Virtual Assistant Research Articles

VividWav2Lip: High-Fidelity Facial Animation Generation Based on Speech-Driven Lip Synchronization

Speech-driven lip synchronization is a crucial technology for generating realistic facial animations, with broad application prospects in virtual reality, education, training, and other fields. However, existing methods still face challenges in generating high-fidelity facial animations, particularly in addressing lip jitter and facial motion instability issues in continuous frame sequences. This study presents VividWav2Lip, an improved speech-driven lip synchronization model. Our model incorporates three key innovations: a cross-attention mechanism for enhanced audio-visual feature fusion, an optimized network structure with Squeeze-and-Excitation (SE) residual blocks, and the integration of the CodeFormer facial restoration network for post-processing. Extensive experiments were conducted on a diverse dataset comprising multiple languages and facial types. Quantitative evaluations demonstrate that VividWav2Lip outperforms the baseline Wav2Lip model by 5% in lip sync accuracy and image generation quality, with even more significant improvements over other mainstream methods. In subjective assessments, 85% of participants perceived VividWav2Lip-generated animations as more realistic compared to those produced by existing techniques. Additional experiments reveal our model’s robust cross-lingual performance, maintaining consistent quality even for languages not included in the training set. This study not only advances the theoretical foundations of audio-driven lip synchronization but also offers a practical solution for high-fidelity, multilingual dynamic face generation, with potential applications spanning virtual assistants, video dubbing, and personalized content creation.

Molecular interactions of photosystem I and ZIF-8 in bio-nanohybrid materials.

Bio-nanohybrid devices featuring natural photocatalysts bound to a nanostructure hold great promise in the search for sustainable energy conversion. One of the major challenges of integrating biological systems is protecting them against harsh environmental conditions while retaining, or ideally enhancing their photophysical properties. In this mainly computational work we investigate an assembly of cyanobacterial photosystem I (PS I) embedded in a metal-organic framework (MOF), namely the zeolitic imidazolate framework ZIF-8. This complex has been reported experimentally [Bennett et al., Nanoscale Adv., 2019, 1, 94] but so far the molecular interactions between PS I and the MOF remained elusive. We show via absorption spectroscopy that PS I remains intact throughout the encapsulation-release cycle. Molecular dynamics (MD) simulations further confirm that the encapsulation has no noticeable structural impact on the photosystem. However, the MOF building blocks frequently coordinate to the Mg2+ ions of chlorophylls in the periphery of the antenna complex. High-level quantum mechanical calculations reveal charge-transfer interactions, which affect the excitonic network and thereby may reversibly change the fluorescence properties of PS I. Nevertheless, our results highlight the stability of PS I in the MOF, as the reaction center remains unimpeded by the heterogeneous environment, paving the way for applications in the foreseeable future.

Conversational Artificial Intelligence: A Catalyst for Rethinking Assessment in Higher Education

Conversational Artificial Intelligence has disrupted higher education by fundamentally altering its landscape. Fuelled by natural language processing and machine learning this technology has gained widespread adoption particularly since the release of ChatGPT in November 2022. As universities embrace digital transformation, assessment practices must evolve to align with the capabilities of Artificial Intelligence-driven chatbots and virtual assistants. This paper explores how conversational artificial intelligence impacts higher education, in particular, student assessment. A fundamental shift in assessment and evaluation of student competencies is necessary to not only consider knowledge retention but also critical thinking, communication, and adaptability skills. A review of the literature was conducted to understand how assignments should change due to the emergence of this disruptive technology. Conversational Artificial Intelligence and its application within the higher education context is uncertain, with disparate practices—in terms of ethical consideration and understanding—across the sector. A case study was conducted in which MSc Management students undertaking a specific module were tasked to use three Artificial Intelligence tools in their report writing of a business, to verify the sources and content provided by the Artificial Intelligence tool, and to critically evaluate the process as well as the output received for each prompt. The paper proposes a collaborative approach to navigate the ethical implementation and utilization of conversational Artificial Intelligence in higher education, advocating for the co-creation of guidelines through forums like Knowledge Cafés, stressing the need to rethink student assignments and its assessment and the adoption of artificial intelligence technologies by students for assignments.

