scholarly journals Quantum semantics of text perception

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ilya A. Surov ◽  
E. Semenenko ◽  
A. V. Platonov ◽  
I. A. Bessmertny ◽  
F. Galofaro ◽  
...  
Keyword(s):  
Cognitive Modeling ◽  
Human Perception ◽  
Machine Intelligence ◽  
Quantum Model ◽  
Cognitive State ◽  
Standard Vector ◽  
Perception Model ◽  
Complex Valued ◽  
Positive Results ◽  
Quantum Semantics

AbstractThe paper presents quantum model of subjective text perception based on binary cognitive distinctions corresponding to words of natural language. The result of perception is quantum cognitive state represented by vector in the qubit Hilbert space. Complex-valued structure of the quantum state space extends the standard vector-based approach to semantics, allowing to account for subjective dimension of human perception in which the result is constrained, but not fully predetermined by input information. In the case of two distinctions, the perception model generates a two-qubit state, entanglement of which quantifies semantic connection between the corresponding words. This two-distinction perception case is realized in the algorithm for detection and measurement of semantic connectivity between pairs of words. The algorithm is experimentally tested with positive results. The developed approach to cognitive modeling unifies neurophysiological, linguistic, and psychological descriptions in a mathematical and conceptual structure of quantum theory, extending horizons of machine intelligence.

Perceptual Image Representations for Support Vector Machine Image Coding

2011 ◽  
pp. 303-324
Author(s):  
Juan Gutiérrez ◽  
Gabriel Gómez-Perez ◽  
Jesús Malo ◽  
Gustavo Camps-Valls
Keyword(s):  
Support Vector Machine ◽  
Image Coding ◽  
General Procedure ◽  
Image Representation ◽  
Rate Distortion ◽  
Human Perception ◽  
Human Vision ◽  
Support Vector ◽  
Perception Model ◽  
Image Representations

Support vector machine (SVM) image coding relies on the ability of SVMs for function approximation. The size and the profile of the e-insensitivity zone of the support vector regression (SVR) at some specific image representation determines (a) the amount of selected support vectors (the compression ratio), and (b) the nature of the introduced error (the compression distortion). However, the selection of an appropriate image representation is a key issue for a meaningful design of the e-insensitivity profile. For example, in image coding applications, taking human perception into account is of paramount relevance to obtain a good rate-distortion performance. However, depending on the accuracy of the considered perception model, certain image representations are not suitable for SVR training. In this chapter, we analyze the general procedure to take human vision models into account in SVR-based image coding. Specifically, we derive the condition for image representation selection and the associated e-insensitivity profiles.

scholarly journals Graphical evolving transformation system machine

2015 ◽  
Author(s):  
◽  
Thanh Thieu
Keyword(s):  
Learning Algorithm ◽  
Minimum Description Length ◽  
Inductive Learning ◽  
Human Perception ◽  
Machine Intelligence ◽  
Transformation System ◽  
Noisy Input ◽  
Wide Range ◽  
Real World Datasets ◽  
Class Representation

For years, scientists have challenged the machine intelligence problem. Learning classes of objects followed by the classification of objects into their classes is a common task in machine intelligence. For this task, two objects representation schemes are often used: a vector-based representation, and a graph-based representation. While the vector representation has sound mathematical background and optimization tools, it lacks the ability to encode relations between the patterns and their parts, thus lacking the complexity of human perception. On the other hand, the graph-based representation naturally captures the intrinsic structural properties, but available algorithms usually have exponential complexity. In this work, we build an inductive learning algorithm that relies on graph-based representation of objects and their classes, and test the framework on a competitive dataset of human actions in static images. The method incorporates three primary measures of class representation: likelihood probability, family resemblance typicality, and minimum description length. Empirical benchmarking shows that the method is robust to the noisy input, scales well to real-world datasets, and achieves comparable performance to current learning techniques. Moreover, our method has the advantage of intuitive representation regarding both patterns and class representation. While applied to a specific problem of human pose recognition, our framework, named graphical Evolving Transformation System (gETS), can have a wide range of applications and can be used in other machine learning tasks.

