Real-time Conversion Research Articles

Large language models meet user interfaces: The case of provisioning feedback

Incorporating Generative Artificial Intelligence (GenAI), especially Large Language Models (LLMs), into educational settings presents valuable opportunities to boost the efficiency of educators and enrich the learning experiences of students. A significant portion of the current use of LLMs by educators has involved using conversational user interfaces (CUIs), such as chat windows, for functions like generating educational materials or offering feedback to learners. The ability to engage in real-time conversations with LLMs, which can enhance educators' domain knowledge across various subjects, has been of high value. However, it also presents challenges to LLMs' widespread, ethical, and effective adoption. Firstly, educators must have a degree of expertise, including tool familiarity, AI literacy and prompting to effectively use CUIs, which can be a barrier to adoption. Secondly, the open-ended design of CUIs makes them exceptionally powerful, which raises ethical concerns, particularly when used for high-stakes decisions like grading. Additionally, there are risks related to privacy and intellectual property, stemming from the potential unauthorised sharing of sensitive information. Finally, CUIs are designed for short, synchronous interactions and often struggle and hallucinate when given complex, multi-step tasks (e.g., providing individual feedback based on a rubric on a large scale). To address these challenges, we explored the benefits of transitioning away from employing LLMs via CUIs to the creation of applications with user-friendly interfaces that leverage LLMs through API calls. We first propose a framework for pedagogically sound and ethically responsible incorporation of GenAI into educational tools, emphasizing a human-centred design. We then illustrate the application of our framework to the design and implementation of a novel tool called Feedback Copilot, which enables instructors to provide students with personalized qualitative feedback on their assignments in classes of any size. An evaluation involving the generation of feedback from two distinct variations of the Feedback Copilot tool, using numerically graded assignments from 338 students, demonstrates the viability and effectiveness of our approach. Our findings have significant implications for GenAI application researchers, educators seeking to leverage accessible GenAI tools, and educational technologists aiming to transcend the limitations of conversational AI interfaces, thereby charting a course for the future of GenAI in education.

Hyperscanning shows friends explore and strangers converge in conversation

During conversation, people often endeavor to convey information in an understandable way (finding common ground) while also sharing novel or surprising information (exploring new ground). Here, we test how friends and strangers balance these two strategies to connect with each other. Using fMRI hyperscanning, we measure a preference for common ground as convergence over time and exploring new ground as divergence over time by tracking dyads’ neural and linguistic trajectories over the course of semi-structured intimacy-building conversations. In our study, 60 dyads (30 friend dyads) engaged in a real-time conversation with discrete prompts and demarcated turns. Our analyses reveal that friends diverge neurally and linguistically: their neural patterns become more dissimilar over time and they explore more diverse topics. In contrast, strangers converge: neural patterns and language become more similar over time. The more a conversation between strangers resembles the exploratory conversations of friends, the more they enjoy it. Our results highlight exploring new ground as a strategy for a successful conversation.

Intraoperative confocal laser endomicroscopy during 5-aminolevulinic acid-guided glioma surgery: significant considerations for resection at the tumor margin.

Because gliomas have poorly defined tumor margins, the ability to achieve maximal resection is limited. To better discern these margins, fluorescence-guided surgery has been used to aid maximal safe resection. The authors describe their experience with the simultaneous use of intraoperative fluorescein sodium (FNa) confocal laser endomicroscopy (CLE) and operating microscope 5-aminolevulinic acid (5-ALA) fluorescence imaging for glioma resection to improve CLE use for better margin discrimination. FNa CLE and 5-ALA wide-field imaging were used in 33 patients with gliomas. CLE imaging was enhanced with the use of a telesurgical pathology software platform that enables real-time conversation between the operating neurosurgeons and the pathologists located remotely. CLE was used for imaging tumor regions that were subjectively regarded as tumor margins under normal visualization with the operative microscope. After FNa CLE imaging, 5-ALA wide-field imaging was performed in the same regions. Tissue was biopsied at imaging locations, and interpretations of FNa CLE and 5-ALA wide-field imaging were compared to those of permanent histological sections. Eighty-eight deep- and superficial-margin regions of interest (ROIs) were imaged with FNa CLE and 5-ALA imaging. Most of the ROIs interpreted by the neuropathologist as infiltrative glioma based on FNa CLE imaging lacked 5-ALA-induced fluorescence. Permanent histological sections from the corresponding regions were concordant with the interpretation of FNa CLE images in 57 of 88 (65%) ROIs and with the interpretation of 5-ALA imaging in 43 of 88 (49%) ROIs. The sensitivity and specificity of FNa CLE for the interpretation of tumor margins were 73% and 41%, respectively, and those of 5-ALA were 38% and 82%, respectively. Positive and negative predictive values for CLE were 79% and 33%, respectively, and those for 5-ALA were 86% and 31%, respectively. Conventional intraoperative evaluation of tumor margins, based on MRI and wide-field fluorescence imaging, can underestimate the invasiveness of gliomas. FNa CLE showed higher accuracy in detecting regions with infiltrating tumors than intraoperative 5-ALA imaging. Future considerations should include more rigorous comparisons of FNa CLE imaging and 5-ALA-guided resections on a larger cohort of patients.

