Abstract
Cloud computing is the latest trend in business for providing software, platforms and services over the Internet. However, a widespread adoption of this paradigm has been hampered by the lack of security mechanisms. In view of this, the aim of this work is to propose a new approach for detecting anomalies in cloud network traffic. The anomaly detection mechanism works on the basis of a Support Vector Machine (SVM). The key requirement for improving the accuracy of the SVM model, in the context of cloud, is to reduce the total amount of data. In light of this, we put forward the Poisson Moving Average predictor which is the core of the feature extraction approach and is able to handle the vast amount of information generated over time. In addition, two case studies are employed to validate the effectiveness of the mechanism on the basis of real datasets. Compared with other approaches, our solution exhibits the best performance in terms of detection and false alarm rates.

Abstract
Nowadays, many companies have migrated their applications and data to the cloud. Among other benefits of this technology, the ability to answer quickly business requirements has been one of the main motivations. Thereby, in cloud environments, resources should be acquired and released automatically and quickly at runtime. This way, to ensure QoS, the major cloud providers emphasize ensuring of availability, CPU instance and cost measure in their SLAs (Service Level Agreements). However, the QoS performance are not completely handled or inappropriately treated in SLAs. Although from the user's point of view, it is considered one of the main QoS parameters. Therefore, the aim of this work consists in development of a solution to efficient query processing on large databases available in the cloud environments. It integrates adaptive re-optimization at query runtime and their costs are based on the SRT (Service Response Time) QoS performance parameter of SLA. Finally, the solution was evaluated in Amazon EC2 cloud infrastructure and the TPC-DS like benchmark was used for generating a database.

Abstract
The integrated use of multiple Synthetic Aperture Radar (SAR) platforms for the deformation monitoring via satellite radar interferometry offers several perspectives for investigation of the behaviour of new and ageing structures, such as buildings and infrastructures, under varying or hazardous environment. Spanning almost 24 years of space-borne radar observations, this study aims to perform classical PSInSAR (Persistent Scatterer Interferometric Synthetic Aperture Radar) analysis incorporating measurements of ERS, Envisat, TerraSAR-X, Sentinel-1A and Radarsat-2 satellites. The results from the processing of different sensing geometries over Bratislava (Slovakia) urban area are presented, focusing on the description of characteristics associated with the specifics of every satellite platform in use. The discussion over technical feasibility of infrastructure monitoring is accompanied by the outline of possible future needs for the utilisation of the wealth source of information provided by the satellite radar imagery. (C) 2016 The Authors. Published by Elsevier B.V.

Abstract
With the increasing importance of software in the aerospace field, as evidenced by its growing size and complexity, a rigorous and reliable software verification and validation process must be applied to ensure conformance with the strict requirements of this software. Although important, traditional validation activities such as testing and simulation can only provide a partial verification of behavior in critical real-time software systems, and thus, formal verification is an alternative to complement these activities. Two useful formal software verification approaches are deductive verification and abstract interpretation, which analyze programs statically to identify defects. This paper explores abstract interpretation and deductive verification by employing Frama-C's value analysis and Jessie plug-ins to verify embedded aerospace control software. The results indicate that both approaches can be employed in a software verification process to make software more reliable.

Abstract
This research aims at tackling a real-world long-haul freight transportation problem where tractors are allowed to exchange semi-trailers through several transshipment points until a request reaches its destiny. The unique characteristics of the considered logistics network allow for providing long-haul services by means of short-haul jobs, drastically reducing empty truck journeys. A greater flexibility is achieved with faster responses. Furthermore, the planning goals as well as the nature of the considered trips led to the definition of a new problem, the long-haul freight transportation problem with multiple transshipment locations. A novel mathematical formulation is developed to ensure resource synchronization while including realistic features, which are commonly found separately in the literature. Considering the complexity and dimension of this routing and scheduling problem, a mathematical programming heuristic (matheuristic) is developed with the objective of obtaining good quality solutions in a reasonable amount of time, considering the logistics business context. We provide a comparison between the results obtained for 79 real-world instances. The developed solution method is now the basis of a decision support system of a Portuguese logistics operator (LO).

Abstract
A number of High Dynamic Range (HDR) video compression algorithms proposed to date have either been developed in isolation or only-partially compared with each other. Previous evaluations were conducted using quality assessment error metrics, which for the most part were developed for qualitative assessment of Low Dynamic Range (LDR) videos. This paper presents a comprehensive objective and subjective evaluation conducted with six published HDR video compression algorithms. The objective evaluation was undertaken on a large set of 39 HDR video sequences using seven numerical error metrics namely: PSNR, logPSNR, puPSNR, puSSIM, Weber MSE, HDR-VDP and HDR-VQM. The subjective evaluation involved six short-listed sequences and two ranking-based subjective experiments with hidden reference at two different output bitrates with 32 participants each, who were tasked to rank distorted HDR video footage compared to an uncompressed version of the same footage. Results suggest a strong correlation between the objective and subjective evaluation. Also, non-backward compatible compression algorithms appear to perform better at lower output bit rates than backward compatible algorithms across the settings used in this evaluation.