Virtual Assistant Research Articles

Replies to Queries in Gynecologic Oncology by Bard, Bing and the Google Assistant

When women receive a diagnosis of a gynecologic malignancy, they can have questions about their diagnosis or treatment that can result in voice queries to virtual assistants for more information. Recent advancement in artificial intelligence (AI) has transformed the landscape of medical information accessibility. The Google virtual assistant (VA) outperformed Siri, Alexa and Cortana in voice queries presented prior to the explosive implementation of AI in early 2023. The efforts presented here focus on determining if advances in AI in the last 12 months have improved the accuracy of Google VA responses related to gynecologic oncology. Previous questions were utilized to form a common basis for queries prior to 2023 and responses in 2024. Correct answers were obtained from the UpToDate medical resource. Responses related to gynecologic oncology were obtained using Google VA, as well as the generative AI chatbots Google Bard/Gemini and Microsoft Bing-Copilot. The AI narrative responses varied in length and positioning of answers within the response. Google Bard/Gemini achieved an 87.5% accuracy rate, while Microsoft Bing-Copilot reached 83.3%. In contrast, the Google VA’s accuracy in audible responses improved from 18% prior to 2023 to 63% in 2024. While the accuracy of the Google VA has improved in the last year, it underperformed Google Bard/Gemini and Microsoft Bing-Copilot so there is considerable room for further improved accuracy.

Advanced Techniques for Real-time Facial Expression Recognition Using Deep Learning

Abstract—Developing systems that can automatically recognize and interpret human emotions from facial expressions is the aim of the quickly expanding field of facial emotion identification and detection research. This technology finds applications in a wide range of areas, including as healthcare, marketing, security, and human-computer interface. Using computer vision and machine learning algorithms, facial emotion recognition systems analyze a face’s features and classify it into numerous emotional categories, including joyful, sorrowful, angry, fearful, and surprised.The three steps in the multi-step process that goes into identifying facial emotions are face detection, facial feature extraction, and emotion categorization. Thanks to recent advances in deep learn- ing, facial emotion detection systems can now identify emotions with high resilience and precision. Further, the development of real-time face expression recognition systems has opened up new avenues for applications such as sentiment analysis, emotional intelligence, and affective computing. This technology could fundamentally alter human-machine interactions and open the way to more compassionate and personalized relationships.A multitude of applications, including virtual assistants, mental health aids, and human-centered technology, will be greatly impacted by the development of systems for identifying and de- tecting facial expressions. Artificial intelligence (AI) technologies that recognize emotions allow people to interact with the digital world in more intelligent and flexible ways. But the complexity of emotion identification lies in the fact that it requires context and geometric elements in addition to facial expressions.

Sawit Kita WebApp Development: Artificial - Based E-Learning Intelligence and Community to Drive Actual Information Collaboration and Innovation of Palm Oil Farmers In Indonesia

Web App development within the scope of the company is very in necessary in today's technological era. Development increasingly advanced technology is proof that the level of technology is high in the field of computer science. Data management is very important for a company companies, especially palm oil companies. A company needs a tool or system for managing and monitoring data company to be more efficient, simple, fast and precise. There fore the author designing a system to make it easier for companies to manage data palm oil plantations using a very easy Web App used. This research aims to develop the Sawit Kita Web App, an Artificial Intelligence based e-learning platform and community forpalm oil farmers in Indonesia. The Sawit Kita Web App consists of three main features, namely GPT 4 chat, e-learning modules and community platform. Chat GPT 4 is an Artificial Intelligence-based virtual assistant that can answer questions, provide suggestions, and produce creative content about oil palm cultivation with customization of data input by developer. The e-learning module is a feature that provides interactive and easy-to-understand learning materials about cultivation sustainable palm oil. The community platform is a feature that facilitate communication and collaboration between oil palm farmers and researchers, extension workers, and other related parties in a discussion forum. Target This research is to improve access and quality of information, literacy and skills, participation and collaboration, as well as productivity and well-being palm oil farmers in Indonesia. The research method used is method research and development with stages of analysis, design, development, implementation, and evaluation. This research is expected to provide contribution to the development of the oil palm plantation sector in Indonesia.

