Showing papers by "Open University of Catalonia published in 2018"
TL;DR: The Places Database is described, a repository of 10 million scene photographs, labeled with scene semantic categories, comprising a large and diverse list of the types of environments encountered in the world, using the state-of-the-art Convolutional Neural Networks as baselines, that significantly outperform the previous approaches.
Abstract: The rise of multi-million-item dataset initiatives has enabled data-hungry machine learning algorithms to reach near-human semantic classification performance at tasks such as visual object and scene recognition. Here we describe the Places Database, a repository of 10 million scene photographs, labeled with scene semantic categories, comprising a large and diverse list of the types of environments encountered in the world. Using the state-of-the-art Convolutional Neural Networks (CNNs), we provide scene classification CNNs (Places-CNNs) as baselines, that significantly outperform the previous approaches. Visualization of the CNNs trained on Places shows that object detectors emerge as an intermediate representation of scene classification. With its high-coverage and high-diversity of exemplars, the Places Database along with the Places-CNNs offer a novel resource to guide future progress on scene recognition problems.
3,215 citations
TL;DR: The aim of this paper is to present the enhancements made to the Mixed Methods Appraisal Tool (MMAT), a unique tool that can be used to appraise the quality of different study designs and can provide a more efficient appraisal.
Abstract: INTRODUCTION: Appraising the quality of studies included in systematic reviews combining qualitative and quantitative evidence is challenging. To address this challenge, a critical appraisal tool was developed: the Mixed Methods Appraisal Tool (MMAT). The aim of this paper is to present the enhancements made to the MMAT. DEVELOPMENT: The MMAT was initially developed in 2006 based on a literature review on systematic reviews combining qualitative and quantitative evidence. It was subject to pilot and interrater reliability testing. A revised version of the MMAT was developed in 2018 based on the results from usefulness testing, a literature review on critical appraisal tools and a modified e-Delphi study with methodological experts to identify core criteria. TOOL DESCRIPTION: The MMAT assesses the quality of qualitative, quantitative, and mixed methods studies. It focuses on methodological criteria and includes five core quality criteria for each of the following five categories of study designs: (a) qualitative, (b) randomized controlled, (c) nonrandomized, (d) quantitative descriptive, and (e) mixed methods. CONCLUSION: The MMAT is a unique tool that can be used to appraise the quality of different study designs. Also, by limiting to core criteria, the MMAT can provide a more efficient appraisal.
1,208 citations
TL;DR: This paper proposes the use of deep learning approaches for breast ultrasound lesion detection and investigates three different methods: a Patch-based LeNet, a U-Net, and a transfer learning approach with a pretrained FCN-AlexNet.
Abstract: Breast lesion detection using ultrasound imaging is considered an important step of computer-aided diagnosis systems. Over the past decade, researchers have demonstrated the possibilities to automate the initial lesion detection. However, the lack of a common dataset impedes research when comparing the performance of such algorithms. This paper proposes the use of deep learning approaches for breast ultrasound lesion detection and investigates three different methods: a Patch-based LeNet, a U-Net, and a transfer learning approach with a pretrained FCN-AlexNet. Their performance is compared against four state-of-the-art lesion detection algorithms (i.e., Radial Gradient Index, Multifractal Filtering, Rule-based Region Ranking, and Deformable Part Models). In addition, this paper compares and contrasts two conventional ultrasound image datasets acquired from two different ultrasound systems. Dataset A comprises 306 (60 malignant and 246 benign) images and Dataset B comprises 163 (53 malignant and 110 benign) images. To overcome the lack of public datasets in this domain, Dataset B will be made available for research purposes. The results demonstrate an overall improvement by the deep learning approaches when assessed on both datasets in terms of True Positive Fraction, False Positives per image, and F-measure.
564 citations
TL;DR: This paper proposes a new MAC layer—RS-LoRa—to improve reliability and scalability of LoRa wide-area networks (LoRaWANs) and implement it in NS-3 and demonstrates the benefit of RS-Lo Ra over the legacy LoRaWan, in terms of packet error ratio, throughput, and fairness.
