Abstract
The present study analyzes the types of interrogative, such as yes/no questions, wh-questions, or alternative questions, and their semantic and pragmatic functions in a multilingual parallel corpus of spoken monologues extracted from the TED Talks transcripts in five languages: English, European Portuguese, Lithuanian, Bulgarian, and Hebrew. The corpus was developed with English as the pivot language, and the examples are aligned in all five languages based on the occurrence of an interrogative. To conduct this study, we designed an annotation scheme that harmonizes two parts of ISO 24617 - Part 8: Semantic relations and Part 2: Dialogue acts. This framework enabled us to determine the discourse relations that questions establish with the segments to which they are connected and that precede them and their communicative function. In our analysis, we observed that, despite the monologic nature of the corpus, interrogatives are very frequent and diversified across the five languages. Our findings also reveal that the questions are mostly used with a pragmatic function and that the range of discourse relations is less varied. Additionally, the analysis disclosed some pertinent differences between the five languages concerning the translation choices.

Abstract
Discourse markers (DMs) are linguistic expressions that convey different semantic and pragmatic values, managing and organizing the structure of spoken and written discourses. They can be either single-word or multiword expressions (MWE), made up of conjunctions, adverbs, and prepositional phrases. Although DMs are the focus of many studies, some questions regarding the interoperability of taxonomies and automatic identification and classification require further research. We aim to tackle these issues by offering a critical analysis and discussing the constitution of a multilingual corpus in 10 languages, i.e., English, Lithuanian, Bulgarian, German, Macedonian, Romanian, Hebrew, Polish, European Portuguese, and Italian. The novel two-level annotation approach is based on (i) signaling the existence or non-existence of DMs in a given text, and (ii) applying the ISO-24617 standard to annotate the DMs' discourse relation and communicative function in the corpora. Additionally, we introduce prediction models for detecting the presence of DMs within a text. Marcatorii discursivi (DM-uri) sunt expresii lingvistice care transmit diverse valori semantice si pragmatice, av & acirc;nd rolul de a gestiona si organiza structura discursurilor vorbite si scrise. Acestia pot fi fie expresii formate dintr-un singur cuv & acirc;nt, fie locutiuni, expresii formate din mai multe cuvinte (MWE), alc & abreve;tuite din conjunctii, adverbe si grupuri prepozitionale. Desi marcatorii discursivi reprezint & abreve; obiectul multor studii, unele & icirc;ntreb & abreve;ri legate de interoperabilitatea taxonomiilor si de identificarea si clasificarea automat & abreve; a acestora necesit & abreve; cercet & abreve;ri suplimentare. Ne propunem s & abreve; abord & abreve;m aceste aspecte printr-o analiz & abreve; critic & abreve; si prin discutarea constituirii unui corpus multilingv & icirc;n 10 limbi, si anume: englez & abreve;, lituanian & abreve;, bulgar & abreve;, german & abreve;, macedonean & abreve;, rom & acirc;n & abreve;, ebraic & abreve;, polonez & abreve;, portughez & abreve; european & abreve; si italian & abreve;. Noua abordare de adnotare pe dou & abreve; niveluri se bazeaz & abreve; pe (i) semnalarea existentei sau inexistentei marcatorilor discursivi & icirc;ntr-un text dat si (ii) aplicarea standardului ISO-24617 pentru a adnota relatia discursiv & abreve; si functia comunicativ & abreve; a marcatorilor & icirc;n corpusuri. & Icirc;n plus, & icirc;n acest articol, introducem modele de predictie pentru detectarea prezentei marcatorilor discursivi & icirc;ntr-un text.

