Abstract
Today digital labor increasingly advocates for the inclusion of people who are excluded from society in someway. The proliferation of crowdsourcing as a new form of digital labor consisting mainly of microtasks that are characterized by a low level of complexity and short time periods in terms of accomplishment has allowed a wide spectrum of people to access the digital job market. However, there is a long-recognized mismatch between the expectations of employers and the capabilities of workers in microwork crowdsourcing marketplaces. Cognitive personalization has the potential to tailor microtasks to crowd workers, thus ensuring increased accessibility by providing the necessary coverage for individuals with disabilities and special needs. In this paper an architecture for a crowdsourcing system intended to support cognitive personalization in the design of microtasks is introduced. The architecture includes an ontology built for the representation of knowledge on the basis of the concepts of microtasks, cognitive abilities, and types of adaptation in order to personalize the interface to the crowd worker. The envisioned system contains a backend and a frontend that serve as an intermediary layer between the crowdsourcing platform and the workers. Finally, some results obtained to evaluate the proposed system are presented.

Abstract
The accelerated changes on our planet have led to a growing interest in climate change and its consequences: natural hazards and adverse socio-economic impacts. However, the development of climate research and the proliferation of datasets require an integrated and efficient approach to the analysis, investigation, and visualization of atmospheric meteorological data. Thus, we propose a literature review of existing systems viewing meteorological phenomena in four and three dimensions. Moreover, we evaluate meteorological occurrences to better understand the dynamics associated with a meteorological phenomenon and visualize different weather data. Based on the investigation of tools and methods, we consider the existence of different ways of representing meteorological data and methodologies. However, it was imperative to obtain knowledge and create our way of visualizing weather data. This article found eleven existing solutions for 4D meteorological visualization and meteorological phenomena.

Abstract
The advance of industrialization regarding the optimization of production to obtain greater productivity and consequently generate more profits has led to the emergence of Industry 4.0, which aims to create an environment called smart manufacturing. On the other hand, the Internet of Things is a global network of interrelated physical devices, such as sensors, actuators, intelligent applications, computers, mechanical machines, objects, and people, becoming an essential part of the Internet. These devices are data sources that provide abundant information on manufacturing processes in an industrial environment. A concern of this type of system is processing large sets of data and generating knowledge. These challenges often raise concerns about security, more specifically cybersecurity. Good cybersecurity practices make it possible to avoid damage to production lines and information. With the growing increase in threats in terms of security, this paper aims to carry out a review of existing technologies about cybersecurity in intelligent manufacturing and an introduction to the architecture of the IoT and smart manufacturing.

Abstract
The current technological advances have pushed the quantification of exercise intensity to new era of physical exercise sciences. Monitoring physical exercise is essential in the process of planning, applying, and controlling loads for performance optimization and health. A lot of research studies applied various statistical approaches to estimate various physiological indices, to our knowledge, no studies found to investigate the relationship of facial color changes and increased exercise intensity. The aim of this study was to develop a non-contact method based on computer vision to determine the heart rate and, ultimately, the exercise intensity. The method was based on analyzing facial color changes during exercise by using RGB, HSV, YCbCr, Lab, and YUV color models. Nine university students participated in the study (mean age = 26.88 +/- 6.01 years, mean weight = 72.56 +/- 14.27 kg, mean height = 172.88 +/- 12.04 cm, six males and three females, and all white Caucasian). The data analyses were carried out separately for each participant (personalized model) as well as all the participants at a time (universal model). The multiple auto regressions, and a multiple polynomial regression model were designed to predict maximum heart rate percentage (maxHR%) from each color models. The results were analyzed and evaluated using Root Mean Square Error (RMSE), F-values, and R-square. The multiple polynomial regression using all participants exhibits the best accuracy with RMSE of 6.75 (R-square = 0.78). Exercise prescription and monitoring can benefit from the use of these methods, for example, to optimize the process of online monitoring, without having the need to use any other instrumentation.

Abstract
Vocational high school is a secondary education whose practice portion is larger than its theoretical portion. This allows students to do more hands-on practice in the laboratory, as skill competency is very important in vocational education. Through practice, students have the skills to become competent and skilled technicians in the future. When students practice in a hands-on laboratory, errors may occur that can injure students, equipment, and components. In addition, short circuits can also endanger student safety. Therefore, to improve practical skills in the laboratory, teachers must find innovative ways to incorporate these methods into the learning process. One of the things that can be done to improve students’ practical skills is to use simulation software before doing direct practice in the laboratory. In-depth interviews were conducted with three electrical engineering teachers to verify the perspective of the proposed model. The results suggest that the proposed design is likely to improve problem-solving skills when an error occurs during the simulation, and it will improve practical skills when using hands-on laboratories so that students learn more about hands-on lab practice in vocational high school. © 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.