social networking media, wikis, really simple syndication feeds, blogs, newsgroups, chat and news feeds. For completeness, it also includes introductions to social media scraping, storage, data cleaning and sentiment analysis. Although principally a review, the paper also provides a methodology and a critique of social media tools. Analyzing social media, in particular Twitter feeds for sentiment analysis, has become a major research and business activity due to the availability of web-based application programming interfaces (APIs) provided by Twitter, Facebook and News services. This has led to an ‘explosion’ of data services, software tools for scraping and analysis and social media analytics platforms. It is also a research area undergoing rapid change and evolution due to commercial pressures and the potential for using social media data for computational (social science) research. Using a simple taxonomy, this paper provides a review of leading software tools and how to use them to scrape, cleanse and analyze the spectrum of social media. In addition, it discussed the requirement of an experimental computational environment for social media research and presents as an illustration the system architecture of a social media (analytics) platform built by University College London. The principal contribution of this paper is to provide an overview (including code fragments) for scientists seeking to utilize social media scraping and analytics either in their research or business. The data retrieval techniques that are presented in this paper are valid at the time of writing this paper (June 2014), but they are subject to change since social media data scraping APIs are rapidly changing

A machine learning based Distributed Congestion Control Protocol for multi-hop wireless networks

The application areas of multi-hop wireless networks are expected to experience sustained growth in the next years. This growth will be further supported by the current possibility of providing low-cost communication capabilities to any device. One of the main issues to consider with this type of networks is congestion control, that is, avoiding an excessive volume of data traffic that could lead to a loss of performance. In this work, a distributed congestion control mechanism is proposed for generic multi-hop networks. Different categories of data traffic are taken into account, each of them with different quality of service requirements. The mechanism is based on machine learning techniques, specifically, the CatBoost algorithm that uses gradient boosting on decision trees. The obtained decision trees are used to predict whether the packets to be transmitted over the network will reach their destination on time or not. This prediction will be made based on the network load state, which will be quantified by means of two parameters: the utilization factor of the different transmission channels, and the occupancy of the buffers of the network nodes. To make the values of these parameters available to all nodes in the network, an appropriate dissemination protocol has also been designed. Besides, a method to assign different transmission priorities to each traffic category, based on the estimation of the network resources required at any time, has also been included. The complete system has been implemented and evaluated through simulations, which show the correct functionality and the improvements obtained in terms of packet delivery ratio, network transit time, and traffic differentiation.Peer ReviewedPostprint (published version

Service Composition for IP Smart Object using Realtime Web Protocols: Concept and Research Challenges

The Internet of Things (IoT) refers to a world-wide network of interconnected physical things using standardized communication protocols. Recent development of Internet Protocol (IP) stacks for resource-constrained devices unveils a possibility for the future IoT based on the stable and scalable IP technology much like today's Internet of computers. One important question remains: how can data and events (denoted as services) introduced by a variety of IP networked things be exchanged and aggregated e ciently in various application domains. Because the true value of IoT lies in the interaction of several services from physical things, answers to this question are essential to support a rapid creation of new IoT smart and ubiquitous applications. The problem is known as service composition. This article explains the practicability of the future full-IP IoT with realtime Web protocols to formally state the problem of service composition for IP smart objects, provides literature review, and discusses its research challenges

The effects of network factors on the performance of 3G UMTS applications

Includes bibliographical references (leaves 139-148).3G is the wireless network technology expected to allow wireless applications to perform on par with wired applications. However 3G has factors which limit its performance. These factors include both device factors such as small screens, limited battery power and life, as well as network factors such as high delay networks and low bandwidths. This thesis investigates the following: how network factors affect the performance of 3G UMTS applications; which network factors have the most significant impact on a specific application; whether there are any minimum requirements needed for an application. Eight popular 3G applications were investigated: FTP, email, MMS, SMS, HTTP web browsing, broadcast media, video calling and streaming media

