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.

Abstract
With cutting edge scientific breakthroughs, human-centred algorithmic approaches have proliferated in recent years and information technology (IT) has begun to redesign socio-technical systems in the context of human-AI collaboration. As a result, distinct forms of interaction have emerged in tandem with the proliferation of infrastructures aiding interdisciplinary work practices and research teams. Concomitantly, large volumes of heterogeneous datasets are produced and consumed at a rapid pace across many scientific domains. This results in difficulties in the reliable analysis of scientific production since current tools and algorithms are not necessarily able to provide acceptable levels of accuracy when analyzing the content and impact of publication records from large continuous scientific data streams. On the other hand, humans cannot consider all the information available and may be adversely influenced by extraneous factors. Using this rationale, we propose an initial design of a human-AI enabled pipeline for performing scientometric analyses that exploits the intersection between human behavior and machine intelligence. The contribution is a model for incorporating central principles of human-machine symbiosis (HMS) into scientometric workflows, demonstrating how hybrid intelligence systems can drive and encapsulate the future of research evaluation.

Abstract

Abstract