This paper introduces WECM, a novel evidential and subspace clustering algorithm. It is based on the Evidential c-means, a variant of the k-means designed to produce a credal partition, allowing a better representation of the partial knowledge regarding the class membership of objects. The WECM algorithm integrates weights on features and clusters to enhance the clustering separability and interpretability. Experiments conducted on synthetic and real data show the positive effects of the weights on the clustering performances.
We propose a novel feature selection (FS) method based on peculiar fuzzy set generation, aggregation, and ordering. In particular, here we propose to elicit the fuzzy membership by a proper probability-possibility transformation of frequencies stemming from the bootstrap application of different filter FS methods. At the same time, we aggregate such vague scores of each feature via the recently introduced SMART-or fuzzy aggregation operator. Finally, to rank the features for the selection proposal we adopt Yager's ordering. Empirical results on benchmark databases show an overperformance of our approach with respect to different generation techniques, or aggregation functions and orderings.
The ongoing digital era is significantly impacted by the dissemination of fake news and disinformation. Addressing this issue involves delving into the realm of Artificial Intelligence, where Natural Language Processing (NLP) stands out as one of the most active areas capable of contributing to solutions. In this paper, we present an analysis grounded in argument mining, with a specific focus on understanding the creation of fake pieces of content in social media. Our research reveals that deceptive narratives in social media often incorporate a substantial component of arguments. This finding not only sheds light on the intricacies of misinformation but also provides valuable insights for future research in combating this pervasive issue.
One of the forms to fight the spread of hate speech is to reply to such utterances with counter speech. In this paper, we present models to identify counter speech, based on an annotated set of comments to Youtube videos spoken in Portuguese. We leverage the sequence of replies to comments, to form a corpus of pairs of comments, where a target is labelled as neutral, hate speech or counter speech, relative to a context, which corresponds to a preceding comment. To the best of our knowledge, this is the first corpus with counter speech examples in Portuguese. Using such corpus, we compute models by fine-tuning pre-trained models based on Transformers, and experiment with both multilingual and Portuguese pre-trained models. Our approach follows a recent work for English, both in corpus design and experimental setup, and we obtain similar performance results in Portuguese. Warning: This work contains offensive and hateful text that some might find upsetting. It does not represent the views of the authors.
In the paper a convex game with discontinuous payoff functions is considered. This paper introduces the concept of quasi-Nash equilibrium, which allows us to analyze games with discontinuous payoff functions by approximating them with continuous and concave functions. We show that if the approximation is close enough, then the quasi-Nash equilibrium is close to the true Nash equilibrium (if it exists).
