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Cities face many challenges concerning their management, security, transportation, public health, the distribution of resources, sustainability, energy efficiency, and many more. As cities grow larger, it is only expected that these problems become more acute and, therefore, they will need solutions to tackle or smooth these problems. With the rise of technologies such as artificial intelligence and the increasing number of social applications that allow citizens to participate in the urban digital ecosystem, researchers and policymakers have seen an opportunity in the application of these technologies to tackle urban challenges. In this editorial article, we review some relevant contributions to this special issue to social and intelligent applications for future cities., (undefined)

For the majority of students, assessment moments are associated with significant levels of stress and anxiety. While a certain amount of stress motivates the individual and improves performance, too much stress will have the contrary effect. Stress has therefore a fundamental role on student performance. It should be the educational organizations’ mission to understand the underlying mechanisms that lead to performance anxiety and provide their students with the best coping tools and strategies. In the present study we analyze student behavior during e-assessment in terms of mouse dynamics. Two major behavioral patterns can be identified, based on ten features that quantify the performance of the student’s interaction with the computer: (1) students who are able to sustain performance during the exam and (2) students whose performance varies significantly. Data shows that the behavior of each student during the exam correlates strongly with the time it takes the student to complete it. Several classifiers were trained that predict the completion time of each exam based on the students’ interaction patterns. Two of them do it with an average error of around twelve minutes. Results show that there are still mechanisms that can be explored to better understand the complex relationship between stress, performance and human behavior, that can be used for the implementation of better stress detection, monitoring and coping strategies., This work has been supported by COMPETE, Portugal: POCI-01- 0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia, Portugal within the Project Scope: UID/CEC/00319/2013. This work was funded by ‘‘EUSTRESS – Sistema de Informação para a monitorização e avaliação dos níveis do stress e previsão de stress crónico, Portugal’’, No. 2015/017832 P2020 SI I&DT, (NUP, Portugal, NORTE-01-0247-FEDER-017832) in co-promotion between Optimizer-Lda and ICVS/3B’s-Uminho.

Learning styles are strongly connected with learning and when it comes to acquiring new knowledge, attention is one the most important mechanisms. The learner's attention affects learning results and can define the success or failure of a student. When students are carrying out learning activities using new technologies, it is extremely important that the teacher has some feedback from the students' work in order to detect potential learning problems at an early stage and then to choose the appropriate teaching methods. In this paper we present a nonintrusive distributed system for monitoring the attention level in students. It is especially suited for classes working at the computer. The presented system is able to provide real-time information about each student as well as information about the class, and make predictions about the best learning style for a student using an ensemble of neural networks. It can be very useful for teachers to identify potentially distracting events and this system might be very useful to the teacher to implement more suited teaching strategies. (C) 2017 Published by Elsevier B.V., COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope:UID/CEC/00319/2013

[Excerpt] 1. Introduction: Society is ageing fast. A number of studies [1, 2, 3, 4] confirm this trend and highlight a negative consequence of this trend: the increased lack of care and assistance for the elderly. Elderly care (paid and unpaid) requires a high cost for the families [3], monetarily, physically, and psychologically. This cost is hard to translate to a single value, and even harder to attain help from governmental sources or external help. Furthermore, family members who provide care to their elderly relatives often report high tension levels in their relationships, degrading the quality of life for all parties [...], This work is supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundação para a Ciencia e Tecnologia within the projects UID/CEC/00319/2013 and Post-Doc scholarship SFRH/BPD/102696/2014 (Angelo Costa) This work is partially supported by the MINECO/FEDER TIN 2015-65515-C4-1-R.

Phenomenology is the empirical study of mind and consciousness. Considering programming activity as a cognitive procedure, phenomenology may be applied to address some of its issues. A particular issue of interest is how one programming approach differs from others, i.e. the cognition performed when using an approach differs from the one performed using another? If they are similar the two approaches are like to be the same, otherwise essentially different. Since adaptive computing proposal is Turing-equivalent it may be discussed about the actual differences among those approaches. As an example, λ-calculus is also Turing-equivalent but since the cognition performed is different it justifies the several λ-based existing approaches. In order to accomplish such analysis, bergsonism will be used as phenomenological method to be applied in particular adaptive structure called adaptive-graph. As a result it will be argued cognition performed using adaptive-graph is different from the one performed when using non-adaptive one. Then it will generalized suggesting adaptive computing cognition shall be further explored., This paper has been supported by COMPETE: POCI-01-0145-FEDER-0070 43 and FCT Fundac ̧ ̃ao para a Ciˆencia e Tecnologia - Project UID/CEC/00319/2013.

The recognition of emotions in spoken words is one of the most important aspects in human communication and social relationships. Traditional approaches to the study of vocal emotional recognition involve instructing listeners to choose which one of several words describing emotion categories best characterize linguistically neutral utterances or vocalizations uttered by actors portraying various emotional states. To this end, generic experiment control software is usually used, which has some disadvantages. In this paper, we present a system that digitalizes the whole process involved in understanding how people perceive and understand vocal emotions, improving data collection, processing and analysis. Moreover, this system provides a new group of features that allows a more comprehensive characterization of the behavioral dimension underlying vocal emotional recognition. In this paper we describe this system and analyze the relationship between emotional perception, gender, age and Human-Computer Interaction. (C) 2018 Elsevier B.V. All rights reserved., FCT — Fundação para a Ciência e Tecnologia, Portugal (PTDC/MHN-PCN/3606/2012) and by COMPETE, Portugal: POCI-01-0145-FEDER-007043 and FCT , Portugal within the Project Scope: UID/CEC/00319/2013

