Abstract
The Industry 4.0 (I4.0) paradigm comes as a direct action by the German government to improve the industrial production process, by enhancing the ability to take action during the process and produce customized products, while maintaining the mass production industrial context. The I4.0 solutions rely on cybernetics systems that can enhance the users' decision-making. Some technologies are particularly suited for this purpose, including data science combined with context sensitive applications and virtual assistants (VA). These types of user application can provide information and features according to the user's context, thus acting proactively and foreseeing the user actions. In this work, we partnered with Continental Advanced Antenna - a manufacturer of radiofrequency devices for the auto industry, to further develop the concept of a VA to assist the production management. A prototype was built to interact and keep the production management team up to date regarding the ongoing execution of the production plan.

Abstract
In this paper, we describe a preliminary study on a multisensory music experience for people with hearing loss. Our main goal is to provide a music event through visual and tactile stimuli, granting a multisensory experience using haptic interfaces and taking advantage of visual feedback, vibrations and pressure to induce feelings. In this context, a mobile application was developed, allowing the user to interact with recorded audio samples that exploit vibrations to trigger emotions, such as fear, adrenaline, anxiety, suspense, drama, adventure, or even more complex moods like when dancing and relaxing. We thus describe our methodology (design, implementation and user assessment) for a preliminary study of a music experience based on a user-centered design approach. Indeed, we gathered promising results as the experience was considered effective and satisfying. We also uncovered some development issues to be addressed in future work, having to do with the use of specific hardware for providing a fully immersive experience. © 2022, Springer Nature Switzerland AG.

Abstract
A typical example of a Brain-Computer Interface (BCI) is a system that allows a person to move a ball displayed on a computer screen to the left or to the right, simply by imagining the movement of the left or right hand, respectively. Since the term Brain-Computer Interface was coined in 1973, the interest and efforts in this field have grown tremendously and there are now thought to be several hundred laboratories worldwide developing research in this topic. This paper aims at summarizing its resulting knowledge in a way that allows for a quick and clear consultation, highlighting the research lines, technologies and the most relevant cases of applications, so that policy makers, professionals and consumers can make effective use of the findings. With this in mind, a Brain-Computer Interface toolkit is proposed with a focus on different target audiences (e.g., children, seniors, people with intellectual disabilities) that can take advantage of this resource and promote an independent life routine. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Due to the rapid development of artificial intelligence, popular Virtual Assistants like Amazon Alexa or Google Assistant, can be applied to a wide variety of business areas. One area in which Virtual Assistants can be very useful is in Education, specially due to the pandemics that is occurring during the last years, as it can provide to students, teachers and staff an alternative administration tool as well as introduce new learning processes in classroom or on online classes. This work reviews and analyses some applications of Virtual Assistants in the education process. The reviewed work relies mainly on three categories: Student engagement with academic life, Education process during lessons and Learning of foreign languages. The presented solutions generally have great potential but the majority are simple proof of concepts and need more development and proper tests to enable retrieving more accurate results.

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
Our study provides valuable insights into the relationship between artificial intelligence (AI) and Human Resource Management (HRM). We have minimised bias and ensured reliable findings by employing a systematic literature review and the PRISMA statement. Our comprehensive syn-thesis of the studies included in this research, along with a bibliometric analysis of articles, journals, indexes, authors' affiliations, citations, keyword co-occurrences, and co-authorship analysis, has produced robust results. The discussion of our findings focuses on critical areas of interest, such as AI and Talent, AI Bias, Ethics and Law, and their impact on Human Resource (HR) management. Our research highlights the recognition by organisations of the importance of talent management in achieving a competitive advantage as higher-level skills become increas-ingly necessary. Although some HR managers have adopted AI technology for talent acquisition, our study reveals that there is still room for improvement. Our study is in line with previous research that acknowledges the potential for AI to revolutionise HR management and the future of work. Our findings emphasise the need for HR managers to be proactive in embracing technology and bridging the technological, human, societal, and governmental gaps. Our study contributes to the growing body of AI and HR management knowledge, providing essential insights and rec-ommendations for future research. The importance of our study lies in its focus on the role of HR in promoting the benefits of AI-based applications, thereby creating a larger body of knowledge from an organisational perspective.