This paper presents an experiment on the effects of inter-agents emotional alignment, a prerequisite for empathic communication, in Human-Robot Interaction (HRI). We describe a pipeline built around the Pepper robot with the idea of verifying the effect of emotionally-aligned communication toward a user. In particular, our goal is twofold, in that we investigate if and to what extent an emotionally-aligned, empathic dialogue impacts on (i) the communication style of the user, and (ii) the persuasive effectiveness of the robot, intended as its ability to alter or reinforce its interlocutors' attitudes and beliefs about the conversation topic. Both these aspects have been assessed in a controlled experiment with 46 participants, comparing a condition where the robot addresses participants with emotionally neutral sentences with a condition where the robot provides answers tailored to the emotions expressed in participants' input utterances. Results show how emotion alignment acts as an effective trigger for the elicitation of different communication styles of the users but also that, contrary to what we expected, it does not play any persuasive effect.

Medical datasets are typically affected by issues such as missing values, class imbalance, a heterogeneous feature types, and a high number of features versus a relatively small number of samples, preventing machine learning models from obtaining proper results in classification and regression tasks. This paper introduces AutoML-Med, an Automated Machine Learning tool specifically designed to address these challenges, minimizing user intervention and identifying the optimal combination of preprocessing techniques and predictive models. AutoML-Med's architecture incorporates Latin Hypercube Sampling (LHS) for exploring preprocessing methods, trains models using selected metrics, and utilizes Partial Rank Correlation Coefficient (PRCC) for fine-tuned optimization of the most influential preprocessing steps. Experimental results demonstrate AutoML-Med's effectiveness in two different clinical settings, achieving higher balanced accuracy and sensitivity, which are crucial for identifying at-risk patients, compared to other state-of-the-art tools. AutoML-Med's ability to improve prediction results, especially in medical datasets with sparse data and class imbalance, highlights its potential to streamline Machine Learning applications in healthcare.

The paper presents an experiment on the effects of adaptive emotional alignment between agents, considered a prerequisite for empathic communication, in Human-Robot Interaction (HRI). Using the NAO robot, we investigate the impact of an emotionally aligned, empathic, dialogue on these aspects: (i) the robot’s persuasive effectiveness, (ii) the user’s communication style, and (iii) the attribution of mental states and empathy to the robot. In an experiment with 42 participants, two conditions were compared: one with neutral communication and another where the robot provided responses adapted to the emotions expressed by the users. The results show that emotional alignment does not influence users’ communication styles or have a persuasive effect. However, it significantly influences attribution of mental states to the robot and its perceived empathy.

This article presents an affective-based sensemaking system for grouping and suggesting stories created by the users about the cultural artefacts in a museum. By relying on the TCL commonsense reasoning framework, the system exploits the spatial structure of the Plutchik’s “wheel of emotions” to organize the stories according to their extracted emotions. The process of emotion extraction, reasoning, and suggestion is triggered by an app, called GAMGame, and integrated with the sensemaking engine. Following the framework of Citizen Curation, the system allows classifying and suggesting stories encompassing cultural items able to evoke not only the very same emotions of already experienced or preferred museum objects but also novel items sharing different emotional stances and, therefore, able to break the filter bubble effect and open the users’ view toward more inclusive and empathy-based interpretations of cultural content. The system has been designed tested, in the context of the H2020EU SPICE project (Social cohesion, Participation, and Inclusion through Cultural Engagement), in cooperation with the community of the d/Deaf and on the collection of the Gallery of Modern Art (GAM) in Turin. We describe the user-centered design process of the web app and of its components and we report the results concerning the effectiveness of the diversity-seeking, affective-driven, recommendations of stories.

In medical process mining, specific domain characteristics have to be dealt with: in particular, in medicine, a significant amount of expert knowledge is typically available; moreover, an interactive approach, letting medical users be involved in the work of process model discovery, is more acceptable than a completely automated strategy. To this end, in our recent work we have defined SIM (Semantic Interactive Miner), an innovative process mining tool able to: (i) support the interaction with medical experts, who can progressively merge parts of the initially mined model, obtaining a more generalized version; (ii) exploit pre-encoded domain knowledge, to move from a model where activities are reported at the ground level to a more user-interpretable high-level version. In this paper we illustrate the features of our tool by showing its application to the case study of neonatal resuscitation simulation: we use SIM to mine the process models produced by two different groups of students of a simulation course, aiming at verifying whether differently skilled young professionals produce different processes, which can finally be compared to the correct guideline.

As nowadays Machine Learning (ML) techniques are generating huge data collections, the problem of how to efficiently engineer their storage and operations is becoming of paramount importance. In this article we propose a new lossless compression scheme for real-valued matrices which achieves efficient performance in terms of compression ratio and time for linear-algebra operations. Experiments show that, as a compressor, our tool is clearly superior to gzip and it is usually within 20% of xz in terms of compression ratio. In addition, our compressed format supports matrix-vector multiplications in time and space proportional to the size of the compressed representation, unlike gzip and xz that require the full decompression of the compressed matrix. To our knowledge our lossless compressor is the first one achieving time and space complexities which match the theoretical limit expressed by the k -th order statistical entropy of the input. To achieve further time/space reductions, we propose column-reordering algorithms hinging on a novel column-similarity score. Our experiments on various data sets of ML matrices show that our column reordering can yield a further reduction of up to 16% in the peak memory usage during matrix-vector multiplication. Finally, we compare our proposal against the state-of-the-art Compressed Linear Algebra (CLA) approach showing that ours runs always at least twice faster (in a multi-thread setting), and achieves better compressed space occupancy and peak memory usage. This experimentally confirms the provably effective theoretical bounds we show for our compressed-matrix approach.