Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine Learning–Based Approach for Identifying Research Gaps: COVID-19 as a Case Study

Background Research gaps refer to unanswered questions in the existing body of knowledge, either due to a lack of studies or inconclusive results. Research gaps are essential starting points and motivation in scientific research. Traditional methods for identifying research gaps, such as literature reviews and expert opinions, can be time consuming, labor intensive, and prone to bias. They may also fall short when dealing with rapidly evolving or time-sensitive subjects. Thus, innovative scalable approaches are needed to identify research gaps, systematically assess the literature, and prioritize areas for further study in the topic of interest. Objective In this paper, we propose a machine learning–based approach for identifying research gaps through the analysis of scientific literature. We used the COVID-19 pandemic as a case study. Methods We conducted an analysis to identify research gaps in COVID-19 literature using the COVID-19 Open Research (CORD-19) data set, which comprises 1,121,433 papers related to the COVID-19 pandemic. Our approach is based on the BERTopic topic modeling technique, which leverages transformers and class-based term frequency-inverse document frequency to create dense clusters allowing for easily interpretable topics. Our BERTopic-based approach involves 3 stages: embedding documents, clustering documents (dimension reduction and clustering), and representing topics (generating candidates and maximizing candidate relevance). Results After applying the study selection criteria, we included 33,206 abstracts in the analysis of this study. The final list of research gaps identified 21 different areas, which were grouped into 6 principal topics. These topics were: “virus of COVID-19,” “risk factors of COVID-19,” “prevention of COVID-19,” “treatment of COVID-19,” “health care delivery during COVID-19,” “and impact of COVID-19. ” The most prominent topic, observed in over half of the analyzed studies, was “the impact of COVID-19. ” Conclusions The proposed machine learning–based approach has the potential to identify research gaps in scientific literature. This study is not intended to replace individual literature research within a selected topic. Instead, it can serve as a guide to formulate precise literature search queries in specific areas associated with research questions that previous publications have earmarked for future exploration. Future research should leverage an up-to-date list of studies that are retrieved from the most common databases in the target area. When feasible, full texts or, at minimum, discussion sections should be analyzed rather than limiting their analysis to abstracts. Furthermore, future studies could evaluate more efficient modeling algorithms, especially those combining topic modeling with statistical uncertainty quantification, such as conformal prediction.
New high‐resolution age data from the Ediacaran–Cambrian boundary indicate rapid, ecologically driven onset of the Cambrian explosion

The replacement of the late Precambrian Ediacaran biota by morphologically disparate animals at the beginning of the Phanerozoic was a key event in the history of life on Earth, the mechanisms and the time‐scales of which are not entirely understood. A composite section in Namibia providing biostratigraphic and chemostratigraphic data bracketed by radiometric dating constrains the Ediacaran–Cambrian boundary to 538.6–538.8 Ma, more than 2 Ma younger than previously assumed. The U–Pb‐CA‐ID TIMS zircon ages demonstrate an ultrashort time frame for the LAD of the Ediacaran biota to the FAD of a complex, burrowing Phanerozoic biota represented by trace fossils to a 410 ka time window of 538.99 ± 0.21 Ma to 538.58 ± 0.19 Ma. The extremely short duration of the faunal transition from Ediacaran to Cambrian biota within less than 410 ka supports models of ecological cascades that followed the evolutionary breakthrough of increased mobility at the beginning of the Phanerozoic.
NEURAL NETWORK MODEL FOR PREFORMED-FOAM CELLULAR CONCRETE

Cellular concrete (CC) is a lightweight material consisting of portland cement paste or mortar with a homogeneous void or cell structure created by introducing air or gas in the form of small bubbles during the mixing process. A major concern with CC production is achieving product consistency and predictability of performance. CC producers have generated much experimental data over the years, but the analysis of such data using traditional statistical tools has not produced reliable predictive models. This research studies the use of artificial neural networks (ANN) to predict the performance of CC mixtures. The ANN method can capture complex interactions among input/output variables in a system without any prior knowledge of the nature of these interactions and without having to explicitly assume a model form, which is generated by the data points themselves. The database assembled, the selection and training process of the ANN model, and its validation are described. Results show that production yield, foamed and unfoamed density, and compressive strength of CC mixtures can be predicted much more accurately using the ANN method compared to existing parametric methods.
The Anterolateral Barrel Subfield Differs from the Posteromedial Barrel Subfield in the Morphology and Cell Density of Parvalbumin-Positive GABAergic Interneurons