MoConVQ: Unified Physics-Based Motion Control via Scalable Discrete Representations

In this work, we present MoConVQ, a novel unified framework for physics-based motion control leveraging scalable discrete representations. Building upon vector quantized variational autoencoders (VQ-VAE) and model-based reinforcement learning, our approach effectively learns motion embeddings from a large, unstructured dataset spanning tens of hours of motion examples. The resultant motion representation not only captures diverse motion skills but also offers a robust and intuitive interface for various applications. We demonstrate the versatility of MoConVQ through several applications: universal tracking control from various motion sources, interactive character control with latent motion representations using supervised learning, physics-based motion generation from natural language descriptions using the GPT framework, and, most interestingly, seamless integration with large language models (LLMs) with in-context learning to tackle complex and abstract tasks.

Children’s conversational voice search as learning: a literature review

Purpose This paper aims to critically review the intersection of searching and learning among children in the context of voice-based conversational agents (VCAs). This study presents the opportunities and challenges around reconfiguring current VCAs for children to facilitate human learning, generate diverse data to empower VCAs, and assess children’s learning from voice search interactions. Design/methodology/approach The scope of this paper includes children’s use of VCAs for learning purposes with an emphasis on conceptualizing their VCA use from search as learning perspectives. This study selects representative works from three areas of literature: children’s perceptions of digital devices, children’s learning and searching, and children’s search as learning. This study also includes conceptual papers and empirical studies focusing on children from 3 to 11 because this age spectrum covers a vital transitional phase in children’s ability to understand and use VCAs. Findings This study proposes the concept of child-centered voice search systems and provides design recommendations for imbuing contextual information, providing communication breakdown repair strategies, scaffolding information interactions, integrating emotional intelligence, and providing explicit feedback. This study presents future research directions for longitudinal and observational studies with more culturally diverse child participants. Originality/value This paper makes important contributions to the field of information and learning sciences and children’s searching as learning by proposing a new perspective where current VCAs are reconfigured as conversational voice search systems to enhance children’s learning.

Chatbots and Virtual Assistants in HRM: Exploring Their Role in Employee Engagement and Support

Chatbots and Virtual Assistants in HRM: Exploring Their Role in Employee Engagement and Support

Sentient libraries: empowering user expeditions with emotional artificial intelligence

Purpose The study aims to forecast emotional artificial intelligence’s (EAI) trajectory in libraries, provide actionable implementation recommendations and outline potential research paths. It seeks to establish ethical benchmarks, considering emotional well-being alongside informational needs. Understanding EAI in libraries becomes pivotal, envisioning libraries as empathetic, user-centric spaces. Design/methodology/approach This research uses a multifaceted approach, intertwining comprehensive literature analysis, illustrative case studies and forward-thinking assessments. It combines foundational knowledge from various academic sources, integrating real-world examples of EAI implementations in library settings. The methodology goes beyond observational analysis, scrutinizing ethical quandaries and societal impacts. It comprehensively explains EAI’s role while delineating guidelines for responsible integration and future exploration. Findings Integrating emotional intelligence (EI) enhances resource curation, fosters inclusive virtual learning environments and elevates continuous service enhancement in libraries. It allows tailored recommendations, emotionally perceptive virtual assistants and adaptive responses to diverse emotional needs, reshaping libraries into empathetic spaces. Research limitations/implications Ethical considerations pose significant challenges, especially regarding privacy, security and fair usage of EAI. Libraries must mitigate biases and ensure equitable emotional services. Adapting to diverse emotional needs within library services requires a delicate balance between empathetic approaches and upholding ethical standards. Practical implications EAI revolutionizes user experiences, offering personalized, engaging interactions while ensuring ethical, inclusive library environments. Implementing EAI requires navigating biases and ethical complexities, demanding continuous refinement and using user-focused strategies. Social implications Integrating EAI redefines the library’s role, transforming libraries into emotionally perceptive hubs catering to users’ intellectual and emotional needs. It emphasizes fairness, trust and ethical conduct in providing empathetic, inclusive services. Originality/value This research pioneers the integration of EI in libraries, offering insights into ethical frameworks, bias mitigation and user-centric approaches. It envisions libraries as empathetic, responsive spaces, enriching lives through emotional understanding and responsiveness.

