Assistive Ambient Living (AAL) in ageing refers to any device used to support ageing related psychological and physical changes aimed at improving seniors’ quality of life and reducing caregivers’ ...burdens. The diffusion of these devices opens the ethical issues related to their use in the human personal space. This is particularly relevant when AAL technologies are devoted to the ageing population that exhibits special bio-psycho-social aspects and needs. In spite of this, relatively little research has focused on ethical issues that emerge from AAL technologies. The present article addresses ethical issues emerging when AAL technologies are implemented for assisting the elderly population and is aimed at raising awareness of these aspects among healthcare providers. The overall conclusion encourages a person-oriented approach when designing healthcare facilities. This process must be fulfilled in compliance with the general principles of ethics and individual nature of the person devoted to. This perspective will develop new research paradigms, paving the way for fulfilling essential ethical principles in the development of future generations of personalized AAL devices to support ageing people living independently at their home.
This work deals with a generalization of the minimum Target Set Selection (TSS) problem, a key algorithmic question in information diffusion research due to its potential commercial value. Firstly ...proposed by Kempe et al., the TSS problem is based on a linear threshold diffusion model defined on an input graph with node thresholds, quantifying the hardness to influence each node. The goal is to find the smaller set of items that can influence the whole network according to the diffusion model defined. This study generalizes the TSS problem on networks characterized by many-to-many relationships modeled via hypergraphs. Specifically, we introduce a linear threshold diffusion process on such structures, which evolves as follows. Let H=(V,E) be a hypergraph. At the beginning of the process, the nodes in a given set S⊆V are influenced. Then, at each iteration, (i) the influenced hyperedges set is augmented by all edges having a sufficiently large number of influenced nodes; (ii) consequently, the set of influenced nodes is enlarged by all the nodes having a sufficiently large number of already influenced hyperedges. The process ends when no new nodes can be influenced. Exploiting this diffusion model, we define the minimum Target Set Selection problem on hypergraphs (TSSH). Being the problem NP-hard (as it generalizes the TSS problem), we introduce four heuristics and provide an extensive evaluation on real-world networks.
This paper investigates effects of participants’ gender and age (adolescents, young adults, and seniors), robots’ gender (male and female robots) and appearance (humanoid vs android) on robots’ ...acceptance dimensions. The study involved 6 differently aged groups of participants (two adolescents, two young adults and two seniors’ groups, for a total of 240 participants) requested to express their willingness to interact and their perception of robots’ usefulness, pleasantness, appeal, and engagement for two different sets of females (Pepper, Erica, and Sophia) and male (Romeo, Albert, and Yuri) humanoid and android robots. Participants were also requested to express their preferred and attributed age ranges and occupations they entrusted to robots among healthcare, housework, protection and security and front office. Results show that neither the age nor participants and robots’ gender, nor robots’ human likeness univocally affected robots’ acceptance by these differently aged users. Robots’ acceptance appeared to be a nonlinear combination of all these factors.
The current study aims at examining the relationship between the perfectionism two-factor model (i.e., concerns and strivings) and burnout dimensions measured by using the BAT (Burnout Assessment ...Tool) through a longitudinal study. A two-wave cross-lagged study was conducted using path analysis in SEM (Structural Equation Modeling) of 191 workers. Results confirmed the predictive role of perfectionistic concerns on the burnout dimensions, whereas perfectionistic strivings were not significantly related, suggesting that perfectionism should be monitored by employers and clinicians to prevent employee burnout. Limitations and future research directions are envisaged.
This paper proposes a systematic approach to investigate the impact of factors such as the gender and age of participants and gender, and age of faces on the decoding accuracy of emotional ...expressions of disgust, anger, sadness, fear, happiness, and neutrality. The emotional stimuli consisted of 76 posed and 76 naturalistic faces, differently aged (young, middle-aged, and older) selected from FACES and SFEW databases. Either a posed or naturalistic faces’ decoding task was administered. The posed faces’ decoding task involved three differently aged groups (young, middle-aged, and older adults). The naturalistic faces’ decoding task involved two groups of older adults. For the posed decoding task, older adults were found significantly less accurate than middle-aged and young participants, and middle-aged significantly less accurate than young participants. Old faces were significantly less accurately decoded than young and middle-aged faces of disgust, and anger, and young faces of fear, and neutrality. Female faces were significantly more accurately decoded than male faces of anger and sadness, significantly less accurately decoded than male faces of neutrality. For the naturalistic decoding task, older adults were significantly less accurate in decoding naturalistic rather than posed faces of disgust, fear, and neutrality, contradicting an older adults’ emended support from a prior naturalistic emotional experience. Young faces were more accurately decoded than old and middle-aged faces of disgust and anger and old faces of neutrality. Female faces were significantly more accurately decoded than male faces of fear, and significantly less accurately decoded than male faces of anger. Significant effects and significant interdependencies were observed among the age of participants, emotional categories, age, and gender of faces, and type of stimuli (naturalistic vs. posed), not allowing to distinctly isolate the effects of each involved variable. Nevertheless, the data collected in this paper weakens both the assumptions on women enhanced ability to display and decode emotions and participants enhanced ability to decode faces closer to their own age (“own age bias” theory). Considerations are made on how these data would guide the development of assessment tools and preventive interventions and the design of emotionally and socially believable virtual agents and robots to assists and coach emotionally vulnerable people in their daily routines.