Abstract: Providing low power and long range (LoRa) connectivity is the goal of most Internet of Things networks, e.g., LoRa, but keeping communication reliable is challenging. LoRa networks are vulnerable to the capture effect. Cell-edge nodes have a high chance of losing packets due to collisions, especially when high spreading factors (SFs) are used that increase time on air. Moreover, LoRa networks face the problem of scalability when they connect thousands of nodes that access the shared channels randomly. In this paper, we propose a new MAC layer—RS-LoRa—to improve reliability and scalability of LoRa wide-area networks (LoRaWANs). The key innovation is a two-step lightweight scheduling : 1) a gateway schedules nodes in a coarse-grained manner through dynamically specifying the allowed transmission powers and SFs on each channel and 2) based on the coarse-grained scheduling information, a node determines its own transmission power, SF, and when and on which channel to transmit. Through the proposed lightweight scheduling, nodes are divided into different groups, and within each group, nodes use similar transmission power to alleviate the capture effect. The nodes are also guided to select different SFs to increase the network reliability and scalability. We have implemented RS-LoRa in NS-3 and evaluated its performance through extensive simulations. Our results demonstrate the benefit of RS-LoRa over the legacy LoRaWAN, in terms of packet error ratio, throughput, and fairness. For instance, in a single-cell scenario with 1000 nodes, RS-LoRa can reduce the packet error ratio of the legacy LoRaWAN by nearly 20%.
187 citations
TL;DR: CUIDATS is presented, an IoT hybrid monitoring system for health care environments which integrates RFID and WSN technologies in a single platform providing location, status, and tracking of patients and assets.
121 citations
TL;DR: In this paper, the authors identify the key enabling factors of ecosystem services (ES) in urban green spaces and highlight the potential for green space planning in cities to steer the stewardship of urban gardens by providing institutional and physical space for civic gardening initiatives.
108 citations
08 Sep 2018
TL;DR: This work introduces bijective Gated Recurrent Units, a double mapping between the input and output of a GRU layer that allows for recurrent auto-encoders with state sharing between encoder and decoder, stratifying the sequence representation and helping to prevent capacity problems.
Abstract: This work introduces double-mapping Gated Recurrent Units (dGRU), an extension of standard GRUs where the input is considered as a recurrent state. An extra set of logic gates is added to update the input given the output. Stacking multiple such layers results in a recurrent auto-encoder: the operators updating the outputs comprise the encoder, while the ones updating the inputs form the decoder. Since the states are shared between corresponding encoder and decoder layers, the representation is stratified during learning: some information is not passed to the next layers. We test our model on future video prediction. Main challenges for this task include high variability in videos, temporal propagation of errors, and non-specificity of future frames. We show how only the encoder or decoder needs to be applied for encoding or prediction. This reduces the computational cost and avoids re-encoding predictions when generating multiple frames, mitigating error propagation. Furthermore, it is possible to remove layers from a trained model, giving an insight to the role of each layer. Our approach improves state of the art results on MMNIST and UCF101, being competitive on KTH with 2 and 3 times less memory usage and computational cost than the best scored approach.
98 citations
TL;DR: In this paper, the authors conduct a systematic review of academic literature to examine the context and attributes of co-management initiatives in small-scale fisheries, and their expected outcomes, and suggest that a supporting legal and institutional framework facilitates the emergence of comanagement, because it contributes to clarify and legitimize property rights over fish resources.
Abstract: Small-scale fisheries are an important source of livelihoods, particularly among poor coastal populations. To improve fisheries’ condition and maximize their contribution to human welfare, co-management approaches have proliferated worldwide. In this article, we conduct a systematic review of academic literature to examine the context and attributes of co-management initiatives in small-scale fisheries, and their expected outcomes. The review suggests that a supporting legal and institutional framework facilitates the emergence of co-management, because it contributes to clarify and legitimize property rights over fish resources. It is also found that co-management delivers both ecological and social benefits: it increases the abundance and habitat of species, fish catches, actors’ participation, and the fishery’s adaptive capacity, as well as it induces processes of social learning. Furthermore, co-management is more effective if artisanal fishers and diverse stakeholders become involved through an adaptive institutional framework. However, the review also suggests that more research is needed to discern when co-management initiatives can transform pre-existing conflicts, challenge power asymmetries and distribute benefits more equitably.