A metric for evaluating speech recognizer output based on human-perception model

2015 ◽  
Author(s):  
Nobuyasu Itoh ◽  
Gakuto Kurata ◽  
Ryuki Tachibana ◽  
Masafumi Nishimura
Keyword(s):  
Human Perception ◽  
Perception Model ◽  
Speech Recognizer

scholarly journals Familiarity and Complexity during a Takeover in Highly Automated Driving

2021 ◽  
Author(s):  
Marlene Susanne Lisa Scharfe-Scherf ◽  
Nele Russwinkel
Keyword(s):  
Cognitive Modeling ◽  
Driving Simulator ◽  
Cognitive State ◽  
Automated Driving ◽  
Cognitive Assistance ◽  
Assistance Systems ◽  
The Impact ◽  
Subjective Complexity ◽  
Highly Automated Driving ◽  
Action Decision

AbstractThis paper shows, how objective complexity and familiarity impact the subjective complexity and the time to make an action decision during the takeover task in a highly automated driving scenario. In the next generation of highly automated driving the driver remains as fallback and has to take over the driving task whenever the system reaches a limit. It is thus highly important to develop an assistance system that supports the individual driver based on information about the drivers’ current cognitive state. The impact of familiarity and complexity (objective and subjective) on the time to make an action decision during a takeover is investigated. To produce replicable driving scenarios and manipulate the independent variables situation familiarity and objective complexity, a driving simulator is used. Results show that the familiarity with a traffic situation as well as the objective complexity of the environment significantly influence the subjective complexity and the time to make an action decision. Furthermore, it is shown that the subjective complexity is a mediator variable between objective complexity/familiarity and the time to make an action decision. Complexity and familiarity are thus important parameters that have to be considered in the development of highly automated driving systems. Based on the presented mediation effect, the opportunity of gathering the drivers’ subjective complexity and adapting cognitive assistance systems accordingly is opened up. The results of this study provide a solid basis that enables an individualization of the takeover by implementing useful cognitive modeling to individualize cognitive assistance systems for highly automated driving.

scholarly journals A technical framework for automatic perceptual evaluation of singing quality

2018 ◽  
Vol 7 ◽  
Author(s):  
Chitralekha Gupta ◽  
Haizhou Li ◽  
Ye Wang
Keyword(s):  
Cognitive Modeling ◽  
Human Perception ◽  
Fusion Method ◽  
Acoustic Features ◽  
Late Fusion ◽  
Perceptual Evaluation ◽  
Early Fusion ◽  
Modeling Concept ◽  
The Individual ◽  
Technical Framework

Human experts evaluate singing quality based on many perceptual parameters such as intonation, rhythm, and vibrato, with reference to music theory. We proposed previously the Perceptual Evaluation of Singing Quality (PESnQ) framework that incorporated acoustic features related to these perceptual parameters in combination with the cognitive modeling concept of the telecommunication standard Perceptual Evaluation of Speech Quality to evaluate singing quality. In this study, we present further the study of the PESnQ framework to approximate the human judgments. First, we find that a linear combination of the individual perceptual parameter human scores can predict their overall singing quality judgment. This provides us with a human parametric judgment equation. Next, the prediction of the individual perceptual parameter scores from the PESnQ acoustic features show a high correlation with the respective human scores, which means more meaningful feedback to learners. Finally, we compare the performance of early fusion and late fusion of the acoustic features in predicting the overall human scores. We find that the late fusion method is superior to that of the early fusion method. This work underlines the importance of modeling human perception in automatic singing quality assessment.

Teaching and Explanation: Aligning Priors between Machines and Humans

2021 ◽  
pp. 171-196
Author(s):  
José Hernández-Orallo ◽  
Cèsar Ferri
Keyword(s):  
Artificial Intelligence ◽  
Human Perception ◽  
Human Learning ◽  
Machine Intelligence ◽  
Human Intelligence ◽  
Machine Perception ◽  
Explainable Artificial Intelligence ◽  
The Future ◽  
The Way

