scholarly journals A Context-Aware Conversational Agent in the Rehabilitation Domain

2019 ◽  
Vol 11 (11) ◽  
pp. 231 ◽  
Author(s):  
Thanassis Mavropoulos ◽  
Georgios Meditskos ◽  
Spyridon Symeonidis ◽  
Eleni Kamateri ◽  
Maria Rousi ◽  
...  
Keyword(s):  
Language Processing ◽  
Speech Synthesis ◽  
Theoretical Background ◽  
Human Interaction ◽  
Context Aware ◽  
Sensor Management ◽  
Vision Sensor ◽  
Conversational Agents ◽  
Research Areas ◽  
Intuitive Manner

Conversational agents are reshaping our communication environment and have the potential to inform and persuade in new and effective ways. In this paper, we present the underlying technologies and the theoretical background behind a health-care platform dedicated to supporting medical stuff and individuals with movement disabilities and to providing advanced monitoring functionalities in hospital and home surroundings. The framework implements an intelligent combination of two research areas: (1) sensor- and camera-based monitoring to collect, analyse, and interpret people behaviour and (2) natural machine–human interaction through an apprehensive virtual assistant benefiting ailing patients. In addition, the framework serves as an important assistant to caregivers and clinical experts to obtain information about the patients in an intuitive manner. The proposed approach capitalises on latest breakthroughs in computer vision, sensor management, speech recognition, natural language processing, knowledge representation, dialogue management, semantic reasoning, and speech synthesis, combining medical expertise and patient history.

Embodied Conversation

2021 ◽  
pp. 1091-1107
Author(s):  
Andrej Zgank ◽  
Izidor Mlakar ◽  
Uros Berglez ◽  
Danilo Zimsek ◽  
Matej Borko ◽  
...  
Keyword(s):  
Language Processing ◽  
Automatic Speech Recognition ◽  
Speech Synthesis ◽  
Conversational Agents ◽  
Embodied Conversational Agent ◽  
Computer Interfaces ◽  
Human Computer Interfaces ◽  
Text To Speech Synthesis ◽  
High Level

The chapter presents an overview of human-computer interfaces, which are a crucial element of an ambient intelligence solution. The focus is given to the embodied conversational agents, which are needed to communicate with users in a most natural way. Different input and output modalities, with supporting methods, to process the captured information (e.g., automatic speech recognition, gesture recognition, natural language processing, dialog processing, text to speech synthesis, etc.), have the crucial role to provide the high level of quality of experience to the user. As an example, usage of embodied conversational agent for e-Health domain is proposed.

Artificial Intelligence in Mental Health

2022 ◽  
pp. 296-319
Author(s):  
Lisa Ogilvie ◽  
Julie Prescott ◽  
Terry Hanley ◽  
Jerome Carson
Keyword(s):  
Mental Health ◽  
Language Processing ◽  
Human Interaction ◽  
Ethical Framework ◽  
Conversational Agents ◽  
Addiction Recovery ◽  
Resultant Solution ◽  
Common Risk Factors ◽  
The Common ◽  
Technological Improvements

Chatbots are programmed conversational agents that emulate communication systematically using natural language processing. They can be programmed to assume a range of roles where regular human interaction occurs. Within mental health services, they are not as well represented as in other areas of healthcare, with research suggesting that uptake has been hindered by concerns over the accuracy of the information they provide, undeveloped technology, lack of adherence to an ethical framework, and the unconvincing portrayal of human authenticity. Technological improvements have addressed some of these concerns, and as the resultant solution choice increases, the potential for chatbots within mental health is receiving greater attention. In this chapter, two novel uses for chatbots are showcased. Foxbot, a recovery friend, accessible at the point of need to help mitigate some of the common risk factors to sustaining addiction recovery; and ERIC, a counselling client who allows trainee counsellors to practise their counselling skills without having to enlist an actual client.

Development and Applications of a Wellhead Protection Area Delineation Computer Program

1991 ◽  
Vol 24 (11) ◽  
pp. 51-62 ◽  
Author(s):  
N. Guiguer ◽  
T. Franz
Keyword(s):  
Case History ◽  
Numerical Models ◽  
Groundwater Resources ◽  
Theoretical Background ◽  
Human Interaction ◽  
Wellhead Protection ◽  
First Case ◽  
Protection Area ◽  
And Storage ◽  
The Town

In the last few years, groundwater management has concentrated on the protection of groundwater quality. An increasing number of countries has adopted policies to protect vital groundwater resources from deterioration by regulating human interaction with the subsurface, the use of potential contaminants, land use restrictions, and waste transport and storage. One of the more common regulatory approaches to the protection of groundwater focuses on public water supplies to reduce the potential of human exposure to hazardous contaminants. Under the framework of the Safe Drinking Water Act amended by U.S. Congress in 1986, The U.S.EPA (1987) issued guidelines for the delineation of wellhead protection areas, recommending the use of analytical and numerical models for the identification of such areas. In this study, the theoretical background for the development of one such numerical model is presented. Two real-world applications are discussed: in the first case history, the model is applied to a Superfund Site in Puerto Rico as a tool for assessment of the effectiveness of a proposed pump-and-treat scheme for aquifer remediation. Based on simulation results for the evolution of the existing contaminant plume it was verified that such a scheme would not work with the proposed purging wells. The second case history is the delineation of a wellhead protection area in the Town of Littleton, Massachusetts, and subsequent design of a monitoring well network.

