Pain is a highly subjective and complex phenomenon. Current methods used to measure pain mostly rely on the patient’s description, which may not always be possible. This way, pain recognition systems based on body language and physiological signals have emerged. As the emotional state of a person can also influence the way pain is perceived, in this work, a protocol for pain induction with previous emotional elicitation was conducted. Eletrocardiogram (ECG), Electrodermal Activity (EDA) and Eletromyogram (EMG) signals were collected during the protocol. Besides the physiological responses, perception was also assessed through reported-scores (using a numeric scale) and times for pain tolerance. In this protocol, 3 different emotional elicitation sessions, negative, positive and neutral, were performed through videos of excerpts of terror, comedy and documentary movies, respectively, and pain was induced using the Cold Pressor Task (CPT) . A total of 56 participants performed the study (with 54 completing all three sessions). The results showed that during the negative emotional state, pain reported-scores were higher and pain threshold and tolerance times were smaller when compared with positive. As expected, the physiological response to pain remain similar despite the emotional elicitation.

In this article, we propose an incremental method for computing seeded watershed cuts for interactive image segmentation.

We propose an algorithm based on the hierarchical image representation called the binary partition tree to compute a seeded watershed cut.

We show that this algorithm fits perfectly in an interactive segmentation process by handling user interactions, seed addition or removal, in time linear with respect to the number of affected pixels.

Run time comparisons with several state-of-the-art interactive and non-interactive watershed methods show that the proposed method can handle user interactions much faster than previous methods achieving significant speedup from 15 to 90, thus improving the user experience on large images.

Transformers gave a considerable boost to Natural Language Processing, but their application to specific scenarios still poses some practical issues.

We present an approach for extracting information from technical documents on different domains, with minimal effort.

It leverages on generic models for Question Answering and on questions formulated with target properties in mind. These are made to specific sections where the answer, then used as the value for the property, should reside.

We further describe how this approach was applied to documents of two very different domains: toxicology and finance.

For both, results extracted from a sample of documents were assessed by domain experts, who also provided feedback on the benefits of this approach.

F-Scores of 0.73 and 0.90, respectively in the toxicological and financial domain, confirm the potential and flexibility of the approach suggesting that, while it cannot yet be fully automated and replace human work, it can support expert decisions, thus reducing time and manual effort.