Abstract
Recently the world knew by the media, that its leading nations follow closely their citizens, disregarding any moral and technological threshold, that internal and external security agencies in the USA and Europe closely follow telephone conversations, e-mail, web traffic of their counterparts, using powerful monitoring and surveillance programs. In other corners of the globe nations in turmoil or wrapped in the cloak of censorship persecute and deny uncontrolled web access without harmful repercussions to their citizens. This work is a research-in-progress project and consists in showing the research done so far to develop a methodology. This consists in the construction of an operative system with an academic scientific source that permits a secure and anonymous use of the web. For such methodology, first is required to comprehend and get acquaintance with the technologies that controls usage of web consumers, solutions that enable and grant some anonymity and security in web traffic.

Abstract
This work addresses the problem of selecting Tensor Factorization algorithms for the Context-aware Filtering recommendation task using a metalearning approach. The most important challenge of applying metalearning on new problems is the development of useful measures able to characterize the data, i.e. metafeatures. We propose an extensive and exhaustive set of metafeatures to characterize Context-aware Filtering recommendation task. These metafeatures take advantage of the tensor's hierarchical structure via slice operations. The algorithm selection task is addressed as a Label Ranking problem, which ranks the Tensor Factorization algorithms according to their expected performance, rather than simply selecting the algorithm that is expected to obtain the best performance. A comprehensive experimental work is conducted on both levels, baselevel and metalevel (Tensor Factorization and Label Ranking, respectively). The results show that the proposed metafeatures lead to metamodels that tend to rank Tensor Factorization algorithms accurately and that the selected algorithms present high recommendation performance.

Abstract
There exist many dataflow applications with timing constraints that require real-time guarantees on safe execution without violating their deadlines. Extraction of timing parameters (offsets, deadlines, periods) from these applications enables the use of real-time scheduling and analysis techniques, and provides guarantees on satisfying timing constraints. However, existing extraction techniques require the transformation of the dataflow application from highly expressive dataflow computational models, for example, Synchronous Dataflow (SDF) and Cyclo-Static Dataflow (CSDF) to Homogeneous Synchronous Dataflow (HSDF). This transformation can lead to an exponential increase in the size of the application graph that significantly increases the runtime of the analysis. In this article, we address this problem by proposing an offline heuristic algorithm called slack-based merging. The algorithm is a novel graph reduction technique that helps in speeding up the process of timing parameter extraction and finding a feasible real-time schedule, thereby reducing the overall design time of the real-time system. It uses two main concepts: (a) the difference between the worst-case execution time of the SDF graph's firings and its timing constraints (slack) to merge firings together and generate a reducedsize HSDF graph, and (b) the novel concept of merging called safe merge, which is a merge operation that we formally prove cannot cause a live HSDF graph to deadlock. The results show that the reduced graph (1) respects the throughput and latency constraints of the original application graph and (2) typically speeds up the process of extracting timing parameters and finding a feasible real-time schedule for real-time dataflow applications. They also show that when the throughput constraint is relaxed with respect to the maximal throughput of the graph, the merging algorithm is able to achieve a larger reduction in graph size, which in turn results in a larger speedup of the real-time scheduling algorithms.

Abstract

Abstract
In this paper the synthesis of a rotational speed closed-loop control system based on a fractional-order proportional-integral (FOPI) controller is presented. In particular, it is proposed the use of the SCoMR-FOPI procedure as the controller tuning method for an unmanned aerial vehicle's propulsion unit. In this framework, both the Hermite-Biehler and Pontryagin theorems are used to predefine a stability region for the controller. Several simulations were conducted in order to try to answer the questions - is the FOPI controller good enough to be an alternative to more complex FOPID controllers? In what circumstances can it be advantageous over the ubiquitous PID? How robust this fractional-order controller is regarding the parametric uncertainty of considered propulsion unit model?

Abstract
This work further clarifies how the MULTIS architecture can be used for integration of virtual worlds in learning management system (LMS) for organizational management of e-learning activities, as an extension to a previous work published in the proceedings of VEAI 2016. Current LMSs provide minimal support for educational use in an organizational context, and other integration efforts assume that educators are inside the virtual world, accessing the LMS as an external service. Our approach enables educators to set up and manage virtual world activities from within the traditional LMS Web interface as an integral part of the overall educational activities of a course. The MULTIS architecture foresees several alternative communication channels between LMS and virtual worlds, including the spooling of automated clients or "bots" and the flexibility to inject code if necessary and possible. In this work, we detail the application of this architecture and its approach in several sample scenarios, based on previous analysis of integration requirements. It is the result of a joint effort by academic and corporate teams, implemented and tested in the Formare LMS for OpenSimulator and Second Life Grid virtual world platforms.