93 citations
TL;DR: This paper proposes to improve the assembling of the committee by introducing supervised learning on the ensemble computation, and trains a CNN on the posterior-class probabilities resulting from the individual members allowing to capture non-linear dependencies among committee members, and to learn this combination from data.
Abstract: Automated emotion recognition from facial images is an unsolved problem in computer vision. Although recent methods achieve close to human accuracy in controlled scenarios, the recognition of emotions in the wild remains a challenging problem. Recent advances in Deep learning have supposed a significant breakthrough in many computer vision tasks, including facial expression analysis. Particularly, the use of Deep Convolutional Neural Networks has attained the best results in the recent public challenges. The current state-of-the-art algorithms suggest that the use of ensembles of CNNs can outperform individual CNN classifiers. Two key considerations influence these results: (i) The design of CNNs involves the adjustment of parameters that allow diversity and complementarity in the partial classification results, and (ii) the final classification rule that assembles the result of the committee. In this paper we propose to improve the assembling of the committee by introducing supervised learning on the ensemble computation. We train a CNN on the posterior-class probabilities resulting from the individual members allowing to capture non-linear dependencies among committee members, and to learn this combination from data. The validation shows an accuracy 5 percent higher with respect to previous state-of-the art results based on averaging classifiers, and 4 percent to the majority voting rule.
88 citations
Memorial Sloan Kettering Cancer Center1, University College London2, National Taiwan University3, University of Southern California4, Humanitas University5, Asan Medical Center6, Trakya University7, University of Amsterdam8, Open University of Catalonia9, Exelixis10, University of California, San Francisco11
TL;DR: This phase 3 trial evaluated C vs P in previously treated pts with advanced HCC and found that C, an inhibitor of MET, VEGFR, and AXL has previously shown clinical activity in pts withAdvanced HCC.
Abstract: 207Background: C, an inhibitor of MET, VEGFR, and AXL, has previously shown clinical activity in pts with advanced HCC. This phase 3 trial (NCT01908426) evaluated C vs P in previously treated pts w...
85 citations
TL;DR: The results of the study indicate that agile strategies are useful for improving students' online project management and collaboration and no significant impact has been observed in students' satisfaction nor in the overall learning outcomes.
Abstract: Unsatisfactory prior experiences in collaborative learning influence students' predisposition towards team-based learning activities. Incorporating strategies for helping teams to effectively regulate group work and enhance planning processes may result in an increase in students' engagement with learning activities and collaborative processes. Taking into account the benefits of the agile method for teamwork organisation, this study sought to analyse the usefulness of agile strategies for team regulation and project management in online higher education. An iterative process of course redesign was conducted in the context of an undergraduate project-based learning course during two consecutive semesters. The new design was piloted and evaluated based on the students' and teacher's views and the learning outcomes. A total of 114 students were surveyed about their satisfaction with the course and their perception of the usefulness of the method. Two interviews were conducted to collect the teacher's opinions. The results of the study indicate that agile strategies are useful for improving students' online project management and collaboration. Nevertheless, no significant impact has been observed in students' satisfaction nor in the overall learning outcomes.
TL;DR: The iCV-MEFED data set, which includes 50 classes of compound emotions and labels assessed by psychologists, is released and experiments indicate that pairs of compound emotion are more difficult to be recognized if compared with the seven basic emotions.
