A principled approach to the analysis of eye movements for behavioural biometrics is laid down. The approach grounds in foraging theory, which provides a sound basis to capture the uniqueness of individual eye movement behaviour. We propose a composite Ornstein-Uhlenbeck process for quantifying the exploration/exploitation signature characterising the foraging eye behaviour. The relevant parameters of the composite model, inferred from eye-tracking data via Bayesian analysis, are shown to yield a suitable feature set for biometric identification; the latter is eventually accomplished via a classical classification technique. A proof of concept of the method is provided by measuring its identification performance on a publicly available dataset. Data and code for reproducing the analyses are made available. Overall, we argue that the approach offers a fresh view on either the analyses of eye-tracking data and prospective applications in this field.

Automatic pain assessment can be defined as the set of computeraided technologies allowing to recognise pain status. Reliable and valid methods for pain assessment are of primary importance for the objective and continuous monitoring of pain in people who are unable to communicate verbally. In the present work, we propose a novel approach for the recognition of pain from the analysis of facial expression. More specifically, we evaluate the effectiveness of Graph Neural Network (GNN) architectures exploiting the inherent graph structure of a set of fiducial points automatically tracked on subject faces. Experiments carried over on the publicly available dataset BioVid, show how the proposed method reaches higher levels of accuracy when compared with baseline models on acted pain, while outmatching state of the art approaches on spontaneous pain.

A core endeavour in current affective computing and social signal processing research is the construction of datasets embedding suitable ground truths to foster machine learning methods. This practice brings up hitherto overlooked intricacies. In this paper, we consider causal factors potentially arising when human raters evaluate the affect fluctuations of subjects involved in dyadic interactions and subsequently categorise them in terms of social participation traits. To gauge such factors, we propose an emulator as a statistical approximation of the human rater, and we first discuss the motivations and the rationale behind the approach.The emulator is laid down in the next section as a phenomenological model where the core affect stochastic dynamics as perceived by the rater are captured through an Ornstein–Uhlenbeck process; its parameters are then exploited to infer potential causal effects in the attribution of social traits. Following that, by resorting to a publicly available dataset, the adequacy of the model is evaluated in terms of both human raters’ emulation and machine learning predictive capabilities. We then present the results, which are followed by a general discussion concerning findings and their implications, together with advantages and potential applications of the approach.

In this work, we explore the effectiveness of multimodal models for estimating the emotional state expressed continuously in the Valence/Arousal space. We consider four modalities typically adopted for the emotion recognition, namely audio (voice), video (face expression), electrocardiogram (ECG), and electrodermal activity (EDA), investigating different mixtures of them. To this aim, a CNN-based feature extraction module is adopted for each of the considered modalities, and an RNN-based module for modelling the dynamics of the affective behaviour. The fusion is performed in three different ways: at feature-level (after the CNN feature extraction), at model-level (combining the RNN layer's outputs) and at prediction-level (late fusion). Results obtained on the publicly available RECOLA dataset, demonstrate that the use of multiple modalities improves the prediction performance. The best results are achieved exploiting the contribution of all the considered modalities, and employing the late fusion, but even mixtures of two modalities (especially audio and video) bring significant benefits.

Five psychrotolerant Alcanivorax spp. strains were isolated from Antarctic coastal waters. Strains were screened for molecular and physiological properties and analyzed regarding their growth capacity. Partial 16S rDNA, alk-B1, and P450 gene sequencing was performed. Biolog EcoPlates and the API 20E test were used to evaluate metabolic and biochemical profiles. Bacterial growth in sodium acetate was determined at 4, 15, 20, and 25 °C to evaluate the optimal temperature. Furthermore, the ability of each strain to grow in a hydrocarbon mixture at 4 and 25 °C was assayed. Biosurfactant production tests (drop-collapse and oil spreading) and emulsification activity tests (E24) were also performed. Concerning results of partial gene sequencing (16S rDNA, alk-B1, and P450), a high similarity of the isolates with the same genes isolated from other Alcanivorax spp. strains was observed. The metabolic profiles obtained by Biolog assays showed no significant differences in the isolates compared to the Alcanivorax borkumensis wild type. The results of biodegradative tests showed their capability to grow at different temperatures. All strains showed biosurfactant production and emulsification activity. Our findings underline the importance to proceed in the isolation and characterization of Antarctic hydrocarbon-degrading bacterial strains since their biotechnological and environmental applications could be useful even for pollution remediation in polar areas.

Within the Svalbard archipelago, Kongsfjorden is an important marine ecosystem that is recognised as one of the main representative Arctic glacial fjords. Prokaryotic organisms are key drivers of important ecological processes such as carbon fluxes, nutrient mineralisation, and energy transfer, as well as sentinels of environmental pollution, especially in sediments, that are a repository of contaminants. In some areas of the Arctic, the structure and metabolic activity of the microbial community in the organic matter turnover and globally in the functioning of the benthic domain are mostly still unknown. A snapshot of the main microbial parameters such as bacterial abundance (by microscopic and plate counts), structure (by 16S rRNA sequencing), and metabolic activity was provided in Ny-Ålesund harbour, contextually in seawater and sediment samples. Fluorogenic substrates were used to assess the microbial ability to utilise organic substrates such as proteins, polysaccharides, and organic phosphates through specific enzymatic assays (leucine aminopeptidase—LAP, beta-glucosidase—ß-GLU, and alkaline phosphatase—AP, respectively). The metabolic profiles of psychrophilic heterotrophic bacterial isolates were also screened using a qualitative assay. The phylogenetic analysis of the microbial community revealed that Proteobacteria prevailed among the observed taxonomic groups. Several of the observed sequences were assigned to clones found in harbours, microbial biofilms, antifouling paints, or oil-polluted facilities of cold environments, highlighting a signature of human pressure on the polar habitat of Ny-Ålesund harbour.