Layer 4 of the rodent somatosensory cortex has unitary structures called barrels that receive tactile information from individual vibrissae. Barrels in the anterolateral barrel subfield (ALBSF) are much smaller and have gained less attention than larger barrels in the posteromedial barrel subfield (PMBSF), though the former outnumber the latter. We compared the morphological features of barrels between the ALBSF and PMBSF in male mice using deformation-free tangential sections and confocal optical slice-based, precise reconstructions of barrels. The average volume of a single barrel in the ALBSF was 34.7% of that in the PMBSF, but the numerical density of parvalbumin (PV)-positive interneurons in the former was 1.49 times higher than that in the latter. Moreover, PV neuron density in septa was 2.08 times higher in the ALBSF than that in the PMBSF. The proportions of PV neuron number to both all neuron number and all GABAergic neuron number in the ALBSF were also higher than those in the PMBSF. Somata of PV neurons in barrels and septa in the ALBSF received 1.64 and 1.50 times more vesicular glutamate transporter Type 2–labeled boutons than those in the PMBSF, suggesting more potent feedforward inhibitory circuits in the ALBSF. The mode of connectivity through dendritic gap junctions among PV neurons also differed between the ALBSF and PMBSF. Clusters of smaller unitary structures containing a higher density of representative GABAergic interneurons with differential morphological features in the ALBSF suggest a division of functional roles in the two vibrissa–barrel systems, as has been demonstrated by behavioral studies.
Named entity recognition specialised for Portuguese 18th-century History research

This paper presents the construction of a corpus and the respective models learned for the Named Entity Recognition (NER) task, specialised for historical research. The entity categories were adapted based on the objectives of the historical analysis of the 18th-century text. We trained and evaluated traditional neural networks and the new Large Language Models (LLMs) for the NER task. In total, we assessed six language models, where the results of traditional architectures were superior to LLMs.
Applying event classification to reveal the Estado da Índia

This paper presents a study based on the application of a Portuguese event detection (extraction and classification) tool (TEFE) to historical texts. It shows how historical analysis and interpretation can use this tool in historical research, on the basis of a historiography analysis of the Portuguese Empire in East. TEFE has been applied to one volume of the Portuguese Livros das Monções (Monsoon Books), concerning the time gap between 1614 and 1616, high-lighting conflict-related categories of events. A historical analysis of this special category of events is performed, revealing aspects of a generalized juncture of war and conflict in Asia in the early seventeenth century.
Bringing Pragmatics to Porttinari - Adding Speech Acts to News Texts

The automatic classification of Speech Acts is a topic of great interest within the NLP area which could be taken as a first step towards the semantic representation of texts. However, in order to carry out this task, a reasonable amount of annotated data is necessary, if one is to apply any Machine Learning (ML) technique to this end. In this work, we present a subset of the Porttinari-base corpus manually annotated with Speech Acts, following an adapted version of the ISO-24617-2 standard, so as to provide the community with a starting point for automatic identification of speech acts in news texts written in (Brazilian) Portuguese. To illustrate the corpus’ usefulness, we also present the re-sults of training an ML distributional model to classify speech acts in such texts.
Toxic Speech Detection in Portuguese: A Comparative Study of Large Language Models

This research addresses the automatic detection of toxic speech in Portuguese. Utilizing the ToLD-Br dataset, which includes 21,000 annotated tweets, we examine the performance of Large Language Models (LLMs) such as Ope-nAI’s ChatGPT and the monolingual MariTalk from Maritaca AI. The study focuses on their effectiveness in identifying Toxic speech, the influence of few-shot learning, and the intricacies of annotating datasets, particularly regarding vulgar language (swear words). Our experiments reveal that MariTalk (Sabiá) demonstrates a nuanced understanding of colloquial Portuguese. Meanwhile, ChatGPT, especially when augmented with few-shot learning, shows robustness comparable to baseline methods. This investigation underscores the value of both monolingual and lower-capacity models in the nuanced field of language-specific Toxic speech detection, offering insights into their competitive edge against models like ChatGPT.
Is it safe to machine translate suicide-related language from English to Galician?

In this article, we present work that uses a pre-trained language model (PLM) from one of the most widely-used machine translation (MT) systems to translate suicide-related language from an English lexicon to Galician, a language commonly spoken in northern Spain. We make the MT system translations publicly available along with other annotations from professional Galician translators. Additionally, we compare and contrast the findings to provide insight into the types of errors that a MT system may commit when translating from English to Galician in life-threatening situations.
RePro: a benchmark for Opinion Mining for Brazilian Portuguese

We introduce RePro, a corpus of e-commerce product reviews in Brazilian Portuguese labeled with sentiment and topic information. We carried out a careful annotation process, whose aim is to introduce an easily available and open benchmark for opinion mining related tasks, namely sentiment analysis and topic modeling tasks. This work describes the corpus design and annotation process as well as the prelimi-nary results of classification tasks. These pre-liminary results can be used as baselines for future work. RePro contains 10,000 humanly annotated reviews, based on data from the largest Brazilian e-commerce platform, which produced the B2W-Reviews01 dataset.
Exploring Open Information Extraction for Portuguese Using Large Language Models

