This paper presents a multimodal human-robot interaction (HRI) system for educational contexts implemented on the humanoid robot Pepper. The system leverages multiple communicative channels, allowing learners to combine speech with tablet interaction while the robot responds through synchronized speech, textual captions and dynamic visual cues. To ensure robustness and reliability, we introduce the use of Metamorphic Testing for multimodal HRI. By validating system behavior through systematic input transformations, we demonstrate how metamorphic testing can uncover inconsistencies across linguistic, visual and cross-modal interactions. This work contributes both a novel methodological framework for evaluating multimodal HRI systems and an application to educational robotics.

This is the ffth edition of the SOcial and Cultural IntegrAtion with PersonaLIZEd Interfaces (SOCIALIZE) workshop. This year’s event, like those before it, focuses on bringing together technology enthusiasts to break down barriers - whether they are cultural, social, or linguistic. We especially want to help people who struggle with making connections. In this context, social robots could play a vital role in achieving these ambitious aims. This year’s edition has been particularly successful in terms of submissions. After the review process, 13 articles were accepted, all addressing relevant and timely topics. The authors hope that their presentations will inspire fruitful and engaging discussions among the participants.

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.

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.

In this paper, we describe an empirical evaluation of the user's perceived usefulness of recommendations for configuration tasks in a smart home scenario. Our results suggest that while overall recommendations help improve the performance in the task, the psychological traits of Self-efficacy and Need for Cognition play an important role in determining the perceived usefulness of the recommendations. These effects are somehow different from those reported in other studies where recommendations are provided for a choice task rather than a constructive task. Thus, our results offer evidence regarding the importance of incorporating the evaluation of personality traits into the design of configuration constructive tasks in order to make informed decisions on whether and how to provide users with recommendations. Additionally, they show that it is crucial to take into account the nature of the task and, where possible, individual competencies.

Artificial Intelligence is undoubtedly becoming pervasive in everyday life of everyone. In this setting, developing correct AI conception since childhood is not only a need to be addressed in educational curricula, but is also a children right. Accordingly, several initiatives at national and international levels aim at promoting AI and emerging technology literacy, supported also by a proliferation in the literature of learning courses covering a variety of topics, learning objectives and targeted ages. Schools are therefore pushed to introduce innovative activities for children in their curricula. In this paper, we report the results of a case study where we tested the contribution of an AI block-based course in developing computational thinking, and human and AI minds understanding in fifth and sixth grade children.

Cultural Heritage (CH) is a challenging domain of application for novel Information and Communication Technologies (ICT), where visualization plays a major role in enhancing the visitors’ experience, whether onsite or online. Technology-supported natural human-computer interaction is a key factor in enabling access, both on-site and online, to CH assets. Recent advances in ICT enhance visitors’ access to online collections as well as their CH experience onsite, bringing even wider audiences than those who visit the physical museums. The range of visualization devices – from tiny smartwatch screens, through large situated public displays, to the latest generation of immersive Head-Mounted Displays – together with the increasing availability of real-time 3D rendering technologies for online and mobile devices and, recently, Internet of Things (IoT) approaches, Social Robotics, requires exploring how they can be applied successfully in CH. Following the successful workshops at AVI 2016, AVI 2018, AVI 2020, AVI 2022 and AVI 2024 and a large number of recent events and projects focusing on CH, the goal of the workshop is to bring together researchers and practitioners interested in presenting and discussing the potential of state of the art advanced visual interfaces in enhancing our daily cultural heritage experience. This year the workshop theme will be: AVI for accessibility in CH.

In this paper, we present an experiment where we study the impact of different types of explainable security warnings in a smart home environment. Results show that detailed informal-style explanations are evaluated more positively and are more effective in promoting safe home automation.

This paper presents an empirical evaluation of mid-air gestures in a web setting. Fifty-six (56) HCI students were divided into 16 groups and involved as designers. Then, they proposed a set of mid-air gestures to carry out the identified actions: 99 different mid-air gestures for 16 different web actions were produced in total. Designers validated their proposals involving external subjects, namely 248 users in total. Finally, we analyzed their results and identified the most recurring or intuitive gestures as well as the potential criticalities associated with their proposals.

Users’ feedback is becoming more and more important in many different contexts of users’ interaction with computerised systems, such as in recommender systems, social network, e-democracy, quantifi...

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.

