Olfactory stimuli have demonstrated the potential to evoke emotional depth and enhance user experiences in HCI. Yet, their role in shaping perceptions of social robots remains largely untapped. This study investigates how olfactory (scent) and auditory (voice) stimuli influence user perceptions of social robots. Using a 2x2 between-subjects design, participants interacted with a social robot under conditions with pleasant/unpleasant scents and friendly/unfriendly voices. The study measured perceived trust, friendliness, competence, and engagement. Our findings show that pleasant scents can enhance the perceptions of friendliness and engagement, while friendly voices can improve trust, friendliness, and engagement. The congruent combination of scents and voices affects friendliness and engagement but does not influence trust and competence. This study contributes to the growing work on multi-sensory Human-Robot Interaction (HRI) design, offering implications for creating more socially interactive robots.

This study aimed to detect stressful hazard perception scenarios subjectively and objectively when using intelligent driving systems. We used electrooculography (EOG), electroencephalography (EEG), subjective ratings, and interviews to identify potential stressful hazard perceptions and record improvements in an intelligent navigation-guided pilot (NGP) system. Moreover, we analyzed electrophysiological data. Our study contributes to the use of engagement, concentration, and phase locking value connectivity based on EEG to support previous research methodologies using beta power, pupil size, fixation ratio, fixation duration, and subjective evaluations for investigating hazard perception. Our analyses showed that stressful hazard perception scenarios occurred mainly when encountering broken and solid lines, frequent lane changes, cars approaching suddenly, several cars driving in parallel, and the decision to change lanes but immediately pulling back upon using the NPG system. Our findings shed light on obtaining accurate results based on subjective and objective evaluations for developing intelligent driving systems.

Aspect-level sentiment classification is a fine-grained task in sentiment analysis whose main purpose is to identify the sentiment polarity of a specific aspect. Current Graph Convolutional Network (GCN) has its distinctive superiority in tackling sentiment classification both semantically and syntactically. However, GCN still has deficiencies in introducing the noise during processing and dealing with sentences of complex structure. To address these issues, we propose a novel Bi-branch GCN (Bi-B GCN). In our model, an attention weight graph, by employing the attention mechanism, is constructed to substitute the basic syntax dependency tree and thus to remove the irrelevant information. Furthermore, a semantic dependency graph is devised to supplement the semantic information to the syntax dependency tree, based on which the connection between different words can be captured. In addition, on the task of sentiment classification, the integration of semantic information and the syntactic information is conducted by using a combinational gated mechanism. Substantial experiments to validate the working performance of Bi-B GCN are performed on a variety of datasets. The encouraging results establish a strong evidence of the high accuracy of the proposed model.