AI‐induced dehumanization

AbstractRecent technological advancements have empowered nonhuman entities, such as virtual assistants and humanoid robots, to simulate human intelligence and behavior. This paper investigates how autonomous agents influence individuals' perceptions and behaviors toward others, particularly human employees. Our research reveals that the socio‐emotional capabilities of autonomous agents lead individuals to attribute a humanlike mind to these nonhuman entities. Perceiving a high level of humanlike mind in the nonhuman, autonomous agents affects perceptions of actual people through an assimilation process. Consequently, we observe “assimilation‐induced dehumanization”: the humanness judgment of actual people is assimilated toward the lower humanness judgment of autonomous agents, leading to various forms of mistreatment. We demonstrate that assimilation‐induced dehumanization is mitigated when autonomous agents possess capabilities incompatible with humans, leading to a contrast effect (Study 2), and when autonomous agents are perceived as having a high level of cognitive capability only, resulting in a lower level of mind perception of these agents (Study 3). Our findings hold across various types of autonomous agents (embodied: Studies 1–2 and disembodied: Studies 3–5), as well as in real and hypothetical consumer choices.

Transmedia skill derived from the process of converting films into educational games with augmented reality and artificial intelligence

Transmedia skill, derived from the process of converting films into educational games using augmented reality and artificial intelligence, involves employing various languages and mediums to adapt an original narrative to another format. This transmedia practice presents an opportunity to cultivate diverse skills in teacher training by transforming film narratives into educational games with Augmented Reality (AR) and Artificial Intelligence (AI). Moreover, these educational games enable student engagement in missions or challenges, enhancing their engagement with educational activities. Thus, this research stems from an Innovation project implemented in the Degree in Early Childhood Education (N=77) with two groups of university students who developed 24 educational games in physical and digital formats. The objectives are: 1) to compare the transmedia process adopted by both groups when converting children's animation films into games, some in digital format and others combining physical and digital resources; and 2) to analyze their transmedia skill reflected in the games developed. The methodology adopted is non-experimental empirical, with a descriptive and comparative nature. Two instruments were designed and validated, one to analyze the transmedia process followed in each case and another to ascertain the level of transmedia skill of the university students. The results reveal that both groups chose different creative approaches to gamify the films, expanding their stories by leveraging the potential of AR and AI to create interactive characters and settings. From this, their transmedia capability could be inferred. Thus, the use of digital applications to collaboratively design games—utilizing film narratives—represented an innovation in their training, holistically enhancing various skills. In conclusion, this experience presents an opportunity to increase the transmedia skills of future educators.

Analysis of Dataset Limitations in Semantic Knowledge-Driven Multi-Variant Machine Translation

In this study, we explore the implications of dataset limitations in semantic knowledge-driven machine translation (MT) for intelligent virtual assistants (IVA). Our approach diverges from traditional single-best translation techniques, utilizing a multi-variant MT method that generates multiple valid translations per input sentence through a constrained beam search. This method extends beyond the typical constraints of specific verb ontologies, embedding within a broader semantic knowledge framework. We evaluate the performance of multi-variant MT models in translating training sets for Natural Language Understanding (NLU) models. These models are applied to semantically diverse datasets, including a detailed evaluation using the standard MultiATIS++ dataset. The results from this evaluation indicate that while multi-variant MT method is promising, its impact on improving intent classification (IC) accuracy is limited when applied to conventional datasets such as MultiATIS++. However, our findings underscore that the effectiveness of multi-variant translation is closely associated with the diversity and suitability of the datasets utilized. Finally, we provide an in-depth analysis focused on generating variant-aware NLU datasets. This analysis aims to offer guidance on enhancing NLU models through semantically rich and variant-sensitive datasets, maximizing the advantages of multi-variant MT.