Abstract: Emotion recognition has a key role in affective computing. Recently, fine-grained emotion analysis, such as compound facial expression of emotions, has attracted high interest of researchers working on affective computing. A compound facial emotion includes dominant and complementary emotions (e.g., happily-disgusted and sadly-fearful), which is more detailed than the seven classical facial emotions (e.g., happy, disgust, and so on). Current studies on compound emotions are limited to use data sets with limited number of categories and unbalanced data distributions, with labels obtained automatically by machine learning-based algorithms which could lead to inaccuracies. To address these problems, we released the iCV-MEFED data set, which includes 50 classes of compound emotions and labels assessed by psychologists. The task is challenging due to high similarities of compound facial emotions from different categories. In addition, we have organized a challenge based on the proposed iCV-MEFED data set, held at FG workshop 2017. In this paper, we analyze the top three winner methods and perform further detailed experiments on the proposed data set. Experiments indicate that pairs of compound emotion (e.g., surprisingly-happy vs happily-surprised) are more difficult to be recognized if compared with the seven basic emotions. However, we hope the proposed data set can help to pave the way for further research on compound facial emotion recognition.
TL;DR: The findings show that designers and teachers should pay special attention to their students during the second quartile of the course (independently of the type of MOOC); the teachers’ presence during the course, his or her interactions with students and the quality of the videos presented are significant determinants of course completion.
Abstract: This study investigated learner support strategies that enable the success and completion of Massive Open Online Courses (MOOCs). It examined five MOOCs categorised into three groups according to their pedagogical approach and used in different learning settings: formal MOOCs, conventional MOOCs and professional MOOCs. A total of 4,202,974 units of variables (student behaviours and MOOC features) were analysed using Semi-Supervised Extreme Learning Machine (SSELM) and Global Sensitivity Analysis. In this study, the use of SSELM was compared to the state-of-art models (e.g. ELM, KELM, OP-ELM, PCA-ELM), and SSELM yielded 97.24% accuracy. Using unlabelled students helped improve the learning accuracy for the model, which confirms that SSELM is a good model to predict completion in MOOCs, considering the difficulty of labelling students in such an open and flexible learning environment. The findings show that designers and teachers should pay special attention to their students during the second quartile of the course (independently of the type of MOOC). The teachers’ presence during the course, his or her interactions with students and the quality of the videos presented are significant determinants of course completion.
Journal Article•
TL;DR: In this paper, the authors test whether parliamentarians' use of Twitter is opening communication flows or confining them to representatives of the same party or ideology, and find that communication flows are polarized along party and ideological lines.
Abstract: Social media is transforming relations among members of parliaments, but are members taking advantage of these new media to broaden their party and ideological communication environment, or they are mainly communicating with other party members and ideologically aligned peers? This article tests whether parliamentarians’ use of Twitter is opening communication flows or confining them to representatives of the same party or ideology. The study is based on a data set spanning the period January 1, 2013, to March 31, 2014, which covers all relations (4,516), retweets (6,045), and mentions (19,507) among Catalan parliamentarians. Our results indicate that communication flows are polarized along party and ideological lines. The degree of polarization of this network depends, however, on where the interactions occur: The relations network is the most polarized; cross-party and cross-ideological interactions are greater in the retweet network and most present in the mention network.
TL;DR: The effect of flow on perceived ease of use, perceived usefulness and on the actual usage of the e-learning environment is identified, demonstrating the importance of this factor as a complement to the components of the TAM.
Abstract: This study advances the understanding of the process by which students accept and use e-learning environments. This is a key aspect in studying the online behaviour of students, as it directly infl...
TL;DR: A simheuristic algorithm for solving the Arc-Routing Problem with Stochastic Demands that combines Monte Carlo Simulation (MCS) with the RandSHARP metaheuristic, which is a biased-randomized version of a savings-based heuristic for the CARP, which allows it to obtain competitive results for this problem in low computational times.
Abstract: This paper proposes a simheuristic algorithm for solving the Arc Routing Problem with Stochastic Demands. Our approach combines Monte Carlo Simulation (MCS) with the RandSHARP metaheuristic, which ...
TL;DR: In this paper, the authors identify contextual, process and proposal related factors that are likely to affect the prospect of proposals being implemented, generating a set of testable hypotheses and test the explanatory power of these hypotheses through multilevel analysis on a diverse set of 571 policy proposals.
