Sep 2026· Companion Publication of the 28th International Conference on Multimodal Interaction· 1 citation· 46 references
Computer Science
Abstract
Physiological affect sensing in naturalistic group interaction is typically limited more by label density than by sensor density: wearable devices produce thousands of time windows, while self-report labels are collected only a few times per session. This paper asks how to mine more supervisory signal from sparsely labelled group sessions. More broadly, we treat this as a step toward collective-state sensing: better individual affect estimates within a team help reveal the affective structure emerging at group level. Using GroupAffect-4, a four-person collaborative dataset with wearable physiology, eye tracking, Big Five personality, and post-task VAD labels, we study a progression of label-mining strategies: no augmentation, Gaussian Process (GP) pseudo-labelling, personality-aware trust weighting, and joint personality-plus-confidence weighting under a shared target-construction pipeline. We further ask whether individual predispositions (here, Big Five personality) together with group-level context improve which pseudo-labels should be trusted. They help, but not in the originally hypothesised graded way: the compressed BFI cosine-similarity range (0.91–0.99) renders weight ordering negligible (perm p = 0.39), so personality similarity acts as a same-team membership filter rather than a calibrated trust dial. In the known-team setting, pseudo-label augmentation improves over the labelled-only baseline, with the clearest benefit coming from within-team filtering rather than from fine-grained personality ranking. With smoothing, A1–A3 are tied to rounding on Valence and Arousal, while A3 has the highest Dominance score among the augmented SVM variants; the practical choice is therefore dimension-dependent. Cross-subject LOSO transfer remains encouraging for such a small and sparsely labelled dataset, especially on Arousal, whereas strict session-isolated LOGO removes the augmentation benefit. At the current scale of only 10 groups, LOGO should be read as a conservative lower bound on unseen-group transfer rather than as a definitive ceiling, but the present evidence shows that personality weighting mainly operates as a within-team mechanism.
This publication proposes a definition and a classification of agile software development approaches and analyses ten software development methods that can be characterized as being "agile" against the defined criterion.
P. Abrahamsson, O. Salo, Jussi Ronkainen et al.· arXiv.org· 727 citations· ⚡54
The study shows that agile practices improve both informal and formal communication, but indicates that, in larger development situations involving multiple external stakeholders, a mismatch of adequate communication mechanisms can sometimes even hinder the communication.
M. Pikkarainen, Jukka Haikara, O. Salo et al.· Empirical Software Engineeri...· 401 citations· ⚡48
The results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.
Nicolò Paternoster, Carmine Giardino, M. Unterkalmsteiner et al.· Information and Software Tec...· 394 citations· ⚡54
The possibility of inferring high-dimensional data inference in a model that consists of a prior and an auxiliary differentiable constraint given some additional information is considered, thereby allowing a range of potential applications in adapting models to new domains and tasks.
Alexandros Graikos, Esmeralda S. Whitammer, N. Jojic et al.· Neural Information Processin...· 316 citations· ⚡15
It is proved that any global minimizer of the trajectory balance objective can define a policy that samples exactly from the target distribution, and empirically demonstrate the benefits of the trajectories balance objective for GFlowNet convergence, diversity of generated samples, and robustness to long action sequenc...
Esmeralda S. Whitammer, Moksh Jain, Emmanuel Bengio et al.· Neural Information Processin...· 302 citations· ⚡60
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
MIT News · Artificial Intelligence· news.mit.eduOct 13, 2026
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
MIT News · Artificial Intelligence· news.mit.eduOct 7, 2026
Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.
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