1,720,992 research outputs found
A framework for creative embodied interfaces
No full textCreative joint activity is a form of real-time dynamic problem solving in which people collaborate to reach a common creative goal (e.g., to solve a mathematical problem, to improvise a piece of music, to write a novel, to sketch a story, and so on). While there exist interfaces able to produce social, emotional, communicative signals while collaborating with single human users to go through the creative process, the design of embodied interfaces able to observe and simultaneously effectively support creative joint activity with multiple human users is still an emerging research field. We define Creative Embodied Interfaces (CEIs) such interfaces, having either anthropomorphic or non-anthropomorphic aspect, and being either physically or virtually present in the real world. We argue that CEIs will enable a novel interaction paradigm that could be exploited in several fields such as science, education, health-care, arts, entertainment, social inclusion, companionship. This paper is aimed at providing definition and a first framework of CEIs combining psychological theories of creativity and computational models of social signal analysis/synthesis in avatars
A Segmentation Framework based on Cognitive Sciences for Empowering Hybrid Co-Working in Industry 5.0
No full textIndustry 5.0 rethinks the role of human operators in production processes with the final goal to promote societal well-being. To achieve such a goal, novel computational approaches reshaping human-machine collaboration are needed. This paper presents a computational framework, stemmed from Cognitive Sciences, to enable human operators and machines to share a cognitive common ground in co-working hybrid processes
Computational Model of Entrainment within Small Groups of People: Toward Novel Approaches to KANSEI information Processing
No full textGet Together in the Middle-earth: A First Step towards Hybrid Intelligence Systems
No full textIn the last decade, the number of computer systems using AI has increased dramatically. To date, indeed, AI is present in almost all the aspects of the human everyday life. This resulted in the attempt of scholars in Computer Science to endow machines with human-like socio-cognitive skills and/or human-like embodiment to try to improve interactions. Such an approach, however, highlights several crucial issues related to the substantial differences between fine-grained human skills and what machines can do and learn. So, although being expensive and sophisticated tools, machines tend to be "idiots savants". Hybrid Intelligence (HI) is aimed to tackle this issue by proposing, as Akata and colleagues say, "systems that operate as mixed teams, where humans and machines cooperate synergistically, proactively, and purposefully to achieve shared goals". To our knowledge, however, HI is at a very early exploratory stage, and few concrete solutions to deal with it exist. In this position paper we introduce and briefly describe "Middle-Earth", a conceptual and experimental ground to study HI. Moreover, we present a first prototype of a software platform based on immersive VR environments, on which we plan to carry out in the future the first pioneering experiments on teams of humans and/or AI-driven agents getting together in Middle Earth to perform collaborative tasks
Towards Analysis of Expressive Gesture in Groups of Users: Computational Models of Expressive Social Interaction
No full textInteraction fidelity vs user’s workload in a VR environment: A pilot study
No full textThis paper describes a preliminary study on how Interaction Fidelity, shaped by a combination of visual, auditory and haptic modalities, impacts the user’s workload. A VR escape room environment consisting of 5 puzzles to be solved in a pre-defined order is presented. Preliminary analysis shows that further investigation on the VR escape room environment could provide insights on how IF influences the user’s workload depending on the type of task
A Hitchhiker's Guide towards Transactive Memory System Modeling in Small Group Interactions
No full textModeling Transactive Memory System (TMS) over time is an actual challenge of Human-Centered Computing. TMS is a group's meta-knowledge indicating the attribute of "who knows what". Conceiving and developing machines able to deal with TMS is a relevant step in the field of Hybrid Intelligence aiming at creating systems where human and artificial teammates cooperate in synergistic fashion. Recently, a TMS dataset has been proposed, where a number of audio and visual automated features and manual annotations are extracted taking inspiration from Social Sciences literature. Is it possible, on top of these, to model relationships between these engineered features and the TMS scores In this work we first build and discuss a processing pipeline; then we propose four possible classifiers, two of which are artificial neural networks-based. We observe that the largest obstacle towards modeling the target relationships currently lies in the little data availability for training an automatic system. Our purpose, with this work, is to provide hints on how to avoid some common pitfalls to train these systems to learn TMS scores from audio/visual features
Audio patterns as independent source of information for the perception and the control of orientation
No full text
Few Labels are Enough! Semi-supervised Graph Learning for Social Interaction
No full textEndowing machines with social intelligence is a fundamental goal of artificial social intelligence. Dealing with human-centered phenomena requires, however, a considerable amount of manually annotated data, making data annotation a costly and challenging task that hinders the training of supervised learning algorithms. In this study, we apply an approach grounded on Graph Convolutional Network (GCN) to alleviate the annotation burden. As a test bed, we select emergent states analysis with specific reference to the team potency. At first, we build the POTENCY dataset by fusing three datasets on social interaction. Next, we compute a set of multimodal features characterizing the social behavior of the team members and the team as one. Finally, we feed the POTENCY dataset to a semi-supervised GCN, trained on a binary node classification task, with variable amounts of labels. We show that GCN can assign team potency labels to an unlabeled team in the dataset by using only a few labeled examples (i.e., 10% of data), with performances comparable to or higher than those of two baseline algorithms carrying out the same task in a fully supervised way
- …