Abstract
Decision Trees (DTs) remain one of the most important algorithms in machine learning for their simplicity, interpretability, and often satisfactory performance. Furthermore, they are critical foundational components for more performant models such as Random Forests (RFs) and Gradient Boosted Trees. Central to DTs is the splitting process, where data is partitioned according to criteria traditionally based on information-theoretic measures such as Shannon entropy or Gini index. In this article, we propose a novel parametric entropy-based information gain criterion designed to generalize and extend classical entropic measures to improve classification performance in DTs and RFs. We introduce a five-parameter entropy formulation capable of replicating and extending known entropy measures. This new criterion was incorporated into DT and RF classifiers and evaluated on a collection of 18 benchmarking datasets, including both synthetic and real-world data retrieved from publicly available repositories. Performance was assessed using 5-fold cross-validation and optimized via Bayesian hyperparameter search, with weighted F1-score as the primary metric. Compared to splitting criteria based on existing entropy/purity measures (e.g., Gini, Shannon, R & eacute;nyi, and Tsallis), our method yielded statistically significant improvements in classification performance across most datasets. On multiclass and imbalanced datasets, such as the Wine Quality dataset, F1-score improvements exceeded 40% using RF algorithms. Bayesian signed-rank tests confirmed the robustness of our method, which never underperformed relative to standard approaches. The proposed entropy-based splitting criterion offers a flexible and effective alternative to classical information-theoretic measures, delivering improvements in classification performance.

Abstract
Rising competitiveness and client requirements make effective use of high volume and complexity real-time industrial data crucial for faster decision-making. However, this potential is hindered by a lack of smart, user-friendly analytic tools for all collaborators. Despite the proliferation of Machine Learning (ML) tools for data scientists, non-experts struggle with converting data into actionable insights and identifying profitable data science projects. A smart tool is thus proposed, allowing non-experts to perform preliminary data evaluations through profiled analysis pathways that execute predefined sets of Exploratory Data Analysis (EDA) methods and ML operations. Further assisting users, the tool solely relies on metadata attributes and textual descriptions of datasets enhanced by interaction with a Large Language Model (LLM). This paper examines profile selection stages, replacing traditional ML methods with Conformal Prediction (CP) techniques. CP identifies multiple potential prospects with statistical confidence and recognizes when correct predictions are impossible. Trials with task-labeled metadata files (derived from publicly available datasets) showed that while classic ML methods had about 80% efficiency, CP techniques improved the selection process, keeping profiling errors below 0.06 with 99% confidence. This approach enables the correct identification (with statistical confidence) of appropriate analysis profiles for data science problems, thus paving the way for more efficient data analysis tools in industrial settings, accessible to users of all skill levels. © 2024 The Authors. Published by Elsevier B.V.

Abstract
Process Mining offers a powerful framework for uncovering, analyzing, and optimizing real-world business processes. Petri nets provide a versatile means of modeling process behavior. However, traditional methods often struggle to effectively compare complex Petri nets, hindering their potential for process enhancement. To address this challenge, we introduce PetriNet2Vec, an unsupervised methodology inspired by Doc2Vec. This approach converts Petri nets into embedding vectors, facilitating the comparison, clustering, and classification of process models. We validated our approach using the PDC Dataset, comprising 96 diverse Petri net models. The results demonstrate that PetriNet2Vec effectively captures the structural properties of process models, enabling accurate process classification and efficient process retrieval. Specifically, our findings highlight the utility of the learned embeddings in two key downstream tasks: process classification and process retrieval. In process classification, the embeddings allowed for accurate categorization of process models based on their structural properties. In process retrieval, the embeddings enabled efficient retrieval of similar process models using cosine distance. These results demonstrate the potential of PetriNet2Vec to significantly enhance process mining capabilities.

Abstract
The power transformer is one of the key components of any electrical grid, and, as such, modern day industrialization activities require constant usage of the asset. This increases the possibility of failures and can potentially diminish the lifespan of a power transformer. Dissolved gas analysis (DGA) is a technique developed to quantify the existence of hydrocarbon gases in the content of the power transformer oil, which in turn can indicate the presence of faults. Since this process requires different chemical analysis for each type of gas, the overall cost of the operation increases with number of gases. Thus said, a machine learning methodology was defined to meet two simultaneous objectives, identify gas subsets, and predict the remaining gases, thus restoring them. Two subsets of equal or smaller size to those used by traditional methods (Duval's triangle, Roger's ratio, IEC table) were identified, while showing potentially superior performance. The models restored the discarded gases, and the restored set was compared with the original set in a variety of validation tasks.