Recognition of social signals, from human facial expressions or prosody of
speech, is a popular research topic in human-robot interaction studies. There
is also a long line of research in the spoken dialogue community that
investigates user satisfaction in relation to dialogue characteristics.
However, very little research relates a combination of multimodal social
signals and language features detected during spoken face-to-face human-robot
interaction to the resulting user perception of a robot. In this paper we show
how different emotional facial expressions of human users, in combination with
prosodic characteristics of human speech and features of human-robot dialogue,
correlate with users' impressions of the robot after a conversation. We find
that happiness in the user's recognised facial expression strongly correlates
with likeability of a robot, while dialogue-related features (such as number of
human turns or number of sentences per robot utterance) correlate with
perceiving a robot as intelligent. In addition, we show that facial expression,
emotional features, and prosody are better predictors of human ratings related
to perceived robot likeability and anthropomorphism, while linguistic and
non-linguistic features more often predict perceived robot intelligence and
interpretability. As such, these characteristics may in future be used as an
online reward signal for in-situ Reinforcement Learning based adaptive
human-robot dialogue systems.

研究发现，人机面对面互动过程中的社交信号、语言特征、情感表达等特征与用户对机器人的印象有关；其中，用户面部表情的快乐程度与机器人的好感度强相关，而对话特征与机器人的智能感知有关。这些特征有望成为在线补偿信号，用于基于增强学习的自适应人机对话系统中。

同情始於微笑，智慧始於語言：在人機交互中使用多模態功能

Sympathy Begins with a Smile, Intelligence Begins with a Word: Use of  Multimodal Features in Spoken Human-Robot Interaction

Contagions such as the spread of popular news stories, or infectious
diseases, propagate in cascades over dynamic networks with unobservable
topologies. However, "social signals" such as product purchase time, or blog
entry timestamps are measurable, and implicitly depend on the underlying
topology, making it possible to track it over time. Interestingly, network
topologies often "jump" between discrete states that may account for sudden
changes in the observed signals. The present paper advocates a switched dynamic
structural equation model to capture the topology-dependent cascade evolution,
as well as the discrete states driving the underlying topologies. Conditions
under which the proposed switched model is identifiable are established.
Leveraging the edge sparsity inherent to social networks, a recursive
$\ell_1$-norm regularized least-squares estimator is put forth to jointly track
the states and network topologies. An efficient first-order proximal-gradient
algorithm is developed to solve the resulting optimization problem. Numerical
experiments on both synthetic data and real cascades measured over the span of
one year are conducted, and test results corroborate the efficacy of the
advocated approach.

本文提出了一种切换动态结构方程模型，用于跟踪动态网络中基础拓扑的离散确认背后的驱动离散状态，一个通过交替最小二乘求解的快速一阶邻域梯度算法，验证试验表明这种方法的功效。