Smart Cities will create many benefits for society. One of the most significant will be real-time, active management of the systems making up a Smart City. Active management will be enabled by processing large amounts of real-time data, by machine learning to deliver timely insights and by large-scale simulation to create effective interventions. To realise active management of Smart Cities requires processing systems that can take in data at large volume and variety, and perform complex processing on these data, all in real-time. Digital twins are used to create a continuous, consistent view of the current state of the city, and simulation of the evolution of digitals twins is used to detect problems arising in the near future and to optimise the impact of interventions. In this paper we propose and validate a Smart City processing architecture that can deliver these benefits by building a digital twin of a city, combining real-time streamed data ingestion, simulation and analytics. Streamed data systems can deliver a real time response with large amount of incoming data but are not suited to time advancing simulations where the interaction between modelled objects is important. We propose a processing architecture that mitigates this limitation by separating object interactions from streamed data processing. Finally, we introduce DEDUCE-PT, a demonstration implementation of this Smart City processing architecture that is aimed at proactive management of public transport systems.

Subject identification has several applications. In transportation companies, knowing who is driving their vehicles might prevent theft or other ill-intended actions. On the other hand, privacy concerns, and the respective legislation, hinder the applicability of several traditional recognition techniques based on invasive technologies, such as video cameras. Moreover, in order to keep the driver's distractions to a minimum, this technologies must be non-disruptive, that is, they must be able to identify the subject seamlessly without them taking any action. In this context, we propose using deep learning applied to smart watch data for recognizing the person driving a vehicle based on a training set. Our proposal relies on the possibility of using transfer learning to avoid long training sessions for new drivers and to deliver a solution which can be deployed in practice. In this paper, we describe the convolutional neural network used in the solution and present results according to a real data-set collected by us, achieving accuracies ranging from 75 to 94%.

In a distributed grid environment with ambitious service demands the job submission and management interfaces provide functionality of major importance. Emerging e-science and grid infrastructures such as EGEE and DEISA rely on highly available services that are capable of managing scientific jobs. It is the adoption of emerging open standard interfaces which allows the distribution of grid resources in such a way that their actual service implementation or grid technologies are not isolated from each other, especially when these resources are deployed in different e-science infrastructures that consist of different types of computational resources. This paper motivates the interoperability of these infrastructures and discusses solutions. We describe the adoption of various open standards that recently emerged from the open grid forum (OGF) in the field of job submission and management by well-known grid technologies, respectively gLite and UNICORE. This has a fundamental impact on the interoperability between these technologies and thus within the next generation e-science infrastructures that rely on these technologies.

Many existing Grid technologies and resource management systems lack a standardized job submission interface in Grid environments or e-Infrastructures. Even if the same language for job description is used, often the interface for job submission is also different in each of these technologies. The evolvement of the standardized Job Submission and Description Language (JSDL) as well as the OGSA - Basic Execution Services (OGSA-BES) pave the way to improve the interoperability of all these technologies enabling cross-Grid job submission and better resource management capabilities. In addition, the BytelO standards provide useful mechanisms for data access that can be used in conjunction with these improved resource management capabilities. This paper describes the integration of these standards into the recently released UNICORE 6 Grid middleware that is based on open standards such as the Web Services Resource Framework (WS-RF) and WS-Addressing (WS-A).

This study is concerned with efficient use of a high-level machine-independent language in the AMT DAP (distributed array processor) series of computers. The AMT DAP is a single instruction multiple data stream computer. The recent enhancement of Fortran-Plus is described. This enhancement removed the restrictions on array shapes and sizes, giving a language which is independent of the available hardware parallelism. The enhanced Fortran-Plus has been implemented on the AMT DAP using the concept of the virtual systems architecture (VSA). The power of enhanced Fortran-Plus in expressing parallel algorithms is demonstrated in certain application areas. To show that the higher level of language has been implemented on the general VSA system without compromising performance, the performance of these codes is discussed for a range of problem sizes.>