In this work, we investigate the potential of Large Language Models (LLMs) for Open Information Extraction (OpenIE) in the Portuguese language. While most OpenIE meth-ods are primarily optimized for English, only few works in the literature explore their uses for cross-lingual and multilingual scenarios. Despite the growing interest in Portuguese OpenIE methods, the use LLMs for Portuguese focused OpenIE is still an underdeveloped topic in the area.Our study addresses this research gap by examining the viability of using open and commercial LLMs with few-shot prompt engineering for Portuguese OpenIE. We provide an analysis of the performance of these LLMs in Ope-nIE tasks, revealing that they achieve performance metrics comparable to state-of-the-art systems. In addition, we have fine-tuned and launched an open LLM for OpenIE (PortOIE-Llama), which outperforms commercial LLMs in our experiments. Our findings highlight the potential of LLMs in Portuguese OpenIE tasks and suggest that further refinement and fine-tuning of larger models could enhance these results.
Identification of Types of Event-Time Temporal Relations in Portuguese Using a Rule-Based Approach

In this article, we present a computational method for identifying types of temporal relations between events and temporal expressions in Portuguese texts. We employ a linguistically-rich approach based on rule learning algorithms, and language-specific manual rules. Experiments on the TimeBankPT corpus demonstrated the effectiveness of our method, outper-forming the baseline in terms of accuracy and F1-score. Through the use of explainable rules, our method enables an enhanced understanding of temporal phenomena in texts, allowing further development of resources and linguistic research on the area.
Exploring Pre-Trained Transformers for Translating Portuguese Text to Brazilian Sign Language

The paper focuses on machine translation from Portuguese text to Brazilian Sign Language (Libras) using Transformer-based models. In recent years, the Transformer architecture has established itself as a state-of-the-art approach for machine translation between written languages. To allow the use of the Transformer architecture for translating Portuguese into Brazilian Sign Language, we represent the sign language in a written form with glosses. As Brazilian Signing Language is a low-research language, the effective training of the Transformer model is challenging. The paper presents experimental results exploring transfer learning from pre-trained models of ten different language pairs: Portuguese-Galician, Galician-Portuguese, Portuguese-Catalan, Catalan-Portuguese, Portuguese-Ukrainian, Ukrainian-Portuguese, English-Spanish, English-French, German-Dutch, and German-Ukrainian. After transfer learning and considering the BLEU metric as the evaluation parameter, the experimental results show that the language pairs whose parent models had the biggest training datasets and vocabulary (English-Spanish, English-French, and German-Dutch) displayed the highest performances. The English-Spanish pair, the pair with the biggest training set, achieved the highest performance, followed by the English-French pair, the second biggest training set. The Galician-Portuguese pair, the pair with the smallest training set and vocabulary, presented the fourth-best BLEU score. One possible conjecture to explain this last result is the close relation between the languages.
RecognaSumm: A Novel Brazilian Summarization Dataset

Research in the field of automatic summarization, particularly in abstractive summarization, for the Portuguese language still faces a significant challenge due to the limited availability of datasets with annotated summaries. Although existing datasets enable research, they are comparatively smaller than those available for the English language, thereby impeding the attainment of more robust results. This paper introduces RecognaSumm, a novel Portuguese dataset comprising a diverse set of journalistic texts annotated with summaries. With a total of 135,272 samples, it stands as the largest known Portuguese summarization dataset to date, to the best of our knowledge. Additionally, this work introduces an abstractive summarization model trained on this dataset 1 , offering a base-line for future studies.
A Multilingual Dataset for Investigating Stereotypes and Negative Attitudes Towards Migrant Groups in Large Language Models

Content Warning: This paper contains examples of xenophobic stereotypes. In recent years, Large Language Models (LLMs) gained a lot of attention due to achieving state-of-the-art performance in many Natural Language Processing tasks. Such models are powerful due to their ability to learn underlying word association patterns present in large volumes of data, however, for the same reason, they reflect stereotypical human biases. Although the presence of biased word associations in language models is a ubiquitous problem that has been studied since the popularization of static embeddings (e.g., Word2Vec ), resources for quantifying stereotypes in LLMs are still quite scarce and primarily focused on the English language. To help close this gap, we release an evaluation dataset comprising sentence templates designed to measure stereotypes and negative attitudes towards migrant groups in contextualized word embedding representations for the Portuguese, Spanish, and Catalan languages. Our multilingual dataset draws inspiration from social surveys that measure perceptions and attitudes towards immigration in European countries.
A Bag-of-Users approach to mental health prediction from social media data

