Showing papers by "Open University of Catalonia published in 2017"

Understanding the Limits of LoRaWAN

Abstract: Low-power wide area networking technology offers long-range communication, which enables new types of services. Several solutions exist; LoRaWAN is arguably the most adopted. It promises ubiquitous connectivity in outdoor IoT applications, while keeping network structures and management simple. This technology has received a lot of attention in recent months from network operators and solution providers. However, the technology has limitations that need to be clearly understood to avoid inflated expectations and disillusionment. This article provides an impartial and fair overview of the capabilities and limitations of LoRaWAN. We discuss those in the context of use cases, and list open research and development questions.

Maximizing Opportunities and Minimizing Risks for Children Online: The Role of Digital Skills in Emerging Strategies of Parental Mediation

Abstract: As internet use becomes widespread at home, parents are trying to maximize their children’s online opportunities while also minimizing online risks. We surveyed parents of 6- to 14-year-olds in eight European countries (N=6,400). A factor analysis revealed two strategies. Enabling mediation is associated with increased online opportunities but also risks. This strategy incorporates safety efforts, responds to child agency and is employed when parent or child is relatively digitally skilled, so may not support harm. Restrictive mediation is associated with fewer online risks but at the cost of opportunities, reflecting policy advice that regards media use as primarily problematic. It is favoured when parent or child digital skills are lower, potentially keeping vulnerable children safe yet undermining their digital inclusion.

Maximizing opportunities and minimizing risks for children online: the role of digital skills in emerging strategies of parental mediation

Abstract: As internet use becomes widespread at home, parents are trying to maximize their children’s online opportunities while also minimizing online risks. We surveyed parents of 6- to 14-year-olds in eight European countries (N=6,400). A factor analysis revealed two strategies. Enabling mediation is associated with increased online opportunities but also risks. This strategy incorporates safety efforts, responds to child agency and is employed when parent or child is relatively digitally skilled, so may not support harm. Restrictive mediation is associated with fewer online risks but at the cost of opportunities, reflecting policy advice that regards media use as primarily problematic. It is favoured when parent or child digital skills are lower, potentially keeping vulnerable children safe yet undermining their digital inclusion.

The Crosstalk between the Gut Microbiota and Mitochondria during Exercise

Abstract: Many physiological changes occur in response to endurance exercise in order to adapt to the increasing energy needs, mitochondria biogenesis, increased reactive oxygen species (ROS) production and acute inflammatory responses. Mitochondria are organelles within each cell that are crucial for ATP production and are also a major producer of ROS and reactive nitrogen species during intense exercise. Recent evidence shows there is a bidirectional interaction between mitochondria and microbiota. The gut microbiota have been shown to regulate key transcriptional co-activators, transcription factors and enzymes involved in mitochondrial biogenesis such as PGC-1α, SIRT1 and AMPK genes. Furthermore, the gut microbiota and its metabolites, such as short chain fatty acids and secondary bile acids, also contribute to host energy production, ROS modulation and inflammation in the gut by attenuating TNFα- mediated immune responses and inflammasomes such as NLRP3. On the other hand, mitochondria, particularly mitochondrial ROS production, have a crucial role in regulating the gut microbiota via modulating intestinal barrier function and mucosal immune responses. Recently, it has also been shown that genetic variants within the mitochondrial genome, could affect mitochondrial function and therefore the intestinal microbiota composition and activity. Diet is also known to dramatically modulate the composition of the gut microbiota. Therefore, studies targeting the gut microbiota can be useful for managing mitochondrial related ROS production, pro-inflammatory signals and metabolic limits in endurance athletes.

A Survey on Deep Learning Based Approaches for Action and Gesture Recognition in Image Sequences

Abstract: The interest in action and gesture recognition has grown considerably in the last years. In this paper, we present a survey on current deep learning methodologies for action and gesture recognition in image sequences. We introduce a taxonomy that summarizes important aspects of deep learning for approaching both tasks. We review the details of the proposed architectures, fusion strategies, main datasets, and competitions. We summarize and discuss the main works proposed so far with particular interest on how they treat the temporal dimension of data, discussing their main features and identify opportunities and challenges for future research.

