Abstract
The study of data quality in crowdsourcing campaigns is currently a prominent research topic, given the diverse range of participants involved. A potential solution to enhancing data quality processes in crowdsourcing is cognitive personalization, which involves appropriately adapting or assigning tasks based on a crowd worker’s cognitive profile. There are two common methods for assessing a crowd worker’s cognitive profile: administering online cognitive tests, and inferring behavior from task fingerprinting based on user interaction log events. This article presents the findings of a study that investigated the complementarity of both approaches in a microtask scenario, focusing on personalizing task design. The study involved 134 unique crowd workers recruited from a crowdsourcing marketplace. The main objective was to examine how the administration of cognitive ability tests can be used to allocate crowd workers to microtasks with varying levels of difficulty, including the development of a deep learning model. Another goal was to investigate if task fingerprinting can be used to allocate crowd workers to different microtasks in a personalized manner. The results indicated that both objectives were accomplished, validating the usage of cognitive tests and task fingerprinting as effective mechanisms for microtask personalization, including the development of a deep learning model with 95% accuracy in predicting the accuracy of the microtasks. While we achieved an accuracy of 95%, it is important to note that the small dataset size may have limited the model’s performance.

Abstract
Peripheral arterial disease (PAD) causes blockage of the arteries, altering the blood flow to the lower limbs. This blockage can cause the individual with PAD to feel severe pain in the lower limbs. The main contribution of this research is the discovery of a solution that allows the automatic detection of the onset of claudication based on data analysis from patients’ smartphones. For the data-collection procedure, 40 patients were asked to walk with a smartphone on a thirty-meter path, back and forth, for six minutes. Each patient conducted the test twice on two different days. Several machine learning models were compared to detect the onset of claudication on two different datasets. The results suggest that we can identify the onset of claudication using inertial sensors with a best case accuracy of 92.25% for the Extreme Gradient Boosting model.

Abstract

Abstract
The use of virtual reality (VR) for industrial training helps minimize risks and costs by allowing more frequent and varied use of experiential learning activities, leading to active and improved learning. However, creating VR training experiences is costly and time-consuming, requiring software development experts. Additionally, current authoring tools lack integration with existing data and are desktop-oriented, which detach the pedagogic process of creating the immersive experience from experiencing it in a situated context. We present a novel interactive approach for immersive authoring of VR-based experiential training by the trainers themselves, from inside the virtual environment and without the support of development experts. The design includes identifying interactable elements, such as 3-D models, equipment, tools, settings, and environment. The trainer also specifies by demonstration the actions to be performed by trainees, as a virtual choreography. During course execution, trainees' activities are also registered as virtual choreographies and matched to those specified by the trainer. Thus trainer and trainee are culturally situated within their area semantics and social discourse, rather than adopting concepts of the VR system for the learning content. We conducted a usability case study with professionals from an international wind energy company, using detailed models of wind turbines and real-world procedures. Trainers set up a training course using the immersive authoring tool, and trainees executed the course. The learning experience and usability were analyzed, and the training was certified by comparing real-world task completion between a user that had undergone virtual training and a user that did not.

Abstract