Computational models of mental health prediction from social media data are typically built from the textual contents produced by the individuals to be assessed, but the use of non-textual information available from the network structure may also have relevant predictive power. Based on these observations, this work presents initial experiments on mental health prediction from textual and non-textual Twitter data in Portuguese, comparing a traditional content-based approach using BERT with models based on social media connections (friends, followers and mentions), and which is inspired from the well-known Bag-of-Words text representation. Results highlight an advantage for the model based on textual contents, but also suggest that the use of non-textual information may provide a significant contribution to these tasks.
BATS-PT: Assessing Portuguese Masked Language Models in Lexico-Semantic Analogy Solving and Relation Completion

This paper presents BATS-PT, the manual translation of the lexicographic portion of the Big-ger Analogy Test Set (BATS) to European Portuguese. BATS-PT covers ten types of lexico-semantic analogies and can be used for assessing word embeddings and language models. Following this, the dataset is showcased while assessing two pretrained language models for Portuguese, BERTimbau and Albertina, in two tasks: analogy solving and relation completion, both in zero-and few-shot mask-prediction approaches. Experiments reveal different performance across relations and, in both tasks, the best overall performance was achieved with BERTimbau, in a five-shot scenario. We further discuss the limitations of the reported experiments and directions towards future improvements in these tasks.
TransAlign: An Automated Corpus Generation through Cross-Linguistic Data Alignment for Open Information Extraction

This paper introduces a comprehensive approach to address the limited availability of training data on Open Information Extraction (OpenIE) for underrepresented languages by leveraging datasets from languages with abundant resources. We present TransAlign, a cross-linguistic data alignment framework for translating and aligning OpenIE datasets to target languages using language-specific grammatical rules. We explore this methodology for the Portuguese language, employing LSOIE, a large-scale dataset for supervised Open Information Extraction, AACTRANS+CLP, and CARB datasets. We employed high-quality translation models and hand-crafted alignment rules, based on grammatical information, to ensure that the triples are correctly aligned according to the grammar of Brazilian Portuguese. This process resulted in the generation of 96.067 high-quality triples, which laid the foundation for our Portuguese-specific OpenIE dataset. We trained two models by utilizing this dataset, which achieved 10.53% improvement in F1 scores compared to the existing state-of-the-art systems for the Portuguese language, such as PortNOIE (Cabral et al., 2022), including LLMs models.
Investigation of expression of genes encoding glycogen degrading enzymes in Gardnerella swidsinskii and identification of reference genes for quantitative real-time PCR

Gardnerella spp. express and export enzymes for the breakdown of glycogen into glucose, maltose, and malto-oligosaccharides for consumption by the vaginal microbiota but how the expression of these “public goods” is affected by substrate and product levels in the environment is not known. Accurate measurement of relative gene expression using real-time quantitative PCR relies on the identification of appropriate reference genes whose expression levels remain constant under the conditions of the study. Currently, no reference genes have been identified for gene expression analysis of Gardnerella spp. The objectives of this study were to identify reference genes and apply them in determining the relative gene expression levels of genes encoding α-amylase and α-amylase-pullulanase in media supplemented with substrate (glycogen) or a preferred product (maltotriose). Ten candidate reference genes were evaluated and analysis of Cq values from qPCR using multiple algorithms identified uppS (encoding polyprenyl diphosphate synthase) as the top comprehensively ranked reference gene followed by gatA (encoding Asp-tRNA/Glu-tRNA amidotransferase subunit gatA). Interpretation of the Cq values for α-amylase and α-amylase-pullulanase was performed by applying these two reference genes in the calculation of relative gene expression levels. α-amylase-pullulanase gene expression was upregulated in media supplemented with 1% glycogen in comparison to media supplemented with 1% maltotriose suggesting a regulatory mechanism in G. swidsinskii that responds to nutrient availability. No significant difference in gene expression of α-amylase was observed suggesting expression is not influenced by substrate availability. The RNA purification protocol and reference genes validated in this study will be useful in future studies of gene expression in Gardnerella. Importance Knowledge of the factors affecting growth of vaginal microbiota is critical to understanding how vaginal dysbiosis is initiated and maintained. Overgrowth of Gardnerella species including G. swidsinskii is a hallmark of bacterial vaginosis. These organisms break down vaginal glycogen and the products become available for uptake by Gardnerella and other microbiota. Measuring how expression of genes encoding glycogen degrading enzymes relates to relative abundance of substrate and products in the environment requires development of protocols for RNA purification and identification of reference genes for RT-qPCR.