Does conversation lead to better searches? Investigating single-shot and multi-turn spoken searches with children

Interactive speech technologies such as Siri and Amazon Echo allow people to search for information via speech. These systems generally take a single spoken statement or query as input, which may not be enough to fully capture the user’s information need. Therefore, multi-turn conversations have been proposed to establish more detail. A conversational approach can particularly benefit children who experience more difficulty formulating precise queries. We carried out a study with 32 children comparing multi-turn conversations with single-shot (or Query-Response) interactions. We compared the descriptions of the information needs, as well as children’s search experience. Findings indicate that more elaborate descriptions of children’s information needs (more keywords) were gathered through conversational search, leading to more focused search results compared with the Query-Response search. Further analysis of children’s responses yielded insights into their preferences for conversing with speech-based interfaces. This paper also offers design and methodological recommendations for conversational agents that support children’s information search.

Video and Audio Deepfake Datasets and Open Issues in Deepfake Technology: Being Ahead of the Curve

The revolutionary breakthroughs in Machine Learning (ML) and Artificial Intelligence (AI) are extensively being harnessed across a diverse range of domains, e.g., forensic science, healthcare, virtual assistants, cybersecurity, and robotics. On the flip side, they can also be exploited for negative purposes, like producing authentic-looking fake news that propagates misinformation and diminishes public trust. Deepfakes pertain to audio or visual multimedia contents that have been artificially synthesized or digitally modified through the application of deep neural networks. Deepfakes can be employed for benign purposes (e.g., refinement of face pictures for optimal magazine cover quality) or malicious intentions (e.g., superimposing faces onto explicit image/video to harm individuals producing fake audio recordings of public figures making inflammatory statements to damage their reputation). With mobile devices and user-friendly audio and visual editing tools at hand, even non-experts can effortlessly craft intricate deepfakes and digitally altered audio and facial features. This presents challenges to contemporary computer forensic tools and human examiners, including common individuals and digital forensic investigators. There is a perpetual battle between attackers armed with deepfake generators and defenders utilizing deepfake detectors. This paper first comprehensively reviews existing image, video, and audio deepfake databases with the aim of propelling next-generation deepfake detectors for enhanced accuracy, generalization, robustness, and explainability. Then, the paper delves deeply into open challenges and potential avenues for research in the audio and video deepfake generation and mitigation field. The aspiration for this article is to complement prior studies and assist newcomers, researchers, engineers, and practitioners in gaining a deeper understanding and in the development of innovative deepfake technologies.

Artificial Intelligence Promoting the Inclusion of Students with Special Needs in Physical Education Classes

School Physical Education (PE), a crucial component for the integral development of students, faces the challenge of promoting the inclusion of students with special needs (SEN). Barriers such as the lack of adaptation of activities and the scarcity of adequate resources hinder the full participation of these students. This article investigates the potential of Artificial Intelligence (AI) as a tool to personalize teaching, adapt activities, and create inclusive learning environments in PE. Through the analysis of case studies and practical examples, it demonstrates how AI can be used to individually assess students, provide real-time feedback, develop adaptive platforms, recognize gestures and movements, create gamified activities, use virtual and augmented reality, translate sign language, implement virtual assistants, and prevent bullying. At the same time, the article discusses the ethical challenges and the need for teacher training to ensure the responsible and effective use of AI in promoting inclusion in PE.

Trust, Workload and Performance in Human-AI Partnering: The Role of AI Attributes in Solving Classification Problems

Abstract Intelligent systems have been rapidly evolving and play a pivotal role in assisting individuals across diverse domains, from healthcare to transportation. Understanding the dynamics of human-Artificial Intelligence (AI) partnering, particularly how humans trust and collaborate with intelligent systems, is becoming increasingly critical to design effective systems. This paper presents an experimental analysis to assess the impact of AI design attributes on users' trust, workload and performance when solving classification problems supported by an AI assistant. Specifically, we study the effect of transparency, fairness, and robustness in the design of an AI assistant and analyze the role of participants' gender and education background on the outcomes. The experiment is conducted with 47 students in undergraduate, master's and Ph.D. programs using a drawing game application where the users are asked to recognize incomplete sketches revealed progressively while receiving recommendations from multiple versions of an AI assistant. The results show that when collaborating with the AI, participants achieve a higher performance than their individual performance or the performance of the AI. The results also show that gender does not have an impact on users' trust and performance when collaborating with different versions of the AI system, whereas education level has a significant impact on the participants' performance but not on trust. Finally, the impact of design attributes on participants' trust and performance highly depends on the accuracy of the AI recommendations, and improvements in participants' performance and trust in some cases come at the expense of increased workload.

