scholarly journals Appoximationism as a new art form in Art Therapy.

2020 ◽  
Author(s):  
Anil Kumar Bheemaiah
Keyword(s):  
Data Mining ◽  
Natural Language ◽  
Art Therapy ◽  
Human Cognition ◽  
Universal Language ◽  
Art Form ◽  
Formal Definitions ◽  
Graphics Calculators ◽  
Visual Description ◽  
Definition Of

Abstract:Approximationism as an art form is defined in a deep definition of human nature, as a model maker, in correlations and autocorrelations with varying degrees of naturalness, human cognition, is defined as symbolic dynamism and abstractionism, in a spectrum ,with algorithms and data mining, defined as approximationism in visual declaration, symbolic mathematics, natural language and metaphors, an ontological API and Taxonomy in cognition. Various metrics of formal elements of approximationism for art therapy are the topic of a future publication.Keywords: approximationism, ontology API, taxonomy, complexity theory, metrics, visual dynamism, visual declaration, graphics calculators, docking stations.What:Mathematics is a universal language, but Godel's incompleteness theorem proves it’s incompleteness, not even closed form expressions for natural language or for consciousness are possible, so approximations are the solution, every model we make and data we collect are mere approximations. Is art too an approximation and a visual description of a model? This is approximationism, the art of approximations of life, art as an approximation.We explore the use of approximation art in art therapy and data mining or sketch to coding, for the formal definitions and the programming of approximationist art therapy.

scholarly journals State Pod Automatism as Art Therapy.

2020 ◽  
Author(s):  
Anil Kumar Bheemaiah
Keyword(s):  
Data Mining ◽  
Art Therapy ◽  
Wilderness Therapy ◽  
Structure Variation ◽  
Data Mining Algorithms ◽  
Art Form ◽  
Color Histograms ◽  
Anchor Points ◽  
Definition Of ◽  
Mining Algorithms

Abstract:Formal art therapy is defined in a definition of the formal elements in art and metrics on structure, contour, variation and balance, leading to adaptability and measures of dynamism and thought as an emergence in the art form, leading to a viable diagnosis. In this paper we examine formal art analysis in art therapy and describe several metrics based on common data mining algorithms.Keywords: Formal Art Therapy, Autopilot, Mindfulness, Wilderness Therapy, structure, variation, segmentation, objects in images, dynamism, balance, color histograms, k means, data mining. What:In understanding the Autopilot, we use art therapy in the form of the state pod automatism,in this coding , the formal elements consist of a flexible line composition with five or more anchor points to create pods, which are painted in a spectrum of colors using a pixel brush, either as oil or water colors, in as many colors and hues as possible, the transitions between the pods, represent the nature of the inertial, the reason for dukka.(Contributors to Wikimedia projects 2001)We present data mining algorithms for structure and color, and contour based metrics for assisting a prognosis and art therapy formulation.How:Three examples are presented and analyzed using k-means algorithms to create statistics for colors, color clusters and pixels, with a database lookup of matching words as a meta description of the images. We present the analysis, and propose contour and structure mining tools for generating metrics.(Contributors to Wikimedia projects 2005)

scholarly journals Synkretismus jako jazykový jev, lingvistická kategorie a lingvistický termín

2018 ◽  
Vol 165 ◽  
pp. 429-440
Author(s):  
Ladislav Vobořil
Keyword(s):  
Language Play ◽  
Broad Definition ◽  
Universal Language ◽  
Linguistic Term ◽  
Definition Of ◽  
Language Universal