Gamification: a systematic review of design frameworks

Abstract: Learner’s motivation difficulties are recognized as a problem in diverse educational scenarios, reaching up to university degrees Among other techniques that are often applied by instructors to counteract this issue, those related to the use of gaming elements seem to very promising In this context, considering the use of game-like properties in learning scenarios, known as gamification, has received increasing interest by academia in recent years However, its application in higher education can be challenging, due to some unwanted effects caused by the lack of proven design methodologies have been detected Choosing the adequate formal process for gamification design has become an important success requirement This work presents a systematic review of the gamification design frameworks discussed in the literature, providing a useful resource to educational practitioners as well as gamification designers and researchers A total of 2314 unique works are initially recorded, based on queries in databases, libraries, journals and search engines After applying a systematic filtering process, a definitive list of 40 works is more closely analysed Next to review over relevant literature, an assessment of the main features found in the discussed approaches is given, while also categorizing them according to their main application field and its suitability in higher educational environments

Emotion Recognition in Context

Abstract: Understanding what a person is experiencing from her frame of reference is essential in our everyday life. For this reason, one can think that machines with this type of ability would interact better with people. However, there are no current systems capable of understanding in detail peoples emotional states. Previous research on computer vision to recognize emotions has mainly focused on analyzing the facial expression, usually classifying it into the 6 basic emotions [11]. However, the context plays an important role in emotion perception, and when the context is incorporated, we can infer more emotional states. In this paper we present the Emotions in Context Database (EMCO), a dataset of images containing people in context in non-controlled environments. In these images, people are annotated with 26 emotional categories and also with the continuous dimensions valence, arousal, and dominance [21]. With the EMCO dataset, we trained a Convolutional Neural Network model that jointly analyzes the person and the whole scene to recognize rich information about emotional states. With this, we show the importance of considering the context for recognizing peoples emotions in images, and provide a benchmark in the task of emotion recognition in visual context.

Minimal IPv6 over the TSCH Mode of IEEE 802.15.4e (6TiSCH) Configuration

Abstract: This document describes a minimal mode of operation for an IPv6 over the TSCH mode of IEEE 802.15.4e (6TiSCH) network. This minimal mode of operation specifies the baseline set of protocols that need to be supported and the recommended configurations and modes of operation sufficient to enable a 6TiSCH functional network. 6TiSCH provides IPv6 connectivity over a Time-Slotted Channel Hopping (TSCH) mesh composed of IEEE Std 802.15.4 TSCH links. This minimal mode uses a collection of protocols with the respective configurations, including the IPv6 Low-Power Wireless Personal Area Network (6LoWPAN) framework, enabling interoperable IPv6 connectivity over IEEE Std 802.15.4 TSCH. This minimal configuration provides the necessary bandwidth for network and security bootstrapping and defines the proper link between the IETF protocols that interface to IEEE Std 802.15.4 TSCH. This minimal mode of operation should be implemented by all 6TiSCH-compliant devices.

Neural Basis of Video Gaming: A Systematic Review.

Abstract: Background Video gaming is an increasingly popular activity in contemporary society, especially among young people, and video games are increasing in popularity not only as a research tool but also as a field of study Many studies have focused on the neural and behavioral effects of video games, providing a great deal of video game derived brain correlates in recent decades There is a great amount of information, obtained through a myriad of methods, providing neural correlates of video games Objectives We aim to understand the relationship between the use of video games and their neural correlates, taking into account the whole variety of cognitive factors that they encompass Methods A systematic review was conducted using standardized search operators that included the presence of video games and neuro-imaging techniques or references to structural or functional brain changes Separate categories were made for studies featuring Internet Gaming Disorder and studies focused on the violent content of video games Results A total of 116 articles were considered for the final selection One hundred provided functional data and twenty two measured structural brain changes One-third of the studies covered video game addiction, and 14% focused on video game related violence Conclusions Despite the innate heterogeneity of the field of study, it has been possible to establish a series of links between the neural and cognitive aspects, particularly regarding attention, cognitive control, visuospatial skills, cognitive workload and reward processing However, many aspects could be improved The lack of standardization in the different aspects of video game-related research, such as the participants’ characteristics, the features of each video game genre and the diverse study goals could contribute to discrepancies in many related studies