Motivated by applications in sociology, economy and medicine, we study variants of the Target Set Selection problem, first proposed by Kempe, Kleinberg and Tardos. In our scenario one is given a ...graph G=(V,E), integer values t(v) for each vertex v (thresholds), and the objective is to determine a small set of vertices (target set) that activates a given number (or a given subset) of vertices of G within a prescribed number of rounds. The activation process in G proceeds as follows: initially, at round 0, all vertices in the target set are activated; subsequently at each round r⩾1 every vertex of G becomes activated if at least t(v) of its neighbors are already active by round r−1. It is known that the problem of finding a minimum cardinality Target Set that eventually activates the whole graph G is hard to approximate to a factor better than O(2log1−ϵ|V|). In this paper we give exact polynomial time algorithms to find minimum cardinality Target Sets in graphs of bounded clique-width, and exact linear time algorithms for trees.
Non-Pharmaceutical Interventions (NPIs) are essential measures that reduce and control a severe outbreak or a pandemic, especially in the absence of drug treatments. However, estimating and ...evaluating their impact on society remains challenging, considering the numerous and closely tied aspects to examine. This article proposes a fine-grain modeling methodology for NPIs, based on high-order relationships between people and environments, mimicking direct and indirect contagion pathways over time. After assessing the ability of each intervention in controlling an epidemic propagation, we devise a multi-objective optimization framework, which, based on the epidemiological data, calculates the NPI combination that should be implemented to minimize the spread of an epidemic as well as the damage due to the intervention. Each intervention is thus evaluated through an agent-based simulation, considering not only the reduction in the fraction of infected but also to what extent its application damages the daily life of the population. We run experiments on three data sets, and the results illustrate how the application of NPIs should be tailored to the specific epidemic situation. They further highlight the critical importance of correctly implementing personal protective (e.g., using face masks) and sanitization measures to slow down a pathogen spreading, especially in crowded places.
In this research, we analyse data obtained from sensors when a user handwrites or draws on a tablet to detect whether the user is in a specific mood state. First, we calculated the features based on ...the temporal, kinematic, statistical, spectral and cepstral domains for the tablet pressure, the horizontal and vertical pen displacements and the azimuth of the pen's position. Next, we selected features using a principal component analysis (PCA) pipeline, followed by modified fast correlation-based filtering (mFCBF). PCA was used to calculate the orthogonal transformation of the features, and mFCBF was used to select the best PCA features. The EMOTHAW database was used for depression, anxiety and stress scale (DASS) assessment. The process involved the augmentation of the training data by first augmenting the mood states such that all the data were the same size. Then, 80% of the training data was randomly selected, and a small random Gaussian noise was added to the extracted features. Automated machine learning was employed to train and test more than ten plain and ensembled classifiers. For all three moods, we obtained 100% accuracy results when detecting two possible grades of mood severities using this architecture. The results obtained were superior to the results obtained by using state-of-the-art methods, which enabled us to define the three mood states and provide precise information to the clinical psychologist. The accuracy results obtained when detecting these three possible mood states using this architecture were 82.5%, 72.8% and 74.56% for depression, anxiety and stress, respectively.
Currently, the diagnosis of major depressive disorder (MDD) and its subtypes is mainly based on subjective assessments and self-reported measures. However, objective criteria as ...Electroencephalography (EEG) features would be helpful in detecting depressive states at early stages to prevent the worsening of the symptoms. Scientific community has widely investigated the effectiveness of EEG-based measures to discriminate between depressed and healthy subjects, with the aim to better understand the mechanisms behind the disorder and find biomarkers useful for diagnosis. This work offers a comprehensive review of the extant literature concerning the EEG-based biomarkers for MDD and its subtypes, and identify possible future directions for this line of research. Scopus, PubMed and Web of Science databases were researched following PRISMA's guidelines. The initial papers' screening was based on titles and abstracts; then full texts of the identified articles were examined, and a synthesis of findings was developed using tables and thematic analysis. After screening 1871 articles, 76 studies were identified as relevant and included in the systematic review. Reviewed markers include EEG frequency bands power, EEG asymmetry, ERP components, non-linear and functional connectivity measures. Results were discussed in relations to the different EEG measures assessed in the studies. Findings confirmed the effectiveness of those measures in discriminating between healthy and depressed subjects. However, the review highlights that the causal link between EEG measures and depressive subtypes needs to be further investigated and points out that some methodological issues need to be solved to enhance future research in this field.
Agent-based models (ABMs) are one of the most effective and successful methods for analyzing real-world complex systems by investigating how modeling interactions on the individual level (i.e., ...micro-level) leads to the understanding of emergent phenomena on the system level (i.e., macro-level). ABMs represent an interdisciplinary approach to examining complex systems, and the heterogeneous background of ABM users demands comprehensive, easy-to-use, and efficient environments to develop ABM simulations. Currently, many tools, frameworks, and libraries exist, each with its characteristics and objectives. This article aims to guide newcomers in the jungle of ABM tools toward choosing the right tool for their skills and needs. This work proposes a thorough overview of open-source general-purpose ABM tools and offers a comparison from a two-fold perspective. We first describe an off-the-shelf evaluation by considering each ABM tool’s features, ease of use, and efficiency according to its authors. Then, we provide a hands-on evaluation of some ABM tools by judging the effort required in developing and running four ABM models and the obtained performance.
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