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
Crowdsourcing has received considerable attention over the last fifteen years and has been the subject of several experiments that demonstrate its large potential for use in real-world situations. With the rapid growth of and access to crowd work environments, there is a need for new ways to ensure more equitable access for all people. Task design is one of the core aspects of the crowdsourcing process and its optimization is a priority for many requesters that want to have their tasks solved in short times and with high levels of accuracy. Aligned with this goal, a cognitive personalization framework can make it feasible to assess the information processing preferences of crowd workers in order to provide a useful user interface (UI) adaptation. In an effort to address this issue, this study recruited a total of 64 crowd workers to take cognitive style tests and perform prototypical tasks. The results indicate that it is possible to apply short tests and then obtain some useful indicators for better matching tasks to workers with implications for improving the general outcomes and acceptance rates in crowdsourcing.

Abstract

Abstract
User experience can be affected by the amount and intensity of information presented. Four scenarios were developed to assess the insertion of information elements (chronometer and hint system) and tested with 37 users to find out if they affected the user's sense of presence and symptoms of cybersickness. In order to instruct users and using virtual reality using the Unity 3D game engine, we created a virtual world where the user has the role of exploring the environment and looking for mushrooms, and can consult a description about it. For tests with users, the IPQp and SSQ questionnaires were applied. The results indicate that it is possible to create a virtual world with the addition of informational components without significantly disturbing the user experience. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Physical activity is movement of the body or part of the body to make muscles more active and to lose the energy from the body. Regular physical activity in the daily routine is very important to maintain good physical and mental health. It can be performed at home, a rehabilitation center, gym, etc., with a regular monitoring system. How long and which physical activity is essential for specific people is very important to know because it depends on age, sex, time, people that have specific diseases, etc. Therefore, it is essential to monitor physical activity either at a physical activity center or even at home. Physiological parameter monitoring using contact sensor technology has been practiced for a long time, however, it has a lot of limitations. In the last decades, a lot of inexpensive and accurate non-contact sensors became available on the market that can be used for vital sign monitoring. In this study, the existing research studies related to the non-contact and video-based technologies for various physiological parameters during exercise are reviewed. It covers mainly Heart Rate, Respiratory Rate, Heart Rate Variability, Blood Pressure, etc., using various technologies including PPG, Video analysis using deep learning, etc. This article covers all the technologies using non-contact methods to detect any of the physiological parameters and discusses how technology has been extended over the years. The paper presents some introductory parts of the corresponding topic and state of art review in that area.