PUBLICACIONES

Educational emergencies are key situations capable of redefining the way society organizes its respective educational systems. The research analyzes the influence of management teams on the development of teacher agency during emergency remote teaching in three schools from different socioeconomic and geographic contexts. Eighteen teachers and six administrators were interviewed, using the teaching agency framework, grounded theory, and thematic analysis to process the data. The results show that the decisions made by administrators during emergency remote teaching allowed for the development of teacheragency in three main dimensions: challenges of teacher adaptation, support from educational management, and improvement in teachers' perceptions of educational management. The study concludes that support from administrators and freedom in curricular decision-making by teachers are key factors in facilitating the development of teacher agency and addressing the educational crisis. The study is novel in its use of the ecological approach to teacher agency as a theoretical framework and in its retrospective analysis of social and educational crises.

Green AI aims to develop accurate AI models that are also sustainable without compromising the environment, especially in terms of carbon emissions. There are few studies on this topic in recommender systems, so we analyzed the trade-offs between recommendation performance and carbon footprint in session-based recommender systems. We use five public e-commerce datasets to predict the next item a user will interact with based solely on their past click events. The GRU4Rec algorithm and five unofficial reimplementations in different deep learning frameworks (Theano, PyTorch, TensorFlow, Keras, and Reckpack) are evaluated. The results indicate a strong effect of the loss function and dataset size on the carbon footprint without significantly affecting the accuracy metrics. We show evidence that the implementation choice for the same algorithm strongly affects the CO emitted, and optimized implementations do not sacrifice recommendation efficiency, which should be considered when choosing a framework or implementation for an algorithm.

Recent advances in deep learning have significantly transformed the field of 3D shape generation, enabling the synthesis of complex, diverse, and semantically meaningful 3D objects. This survey provides a comprehensive overview of the current state-of-the-art in 3D shape generation, organizing the discussion around three core components: shape representations, generative modeling approaches, and evaluation protocols. We begin by categorizing 3D representations into explicit, implicit, and hybrid setups, highlighting their structural properties, advantages, and limitations. Next, we review a wide range of generation methods, focusing on feedforward architectures. We further summarize commonly used datasets and evaluation metrics that assess fidelity, diversity, and realism of generated shapes. Finally, we identify open challenges and outline future research directions that could drive progress in controllable, efficient, and high-quality 3D shape generation. This survey aims to serve as a valuable reference for researchers and practitioners seeking a structured and in-depth understanding of this rapidly evolving field.

Participatory society has often been regarded positively, frequently associated with the ideals of a more democratic and equitable civilization. Nevertheless, the idea of participation may act as a two-sided phenomenon in terms of empowerment, especially in the realm of social media platforms. This dichotomy is evident as increased participation often leads to a rise in offensive and divisive language, reflecting the challenging balance between open dialogue and the maintenance of respectful discourse on these platforms. In this work, we comprehensively examine the use of offensive language during a highly polarizing event on two online platforms, Twitter and Whatsapp. In our study, we focus in the 2021 Chilean Presidential Elections, a political event where candidates from two opposing parties faced each other. Using a state-of-the-art model and all available labeled data in literature, we determine the level of offensive language across platforms and parties. Our results show that Twitter messages contain, on average, up to 15% more of offensive language than Whatsapp.

Publisher: Elsevier, Data in Brief  Link>

ABSTRACT

The COVID-19 pandemic has underlined the need for reliable information for clinical decision-making and public health policies. As such, evidence-based medicine (EBM) is essential in identifying and evaluating scientific documents pertinent to novel diseases, and the accurate classification of biomedical text is integral to this process. Given this context, we introduce a comprehensive, curated dataset composed of COVID-19-related documents.

This dataset includes 20,047 labeled documents that were meticulously classified into five distinct categories: systematic reviews (SR), primary study randomized controlled trials (PS-RCT), primary study non-randomized controlled trials (PS-NRCT), broad synthesis (BS), and excluded (EXC). The documents, labeled by collaborators from the Epistemonikos Foundation, incorporate information such as document type, title, abstract, and metadata, including PubMed id, authors, journal, and publication date.

Uniquely, this dataset has been curated by the Epistemonikos Foundation and is not readily accessible through conventional web-scraping methods, thereby attesting to its distinctive value in this field of research. In addition to this, the dataset also includes a vast evidence repository comprising 427,870 non-COVID-19 documents, also categorized into SR, PS-RCT, PS-NRCT, BS, and EXC. This additional collection can serve as a valuable benchmark for subsequent research. The comprehensive nature of this open-access dataset and its accompanying resources is poised to significantly advance evidence-based medicine and facilitate further research in the domain.