Development and implementation of a digital health intervention (DHI) electronic patient-reported outcome measure (ePROM) platform to monitor patients with cancer.

163 Background: Cancer is heterogeneous diseases associated with a significant burden of symptoms and treatment-related adverse events. Digital Health Interventions (DHIs) emerge as promising tools for active/continuous monitoring of patients (pts). Evidence suggests that DHIs may enhance healthcare delivery and improve clinical outcomes by facilitating symptom detection and enabling more effective real-time informed decisions. We aim to develop and implement an electronic Patient-reported Outcome Measure (ePROM) platform to monitor pts undergoing cancer treatment. Methods: We developed ThummiOnco, a DHI ePROM platform designed for monitoring of pts during cancer treatment. It allows pts to report their symptoms (reported and graded according to the CTCAE), quality-of-life (QoL) (EORTC EQ-5D questionnaire), emotional state, treatment adherence (Morisky-Green questionnaire), Emergency Room (ER) visits and hospitalizations through a mobile application. Upon registration, pts consent to share their data with their medical team and provide essential demographic information such as age, sex, cancer type, and treatment regimen. A medical algorithm analyzes the collected data to generate instant personalized recommendations and guidance, including indicating the need of seeking emergency care in the case of serious high-grade or worrisome complaints. Patients also have the option to engage in real-time conversations with the medical team through an online chat feature. Treating physicians and the medical team can continuously access pts data real-time, through an online dashboard, enabling them to visualize information and statistics, address symptom alerts, and make informed management decisions. ThummiOnco is available at no cost to pts at htt ps://thummi.global . Results: Between August 2021 and March 2024, ThummiOnco monitored 925 pts across 7 institutions in Brazil, encompassing both private and public health systems. The majority of pts (72%) were female, with 28% being male. The most frequently monitored cancer types were Breast Cancer (39%), Lung Cancer (7%) and Colon Cancer (6%). During this period, a total of 71.373 interactions were recorded, including 36.175 (51%) chat messages, 21.644 (30%) symptom reports, 10.597 (15%) emotional state reflections, 2.819 (4%) QoL assessments, and 138 (0.2%) ER registries. A total of 23 different symptoms were reported, with the most frequent being: fatigue (16%), joint pain (8%), and nausea (7%). 13.187 (61%) grade 1, 6.545 (30%) grade 2, and 1.912 (9%) grade 3 symptoms were registered. In total, the medical algorithm generated 1.921 recommendations for seeking emergency care. Conclusions: ThummiOnco's capacity to detect a wide range of symptoms and enable real-time informed decisions underscores its potential to enhance patient outcomes and healthcare service delivery.