Model-Driven Software Engineering in Practice: Second Edition

Abstract: This book discusses how model-based approaches can improve the daily practice of software professionals. This is known as Model-Driven Software Engineering (MDSE) or, simply, Model-Driven Engineering (MDE). MDSE practices have proved to increase efficiency and effectiveness in software development, as demonstrated by various quantitative and qualitative studies. MDSE adoption in the software industry is foreseen to grow exponentially in the near future, e.g., due to the convergence of software development and business analysis. The aim of this book is to provide you with an agile and flexible tool to introduce you to the MDSE world, thus allowing you to quickly understand its basic principles and techniques and to choose the right set of MDSE instruments for your needs so that you can start to benefit from MDSE right away. The book is organized into two main parts. The first part discusses the foundations of MDSE in terms of basic concepts (i.e., models and transformations), driving principles, application scenarios, and current standards, like the well-known MDA initiative proposed by OMG (Object Management Group) as well as the practices on how to integrate MDSE in existing development processes. The second part deals with the technical aspects of MDSE, spanning from the basics on when and how to build a domain-specific modeling language, to the description of Model-to-Text and Model-to-Model transformations, and the tools that support the management of MDSE projects. The second edition of the book features: a set of completely new topics, including: full example of the creation of a new modeling language (IFML), discussion of modeling issues and approaches in specific domains, like business process modeling, user interaction modeling, and enterprise architecture complete revision of examples, figures, and text, for improving readability, understandability, and coherence better formulation of definitions, dependencies between concepts and ideas addition of a complete index of book content In addition to the contents of the book, more resources are provided on the book's website http://www.mdse-book.com, including the examples presented in the book.

Why So Many Published Sensitivity Analyses Are False. A Systematic Review of Sensitivity Analysis Practices

Abstract: Sensitivity analysis (SA) has much to offer for a very large class of applications, such as model selection, calibration, optimization, quality assurance and many others. Sensitivity analysis offers crucial contextual information regarding a prediction by answering the question "Which uncertain input factors are responsible for the uncertainty in the prediction?" SA is distinct from uncertainty analysis (UA), which instead addresses the question "How uncertain is the prediction?" As we discuss in the present paper much confusion exists in the use of these terms. A proper uncertainty analysis of the output of a mathematical model needs to map what the model does when the input factors are left free to vary over their range of existence. A fortiori, this is true of a sensitivity analysis. Despite this, most UA and SA still explore the input space; moving along mono-dimensional corridors which leave the space of variation of the input factors mostly unscathed. We use results from a bibliometric analysis to show that many published SA fail the elementary requirement to properly explore the space of the input factors. The results, while discipline-dependent, point to a worrying lack of standards and of recognized good practices. The misuse of sensitivity analysis in mathematical modelling is at least as serious as the misuse of the p-test in statistical modelling. Mature methods have existed for about two decades to produce a defensible sensitivity analysis. We end by offering a rough guide for proper use of the methods.

A Systematic Mapping Study of Software Development With GitHub

Abstract: Context: GitHub, nowadays the most popular social coding platform, has become the reference for mining Open Source repositories, a growing research trend aiming at learning from previous software projects to improve the development of new ones. In the last years, a considerable amount of research papers have been published reporting findings based on data mined from GitHub. As the community continues to deepen in its understanding of software engineering thanks to the analysis performed on this platform, we believe that it is worthwhile to reflect on how research papers have addressed the task of mining GitHub and what findings they have reported. Objective: The main objective of this paper is to identify the quantity, topic, and empirical methods of research works, targeting the analysis of how software development practices are influenced by the use of a distributed social coding platform like GitHub. Method: A systematic mapping study was conducted with four research questions and assessed 80 publications from 2009 to 2016. Results: Most works focused on the interaction around coding-related tasks and project communities. We also identified some concerns about how reliable were these results based on the fact that, overall, papers used small data sets and poor sampling techniques, employed a scarce variety of methodologies and/or were hard to replicate. Conclusions: This paper attested the high activity of research work around the field of Open Source collaboration, especially in the software domain, revealed a set of shortcomings and proposed some actions to mitigate them. We hope that this paper can also create the basis for additional studies on other collaborative activities (like book writing for instance) that are also moving to GitHub.

