Monitoring the waste to energy plant using the latest AI methods and tools

Solid wastes for instance, municipal and industrial wastes present great environmental concerns and challenges all over the world. This has led to development of innovative waste-to-energy process technologies capable of handling different waste materials in a more sustainable and energy efficient manner. However, like in many other complex industrial process operations, waste-to-energy plants would require sophisticated process monitoring systems in order to realize very high overall plant efficiencies. Conventional data-driven statistical methods which include principal component analysis, partial least squares, multivariable linear regression and so forth, are normally applied in process monitoring. But recently, latest artificial intelligence (AI) methods in particular deep learning algorithms have demostrated remarkable performances in several important areas such as machine vision, natural language processing and pattern recognition. The new AI algorithms have gained increasing attention from the process industrial applications for instance in areas such as predictive product quality control and machine health monitoring. Moreover, the availability of big-data processing tools and cloud computing technologies further support the use of deep learning based algorithms for process monitoring. In this work, a process monitoring scheme based on the state-of-the-art artificial intelligence methods and cloud computing platforms is proposed for a waste-to-energy industrial use case. The monitoring scheme supports use of latest AI methods, laveraging big-data processing tools and taking advantage of available cloud computing platforms. Deep learning algorithms are able to describe non-linear, dynamic and high demensionality systems better than most conventional data-based process monitoring methods. Moreover, deep learning based methods are best suited for big-data analytics unlike traditional statistical machine learning methods which are less efficient. Furthermore, the proposed monitoring scheme emphasizes real-time process monitoring in addition to offline data analysis. To achieve this the monitoring scheme proposes use of big-data analytics software frameworks and tools such as Microsoft Azure stream analytics, Apache storm, Apache Spark, Hadoop and many others. The availability of open source in addition to proprietary cloud computing platforms, AI and big-data software tools, all support the realization of the proposed monitoring scheme

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Securing cloud-based data analytics: A practical approach

The ubiquitous nature of computers is driving a massive increase in the amount of data generated by humans and machines. The shift to cloud technologies is a paradigm change that offers considerable financial and administrative gains in the effort to analyze these data. However, governmental and business institutions wanting to tap into these gains are concerned with security issues. The cloud presents new vulnerabilities and is dominated by new kinds of applications, which calls for new security solutions. In the direction of analyzing massive amounts of data, tools like MapReduce, Apache Storm, Dryad and higher-level scripting languages like Pig Latin and DryadLINQ have significantly improved corresponding tasks for software developers. The equally important aspect of securing computations performed by these tools and ensuring confidentiality of data has seen very little support emerge for programmers. In this dissertation, we present solutions to a. secure computations being run in the cloud by leveraging BFT replication coupled with fault isolation and b. secure data from being leaked by computing directly on encrypted data. For securing computations (a.), we leverage a combination of variable-degree clustering, approximated and offline output comparison, smart deployment, and separation of duty to achieve a parameterized tradeoff between fault tolerance and overhead in practice. We demonstrate the low overhead achieved with our solution when securing data-flow computations expressed in Apache Pig, and Hadoop. Our solution allows assured computation with less than 10 percent latency overhead as shown by our evaluation. For securing data (b.), we present novel data flow analyses and program transformations for Pig Latin and Apache Storm, that automatically enable the execution of corresponding scripts on encrypted data. We avoid fully homomorphic encryption because of its prohibitively high cost; instead, in some cases, we rely on a minimal set of operations performed by the client. We present the algorithms used for this translation, and empirically demonstrate the practical performance of our approach as well as improvements for programmers in terms of the effort required to preserve data confidentiality

A study of EU data protection regulation and appropriate security for digital services and platforms

A law often has more than one purpose, more than one intention, and more than one interpretation. A meticulously formulated and context agnostic law text will still, when faced with a field propelled by intense innovation, eventually become obsolete. The European Data Protection Directive is a good example of such legislation. It may be argued that the technological modifications brought on by the EU General Data Protection Regulation (GDPR) are nominal in comparison to the previous Directive, but from a business perspective the changes are significant and important. The Directive’s lack of direct economic incentive for companies to protect personal data has changed with the Regulation, as companies may now have to pay severe fines for violating the legislation. The objective of the thesis is to establish the notion of trust as a key design goal for information systems handling personal data. This includes interpreting the EU legislation on data protection and using the interpretation as a foundation for further investigation. This interpretation is connected to the areas of analytics, security, and privacy concerns for intelligent service development. Finally, the centralised platform business model and its challenges is examined, and three main resolution themes for regulating platform privacy are proposed. The aims of the proposed resolutions are to create a more trustful relationship between providers and data subjects, while also improving the conditions for competition and thus providing data subjects with service alternatives. The thesis contributes new insights into the evolving privacy practices in the digital society at an important time of transition from the service driven business models to the platform business models. Firstly, privacy-related regulation and state of the art analytics development are examined to understand their implications for intelligent services that are based on automated processing and profiling. The ability to choose between providers of intelligent services is identified as the core challenge. Secondly, the thesis examines what is meant by appropriate security for systems that handle personal data, something the GDPR requires that organisations use without however specifying what can be considered appropriate. We propose a method for active network security in web software that is developed through the use of analytics for detection and by inserting data generators into a software installation. The active network security method is proposed as a framework for achieving compliance with the GDPR requirements for services and platforms to use appropriate security. Thirdly, the platform business model is considered from the privacy point of view and the implication of “processing silos” for intelligent services. The centralised platform model is considered problematic from both the data subject and from the competition standpoint. A resolution is offered for enabling user-initiated open data flow to counter the centralised “processing silos”, and thereby to facilitate the introduction of decentralised platforms. The thesis provides an interdisciplinary analysis considering the legal study (lex lata) and additionally the resolution (lex ferenda) is defined through argumentativist legal dogmatics and (de lege ferenda) of how the legal framework ought to be adapted to fit the described environment. User-friendly Legal Science is applied as a theory framework to provide a holistic approach to answering the research questions. The User-friendly Legal Science theory has its roots in design science and offers a way towards achieving interdisciplinary research in the fields of information systems and legal science