Abstract: What happens to the proposals generated by participatory processes? One of the key aspects of research on public participation that has been the subject of rare systematic analysis and comparison is the fate of the output from participatory processes: their proposals. Which specific factors explain whether proposals are accepted, rejected or transformed by public authorities? This paper contributes to this gap in our understanding in two steps. First, we identify contextual, process and proposal related factors that are likely to affect the prospect of proposals being implemented, generating a set of testable hypotheses. Second, we test the explanatory power of these hypotheses through multilevel analysis on a diverse set of 571 policy proposals. Our findings offer evidence that while there is no effect for contextual factors, both process and proposal related variables have significant explanatory power. The design of participatory processes affects the degree of implementation, with participatory budgeting and higher quality processes being particularly effective. But most significant for explaining implementation are proposal level economic and political factors: a proposal's cost, the extent to which it challenges existing policy and the degree of support it has within the municipality all strongly affect the chance of implementation.
TL;DR: This study presents and evaluates several models trained on a large dataset of short YouTube video blog posts for predicting apparent Big Five personality traits of people and whether they seem suitable to be recommended to a job interview.
Abstract: People form first impressions about the personalities of unfamiliar individuals even after very brief interactions with them. In this study we present and evaluate several models that mimic this automatic social behavior. Specifically, we present several models trained on a large dataset of short YouTube video blog posts for predicting apparent Big Five personality traits of people and whether they seem suitable to be recommended to a job interview. Along with presenting our audiovisual approach and results that won the third place in the ChaLearn First Impressions Challenge, we investigate modeling in different modalities including audio only, visual only, language only, audiovisual, and combination of audiovisual and language. Our results demonstrate that the best performance could be obtained using a fusion of all data modalities. Finally, in order to promote explainability in machine learning and to provide an example for the upcoming ChaLearn challenges, we present a simple approach for explaining the predictions for job interview recommendations.
TL;DR: This paper discusses the details of the 2017 IPIN indoor localization competition, the different datasets created, the teams participating in the event, and the results they obtained, and compares these results with other competition-based approaches and on-line evaluation web sites.
Abstract: The development of indoor positioning solutions using smartphones is a growing activity with an enormous potential for everyday life and professional applications. The research activities on this topic concentrate on the development of new positioning solutions that are tested in specific environments under their own evaluation metrics. To explore the real positioning quality of smartphone-based solutions and their capabilities for seamlessly adapting to different scenarios, it is needed to find fair evaluation frameworks. The design of competitions using extensive pre-recorded datasets is a valid way to generate open data for comparing the different solutions created by research teams. In this paper, we discuss the details of the 2017 IPIN indoor localization competition, the different datasets created, the teams participating in the event, and the results they obtained. We compare these results with other competition-based approaches (Microsoft and Perf-loc) and on-line evaluation web sites. The lessons learned by organising these competitions and the benefits for the community are addressed along the paper. Our analysis paves the way for future developments on the standardization of evaluations and for creating a widely-adopted benchmark strategy for researchers and companies in the field.
TL;DR: A physical, nutritional, neurocognitive, and pharmacological multifaceted intervention was effective in reversing frailty measures both at short-term and 18 months.