Knowledge-Based System supported by Chatbot to assist the Risk Classification Process of Patients in Hospital Emergency care (Preprint)

2021 ◽  
Author(s):  
Marciane Mueller ◽  
Rejane Frozza ◽  
Liane Mählmann Kipper ◽  
Ana Carolina Kessler
Keyword(s):  
Medical Care ◽  
Knowledge Base ◽  
Language Processing ◽  
Emergency Care ◽  
Risk Classification ◽  
Conversational Agent ◽  
Conversational Agents ◽  
Hospital Emergency ◽  
Knowledge Based System ◽  
Knowledge Based

BACKGROUND This article presents the modeling and development of a Knowledge Based System, supported by the use of a virtual conversational agent called Dóris. Using natural language processing resources, Dóris collects the clinical data of patients in care in the context of urgency and hospital emergency. OBJECTIVE The main objective is to validate the use of virtual conversational agents to properly and accurately collect the data necessary to perform the evaluation flowcharts used to classify the degree of urgency of patients and determine the priority for medical care. METHODS The agent's knowledge base was modeled using the rules provided for in the evaluation flowcharts comprised by the Manchester Triage System. It also allows the establishment of a simple, objective and complete communication, through dialogues to assess signs and symptoms that obey the criteria established by a standardized, validated and internationally recognized system. RESULTS Thus, in addition to verifying the applicability of Artificial Intelligence techniques in a complex domain of health care, a tool is presented that helps not only in the perspective of improving organizational processes, but also in improving human relationships, bringing professionals and patients closer. The system's knowledge base was modeled on the IBM Watson platform. CONCLUSIONS The results obtained from simulations carried out by the human specialist allowed us to verify that a knowledge-based system supported by a virtual conversational agent is feasible for the domain of risk classification and priority determination of medical care for patients in the context of emergency care and hospital emergency.

scholarly journals Application of Latent Dirichlet Allocation (LDA) for clustering financial tweets

2021 ◽  
Vol 297 ◽  
pp. 01071
Author(s):  
Sifi Fatima-Zahrae ◽  
Sabbar Wafae ◽  
El Mzabi Amal
Keyword(s):  
Language Processing ◽  
Latent Dirichlet Allocation ◽  
Sentiment Classification ◽  
Research Areas ◽  
Preprocessing Method ◽  
Long Time ◽  
Standard Text ◽  
The Given ◽  
Dirichlet Allocation

Sentiment classification is one of the hottest research areas among the Natural Language Processing (NLP) topics. While it aims to detect sentiment polarity and classification of the given opinion, requires a large number of aspect extractions. However, extracting aspect takes human effort and long time. To reduce this, Latent Dirichlet Allocation (LDA) method have come out recently to deal with this issue.In this paper, an efficient preprocessing method for sentiment classification is presented and will be used for analyzing user’s comments on Twitter social network. For this purpose, different text preprocessing techniques have been used on the dataset to achieve an acceptable standard text. Latent Dirichlet Allocation has been applied on the obtained data after this fast and accurate preprocessing phase. The implementation of different sentiment analysis methods and the results of these implementations have been compared and evaluated. The experimental results show that the combined uses of the preprocessing method of this paper and Latent Dirichlet Allocation have an acceptable results compared to other basic methods.

scholarly journals The state of voice user interfaces in Latin America

2020 ◽  
pp. 28
Author(s):  
Adriana L Iñiguez-Carrillo ◽  
Laura S Gaytán-Lugo ◽  
Rocío Maciel-Arellano ◽  
Miguel A García-Ruiz ◽  
Daniel Aréchiga
Keyword(s):  
Latin America ◽  
Latin American ◽  
Language Processing ◽  
User Interfaces ◽  
Speech Processing ◽  
The United States ◽  
The State ◽  
Conversational Agents ◽  
Voice User ◽  
Latin American Countries

This paper describes and analyzes the state of research in Voice User Interfaces (VUIs) in Latin America based on the review of scientific documents published in SCOPUS from 1999 to June 2020, through a bibliometric analysis. We analyzed 419 academic papers. Although a gradual increase is observed over the years, the number of published documents has increased considerably since 2014. Brazil (44%) and Mexico (28%) are the countries with more documents published. Co-authorship occurs between Latin American countries (Brazil, Argentina, Mexico, Ecuador, and Costa Rica). However, the mayor collaboration from Latin American countries occurs with the United States, France, Germany, Spain, Portugal, the United Kingdom, and Japan. The main researched topics are studies of automatic speech recognition, artificial intelligence, speech processing, and human-computer interaction, which have grown over the past few years. Natural language processing, conversational agents, user experience, and chatbots are keywords related to more recent studies. Our analysis reveals that the primary active research developed in the short-term future are personal assistants and assistive technology using voice user interfaces.

