Leveraging inter-tourists interactions via chatbots to bridge academia, tourism industries and future societies

Purpose – The tourism and hospitality sectors are experiencing radical innovation boosted by the advancements in Information and Communication Technologies. Increasingly sophisticated chatbots are introducing novel approaches, re-shaping the dynamics among tourists and service providers, and fostering a remarkable behavioral change in the overall sector. Therefore, the objective of this paper is two-folded: (1) to highlight the academic and industrial standing points with respect to the current chatbots designed/deployed in the tourism sector and (2) to develop a proof-of-concept embodying the most prominent opportunities in the tourism sector. Design/methodology/approach – This work elaborates on the outcomes of a Systematic Literature Review (SLR) and a Focus Group (FG) composed of experts from the tourism industry. Moreover, it presents a proof-of-concept relying on the outcomes obtained from both SLR and FG. Eventually, the proof-of-concept has been tested with experts and practitioners of the tourism sector. Findings – Among the findings elicited by this paper, we can mention the quick evolution of chatbot-based solutions, the need for continuous investments, upskilling, system innovation to tackle the eTourism challenges and the shift toward new dimensions (i.e. tourist-to-tourist-to-chatbot and personalized multi-stakeholder systems). In particular, we focus on the need for chatbot-based activity and thematic aggregation for next-generation tourists and service providers. Originality/value – Both academic- and industrial-centered findings have been structured and discussed to foster the practitioners' future research. Moreover, the proof-of-concept presented in the paper is the first of its kind, which raised considerable interest from both technical and business-planning perspectives

MEDIATION: An eMbEddeD System for Auditory Feedback of Hand-water InterAcTION while Swimming

Cesarini D, Calvaresi D, Farnesi C, et al. MEDIATION: An eMbEddeD System for Auditory Feedback of Hand-water InterAcTION while Swimming. Procedia Engineering. 2016;147:324-329.In swimming sport, the proper perception of moving water masses is a key factor. This paper presents an embedded system for the acquisition of values of pressure on swimmers hands and their transformation into sound. The sound, obtained using sonification, is used as an auditive representation of hand-water interactions while swimming in water. The sound obtained is used as an auditive feedback for the swimmer and as an augmented communication channel between the swimming trainer and the athlete. The developed system is self-contained, battery powered and able to work continuously for over eight hours, thus, representing a viable solution for daily usage in swimmers training. Preliminary results from in-pool experiments with both novel and experienced swimmers demonstrate the high acceptability of this technology and its promising future evolution and usage possibilities

Breast cancer survival analysis agents for clinical decision support.

peer reviewedPersonalized support and assistance are essential for cancer survivors, given the physical and psychological consequences they have to suffer after all the treatments and conditions associated with this illness. Digital assistive technologies have proved to be effective in enhancing the quality of life of cancer survivors, for instance, through physical exercise monitoring and recommendation or emotional support and prediction. To maximize the efficacy of these techniques, it is challenging to develop accurate models of patient trajectories, which are typically fed with information acquired from retrospective datasets. This paper presents a Machine Learning-based survival model embedded in a clinical decision system architecture for predicting cancer survivors' trajectories. The proposed architecture of the system, named PERSIST, integrates the enrichment and pre-processing of clinical datasets coming from different sources and the development of clinical decision support modules. Moreover, the model includes detecting high-risk markers, which have been evaluated in terms of performance using both a third-party dataset of breast cancer patients and a retrospective dataset collected in the context of the PERSIST clinical study

Reinterpreting vulnerability to tackle deception in principles-based XAI for human-computer interaction

Artificial intelligence (AI) systems have been increasingly adopted for decision support, behavioral change purposes, assistance, and aid in daily activities and decisions. Thus, focusing on design and interaction that, in addition to being functional, foster users’ acceptance and trust is increasingly necessary. Human-computer interaction (HCI) and human-robot interaction (HRI) studies focused more and more on the exploitation of communication means and interfaces to possibly enact deception. Despite the literal meaning often attributed to the term, deception does not always denote a merely manipulative intent. The expression “banal deception” has been theorized to specifically refer to design strategies that aim to facilitate the interaction. Advances in explainable AI (XAI) could serve as technical means to minimize the risk of distortive effects on people’s perceptions and will. However, this paper argues that how the provided explanations and their content can exacerbate the deceptive dynamics or even manipulate the end user. Therefore, in order to avoid similar consequences, this analysis suggests legal principles to which the explanation must conform to mitigate the side effects of deception in HCI/HRI. Such principles will be made enforceable by assessing the impact of deception on the end users based on the concept of vulnerability – understood here as the rationalization of the inviolable right of human dignity – and control measures implemented in the given systems

