Abstract
Understanding the underlying differences between groups or classes in certain contexts can be of the utmost importance. Contrast set mining relies on discovering significant patterns by contrasting two or more groups. A contrast set is a conjunction of attribute-value pairs that differ meaningfully in its distribution across groups. A previously proposed technique is rules for contrast sets, which seeks to express each contrast set found in terms of rules. This work extends rules for contrast sets to a temporal data mining task. We define a set of temporal patterns in order to capture the significant changes in the contrasts discovered along the considered time line. To evaluate the proposal accuracy and ability to discover relevant information, two different real-life data sets were studied using this approach.

Abstract
Humans perform and rely on face recognition routinely and effortlessly throughout their daily lives. Multiple works in recent years have sought to replicate this process in a robust and automatic way. However, it is known that the performance of face recognition algorithms is severely compromised in non-ideal image acquisition scenarios. In an attempt to deal with conditions, such as occlusion and heterogeneous illumination, we propose a new approach motivated by the global precedent hypothesis of the human brain's cognitive mechanisms of perception. An automatic modeling of SIFT keypoint descriptors using a Gaussian mixture model (GMM)-based universal background model method is proposed. A decision is, then, made in an innovative hierarchical sense, with holistic information gaining precedence over a more detailed local analysis. The algorithm was tested on the ORL, ARand Extended Yale B Face databases and presented state-of-the-art performance for a variety of experimental setups.

Abstract
We propose a new paradigm for public participation in urban planning, a field which presents significant challenges for public understanding and participation. Our approach is based on leveraging the rich diversity of meaning associated with cultural gestures, traditions, folklore, and rituals, and using them in augmented reality systems, in order for citizens' to explore, understand, and communicate the complex, systemic ideas and concepts associated with urban planning. At an immediate level, this approach holds the potential for enabling increased public awareness of what is at stake in urban planning - both on the part of citizens and on the part of public officials, policy-makers, and decision-makers - and consequently enhancing understanding and improving participation in public life and citizenship. It may also open up a new field of research and development in human-computer interaction, to leverage the richness of meaning and modes of expression which exist in various cultures and societies, rather than ignoring them and imposing dumbed-down or prescribed command methods. Thus, it aims to facilitate new levels of empowerment of users in the use of digital systems and data. The active utilization of cultural meaning in gestures, rituals, and social practices may also support and promote better inclusion and participation of minority groups and migrant communities in contemporary, technology-rich life. (c) 2015 The Authors. Published by Elsevier B.V.

Abstract
Assessing firms' Eco-efficiency is important to ensure they succeed in creating wealth without compromising the needs of future generations. This work aims to extend the Eco-efficiency concept by including in the assessment new features related to environmental benefits. Eco-efficiency is evaluated using a DEA model specified with a Directional Distance Function. The new methodology proposed in this paper is illustrated with an application to world-class mining companies, whose results and managerial implications are discussed.

Abstract
Massive procedural content creation, for example, for virtual urban environments, is a difficult, yet important challenge. While shape grammars are a popular example of effectiveness in architectural modeling, they have clear limitations regarding readability, manageability, and expressive power when addressing a variety of complex structural designs. Moreover, shape grammars aim at geometry specification and do not facilitate integration with other types of content, such as textures or light sources, which could rather accompany the generation process. We present procedural content graphs, a graph-based solution for procedural generation that addresses all these issues in a visual, flexible, and more expressive manner. Besides integrating handling of diverse types of content, this approach introduces collective entity manipulation as lists, seamlessly providing features such as advanced filtering, grouping, merging, ordering, and aggregation, essentially unavailable in shape grammars. Hereby, separated entities can be easily merged or just analyzed together in order to perform a variety of context-based decisions and operations. The advantages of this approach are illustrated via examples of tasks that are either very cumbersome or simply impossible to express with previous grammar approaches.

Abstract
Nowadays, there are several communication gaps that isolate deaf people in several social activities. This work studies the expressiveness of gestures in Portuguese Sign Language (PSL) speakers and their differences between deaf and hearing people. It is a first effort towards the ultimate goal of understanding emotional and behaviour patterns among such populations. In particular, our work designs solutions for the following problems: (i) differentiation between deaf and hearing people, (ii) identification of different conversational topics based on body expressiveness, (iii) identification of different levels of mastery of PSL speakers through feature analysis. With these aims, we build up a complete and novel dataset that reveals the duo-interaction between deaf and hearing people under several conversational topics. Results show high recognition and classification rates.