Algorithmic bias gained notoriety in over the years due to impacts caused by Artificial Intelligence (AI) systems in historically discriminated vulnerable groups [1] [2] [3]. Hate speech detection models are used to identify unwanted contents in social media platforms like Instagram, Threads and X. These platforms are often used as data source during data collection process for dataset constructions utilized in AI models training for hate speech detection. However, the acquisition of bias can occur in many steps of development cycle of AI models. Therefore, this work contributes to diminish algorithmic racism by a methodology that evaluates the presence of racial bias in databases and trained classifiers for hate speech detection in Brazilian Portuguese. The models Logistic Regression (LR), Multilayer Perceptron (MLP), BERTimbaubaseand Tucano-1b1 were trained with OFFCOMBR-2, HateBR and TUPY-E databases for hate speech detection and their respective models were used to predict hate speech over BR-RAPData, a database constructed through data collection of Brazilian RAP lyrics. Our results show that at least one model trained with OFFCOMBR-2, HateBR or TUPY-E achieved F1 score over 70%, a substantial result according to the literature [4] [5] [6]. Nonetheless, analyses of the BR-RAPData prediction for hate speech shows that, in some cases, over 50% of the content in the dataset was targeted as hate speech by the trained classifiers.

Sequence alignment supports numerous tasks in bioinformatics, natural language processing, pattern recognition, social sciences, and other fields. While the alignment of two sequences may be performed swiftly in many applications, the simultaneous alignment of multiple sequences proved to be naturally more intricate. Although most multiple sequence alignment (MSA) formulations are NP-hard, several approaches have been developed, as they can outperform pairwise alignment methods or are necessary for some applications. Taking into account not only similarities but also the lengths of the compared sequences (i.e. normalization) can provide better alignment results than both unnormalized or post-normalized approaches. While some normalized methods have been developed for pairwise sequence alignment, none have been proposed for MSA. This work is a first effort towards the development of normalized methods for MSA. We discuss multiple aspects of normalized multiple sequence alignment (NMSA). We define three new criteria for computing normalized scores when aligning multiple sequences, showing the NP-hardness and exact algorithms for solving the NMSA using those criteria. In addition, we provide approximation algorithms for MSA and NMSA for some classes of scoring matrices.

BACKGROUND: The genomic similarity is a large-scale measure for comparing two given genomes. In this work we study the (NP-hard) problem of computing the genomic similarity under the DCJ model in a setting that does not assume that the genes of the compared genomes are grouped into gene families. This problem is called family-free DCJ similarity. RESULTS: We propose an exact ILP algorithm to solve the family-free DCJ similarity problem, then we show its APX-hardness and present four combinatorial heuristics with computational experiments comparing their results to the ILP. CONCLUSIONS: We show that the family-free DCJ similarity can be computed in reasonable time, although for larger genomes it is necessary to resort to heuristics. This provides a basis for further studies on the applicability and model refinement of family-free whole genome similarity measures.

Finding the smallest sequence of operations to transform one genome into another is an important problem in comparative genomics. The breakpoint graph is a discrete structure that has proven to be effective in solving distance problems, and the number of cycles in a cycle decomposition of this graph is one of the remarkable parameters to help in the solution of related problems. For a fixed k, the number of linear unichromosomal genomes (signed or unsigned) with n elements such that the induced breakpoint graphs have k disjoint cycles, known as the Hultman number, has been already determined. In this work we extend these results to multichromosomal genomes, providing formulas to compute the number of multichromosal genomes having a fixed number of cycles and/or paths. We obtain an explicit formula for circular multichromosomal genomes and recurrences for general multichromosomal genomes, and discuss how these series can be used to calculate the distribution and expected value of the rearrangement distance between random genomes.