Visualized Emotion Ontology: a model for representing visual cues of emotions

Knowledge Engineering Framework for IoT Robotics Applied to Smart Healthcare and Emotional Well-Being

Social companion robots are getting more attention to assist elderly people to stay independent at home and to decrease their social isolation. When developing solutions, one remaining challenge is to design the right applications that are usable by elderly people. For this purpose, co-creation methodologies involving multiple stakeholders and a multidisciplinary researcher team (e.g., elderly people, medical professionals, and computer scientists such as roboticists or IoT engineers) are designed within the ACCRA (Agile Co-Creation of Robots for Ageing) project. This paper will address this research question: How can Internet of Robotic Things (IoRT) technology and co-creation methodologies help to design emotional-based robotic applications? This is supported by the ACCRA project that develops advanced social robots to support active and healthy ageing, co-created by various stakeholders such as ageing people and physicians. We demonstra this with three robots, Buddy, ASTRO, and RoboHon, used for daily life, mobility, and conversation. The three robots understand and convey emotions in real-time using the Internet of Things and Artificial Intelligence technologies (e.g., knowledge-based reasoning).

Emotion Detection for Social Robots Based on NLP Transformers and an Emotion Ontology

For social robots, knowledge regarding human emotional states is an essential part of adapting their behavior or associating emotions to other entities. Robots gather the information from which emotion detection is processed via different media, such as text, speech, images, or videos. The multimedia content is then properly processed to recognize emotions/sentiments, for example, by analyzing faces and postures in images/videos based on machine learning techniques or by converting speech into text to perform emotion detection with natural language processing (NLP) techniques. Keeping this information in semantic repositories offers a wide range of possibilities for implementing smart applications. We propose a framework to allow social robots to detect emotions and to store this information in a semantic repository, based on EMONTO (an EMotion ONTOlogy), and in the first figure or table caption. Please define if appropriate. an ontology to represent emotions. As a proof-of-concept, we develop a first version of this framework focused on emotion detection in text, which can be obtained directly as text or by converting speech to text. We tested the implementation with a case study of tour-guide robots for museums that rely on a speech-to-text converter based on the Google Application Programming Interface (API) and a Python library, a neural network to label the emotions in texts based on NLP transformers, and EMONTO integrated with an ontology for museums; thus, it is possible to register the emotions that artworks produce in visitors. We evaluate the classification model, obtaining equivalent results compared with a state-of-the-art transformer-based model and with a clear roadmap for improvement.

Examining Potential Usability and Health Beliefs Among Young Adults Using a Conversational Agent for HPV Vaccine Counseling

The human papillomavirus (HPV) vaccine is the most effective way to prevent HPV-related cancers. Integrating provider vaccine counseling is crucial to improving HPV vaccine completion rates. Automating the counseling experience through a conversational agent could help improve HPV vaccine coverage and reduce the burden of vaccine counseling for providers. In a previous study, we tested a simulated conversational agent that provided HPV vaccine counseling for parents using the Wizard of OZ protocol. In the current study, we assessed the conversational agent among young college adults (n=24), a population that may have missed the HPV vaccine during their adolescence when vaccination is recommended. We also administered surveys for system and voice usability, and for health beliefs concerning the HPV vaccine. Participants perceived the agent to have high usability that is slightly better or equivalent to other voice interactive interfaces, and there is some evidence that the agent impacted their beliefs concerning the harms, uncertainty, and risk denials for the HPV vaccine. Overall, this study demonstrates the potential for conversational agents to be an impactful tool for health promotion endeavors.

Early Usability Assessment of a Conversational Agent for HPV Vaccination

With the emerging use of speech technology in consumer goods, we experimented with the application of conversational agents for the communication of health information relating to HPV vaccine. Research have stated that one-to-one contact between providers and patients have a variety of positive influences on patients' perception towards vaccines, even leading to uptake, compared to paper-based methods. We implemented a Wizard of Oz experiment that counsels adults with children (n=18) on the HPV vaccine, using an iPad tablet and dialogue script developed by public health collaborators, and for early testing of a prospective conversational agent in this area. Our early results show that non-vaccine hesitant parents believed that agent was easy to use and had capabilities needed, despite the desire for additional features. Our future work will involve developing a dialogue engine to provide automated dialogue interaction and future improvements and experimentation for the speech interface.

Introduction: selected extended articles from the 2nd International Workshop on Semantics-Powered Data Analytics (SEPDA 2017)

In this editorial, we first summarize the 2nd International Workshop on Semantics-Powered Data Analytics (SEPDA 2017) held on November 13, 2017 in Kansas City, Missouri, U.S.A., and then briefly introduce 13 research articles included in this supplement issue, covering topics such as Semantic Integration, Deep Learning, Knowledge Base Construction, and Natural Language Processing.

VEO-Engine: Interfacing and Reasoning with an Emotion Ontology for Device Visual Expression

In order for machines to understand or express emotion to users, the specific emotions must be formally defined and the software coded to how those emotions are to be expressed. This is particularly important if devices or computer-based tools are utilized in clinical settings, which may be stressful for patients and where emotions can dominate their decision making. We have reported our development and feasibility results of an ontology, Visualized Emotion Ontology (VEO), that links abstract visualizations that express specific emotions. Here, we used VEO with the VEO-Engine, a software API package that interfaces with the VEO. The VEO-Engine was developed in Java using Apache Jena and OWL-API. The software package was tested on a Raspberry Pi machine with a small touchscreen display that linked each visualization to an emotion. The VEO-Engine stores input parameters of emotional situations and valences to reason and interpret users' emotions using the ontology-based reasoner. With this software, devices can interfaced wirelessly, so smart devices with visual displays can interact with the ontology. By means of the VEO-Engine, we show the portability and usability of the VEO in human-computer interaction.