A systematic approach to literature analysis: traveling through stories

2021 ◽  
Author(s):  
Ryusei Uenishi ◽  
Claudio Ortega ◽  
Ángel Pérez Martinez ◽  
Michelle Rodríguez-Serra ◽  
Paula Elías
Keyword(s):  
Language Processing ◽  
Travel Literature ◽  
Relevant Information ◽  
Quantitative Information ◽  
Literary Analysis ◽  
Christopher Columbus ◽  
Human Machine Interaction ◽  
The People ◽  
Machine Interaction ◽  
Real Places

Abstract Travel literature has captured humanity’s imagination ever since the emergence of famous works such as The Wonders of The World by Marco Polo and The Journal of Christopher Columbus. Authors in this genre must process large and diverse volumes of data (visual, sensory, and written) obtained on their trips, before synthesizing it humanly in such a way as to move and communicate personally with the reader, without losing the factual nature of the story. This is the ultimate goal of the natural language processing (NLP) field: to process and generate human–machine interaction as naturally as possible. Hence, this article’s purpose is to analyze and describe a nonfictional literary text, which is a type of documentary text that contains objective, qualitative, and quantitative information based on evidence. In this analysis, traditional methods will not be used. Instead, it will leverage NLP techniques to process and extract relevant information from the text. This literary analysis is a new kind of approach that encourages further discussions about the methodologies currently used. The proposed methodology enables exploratory analysis of both individual and unstructured corpus databases while also allowing geospatial data to complement the textual analysis by connecting the people in the text with real places.

scholarly journals Automata for the People: Machine Noise and Attention

2012 ◽  
Vol 18 (3) ◽  
Author(s):  
David Ellison
Keyword(s):  
White Noise ◽  
William James ◽  
Human Machine Interaction ◽  
Musical Performances ◽  
The People ◽  
Eighteenth And Nineteenth Centuries ◽  
Attention Demands ◽  
Machine Interaction ◽  
Physical Mechanics ◽  
Over Time

This article examines the production and reception of incidental machine noise, specifically the variably registered sounds emanating from automata in the eighteenth and nineteenth centuries. The argument proposed here is that the audience for automata performances demonstrated a capacity to screen out mechanical noise that may have otherwise interfered with the narrative theatricality of their display. In this regard the audience may be said to resemble auditors at musical performances who learned to suppress the various noises associated with the physical mechanics of performance, and the faculty of attention itself. For William James among others, attention demands selection among competing stimuli. As the incidental noise associated with automata disappears from sensibility over time, its capacity to signify in other contexts emerges. In the examples traced here, such noise is a means of distinguishing a specifically etherealised human-machine interaction. This is in sharp distinction from other more degrading forms of relationship such as the sound of bodies labouring at machines. In this regard, the barely detected sound of the automata in operation may be seen as a precursor to the white noise associated with modern, corporate productivity.

scholarly journals On the Gap between Domestic Robotic Applications and Computational Intelligence

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 793
Author(s):  
Junpei Zhong ◽  
Chaofan Ling ◽  
Angelo Cangelosi ◽  
Ahmad Lotfi ◽  
Xiaofeng Liu
Keyword(s):  
Language Processing ◽  
Computational Intelligence ◽  
Intelligent Agents ◽  
State Of The Art ◽  
Daily Life ◽  
The State ◽  
Computational Techniques ◽  
Human Machine Interaction ◽  
Domestic Robots ◽  
Machine Interaction

Aspired to build intelligent agents that can assist humans in daily life, researchers and engineers, both from academia and industry, have kept advancing the state-of-the-art in domestic robotics. With the rapid advancement of both hardware (e.g., high performance computing, smaller and cheaper sensors) and software (e.g., deep learning techniques and computational intelligence technologies), robotic products have become available to ordinary household users. For instance, domestic robots have assisted humans in various daily life scenarios to provide: (1) physical assistance such as floor vacuuming; (2) social assistance such as chatting; and (3) education and cognitive assistance such as offering partnerships. Crucial to the success of domestic robots is their ability to understand and carry out designated tasks from human users via natural and intuitive human-like interactions, because ordinary users usually have no expertise in robotics. To investigate whether and to what extent existing domestic robots can participate in intuitive and natural interactions, we survey existing domestic robots in terms of their interaction ability, and discuss the state-of-the-art research on multi-modal human–machine interaction from various domains, including natural language processing and multi-modal dialogue systems. We relate domestic robot application scenarios with state-of-the-art computational techniques of human–machine interaction, and discuss promising future directions towards building more reliable, capable and human-like domestic robots.