AVI-CH is the 14th workshop in the series of PATCH workshops, since 2007 and the 4th in a row at AVI. It is the meeting place for researchers and practitioners focusing on the application of advanced information and communication technology (ICT) in cultural heritage with a specific focus on user interfaces, visualization and interaction. This year, eight papers were submitted by researchers from Greece, Italy and Israel. All were accepted.

Our Computer science k-12 education research group the educational toy company Quercetti have been collaborating together to design and manufacture toys that help stimulate and consolidate so-called computational thinking. This approach has been inspired by methods already consolidated in the literature and widespread worldwide such as the Bebras tasks and CS-Unplugged. This paper describes two of these smart toys, their design process, some evaluation’s results, and finally feedback and reviews from buyers. The main activities proposed by these toys leverage visual coding through small colored physical items (e.g., pegs and balls) to deliver the unplugged activities to young users.

Nowadays, most interactive social systems allow users to react to their contents and exploit user reactions to provide intelligent behaviours, such as adaptation or recommendation. Therefore, carefully understanding and designing the user/system dialogue that revolves around reaction provisioning is a crucial aspect. In this paper, we introduce the UpRISEmodel with the aim of formally describing the user/system interaction while providing and using reactions. Then, we show how this model can be used to formally represent and describe interactive social systems that collect user reactions, as well as to compare them. In addition, we exemplify how the UpRISEmodel can provide a sort of checklist that stimulates system designers to approach design/redesign tasks involving user reactions in a thorough and well-structured manner, suggesting all the possibly relevant points with respect to different usability and performance-related goals. This approach can be seen as the first step towards more transparency in the design of intelligent interactive systems.

This paper describes the Nao_PRM application aimed at expanding the limited functionality of the simulated Nao robot, already integrated into the Choregraphe application, creating a robot simulation with all the required functionalities for social interaction.

AVI2CH is a meeting place for researchers and practitioners focusing on the application of advanced information and communication technology in cultural heritage (CH) with a specific focus on user interfaces, visualization and interaction. It builds on a series of PATCH workshops, since 2007 including three at AVI and also a series of European workshops on cultural informatics. Eleven papers range from novel interfaces in museums to wider community engagement; all share a common mission to ensure that the latest digital technology helps preserve the past in ways that enrich the lives of current and future generations

In this paper we study whether visualizations based on color maps can encourage the intuitive interpretation of detailed descriptions, as the ones proposed in the formal model UPRISE, designed to analyze interactive system components, such as reaction tools, which allow users to provide reactions. We carried out a between-subjects experiment where 56 participants had to group 6 systems according to similarity using either color maps or textual descriptions. Results showed that color maps seem to favour inter-user agreement in comparison and grouping tasks.

The Internet of Things is a pervasive technology widely adopted in several contexts ranging from smart homes to automotive cars. In this context, the End User Development is gaining momentum: different solutions support non-technical users to define the smart objects behavior to better satisfy their needs. This workshop aimed to stimulates participants in providing discussions in line with the workshop themes and goals, for example, user mental models, acceptance of EUD solutions, the role of AI in EUD tools, humanoid robots were discussed.

In this paper we present the results of two experimental evaluations carried out in the broader context of a research project on Industry 4.0. We envision in large displays operated through gestural interaction one of the key technologies for enabling real-time management of production processes and supporting decision-making. We developed prototype applications with the twofold aim of: (1) designing a vocabulary of one-handed, touchless gestures meant for interaction in a CAD-like environment, and (2) investigating the role of the cursor feedback. Our results provide a basis for the development of a new integrated wearable sensor which will be assessed with industry workers and in a more realistic setting, for better ecological validity.

Structured user model data not only allow system personalization, but also may be of interest as a source for analysis: in particular, for the study of general trends and for the detection of anomalies in preferences and mutually-referenced features among different user models. Such sources are multidimensional and interrelated, and recently started to be represented as graph-based datasets. Among the most effective ways of studying such data is visual exploration based on data-driven graph drawing approaches: in particular, node-link and node-link-group diagrams. The paper provides an overview of advanced approaches to the graphical representation of multidimensional data derived from user modeling and presents a proposal for developing flexible and scalable user interfaces for the hypergraph-based visual exploration of relations within a user model (UM). Then, we propose these principles in the visualization of an existing adaptive system.

