Abstract
Thresholding on coherence is a common practice for identifying the surface scatterers that are less affected by decorrelation noise during post-processing and visualisation of the results from multi-temporal InSAR techniques. Simple selection of the points with coherence greater than a specific value is, however, challenged by the presence of spatial dependence among observations. If the discrepancies in the areas of moderate coherence share similar behaviour, it appears important to take into account their spatial correlation for correct inference. Low coherence areas thus could serve as clear indicators of measurement noise or imperfections in mathematical models. Once exhibiting properties of statistical similarity, they allow for detection of observations that could be considered as outliers and trimmed from the dataset. In this paper we propose an approach based on renowned data mining and exploratory data analysis procedures for mitigating the impact of outlying observations in the final results.

Abstract
Anurans (frogs or toads) are closely related to the ecosystem and they are commonly used by biologists as early indicators of ecological stress. Automatic classification of anurans, by processing their calls, helps biologists analyze the activity of anurans on larger scale. Wireless Sensor Networks (WSNs) can be used for gathering data automatically over a large area. WSNs usually set restrictions on computing and transmission power for extending the network's lifetime. Deep Learning algorithms have gathered a lot of popularity in recent years, especially in the field of image recognition. Being an eager learner, a trained Deep Learning model does not need a lot of computing power and could be used in hardware with limited resources. This paper investigates the possibility of using Convolutional Neural Networks with Mel-Frequency Cepstral Coefficients (MFCCs) as input for the task of classifying anuran sounds. © 2016 ACM.

Abstract
There are some obstacles, towards a paperless office. One of them is the collection of signatures, since nearly half of all documents are printed for the sole purpose of collecting them. Digital signatures can have the same legal evidential validity as handwritten signatures, provided they are based on certificates issued by accredited certification authorities and the associated private keys are stored on tamper proof token security devices like smart cards. In this article, we propose a platform for secure digital signature workflow management that integrates secure token based digital signatures with the Enterprise Content Management Alfresco, where each user can associate a set of smart cards to his account. The documents can then be signed with the citizen card or other smart card that has digital signatures capabilities. We have implemented an Alfresco module that allows us to explore several workflow techniques to implement real task secure digital signatures workflows, as people for example do when they pass a paper document between various departments to be signed. Since all users can see the current state of the documents being signed during the entire signage process, important security properties like system trust are preserved. We also describe an external validation web service, that provides a way for users to validate signed documents. The validation service then shows to the user important document security properties like timestamps, certificates attributes and highlights the document integrity in face of the digital signatures that have been collected in the workflows defined by our module in Alfresco. © Springer International Publishing Switzerland 2016.

Abstract
The practice of harmonic mixing is a technique used by DJs for the beat-synchronous and harmonic alignment of two or more pieces of music. In this paper, we present a new harmonic mixing method based on psychoacoustic principles. Unlike existing commercial DJ-mixing software, which determines compatible matches between songs via key estimation and harmonic relationships in the circle of fifths, our approach is built around the measurement of musical consonance. Given two tracks, we first extract a set of partials using a sinusoidal model and average this information over sixteenth note temporal frames. By scaling the partials of one track over +/- 6 semitones (in 1/8th semitone steps), we determine the pitch-shift that maximizes the consonance of the resulting mix. For this, we measure the consonance between all combinations of dyads within each frame according to psychoacoustic models of roughness and pitch commonality. To evaluate our method, we conducted a listening test where short musical excerpts were mixed together under different pitch shifts and rated according to consonance and pleasantness. Results demonstrate that sensory roughness computed from a small number of partials in each of the musical audio signals constitutes a reliable indicator to yield maximum perceptual consonance and pleasantness ratings by musically-trained listeners.

Abstract
Cutting and packing (C&P) is an important area of operational research and its problems arise in various industries such as: textile, wood, glass and shipbuilding. The main objective is to maximize the efficiency of a layout by rearranging and/or reassigning items inside containers in order to reduce costs and environmental impact. In this work, a raster solution to the bidimensional irregular strip packing problem, which consists of placing irregular shapes items inside a single rectangular container with variable length, is studied. In raster methods, the selection of the grid size is very important to the outcome of the algorithm. It influences the size of the search space, the overlap algorithm efficiency, as well as the memory requirements of the packing algorithm. An analysis of the impact of the choice of grid size is performed using 15 benchmark cases from the literature and, through careful observation of such test results, a simple rule to define the grid size is suggested.

Abstract