Syncretism as language phenomenon, linguistic term and categoryThe author deals with syncretism as both non-linguistic, and linguistic term, notion, language universal, language phenomenon, resulting and deeply interconnected with the language economy principle. First, a broad definition of the term syncretism is given, then the author focuses on theoretical aspects of syncretic issues, taking into account research works by many world-known linguists, predominantly Russian, Czech and some others. Second, syncretism is compared with other notions and terms, used sometimes to described very similar language phenomena, very close to syncretism, such as neutralisation, homonymy, polyfunctionality, polysemy, contamination, language play. The author comes to the conclusion that very often the same language phenomena are called using various terms, syncretic phenomena are not the exception; in various studies the term and notion of syncretism can be understood in different ways.Синкретизм как языковое явление, лингвистический термин и категорияАвтором статьи рассматриваются нелингвистические и лингвистические аспекты син­кретизма как языкового термина и понятия, языковой универсалии, языкового явления, воз­никающего как следствие действия закона языковой экономии. Дается общепринятое опре­деление термина синкретизма, затем автор фокусируется на теоретическом обосновании явления синкретизма, опираясь на работы выдающихся мировых ученых, главным образом, русских, чешских и др. Во второй части статьи синкретизм сопоставляется с другими явлени­ями иизбранными для их наименования в лингвистике терминами, как, например, нейтрали­зация, омонимия, полифункциональность, полисемия, контаминация, языковая игра. Конста­тируется, что не всегда те же самые явления обозначены с помощью тех же самых терминов, явления синкретизма могут обозначаться, используя разные термины, и сама наполненность термина синкретизм варьируется от автора к автору.

scholarly journals From Text to Thought: How Analyzing Language Can Advance Psychological Science

2020 ◽  
Author(s):  
Joshua Conrad Jackson ◽  
Joseph Watts ◽  
Johann-Mattis List ◽  
Ryan Drabble ◽  
Kristen Lindquist
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Statistical Power ◽  
Culturally Diverse ◽  
Large Scale ◽  
Human Cognition ◽  
Comparative Linguistics ◽  
Psychological Science ◽  
Language Analysis ◽  
The Mind

Humans have been using language for thousands of years, but psychologists seldom consider what natural language can tell us about the mind. Here we propose that language offers a unique window into human cognition. After briefly summarizing the legacy of language analyses in psychological science, we show how methodological advances have made these analyses more feasible and insightful than ever before. In particular, we describe how two forms of language analysis—comparative linguistics and natural language processing—are already contributing to how we understand emotion, creativity, and religion, and overcoming methodological obstacles related to statistical power and culturally diverse samples. We summarize resources for learning both of these methods, and highlight the best way to combine language analysis techniques with behavioral paradigms. Applying language analysis to large-scale and cross-cultural datasets promises to provide major breakthroughs in psychological science.

Outlier Detection Techniques for Data Mining

2011 ◽  
pp. 1483-1488
Author(s):  
Fabrizio Angiulli
Keyword(s):  
Data Mining ◽  
Outlier Detection ◽  
Credit Card ◽  
Detection Methods ◽  
Distribution Model ◽  
Main Task ◽  
Data Set ◽  
Homogeneous Groups ◽  
Definition Of ◽  
Dependency Detection

Data mining techniques can be grouped in four main categories: clustering, classification, dependency detection, and outlier detection. Clustering is the process of partitioning a set of objects into homogeneous groups, or clusters. Classification is the task of assigning objects to one of several predefined categories. Dependency detection searches for pairs of attribute sets which exhibit some degree of correlation in the data set at hand. The outlier detection task can be defined as follows: “Given a set of data points or objects, find the objects that are considerably dissimilar, exceptional or inconsistent with respect to the remaining data”. These exceptional objects as also referred to as outliers. Most of the early methods for outlier identification have been developed in the field of statistics (Hawkins, 1980; Barnett & Lewis, 1994). Hawkins’ definition of outlier clarifies the approach: “An outlier is an observation that deviates so much from other observations as to arouse suspicions that it was generated by a different mechanism”. Indeed, statistical techniques assume that the given data set has a distribution model. Outliers are those points that satisfy a discordancy test, that is, that are significantly far from what would be their expected position given the hypothesized distribution. Many clustering, classification and dependency detection methods produce outliers as a by-product of their main task. For example, in classification, mislabeled objects are considered outliers and thus they are removed from the training set to improve the accuracy of the resulting classifier, while in clustering, objects that do not strongly belong to any cluster are considered outliers. Nevertheless, it must be said that searching for outliers through techniques specifically designed for tasks different from outlier detection could not be advantageous. As an example, clusters can be distorted by outliers and, thus, the quality of the outliers returned is affected by their presence. Moreover, other than returning a solution of higher quality, outlier detection algorithms can be vastly more efficient than non ad-hoc algorithms. While in many contexts outliers are considered as noise that must be eliminated, as pointed out elsewhere, “one person’s noise could be another person’s signal”, and thus outliers themselves can be of great interest. Outlier mining is used in telecom or credit card frauds to detect the atypical usage of telecom services or credit cards, in intrusion detection for detecting unauthorized accesses, in medical analysis to test abnormal reactions to new medical therapies, in marketing and customer segmentations to identify customers spending much more or much less than average customer, in surveillance systems, in data cleaning, and in many other fields.

