FaceMashup: An end-user development tool for social network data

Every day, each active social network user produces and shares texts, images and videos. While developers can access such data through application programming interfaces (APIs) for creating games, visualizations and routines, end users have less control on such information. Their access is mediated by the social application features, which limits them in combining sources, filtering results and performing actions on groups of elements. In order to fill this gap, we introduce FaceMashup, an end user development (EUD) environment supporting the manipulation of the Facebook graph. We describe the tool interface, documenting the choices we made during the design iterations. Data types are represented through widgets containing user interface (UI) elements similar to those used in the social network application. Widgets can be connected with each other with the drag and drop of their inner fields, and the application updates their content. Finally, we report the results of a user-test on the FaceMashup prototype, which shows a good acceptance of the environment by end-users

Promoting well-being within socio-work contexts: an investigation into the role of politeness in preventing burnout

openLa seguente tesi sperimentale focalizza la ricerca sulla possibile correlazione tra comportamenti positivi quali la cortesia, l’ottimismo, la speranza, la resilienza e l’efficacia personale con il burnout. Si effettua quindi una panoramica sulla letteratura presente ad oggi del costrutto del burnout, per poi fare una revisione del concetto di cortesia e di come esso si è sviluppato a livello teorico e pratico. La ricerca effettuata tramite questionario mira ad evidenziare i principali fattori di correlazione, proponendo delle strategie e fornendo degli spunti utili al fine di mettere in risalto l’importanza di tali comportamenti positivi in ottica di una riduzione dell’esaurimento nei contesti socio-lavorativi.The following experimental thesis focuses research on the possible correlation between positive behaviors such as courtesy, optimism, hope, resilience and personal effectiveness with burnout. An overview of the literature present to date on the burnout construct is then carried out, and then a review of the concept of courtesy and how it has developed on a theoretical and practical level. The research carried out via questionnaire aims to highlight the main correlation factors, proposing strategies and providing useful ideas in order to highlight the importance of such positive behaviors with a view to reducing exhaustion in socio-work contexts

Explainable AI-powered graph neural networks for HD EMG-based gesture intention recognition

The ability to recognize fine-grained gestures enables several applications in different domains, including healthcare, robotics, remote control, and human-computer interaction. Traditional gesture recognition systems rely on data acquired from cameras, depth sensors, or smart gloves. More recently, techniques for recognizing gestures based on signals acquired by high-density (HD) EMG electrodes worn on the forearm have been proposed. An advantage of these techniques is that they do not rely on the use of external devices, and they are feasible also to people who underwent amputation. Unfortunately, the extraction of complex features from raw HD EMG signals may introduce delays that deter the real-time requirements of the system. To address this issue, in a preliminary investigation we proposed to use graph neural networks for gesture recognition from raw HD EMG data. In this paper, we extend our previous work by exploiting Explainable AI algorithms to automatically refine the graph topology based on the data in order to improve recognition rates and reduce the computational cost. We performed extensive experiments with a large dataset collected from 20 volunteers regarding the execution of 65 fine-grained gestures, comparing our technique with state-of-the-art methods based on handcrafted features and different machine learning algorithms. Experimental results show that our technique outperforms the state of the art in terms of recognition performance while incurring significantly lower computational cost at run-time

Advances in irrigation management in greenhouse cultivation

The advantages of greenhouse include the ability to secure better conditions than outdoor environment for crop growth and development, increased off-season production and autonomy from external weather conditions. This chapter provides an up-to-date critical overview of scientific advances in irrigation management for greenhouse vegetables and ornamentals. The chapter presents a technical design of a typical greenhouse irrigation system, before covering water balance and crop evapotranspiration techniques as well as the use of high-tech moisture sensors for irrigation scheduling. In the context of enhancing the water use efficiency of greenhouse crops, the chapter also discusses innovative management practices such as biostimulants and grafting. Finally, the chapter concludes by looking ahead to future prospects and research breakthroughs

Recognition of cooking activities through air quality sensor data for supporting food journaling

