In this paper, we propose a new annotation scheme to classify different types of clauses in Terms-and-Conditions contracts with the ultimate goal of supporting legal experts to quickly identify and assess problematic issues in this type of legal documents. To this end, we built a small corpus of Terms-and-Conditions contracts and finalized an annotation scheme of 14 categories, eventually reaching an inter-annotator agreement of 0.92. Then, for 11 of them, we experimented with binary classification tasks using few-shot prompting with a multilingual T5 and two fine-tuned versions of two BERT-based LLMs for Italian. Our experiments showed the feasibility of automatic classification of our categories by reaching accuracies ranging from .79 to .95 on validation tasks.

This paper presents key aspects that designers and Human–Computer Interaction practitioners might encounter when designing multimodal interaction for older adults, focusing on the trade-offs that might occur as part of the design process. The paper gathers literature on multimodal interaction and assistive technology, and describes a set of design challenges specific for older users. Building on these main design challenges, four trade-offs in the design of multimodal technology for this target group are presented and discussed. To highlight the relevance of the trade-offs in the design process of multimodal technology for older adults, two of the four reported trade-offs are illustrated with two user studies that investigate mid-air and speech-based interaction with a tablet device. The first study explores the design trade-offs related to redundant multimodal commands in older, middle-aged and younger adults, whereas the second one investigates the design choices related to the definition of a set of mid-air one-hand gestures and voice input commands for older adults. Further reflections highlight the design trade-offs that such considerations bring in the process, providing an overview of the design choices involved and of their potential consequences.

Since they can integrate a wide range of interactive modalities, multimodal interfaces are considered to improve accessibility for a variety of users, including older adults. However, only few works have actually explored how older adults approach multimodal interaction outside specific contexts and have done so mainly in comparison to much younger users. This study explores how older (65+ years old), middle-aged (55-65 years old) and younger adults (25-35 years old) use mobile multimodal interaction in an everyday activity (i.e. taking photos with a tablet) by using midair gestures and voice commands, and investigates the differences and similarities between the considered age groups. Preliminary findings from a video-analysis show that all groups easily combine the proposed modalities when interacting with a tablet device. Furthermore, compared to younger adults, older and middle-aged adults show similarities in the way they perform gesture and voice commands.

Incorporating research on personality recognition into computers, both from a cognitive as well as an engineering perspective, would facilitate the interactions between humans and machines. Previous attempts on personality recognition have focused on a variety of different corpora (ranging from text to audiovisual data), scenarios (interviews, meetings), channels of communication (audio, video, text), and different subsets of personality traits (out of the five ones from the Big Five Model). Our study uses simple acoustic and visual nonverbal features extracted from multimodal data, which have been recorded in previously uninvestigated scenarios, and consider all five personality traits and not just a subset. First, we look at the human-machine interaction scenario, where we introduce the display of different “collaboration levels.” Second, we look at the contribution of the human-human interaction (HHI) scenario on the emergence of personality traits. Investigating the HHI scenario creates a stronger basis for future human-agents interactions. Our goal is to study, from a computational approach, the emergence degree of the five personality traits in these two scenarios. The results demonstrate the relevance of each of the two scenarios when it comes to the degree of emergence of certain traits and the feasibility to automatically recognize personality under different conditions.

The paper presents the preliminary studies carried out in ECOMODE (Event-Driven Compressive Vision for Multimodal Interaction with Mobile Devices), an EU project aiming to design a multimodal interaction suitable for elderly people using mobile technology. The project exploits EDC (Event Driven Compressive) paradigm to realize a new generation of low-power multimodal human-computer interfaces for mobile devices. In our studies we investigated (1) how older adults interact with mobile devices and (2) how they use applications based on mid-air gesture interaction. Our studies basically confirm the findings of previous works about usability and accessibility issues, specifically characterizing elderly users' interaction with mobile technology.

Children with reading difficulties face several obstacles in learning to fluently read written material. Multimedia applications integrating text-to-speech (TTS) synthesisers are valuable tools for supporting reading activities. The paper presents GARY, an application that combines TTS synthesis with eye tracking. GARY is meant to be used on a tablet device coupled with an eye tracker. Making use of the information from reader's eye movement, the system allows users to adapt the speed rate of the synthesised voice to their pace of reading. The paper describes the system, its functioning and future steps in designing a tool for supporting readers' ability in making the connection between the sounds heard and the letters read.