Individual Differential Privacy: A Utility-Preserving Formulation of Differential Privacy Guarantees

Abstract: Differential privacy is a popular privacy model within the research community because of the strong privacy guarantee it offers, namely that the presence or absence of any individual in a data set does not significantly influence the results of analyses on the data set. However, enforcing this strict guarantee in practice significantly distorts data and/or limits data uses, thus diminishing the analytical utility of the differentially private results. In an attempt to address this shortcoming, several relaxations of differential privacy have been proposed that trade off privacy guarantees for improved data utility. In this paper, we argue that the standard formalization of differential privacy is stricter than required by the intuitive privacy guarantee it seeks. In particular, the standard formalization requires indistinguishability of results between any pair of neighbor data sets, while indistinguishability between the actual data set and its neighbor data sets should be enough. This limits the data controller’s ability to adjust the level of protection to the actual data, hence resulting in significant accuracy loss. In this respect, we propose individual differential privacy , an alternative differential privacy notion that offers the same privacy guarantees as standard differential privacy to individuals (even though not to groups of individuals). This new notion allows the data controller to adjust the distortion to the actual data set, which results in less distortion and more analytical accuracy. We propose several mechanisms to attain individual differential privacy and we compare the new notion against standard differential privacy in terms of the accuracy of the analytical results.

Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs

Abstract: Abstract This paper reviews the existing literature on the combination of metaheuristics with machine learning methods and then introduces the concept of learnheuristics, a novel type of hybrid algorithms. Learnheuristics can be used to solve combinatorial optimization problems with dynamic inputs (COPDIs). In these COPDIs, the problem inputs (elements either located in the objective function or in the constraints set) are not fixed in advance as usual. On the contrary, they might vary in a predictable (non-random) way as the solution is partially built according to some heuristic-based iterative process. For instance, a consumer’s willingness to spend on a specific product might change as the availability of this product decreases and its price rises. Thus, these inputs might take different values depending on the current solution configuration. These variations in the inputs might require from a coordination between the learning mechanism and the metaheuristic algorithm: at each iteration, the learning method updates the inputs model used by the metaheuristic.

Elements of Gameful Design Emerging from User Preferences

Abstract: Several studies have developed models to explain player preferences. These models have been developed for digital games; however, they have been frequently applied in gameful design (i.e., designing non-game applications with game elements) without empirical validation of their fit to this different context. It is not clear if users experience game elements embedded in applications similarly to how players experience them in games. Consequently, we still lack a conceptual framework of design elements built specifically for a gamification context. To fill this gap, we propose a classification of eight groups of gameful design elements produced from an exploratory factor analysis based on participants' self-reported preferences. We describe the characteristics of the users who are more likely to enjoy each group of design elements in terms of their gender, age, gamification user type, and personality traits. Our main contribution is providing an overview of which design elements work best for what demographic clusters and how we can apply this knowledge to design effective gameful systems.

Places: An Image Database for Deep Scene Understanding

Abstract: The rise of multi-million-item dataset initiatives has enabled data-hungry machine learning algorithms to reach near-human semantic classification at tasks such as object and scene recognition. Here we describe the Places Database, a repository of 10 million scene photographs, labeled with scene semantic categories and attributes, comprising a quasi-exhaustive list of the types of environments encountered in the world. Using state of the art Convolutional Neural Networks, we provide impressive baseline performances at scene classification. With its high-coverage and high-diversity of exemplars, the Places Database offers an ecosystem to guide future progress on currently intractable visual recognition problems.