Scam Call Detection Using NLP and Naïve Bayes Classifier

Financial fraud, particularly credit card fraud, is a pressing concern in the realm of digital transactions. The number of phone scams is increasing daily as con artists use phone calls to target victims for nefarious ends. Individuals are falling for con artists' proposals, becoming victims and giving up their personal information, leaving them open to abuse. Effective detection techniques are becoming more and more necessary. In this study, we offer an efficient approach to scam call identification utilizing speech-to-text libraries and the machine learning technique Naïve Bayes classifier. Our technology, which translates voice to text, uses this text to evaluate conversations in real time. It looks for trends and suspicious phrases that point to attempted scams, including asking for credit card numbers, passwords, or other sensitive information. The user will be able to decide whether or not to trust and continue with the call by using the alert prompt that appears as a pop-up message if the words are found to be suspicious. The user will take certain measures, such as ending the conversation right away, blocking the number, and reporting it further, if they don't trust the call. Our strategy is to successfully handle scam calls through ongoing adaptation and learning, boosting user security and confidence in phone conversations. The user will be able to decide whether or not to trust and continue with the call by using the alert prompt that appears as a pop-up message if the words are found to be suspicious. The user will take certain measures, such as ending the conversation right away, blocking the number, and reporting it further, if they don't trust the call. Our strategy is to successfully handle scam calls through ongoing adaptation and learning, boosting user security and confidence in phone conversations. Keyword: Spam Detection, Naïve Bayes, Natural Language Processing, Machine Learning.

Development of AI-generated medical responses using the ChatGPT for cancer patients

Background and objectiveTo develop a healthcare chatbot service (AI-guided bot) that conducts real-time conversations using large language models to provide accurate health information to patients. MethodsTo provide accurate and specialized medical responses, we integrated several cancer practice guidelines. The size of the integrated meta-dataset was 1.17 million tokens. The integrated and classified metadata were extracted, transformed into text, segmented to specific character lengths, and vectorized using the embedding model. The AI-guide bot was implemented using Python 3.9. To enhance the scalability and incorporate the integrated dataset, we combined the AI-guide bot with OpenAI and the LangChain framework. To generate user-friendly conversations, a language model was developed based on Chat-Generative Pretrained Transformer (ChatGPT), an interactive conversational chatbot powered by GPT-3.5. The AI-guide bot was implemented using ChatGPT3.5 from Sep. 2023 to Jan. 2024. ResultsThe AI-guide bot allowed users to select their desired cancer type and language for conversational interactions. The AI-guided bot was designed to expand its capabilities to encompass multiple major cancer types. The performance of the AI-guide bot responses was 90.98 ± 4.02 (obtained by summing up the Likert scores). ConclusionsThe AI-guide bot can provide medical information quickly and accurately to patients with cancer who are concerned about their health.

Navigating cancer care through digital dialogue: Analyzing key phrases shared by patients in real-time exchange messages using an electronic patient-reported outcome measure (ePROM).

e13661 Background: Cancer is a complex ailment, giving rise to a myriad of symptoms, side effects, and questions that may arise unexpectedly. There is a growing interest in adopting more efficient approaches for the continuous monitoring of patients, particularly through virtual platforms. This study focuses on analyzing the word frequency in the context of cancer assistance using an ePROM. Methods: We conducted an analysis of an ePROM embedded in a mobile app designed for monitoring patients undergoing cancer treatment. Developed collaboratively by medical oncologists and software engineers, this app empowers patients to document their symptoms, quality of life, emotions, emergency room visits, and hospitalizations. Upon app registration, patients willingly share their data with their medical team, along with essential demographic information. A standout feature is the online chat facilitating real-time communication with the medical team, consolidating all information securely in one place. This patient-centric app is provided free of charge. Our study involved a retrospective and descriptive analysis focusing solely on data related to exchanged messages in the online chat between cancer patients and the medical team. These conversations spanned from February 2022 to January 2024 and included patients referred by physicians recommending our platform. The analysis centered on the frequency of non-technical words used in real-time conversations, clustered into semantic groups. Results: Our findings revealed a total exchange of 470,597 words across 684 conversations over a two-year period. In the healthcare context, the most frequent words were categorized as follows: 1) Seeking in-person assistance (568; 0.12%): hospital (424 mentions), emergency (82), urgent/urgency (51), and primary care (UBS, Portuguese acronym) (11); 2) Doubts regarding anything (979; 0.20%): doubt (694 mentions) and question (285); 3) Non-technical one-word symptoms (5213; 1.10%): pain (1821 mentions), nausea (961), tiredness (514), diarrhea (428), vomit (364), fever (304), cough (221), numbness (197), dizziness (162), insomnia (130), constipation (101), and wheezing (10). Conclusions: Our analysis showed two key insights: first, doubts persist throughout treatment, intertwined with the management of side effects; and second, the burden on patients is substantial, whether stemming from uncertainty about the next steps or the necessity for in-person assistance. Cancer's mental and physical toll underscores the importance of having a readily available team to provide guidance, even on simple instructions. In this light, virtual platforms may represent the future of care delivery. Continuous patient monitoring emerges as pivotal for enhancing healthcare assistance, treatment, and outcomes across various cancer types.