The Future of Instruction Modeling

2020 ◽  
pp. 205-228
Author(s):  
George A. Khachatryan
Keyword(s):  
Data Mining ◽  
Natural Language Processing ◽  
Foreign Language ◽  
Natural Language ◽  
Blended Learning ◽  
Language Processing ◽  
Educational Data Mining ◽  
Government Policies ◽  
Free Form ◽  
Learning Programs

Instruction modeling is still in its early stages. This chapter discusses promising directions in which instruction modeling could develop in coming years. This includes increasing the richness of interfaces used in instruction modeling programs (e.g., by allowing students to enter responses in free form and have them graded via natural language processing); applying instruction modeling to subjects beyond mathematics, including English, foreign language, and science; using educational data mining to create automated “coaches” to help teachers better implement instruction modeling programs in their classrooms; creating approaches to instruction modeling that allow for rapid authorship of content; redesigning schools (in schedules as well as architecture) to optimize the use of instruction modeling; and putting in place government policies to encourage the use of comprehensive blended learning programs (such as those developed through instruction modeling).

Migrating from ISO/IEC 9126 to SQUARE

2017 ◽  
pp. 160-178
Author(s):  
Paula Estrella ◽  
Nikos Tsourakis
Keyword(s):  
Natural Language ◽  
Potential User ◽  
Use Case ◽  
Relative Importance ◽  
Quality Model ◽  
Evaluation Methodologies ◽  
Model Based ◽  
Fair Comparison ◽  
Definition Of ◽  
Current Standards

When it comes to the evaluation of natural language systems, it is well acknowledged that there is a lack of common evaluation methodologies, making the fair comparison of such systems a difficult task. Many attempts to standardize this process have used a quality model based on the ISO/IEC 9126 standards. The authors have also used these standards for the definition of a weighted quality model for the evaluation of a medical speech translator, showing the relative importance of the system's features depending on the potential user (patient or doctor, developer). More recently, ISO/IEC 9126 has been replaced by a new series of standards, the 25000 or SQuaRE series, indicating that the model should be migrated to the new series in order to maintain compliance adherence to current standards. This chapter demonstrates how to migrate from ISO/IEC 9126 to ISO 25000 by using the authors' previous work as a use case.

Ontologies in the Health Field

2011 ◽  
pp. 37-56 ◽  
Author(s):  
Michel Simonet ◽  
Radja Messai ◽  
Gayo Diallo
Keyword(s):  
Data Mining ◽  
Information Retrieval ◽  
Semantic Web ◽  
Knowledge Bases ◽  
Health Data ◽  
Efficient Manner ◽  
Common Definition ◽  
Ontology Design ◽  
Definition Of ◽  
Health Field