Human Data Model: Improving Programmability of Health and Well-Being Data for Enhanced Perception and Interaction

Peer reviewe

Networked world: Risks and opportunities in the Internet of Things

The Internet of Things (IoT) – devices that are connected to the Internet and collect and use data to operate – is about to transform society. Everything from smart fridges and lightbulbs to remote sensors and cities will collect data that can be analysed and used to provide a wealth of bespoke products and services. The impacts will be huge - by 2020, some 25 billion devices will be connected to the Internet with some studies estimating this number will rise to 125 billion in 2030. These will include many things that have never been connected to the Internet before. Like all new technologies, IoT offers substantial new opportunities which must be considered in parallel with the new risks that come with it. To make sense of this new world, Lloyd’s worked with University College London’s (UCL) Department of Science, Technology, Engineering and Public Policy (STEaPP) and the PETRAS IoT Research Hub to publish this report. ‘Networked world’ analyses IoT’s opportunities, risks and regulatory landscape. It aims to help insurers understand potential exposures across marine, smart homes, water infrastructure and agriculture while highlighting the implications for insurance operations and product development. The report also helps risk managers assess how this technology could impact their businesses and consider how they can mitigate associated risks

Towards Data Sharing across Decentralized and Federated IoT Data Analytics Platforms

In the past decade the Internet-of-Things concept has overwhelmingly entered all of the fields where data are produced and processed, thus, resulting in a plethora of IoT platforms, typically cloud-based, that centralize data and services management. In this scenario, the development of IoT services in domains such as smart cities, smart industry, e-health, automotive, are possible only for the owner of the IoT deployments or for ad-hoc business one-to-one collaboration agreements. The realization of "smarter" IoT services or even services that are not viable today envisions a complete data sharing with the usage of multiple data sources from multiple parties and the interconnection with other IoT services. In this context, this work studies several aspects of data sharing focusing on Internet-of-Things. We work towards the hyperconnection of IoT services to analyze data that goes beyond the boundaries of a single IoT system. This thesis presents a data analytics platform that: i) treats data analytics processes as services and decouples their management from the data analytics development; ii) decentralizes the data management and the execution of data analytics services between fog, edge and cloud; iii) federates peers of data analytics platforms managed by multiple parties allowing the design to scale into federation of federations; iv) encompasses intelligent handling of security and data usage control across the federation of decentralized platforms instances to reduce data and service management complexity. The proposed solution is experimentally evaluated in terms of performances and validated against use cases. Further, this work adopts and extends available standards and open sources, after an analysis of their capabilities, fostering an easier acceptance of the proposed framework. We also report efforts to initiate an IoT services ecosystem among 27 cities in Europe and Korea based on a novel methodology. We believe that this thesis open a viable path towards a hyperconnection of IoT data and services, minimizing the human effort to manage it, but leaving the full control of the data and service management to the users' will

An Approach to Guide Users Towards Less Revealing Internet Browsers

When browsing the Internet, HTTP headers enable both clients and servers send extra data in their requests or responses such as the User-Agent string. This string contains information related to the sender’s device, browser, and operating system. Previous research has shown that there are numerous privacy and security risks result from exposing sensitive information in the User-Agent string. For example, it enables device and browser fingerprinting and user tracking and identification. Our large analysis of thousands of User-Agent strings shows that browsers differ tremendously in the amount of information they include in their User-Agent strings. As such, our work aims at guiding users towards using less exposing browsers. In doing so, we propose to assign an exposure score to browsers based on the information they expose and vulnerability records. Thus, our contribution in this work is as follows: first, provide a full implementation that is ready to be deployed and used by users. Second, conduct a user study to identify the effectiveness and limitations of our proposed approach. Our implementation is based on using more than 52 thousand unique browsers. Our performance and validation analysis show that our solution is accurate and efficient. The source code and data set are publicly available and the solution has been deployed