Abstract
Malaria is one of the most severe public health problems worldwide. It is estimated that 3.3 billion people live in areas at risk of malaria transmission, and in 2010 caused around 655,000 deaths, 91% of them in the African Region. In this study we assess if the mHealth application “MalariaScope” developed by Fraunhofer Portugal AICOS (FhP AICOS) found to be usable and satisfactory by users from a European country, Portugal, can achieve similar positive results in an African country, Mozambique, which is one of its intended contexts of use. To this end, an academic partner from that African country conducted locally a usability evaluation of the application following the same procedure with participants with similar scientific backgrounds to the Portuguese counterparts. A comparison of the usability metrics of the two evaluations found no significant differences between the Portuguese and Mozambican set of users. © Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2015.

Abstract
With the emergence of new low-cost gestural interaction devices various studies have been developed on multi-modal human-computer interaction to improve user experience. We present an exploratory study which analysed the user experience with a multimodal interaction game prototype. As a result, we propose a set of preliminary recommendations for combined use of such devices and present implications for advancing the multimodal field in human-computer interaction.

Abstract
Breast cancer is the most frequently diagnosed cancer in women. Using historical patient information stored in clinical datasets, data mining and machine learning approaches can be applied to predict the survival of breast cancer patients. A common drawback is the absence of information, i.e., missing data, in certain clinical trials. However, most standard prediction methods are not able to handle incomplete samples and, then, missing data imputation is a widely applied approach for solving this inconvenience. Therefore, and taking into account the characteristics of each breast cancer dataset, it is required to perform a detailed analysis to determine the most appropriate imputation and prediction methods in each clinical environment This research work analyzes a real breast cancer dataset from Institute Portuguese of Oncology of Porto with a high percentage of unknown categorical information (most clinical data of the patients are incomplete), which is a challenge in terms of complexity. Four scenarios are evaluated: (I) 5-year survival prediction without imputation and 5-year survival prediction from cleaned dataset with (II) Mode imputation, (Ill) Expectation-Maximization imputation and (IV) K-Nearest Neighbors imputation. Prediction models for breast cancer survivability are constructed using four different methods: K-Nearest Neighbors, Classification Trees, Logistic Regression and Support Vector Machines. Experiments are performed in a nested ten-fold cross-validation procedure and, according to the obtained results, the best results are provided by the K-Nearest Neighbors algorithm: more than 81% of accuracy and more than 0.78 of area under the Receiver Operator Characteristic curve, which constitutes very good results in this complex scenario.

Abstract
The automatic classification of urban sounds is important for environmental monitoring. In this work we employ SAX-based Multiresolution Motif Discovery to generate features for Urban Sound Classification. Our approach consists in the discovery of relevant frequent motifs in the audio signals and use the frequency of discovered motifs as characterizing attributes. We explore and evaluate different configurations of motif discovery for defining attributes. In the automatic classification step we use a decision tree based algorithm, random forests and SVM. Results obtained are compared with the ones using Mel-Frequency Cepstral Coefficients (MFCC) as features. MFCCs are commonly used in environmental sound analysis, as well as in other sound classification tasks. Experiments were performed on the Urban Sound dataset, which is publicly available. Our results indicate that we can separate difficult pairs of classes (where MFCC fails) using the motif approach for feature construction. © 2015 SERSC.

Abstract
Dynamic logic combines logic with programs, which at a certain level of abstraction, can be regarded as behaviours changing the system state and, therefore, the truth value of formulas. This paper suggests a method for generating such logics for the domain of robot controllers and illustrates it with a logic for handling resource consumption.

Abstract
Automated Guided Vehicle System (AGVS) has become an important strategic tool for automated warehouses. In a very competitive business scenario, they can increase productivity and reduce costs of FMS (Flexible Manufacturing System) transportation systems. The AGV System provides efficient material flow and distribution among workstations at the right time and place. To attend such requirements, AGVS involves dispatching and scheduling of tasks and routing of AGVs. Some studies have approached such procedures in a similar form, although they have different functionalities. This paper reviews the literature related to the dispatching, scheduling and routing of AGVs (Automated Guided Vehicles) and highlights their main differences in comparison with the common management of vehicles transportation systems. To obtain a theoretical base, the definitions of dispatching, routing and scheduling procedures for materials handling applications are presented and the main methods to solve them are discussed.