Health data and knowledge had been structured through medical classifications and taxonomies long before ontologies had acquired their pivot status of the Semantic Web. Although there is no consensus on a common definition of an ontology, it is necessary to understand their main features to be able to use them in a pertinent and efficient manner for data mining purposes. This chapter introduces the basic notions about ontologies, presents a survey of their use in medicine and explores some related issues: knowledge bases, terminology, and information retrieval. It also addresses the issues of ontology design, ontology representation, and the possible interaction between data mining and ontologies.

scholarly journals Contralog: a Prolog conform forward-chaining environment and its application for dynamic programming and natural language parsing

2016 ◽  
Vol 8 (1) ◽  
pp. 41-62
Author(s):  
Imre Kilián
Keyword(s):  
Dynamic Programming ◽  
Natural Language ◽  
Inference Engine ◽  
Recursive Definition ◽  
Target Model ◽  
Forward Chaining ◽  
Natural Language Parsing ◽  
Multiplication Problem ◽  
Inference Strategy ◽  
Definition Of

Abstract The backward-chaining inference strategy of Prolog is inefficient for a number of problems. The article proposes Contralog: a Prolog-conform, forward-chaining language and an inference engine that is implemented as a preprocessor-compiler to Prolog. The target model is Prolog, which ensures mutual switching from Contralog to Prolog and back. The Contralog compiler is implemented using Prolog's de facto standardized macro expansion capability. The article goes into details regarding the target model. We introduce first a simple application example for Contralog. Then the next section shows how a recursive definition of some problems is executed by their Contralog definition automatically in a dynamic programming way. Two examples, the well-known matrix chain multiplication problem and the Warshall algorithm are shown here. After this, the inferential target model of Prolog/Contralog programs is introduced, and the possibility for implementing the ReALIS natural language parsing technology is described relying heavily on Contralog's forward chaining inference engine. Finally the article also discusses some practical questions of Contralog program development.

Constructing Corpora for the Development and Evaluation of Paraphrase Systems

2008 ◽  
Vol 34 (4) ◽  
pp. 597-614 ◽  
Author(s):  
Trevor Cohn ◽  
Chris Callison-Burch ◽  
Mirella Lapata
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Syntactic Structure ◽  
Nominal Data ◽  
Parallel Corpus ◽  
Word Alignments ◽  
Definition Of

Automatic paraphrasing is an important component in many natural language processing tasks. In this article we present a new parallel corpus with paraphrase annotations. We adopt a definition of paraphrase based on word alignments and show that it yields high inter-annotator agreement. As Kappa is suited to nominal data, we employ an alternative agreement statistic which is appropriate for structured alignment tasks. We discuss how the corpus can be usefully employed in evaluating paraphrase systems automatically (e.g., by measuring precision, recall, and F1) and also in developing linguistically rich paraphrase models based on syntactic structure.

Frames at the Interface of Language and Cognition

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Sebastian Löbner
Keyword(s):  
Research Group ◽  
Basic Mechanism ◽  
Semantic Theory ◽  
Theory And Practice ◽  
Human Cognition ◽  
Annual Review ◽  
Publication Date ◽  
World Knowledge ◽  
Formal Definitions ◽  
Language And Cognition

This article reviews the work on frames in the last decade by a Düsseldorf research group. The research is based on Barsalou's notion of frames and the hypothesis that the frame is the general format of categorization in human cognition. The Düsseldorf frame group developed formal definitions and interpretations of Barsalou frames and applied the theory in linguistics, philosophy, and psychology. This review focuses on applications of the theory in semantics. The Düsseldorf approach grounds the analysis of composition in deep decomposition of lexical meanings with frames. The basic mechanism of composition is unification, which has deep repercussions on semantic theory and practice: Composition produces structured meanings and is not necessarily deterministic. The interaction of semantic and world knowledge can be modeled in an overall frame model across levels of linguistic analysis. The review concludes with a brief report on the development of hyperframes for dynamic verbs and for cascades, a model for multilevel categorization of action. Expected final online publication date for the Annual Review of Linguistics, Volume 7 is January 14, 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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