Cultural Heritage (CH) is a challenging domain of application for novel Information and Communication Technologies (ICT), where visualization plays a major role in enhancing visitors' experience, either onsite or online. Technology-supported natural human-computer interaction is a key factor in enabling access to CH assets. Advances in ICT ease visitors to access collections online and better experience CH onsite. The range of visualization devices - from tiny smart watch screens and wall-size large situated public displays to the latest generation of immersive head-mounted displays - together with the increasing availability of real-time 3D rendering technologies for online and mobile devices and, recently, Internet of Things (IoT) approaches, require exploring how they can be applied successfully in CH. Following the successful workshop at AVI 2016 and the large numbers of recent events and projects focusing on CH and, considering that 2018 has been declared the European Year of Cultural Heritage, the goal of the workshop is to bring together researchers and practitioners interested in presenting and discussing the potential use of state-of-the-art advanced visual interfaces in enhancing our daily CH experience.

Polar coordinates have been widely used in various techniques of interactive data visualization. The spatial organization through circular and radial layouts is implemented in a wide range of statistical charts and plots and is applicable for space-filling techniques and for node-link-group diagrams. Different arrangements of dots, lines and areas in polar coordinates create grids for data distribution, aggregation and linking.

The exploration of moderately dense networks is used in various challenges of visual data analysis. Frequently, the solutions lay in graph drawing, based on automatic force-directed layout, which results in a spontaneous and irreproducible node-link diagram. Currently available approaches to improve its readability are generally oriented to finite rendering without providing to the analyst handy tools for post-layout manipulations. Enabling indirect manual control on visualizations through multi-step menus may appear difficult to learn and use. Thus, this problem requires a more intuitive way of solving. This paper presents an original toolset for user-guided refinement of the force-directed graph layout, with a bias on pen-centric sketching techniques.

Many websites allow users to rate items and share their ratings with others, for social or personalisation purposes. In recommender systems in particular, personalised suggestions are generated by predicting ratings for items that users are unaware of, based on the ratings users provided for other items. Explicit user ratings are collected by means of graphical widgets referred to as ‘rating scales’. Each system or website normally uses a specific rating scale, in many cases differing from scales used by other systems in their granularity, visual metaphor, numbering or availability of a neutral position. While many works in the field of survey design reported on the effects of rating scales on user ratings, these, however, are normally regarded as neutral tools when it comes to recommender systems. In this paper, we challenge this view and provide new empirical information about the impact of rating scales on user ratings, presenting the results of three new studies carried out in different domains. Based on these results, we demonstrate that a static mathematical mapping is not the best method to compare ratings coming from scales with different features, and suggest when it is possible to use linear functions instead.

This brief introduction begins with an overview of the types of research that are relevant to the special issue on Big Personal Data in Interactive Intelligent Systems. The overarching question is: How can big personal data be collected, analyzed, and exploited so as to provide new or improved forms of interaction with intelligent systems, and what new issues have to be taken into account? The three articles accepted for the special issue are then characterized in terms of the concepts of this overview.

Cultural heritage traditionally draws a lot of research attention when it comes to exploring the potential benefits from application of novel technology in realistic settings. The domain is rich of physical as well as virtual sites objects and infinite information about them. Hence, it is only natural that whenever new technology appears, it is experimented in cultural heritage -- from early dialog systems to state of the art Humanoid robots, eye trackers, virtual/augmented reality devices, and the Internet of Things (IoT). The AVI-CH workshop nicely demonstrate this with the diversity of topics presented by the papers accepted to the workshop.

The paper presents the result on cross-representation mediation of user models in the context of movie recommendation. We analyze the possibility of initializing the user models for a content-based recommender starting from movie ratings provided by users in other social applications. We focus in particular on (i) an approach for inferring user model preferences from rating and (ii) the experimentation of several methods to solve the missing value problem exploiting community-based ratings. We tested different variations of the proposed approach exploiting a subset of the MovieLens 10M Dataset, computing rating predictions, and MAE.

This paper describes a novel methodology to evaluate a social media application in its formative phase of design. Taking advantage of the experiences developed in the Alternate Reality Games, we propose to insert game mechanics in the test setting of a formative evaluation of a prototypical social system. As a use case, we present the evaluation of WantEat, a prototypical social mobile application in the gastronomical domain. The evaluation highlighted how the gamification of a field trial can yield good results when evaluating social applications in prototypical status. From a methodological point of view, gamifying a field trial overcomes the cold start problem, caused by the absence of active communities, which can prevent the participation of users and therefore the collection of reliable data. Our experience showed that the gamification of a field evaluation is feasible and can likely increase the quantity of both browsing actions and social actions performed by users. Based on these results, we then are able to provide a set of guidelines to gamify the evaluation session of an interactive system.