scholarly journals Beyond Robotic Speech: Mutual Benefits to Cognitive Psychology and Artificial Intelligence from the Study of Multimodal Communication

2021 ◽  
pp. 274-294
Author(s):  
Beata Grzyb ◽  
Gabriella Vigliocco
Keyword(s):  
Language Learning ◽  
Language Processing ◽  
Computational Models ◽  
Human Communication ◽  
Embodied Agents ◽  
Human Machine Interaction ◽  
Human Likeness ◽  
Neural Underpinnings ◽  
Underlying Mechanisms ◽  
Machine Interaction

Recently, cognitive scientists have started to realise the potential importance of multimodality for the understanding of human communication and its neural underpinnings; while AI scientists have begun to address how to integrate multimodality in order to improve communication between human and embodied agent. We review here the existing literature on multimodal language learning and processing in humans and the literature on perception of embodied agents, their comprehension and production of multimodal cues and we discuss their main limitations. We conclude by arguing that by joining forces AI scientists can improve the effectiveness of human-machine interaction and increase the human-likeness and acceptance of embodied agents in society. In turn, computational models that generate language in artificial embodied agents constitute a unique research tool to investigate the underlying mechanisms that govern language processing and learning in humans.

scholarly journals Crowdsourcing Ecologically-Valid Dialogue Data for German

2021 ◽  
Vol 3 ◽  
Author(s):  
Yannick Frommherz ◽  
Alessandra Zarcone
Keyword(s):  
Language Processing ◽  
Large Scale ◽  
Amazon Mechanical Turk ◽  
Human Machine Interaction ◽  
User Interactions ◽  
Wizard Of Oz ◽  
Low Resource ◽  
The Cost ◽  
Vague Language ◽  
Machine Interaction

Despite their increasing success, user interactions with smart speech assistants (SAs) are still very limited compared to human-human dialogue. One way to make SA interactions more natural is to train the underlying natural language processing modules on data which reflects how humans would talk to a SA if it was capable of understanding and producing natural dialogue given a specific task. Such data can be collected applying a Wizard-of-Oz approach (WOz), where user and system side are played by humans. WOz allows researchers to simulate human-machine interaction while benefitting from the fact that all participants are human and thus dialogue-competent. More recent approaches have leveraged simple templates specifying a dialogue scenario for crowdsourcing large-scale datasets. Template-based collection efforts, however, come at the cost of data diversity and naturalness. We present a method to crowdsource dialogue data for the SA domain in the WOz framework, which aims at limiting researcher-induced bias in the data while still allowing for a low-resource, scalable data collection. Our method can also be applied to languages other than English (in our case German), for which fewer crowd-workers may be available. We collected data asynchronously, relying only on existing functionalities of Amazon Mechanical Turk, by formulating the task as a dialogue continuation task. Coherence in dialogues is ensured, as crowd-workers always read the dialogue history, and as a unifying scenario is provided for each dialogue. In order to limit bias in the data, rather than using template-based scenarios, we handcrafted situated scenarios which aimed at not pre-script-ing the task into every single detail and not priming the participants’ lexical choices. Our scenarios cued people’s knowledge of common situations and entities relevant for our task, without directly mentioning them, but relying on vague language and circumlocutions. We compare our data (which we publish as the CROWDSS corpus; n = 113 dialogues) with data from MultiWOZ, showing that our scenario approach led to considerably less scripting and priming and thus more ecologically-valid dialogue data. This suggests that small investments in the collection setup can go a long way in improving data quality, even in a low-resource setup.

Robots for the people, by the people: Personalizing human-machine interaction

2018 ◽  
Vol 3 (21) ◽  
pp. eaat7451 ◽  
Author(s):  
Caitlyn Clabaugh ◽  
Maja Matarić
Keyword(s):  
Human Machine Interaction ◽  
The People ◽  
Machine Interaction

A Study for Improving Device-Directed Speech Detection Toward Frictionless Human-Machine Interaction

2019 ◽  
Author(s):  
Che-Wei Huang ◽  
Roland Maas ◽  
Sri Harish Mallidi ◽  
Björn Hoffmeister
Keyword(s):  
Human Machine Interaction ◽  
Speech Detection ◽  
Machine Interaction