Enhancing Reality: Exploring the Potential of Generative Artificial Intelligence

This paper delves into the realm of recent advancements in artificial intelligence, with a particular focus on Generative AI. Generative AI, an emerging field within AI, leverages machine learning algorithms and neural networks to generate original content across various mediums such as images, music, speech, and text. Its potential to revolutionize industries like advertising, gaming, and healthcare through personalized content creation, task automation, and enhanced accuracy in complex endeavors like drug discovery and medical diagnosis is profound. We explore different models of Generative AI, highlighting their strengths and limitations. Despite being in its early stages, Generative AI presents a promising avenue for research and development, offering numerous unexplored opportunities. Examples of prominent Generative AI models such as ChatGPT and DALL-E are provided, elucidating their applications across diverse domains. Looking forward, the potential applications of Generative AI are vast, including the development of virtual assistants for human interaction, bolstering cybersecurity, and designing intelligent robots for industrial tasks. As Generative AI continues to advance, it holds the promise of driving innovation and transformation across industries, paving the way for growth and progress in the future. Key Words: Generative AI, artificial intelligence, content generation, machine learning, neural networks, industry applications, innovation.

Prompt Engineering in AI driven Indian Healthcare

The phenomenal growth of artificial intelligence (AI) and machine learning (ML) in recent years has revolutionized various sectors, including healthcare. Particularly the large language models (LLMs) developed recently by various companies such as Google, Microsoft, Nvidia, OpenAI have demonstrated remarkable capabilities in understanding and generating contextualized text thus making them valuable tools that can be effectively utilized by healthcare industry and service providers. Prompt engineering plays a crucial role leveraging full potential of such LLMs. Prompt Engineering is the technique used to design and refine inputs to improve the performance of AI models and maximize the efficacy of AI applications. In India, with diverse population and varying healthcare needs and resource constraints specially in tier 3 cities, villages and remote areas. This paper highlights pivotal role that Prompt Engineering can play in supplementing solutions and initiatives to address healthcare needs. It explores applications in diagnostics, electronic health records (EHRs), virtual health assistants and the overall impact on patient care and administrative efficiency. Through case studies and theoretical analysis, this paper aims to demonstrate how prompt engineering can enhance diagnostic accuracy, streamline administrative tasks, and improve patient care. Keywords— AI, healthcare delivery, prompt engineering, LLM, EHRs, virtual health assistants, patient care

3D facial modeling, animation, and rendering for digital humans: A survey

With the continuous advancement of 3D and human–computer interaction technologies, digital human systems have been widely implemented in our daily lives, such as 3D games, online education, and virtual assistants. This paper provides a comprehensive literature survey of the technologies vitally important to digital humans in 3D facial modeling, animation, and rendering. Firstly, we combed through the research development of 3D face modeling technology and summarized the 3D facial models. Then, we summed up various methods for animating the 3D face based on data from images, videos, and the like. Lastly, we delved deeply into the techniques for 3D face rendering, comparing traditional rendering with neural network rendering. In conclusion, we provided a forward-looking perspective on the current challenges and future development of 3D digital humans.

Network-integrated medical chatbot for enhanced healthcare services

Technology has a significant role in improving medical care, such as enhancing communication between patients and healthcare virtual assistants. By including artificial intelligence and natural language processing (NLP), the main aim of this research paper is to design a medical chatbot framework, and application design prototype, and use the Dialog Flow tool to build an auto-response system known as a chatbot. The goal is to provide medical services and meet patients' needs. It will communicate with the user in the language they follow. With the help of keywords such as symptoms entered by the patients, it will help to identify the disease. Moreover, the application provides the patients with the ability to record their medicine to remind them to take it. Additionally, it will make it easier for patients to learn more about their disease and motivate them to take the necessary precautions to stay healthy.

