Abstract
Machine Learning systems are generally thought of as fully automatic. However, in recent years, interactive systems in which Human experts actively contribute towards the learning process have shown improved performance when compared to fully automated ones. This may be so in scenarios of Big Data, scenarios in which the input is a data stream, or when there is concept drift. In this paper we present a system for supporting auditors in the task of financial fraud detection. The system is interactive in the sense that the auditors can provide feedback regarding the instances of the data they use, or even suggest new variables. This feedback is incorporated into newly trained Machine Learning models which improve over time. In this paper we show that the order by which instances are evaluated by the auditors, and their feedback incorporated, influences the evolution of the performance of the system over time. The goal of this paper is to study of different instance selection strategies for Human evaluation and feedback can improve the learning speed. This information can then be used by the system to determine, at each moment, which instances would improve the system the most, so that these can be suggested to the users for validation. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Background: Suffering in nurses is associated with the delivery of care to patients in suffering and factors related to the working conditions. It is a multidimensional experience that occurs in situations of loss, damage, or threat to human integrity. Objective: To compare the mean scores in the dimensions of suffering (Emotional Pain, Relational Loss, and Avoidance) based on the sociodemographic and professional variables of nurses. Methodology: A descriptive and cross-sectional study with a quantitative approach was conducted with a sample of 100 nurses. A self-administered questionnaire was applied, as well as the Caregiver Grief Scale for assessing suffering. Results: Women with children, with a partner, without specialization in nursing, and with more years of service had higher mean scores of suffering. In men, the highest mean scores were found in nurses without children, without a partner, with specialization in nursing, and with more years of service. Conclusion: Nurses showed higher mean scores of suffering in the dimension of Emotional Pain, followed by Relational Loss, and Avoidance, and suffering was higher among women.

Abstract
Deep brain stimulation (DBS) surgery is the gold standard therapeutic intervention in Parkinson's disease (PD) with motor complications, notwithstanding drug therapy. In the intraoperative evaluation of DBS's efficacy, neurologists impose a passive wrist flexion movement and qualitatively describe the perceived decrease in rigidity under different stimulation parameters and electrode positions. To tackle this subjectivity, we designed a wearable device to quantitatively evaluate the wrist rigidity changes during the neurosurgery procedure, supporting physicians in decision-making when setting the stimulation parameters and reducing surgery time. This system comprises a gyroscope sensor embedded in a textile band for patient's hand, communicating to a smartphone via Bluetooth and has been evaluated on three datasets, showing an average accuracy of 80%. In this work, we present a system that has seen four iterations since 2015, improving on accuracy, usability and reliability. We aim to review the work done so far, outlining the iHandU system evolution, as well as the main challenges, lessons learned, and future steps to improve it. We also introduce the last version (iHandU 4.0), currently used in DBS surgeries at SAo JoAo Hospital in Portugal.

Abstract
This paper introduces a sort of automata and associated languages, often arising in modelling natural phenomena, in which both vagueness and simultaneity are taken as first class citizens. This requires a fuzzy semantics assigned to transitions and a precise notion of a synchronous product to enforce the simultaneous occurrence of actions. The expected relationships between automata and languages are revisited in this setting; in particular it is shown that any subset of a fuzzy synchronous language with the suitable signature forms a synchronous Kleene algebra.

Abstract

Abstract
Magnetic resonance imaging (MRI) is the most commonly used exam for diagnosis and follow-up of neurodegenerative diseases, such as multiple sclerosis (MS). MS is a neuroinflammatory and neurodegenerative disease characterized by demyelination of neuron axon. This demyelination process causes lesions in white matter that can be observed in vivo by MRI. Such lesions may provide quantitative assessments of the inflammatory activity of the disease. Quantitative measures based on various features of lesions have been shown to be useful in clinical trials for evaluating therapies. Although manual segmentations are considered as the gold standard, this process is time consuming and error prone. Therefore, automated lesion identification and quantification of the MRI are active areas in MS research. The purpose of this study was to perform the brain lesions volumetric quantification in MS patients, after segmentation via a convolutional neural network (CNN) model. Initially, MRI was rigidly registered, skullstripped and bias corrected. After, we use the CNN for brain lesions segmentation, which used training data to identify lesions within new test subjects. Finally, volume quantification was performed with a count of segmented voxels and represented by mm(3). We did not observe a statistical difference between the volume of brain lesion automatically identified and the volume manually segmented. The use of deep learning techniques in health is constantly developing. We observed that the use of these computational method for segmentation and quantification of brain lesions can be applied to aid in diagnosis and follow-up of MS.