Neglected ecosystem services: Highlighting the socio-cultural perception of mangroves in decision-making processes

Abstract: Despite the increasing recognition of the need to conserve mangroves, degradation has continued during the last two decades due to ineffective and non-inclusive decision-making processes exclusively based on economic factors. The purpose of the present study is to give tools to mangrove conservation management and policy, exploring the sociocultural valuation of the ecosystem services of mangroves through a case study in northeastern Brazil, an area highly impacted by shrimp aquaculture. We used a mix of methods to complement ecosystem services identified in the academic literature with those perceived as such by local people. We analyzed these locally perceived mangrove services in relation to community livelihoods, and highlighted that local people identified four additional cultural services related to maintenance of Traditional Ecological Knowledge (TEK), creation and maintenance of social relationship, personal satisfaction and mental and physical relaxation. This demonstrates that local people have a symbolic relationship with the mangrove forest, which goes beyond the material approach normally used to evaluate ecosystem services. Such findings suggest that the socio-cultural dimension of mangrove services needs to be considered by policy-makers as an indispensable criterion for confronting the key challenges in coastal ecosystems conservation.

Energy- and Spectrum-Efficient User Association in Millimeter-Wave Backhaul Small-Cell Networks

Abstract: Macrocells are expected to be densely overlaid by small cells (SCs) to meet increasing capacity demands. Due to their dense deployment, some SCs will not be connected directly to the core network, and thus, they may forward their traffic to the neighboring SCs until they reach it, thereby forming a multihop backhaul (BH) network. This is a promising solution, since the expected short length of BH links enables the use of millimeter-wave (mmWave) frequencies to provide high-capacity BH. In this context, user association becomes challenging due to the multihop BH architecture, and therefore, new optimal solutions should be developed. Thus, in this paper, we study the user association problem aiming at the joint maximization of network energy efficiency (EE) and spectrum efficiency (SE), without compromising the user quality of service (QoS). The problem is formulated as an $\varepsilon$ -constraint problem, which considers the transmit energy consumption both in the access network, i.e., the links between the users and their serving cells, and the BH links. The optimal Pareto-front solutions of the problem are analytically derived for different BH technologies, and insights are gained into the EE and SE tradeoff. The proposed optimal solutions, despite their high complexity, can be used as a benchmark for the performance evaluation of user association algorithms. We also propose a heuristic algorithm, which is compared with reference solutions under different traffic scenarios and BH technologies. Our results motivate the use of mmWave BH, whereas the proposed algorithm achieves near-optimal performance.

mHealth Assessment: Conceptualization of a Global Framework.

Abstract: Background: The mass availability and use of mobile health (mHealth) technologies offers the potential for these technologies to support or substitute medical advice. However, it is worrisome that most assessment initiatives are still not able to successfully evaluate all aspects of mHealth solutions. As a result, multiple strategies to assess mHealth solutions are being proposed by medical regulatory bodies and similar organizations. Objective: We aim to offer a collective description of a universally applicable description of mHealth assessment initiatives, given their current and, as we see it, potential impact. In doing so, we recommend a common foundation for the development or update of assessment initiatives by addressing the multistakeholder issues that mHealth technology adds to the traditional medical environment. Methods: Organized by the Mobile World Capital Barcelona Foundation, we represent a workgroup consisting of patient associations, developers, and health authority representatives, including medical practitioners, within Europe. Contributions from each group’s diverse competencies has allowed us to create an overview of the complex yet similar approaches to mHealth evaluation that are being developed today, including common gaps in concepts and perspectives. In response, we summarize commonalities of existing initiatives and exemplify additional characteristics that we believe will strengthen and unify these efforts. Results: As opposed to a universal standard or protocol in evaluating mHealth solutions, assessment frameworks should respect the needs and capacity of each medical system or country. Therefore, we expect that the medical system will specify the content, resources, and workflow of assessment protocols in order to ensure a sustainable plan for mHealth solutions within their respective countries. Conclusions: A common framework for all mHealth initiatives around the world will be useful in order to assess whatever mHealth solution is desirable in different areas, adapting it to the specifics of each context, to bridge the gap between health authorities, patients, and mHealth developers. We aim to foster a more trusting and collaborative environment to safeguard the well-being of patients and citizens while encouraging innovation of technology and policy. [JMIR Mhealth Uhealth 2017;5(5):e60]

Building Agent-Based Decision Support Systems for Word-of-Mouth Programs: A Freemium Application

Abstract: Marketers must constantly decide how to implement word-of-mouth (WOM) programs, and a well-developed decision support system (DSS) can provide them valuable assistance in doing so. The authors propose an agent-based framework that aggregates social network–level individual interactions to guide the construction of a successful DSS for WOM. The framework presents a set of guidelines and recommendations to (1) involve stakeholders, (2) follow a data-driven iterative modeling approach, (3) increase validity through automated calibration, and (4) understand the DSS behavior. This framework is applied to build a DSS for a freemium app in which premium users discuss the product with their social network and promote its viral adoption. After its validation, the agent-based DSS forecasts the aggregate number of premium sales over time and the most likely users to become premium in the near future. The experiments show how the DSS can help managers by forecasting premium conversions and increasing the numb...

