Abstract
In query-by-semantic-example image retrieval, images are ranked by similarity of semantic descriptors. These descriptors are obtained by classifying each image with respect to a pre-defined vocabulary of semantic concepts. In this work, we consider the problem of improving the accuracy of semantic descriptors through cross-modal regularization, based on auxiliary text. A cross-modal regularizer, composed of three steps, is proposed. Training images and text are first mapped to a common semantic space. A regularization operator is then learned for each concept in the semantic vocabulary. This is an operator which maps the semantic descriptors of images labeled with that concept to the descriptors of the associated texts. A convex formulation of the learning problem is introduced, enabling the efficient computation of concept-specific regularization operators. The third step is the selection of the most suitable operator for the image to regularize. This is implemented through a quantization of the semantic space, where a regularization operator is associated with each quantization cell. Overall, the proposed regularizer is a non-linear mapping, implemented as a piecewise linear transformation of the semantic image descriptors to regularize. This transformation is a form of cross-modal domain adaptation. It is shown to achieve better performance than recent proposals in the domain adaptation literature, while requiring much simpler optimization.

Abstract
This paper presents an original short-term forecasting model of the hourly electric power production for aggregated regional hydropower generation. The inputs of the model are previously recorded values of the aggregated hourly production of hydropower plants and hourly water precipitation forecasts using Numerical Weather Prediction tools, as well as other hourly data (load demand and wind generation). This model is composed of three modules; the first one gives the prediction of the "monthly" hourly power production of the hydropower plants; the second module gives the prediction of hourly power deviation values, which are added to that obtained by the first module to achieve the final forecast of the hourly hydropower generation; the third module allows a periodic adjustment of the prediction of the first module to improve its BIAS error. The model has been applied successfully to the real-life case study of the short-term forecasting of the aggregated hydropower generation in Spain and Portugal (Iberian Peninsula Power System), achieving satisfactory results for the next-day forecasts. The model can be valuable for agents involved in electricity markets and useful for power system operations.

Abstract
This paper presents a holistic framework for electric vehicles integration in electric power systems together with their charging management and control methodologies that allow minimizing the negative impacts in the grid of the charging process and maximize the benefits that charging controllability may bring to their owners, energy retailers and system operators. The performance of these management and control methods will be assessed through steady state computational simulations and then validated in a microgrid laboratory environment.

Abstract
Understanding the factors that affect the productivity of software developers and may cause productivity variations among individuals and projects is important for anyone interested in improving software engineering performance and estimates, and in particular for users of high-maturity processes, such as the Personal Software Process (PSP) and the Team Software Process (TSP). In order to contribute to the understanding of the personal and non-personal factors that affect productivity, we analyzed the data from more than 3000 developers that concluded successfully the 10 projects of the PSP for Engineers I/II training course. Regarding non-personal factors, by conducting a detailed per-phase analysis, we found significant variations of productivity among projects that can be partially explained by process changes. Regarding personal factors, we found significant variations among individuals that can be partially explained by personal experience.

Abstract
This paper presents an interactive video system that enables users to change the flow of video playback by interacting with hotspots that were predefined throughout the video streams. These hotspots are synchronized with the underlying video streams and the interactions result in smooth transitions between the preloaded targets. This approach allows the dynamic visualization of content by interacting with the hotspots and producing the consequent changes in the flow of the story. The system includes web-based and mobile video players specifically developed to deal with the interactive features, as well as a configuration tool that allows content managers to choose which pre-produced interaction possibilities will be used for a specific target audience. The interactive video solution presented herein has potential to be used as a powerful communication tool, in commercial, e-learning, accessibility and entertainment contexts. © 2014 Springer International Publishing.

Abstract
This paper is motivated by a conjecture [1,5] that BPP can be characterized in terms of polynomial-time nonadaptive reductions to the set of Kolmogorov-random strings. In this paper we show that an approach laid out in [5] to settle this conjecture cannot succeed without significant alteration, but that it does bear fruit if we consider time-bounded Kolmogorov complexity instead. We show that if a set A is reducible in polynomial time to the set of time-t-bounded Kolmogorov-random strings (for all large enough time bounds t), then A is in P/poly, and that if in addition such a reduction exists for any universal Turing machine one uses in the definition of Kolmogorov complexity, then A is in PSPACE. © 2012 Springer-Verlag.