Impact of Artificial Intelligence in Customer Journey

The entire gamut of Customer journey is undergoing a massive transformation due to the rapid advancement of Artificial Intelligence (AI). Leveraging the power of AI , CRM & systems have refined the aspect of how businesses manage and optimize the customer journey. AI-powered systems have significant impact across various stages of the customer lifecycle by use of techniques such as machine learning to empower businesses to use systems that can analyse vast amounts of customer dataset in real-time, enabling them to gain deeper insights in customer behaviours, preferences, & sentiment. The AI-driven techniques help businesses to drive more personalized & targeted marketing campaigns, tailored recommendations, and extend efficient customer service leading ultimately to enhancing customer satisfaction and loyalty. Moreover, AI-powered systems have capabilities of offering predictive analytics which empower businesses to forecast customer behaviours and anticipate their needs. The capabilities help businesses in effective resource optimization and improve efficiency. For customer service AI-powered chatbots and virtual assistants are used to enhance engagement by providing instant responses and ability to handle resolving issues promptly.

Comparative Study for Virtual Personal Assistants (VPA) and State-of-the-Art Speech Recognition Technology

Numerous types of virtual assistants have emerged as a result of the widespread use of smartphones, the expansion of their services, the tremendous advancements in automatic speech recognition and AI, and the growing reliance on virtual personal assistants (VPAs) for basic daily tasks like playing music, sending texts, making restaurant reservations, and getting weather updates. The popularity of virtual personal assistants is largely attributable to their convenient blend of user-friendliness and natural language interaction. This study comprehensively examines various virtual personal assistants powered by AI. It briefly overviews each, such as Microsoft Cortana, Samsung Bixby, Apple SIRI, Google Assistant, and Amazon Alexa. This study also includes a comprehensive overview of the state-of-the-art speech recognition used in virtual personal assistants. The findings show that each Virtual Personal Assistant has advantages, and a user may select any of them depending on his preferences and needs.

Intelligent personal assistants in self-access L2 vocabulary learning

Intelligent personal assistants in self-access L2 vocabulary learning

Retraction notice: Intelligent personal assistant for personal computers using long short-term memory-based verbalizer

Retraction notice: Intelligent personal assistant for personal computers using long short-term memory-based verbalizer

Role, Needs, and State of Cognitive Assistants in Single-Pilot Operations

Commercial aviation crew sizes have historically shrunk and are only projected to shrink down further to a one-member crew, known as single-pilot operations. Single-pilot operations involve a redesign of the modern two-person flight deck, as the work of the second pilot is now displaced to either enhanced ground support or increased automation. This paper focuses on cognitive assistants, a solution for work displacement within the latter domain of increased automation, where artificial intelligence may take the role of the second pilot and provide crucial decision-making, planning, and flight support to the remaining pilot. Work within this area is numerous, but a complete survey of current theory and implementations for cognitive assistants has yet to be completed. Therefore, this paper reviews current analyses and implementations of cognitive assistants within aviation broadly and discusses their potential within single-pilot operations. Gaps and constraints that remain within cognitive assistants for single-pilot operations are also analyzed, and future pathways for the incorporation of cognitive assistants in commercial aviation are identified.

PAINe: An Artificial Intelligence–based Virtual Assistant to Aid in the Differentiation of Pain of Odontogenic versus Temporomandibular Origin