Role of Digital Communication in Learning the English language

Digital communication technologies have transformed how people learn languages, especially English. This article looks at how these technologies help people learn English better. It covers various digital tools and platforms like social media, online forums, language learning apps, and virtual classrooms that create interactive and engaging learning experiences. By using these technologies, learners can access many resources, have real-time conversations with native speakers, and get quick feedback, making the learning process faster. The article also explores how digital communication affects the development of key language skills, such as listening, speaking, reading, and writing. The findings show the benefits of using digital communication in English language education, such as increased motivation, personalized learning experiences, and the chance to practice language skills in real-life contexts. However, it also discusses challenges like differences in digital literacy and the need for effective teaching strategies to get the most out of digital tools. Overall, this study highlights the important role of digital communication in modern English language learning, offering insights for teachers, learners, and policymakers to fully utilize its potential.

292 Intraoperative Confocal Laser Endomicroscopy During 5-ALA Guided Glioma Surgery: Significant Considerations for Resection at the Tumor Margins

INTRODUCTION: Fluorescence-guided surgery using 5-aminolevulinic acid (5-ALA) has proven effective in aiding the detection of glioma marginal zones and enhancing maximal safe resection. FDA-cleared confocal laser endomicroscopy is another imaging modality for intraoperative brain tissue discrimination on cellular level that could improve visualization at the glioma margin. METHODS: 5-ALA wide-field imaging and CLE were used in 9 patients with gliomas (6 enhancing, 3 non-enhancing). CLE imaging was enhanced by the use of a telesurgical pathology software platform to enable real-time conversation between neurosurgeons and pathologists located remotely. CLE was used for imaging tumor regions subjectively regarded as tumor margins under normal visualization with the operative microscope. After CLE imaging, 5-ALA wide-field imaging was performed in the same regions. A tissue was harvested at imaging locations, and interpretations of CLE and 5-ALA wide-field imaging were compared to permanent histological sections. RESULTS: Overall, 20 deep and superficial margin regions of interest (ROI) were imaged with CLE and 5-ALA imaging. Most of the ROIs interpreted by the neuropathologist as infiltrative glioma based on CLE imaging lacked 5-ALA-induced fluorescence. Permanent histological sections from the corresponding regions were concordant with the interpretation of CLE images in 15 of 20 (75%) ROIs and with the interpretation of 5-ALA imaging in 11 of 20 (55%) ROIs. Sensitivity/specificity of CLE and 5-ALA for interpretation of tumor margins were 79%/67%, and 50%/67%, respectively. Positive/negative predictive values for CLE and 5-ALA were 85%/57%, and 78%/37%, respectively. CONCLUSIONS: Conventional intraoperative understanding of tumor margins based on wide-field fluorescence could underestimate the invasiveness of gliomas. The ongoing study demonstrates that CLE shows higher accuracy in detecting regions with infiltrating tumor than intraoperative 5-ALA imaging.

Cartoonization of Images Using Generative Adversarial Network

Abstract: This research paper introduces a web-based image style conversion system powered by a trained AnimeGANv2 model. The system enables real-time conversion of images into anime-style artwork within a web application. Through the integration of TensorFlow, Protobuf, and ONNX formats, the AnimeGANv2 model is seamlessly deployed for efficient style conversion. The web application incorporates user authentication, secure image uploading, and instant style conversion features. Evaluation through user testing assesses the system's usability, performance, and user satisfaction. Results demonstrate the successful implementation of real- time image style conversion, offering a practical solution for users interested in artistic rendering. This research contributes to understanding GAN-based image style conversion applications and their integration into web platforms for creative expression and visual content generation.