With the size digital collections are currently reaching, retrieving the best match of a document from large collections by comparing hundreds of tags is a task that involves considerable algorithm complexity, even more so if the number of tags in the collection is not fixed. For these cases, similarity search appears to be the best retrieval method, but there is a lack of techniques suited for these conditions. This work presents a combination of machine learning algorithms put together to find the most similar object of a given one in a set of pre-processed objects based only on their metadata tags. The algorithm represents objects as character frequency curves and is capable of finding relationships between objects without an apparent association. It can also be parallelized using MapReduce strategies to perform the search. This method can be applied to a wide variety of documents with metadata tags. The case-study used in this work to demonstrate the similarity search technique is that of a collection of image objects in JavaScript Object Notation (JSON) containing metadata tags., This work has been done in the context of the project “ASASEC (Advisory System Against Sexual Exploitation of Children)” (HOME/2010/ISEC/AG/043) supported by the European Union with the program “Prevention and fight against crime”., info:eu-repo/semantics/publishedVersion

Monitoring and managing performance in the workplace is nowadays an important aspect, in a time in which methodologies like Agile push individual and team limits further. Current performance monitoring approaches are either intrusive or based on productivity measures and are thus often dreaded by workers. Moreover, these approaches do not take into account the importance and role of the numerous external factors that influence productivity. We present a non-intrusive performance monitoring environment based on behavioral biometrics and real time analytics. It monitors and analyzes 15 features extracted from the workers’ interaction with the computer and can provide a measure of performance that is completely transparent. This measure is sensitive to external factors such as mental fatigue, stress or emotional valence. We validate this environment by assessing the effects of musical selection on Human–Computer Interaction. Results show a significant improvement on mouse motion when participants listen to the selected auditory stimuli and a negative effect on typing performance, especially with stimuli with positive tension. This work will enable the development of performance monitoring and management environments, with benefits for both organizations and individuals., FCT - Fuel Cell Technologies Program (POCI-01-0145-FEDER-007043), info:eu-repo/semantics/publishedVersion

Human performance, in all its different dimensions, is a very complex and interesting topic. In this paper we focus on performance in the workplace which, asides from complex is often controversial. While organizations and generally competitive working conditions push workers into increasing performance demands, this does not necessarily correlates positively to productivity. Moreover, existing performance monitoring approaches (electronic or not) are often dreaded by workers since they either threat their privacy or are based on productivity measures, with specific side effects. We present a new approach for the problem of performance monitoring that is not based on productivity measures but on the workers’ movements while sitting and on the performance of their interaction with the machine. We show that these features correlate with mental fatigue and provide a distributed architecture for the non-intrusive and transparent collection of this data. The easiness in deploying this architecture, its non-intrusive nature, the potential advantages for better human resources management and the fact that it is not based on productivity measures will, in our belief, increase the willingness of both organizations and workers to implement this kind of performance management initiatives., This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundacao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2013. The work of Davide Carneiro is supported by a Post-Doctoral Grant by FCT (SFRH/BPD/109070/2015)., info:eu-repo/semantics/publishedVersion

"Manuscript", Learning Non-Taxonomic Relationships is a sub-field of Ontology Learning that aims at automating the extraction of these relationships from text. Several techniques have been proposed based on Natural Language Processing and Machine Learning. However just like for other techniques for Ontology Learning, evaluating techniques for Learning Non-Taxonomic Relationships is an open problem. Three general proposals suggest that the learned ontologies can be evaluated in an executable application or by domain experts or even by a comparison with a predefined reference ontology. This article proposes two procedures to evaluate techniques for Learning Non-Taxonomic Relationships based on the comparison of the relationships obtained with those of a reference ontology. Also, these procedures are used in the evaluation of two state of the art techniques performing the extraction of relationships from two corpora in the domains of biology and Family Law., This work is supported by CNPq, CAPES and FAPEMA, research funding agencies of the Brazilian government.

Tourist recommendation systems have been growing over the last few years, mainly because of the use of mobile devices to obtain user context. This work discusses some of the most relevant systems on the field and presents PSiS Mobile, which is a mobile recommendation and planning application designed to support a tourist during his vacations. It provides recommendations about points of interest to visit based on tourist preferences and on user and sight context. Also, it suggests a visit planning which can be dynamically adapted based on current user and sight context. This tool works also like a journey dairy since it records the tourist moves and tasks to help him remember how the trip was like. To conclude, some field experiences will be presented., This work is part-funded by the ERDF European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by the National Funds through the FCT Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within projects PSIS (PTDC/TRA /72152/2006), FCOMP-01- 0124 - FEDER-028980 (PTDC/EEI-SII/1386/2012) and PEst- OE / EEI / UI0752 / 2011.

This study presents and approach to measure the levels of acute stress in humans by analysing their behavioural patterns when interacting with technological devices. We study the effects of stress on eight behavioural, physical and cognitive features. The data was collected with the participation of 19 users in different phases, with different levels of stress induced. A non-parametric statistical hypothesis test is used to determine which features show statistically significant differences, for each user, when under stress. It is shown that the features more related to stress are the acceleration and the mean and maximum intensity of the touch. It is also shown that each user is affected by stress in a specific way. Moreover, all the process of estimating stress is undertaken in a non-invasive way. This work constitutes the foundation of a context layer for a virtual environment for conflict resolution. The main objective is to overcome some of the main drawbacks of communicating online, namely the lack of contextual information such as body language or gestures., The work of Davide Carneiro is supported by a doctoral grant by FCT (SFRH/BD/64890/2009). This work is also partially supported by the Spanish Ministerio de Economia y Competitividad/ FEDER under project TIN2010-20845-C03-01 and by the Spanish Junta de Comunidades de Castilla-La Mancha/ FEDER under project PII2I09-0069-0994.