Model checking web applications

The modelling of web-based applications can assist in capturing and understanding their behaviour. The development of such applications requires the use of sound methodologies to ensure that the intended and actual behaviour are the same. As a verification technique, model checking can assist in finding design flaws and simplifying the design of a web application, and as a result the design and the security of the web application can be improved. Model checking has the advantage of using an exhaustive search of the state space of a system to determine if the specifications are true or not in a given model. In this thesis we present novel approaches in modelling and verifying web applications' properties to ensure their design correctness and security. Since the actions in web applications rely on both the user input and the server status; we propose an approach for modelling and verifying dynamic navigation properties. The Spin model checker has been used successfully in verifying communication protocols. However, the current version of Spin does not support modelling time. We integrate discrete time in the Spin model to allow the modelling of realistic properties that rely on time constraints and to analyse the sequence of actions and time. Examining the sequence of actions in web applications assists in understanding their behaviour in different scenarios such as navigation errors and in the presence of an intruder. The model checker Uppaal is presented in the literature as an alternative to Spin when modelling real-time systems. We develop models with real time constraints in Uppaal in order to validate the results from the Spin models and to compare the differences between modelling with real time and with discrete time as in Spin. We also compare the complexity and expressiveness of each model checker in verifying web applications' properties. The web application models in our research are developed gradually to ensure their correctness and to manage the complexities of specifying the security and navigation properties. We analyse the compromised model to compare the differences in the sequence of actions and time with the secure model to assist in improving early detections of malicious behaviour in web applications

SECURING THE DATA STORAGE AND PROCESSING IN CLOUD COMPUTING ENVIRONMENT

Organizations increasingly utilize cloud computing architectures to reduce costs and en- ergy consumption both in the data warehouse and on mobile devices by better utilizing the computing resources available. However, the security and privacy issues with publicly available cloud computing infrastructures have not been studied to a sufficient depth for or- ganizations and individuals to be fully informed of the risks; neither are private nor public clouds prepared to properly secure their connections as middle-men between mobile de- vices which use encryption and external data providers which neglect to encrypt their data. Furthermore, cloud computing providers are not well informed of the risks associated with policy and techniques they could implement to mitigate those risks. In this dissertation, we present a new layered understanding of public cloud comput- ing. On the high level, we concentrate on the overall architecture and how information is processed and transmitted. The key idea is to secure information from outside attack and monitoring. We use techniques such as separating virtual machine roles, re-spawning virtual machines in high succession, and cryptography-based access control to achieve a high-level assurance of public cloud computing security and privacy. On the low level, we explore security and privacy issues on the memory management level. We present a mechanism for the prevention of automatic virtual machine memory guessing attacks

Malware-Resistant Protocols for Real-World Systems

Cryptographic protocols are widely used to protect real-world systems from attacks. Paying for goods in a shop, withdrawing money or browsing the Web; all these activities are backed by cryptographic protocols. However, in recent years a potent threat became apparent. Malware is increasingly used in attacks to bypass existing security mechanisms. Many cryptographic protocols that are used in real-world systems today have been found to be susceptible to malware attacks. One reason for this is that most of these protocols were designed with respect to the Dolev-Yao attack model that assumes an attacker to control the network between computer systems but not the systems themselves. Furthermore, most real-world protocols do not provide a formal proof of security and thus lack a precise definition of the security goals the designers tried to achieve. This work tackles the design of cryptographic protocols that are resilient to malware attacks, applicable to real-world systems, and provably secure. In this regard, we investigate three real-world use cases: electronic payment, web authentication, and data aggregation. We analyze the security of existing protocols and confirm results from prior work that most protocols are not resilient to malware. Furthermore, we provide guidelines for the design of malware-resistant protocols and propose such protocols. In addition, we formalize security notions for malware-resistance and use a formal proof of security to verify the security guarantees of our protocols. In this work we show that designing malware-resistant protocols for real-world systems is possible. We present a new security notion for electronic payment and web authentication, called one-out-of-two security, that does not require a single device to be trusted and ensures that a protocol stays secure as long as one of two devices is not compromised. Furthermore, we propose L-Pay, a cryptographic protocol for paying at the point of sale (POS) or withdrawing money at an automated teller machine (ATM) satisfying one-out-of-two security, FIDO2 With Two Displays (FIDO2D) a cryptographic protocol to secure transactions in the Web with one-out-of-two security and Secure Aggregation Grouped by Multiple Attributes (SAGMA), a cryptographic protocol for secure data aggregation in encrypted databases. In this work, we take important steps towards the use of malware-resistant protocols in real-world systems. Our guidelines and protocols can serve as templates to design new cryptographic protocols and improve security in further use cases