IntroductionPain associated with temporomandibular dysfunction (TMD) is often confused with odontogenic pain, which is a challenge in endodontic diagnosis. Validated screening questionnaires can aid in the identification and differentiation of the source of pain. Therefore, this study aimed to develop a virtual assistant based on artificial intelligence using natural language processing techniques to automate the initial screening of patients with tooth pain. MethodsThe PAINe chatbot was developed in Python language using the PyCharm environment and the openai library to integrate the ChatGPT 4 API and the Streamlit library for interface construction. The validated TMD Pain Screener questionnaire and 1 question regarding the current pain intensity were integrated into the chatbot to perform the differential diagnosis of TMD in patients with tooth pain. The accuracy of the responses was evaluated in 50 random scenarios to compare the chatbot with the validated questionnaire. The kappa coefficient was calculated to assess the agreement level between the chatbot responses and the validated questionnaire. ResultsThe chatbot achieved an accuracy rate of 86% and a substantial level of agreement (κ = 0.70). Most responses were clear and provided adequate information about the diagnosis. ConclusionsThe implementation of a virtual assistant using natural language processing based on large language models for initial differential diagnosis screening of patients with tooth pain demonstrated substantial agreement between validated questionnaires and the chatbot. This approach emerges as a practical and efficient option for screening these patients.

Design and Implementation of a Cognitive Cloud Assistant: A Text Classification Model Approach

Abstract The main objective of this research was to develop a classification model for the design of a cloud cognitive assistant. The classification model should be able to classify the textual description of the cloud architecture into desired multiple classes. For the purpose of implementation of such assistant, the analysis of current state of cognitive assistants and cloud computing was researched. As for the implementation of classifier, the overview of the possible ways how to create such classifier was also added to the research. Based on the analysis, a solution was proposed for the implementation of a model for text classification, and also a proposal for the implementation of an assistant that would use the proposed model. The Keras library was used to create a sequential text classifier. The IBM Watson cloud services were used to deploy the created model into live environment, and the services from the same group were used to develop a proposed cognitive assistant that was in the end connected to the classifier model.

The Medicine Revolution Through Artificial Intelligence: Ethical Challenges of Machine Learning Algorithms in Decision-Making.

The integration of artificial intelligence (AI) and its autonomous learning processes (or machine learning) in medicine has revolutionized the global health landscape, providing faster and more accurate diagnoses, personalization of medical treatment, and efficient management of clinical information. However, this transformation is not without ethical challenges, which require a comprehensive and responsible approach. There are many fields where AI and medicine intersect, such as health education, patient-doctor interface, data management, diagnosis, intervention, and decision-making processes. For some of these fields, there are some guidelines to regulate them. AI has numerous applications in medicine, including medical imaging analysis, diagnosis, predictive analytics for patient outcomes, drug discovery and development, virtual health assistants, and remote patient monitoring. It is also used in robotic surgery, clinical decision support systems, AI-powered chatbots for triage, administrative workflow automation, and treatment recommendations. Despite numerous applications, there are several problems related to the use of AI identified in the literature in general and in medicine in particular. These problems are data privacy and security, bias and discrimination, lack of transparency (Black Box Problem), integration with existing systems, cost and accessibility disparities, risk of overconfidence in AI, technical limitations, accountability for AI errors, algorithmic interpretability, data standardization issues, unemployment, and challenges in clinical validation. Of the various problems already identified, the most worrying are data bias, the black box phenomenon, questions about data privacy, responsibility for decision-making, security issues for the human species, and technological unemployment. There are still several ethical problems associated with the use of AI autonomous learning algorithms, namely epistemic, normative, and comprehensive ethical problems (overarching). Addressing all these issues is crucial to ensure that the use of AI in healthcare is implemented ethically and responsibly, providing benefits to populations without compromising fundamental values. Ongoing dialogue between healthcare providers and the industry, the establishment of ethical guidelines and regulations, and considering not only current ethical dilemmas but also future perspectives are fundamental points for the application of AI to medical practice. The purpose of this review is to discuss the ethical issues of AI algorithms used mainly in data management, diagnosis, intervention, and decision-making processes.