Following Gaze in Video

Abstract: Following the gaze of people inside videos is an important signal for understanding people and their actions. In this paper, we present an approach for following gaze in video by predicting where a person (in the video) is looking even when the object is in a different frame. We collect VideoGaze, a new dataset which we use as a benchmark to both train and evaluate models. Given one frame with a person in it, our model estimates a density for gaze location in every frame and the probability that the person is looking in that particular frame. A key aspect of our approach is an end-to-end model that jointly estimates: saliency, gaze pose, and geometric relationships between views while only using gaze as supervision. Visualizations suggest that the model learns to internally solve these intermediate tasks automatically without additional supervision. Experiments show that our approach follows gaze in video better than existing approaches, enabling a richer understanding of human activities in video.

Research Trends in Mobile Learning in Higher Education: A Systematic Review of Articles (2011 – 2015)

Abstract: The potential and use of mobile devices in higher education has been a key issue for educational research and practice since the widespread adoption of these devices. Due to the evolving nature and affordances of mobile technologies, it is an area that requires ongoing investigation. This study aims to identify emerging trends in mobile learning research in higher education in order to provide insights for researchers and educators around research topics and issues for further exploration. This study analysed the research themes, methods, settings, and technologies in mobile learning research in higher education from 2011 to 2015. A total of 233 refereed articles were selected and analysed from peer reviewed journals. The results were compared to three previous literature review-based research studies focused between 2001 and 2010 to identify similarities and differences. Key findings indicated that: (a) mobile learning in higher education is a growing field as evidenced by the increasing variety of research topics, methods, and researchers; (b) the most common research topic continues to be about enabling m-learning applications and systems; and (c) mobile phones continue to be the most widely used devices in mobile learning studies, however, more and more studies work across different devices, rather than focusing on specific devices.

Solving the deterministic and stochastic uncapacitated facility location problem: from a heuristic to a simheuristic

Abstract: The uncapacitated facility location problem (UFLP) is a popular combinatorial optimization problem with practical applications in different areas, from logistics to telecommunication networks. While most of the existing work in the literature focuses on minimizing total cost for the deterministic version of the problem, some degree of uncertainty (e.g., in the customers’ demands or in the service costs) should be expected in real-life applications. Accordingly, this paper proposes a simheuristic algorithm for solving the stochastic UFLP (SUFLP), where optimization goals other than the minimum expected cost can be considered. The development of this simheuristic is structured in three stages: (i) first, an extremely fast savings-based heuristic is introduced; (ii) next, the heuristic is integrated into a metaheuristic framework, and the resulting algorithm is tested against the optimal values for the UFLP; and (iii) finally, the algorithm is extended by integrating it with simulation techniques, and the resulting simheuristic is employed to solve the SUFLP. Some numerical experiments contribute to illustrate the potential uses of each of these solving methods, depending on the version of the problem (deterministic or stochastic) as well as on whether or not a real-time solution is required.

Fair Coexistence of Scheduled and Random Access Wireless Networks: Unlicensed LTE/WiFi

Abstract: We study the fair coexistence of scheduled and random access transmitters sharing the same frequency channel. Interest in coexistence is topical due to the need for emerging unlicensed LTE technologies to coexist fairly with WiFi. However, this interest is not confined to LTE/WiFi as coexistence is likely to become increasingly commonplace in IoT networks and beyond 5G. In this paper, we show that mixing scheduled and random access incurs an inherent throughput/delay cost and the cost of heterogeneity. We derive the joint proportional fair rate allocation, which casts useful light on current LTE/WiFi discussions. We present experimental results on inter-technology detection and consider the impact of imperfect carrier sensing.