Abstract
Identifying bad design patterns in software is a successful and inspiring research trend. While these patterns do not necessarily correspond to software errors, the fact is that they raise potential problematic issues, often referred to as code smells, and that can for example compromise maintainability or evolution. The identification of code smells in spreadsheets, which can be viewed as software development environments for non-professional programmers, has already been the subject of confluent researches by different groups. While these research groups have focused on detecting smells on concrete spreadsheets, or spreadsheet instances, in this paper we propose a comprehensive set of smells for abstract representations of spreadsheets, or spreadsheet models. We also propose a set of refactorings suggesting how spreadsheet models can become simpler to understand, manipulate and evolve. Finally we present the integration of both smells and refactorings under the MDSheet framework.

Abstract
We present a structure editor that aims to facilitate the presentation and manipulation of handwritten mathematical expressions. The editor is oriented to the calculational mathematics involved in algorithmic problem solving and it provides features that allow reliable structure manipulation of mathematical formulae, as well as flexible and interactive presentations. We describe some of its most important features, including the use of gestures to manipulate algebraic formulae, the structured selection of expressions, definition and redefinition of operators in runtime, gesture's editor, and handwritten templates. The editor is made available in the form of a C# class library which can be easily used to extend existing tools. For example, we have extended Classroom Presenter, a tool for ink-based teaching presentations and classroom interaction. We have tested and evaluated the editor with target users. The results obtained seem to indicate that the software is usable, suitable for its purpose and a valuable contribution to teaching and learning algorithmic problem solving.

Abstract

Abstract
Rumination is a psychological process that is featured by a repetitive pattern of intrusive thoughts that, by focusing the person into painful cognitive contents, elicits suffering. Usually people try to deal with these ruminative thoughts by trying to avoid thinking, which paradoxically makes the thoughts even more intense. Several researchers proposed that ruminative thoughts are central in anxiety and depressive disorders and that the treatment of rumination is also pivotal in the treatment of these disorders. This study is a first attempt to construct a game that trains players in shifting the attention from ruminative thoughts to problem-solving tasks, illustrating the use of the game. We expect that training such an ability will reduce the intensity of that thoughts. It is our expectation that the continuous use of games with these kind of features will reduce the intensity of ruminative thoughts, improving the wellbeing and reducing the psychological suffering of players.

Abstract

Abstract
Wireless Sensor Networks (WSNs) are made of a large amount of small devices that are able to sense changes in the environment, and communicate these changes throughout the network. An example of such network is a photo voltaic (PV) power plant, where there is a sensor connected to each solar panel. Because such a network covers a large area, the number of sensors can be very large. The task of each sensor is to sense the output of the panel which is then sent to a central node for processing. As the network grows, it becomes impractical and even impossible to configure all these nodes manually. And so, the use of self-organization and auto-configuration algorithms becomes essential. In this paper, two algorithms are proposed that can be used to allow each node in the network to automatically identify its closest neighbors as well as its relative location in the network using the value of the Received Signal Strength indicator (RSSI) of the messages sent back and forth during the setup phase. Results show that the error in neighbor identification decreases as we increase the number of RSSI values used for decision making. Additionally, the number of nodes in the network affects the setup error greatly. However, the value of the error is still acceptable even for high number of simulated columns.