As human‐machine interaction (HMI) in healthcare continues to evolve, the issue of trust in HMI in healthcare has been raised and explored. It is critical for the development and safety of healthcare that humans have proper trust in medical machines. Intelligent machines that have applied machine learning (ML) technologies continue to penetrate deeper into the medical environment, which also places higher demands on intelligent healthcare. In order to make machines play a role in HMI in healthcare more effectively and make human‐machine cooperation more harmonious, the authors need to build good human‐machine trust (HMT) in healthcare. This article provides a systematic overview of the prominent research on ML and HMT in healthcare. In addition, this study explores and analyses ML and three important factors that influence HMT in healthcare, and then proposes a HMT model in healthcare. Finally, general trends are summarised and issues to consider addressing in future research on HMT in healthcare are identified.

Keyword spotting (KWS) deals with the identification of keywords in unconstrained speech, which is a natural, straightforward and friendly way for human-robot interaction (HRI). Most keyword spotters have the common problem of noise-robustness when applied to real-world environment with dramatically changing noises. Since visual information won't be affected by the acoustic noise, it can be utilized to complementarily improve the noise-robustness. In this paper, a novel audio-visual keyword spotting approach based on adaptive decision fusion under noisy conditions is proposed. In order to accurately represent the appearance and movement of mouth region, an improved local binary pattern from three orthogonal planes (ILBP-TOP) is proposed. Besides, a parallel two-step recognition based on acoustic and visual keyword candidates is conducted and generates corresponding acoustic and visual scores for each keyword candidate. Optimal weights for combining acoustic and visual contributions under diverse noise conditions are generated using a neural network based on reliabilities of the two modalities. Experiments show that our proposed audio-visual keyword spotting based on decision fusion significantly improves the noise robustness and attains better performance than feature fusion based audiovisual spotter. Additionally, ILBP-TOP shows more competitive performance than LBP-TOP.

Segmentation based methods have become the mainstream for detecting scene text with arbitrary orientations and shapes. In order to address challenging problems such as separating the text instances that are very close to each other, however, these methods often require time-consuming post-processing. In this paper, we propose an instance segmentation network (ISNet), which simultaneously generates prototype masks and per-instance mask coefficients. After linearly combining the two components, ISNet can implement fast text location. Furthermore, we apply self-distillation to train the ISNet and refine its detection accuracy. We have evaluated the proposed method on four popular benchmarks, i.e., ICDAR2015, ICDAR2017 MLT, CTW1500 and Total-Text, and the experimental results show that it can achieve better tradeoff between accuracy and efficiency for scene text detection.

The reliability and sustainability of innovation in megaprojects are of critical importance with complex stakeholders and substantial resource investments. In that respect, responsible innovation management is the key to the sustainable development of megaprojects. This paper proposes the concept of megaproject responsible innovation (MRI) and develops a holistic framework for MRI with four dimensions: (1) anticipation across the megaproject life cycle, (2) inclusion of various parties in a relationship of symbiosis, competition, and cooperation, (3) reflexivity of social responsibility, and (4) responsiveness to social expectations. Based on an ecosystem approach to megaproject innovation, we propose an ecological governance strategy for MIR. Moreover, different approaches are needed in innovation organizations which are part of specific ecological niches for effective and efficient governance.

As a typical biometric cue with great diversities, smile is a fairly influential signal in social interaction, which reveals the emotional feeling and inner state of a person. Spontaneous and posed smiles initiated by different brain systems have differences in both morphology and dynamics. Distinguishing the two types of smiles remains challenging as discriminative subtle changes need to be captured, which are also uneasily observed by human eyes. Most previous related works about spontaneous versus posed smile recognition concentrate on extracting geometric features while appearance features are not fully used, leading to the loss of texture information. In this paper, we propose a region-specific texture descriptor to represent local pattern changes of different facial regions and compensate for limitations of geometric features. The temporal phase of each facial region is divided by calculating the intensity of the corresponding facial region rather than the intensity of only the mouth region. A mid-level fusion strategy of support vector machine is employed to combine the two feature types. Experimental results show that both our proposed appearance representation and its combination with geometry-based facial dynamics achieve favorable performances on four baseline databases: BBC, SPOS, MMI, and UvA-NEMO.