Abstract
In this study, a multimodal interferometer based on a suspended core photonic crystal fiber (PCF) for simultaneous strain and temperature measurements is proposed. The structure is also employed for angle measurements. The sensor comprises a 3-mm-suspended core PCF between SMFs and is based on the combination of two multimodal interferences with different frequency fringe patterns. The interferometric patterns show different sensitivity responses to strain and temperature. Through a low-pass frequency filtering of the detected spectrum, the wavelength shift of the two patterns can be measured allowing the discrimination of strain and temperature effects with resolutions of 0.45 degrees C and 4.02 mu epsilon, respectively. The sensor is also characterized for angle measurements showing a maximum sensitivity of 9.17 pm/degrees in the range from 0 degrees to 90 degrees. It is demonstrated that with this sensing structure is possible to obtain simultaneous measurement of bend angle and temperature with resolutions of 1.69 degrees and 0.92 degrees C, respectively.

Abstract
These tutorial notes present a methodology for spreadsheet engineering. First, we present data mining and database techniques to reason about spreadsheet data. These techniques are used to compute relationships between spreadsheet elements (cells/columns/rows), which are later used to infer a model defining the business logic of the spreadsheet. Such a model of a spreadsheet data is a visual domain specific language that we embed in a well-known spreadsheet system. The embedded model is the building block to define techniques for model-driven spreadsheet development, where advanced techniques are used to guarantee the model-instance synchronization. In this model-driven environment, any user data update has to follow the model-instance conformance relation, thus, guiding spreadsheet users to introduce correct data. Data refinement techniques are used to synchronize models and instances after users update/evolve the model. These notes briefly describe our model-driven spreadsheet environment, the MDSheet environment, that implements the presented methodology. To evaluate both proposed techniques and the MDSheet tool, we have conducted, in laboratory sessions, an empirical study with the summer school participants. The results of this study are presented in these notes.

Abstract
It is increasingly important to assess and ensure the correct behaviour of mobile applications as their importance in everyday life keeps increasing. This paper presents an automatic testing approach combining reverse engineering with testing. The algorithm tries to identify existing User Interface (UI) patterns on a mobile application under test through a reverse engineering process and then tests them using generic test strategies called Test Patterns. The overall testing approach was implemented in the iMPAcT (Mobile PAttern Testing) tool and is illustrated in a case study performed over some mobile applications as a proof-of-concept.

Abstract

Abstract
For the project of an Agile/Virtual Enterprise (A/V E) the resources selection is a key factor. The resources systems (output of the selection process) should be prepared to guarantee quality, efficiency and cost-attractiveness, in order to ensure the agility and integrability of the A/V E. This is a difficult matter because it can be a combinatorial and multi-criteria problem. Despite the potential of Value Analysis (VA), none of the resources selection models found in the literature incorporates the evaluation of the resources value. They approach mainly the factors cost and/or time. So, our model constitutes an innovative approach because it gives the highest importance to the value of the resources systems, through the incorporation of VA. The main objective is to quantify the selection process performance with VA integrated into the pre-selection of resources in accordance with the developed model. The paper contribution is the positive confirmation, through the simulation results analysis, of the benefits of VA integration in the resources selection process: greater applicability domain for candidate resources and number of tasks; and reduction of the selection time. In conclusion, the increased efficiency and the superior applicability domain of the model are demonstrated.

Abstract
Assessing physical activity (PA) is a challenging task and many different approaches have been proposed. Direct observation (DO) techniques can objectively code both the behavior and the context in which it occurred, however, they have significant limitations such as the cost and burden associated with collecting and processing data. Therefore, this study evaluated the utility of an automated video analysis system (CAM) designed to record and discriminate the intensity of PA using a subject tracking methodology. The relative utility of the CAM system and DO were compared with criterion data from an objective accelerometry-based device (Actigraph GT3X+). Eight 10 year old children (three girls and five boys) wore the GT3X+ during a standard basketball session. PA was analyzed by two observers using the SOPLAY instrument and by the CAM system. The GT3X+ and the CAM were both set up to collect data at 30 Hz while the DO was performed every two minutes, with 10s of observation for each gender. The GT3X+ was processed using cut points by Evanson and the outcome measure was the percentage of time spent in different intensities of PA. The CAM data were processed similarly using the same speed thresholds as were used in establishing the Evenson cut-off points (light: <2 mph; walking: 2-4 mph; very active: >4 mph). Similar outcomes were computed from the SOPLAY default analyses. A chi-square test was used to test differences in the percentage of time at the three intensity zones (light, walking and very active). The Yates' correction was used to prevent overestimation of statistical significance for small data. When compared with GT3X+, the CAM had better results than the SOPLAY. The chi-square test yielded the following pairwise comparisons: CAM versus GT3x+ was chi(2) (5) = 24.18, p < .001; SOPLAY2 versus GT3x+ was chi(2) (5) = 144.44, p < .001; SOPLAY1 versus GT3x+ was chi(2) (5) = 119.55, p < .001. The differences were smaller between CAM and GT3x+, suggesting that the video tracking system provided better agreement than DO. The small sample size precludes a definitive evaluation but the results show that the CAM video system may have promise for automated coding of physical activity behavior.