Being able to be attentive as necessary on an e-learning task is not always easy for a young student, especially for children affected by Attention Deficit Hyperactivity Disorder. To support this category of students, we aim to design a software module able to: (a) automatically detect their level of attention when working on a learning task and, (b) propose suitable stimuli to re-capture their attention on it. This paper describes the first two steps towards this application: (1) the building of an attention model, meant as the base of the software module for automatically measuring the attention level, and (2) the study of a face tracker to measure the attention level in real time. Finally, it proposes some possible stimuli which can be used to re-capture the lost attention.

This one-day workshop is going to bring together top researchers and practitioners working in the area of interactive e-books for children. The goal of the workshop is defining key directions for future research in the design process and implementation of this kind of books. The workshop critically explores opportunities and challenges for making interactive e-books effective for children's learning and entertainment.

The user's personality in Human-Computer Interaction (HCI) plays an important role for the overall success of the interaction. The present study focuses on automatically recognizing the Big Five personality traits from 2-5 min long videos, in which the computer interacts using different levels of collaboration, in order to elicit the manifestation of these personality traits. Emotional Stability and Extraversion are the easiest traits to automatically detect under the different collaborative settings: all the settings for Emotional Stability and intermediate and fully-non collaborative settings for Extraversion. Interestingly, Agreeableness and Conscientiousness can be detected only under a moderately non-collaborative setting. Finally, our task does not seem to activate the full range of dispositions for Creativity.

Personality plays an important role in the way people manage the images they convey in self-presentations and employment interviews, trying to affect the other"s first impressions and increase effectiveness. This paper addresses the automatically detection of the Big Five personality traits from short (30-120 seconds) self-presentations, by investigating the effectiveness of 29 simple acoustic and visual non-verbal features. Our results show that Conscientiousness and Emotional Stability/Neuroticism are the best recognizable traits. The lower accuracy levels for Extraversion and Agreeableness are explained through the interaction between situational characteristics and the differential activation of the behavioral dispositions underlying those traits.

This paper targets the automatic detection of individual performances in group tasks by means of short sequences, "thin slices", of nonverbal behavior. We designed our task as a classification one. We also investigated the relevance of social context in our task and the effectiveness of our feature selection.

This paper targets the automatic detection of personality traits in a meeting environment by means of audio and visual features; information about the relational context is captured by means of acoustic features designed to that purpose. Two personality traits are considered: Extraversion (from the Big Five) and the Locus of Control. The classification task is applied to thin slices of behaviour, in the form of 1-minute sequences. SVM were used to test the performances of several training and testing instance setups, including a restricted set of audio features obtained through feature selection. The outcomes improve considerably over existing results, provide evidence about the feasibility of the multimodal analysis of personality, the role of social context, and pave the way to further studies addressing different features setups and/or targeting different personality traits.

One of the research goals in the human-computer interaction community is to build believable Embodied Conversational Agents, that is, agents able to communicate complex information with human-like expressiveness and naturalness. Since emotions play a crucial role in human communication and most of them are expressed through the face, having more believable ECAs implies to give them the ability of displaying emotional facial expressions.This paper presents a system based on Hidden Markov Models (HMMs) for the synthesis of emotional facial expressions during speech. The HMMs were trained on a set of emotion examples in which a professional actor uttered Italian non-sense words, acting various emotional facial expressions with different intensities.The evaluation of the experimental results, performed comparing the "synthetic examples" (generated by the system) with a reference "natural example" (one of the actor's examples) in three different ways, shows that HMMs for emotional facial expressions synthesis have some limitations but are suitable to make a synthetic Talking Head more expressive and realistic.

In this paper we present the multilingual VoIP (Voice over Internet Protocol networks) corpora collected for the second showcase of the Nespole! project in the tourism and medical domains. The corpora comprise over 20 hours of human-to-human monolingual dialogues in English, French, German and Italian: 66 dialogues in the tourism domain and 49 in the medical domain. We describe in detail the data collection (technical set-up, scenarios for each domain, recording procedure and data transcription), as well as statistically illustrated corpora and a preliminary data analysis. 1.