Abstract: Background Detecting and managing frailty at early stages can prevent disability and other adverse outcomes. The study aim was to evaluate whether a multifactorial intervention program could modify physical and cognitive frailty parameters in elderly individuals. Methods We conducted a multicenter, randomized, single-blind, parallel-group trial in community-living prefrail/frail elderly individuals in Barcelona. A total of 352 patients, aged ≥65 years old with positive frailty screening, was randomized into two groups to receive a 12-week multidisciplinary intervention or usual care, with concealed allocation. The intervention consisted of: exercise training, intake of hyperproteic nutritional shakes, memory training, and medication review. Main outcome assessments with multivariate analysis were conducted at 3 and 18 months. Results A total of 347 participants (98.6%) completed the study, mean age 77.3 years, 89 prefrail subjects (25.3%), and 75.3% female (n = 265). Eighteen-month assessments were performed in 76% of the sample. After 3 and 18 months, adjusted means difference between-groups showed significant improvements for the intervention group in all comparisons: Short Physical Performance Battery score improved 1.58 and 1.36 points (p < .001), handgrip strength 2.84 and 2.49 kg (p < .001), functional reach 4.3 and 4.52 cm (p < .001), and number of prescriptions decreased 1.39 and 1.09 (p < .001), respectively. Neurocognitive battery also showed significant improvements across all dimensions at 3 and 18 months. Conclusions A physical, nutritional, neurocognitive, and pharmacological multifaceted intervention was effective in reversing frailty measures both at short-term and 18 months. Lasting benefits of a multi-intervention program among frail elderly individuals encourage its prioritization.
TL;DR: A variable neighborhood search metaheuristic hybridized with simulation to solve the IRP under demand uncertainty is presented, able to solve large sized instances for the single period IRP with stochastic demands and stock-outs in very short computing times.
09 Dec 2018
TL;DR: This paper reviews both initial as well as recent applications of simheuristics, mainly in the area of logistics and transportation, and discusses current trends and open research lines in this field.
Abstract: Optimization problems arising in real-life transportation and logistics need to consider uncertainty conditions (e.g., stochastic travel times, etc.). Simulation is employed in the analysis of complex systems under such non-deterministic environments. However, simulation is not an optimization tool, so it needs to be combined with optimization methods whenever the goal is to: (i) maximize the system performance using limited resources; or (ii) minimize its operations cost while guaranteeing a given quality of service. When the underlying optimization problem is NP-hard, metaheuristics are required to solve large-scale instances in reasonable computing times. Simheuristics extend metaheuristics by adding a simulation layer that allows the optimization component to deal with scenarios under uncertainty. This paper reviews both initial as well as recent applications of simheuristics, mainly in the area of logistics and transportation. The paper also discusses current trends and open research lines in this field.
TL;DR: Limited effects were found for mHealth interventions to reduce ED-related symptoms and a common evaluation framework for ED m health interventions should be proposed to assess the validity of interventions before implemented on a larger scale in clinical practice.
Abstract: Objective To systematically review the existing evidence of mobile health (mHealth) tools for the treatment of eating disorders (ED). Method Electronic databases (Pubmed, PsycInfo, and SCOPUS) were searched, and PRISMA guidelines were followed. Selected studies were divided into three categories according to the intended purpose of the mHealth tools used: (a) sole means of support, (b) complementary to standard face-to-face treatment, and (c) for relapse prevention. Additionally, studies were assessed on efficacy, qualitative information, and methodological quality. Results Fifteen studies were identified. Most studies using mHealth as a sole means of intervention or adjunct to traditional therapy showed no effects, although an improvement at postassessment was present in vodcast, smartphone application, and text-messaging interventions. Between group effects were only found for a text-messaging intervention for relapse prevention. Qualitative analyses showed that most mHealth interventions were considered as acceptable, supporting, and motivating by patients and therapists, although different important problems were observed in individual studies. Conclusions Limited effects were found for mHealth interventions to reduce ED-related symptoms. A common evaluation framework for ED mHealth interventions should be proposed to assess the validity of interventions before implementing them on a larger scale in clinical practice.
TL;DR: A model of the 2L-VRP with stochastic travel times that also includes penalty costs generated by overtime is offered and a hybrid simheuristic algorithm is proposed that combines Monte Carlo simulation, an iterated local search framework, and biased-randomised routing and packing heuristics.
TL;DR: A simheuristic algorithm is proposed in order to minimize each of the following key performance indicators: the makespan in the deterministic version; and the expected makespan or a makespan percentile in the stochastic version.
TL;DR: The model developed takes advantage of the potential for technologies to go beyond traditional assessment approaches and proposes a classification of e-assessment activities organized by competences, which can help teachers and students better understand the meaning of competence-based learning.