Online Conversion Rate Prediction via Multi-Interval Screening and Synthesizing under Delayed Feedback

Due to the widespread adoption of the cost-per-action(CPA) display strategy that demands a real-time conversion rate prediction(CVR), delayed feedback is becoming one of the major challenges in online advertising. As the true labels of a significant quantity of samples are only available after long delays, the observed training data are usually biased, harming the performance of models. Recent studies show integrating models with varying waiting windows to observe true labels is beneficial, but the aggregation framework remains far from reaching a consensus. In this work, we propose the Multi-Interval Screening and Synthesizing model (MISS for short) for online CVR prediction. We first design a multi-interval screening model with various output heads to produce accurate and distinctive estimates. Then a light-weight synthesizing model with an assembled training pipeline is applied to thoroughly exploit the knowledge and relationship among heads, obtaining reliable predictions. Extensive experiments on two real-world advertising datasets validate the effectiveness of our model.

Interfacial complexation between Fe3+ and Bi2MoO6 promote efficient persulfate activation via Fe3+/Fe2+ cycle for organic contaminates degradation upon visible light irradiation

To address the observed decrease in efficiency during Fe2+-mediated persulfate (PDS) activation caused by slow electron transfer rates and challenges in cycling between Fe3+/Fe2+ states, we devised a strategy to establish interfacial complexation between Fe3+ and Bi2MoO6 in the presence of PDS. The proposed approach facilitates more efficient capture of photogenerated electrons, thereby accelerating the rate-limiting reduction process of the Fe3+/Fe2+ cycle under visible light irradiation and promoting PDS activation. The Bi2MoO6/Fe3+/PDS/Vis system demonstrates complete degradation of organic pollutants, including Atrazine (ATZ), carbamazepine (CBZ), bisphenol A (BPA), and 2,4-dichlorophenol (DCP) at a concentration of 10 mg/L within a rapid reaction time of 30 min. Radical scavenging experiments and electron paramagnetic resonance spectra (EPR) confirm that the sulfate radical (•SO4−) is the dominant species responsible for organic contaminant degradation. The real-time conversion process between Fe3+ and Fe2+ was monitored by observing changes in iron species forms and concentrations within the reaction system. X-ray photoelectron spectroscopy (XPS) and Raman spectroscopy verify the formation of a complexation between Fe3+ and Bi2MoO6, facilitating anchoring of Fe3+ onto material surface. Based on these findings, we propose a reliable mechanism for the activation reaction. This work presents a promising heterogeneous PDS activation method based on Fe3+/Fe2+ cycle for water treatment.

Research on Speech Communication Enhancement of English Web-based Learning Platform based on Human-computer Intelligent Interaction

This study presents a novel web-based learning platform that leverages human-computer intelligent interaction to enhance English communication skills. The platform integrates cutting-edge technologies to create an immersive learning experience, combining natural language processing, speech recognition, and interactive exercises. Learners engage in real-time conversations with virtual tutors, receive personalized feedback, and access a vast repository of educational resources. The platform not only facilitates language acquisition but also encourages self-paced learning, making it a valuable tool for both educators and students. By harnessing the power of artificial intelligence, this web-based platform represents a significant advancement in the realm of English language education. To overcome these issues this paper proposed SVM with an improved satin Bower bird optimization algorithm (SVM-ISBBO). SVM-ISBBO uses fog computing services that minimize the latency and speeds up the process, effectively handling huge wearable devices. In this proposed work SVM-ISBBO monitors the students communication, vocal parameters, blood pressure, etc, and these values are obtained from wearable sensor devices and their notifications are sent back to teachers. Teachers diagnosed the student information and sent back the alert notifications to the students for taking proper medications. All this information is stored in fog-based cloud storage in a secure manner. The accuracy rate of KNN got 78.56%, NB got 81.74%, SVM got 85.15% and the proposed work of SVM-ISBBO got 92.34%.

