The virtual playground: an educational virtual reality environment for evaluating interactivity and conceptual learning

The research presented in this paper aims at investigating user interaction in immersive virtual learning environments (VLEs), focusing on the role and the effect of interactivity on conceptual learning. The goal has been to examine if the learning of young users improves through interacting in (i.e. exploring, reacting to, and acting upon) an immersive virtual environment (VE) compared to non interactive or non-immersive environments. Empirical work was carried out with more than 55 primary school students between the ages of 8 and 12, in different between-group experiments: an exploratory study, a pilot study, and a large-scale experiment. The latter was conducted in a virtual environment designed to simulate a playground. In this ‘Virtual Playground’, each participant was asked to complete a set of tasks designed to address arithmetical ‘fractions’ problems. Three different conditions, two experimental virtual reality (VR) conditions and a non-VR condition, that varied the levels of activity and interactivity, were designed to evaluate how children accomplish the various tasks. Pre-tests, post-tests, interviews, video, audio, and log files were collected for each participant, and analyzed both quantitatively and qualitatively. This paper presents a selection of case studies extracted from the qualitative analysis, which illustrate the variety of approaches taken by children in the VEs in response to visual cues and system feedback. Results suggest that the fully interactive VE aided children in problem solving but did not provide as strong evidence of conceptual change as expected; rather, it was the passive VR environment, where activity was guided by a virtual robot, that seemed to support student reflection and recall, leading to indications of conceptual change

From SpaceStat to CyberGIS: Twenty Years of Spatial Data Analysis Software

This essay assesses the evolution of the way in which spatial data analytical methods have been incorporated into software tools over the past two decades. It is part retrospective and prospective, going beyond a historical review to outline some ideas about important factors that drove the software development, such as methodological advances, the open source movement and the advent of the internet and cyberinfrastructure. The review highlights activities carried out by the author and his collaborators and uses SpaceStat, GeoDa, PySAL and recent spatial analytical web services developed at the ASU GeoDa Center as illustrative examples. It outlines a vision for a spatial econometrics workbench as an example of the incorporation of spatial analytical functionality in a cyberGIS.

Better Safe Than Sorry: An Adversarial Approach to Improve Social Bot Detection

The arm race between spambots and spambot-detectors is made of several cycles (or generations): a new wave of spambots is created (and new spam is spread), new spambot filters are derived and old spambots mutate (or evolve) to new species. Recently, with the diffusion of the adversarial learning approach, a new practice is emerging: to manipulate on purpose target samples in order to make stronger detection models. Here, we manipulate generations of Twitter social bots, to obtain - and study - their possible future evolutions, with the aim of eventually deriving more effective detection techniques. In detail, we propose and experiment with a novel genetic algorithm for the synthesis of online accounts. The algorithm allows to create synthetic evolved versions of current state-of-the-art social bots. Results demonstrate that synthetic bots really escape current detection techniques. However, they give all the needed elements to improve such techniques, making possible a proactive approach for the design of social bot detection systems.Comment: This is the pre-final version of a paper accepted @ 11th ACM Conference on Web Science, June 30-July 3, 2019, Boston, U

A unified view of data-intensive flows in business intelligence systems : a survey

Data-intensive flows are central processes in today’s business intelligence (BI) systems, deploying different technologies to deliver data, from a multitude of data sources, in user-preferred and analysis-ready formats. To meet complex requirements of next generation BI systems, we often need an effective combination of the traditionally batched extract-transform-load (ETL) processes that populate a data warehouse (DW) from integrated data sources, and more real-time and operational data flows that integrate source data at runtime. Both academia and industry thus must have a clear understanding of the foundations of data-intensive flows and the challenges of moving towards next generation BI environments. In this paper we present a survey of today’s research on data-intensive flows and the related fundamental fields of database theory. The study is based on a proposed set of dimensions describing the important challenges of data-intensive flows in the next generation BI setting. As a result of this survey, we envision an architecture of a system for managing the lifecycle of data-intensive flows. The results further provide a comprehensive understanding of data-intensive flows, recognizing challenges that still are to be addressed, and how the current solutions can be applied for addressing these challenges.Peer ReviewedPostprint (author's final draft

Ontology-driven conceptual modeling: A'systematic literature mapping and review

All rights reserved. Ontology-driven conceptual modeling (ODCM) is still a relatively new research domain in the field of information systems and there is still much discussion on how the research in ODCM should be performed and what the focus of this research should be. Therefore, this article aims to critically survey the existing literature in order to assess the kind of research that has been performed over the years, analyze the nature of the research contributions and establish its current state of the art by positioning, evaluating and interpreting relevant research to date that is related to ODCM. To understand and identify any gaps and research opportunities, our literature study is composed of both a systematic mapping study and a systematic review study. The mapping study aims at structuring and classifying the area that is being investigated in order to give a general overview of the research that has been performed in the field. A review study on the other hand is a more thorough and rigorous inquiry and provides recommendations based on the strength of the found evidence. Our results indicate that there are several research gaps that should be addressed and we further composed several research opportunities that are possible areas for future research