The underlying mechanism of user response to AI assistants: from interactivity to loyalty

The underlying mechanism of user response to AI assistants: from interactivity to loyalty

Emotional banking with a digital avatar: A PLS-SEM based study based on para-social relationship theory

Purpose: The major objective of this study is to measure the impact of various attributes, such as social attraction, physical attraction, and task attraction on para-social relationships. The study also seeks to measure how the para-social relationship mediates the association between the three attributes (above-mentioned) on perceived credibility and informational influence, and consumers’ intention to purchase banking products. Study design/methodology: PLS-SEM has been used as it is believed to be most suited for the study due to the multivariate non-normality in the data, and the small sample size. Data has been collected using the 5-point Likert scale from approximately 151 respondents, who were selected using the non-random sampling method based on purposive sampling coupled with convenience-based sampling. The data was collected from January 2023 to August 2023. Findings: Largely, the findings reveal that both social and physical attractions do have a positive impact on the para-social relationship, further leading to perceived credibility and informational influence. Notably, this perceived credibility and informational influence lead to consumers’ intentions to purchase banking products, albeit with the use of artificial intelligence-based chatbots and digital assistants. Originality: This is possibly among the first-ever studies extending the para-social theory for purchasing banking products and services using artificial intelligence-based chatbots and virtual assistants.

Virtual resonance: analyzing IPA usage intensity under COVID-19's isolating canopy

The widespread adoption of smartphones coupled with advancements in artificial intelligence has significantly propelled the use of intelligent personal assistants (IPAs). These digital assistants have become indispensable for many users, particularly during the COVID-19 pandemic. Employing coviance-based structural equation modeling (CB-SEM) and analyzing data from 260 participants, this study explores the key factors influencing IPA usage intensity. Contrary to expectations, affective risk perception showed no significant impact on either IPA usage or parasocial interaction during the pandemic. In stark contrast, cabin fever syndrome significantly influenced both IPA usage and parasocial interaction, underscoring the role of environmental and psychological stressors in shaping technology use. Furthermore, loneliness was found to significantly enhance parasocial interaction with IPAs, though it did not affect usage intensity. The findings highlight a substantial connection between parasocial interaction and IPA usage intensity, suggesting that users who engage in human-like interactions with IPAs tend to use them more extensively. These insights not only deepen our understanding of how IPAs are utilized during health crises but also point to potential directions for developing IPAs that are more responsive to users' emotional and social needs.

Modern Broadcasting: Leveraging Artificial Intelligence and Big Data for More Personalized Content

Artificial intelligence (AI) technology and big data analytics have changed the landscape of the modern broadcast industry by providing a deeper understanding of audiences, increasing content personalization and strengthening engagement with viewers. In this study, we investigate the impact of the use of this technology in the broadcasting industry, with a focus on the analysis of demographic data, the use of recommendation algorithms, personalized interactions through AI technology, and optimization of broadcast time. Analysis of demographic data allows broadcasters to understand their audiences better, while the use of recommendation algorithms provides the opportunity to serve more personalized content to audiences, increasing engagement and interaction. The use of AI technology in audience interactions, such as chatbots and virtual assistants, also provides viewers with a more personalized experience, while optimizing broadcast time allows broadcasters to reach a wider audience and increase broadcast effectiveness. Although AI technology and big data analysis bring significant benefits, ethical challenges related to data privacy and security remain a major concern. By considering these challenges, broadcasters can leverage this technology effectively to improve content quality, increase engagement with audiences, and remain relevant in an ever-changing broadcast environment.

Voice Assistants for Mental Health Services: Designing Dialogues with Homebound Older Adults.

The number of older adults who are homebound with depressive symptoms is increasing. Due to their homebound status, they have limited access to trained mental healthcare support, which leaves this support often to untrained family caregivers. To increase access, a growing interest is placed on using technology-mediated solutions, such as voice-assisted intelligent personal assistants (VIPAs), to deliver mental health services to older adults. To better understand how older adults and family caregivers intend to interact with a VIPA for mental health interventions, we conducted a participatory design study during which 6 older adults and 7 caregivers designed VIPA-human dialogues for various scenarios. Using conversation style preferences as a starting point, we present aspects of human-likeness older adults and family caregivers perceived as helpful or uncanny, specifically in the context of the delivery of mental health interventions, which helps inform potential roles VIPAs can play in mental healthcare for older adults.