The integration of artificial intelligence into dermatological research has underscored the need for robust and well-structured dermatological datasets. However, these datasets vary widely in their development processes, and there is currently no standard methodology to create such datasets. This work identifies three pressing needs for the building of dermatological datasets focus on skin tumor classification: the need for multimodal datasets, the definition of minimum metadata requirements, and the inclusion of underrepresented populations to address the scarcity of health data. We propose a practical methodology to create dermatological datasets from clinical records, incorporating both images and patient metadata. The process consists of four key stages: getting the institutional review board approval and analysis of clinical information sources, data recording and structuring, processing of clinical data and images, and quality assessment. This methodology was derived from hands-on experience in building two datasets from Chilean and Mexican populations, respectively. The methodology allows the creation of well-structured datasets by simplifying data organization and enabling replication. Each step includes practical guidance for dealing with typical challenges, such as image metadata categorization and technical validation by dermatologists and computer scientists. Our contribution offers a reproducible, scalable, and interdisciplinary framework for creating dermatological datasets, especially useful for countries initiating dataset creation. In addition to the methodological proposal, we highlight common pitfalls and offer recommendations to mitigate them.

Publisher: Multimedia Tools and Applications, Link>

ABSTRACT

This paper proposes a novel online self-learning detection system for different types of objects. It allows users to random select detection target, generating an initial detection model by selecting a small piece of image sample and continue training the detection model automatically. The proposed framework is divided into two parts: First, the initial detection model and the online reinforcement learning. The detection model is based on the proportion of users of the Haar-like features to generate feature pool, which is used to train classifiers and get positive-negative (PN) classifier model. Second, as the videos plays, the detecting model detects the new sample by Nearest Neighbor (NN) Classifier to get the PN similarity for new model. Online reinforcement learning is used to continuously update classifier, PN model and new classifier. The experiment shows the result of less detection sample with automatic online reinforcement learning is satisfactory.

The fruit industry in Chile has achieved global recognition for its productivity and leadership in fruit exportation, being the main exporter in the Southern Hemisphere, especially of cherries, grapes, and blueberries. Agricultural automation is a growing trend aimed at reducing laborious work and the consumption of time and personnel. Advances in artificial intelligence are enabling the automation of various processes, such as fruit categorization, though there are still gaps in the precision of classifying fruits in good and bad condition, particularly when considering specialized multimodal models. This work addresses this gap by combining convolutional neural network models and the multimodal CLIP technique, evaluating the effectiveness of convolutional architectures such as ResNet50, Xception, and MobileNet. The experiments show interesting results among different architectures, with ViT-B/16 model standing out for its higher precision in this task.

Fuzzy Cognitive Maps (FCMs) are a type of recurrent neural network with built-in meaning in their architecture, originally devoted to modeling and scenario simulation tasks. These knowledge-based neural systems support feedback loops that handle static and temporal data. Over the last decade, there has been a noticeable increase in the number of contributions dedicated to developing FCM-based models and algorithms for structured pattern classification and time series forecasting. These models are attractive since they have proven competitive compared to black boxes while providing highly desirable interpretability features. Equally important are the theoretical studies that have significantly advanced our understanding of the convergence behavior and approximation capabilities of FCM-based models. These studies can challenge individuals who are not experts in Mathematics or Computer Science. As a result, we can occasionally find flawed FCM studies that fail to benefit from the theoretical progress experienced by the field. To address all these challenges, this survey paper aims to cover relevant theoretical and algorithmic advances in the field, while providing clear interpretations and practical pointers for both practitioners and researchers. Additionally, we will survey existing tools and software implementations, highlighting their strengths and limitations towards developing FCM-based solutions.

In this paper, we apply a method to quantify biases associated with named entities from various countries. We create counterfactual examples with small perturbations on target-domain data instead of relying on templates or specific datasets for bias detection. On widely used classifiers for subjectivity analysis, including sentiment, emotion, hate speech, and offensive text using Twitter data, our results demonstrate positive biases related to the language spoken in a country across all classifiers studied. Notably, the presence of certain country names in a sentence can strongly influence predictions, up to a 23% change in hate speech detection and up to a 60% change in the prediction of negative emotions such as anger. We hypothesize that these biases stem from the training data of pre-trained language models (PLMs) and find correlations between affect predictions and PLMs likelihood in English and unknown languages like Basque and Maori, revealing distinct patterns with exacerbate correlations. Further, we followed these correlations in-between counterfactual examples from a same sentence to remove the syntactical component, uncovering interesting results suggesting the impact of the pre-training data was more important for English-speaking-country names. Our anonymized code is available here.