Machine intelligence differs signficantly from human intelligence. While human perception has similarities to the way machine perception works, human learning is mostly a directed process, guided by other people: parents, teachers, ... The area of machine teaching is becoming increasingly popular as a different paradigm for making machines learn. In this chapter, we start from recent results in machine teaching that show the relevance of prior alignment between humans and machines. From here, we focus on the scenario when a machine has to teach humans, a situation more and more common in the future. Specifically, we analyse how machine teaching relates to explainable artificial intelligence, and how simplicity priors play a role beyond intelligibility. We illustrate this with a general teaching protocol and a few examples in several representation languages: feature-value vectors and sequences. Some straightforward experiments with humans indicate when a strong simplicity prior is --and is not-- sufficient.

scholarly journals Main Problems of Improving Scientific and Methodological Support for Determining the Development Goals System in a Region

2020 ◽  
Vol 4 (46) ◽  
pp. 98-105
Author(s):  
N. V. Bielikova ◽  
◽  
M. L. Bekker ◽  
Y. M. Kriachko ◽  
◽  
...  
Keyword(s):  
Economic Development ◽  
Cognitive Modeling ◽  
System Analysis ◽  
Cognitive Model ◽  
Human Perception ◽  
Scientific Basis ◽  
Fuzzy Cognitive Maps ◽  
Cognitive Approach ◽  
Socio Economic Development ◽  
Development Goals

The relevance of the article is confirmed by the need to develop scientific and methodological support for determining the development goals system in a region.It is one of the priorities for both the scientific community and public authorities. In particular, scientific basis, which coulddesign the development goals system in a region, requires further development and improvement. It should be based onboth the traditional and non-traditional methods of system analysis. The purpose of the article is to suggest recommendations for improving the scientific and methodological support for determining the development goals system in a region through using specific tools of fuzzy cognitive modeling. It has been proved that comprehensive assessment of regional socio-economic systems is impossible without the application of systemic approach, which allows interconnecting a large number of processes occurring in the economic, social and environmental systems in a region. Such researchis successfully carried outusing a cognitive approach to study complex systems of different nature. The authors of the article have considered peculiarities of drawing fuzzy cognitive maps, the latter being used as a tool for displaying real dynamic systems in a form that corresponds to human perception of such processes. A logical patternfor carrying out structural analysis of the socio-economic development of the region has been suggested.The patterncomprises five interrelated stages and includes constructing a fuzzy cognitive model of socio-economic development in the Ukrainian regions.The systemic indicators of fuzzy cognitive models of socio-economic development in the Ukrainian regions have beensuggested, and the analysis of their influence on the development of in the Ukrainian regions has been carried out.

Perception modeling based on vision and touch: “authenticity” of leather defined through observational experience

2021 ◽  
pp. 004051752199803
Author(s):  
Shuhei Watanabe ◽  
Takahiko Horiuchi
Keyword(s):  
Physical Properties ◽  
Animal Rights ◽  
Subjective Evaluation ◽  
Human Perception ◽  
Cost Effective ◽  
Effective Alternative ◽  
Artificial Leather ◽  
Perception Model ◽  
Cost Effective Alternative ◽  
By Products

Nowadays, numerous products use artificial leather as it is a cost-effective alternative to genuine leather. However, products made from artificial leather may leave impressions on consumers that are dissimilar to those left by products made of genuine leather. In other words, products that use artificial leather but are perceived as genuine leather are more attractive to consumers. Therefore, in this study, we aimed to understand and quantify the factors that affect the mechanism via which consumers perceive a leather product to be made of genuine leather. We conducted several experiments to evaluate the hypothesis regarding human perception. Measurement experiments were performed to obtain the visual and physical properties of such impressions. We estimated the representative impressions formed by people during their interaction with leather samples through subjective experiments and derived models of these impressions in terms of the measured properties. Subjective evaluation experiments were performed under visual, tactile, and visual–tactile conditions. Finally, we quantified leather “authenticity” using these representative impressions. Participants, who are general consumers, were divided into two groups according to their familiarity with leather. The “authenticity” perception model of the group familiar with leather was constructed under visual and visual–tactile conditions, whereas the model of the group unfamiliar with leather was constructed under visual–tactile conditions, suggesting the influence of a cross-modal phenomenon. The results of this study can be applied to develop attractive artificial leather, which is expected to contribute to the protection of animal rights while promoting the sale of artificial leather products.

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