BLUE EYES TECHNOLOGY

Our paper deals with BLUE EYES TECHNOLOGY. Blue eye is a technology, which aims at creating computational machines that have perceptual and sensory abilities like those of human beings. The basic idea behind this technology is to give computer human power. For example, we can understand humans’ emotional state by his facial expressions. If we add these perceptual abilities to computers, we would enable them to work together with human beings as intimate partners. It provides technical means for monitoring and recording human-operator’s physiological condition. It has the ability to gather information about you and interact with you through special techniques like facial recognition, speech recognition, etc. It can even understand your emotions at the touch of the mouse. It can verify your identity, feel your presence, and start interacting with you. The machine can understand what a user wants, where he is looking at, and even realize his physical or emotional states. It realizes the urgency of the situation through the mouse. For instance if you ask the computer to dial to your friend at his office, it understands the situation and establishes a connection. It can reconstruct the course of operator’s work

Perceptions of Human Interactions in Adult Patients After Using Digital Therapeutic Mobile Applications

AbstractWhile the use of technology in healthcare has been present for many years, the recent rise of digital therapeutics in healthcare has been understudied, specifically regarding the perceptions and experiences of patients using mobile applications. Traditional healthcare involves face-to-face human interactions, requiring scheduled check-ins and provides structured interventions. Conversely, Digital therapeutics is primarily comprised of human to machine interactions. Digital therapeutics allows the user to access intervention in their time of individual need. The decrease of human-to-human interaction and the rise of human-to-machine interaction was explored in this qualitative descriptive phenomenological study from the patient perspective. Watson’s theory of human caring was used as a basis to understand patient’s past and present perceptions of traditional healthcare practices in a conceptual manner. Locsin’s theory of technology competency as caring in nursing provided a framework for the combination of caring and technology. Thematic coding of the data derived from semistructured interview questions from six participants over the age of 18 revealed four key themes: (a) needing something more, (b) help in my time, (c) one more thing, and (d) who is behind the curtain. These themes provide actionable evidence that supports positive social change through understanding of patient’s expectations that led to satisfaction in their healthcare. Social change can be positively impacted through this research and findings to provide the patient perspectives to create meaningful healthcare experiences that align with current patient expectations and requirements

Non-verbal signals for grounding in embodied conversational agent

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2002.Includes bibliographical references (leaves 79-82).In face-to-face conversation, speakers present non-verbal signals collateral with verbal information. Nodding and gazing at a speaker are known to provide positive feedback from listeners, which contributes to establishing common ground (a process called grounding). However, previous theories and computational models of grounding were mainly concerned with verbal grounding acts, and there have not been enough discussion about how nonverbal behaviors are used in the process of grounding. This thesis first compares face-to-face conversation to conversation without co-presence, revealing how nonverbal behaviors are used in the process of grounding in human communication. Results of the analysis show that, in face-to-face communication, non-verbal behaviors are changing during an utterance and a typical transition pattern of non-verbal behaviors is also different depending on the type of verbal act. Then, the implementation of grounding functionality onto an Embodied Conversational Agent is presented. The dialogue state updating mechanism in the Dialogue Manager accesses non-verbal information conveyed by a user and judges the groundedness of presented materials based on the results of empirical study.by Yukiko I. Nakano.S.M

A semantic metadata enrichment software ecosystem (SMESE) : its prototypes for digital libraries, metadata enrichments and assisted literature reviews

Contribution 1: Initial design of a semantic metadata enrichment ecosystem (SMESE) for Digital Libraries The Semantic Metadata Enrichments Software Ecosystem (SMESE V1) for Digital Libraries (DLs) proposed in this paper implements a Software Product Line Engineering (SPLE) process using a metadata-based software architecture approach. It integrates a components-based ecosystem, including metadata harvesting, text and data mining and machine learning models. SMESE V1 is based on a generic model for standardizing meta-entity metadata and a mapping ontology to support the harvesting of various types of documents and their metadata from the web, databases and linked open data. SMESE V1 supports a dynamic metadata-based configuration model using multiple thesauri. The proposed model defines rules-based crosswalks that create pathways to different sources of data and metadata. Each pathway checks the metadata source structure and performs data and metadata harvesting. SMESE V1 proposes a metadata model in six categories of metadata instead of the four currently proposed in the literature for DLs; this makes it possible to describe content by defined entity, thus increasing usability. In addition, to tackle the issue of varying degrees of depth, the proposed metadata model describes the most elementary aspects of a harvested entity. A mapping ontology model has been prototyped in SMESE V1 to identify specific text segments based on thesauri in order to enrich content metadata with topics and emotions; this mapping ontology also allows interoperability between existing metadata models. Contribution 2: Metadata enrichments ecosystem based on topics and interests The second contribution extends the original SMESE V1 proposed in Contribution 1. Contribution 2 proposes a set of topic- and interest-based content semantic enrichments. The improved prototype, SMESE V3 (see following figure), uses text analysis approaches for sentiment and emotion detection and provides machine learning models to create a semantically enriched repository, thus enabling topic- and interest-based search and discovery. SMESE V3 has been designed to find short descriptions in terms of topics, sentiments and emotions. It allows efficient processing of large collections while keeping the semantic and statistical relationships that are useful for tasks such as: 1. topic detection, 2. contents classification, 3. novelty detection, 4. text summarization, 5. similarity detection. Contribution 3: Metadata-based scientific assisted literature review The third contribution proposes an assisted literature review (ALR) prototype, STELLAR V1 (Semantic Topics Ecosystem Learning-based Literature Assisted Review), based on machine learning models and a semantic metadata ecosystem. Its purpose is to identify, rank and recommend relevant papers for a literature review (LR). This third prototype can assist researchers, in an iterative process, in finding, evaluating and annotating relevant papers harvested from different sources and input into the SMESE V3 platform, available at any time. The key elements and concepts of this prototype are: 1. text and data mining, 2. machine learning models, 3. classification models, 4. researchers annotations, 5. semantically enriched metadata. STELLAR V1 helps the researcher to build a list of relevant papers according to a selection of metadata related to the subject of the ALR. The following figure presents the model, the related machine learning models and the metadata ecosystem used to assist the researcher in the task of producing an ALR on a specific topic