Intergenerational Education for Social Inclusion and Solidarity: The Case Study of the EU Funded Project "Connecting Generations"

This paper reflects on lessons learned from a validated model of international collaboration based on research and practice. During the European Year for Active Ageing, a partnership of seven organizations from the European Union plus Turkey implemented the Lifelong Learning Programme partnership “Connecting Generations‘ which involved universities, non-governmental organizations, third age Universities and municipalities in collaboration with local communities. Reckoning that Europe has dramatically changed in its demographic composition and is facing brand new challenges regarding intergenerational and intercultural solidarity, each partner formulated and tested innovative and creative practices that could enhance better collaboration and mutual understanding between youth and senior citizens, toward a more inclusive Europe for all. Several innovative local practices have experimented, attentively systematized and peer-valuated among the partners. On the basis of a shared theoretical framework coherent with EU and Europe and Training 2020 Strategy, an action-research approach was adopted throughout the project in order to understand common features that have been replicated and scaled up since today

Measuring Membership Privacy on Aggregate Location Time-Series

While location data is extremely valuable for various applications, disclosing it prompts serious threats to individuals' privacy. To limit such concerns, organizations often provide analysts with aggregate time-series that indicate, e.g., how many people are in a location at a time interval, rather than raw individual traces. In this paper, we perform a measurement study to understand Membership Inference Attacks (MIAs) on aggregate location time-series, where an adversary tries to infer whether a specific user contributed to the aggregates. We find that the volume of contributed data, as well as the regularity and particularity of users' mobility patterns, play a crucial role in the attack's success. We experiment with a wide range of defenses based on generalization, hiding, and perturbation, and evaluate their ability to thwart the attack vis-a-vis the utility loss they introduce for various mobility analytics tasks. Our results show that some defenses fail across the board, while others work for specific tasks on aggregate location time-series. For instance, suppressing small counts can be used for ranking hotspots, data generalization for forecasting traffic, hotspot discovery, and map inference, while sampling is effective for location labeling and anomaly detection when the dataset is sparse. Differentially private techniques provide reasonable accuracy only in very specific settings, e.g., discovering hotspots and forecasting their traffic, and more so when using weaker privacy notions like crowd-blending privacy. Overall, our measurements show that there does not exist a unique generic defense that can preserve the utility of the analytics for arbitrary applications, and provide useful insights regarding the disclosure of sanitized aggregate location time-series

Possibilities for pedagogy in Further Education: Harnessing the abundance of literacy

In this report, it is argued that the most salient factor in the contemporary communicative landscape is the sheer abundance and diversity of possibilities for literacy, and that the extent and nature of students' communicative resources is a central issue in education. The text outlines the conceptual underpinnings of the Literacies for Learning in Further Education project in a social view of literacy, and the associated research design, methodology and analytical framework. It elaborates on the notion of the abundance of literacies in students' everyday lives, and on the potential for harnessing these as resources for the enhancement of learning. It provides case studies of changes in practice that have been undertaken by further education staff in order to draw upon students' everyday literacy practices on Travel and Tourism and Multimedia courses. It ends with some of the broad implications for conceptualising learning that arise from researching through the lens of literacy practices

Towards a European higher education market

With the aim of building the European Higher Education Area, the Bologna Declarationindicates three main goals:â–¡ International competitiveness,â–¡ Mobility andâ–¡ EmployabilityMobility of students and academic- and administrative staff is the basis for establishing the EuropeanHigher Education Area. Using methods of open and distance learning and creating synergies betweennational and regional institutions and industry to promote cross-border business education mightstrengthen the international competitiveness, mobility and employability of HE graduates andEuropean Industry.This is not just a technological process, but also an ongoing political process involving the EuropeanHEIs, industrial partners, national governments and the European authorities (Council, Commission,Parliament) and the main theme of this SPACE conference and workshop: Bridging the Gap betweenbusiness and business schools - Being Mobil

DATGAN: Integrating expert knowledge into deeplearning for population synthesis

Agent-based simulations and activity-based models used to analyse nationwide transport networks require detailed synthetic populations. These applications are becoming more and more complex and thus require more precise synthetic data. However, standard statistical techniques such as Iterative Proportional Fitting (IPF) or Gibbs sampling fail to provide data with a high enough standard, e.g. these techniques fail to generate rare combinations of attributes, also known as sampling zeros in the literature. Researchers have, thus, been investigating new deep learning techniques such as Generative Adversarial Networks (GANs) for population synthesis. These methods have already shown great success in other fields. However, one fundamental limitation is that GANs are data-driven techniques, and it is thus not possible to integrate expert knowledge in the data generation process. This can lead to the following issues: lack of representativity in the generated data, the introduction of bias, and the possibility of overfitting the sample’s noise. To address these limitations, we present the Directed Acyclic Tabular GAN (DATGAN) to integrate expert knowledge in deep learning models for synthetic populations. This approach allows the interactions between variables to be specified explicitly using a Directed Acyclic Graph (DAG). The DAG is then converted to a network of modified Long Short-Term Memory (LSTM) cells. Two types of multi-input LSTM cells have been developed to allow such structure in the generator. The DATGAN is then tested on the Chicago travel survey dataset. We show that our model outperforms state-of-the-art methods on Machine Learning efficacy and statistical metrics