Virtual Assistant for Local Government Units Using BERT-Based Natural Language Processing

Local Government Units (LGUs) play a critical role in public service delivery, yet they often encounter challenges such as limited resources, bureaucratic inefficiencies, and high volumes of citizen inquiries. To address these issues, this paper proposes the development of a virtual assistant powered by Natural Language Processing (NLP), specifically utilizing the Bidirectional Encoder Representations from Transformers (BERT) model. This assistant is designed to automate the handling of common citizen inquiries, thereby enhancing the efficiency of service delivery. The virtual assistant was implemented as a web application using the Django framework, with an evaluation based on ISO 9126 standards, including aspects of functionality, reliability, usability, and efficiency. The findings demonstrate that the BERT-based NLP system significantly improves the responsiveness and accuracy of LGU services, leading to enhanced citizen satisfaction and more streamlined public service operations. The model achieved an Exact Match score of 48.0 and an F1 score of 68.01, indicating its effectiveness in accurately understanding and responding to citizen queries. Additionally, the system scored highly in usability, efficiency, maintainability, and portability, confirming its ease of use and operational effectiveness. These results give rather strong evidence that the use of the NLP-driven virtual assistant can be beneficial for increasing the citizen satisfaction and improving the effectiveness of the public service delivery.

Graphic association learning: Multimodal feature extraction and fusion of image and text using artificial intelligence techniques

With the advancement of technology in recent years, the application of artificial intelligence in real life has become more extensive. Graphic recognition is a hot spot in the current research of related technologies. It involves machines extracting key information from pictures and combining it with natural language processing for in-depth understanding. Existing methods still have obvious deficiencies in fine-grained recognition and deep understanding of contextual context. Addressing these issues to achieve high-quality image-text recognition is crucial for various application scenarios, such as accessibility technologies, content creation, and virtual assistants. To tackle this challenge, a novel approach is proposed that combines the Mask R-CNN, DCGAN, and ALBERT models. Specifically, the Mask R-CNN specializes in high-precision image recognition and segmentation, the DCGAN captures and generates nuanced features from images, and the ALBERT model is responsible for deep natural language processing and semantic understanding of this visual information. Experimental results clearly validate the superiority of this method. Compared to traditional image-text recognition techniques, the recognition accuracy is improved from 85.3% to 92.5%, and performance in contextual and situational understanding is enhanced. The advancement of this technology has far-reaching implications for research in machine vision and natural language processing and open new possibilities for practical applications.

O USO DE ASSISTENTES VIRTUAIS POR ESTUDANTES COM TDAH NO ENSINO SUPERIOR

The objective of this article was to verify how virtual assistants have contributed to the accessibility of students with ADHD in higher education. As a methodological procedure, we interviewed students with ADHD enrolled in undergraduate and graduate courses at higher education institutions. All those selected are from different Brazilian states and use virtual assistants for at least six months. The approach was carried out through social networks, after some posts by the selected ones about the use of virtual assistants and their diagnosis of ADHD. The in-depth interviews were structured around three axes: user profile, use of virtual assistants and use with an educational focus. The results highlight the importance of developing technologies already thinking about the accessibility requirement. We understand that access to different technologies can facilitate academic performance and the development of potential for students with ADHD.

Transforming Health Care Through Chatbots for Medical History-Taking and Future Directions: Comprehensive Systematic Review.

