Abstract
Background Self-reported client assessments during online treatments enable the development of statistical models for the prediction of client improvement and symptom development. Evaluation of these models is mandatory to ensure their validity. Methods For this purpose, we suggest besides a model evaluation based on study data the use of a simulation analysis. The simulation analysis provides insight into the model performance and enables to analyse reasons for a low predictive accuracy. In this study, we evaluate a temporal causal model (TCM) and show that it does not provide reliable predictions of clients' future mood levels. Results Based on the simulation analysis we investigate the potential reasons for the low predictive performance, for example, noisy measurements and sampling frequency. We conclude that the analysed TCM in its current form is not sufficient to describe the underlying psychological processes. Conclusions The results demonstrate the importance of model evaluation and the benefit of a simulation analysis. The current manuscript provides practical guidance for conducting model evaluation including simulation analysis.

Abstract
Background: Internet-based cognitive-behavioral therapy (iCBT) is more effective when it is guided by human support than when it is unguided. This may be attributable to higher adherence rates that result from a positive effect of the accompanying support on motivation and on engagement with the intervention. This protocol presents the design of a pilot randomized controlled trial that aims to start bridging the gap between guided and unguided interventions. It will test an intervention that includes automated support delivered by an embodied conversational agent (ECA) in the form of a virtual coach. Methods/design: The study will employ a pilot two-armed randomized controlled trial design. The primary outcomes of the trial will be (1) the effectiveness of iCBT, as supported by a virtual coach, in terms of improved intervention adherence in comparison with unguided iCBT, and (2) the feasibility of a future, larger-scale trial in terms of recruitment, acceptability, and sample size calculation. Secondary aims will be to assess the virtual coach's effect on motivation, users' perceptions of the virtual coach, and general feasibility of the intervention as supported by a virtual coach. We will recruitN = 70 participants from the general population who wish to learn how they can improve their mood by using Moodbuster Lite, a 4-week cognitive-behavioral therapy course. Candidates with symptoms of moderate to severe depression will be excluded from study participation. Included participants will be randomized in a 1:1 ratio to either (1) Moodbuster Lite with automated support delivered by a virtual coach or (2) Moodbuster Lite without automated support. Assessments will be taken at baseline and post-study 4 weeks later. Discussion: The study will assess the preliminary effectiveness of a virtual coach in improving adherence and will determine the feasibility of a larger-scale RCT. It could represent a significant step in bridging the gap between guided and unguided iCBT interventions.

Abstract
Index structures are fast-access methods. In the past, they were often used to minimise fetch operations to external storage devices (secondary memory). Nowadays, this also holds for increasingly large amounts of data residing in main-memory (primary memory). Examples of software that deals with this fact are in-memory databases and mobile device applications. Within this scope, this paper focuses on index structures to store, access and delete interval-based time-dependent (temporal) data from very large datasets, in the most efficient way. Index structures for this domain have specific characteristics, given the nature of time and the requirement to index time intervals. This work presents an open-source time-efficiency focused variant of the original Interval B+ tree. We designate this variant Improved Interval B+ tree (I2B+ tree). Our contribution adds to the performance of the delete operation by reducing the amount of traversed nodes to access siblings. We performed an extensive analysis of insert, range queries and deletion operations, using multiple datasets with growing volumes of data, distinct temporal distributions and tree parameters (time-split and node order). Results of the experiments validate the logarithmic performance of these operations and propose the best-observed tree parameter ranges.

Abstract
Delivering information timely across the logistics network is a challenge considering the diversity of entities, technologies, IT maturity and legislation. A way to overcome those challenges is to bring all the parties to a common level by providing technology able to fast and easily connect every system and a common language to allow efficient communication between logistics stakeholders. When we apply the Synchromodality and the Digital twin concepts to logistics, significant improvements can be achieved. Shippers and Integrators can act more precisely, deciding in any step of the global operation by what mode should be selected or combined and following the strict regulations and issuing the mandatory documents timely to avoid bottlenecks. This work produced one real business scenario where the 4-corner model technology-based solution enables synchromodality across the logistics network of one industry unit and its providers and the Digital twin for the process and the VGM (Verified Gross Mass) formality documents. As a solution to this scenario we propose collaboration networks between logistics stakeholders that provide interoperable, low-cost, reliable and secure data exchange, without requiring significant IT developments. To this purpose we studied and developed a demonstrator and tested our solution that consists of the adoption of the 4-corner model as described by Connecting Europe Facility (CEF) eDelivery using access points and the adoption of the standard e-Freight to exchange data over the collaboration network. The widespread use of this solution will increase global efficiency, make the inclusion of every client and provider regardless of resources available, and avoid imponderables in the logistics network. © 2020 IEEE.

Abstract
Information visualization commonly aids the understanding of the evolution of spatiotemporal phenomena. The current work proposes a novel approach to visually represent spatiotemporal phenomena based on the automated generation of static and interactive visual narratives that summarize the evolution of a spatiotemporal phenomenon. The visual narrative is composed of an interactive storyboard that consists of a set of frames that represent events of interest in the phenomenon. Towards corroborating the hypothesis that this approach would effectively and efficiently transmit the evolution of spatiotemporal phenomena, we conceptualized a visualization framework, identifying visual metaphors that map spatiotemporal transformations into visual content and defining the parameterization approaches for spatiotemporal features. We developed a functional prototype implementing the conceptual solution and presented issues encountered regarding visual clutter and parameterization. We conducted a user study based on a questionnaire which concluded that the proposed approach can be effective and efficient for understanding the evolution of these phenomena in terms of transformations for a subset of possible scenarios.

Abstract
Researchers in a variety of fields are increasingly adopting crowdsourcing as a reliable instrument for performing tasks that are either complex for humans and computer algorithms. As a result, new forms of collective intelligence have emerged from the study of massive crowd-machine interactions in scientific work settings as a field for which there is no known theory or model able to explain how it really works. Such type of crowd work uses an open participation model that keeps the scientific activity (including datasets, methods, guidelines, and analysis results) widely available and mostly independent from institutions, which distinguishes crowd science from other crowd-assisted types of participation. In this paper, we build on the practical challenges of crowd-AI supported research and propose a conceptual framework for addressing the socio-technical aspects of crowd science from a CSCW viewpoint. Our study reinforces a manifested lack of systematic and empirical research of the symbiotic relation of AI with human computation and crowd computing in scientific endeavors.