Empowering Financial Peace through Chatbot Guidance

Real-time conversations with humans can be held by intelligent chatbots, utilized by businesses to enhance customer service and streamline operations. A gap is perceived in existing chatbots' achievement of human-like conversational abilities, despite their widespread acceptance and implementation across various sectors. This research paper discusses the development of a unique chatbot providing not only financial guidance but also emotional support, thus sparking a blend of technological innovation and human connection. Emotional states of users are deciphered by the chatbot, using natural language processing (NLP) and sentiment analysis. Tailored responses are generated, offering practical financial advice as well as emotional support during times of stress. Aiming to empower users in managing their financial challenges with serenity, the chatbot integrates emotional support, financial education, goal setting and planning, stress management, and continuous support and monitoring. Cutting-edge technologies, including the Python programming language and NLP libraries like spaCy and NLTK, are incorporated in the project. It interfaces with external financial APIs to provide real-time data enabling informed decision-making. Our chatbot's focus lies in providing a secure and positive environment for users to share financial worries, with the unique ability to comprehend and respond to financial inquiries and emotional nuances in users' messages. By documenting the creation of this chatbot, a contribution is made to the expanding field of emotional intelligence in artificial intelligence applications. The expected outcomes of this research project include an enhanced sense of financial well-being for users, bridging the gap between technological solutions and human-centered support

Developing conversational Virtual Humans for social emotion elicitation based on large language models

Emotions play a critical role in numerous processes, including, but not limited to, social interactions. Consequently, the ability to evoke and recognize emotions is a challenging task with widespread implications, notably in the field of mental health assessment systems. However, up until now, emotional elicitation methods have not utilized simulated open social conversations. Our study introduces a comprehensive Virtual Human (VH), equipped with a realistic avatar and conversational abilities based on a Large Language Model. This architecture integrates psychological constructs – such as personality, mood, and attitudes – with emotional facial expressions, lip synchronization, and voice synthesis. All these features are embedded into a modular, cognitively-inspired framework, specifically designed for voice-based semi-guided emotional conversations in real time. The validation process involved an experiment with 64 participants interacting with six distinct VHs, each designed to provoke a different basic emotion. The system took an average of 4.44 s to generate the VH’s response. Participants assessed the naturalness and realism of the conversation, scoring averages of 4.61 and 4.44 out of 7, respectively. The VHs successfully generated the intended emotional valence in the users, while arousal was not evoked, though it could be recognized in the VHs. Our findings underscore the feasibility of employing VHs within affective computing to elicit emotions in socially and ecologically valid contexts. This development holds significant potential for application in sectors such as health, education, and marketing, among others.

Examining the experience of healthcare workers who led staff wellness rounding during the COVID-19 pandemic.

ObjectiveStaff wellness rounding (SWR) is a process in which healthcare leaders have real-time conversations with healthcare workers (HCW) to identify safety and wellness issues. This study examined the experience of multidisciplinary healthcare leaders, who were not trained in psychology, who delivered SWR during the surge phase of the COVID-19 pandemic.MethodsA mixed methods approach was used. Phase 1 included a survey of HCW who participated in SWR during July-October 2021. Phase 2 included semi-structured interviews of leaders who delivered SWR.ResultsThere were 403 respondents to the survey, with 169 participants (41.9%) being eligible for the study. More than 67% of eligible respondents would recommend SWR to other colleagues, and 77.5% reported that SWR provided an opportunity to escalate issues or concerns about COVID-19. Eleven SWR leaders were interviewed about their experience of leading SWR. Four key themes were identified: SWR (1) offered a defined process for communication between executive leaders and HCW; (2) enabled escalation and actioning of issues to and from executive teams in the organisation; (3) required flexible scheduling to meet varied work schedules of HCW; and (4) required the leaders to have a core set of skills and competencies.ConclusionThis study examined the contribution that an SWR intervention can make to support HCW wellbeing during crisis-type events. The model facilitated interactions between executive leaders, managers and frontline staff. It fostered collegiality with peers, managers and leaders, supported recognition and acknowledgment of peers and used available resources effectively to support staff wellness during the surge phase of the COVID-19 pandemic.

