Modeling 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.

A Hitchhiker's Guide towards Transactive Memory System Modeling in Small Group Interactions / Tartaglione, E.; Biancardi, B.; Mancini, M.; Varni, G.. - (2021), pp. 254-262. (Intervento presentato al convegno 23rd ACM International Conference on Multimodal Interaction, ICMI 2021 tenutosi a Montreal) [10.1145/3461615.3485414].

A Hitchhiker's Guide towards Transactive Memory System Modeling in Small Group Interactions

Mancini M.;
2021

Abstract

Modeling 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.
2021
23rd ACM International Conference on Multimodal Interaction, ICMI 2021
Explainable Models; Multi-modal Group Behaviour Analysis; Social Signal Processing; Transactive Memory System
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
A Hitchhiker's Guide towards Transactive Memory System Modeling in Small Group Interactions / Tartaglione, E.; Biancardi, B.; Mancini, M.; Varni, G.. - (2021), pp. 254-262. (Intervento presentato al convegno 23rd ACM International Conference on Multimodal Interaction, ICMI 2021 tenutosi a Montreal) [10.1145/3461615.3485414].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1679360
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