Abstract
The expectations of Machine Learning systems are becoming increasingly demanding, namely in what concerns the diversity of applications, the expected accuracy, and the pressure for results. However, there are cases in which Human experts are needed to label the data, which may have a significant cost in terms of human resources and time. In these cases, it is often best to learn on-the-fly, without expecting for the whole data to be labeled. Often, it is desirable to guide the Human annotators into focusing on the more relevant instances: this constitutes the so-called active learning. In this paper we propose an approach in which a clustering algorithm is used to find groups of similar instances. Then, the procedure is guided with the objective of favoring the annotation of the groups that are under-represented in the labeled dataset. Results show that this approach leads to models that are, over time, more accurate and reliable.

Abstract
Given the new requirements of Machine Learning problems in the last years, especially in what concerns the volume, diversity and speed of data, new approaches are needed to deal with the associated challenges. In this paper we describe CEDEs - a distributed learning system that runs on top of an Hadoop cluster and takes advantage of blocks, replication and balancing. CEDEs trains models in a distributed manner following the principle of data locality, and is able to change parts of the model through an optimization module, thus allowing a model to evolve over time as the data changes. This paper describes its generic architecture, details the implementation of the first modules, and provides a first validation.

Abstract
In recent years, several regulatory initiatives have been carried out at the European Commission level to ensure the ethical use of Artificial Intelligence, including the General Data Protection Regulation, Data Governance Act, or the Artificial Intelligence Act. However, there is also a need for technological solutions that effectively enable the implementation of this regulation in a realistic and efficient way. The main goal of this work is to propose and implement such a technological solution, relying on the notion of observability. The hypothesis is that a set of ethics metrics can be implemented along a domain-agnostic Data Science/Artificial Intelligence pipeline. These metrics, when observed in real time, will allow not only to assess the level of compliance of the pipeline with ethics standards at different levels, but also allow for a timely reaction by the organization when the data, the model or any other artifact in the pipeline exhibits undesired behavior. In this way, some of the most important ethical principles of AI are guaranteed: responsibility and prevention of harm. This work aims to identify a large group of ethics metrics, implement them, map them onto the different stages of a typical Data Science / AI process, and determine whether the presence of these metrics ensures or contributes to the development of AI solutions that can be considered ethical according to the latest European regulation.

Abstract
We live in an era in which the preservation of the environment is being widely discussed, driven by growing concerns over climate issues. One major factor contributing to this situation is the lack of attention societies give to maintaining high sustainability levels. Data plays a crucial role in understanding and assessing sustainability impacts in both urban and rural areas. However, obtaining comprehensive data on a country's sustainability is challenging due to the lack of simple and accessible sources. Existing solutions for sustainability analysis are limited by high costs and implementation difficulties, which restrict their spatial coverage. In this paper, we propose a solution using low-cost hardware and open-source technologies to collect data about the movement of people and vehicles. This solution involves low-cost video-based meters that can be flexibly deployed to various locations. Specifically, we developed a prototype using Raspberry Pi and YOLO which is able to correctly classify 91% of the vehicles by type, and 100% of the events (entering of leaving). The results indicate that this system can effectively and affordably identify and count people and vehicles, allowing for its implementations namely in remote sensitive areas such as natural parks, in which the access of people and vehicles must be controlled and monitored. © 2023 The authors and IOS Press.

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