Abstract: This article presents a model for designing e-assessment processes aligned with competences and learning activities. The authors examined assessment in student-centered, competence-based learning in online contexts. We analyzed the importance of alignment for properly selecting the learning activities that best guide students towards the desired level of competence acquisition (i.e. learning outcomes). We explored the leading types of assessment and new opportunities for assessment derived from the use of technologies. The model developed takes advantage of the potential for technologies to go beyond traditional assessment approaches and proposes a classification of e-assessment activities organized by competences. When the model was applied in a real online course, results suggested it can help teachers and students better understand the meaning of competence-based learning and how the formative assessment approach is useful for helping students attain the desired competence levels.
01 Oct 2018
TL;DR: This paper proposes an approach to generate specification-based test cases for REST APIs to make sure that such APIs meet the requirements defined in their specifications, and particularly the OpenAPI one.
Abstract: The REpresentation State Transfer (REST) has gained momentum as the preferred technique to design Web APIs. REST allows building loosely coupled systems by relying on HTTP and the Web-friendly format JSON. However, REST is not backed by any standard or specification to describe how to create/consume REST APIs, thus creating new challenges for their integration, testing and verification. To face this situation, several specification formats have been proposed (e.g., OpenAPI, RAML, and API Blueprint), which can help automate tasks in REST API development (e.g., testing) and consumption (e.g., SDKs generation). In this paper we focus on automated REST API testing relying on API specifications, and particularly the OpenAPI one. We propose an approach to generate specification-based test cases for REST APIs to make sure that such APIs meet the requirements defined in their specifications. We provide a proof-of-concept tool implementing our approach, which we have validated with 91 OpenAPI definitions. Our experiments show that the generated test cases cover on average 76.5% of the elements included in the OpenAPI definitions. Furthermore, our experiments also reveal that 40% of the tested APIs fail.
TL;DR: It is shown that there is a need to guarantee proper records management, which includes transparency throughout a record’s lifecycle, and that legislation assessed does not properly reflect these ideals.
12 Apr 2018
TL;DR: The concept of learning ecologies provides a framework of analysis to know how we learn, and what contexts and / or elements we use to train ourselves, in order to provide us with new learning opportunities.
Abstract: The immersion of society in the digital age has decisively influenced people’s ways of behaving, in the field of work, economy, entertainment and teaching. Higher education is undergoing a great transformation due to the technological development in which we are immersed, and these continuous changes have shown the need to keep us updated permanently, thus adopting the idea of life-long learning. Each person and each professional has a wide and diverse range of possibilities to be trained and to learn, which requires individuals to take more and more control over their own learning process. The concept of learning ecologies provides a framework of analysis to know how we learn, and what contexts and / or elements we use to train ourselves, in order to provide us with new learning opportunities. Being aware of the elements and / or contexts that make up our learning ecologies can be a very useful strategy to help us update ourselves in a self-directed and effective way. This has led us to carry out a bibliographic studyaimed at identifying some of the aspects that characterize the new ways in which we learn, which will allow us to understand the role that the university should play in today’s society.
TL;DR: In this article, aproximación contextual del fenomeno de las noticias falsas in relation with el campo de la informacion y la documentacion and the papel que los profesionales del sector podemos ejercer eficaz y eficientemente.
Abstract: Presentamos en este articulo una aproximacion contextual del fenomeno de las noticias falsas en relacion con el campo de la informacion y la documentacion y el papel que los profesionales del sector podemos ejercer eficaz y eficientemente. Hacemos una descripcion de iniciativas y proyectos, tanto de las instituciones bibliotecarias y de sus profesionales, como de los sectores de la educacion y de la comunicacion, tambien afectados e involucrados en la problematica de las noticias falsas y de la posverdad. En las conclusiones planteamos la necesaria revision de una serie de practicas y actividades desarrolladas hasta ahora, la participacion y colaboracion con otros sectores profesionales implicados, y la potenciacion de proyectos de formacion en competencias digitales y mediaticas.