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The study of the use of media by the elderly has been producing an ever-increasing number of social researches. ICTs are seen by many as tools for a more actively facing ageing processes. Moreover, due to the reduction of the age-related digital divide, older adults represent an interesting type of users. From this standpoint, social researchers have been increasingly investigating older adults’ uses and non-uses of ICTs, both with quantitative and qualitative methods, online and offline tools. Starting from the presentation of three empirical studies carried out by the Università Cattolica del Sacro Cuore (Italy) between 2013 and 2020, the following paper aims to reflect on limits and opportunities offered by different research designs in this field of studies. To this purpose, we provide an overview on methods adopted in order to grasp the relationship between the elderly and technologies in complex and articulated social contexts. Furthermore, we will reflect on future lines of inquiry in this field of research, advocating for a greater interdisciplinarity in ageing studies as well as underlining the benefits offered by ethnographic methods and overall qualitative research. Finally, we sketch potential issues referring to the future of empirical research on the elderly in the upcoming years of the post-pandemic era.

Switches are operations which make local changes to the edges of a graph, usually with the aim of preserving the vertex degrees. We study a restricted set of switches, called triangle switches. Each triangle switch creates or deletes at least one triangle. Triangle switches can be used to define Markov chains which generate graphs with a given degree sequence and with many more triangles (3-cycles) than is typical in a uniformly random graph with the same degrees. We show that the set of triangle switches connects the set of all d-regular graphs on n vertices, for all d≥ 3. Hence, any Markov chain which assigns positive probability to all triangle switches is irreducible on these graphs. We also investigate this question for 2-regular graphs.

© 2019, Springer Nature Switzerland AG. We have seen a rise in mixed (MR) and augmented reality (AR) applications and devices in recent years. Subsequently, we have become familiar with the sensing power of these applications and devices, and we are only starting to realize the nascent risks that these technology puts over our privacy and security. Current privacy protection measures are primarily aimed towards known and well-utilised data types (i.e. location, on-line activity, biometric, and so on) while a few works have focused on looking into the security and privacy risks of and providing protection on MR data, particularly on 3D MR data. In this work, we primarily reveal the privacy leakage from released 3D MR data and how the leakage persist even after implementing spatial generalizations and abstractions. Firstly, we formalize the spatial privacy problem in 3D mixed reality data as well as the adversary model. Then, we demonstrate through an inference model how adversaries can identify 3D spaces and, potentially, infer more spatial information. Moreover, we also demonstrate how compact 3D MR Data can be in terms of memory usage which allows adversaries to create lightweight 3D inference models of user spaces.

Enterprises today are presented with a plethora of data, tools and analytics techniques, but lack systems which help analysts to navigate these resources and identify best fitting solutions for their analytics problems. To support enterprise-level data analytics research, this paper presents Research Variable Ontology (RVO), an ontology designed to catalogue and explore essential data analytics design elements such as variables, analytics models and available data sources. RVO is specialised to support researchers with exploratory and predictive analytics problems, popularly practiced in economics and social science domains. We present the RVO design process, its schema, how it links and extends existing ontologies to provide a holistic view of analytics related knowledge and how data analysts at the enterprise level can use it. Capabilities of RVO are illustrated through a case study on House Price Prediction.

© Springer Nature Switzerland AG 2019. The increasing reliance on data for decision making has led to a number of techniques for automatic knowledge acquisition such as Formal Concept Analysis (FCA). FCA creates a lattice comprising partial order relationships between sets of object instances in a domain (extent) and their properties (intent). This is mapped onto a semantic knowledge structure comprising domain concepts with their instances and properties. However, this automatic extraction of structure from a large number of instances usually leads to a lattice which is too complex for practical use. Algorithms to reduce the lattice exist. However, these mainly rely on the lattice structure and are agnostic about any prior knowledge about the domain. In contrast, this paper uses existing domain knowledge encoded in a semantic ontology and a novel relevance index to inform the reduction process. We demonstrate the utility of the proposed approach, achieving a significant reduction of lattice nodes, even when the ontology only provides partial coverage of the domain of interest.

© Springer Nature Switzerland AG 2019. This paper expands on prior research into the generation of audio through the evolution of Continuous Time Recurrent Neural Networks (CTRNNs). CTRNNs are a type of recurrent neural network that can be used to model dynamical systems and can exhibit many different characteristics that can be used for music creation such as the generation of non-linear audio signals which unfold with a level of generative agency or unpredictability. Furthermore, their compact structure makes them ideal for use as an evolvable genotype for musical search as a finite set of CTRNN parameters can be manipulated to discover a vast audio search space. In prior research, we have successfully evolved CTRNNs to generate timbral and melodic content that can be used for electronic music composition. However, although the initial adopted CTRNN algorithm produced oscillations similar to some conventional synthesis algorithms and timbres reminiscent of acoustic instruments, it was hard to find configurations that produced the timbral and temporal richness we expected. Within this paper, we look into modifying the currently used tanh transfer function by modulating it with a sine function to further enhance the idiosyncratic characteristics of CTRNNs. We explore to what degree they can aid musicians in the search for unique sounds and performative dynamics in which some creative control is given to a CTRNN agent. We aim to measure the difference between the two transfer functions by discovering two populations of CTRNNs using a novelty search evolutionary algorithm, each utilising a different transfer function. The effect that each transfer function has on the respective novelty of each CTRNN population is compared using quantitative analysis as well as through a compositional study.