Since 2007, the PATCH workshop series (https://patchworkshopseries.wordpress.com/) have been gathering successfully researchers and professionals from various countries and institutions to discuss the topics of digital access to cultural heritage and specifically the personalization aspects in this process. Due to this rich history, the reach of the PATCH workshop in various research communities is extensive.

Abstract Learning can benefit from the modern Web structure through the convergence of top‐down encyclopedic institutional knowledge and bottom‐up user‐generated annotations. A promising approach to such convergence consists in leveraging the social functionalities in 3.0 executable environments through the recommendation of tags with the mediation of lexical and semantic resources. This paper addresses such issues through the design and evaluation of a tag recommendation system in a Web 3.0 Web portal, ‘150 Digit’. Designed for schools, 150 Digit encourages students and teachers to interact with a set of four exhibitions on the historical and social aspects of the Italian unification process in a virtual environment. The website displays the exhibits and their related documents promoting the users' active participation through tagging, voting and commenting on the exhibits. Tags become a way for students to create and explore new relations among the site contents, orthogonal to the institutional viewpoint. In this paper, we illustrate the recommendation strategy incorporated in 150 Digit, which relies on a semantic middleware to mediate between the input expressed by the users through tags and the top‐down institutional classification provided by the curators of the exhibitions. Following this, we describe the evaluation process conducted in a real experimental setting and discuss the evaluation results and their implications for learning environments.

This article introduces a framework for creating rich augmented environments based on a social web of intelligent things and people. We target outdoor environments, aiming to transform a region into a smart environment that can share its cultural heritage with people, promoting itself and its special qualities. Using the applications developed in the framework, people can interact with things, listen to the stories that these things tell them, and make their own contributions. The things are intelligent in the sense that they aggregate information provided by users and behave in a socially active way. They can autonomously establish social relationships on the basis of their properties and their interaction with users. Hence when a user gets in touch with a thing, she is also introduced to its social network consisting of other things and of users; she can navigate this network to discover and explore the world around the thing itself. Thus the system supports serendipitous navigation in a network of things and people that evolves according to the behavior of users. An innovative interaction model was defined that allows users to interact with objects in a natural, playful way using smartphones without the need for a specially created infrastructure. The framework was instantiated into a suite of applications called WantEat, in which objects from the domain of tourism and gastronomy (such as cheese wheels or bottles of wine) are taken as testimonials of the cultural roots of a region. WantEat includes an application that allows the definition and registration of things, a mobile application that allows users to interact with things, and an application that supports stakeholders in getting feedback about the things that they have registered in the system. WantEat was developed and tested in a real-world context which involved a region and gastronomy-related items from it (such as products, shops, restaurants, and recipes), through an early evaluation with stakeholders and a final evaluation with hundreds of users.

Wanteat is a framework and a suite of applications which allow users to interact with and explore mixed social networks of smart objects and people in the gastronomy domain, thus promoting the cultural heritage of a territory. Wanteat interaction model is based on the concept of a "wheel" [1].

We focus on collaborative filtering dealing with self-organizing communities, host mobility, wireless access, and ad-hoc communications. In such a domain, knowledge representation and users profiling can be hard; remote servers can be often unreachable due to client mobility; and feedback ratings collected during random connections to other users' ad-hoc devices can be useless, because of natural differences between human beings. Our approach is based on so called Affinity Networks, and on a novel system, called MobHinter, that epidemically spreads recommendations through spontaneous similarities between users. Main results of our study are two fold: firstly, we show how to reach comparable recommendation accuracies in the mobile domain as well as in a complete knowledge scenario; secondly, we propose epidemic collaborative strategies that can reduce rapidly and realistically the cold start problem.

This position paper describes the role ontologies can play in Mobile Context-Aware recommender systems. In a Semantic Web vision of recommender systems, the adoption of ontologies for modeling the domain, the context and the adaptation process can contribute to tailor the right information/service to users and thus facilitate the user-system interaction and the system communication with other agents.

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