Machine Learning Based Customized Solution for Deafness and Mute People

Individuals with hearing or speech impairments often possess a higher degree of familiarity with sign language due to its widespread usage. Consequently, a noticeable communication divide exists between those with such impairments and the general populace. A pivotal solution in bridging this gap involves employing human sign language interpreters. Regrettably, the scarcity of sign language interpreters worldwide in comparison to the number of individuals with hearing or speech impairments has resulted in certain individuals being unable to bear the cost of a human interpreter each time they engage in conversation. To address this issue, it is imperative to automate communication in a manner that liberates the deaf community from dependence on human translators. This article centers on the development of an application capable of real-time conversion between American Sign Language and text, along with supplementary functionalities aimed at dismantling the communication barriers faced by individuals with hearing or speech challenges when interacting with the broader public. the primary objective of this application is to accurately perceive and interpret the user's sign language. The initial stage involves training the system to recognize and interpret signs utilizing algorithms for object recognition and motion tracking. For this purpose, a convolutional neural network model was trained employing a meticulously curated American Sign Language dataset. Subsequently, the recognized signs are translated into English, forming grammatically sound words. Incorporated within the system are a language translator and a virtual sign keyboard, augmenting its capabilities. Moreover, a text-to-text transformer, structured on an encoder-decoder framework, is harnessed to identify grammatical inaccuracies and generate coherent phrases. For a comprehensive understanding of the process, detailed elaboration will follow in subsequent sections.

Revolutionizing User Engagement: Integrating Chatbot Technology for Real-Time Assistance and Interactive Dialogue in Human-Robot Interaction

This research paper explores the integration of chatbot technology to revolutionize human-robot interaction, focusing on enhancing user engagement through real-time assistance and interactive dialogue. The study delves into the architecture, functionalities, and practical applications of chatbot integration in robots, aiming to optimize user experience and interaction dynamics. Advancements in artificial intelligence and robotics have led to the development of innovative applications, one of which is the integration of chatbot programs into robots for guiding and engaging in real-time conversations with users. This research article presents an in-depth exploration of a robot equipped with a chatbot program, designed to assist users in various tasks while maintaining interactive and responsive communication. The study discusses the architecture, functionality, and practical implications of such a system, highlighting its potential in enhancing user experience and addressing challenges in human-robot interactions.

Web Real Time Communication (RTC)

Web Real-Time Communication (WebRTC) is an open-source challenge that allows real-time communique talents immediately within internet browsers and cellular applications. It permits peer- to-peer communique, which includes audio, video, and information sharing, without the want for any extra plugins or 1/3-party software program. WebRTC abstracts the complexities of organising direct communique channels among devices, allowing developers to integrate seamless real-time conversation functionalities into web applications. It leverages a aggregate of standardized protocols, such as Session Initiation Protocol (SIP) and Interactive Connectivity Establishment (ICE), and JavaScript APIs to allow steady and efficient conversation among customers, enhancing the general person enjoy for diverse online collaboration, conferencing, and conversation programs.

GPS-SBAS-Based Orbit Determination for Low Earth Orbiting Satellites

A space-based augmentation system (SBAS) provides real-time GNSS correction signals via geostationary satellites for near-ground GNSS users. To use the SBAS correction for low Earth orbit (LEO) satellites, the correction, especially the ionosphere correction, must be adjusted for the LEO altitude. We apply modified SBAS data to LEO satellite onboard navigator to improve the positioning accuracy of a LEO satellite for possible real-time use. The onboard navigator requires high positioning reliability, and code pseudoranges, rather than phase pseudoranges, are used for the primary measurements. The Galileo NeQuick G model is used to determine the real-time conversion factor of the SBAS ionosphere correction for a LEO satellite. The GPS L1 data from GRACE satellite are combined with the SBAS data from the ground receiver. The onboard navigator combines the precise satellite dynamic model with an extended Kalman filter to improve positioning accuracy and stability. The kinematic positioning method, which uses the weighted least square method without the dynamic model, is also performed for comparison. The SBAS correction reduces the positioning error in both the kinematic positioning and the dynamic positioning. The positioning error reduction of the GPS and WAAS case over the GPS-only case is 25.2% for the kinematic method and 30.6% for the dynamic method. In the case of the dynamic method with the SBAS corrections, the positioning error remains smaller than that of the GPS-only dynamic method even after the satellite has left the SBAS service area.