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This paper introduces the Disturbance Description Language (DDL), an XML dialect intended to describe a number of anomalous elements that can occur in a given scenario (including people, vehicles, fire or focus of pollution) and their respective properties, such as temporal availability, location, motion pattern and details for individual components, such as growth pattern and detectability. This dialect is part of a framework to support the execution of cooperative missions by a group of vehicles, in a simulated, augmented or real environment. An interface was incorporated into the framework, for creating and editing XML files following the defined schema. Once the information is correctly specified, it can be used in the framework, thus facilitating the process of environment disturbances specification and deployment. A survey answered by both practitioners and researchers shows that the degree of satisfaction with DDL is elevated (the overall evaluation of DDL achieved a 4.14 score (out of 5), with 81.1% of the answers being equal to or above 4); also, the usability of the interface was evaluated, having achieved a score of 83.6 in the SUS scale. These results imply that DDL is flexible enough to represent several types of disturbances, through a user-friendly interface.

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The overwhelming amount of news content published online every day has made it increasingly difficult to perform macro-level analysis of the news landscape. Visual exploration tools harness both computing power and human perception to assist in making sense of large data collections. In this paper, we employed three visualization tools to explore a dataset comprising one million articles published by news organizations and blogs. The visual analysis of the dataset revealed that 1) news and blog sources evaluate very differently the importance of similar events, granting them distinct amounts of coverage, 2) there are both dissimilarities and overlaps in the publication patterns of the two source types, and 3) the content's direction and diversity behave differently over time. Copyright © 2016 for the individual papers by the paper's authors.

Abstract
We present D'accord, a generative music system for creating harmonically compatible accompaniments of symbolic and musical audio inputs with any number of voices, instrumentation, and complexity. The main novelty of our approach centers on offering multiple ranked solutions between a database of pitch configurations and a given musical input based on tonal pitch relatedness and consonance indicators computed in a perceptually motivated Tonal Interval Space. Furthermore, we detail a method to estimate the key of symbolic and musical audio inputs based on attributes of the space, which underpins the generation of key-related pitch configurations. The system is controlled via an adaptive interface implemented for Ableton Live, MAX, and Pure Data, which facilitates music creation for users regardless of music expertise and simultaneously serves as a performance, entertainment, and learning tool. We perform a threefold evaluation of D'accord, which assesses the level of accuracy of our key-finding algorithm, the user enjoyment of generated harmonic accompaniments, and the usability and learnability of the system.

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Ongoing declines in biodiversity caused by global environmental changes call for adaptive conservation management, including the assessment of habitat suitability spatiotemporal dynamics potentially affecting species persistence. Remote sensing (RS) provides a wide-range of satellite-based environmental variables that can be fed into species distribution models (SDMs) to investigate species-environment relations and forecast responses to change. We address the spatiotemporal dynamics of species' habitat suitability at the landscape level by combining multi-temporal RS data with SDMs for analysing inter-annual habitat suitability dynamics. We implemented this framework with a vulnerable plant species (Veronica micrantha), by combining SDMs with a time-series of RS-based metrics of vegetation functioning related to primary productivity, seasonality, phenology and actual evapotranspiration. Besides RS variables, predictors related to landscape structure, soils and wildfires were ranked and combined through multi-model inference (MMI). To assess recent dynamics, a habitat suitability time-series was generated through model hindcasting. MMI highlighted the strong predictive ability of RS variables related to primary productivity and water availability for explaining the test-species distribution, along with soil, wildfire regime and landscape composition. The habitat suitability time-series revealed the effects of short-term land cover changes and inter-annual variability in climatic conditions. Multi-temporal SDMs further improved predictions, benefiting from RS time-series. Overall, results emphasize the integration of landscape attributes related to function, composition and spatial configuration for improving the explanation of ecological patterns. Moreover, coupling SDMs with RS functional metrics may provide early-warnings of future environmental changes potentially impacting habitat suitability. Applications discussed include the improvement of biodiversity monitoring and conservation strategies.

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