Abstract Unhealthy behaviors regarding nutrition are a global risk for health. Therefore, the healthiness of an individual's nutrition should be monitored in the medium and long term. A powerful tool for monitoring nutrition is a food diary; i.e., a daily list of food taken by the individual, together with portion information. Unfortunately, frail people such as the elderly have a hard time filling food diaries on a continuous basis due to forgetfulness or physical issues. Existing solutions based on mobile apps also require user's effort and are rarely used in the long term, especially by elderly people. For these reasons, in this paper we propose a novel architecture to automatically recognize the preparation of food at home in a privacy-preserving and unobtrusive way, by means of air quality data acquired from a commercial sensor. In particular, we devised statistical features to represent the trend of several air parameters, and a deep neural network for recognizing cooking activities based on those data. We collected a large corpus of annotated sensor data gathered over a period of 8 months from different individuals in different homes, and performed extensive experiments. Moreover, we developed an initial prototype of an interactive system for acquiring food information from the user when a cooking activity is detected by the neural network. To the best of our knowledge, this is the first work that adopts air quality sensor data for cooking activity recognition

The Psychological Implications of Companion Robots: A Theoretical Framework and an Experimental Setup

In this paper we present a theoretical framework to understand the underlying psychological mechanism involved in human-Companion Robot interactions. At first, we take the case of Sexual Robotics, where the psychological dynamics are more evident, to thereafter extend the discussion to Companion Robotics in general. First, we discuss the differences between a sex-toy and a Sexual Robots, concluding that the latter may establish a collusive and confirmative dynamics with the user. We claim that the collusiveness leads to two main consequences, such as the fixation on a specific and atypical type of sexual interaction, called paraphilic, and to the infantilization of the user, which we explain through the theoretical framework of “object-relation theory”. We argue that these dynamics may degrade to an infantile stage the relational abilities of users, extending this argument to Companion Robots in general. Then, we enquire if and how the relational dynamics enacted in HRI may shift to human relations: we discuss the analogy with virtual reality concluding that, under certain condition, a symbolic shift might happen. In the last part of this work, we propose an experimental setup to verify if a collusive and confirmative interaction with a Companion Robot can, over time, impact on the user’s ability to manage relational frustration

Experimental and numerical evaluations on palm microwave heating for Red Palm Weevil pest control

The invasive Red Palm Weevil is the major pest of palms. Several control methods have been applied, however concern is raised regarding the treatments that can cause significant environmental pollution. In this context the use of microwaves is particularly attractive. Microwave heating applications are increasingly proposed in the management of a wide range of agricultural and wood pests, exploiting the thermal death induced in the insects that have a thermal tolerance lower than that of the host matrices. This paper describes research aiming to combat the Red Palm pest using microwave heating systems. An electromagnetic-thermal model was developed to better control the temperature profile inside the palm tissues. In this process both electromagnetic and thermal parameters are involved, the latter being particularly critical depending on plant physiology. Their evaluation was carried out by fitting experimental data and the thermal model with few free parameters. The results obtained by the simplified model well match with both that of a commercial software 3D model and measurements on treated Phoenix canariensis palms with a ring microwave applicator. This work confirms that microwave heating is a promising, eco-compatible solution to fight the spread of weevil

Long-range dependence in earthquake-moment release and implications for earthquake occurrence probability

Since the beginning of the 1980s, when Mandelbrot observed that earthquakes occur on 'fractal' self-similar sets, many studies have investigated the dynamical mechanisms that lead to self-similarities in the earthquake process. Interpreting seismicity as a self-similar process is undoubtedly convenient to bypass the physical complexities related to the actual process. Self-similar processes are indeed invariant under suitable scaling of space and time. In this study, we show that long-range dependence is an inherent feature of the seismic process, and is universal. Examination of series of cumulative seismic moment both in Italy and worldwide through Hurst's rescaled range analysis shows that seismicity is a memory process with a Hurst exponent H 48 0.87. We observe that H is substantially space-and time-invariant, except in cases of catalog incompleteness. This has implications for earthquake forecasting. Hence, we have developed a probability model for earthquake occurrence that allows for long-range dependence in the seismic process. Unlike the Poisson model, dependent events are allowed. This model can be easily transferred to other disciplines that deal with self-similar processe