Abstract
This study investigates spatial distribution and chemical elemental composition screening in soils in Rome (Italy) using X-ray fluorescence analysis. Fifty-nine soil samples were collected from various locations within the urban areas of the Rome municipality and were analyzed for 19 elements. Multivariate statistical techniques, including nonlinear mapping, principal component analysis, and hierarchical cluster analysis, were employed to identify clusters of similar soil samples and their spatial distribution and to try to obtain environmental quality information. The soil sample clusters result from natural geological processes and anthropogenic activities on soil contamination patterns. Spatial clustering using the k-means algorithm further identified six distinct clusters, each with specific geographical distributions and elemental characteristics. Hence, the findings underscore the importance of targeted soil assessments to ensure the sustainable use of land resources in urban areas.

Abstract
In healthcare, diagnoses usually rely on physician expertise. However, complex cases may benefit from consulting similar past clinical reports cases. In this paper, we present MedLink (http://medlink.inesctec.pt), a tool that given a free-text medical report, retrieves and ranks relevant clinical case reports published in health conferences and journals, aiming to support clinical decision-making, particularly in challenging or complex diagnoses. To this regard, we trained two BERT models on the sentence similarity task: a bi-encoder for retrieval and a cross-encoder for reranking. To evaluate our approach, we used 10 medical reports and asked a physician to rank the top 10 most relevant published case reports for each one. Our results show that MedLink’s ranking model achieved NDCG@10 of 0.747. Our demo also includes the visualization of clinical entities (using a NER model) and the production of a textual explanation (using a LLM) to ease comparison and contrasting between reports.

Abstract
We present NarratEX, a dataset designed for the task of explaining the choice of the Dominant Narrative in a news article, and intended to support the research community in addressing challenges such as discourse polarization and propaganda detection. Our dataset comprises 1,056 news articles in four languages, Bulgarian, English, Portuguese, and Russian, covering two globally significant topics: the Ukraine-Russia War (URW) and Climate Change (CC). Each article is manually annotated with a dominant narrative and sub-narrative labels, and an explanation justifying the chosen labels. We describe the dataset, the process of its creation, and its characteristics. We present experiments with two new proposed tasks: Explaining Dominant Narrative based on Text, which involves writing a concise paragraph to justify the choice of the dominant narrative and sub-narrative of a given text, and Inferring Dominant Narrative from Explanation, which involves predicting the appropriate dominant narrative category based on an explanatory text. The proposed dataset is a valuable resource for advancing research on detecting and mitigating manipulative content, while promoting a deeper understanding of how narratives influence public discourse.

Abstract
The development of a robust annotation scheme and corresponding guidelines is crucial for pro- ducing annotated datasets that advance both lin- guistic and computational research. This paper presents a case study that outlines a method- ology for designing an annotation scheme and its guidelines, specifically aimed at represent- ing morphosyntactic and semantic information regarding temporal features, as well as medi- cal information in medical reports written in Portuguese. We detail a multi-step process that includes reviewing existing frameworks, con- ducting an annotation experiment to determine the optimal approach, and designing a model based on these findings. We validated the ap- proach through a pilot experiment where we assessed the reliability and applicability of the annotation scheme and guidelines. In this ex- periment, two annotators independently anno- tated a patient's medical report consisting of six documents using the proposed model, while a curator established the ground truth. The analy- sis of inter-annotator agreement and the annota- tion results enabled the identification of sources of human variation and provided insights for further refinement of the annotation scheme and guidelines.

Abstract
Detecting anomalies in Waste transportation networks is vital for uncovering illegal or unsafe activities, that can have serious environmental and regulatory consequences. Identifying anomalies in such networks presents a significant challenge due to the limited availability of labeled data and the subtle nature of illicit activities. Moreover, traditional anomaly detection methods relying solely on individual transaction data may overlook deeper, network-level irregularities that arise from complex interactions between entities, especially in the absence of labeled data. This study explores anomaly detection in a waste transport network using unsupervised learning, enhanced by limited supervision and enriched with network structure information. Initially, unsupervised models like Isolation Forest, K-Means, LOF, and Autoencoders were applied using statistical and graph-based features. These models detected outliers without prior labels. Later, information on a few confirmed anomalous users enabled weak supervision, guiding feature selection through statistical tests like Kolmogorov-Smirnov and Anderson-Darling. Results show that models trained on a reduced, graph-focused feature set improved anomaly detection, particularly under extreme class imbalance. Isolation Forest notably ranked known anomalies highly. Ego network visualizations supported these findings, demonstrating the value of integrating structural features and limited labels for identifying subtle, relational anomalies.

Abstract
The learning from continuous data streams is a relevant area within machine learning, focusing on the creation and updating of predictive models in real time as new data becomes available for training and prediction. Among the most widely used methods for this type of task, Hoeffding Trees are highly valued for their simplicity and robustness across a variety of applications and are considered the primary choice for generating decision trees in data stream contexts. However, Hoeffding Trees tend to continuously expand as new data is incorporated, resulting in increased processing time and memory consumption, often without providing significant gains in accuracy. In this study, we propose an instance selection scheme that combines different strategies to regularize Hoeffding Trees and their variants, mitigating excessive growth without compromising model accuracy. The method selects misclassified instances and a fraction of correctly classified instances during the training phase. After extensive experimental evaluation, the instance selection scheme demonstrates superior predictive performance compared to the original models (without selection), for both real and synthetic datasets for data streams, using a reduced subset of examples. Additionally, the method achieves relevant improvements in processing time, model complexity, and memory consumption, highlighting the effectiveness of the proposed instance selection scheme.