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Conference proceedings front matter may contain various advertisements, welcome messages, committee or program information, and other miscellaneous conference information. This may in some cases also include the cover art, table of contents, copyright statements, title-page or half title-pages, blank pages, venue maps or other general information relating to the conference that was part of the original...
In his epoch-making work on information theory, Shannon defended information in terms of entropy. Entropy-based definitions of information relate to quantity of information, but not to its meaning. Subsequent attempts to introduce semantics into information theory have made some progress but fell short of having a capability to deal with information described in natural language. This paper is aimed...
Hebbian learning is one of the fundamental premises of neuroscience. The LMS (least mean square) algorithm of Widrow and Hoff is the world's most widely used learning algorithm. Hebbian learning is unsupervised. LMS learning is supervised. However, a form of LMS can be constructed to perform unsupervised learning and to implement Hebbian learning. Combining the two paradigms creates a new unsupervised...
Recent basic studies reveal that AI problems are deeply rooted in both the understanding of the natural intelligence and the adoption of suitable mathematical means for rigorously modeling the brain in machine understandable forms. Learning is a cognitive process of knowledge and behavior acquisition. Learning can be classified into five categories known as object identification, cluster classification,...
In the last ten years, speech recognition has evolved from a science fiction dream to a widespread input method for mobile devices. In this talk, I will describe how speech recognition works, the problems we have solved and the challenges that remain. I will touch upon some of Google's main efforts in language and pronunciation modeling, and describe how the adoption of neural networks for acoustic...
Concept elicitation is centric for machine knowledge extraction and representation in cognitive robot learning. This paper presents a supervised methodology for machine concept elicitation from informal counterparts described in natural languages. The collective opinions of a given concept in ten selected dictionaries are quantitatively analyzed and formally represented according to the attribute-object-relation...
Multiobjective memetic optimization algorithms (MMOAs) are recently applied to solve nonlinear optimization problems with conflicting objectives. An important issue in an MMOA is how to identify the relative best solutions to guide its adaptive processes. Pareto dominance has been used extensively to find the relative relations between solutions for the fitness assessment in multiobjective optimization...
We claim that computing forms of consensus among several agents about their solutions to past problems can play a useful pre-treatment role in case-based reasoning. Intuitively, we define a consensus as a subset of the plain accumulation of all the agents' individual past discovered solutions such that every agent can agree on all the information in this subset. A consensus can be expected to form...
Attention is the primary cognitive process to induce a response to a stimulus. Maintaining the attentive state continuously for a prolonged period of time is known as sustained attention which is vital for performing any task. The present study aims at evaluating the activation of different brain regions while performing an attention requiring task. A standard attention task called the Visual Continuous...
As hot topics in current research, music emotion recognition (MER) have been addressed by different disciplines such as physiology, psychology, musicology, cognitive science, etc. In this paper, music emotions was modeled as continuous variables composed of valence and arousal values (VA values) based on Valence-Arousal model, and MER is formulated as a regression problem. 548 dimensions of music...
Host Based Intrusion Detection Systems (HIDS) are gaining traction in discovering malicious software inside a host operating system. In this paper, the authors have developed a new cognitive host based anomaly detection system based on supervised AdaBoost machine learning algorithm. Particularly, information fractal dimension based approach is incorporated in the original AdaBoost machine learning...
We show and discuss how computational information conservation theory (CICT) can help us to develop even competitive advanced quantum cognitive computational systems towards deep computational cognitive intelligence. CICT new awareness of a discrete HG (hyperbolic geometry) subspace (reciprocal space, RS) of coded heterogeneous hyperbolic structures, underlying the familiar Q Euclidean (direct space,...
In the present paper we describe a bio-inspired non von Neumann controller for a simple sensorimotor robotic system. This controller uses a bitwise version of the Gibbs sampling algorithm to select commands so the robot can adapt its course of action and avoid perceived obstacles in the environment. The VHDL specification of the circuit implementation of this controller is based on stochastic computation...
Chunking has emerged as a basic property of human cognition. Computationally, chunking has been proposed as a process for compressing information also has been identified in neural processes in the brain and used in models of these processes. Our purpose in this paper is to expand understanding of how chunking impacts both learning and performance using the Computational-Unified Learning Model (C-ULM)...
Concept elicitation is a fundamental methodology for knowledge extraction and representation in cognitive robot learning. Traditional machine learning technologies deal with object identification, cluster classification, functional regression, and behavior acquisition. This paper presents a supervised machine knowledge learning methodology for concept elicitation from sample dictionaries in natural...
It is recognized that the semantic space of knowledge is a hierarchical concept network. This paper presents theories and algorithms of hierarchical concept classification by quantitative semantic relations via machine learning based on concept algebra. The equivalence between formal concepts are analyzed by an Algorithm of Concept Equivalence Analysis (ACEA), which quantitatively determines the semantic...
This paper proposes an image-to-image face recognition algorithm that uses Dual Linear Regression based Classification (DLRC) and an Electoral College voting approach. Each face image involved is first converted into a cluster of images; each image in the cluster is obtained by shifting the original image a few pixels. The similarity of a pair of face images can be measured by comparing the distance...
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