Qos-aware fine-grained power management in networked computing systems

Power is a major design concern of today\u27s networked computing systems, from low-power battery-powered mobile and embedded systems to high-power enterprise servers. Embedded systems are required to be power efficiency because most embedded systems are powered by battery with limited capacity. Similar concern of power expenditure rises as well in enterprise server environments due to cooling requirement, power delivery limit, electricity costs as well as environment pollutions. The power consumption in networked computing systems includes that on circuit board and that for communication. In the context of networked real-time systems, the power dissipation on wireless communication is more significant than that on circuit board. We focus on packet scheduling for wireless real-time systems with renewable energy resources. In such a scenario, it is required to transmit data with higher level of importance periodically. We formulate this packet scheduling problem as an NP-hard reward maximization problem with time and energy constraints. An optimal solution with pseudo polynomial time complexity is presented. In addition, we propose a sub-optimal solution with polynomial time complexity. Circuit board, especially processor, power consumption is still the major source of system power consumption. We provide a general-purposed, practical and comprehensive power management middleware for networked computing systems to manage circuit board power consumption thus to affect system-level power consumption. It has the functionalities of power and performance monitoring, power management (PM) policy selection and PM control, as well as energy efficiency analysis. This middleware includes an extensible PM policy library. We implemented a prototype of this middleware on Base Band Units (BBUs) with three PM policies enclosed. These policies have been validated on different platforms, such as enterprise servers, virtual environments and BBUs. In enterprise environments, the power dissipation on circuit board dominates. Regulation on computing resources on board has a significant impact on power consumption. Dynamic Voltage and Frequency Scaling (DVFS) is an effective technique to conserve energy consumption. We investigate system-level power management in order to avoid system failures due to power capacity overload or overheating. This management needs to control the power consumption in an accurate and responsive manner, which cannot be achieve by the existing black-box feedback control. Thus we present a model-predictive feedback controller to regulate processor frequency so that power budget can be satisfied without significant loss on performance. In addition to providing power guarantee alone, performance with respect to service-level agreements (SLAs) is required to be guaranteed as well. The proliferation of virtualization technology imposes new challenges on power management due to resource sharing. It is hard to achieve optimization in both power and performance on shared infrastructures due to system dynamics. We propose vPnP, a feedback control based coordination approach providing guarantee on application-level performance and underlying physical host power consumption in virtualized environments. This system can adapt gracefully to workload change. The preliminary results show its flexibility to achieve different levels of tradeoffs between power and performance as well as its robustness over a variety of workloads. It is desirable for improve energy efficiency of systems, such as BBUs, hosting soft-real time applications. We proposed a power management strategy for controlling delay and minimizing power consumption using DVFS. We use the Robbins-Monro (RM) stochastic approximation method to estimate delay quantile. We couple a fuzzy controller with the RM algorithm to scale CPU frequency that will maintain performance within the specified QoS

User friendly knowledge acquisition system for medical devices actuation

Dissertação para obtenção do Grau de Mestre em Engenharia BiomédicaInternet provides a new environment to develop a variety of applications. Hence, large amounts of data, increasing every day, are stored and transferred through the internet. These data are normally weakly structured making information disperse, uncorrelated, non-transparent and difficult to access and share. Semantic Web, proposed by theWorldWideWeb Consortium (W3C), addresses this problem by promoting semantic structured data, like ontologies, enabling machines to perform more work involved in finding, combining, and acting upon information on theWeb. Pursuing this vision, a Knowledge Acquisition System (KAS) was created, written in JavaScript using JavaScript Object Notation (JSON) as the data structure and JSON Schema to define that structure. It grants new ways to acquire and store knowledge semantically structured and human readable. Plus, structuring data with a Schema generates a software robust and error – free. A novel Human Computer Interaction (HCI) framework was constructed employing this KAS, allowing the end user to configure and control medical devices. To demonstrate the potential of this tool, we present the configuration and control of an electrostimulator. Nowadays, most of the software for Electrostimulation is made with specific purposes, and in some cases they have complicated user interfaces and large, bulky designs that deter usability and acceptability. The HCI concedes the opportunity to configure and control an electrostimulator that surpasses the specific use of several electrostimulator software. In the configuration the user is able to compile different types of electrical impulses (modes) in a temporal session, automating the control, making it simple and user-friendly