Abstract
The internet is increasingly present in people's lives, being used in diverse tasks, such as checking e-mail up to online gaming and streaming. The so-called "killer applications" are applications that, when not properly identified and prevented, have more impact on the network, making it slow. When these applications are used on networks with limited resources, as happens in rural networks, they cause a large load on the network, making it difficult its use for work purposes. It is important then to recognize and characterize this traffic to take action so that it does not cause network problems. With that in mind, the work presented in this paper describes the research and identification of cost free traffic analysis solutions that can help to overcome such problems. For that, we perform preliminary testing and a performance comparison of those tools, focusing on testing particular types of network traffic. After that, we describe the analysis and subsequent modification of the source code for storing important traffic data for the tests, as well as the test scenarios in laboratory and reallife environments. These tasks are aimed on collecting information that assists in taking action to improve the allocation of network resources to priority traffic.

Abstract

Abstract
In order to study the mechanisms behind the human motion and its disorders, and to comprehend how do neuromuscular impairments affect the human movements, it is crucial to know the human musculoskeletal system. This knowledge is also essential to effectively model and analyse the musculoskeletal system, and to classify the human movement. With this work we conducted preliminary studies over a musculoskeletal model in motion, and performed a comparative analysis in gait between two models: one with a non-pathological gait and another with a disordered gait.

Abstract
Scheduling problems occurring in parallel machines manufacturing environments are quite usual and many different methods have been applied for solving it. These methods vary from the application of more or less simple heuristics and rules up to more complex methods, including distinct kind of metaheuristics. In this paper we discuss a fuzzy optimization method using simulated annealing (Fuzzy-SA) for solving an unrelated parallel machines manufacturing scheduling problem. To demonstrate the potential of our method we use an illustrative example of a parallel machines scheduling (PMS) problem and then we analyse it and perform statistical tests with 20 instances.

Abstract
In an embedded system, functions often operate under different requirements. In the extreme, a failing safety critical function may cause collateral damage (and hence consider to be a system failure) while non critical functions affect only the quality of service. Approaches by partitioning the system's functions into sandboxes require virtualization mechanisms by the underlying platform and thus prohibit deployment to the bulk of microcontroller based systems. In this paper we discuss an alternative approach based on static semantic analysis performed directly on the system specification expressed in the form of an object oriented (00) model in the experimental language RTFM-lang. This would allow to (at compile time) to discriminate in between critical and non-critical functions, and assign these (by means of statically checkable typing rules) appropriate access rights. In particular, one can imagine dynamic memory allocations to be allowed only in non-critical functions, while on the other hand, direct interaction with the environment may be restricted to the critical parts. With respect to scheduling, a static task and resource configuration allows e.g. Stack Resource Policy (SRP) based approaches to be deployed. In this paper we discuss how this can be achieved in a mixed critical setting.

Abstract
Computational systems operating in open, dynamic and decentralized environments are required to share data with previously unknown computational systems. Due to this ill specification and emergent operation the systems are required to share the data's respective schemas and semantics so that the systems can correctly manipulate, understand and reason upon the shared data. The schemas and semantics are typically provided by ontologies using specific semantics provided by the ontology language. Because computational systems adopt different ontologies to describe their domain of discourse, a consistent and compatible communication relies on the ability to reconcile (in run-time) the vocabulary used in their ontologies. Since each computational system might have its own perspective about what are the best correspondences between the adopted ontologies, conflicts can arise. To address such conflicts, computational systems may engage in any kind of negotiation process that is able to lead them to a common and acceptable agreement. This paper proposes an argumentation-based approach where the computational entities describe their own arguments according to a commonly agreed argumentation meta-model. In order to support autonomy and conceptual differences, the community argumentation model can be individually extended yet maintaining computational effectiveness. Based on the formal specification, a software development framework is proposed.