The integration of artificial intelligence and chatbot technology in health care has attracted significant attention due to its potential to improve patient care and streamline history-taking. As artificial intelligence-driven conversational agents, chatbots offer the opportunity to revolutionize history-taking, necessitating a comprehensive examination of their impact on medical practice. This systematic review aims to assess the role, effectiveness, usability, and patient acceptance of chatbots in medical history-taking. It also examines potential challenges and future opportunities for integration into clinical practice. A systematic search included PubMed, Embase, MEDLINE (via Ovid), CENTRAL, Scopus, and Open Science and covered studies through July 2024. The inclusion and exclusion criteria for the studies reviewed were based on the PICOS (participants, interventions, comparators, outcomes, and study design) framework. The population included individuals using health care chatbots for medical history-taking. Interventions focused on chatbots designed to facilitate medical history-taking. The outcomes of interest were the feasibility, acceptance, and usability of chatbot-based medical history-taking. Studies not reporting on these outcomes were excluded. All study designs except conference papers were eligible for inclusion. Only English-language studies were considered. There were no specific restrictions on study duration. Key search terms included "chatbot*," "conversational agent*," "virtual assistant," "artificial intelligence chatbot," "medical history," and "history-taking." The quality of observational studies was classified using the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) criteria (eg, sample size, design, data collection, and follow-up). The RoB 2 (Risk of Bias) tool assessed areas and the levels of bias in randomized controlled trials (RCTs). The review included 15 observational studies and 3 RCTs and synthesized evidence from different medical fields and populations. Chatbots systematically collect information through targeted queries and data retrieval, improving patient engagement and satisfaction. The results show that chatbots have great potential for history-taking and that the efficiency and accessibility of the health care system can be improved by 24/7 automated data collection. Bias assessments revealed that of the 15 observational studies, 5 (33%) studies were of high quality, 5 (33%) studies were of moderate quality, and 5 (33%) studies were of low quality. Of the RCTs, 2 had a low risk of bias, while 1 had a high risk. This systematic review provides critical insights into the potential benefits and challenges of using chatbots for medical history-taking. The included studies showed that chatbots can increase patient engagement, streamline data collection, and improve health care decision-making. For effective integration into clinical practice, it is crucial to design user-friendly interfaces, ensure robust data security, and maintain empathetic patient-physician interactions. Future research should focus on refining chatbot algorithms, improving their emotional intelligence, and extending their application to different health care settings to realize their full potential in modern medicine. PROSPERO CRD42023410312; www.crd.york.ac.uk/prospero.

Editorial

Volume eleven, number two of the Journal of Dialogues and Perspectives in Special Education (RDPEE) for the year 2024, consists of 13 articles, in which the specificities of the target audience of Special Education, from an inclusive perspective, are addressed in different national and international contexts. In the first section, this issue presents studies on inclusion and autism, from a more generalist approach, proposing a reflection on the inclusion of these students in Brazil, as well as the challenges and possibilities presented in the educational system, with an emphasis on elementary school I. We also highlight in this section the study that addresses the conception of man in the exercise of fatherhood in relation to Autism Spectrum Disorder (ASD) and the proposal for a reflection on giftedness and the condition of double exceptionality. In a second block, it is possible to access studies on the inclusion of people with disabilities in different approaches, from the experience of emergency remote teaching in Brazil and also through reflection on this same situation, but experienced in Mexico, highlighting how this contributed to (re)thinking a more inclusive school environment and fostering a culture of peace. Also in the second block, we have studies on the role of educational interpreters in high school, the use of virtual assistants by students with ADHD in higher education, the training of Physical Education teachers who work with students with disabilities, as well as the challenges that teachers encounter when dealing with students with disabilities in the daily life of regular schools. In the third and final block of articles presented in this issue, we have two articles addressing Sexual Education and disability. In the first, we find the discussion on the violation of the rights of people with disabilities and sexual abuse. In the second article, the discussion on the importance of Sexual Education for people with intellectual disabilities.Enviar feedbackPainéis lateraisHistóricoSalvas

User-centered Social Interaction Design in Intelligent Personal Assistants and Social Robots

User-centered design practices are used to develop the social interaction characteristics of intelligent personal assistants and social robots. In this approach, developers establish characteristics favored by the majority of users. However, catering to target users may negate the benefits in user experience and use intention achieved from matching a product’s characteristics to the preferences of individual users. The present study examines differences between target versus individual user based approaches to user-centered design. Specifically, a framework is presented on how to develop a product to meet individual user preferences. Results show that individual user preferences, while similar to the target user preferences, can vary significantly from the baseline. Machine learning and artificial intelligence approaches should be employed to help adjust social interaction approaches from a baseline to individual user preferences.