scholarly journals Human Gaze Assisted Artificial Intelligence: A Review

2020 ◽  
Author(s):  
Ruohan Zhang ◽  
Akanksha Saran ◽  
Bo Liu ◽  
Yifeng Zhu ◽  
Sihang Guo ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Cognitive State ◽  
Data Sets ◽  
Common Goal ◽  
Research Areas ◽  
Future Challenges ◽  
Potential Applications ◽  
High Level ◽  
And Robotics

Human gaze reveals a wealth of information about internal cognitive state. Thus, gaze-related research has significantly increased in computer vision, natural language processing, decision learning, and robotics in recent years. We provide a high-level overview of the research efforts in these fields, including collecting human gaze data sets, modeling gaze behaviors, and utilizing gaze information in various applications, with the goal of enhancing communication between these research areas. We discuss future challenges and potential applications that work towards a common goal of human-centered artificial intelligence.

Expanding a Large Inclusive Study of Human Listening Rates

2021 ◽  
Vol 14 (3) ◽  
pp. 1-26
Author(s):  
Danielle Bragg ◽  
Katharina Reinecke ◽  
Richard E. Ladner
Keyword(s):  
Speech Synthesis ◽  
Large Scale ◽  
Speech Rate ◽  
Synthetic Speech ◽  
Conversational Agents ◽  
Screen Reader ◽  
Audio Cues ◽  
Agent Interaction ◽  
Personal Devices ◽  
Answering Questions

As conversational agents and digital assistants become increasingly pervasive, understanding their synthetic speech becomes increasingly important. Simultaneously, speech synthesis is becoming more sophisticated and manipulable, providing the opportunity to optimize speech rate to save users time. However, little is known about people’s abilities to understand fast speech. In this work, we provide an extension of the first large-scale study on human listening rates, enlarging the prior study run with 453 participants to 1,409 participants and adding new analyses on this larger group. Run on LabintheWild, it used volunteer participants, was screen reader accessible, and measured listening rate by accuracy at answering questions spoken by a screen reader at various rates. Our results show that people who are visually impaired, who often rely on audio cues and access text aurally, generally have higher listening rates than sighted people. The findings also suggest a need to expand the range of rates available on personal devices. These results demonstrate the potential for users to learn to listen to faster rates, expanding the possibilities for human-conversational agent interaction.

Weighting-based semantic similarity measure based on topological parameters in semantic taxonomy

2018 ◽  
Vol 24 (6) ◽  
pp. 861-886 ◽  
Author(s):  
ABDULGABBAR SAIF ◽  
UMMI ZAKIAH ZAINODIN ◽  
NAZLIA OMAR ◽  
ABDULLAH SAEED GHAREB
Keyword(s):  
Semantic Similarity ◽  
Similarity Measure ◽  
Language Processing ◽  
Knowledge Engineering ◽  
Semantic Representation ◽  
Semantic Similarity Measure ◽  
Topological Parameters ◽  
Research Areas ◽  
Comparison Results ◽  
Feature Based

AbstractSemantic measures are used in handling different issues in several research areas, such as artificial intelligence, natural language processing, knowledge engineering, bioinformatics, and information retrieval. Hierarchical feature-based semantic measures have been proposed to estimate the semantic similarity between two concepts/words depending on the features extracted from a semantic taxonomy (hierarchy) of a given lexical source. The central issue in these measures is the constant weighting assumption that all elements in the semantic representation of the concept possess the same relevance. In this paper, a new weighting-based semantic similarity measure is proposed to address the issues in hierarchical feature-based measures. Four mechanisms are introduced to weigh the degree of relevance of features in the semantic representation of a concept by using topological parameters (edge, depth, descendants, and density) in a semantic taxonomy. With the semantic taxonomy of WordNet, the proposed semantic measure is evaluated for word semantic similarity in four gold-standard datasets. Experimental results show that the proposed measure outperforms hierarchical feature-based semantic measures in all the datasets. Comparison results also imply that the proposed measure is more effective than information-content measures in measuring semantic similarity.

Medical Reports Analysis Using Natural Language Processing for Disease Classification

2020 ◽  
pp. 155-172
Author(s):  
Sumathi S. ◽  
Indumathi S. ◽  
Rajkumar S.
Keyword(s):  
Natural Language Processing ◽  
Language Processing ◽  
Text Classification ◽  
Medical Data ◽  
Disease Classification ◽  
Human Interaction ◽  
Time Consumption ◽  
Medical Reports ◽  
Medical Documents

Text classification in medical domain could result in an easier way of handling large volumes of medical data. They can be segregated depending on the type of diseases, which can be determined by extracting the decisive key texts from the original document. Due to various nuances present in understanding language in general, a requirement of large volumes of text-based data is required for algorithms to learn patterns properly. The problem with existing systems such as MedScape, MedLinePlus, Wrappin, and MedHunt is that they involve human interaction and high time consumption in handling a large volume of data. By employing automation in this proposed field, the large involvement of manpower could be removed which in turn speeds up the process of classification of the medical documents by which the shortage of medical technicians in third world countries are addressed.

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