Abstract
A substantial amount of work is often overlooked due to the exponential rate of growth in global scientific output across all disciplines. Current approaches for addressing this issue are usually limited in scope and often restrict the possibility of obtaining multidisciplinary views in practice. To tackle this problem, researchers can now leverage an ecosystem of citizens, volunteers and crowd workers to perform complex tasks that are either difficult for humans and machines to solve alone. Motivated by the idea that human crowds and computer algorithms have complementary strengths, we present an approach where the machine will learn from crowd behavior in an iterative way. This approach is embodied in the architecture of SciCrowd, a crowd-powered human-machine hybrid system designed to improve the analysis and processing of large amounts of publication records. To validate the proposal's feasibility, a prototype was developed and an initial evaluation was conducted to measure its robustness and reliability. We conclude this paper with a set of implications for design.

Abstract

Abstract
Aim To explore the potential of a nurse health triage telephone line to advise and guide elderly users' decisions regarding the appropriate health care setting and self-care. Background Ageing is a concern in many countries and poses challenges to health care services. Triage and advice lines can play an important role for the (re)organisation of health care delivery. Discussion has been focused on the capacity of these lines to reduce inappropriate demand for acute and emergency departments. Methods Cross-sectional descriptive analysis. Results Nurses directed elders to a health care service both by downgrading their initial intentions (concurring to the most common objective) and by upgrading them (e.g., directing elders that intended to stay at home to acute and emergency care). The intention to comply with the nurse's disposition was high. Conclusions The line helped to improve the appropriateness of acute and emergency care demand and to reduce the overall demand for care by elders. There is nonetheless space for improvement given the underuse of the line by elders. Implications for Nursing Management Health telephone-based triage and advice should be promoted to increase the match between the needs of elderly patients and health resources, thus improving health equity.

Abstract
The 2nd workshop on User Interfaces for Spatial-Temporal Data Analysis (UISTDA2019)(1) took place in conjunction with the 24th Annual Meeting of the Intelligent Interfaces community (ACM IUI2019) in Los Angeles, USA on March 20, 2019. The goal of this workshop is to share latest progress and developments, current challenges and potential applications for exploring and exploiting large amounts of spatial and temporal data. Four papers and a keynote talk were presented in this edition of the workshop.

Abstract
Information storage, reflecting the capability of a dynamical system to keep predictable information during its evolution over time, is a key element of intrinsic distributed computation, useful for the description of the dynamical complexity of several physical and biological processes. Here we introduce a parametric approach which allows one to compute information storage across multiple timescales in stochastic processes displaying both short-term dynamics and long-range correlations (LRC). Our analysis is performed in the popular framework of multiscale entropy, whereby a time series is first "coarse grained" at the chosen timescale through low-pass filtering and downsampling, and then its complexity is evaluated in terms of conditional entropy. Within this framework, our approach makes use of linear fractionally integrated autoregressive (ARFI) models to derive analytical expressions for the information storage computed at multiple timescales. Specifically, we exploit state space models to provide the representation of lowpass filtered and downsampled ARFI processes, from which information storage is computed at any given timescale relating the process variance to the prediction error variance. This enhances the practical usability of multiscale information storage, as it enables a computationally reliable quantification of a complexity measure which incorporates the effects of LRC together with that of short-term dynamics. The proposed measure is first assessed in simulated ARFI processes reproducing different types of autoregressive dynamics and different degrees of LRC, studying both the theoretical values and the finite sample performance. We find that LRC alter substantially the complexity of ARFI processes even at short timescales, and that reliable estimation of complexity can be achieved at longer timescales only when LRC are properly modeled. Then, we assess multiscale information storage in physiological time series measured in humans during resting state and postural stress, revealing unprecedented responses to stress of the complexity of heart period and systolic arterial pressure variability, which are related to the different role played by LRC in the two conditions.

Abstract
Tele-media-art is a web-based asynchronous e-learning platform, enabling blind students to have dance and theatre classes remotely, using low-cost motion tracking technology feasible for home use. Teachers and students submit dance recordings augmented with sound synthesis of their motions. Sound synthesis is generated by processing Kinect motion capture data, enabling blind students to compare the audio feedback of their motions with the audio generated by the teacher's motions. To study the feasibility of this approach, the authors present data on early testing of the prototype, performed with blindfolded users.