A Mobile Collaborative System (MCOS) enable the cooperation of the members of a team to achieve a common goal by using a combination of mobile and fixed technologies. MCOS can be enhanced if the context of the group of users is considered in the execution of activities. This paper proposes a novel model for Context-Aware Mobile COllaborative Systems (CAMCOS) and a functional architecture based on that model. In order to validate both the model and the architecture, a prototype system in the tourism domain was implemented and evaluated.

Brain Computer Interfaces- BCI allow users to communicate with the software system through cognitive functions measurable by brain signals, identified as Electroencephalography-EEG. User tests have been the most used method for the evaluation of BCI software applications. In user tests, the data collected comes from the opinions of users through questionnaires, these tests require a lot of time, since they include not only performing interaction task and the application of the questionnaires, but also include placing and calibrating the EEG device. All this makes the evaluation process a very heavy task for the participants and can mean that the data collected is not entirely reliable. That is why we are interested in including EEG signals in the usability evaluation process of applications with BCI. Therefore, we present in this paper the result of the systematic mapping of the literature in order to identify the relevant works in the area and future lines of research.

Program synthesis is the process of automatically generating software from a requirement specification. This paper presents a systematic literature review focused on program synthesis from specifications expressed in natural language. The research problem centers on the complexity of automatically generating accurate and robust code from high-level, ambiguous natural language descriptions – a barrier that limits the broader adoption of automatic code generation in software development. To address this issue, the study systematically examines research published between 2014 and 2024, focusing on works that explore various approaches to program synthesis from natural language inputs. The review follows a rigorous methodology, incorporating search strings tailored to capture relevant studies from five major data sources: IEEE, ACM, Springer, Elsevier, and MDPI. The selection process applied strict inclusion and exclusion criteria, resulting in a final set of 20 high-quality studies. The findings reveal significant advancements in the field, particularly in the integration of large language models (LLMs) with program synthesis techniques. The review also highlights the challenges and concludes by outlining key trends and proposing future research directions aimed at overcoming these challenges and expanding the applicability of program synthesis across various domains.

Today there are smart cities that, through the use of information technologies, sensors, and specialized infrastructure, focus their efforts on improving the quality of life of their inhabitants. From these efforts arose the need to analyze and represent data within a system to make it useful and understandable to people, for which dashboards emerge. The objective of these systems is to provide users with information to support decision-making, so it is essential to adapt the visualization of the information provided to their needs and preferences. However, the analysis of adaptability through user interaction and its benefits is a topic still under exploration. This paper analyzes the literature on information visualization in adaptable dashboards for smart cities. Based on the elements of adaptable dashboards identified in the literature review, we propose an adaptable dashboard architecture, identify the main characteristics of the users of a smart city dashboard, and build an adaptable dashboard prototype using user-centered techniques.