Real-time compliant stream processing agents for physical rehabilitation

Digital rehabilitation is a novel concept that integrates state-of-the-art technologies for motion sensing and monitoring, with personalized patient-centric methodologies emerging from the field of physiotherapy. Thanks to the advances in wearable and portable sensing technologies, it is possible to provide patients with accurate monitoring devices, which simplifies the tracking of performance and effectiveness of physical exercises and treatments. Employing these approaches in everyday practice has enormous potential. Besides facilitating and improving the quality of care provided by physiotherapists, the usage of these technologies also promotes the personalization of treatments, thanks to data analytics and patient profiling (e.g., performance and behavior). However, achieving such goals implies tackling both technical and methodological challenges. In particular, (i) the capability of undertaking autonomous behaviors must comply with strict real-time constraints (e.g., scheduling, communication, and negotiation), (ii) plug-and-play sensors must seamlessly manage data and functional heterogeneity, and finally (iii) multi-device coordination must enable flexible and scalable sensor interactions. Beyond traditional top-down and best-effort solutions, unsuitable for safety-critical scenarios, we propose a novel approach for decentralized real-time compliant semantic agents. In particular, these agents can autonomously coordinate with each other, schedule sensing and data delivery tasks (complying with strict real-time constraints), while relying on ontology-based models to cope with data heterogeneity. Moreover, we present a model that represents sensors as autonomous agents able to schedule tasks and ensure interactions and negotiations compliant with strict timing constraints. Furthermore, to show the feasibility of the proposal, we present a practical study on upper and lower-limb digital rehabilitation scenarios, simulated on the MAXIM-GPRT environment for real-time compliance. Finally, we conduct an extensive evaluation of the implementation of the stream processing multi-agent architecture, which relies on existing RDF stream processing engines

GB-Flex ::automated and distributed decision-making in energy balancing groups

This paper briefly summarizes the possible strategies and shows the feasibility and profits of automatizing the BGs

Integration of local and global features explanation with global rules extraction and generation tools

Widely used in a growing number of domains, Deep Learning predictors are achieving remarkable results. However, the lack of transparency (i.e., opacity) of their inner mechanisms has raised trust and employability concerns. Nevertheless, several approaches fostering models of interpretability and explainability have been developed in the last decade. This paper combines approaches for local feature explanation (i.e., Contextual Importance and Utility – CIU) and global feature explanation (i.e., Explainable Layers) with a rule extraction system, namely ECLAIRE. The proposed pipeline has been tested in four scenarios employing a breast cancer diagnosis dataset. The results show improvements such as the production of more human-interpretable rules and adherence of the produced rules with the original model

Risk and exposure of XAI in persuasion and argumentation ::the case of manipulation

In the last decades, Artificial intelligence (AI) systems have been increasingly adopted in assistive (possibly collaborative) decision-making tools. In particular, AI-based persuasive technologies are designed to steer/influence users’ behaviour, habits, and choices to facilitate the achievement of their own – predetermined – goals. Nowadays, the inputs received by the assistive systems leverage heavily AI data-driven approaches. Thus, it is imperative to have transparent and understandable (to the user) both the process leading to the recommendations and the recommendations. The Explainable AI (XAI) community has progressively contributed to “opening the black box”, ensuring the interaction’s effectiveness, and pursuing the safety of the individuals involved. However, principles and methods ensuring the efficacy and information retain on the human have not been introduced yet. The risk is to underestimate the context dependency and subjectivity of the explanations’ understanding, interpretation, and relevance. Moreover, even a plausible (and possibly expected) explanation can lead to an imprecise or incorrect outcome or its understanding. This can lead to unbalanced and unfair circumstances, such as giving a financial advantage to the system owner/provider and the detriment of the user. This paper highlights that the sole explanations – especially in the context of persuasive technologies – are not self-sufficient to protect users’ psychological and physical integrity. Conversely, explanations could be misused, becoming themselves a tool of manipulation. Therefore, we suggest characteristics safeguarding the explanation from being manipulative and legal principles to be used as criteria for evaluating the operation of XAI systems, both from an ex-ante and ex-post perspective

Decentralized management of patient profilesand trajectories through semantic web agents

The usage of healthcare data for analytics and patient applications has increased in recent years opening a number of technical, ethical and scientific challenges. Among these, those related to the management of personal and sensitive health data have been addressed through decentralized solutions for patient data, often implemented and modelled using distributed agents and semantic technologies. In this paper, we present a technical summary of our previous works in this area, comprising efforts to: (i) use ontology models to represent patient trajectories,(ii) employ agent-based architectures to model and employ decentralized patient data exchanges, (iii) define agent cooperation and negotiation strategies for healthcare data interactions, (iv) adopt semantic data models for privacy-aware agents, and (v) implement multi-agent systems for real-time healthcare data processin