Concise CNN model for face expression recognition

2021 ◽  
pp. 1-9
Author(s):  
Harshadkumar B. Prajapati ◽  
Ankit S. Vyas ◽  
Vipul K. Dabhi
Keyword(s):  
Network Architecture ◽  
Expression Recognition ◽  
Human Machine Interaction ◽  
Face Expression ◽  
Computational Overhead ◽  
Average Accuracy ◽  
Proposed Model ◽  
Face Expression Recognition ◽  
Expression Classification ◽  
Machine Interaction

Face expression recognition (FER) has gained very much attraction to researchers in the field of computer vision because of its major usefulness in security, robotics, and HMI (Human-Machine Interaction) systems. We propose a CNN (Convolutional Neural Network) architecture to address FER. To show the effectiveness of the proposed model, we evaluate the performance of the model on JAFFE dataset. We derive a concise CNN architecture to address the issue of expression classification. Objective of various experiments is to achieve convincing performance by reducing computational overhead. The proposed CNN model is very compact as compared to other state-of-the-art models. We could achieve highest accuracy of 97.10% and average accuracy of 90.43% for top 10 best runs without any pre-processing methods applied, which justifies the effectiveness of our model. Furthermore, we have also included visualization of CNN layers to observe the learning of CNN.

scholarly journals A Bibliometric Analysis of Human-Machine Interaction Methodology for Electric-Powered Wheelchairs Driving from 1998 to 2020

2021 ◽  
Vol 18 (14) ◽  
pp. 7567
Author(s):  
Xiaochen Zhang ◽  
Lanxin Hui ◽  
Linchao Wei ◽  
Fuchuan Song ◽  
Fei Hu
Keyword(s):  
The Elderly ◽  
Research Field ◽  
Future Research ◽  
Human Machine Interaction ◽  
Research Directions ◽  
The Past ◽  
Power Wheelchairs ◽  
The Disabled ◽  
Future Research Directions ◽  
Machine Interaction

Electric power wheelchairs (EPWs) enhance the mobility capability of the elderly and the disabled, while the human-machine interaction (HMI) determines how well the human intention will be precisely delivered and how human-machine system cooperation will be efficiently conducted. A bibliometric quantitative analysis of 1154 publications related to this research field, published between 1998 and 2020, was conducted. We identified the development status, contributors, hot topics, and potential future research directions of this field. We believe that the combination of intelligence and humanization of an EPW HMI system based on human-machine collaboration is an emerging trend in EPW HMI methodology research. Particular attention should be paid to evaluating the applicability and benefits of the EPW HMI methodology for the users, as well as how much it contributes to society. This study offers researchers a comprehensive understanding of EPW HMI studies in the past 22 years and latest trends from the evolutionary footprints and forward-thinking insights regarding future research.

scholarly journals Sentiment Analysis Techniques Applied to Raw-Text Data from a Csq-8 Questionnaire about Mindfulness in Times of COVID-19 to Improve Strategy Generation

2021 ◽  
Vol 18 (12) ◽  
pp. 6408
Author(s):  
Mario Jojoa Acosta ◽  
Gema Castillo-Sánchez ◽  
Begonya Garcia-Zapirain ◽  
Isabel de la Torre Díez ◽  
Manuel Franco-Martín
Keyword(s):  
Health Care ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Language Processing ◽  
Health Care Professionals ◽  
Ground Truth ◽  
Relevant Information ◽  
Free Text

The use of artificial intelligence in health care has grown quickly. In this sense, we present our work related to the application of Natural Language Processing techniques, as a tool to analyze the sentiment perception of users who answered two questions from the CSQ-8 questionnaires with raw Spanish free-text. Their responses are related to mindfulness, which is a novel technique used to control stress and anxiety caused by different factors in daily life. As such, we proposed an online course where this method was applied in order to improve the quality of life of health care professionals in COVID 19 pandemic times. We also carried out an evaluation of the satisfaction level of the participants involved, with a view to establishing strategies to improve future experiences. To automatically perform this task, we used Natural Language Processing (NLP) models such as swivel embedding, neural networks, and transfer learning, so as to classify the inputs into the following three categories: negative, neutral, and positive. Due to the limited amount of data available—86 registers for the first and 68 for the second—transfer learning techniques were required. The length of the text had no limit from the user’s standpoint, and our approach attained a maximum accuracy of 93.02% and 90.53%, respectively, based on ground truth labeled by three experts. Finally, we proposed a complementary analysis, using computer graphic text representation based on word frequency, to help researchers identify relevant information about the opinions with an objective approach to sentiment. The main conclusion drawn from this work is that the application of NLP techniques in small amounts of data using transfer learning is able to obtain enough accuracy in sentiment analysis and text classification stages.

Sign in / Sign up

Export Citation Format

Share Document