Abstract
The main purpose for conducting this research is to analyze the core advantages and disadvantages of automatic assessment tools. With this in mind, we considered two different web sites of two distinct Portuguese Universities and compare the accessibility and usability issues found, aiming at identifying core problems through errors / warnings. This outcome will help us understand the weaknesses of such automatic tools and, thus, suggest features that could be added to improve their analysis. These findings will serve as a basis for a proposal of a new type of platform capable of making any web or mobile application accessible to all types of users, regardless of their impairments (e.g., blindness, deafness, motor or intellectual disabilities). © 2022 IEEE.

Abstract
Information and Communication Technologies are driving the improvement of industrial processes. According to the Industry 4.0 (I4.0) paradigm, digital systems provide real-time information to humans and machines, increasing flexibility and efficiency in production environments. Based on the I4.0 Design Principles concept, Virtual Assistants can play a vital role in processing production data and offer contextualized and real-time information to the workers in the production environment. This systematic review paper explored Virtual Assistant applications in the context of I4.0, discussing the Technical Assistance Design Principle and identifying the characteristics, services, and limitations regarding Virtual Assistant use in the production environments. The results showed that Virtual Assistants offer Physical and Virtual Assistance. Virtual Assistance provides real-time contextualized information mainly for support, while Physical Assistance is oriented toward task execution. Regarding services, the applications include integration with legacy systems and static information treatment. The limitations of the applications incorporate concerns about information security and adapting to noisy and unstable environments. It is possible to assume that the terminology of Virtual Assistants is not standardized and is mentioned as chatbots, robots, and others. Besides the worthy insights of this research, the small number of resulting papers did not allow for generalizations. Future research should focus on broadening the search scope to provide more-significant conclusions and research possibilities with new AI models and services, including the emergent Industry 5.0 concept.

Abstract
This research focuses on developing a wearable device that aims to enhance problem-solving and communication abilities within the context of Industry 4.0. The wearable is being developed in the Continental Advanced Antenna, and it allows operators to notify material shortages on the manufacturing line and helps minimize workflow disturbance. The wearable gives a list of missing materials using context-aware computing, allowing operators to identify and prioritize the missing item quickly. We used the Quick and Dirty usability testing approach to ensure the device's usability and efficacy, allowing quick feedback and iterative modifications throughout the development process. Experienced consultants of project participated initial tests on the device and found that it has the potential to improve efficiency and communication in an industrial setting. However, further testing involving end users is necessary to optimize the device for the unique demands of the production environment. This paper offers valuable insights into the lessons learned from the project and proposes potential future research directions.

Abstract
Virtual assistants offer a new type of solution to handle interaction between human and machine and can be applied in various business contexts such as Industry or Education. When designing and building a virtual assistant the developers must ensure a set of parameters to achieve a good solution. Various platforms and frameworks emerged to allow developers to create virtual assistant solutions easier and faster. This paper provides a review of available platforms and frameworks used by authors to create their own solutions in different areas. Big tech companies like Google with Dialogflow, IBM with Watson Assistant and Microsoft with Bot Framework, present mature solutions to build virtual assistants that provide to the developer all components of the basic architecture to build a fast and solid solution. Open-Source solutions focus on providing to the developer the main components to build a virtual assistant, namely language understanding and response generation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Abstract
Technological developments have had a major impact on the intensive use of electronic equipment, networked or connected to the internet, factors that have boosted the emergence and growth of cybercrime. Measures to mitigate and combat the phenomenon, taking into account its complexity and specificity, must involve all public entities with responsibility in the sector, in a global effort to promote digital literacy in the areas of cybersecurity and computer crime prevention. These comprehensive actions should use digital technologies based on artificial intelligence (AI), such as virtual assistants, whose characteristics allow the massification of information transmission, while enhancing the digital inclusion of users. Government entities are engaged in adopting technologies based on chatbots, with their presence in several areas of public administration. Despite the evolution, these resources have not yet been made available by the entities responsible for mitigating computer crime. On the other hand, although there are government programs aimed at increasing the digital skills of citizens, namely regarding the protection of devices, digital content or personal data, they are not designed for the specificities of cybercrime. In this context, a system based on chatbots, implemented in a digital governance context, by law enforcement agencies, with resources shared with other government entities can contribute to the prevention of cybercrime. © 2022 IEEE.

Abstract
New challenges arise with the upsurge of a Big Data era. Huge volumes of data, from the most varied natures, gathered from different sources, collected in different timings, often with high associated uncertainty, make the decision-making process a harsher task. Current methods are not ready to deal with characteristics of the new problems. This paper proposes a novel data selection methodology that filters big volumes of data, so that only the most correlated information is used in the decision-making process in each given context. The proposed methodology uses a clustering algorithm, which creates sub-groups of data according to their correlation. These groups are then used to feed a forecasting process that uses the relevant data for each situation, while discarding data that is not expected to contribute to improving the forecasting results. In this way, a faster, less computationally demanding, and effective forecasting is enabled. A case study is presented, considering the application of the proposed methodology to the filtering of electricity market data used by forecasting approaches. Results show that the data selection increases the forecasting effectiveness of forecasting methods, as well as the computational efficiency of the forecasts, by using less yet more adequate data. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.