Conversational agents (CAs) offer new functionality and convenience. While their sales have been soaring, they have also rapidly become victims of verbal abuse by their users. Without proper handling of abusive usage, abusers’ actions can be reinforced and transferred to real life. This study investigates whether alternative response styles of empathy orientation and emotional expressivity of voice-activated virtual assistants influence users’ moral emotions found to reduce verbal aggression as well as whether they affect user perceptions of the agent’s capability. Ninety-eight participants were assigned to one of the three emotional expressivity conditions (no-facial expression, fixed-facial expression, varied-facial expression) and interacted with two alternative empathy orientation conditions (other-oriented, self-oriented) of agents. The experimental results show that, regardless of the emotional expressivity types, the agent’s empathy orientation has a significant effect on the moral emotions and agent capability perceptions. Overall, an agent that employed other-oriented empathy style elicited most positive responses from the users. However, the preference was not across the board, as about one-third of the participants showed preference to the self-oriented CA. Users valued agents’ verbal contents and vocal characteristics above their facial expressions. Based on the study findings, we draw several design guidelines and suggest avenues for future research.

This paper proposes a deep learning-based patch label denoising method (LossDiff) for improving the classification of whole-slide images of cancer using a convolutional neural network (CNN). Automated whole-slide image classification is often challenging, requiring a large amount of labeled data. Pathologists annotate the region of interest by marking malignant areas, which pose a high risk of introducing patch-based label noise by involving benign regions that are typically small in size within the malignant annotations, resulting in low classification accuracy with many Type-II errors. To overcome this critical problem, this paper presents a simple yet effective method for noisy patch classification. The proposed method, validated using stomach cancer images, provides a significant improvement compared to other existing methods in patch-based cancer classification, with accuracies of 98.81%, 97.30% and 89.47% for binary, ternary, and quaternary classes, respectively. Moreover, we conduct several experiments at different noise levels using a publicly available dataset to further demonstrate the robustness of the proposed method. Given the high cost of producing explicit annotations for whole-slide images and the unavoidable error-prone nature of the human annotation of medical images, the proposed method has practical implications for whole-slide image annotation and automated cancer diagnosis.

With the advent of many movie content platforms, users face a flood of content and consequent difficulties in selecting appropriate movie titles. Although much research has been conducted in developing effective recommender systems to provide personalized recommendations based on customers’ past preferences and behaviors, not much attention has been paid to leveraging users’ sentiments and emotions together. In this study, we built a new graph-based movie recommender system that utilized sentiment and emotion information along with user ratings, and evaluated its performance in comparison to well known conventional models and state-of-the-art graph-based models. The sentiment and emotion information were extracted using fine-tuned BERT. We used a Kaggle dataset created by crawling movies’ meta-data and review data from the Rotten Tomatoes website and Amazon product data. The study results show that the proposed IGMC-based models coupled with emotion and sentiment are superior over the compared models. The findings highlight the significance of using sentiment and emotion information in relation to movie recommendation.