Concept Classes Research Articles

Effects of design thinking on artificial intelligence learning and creativity

ABSTRACT This study explored the effects of design thinking on the conceptual cognition of artificial intelligence (AI) learning, attitudes toward AI, idea creativity, and the product creativity of AI applications. The concept map indicated that design thinking had a significant effect on the conceptual cognition of AI learning, particularly the relational conjunctions and classes of AI concepts. However, effects of cross-linking and examples were nonsignificant. In addition, it had a significant and positive effect on learning attitudes toward AI, in particular AI input and AI processing. Moreover, design thinking significantly and positively affected the idea creativity of AI applications, particularly the effects of novelty and value. It also had a significant and positive effect on the product creativity of AI applications, particularly functionality and elaboration. However, material novelty, style, idea structure, and product creativity had no significant difference.

Başkalık Üzerine: İbn Sînâ’nın Görelilik Teorisi Bağlamında Bir İnceleme

The mode of existence of relatives has been a matter of debate throughout the history of thought. Having evaluated the debate through the contrast between a first intelligible that has individuals in the external world and a second intelligible that has no counterpart at the individual level in the external world, Ibn Sīnā believed the relative to be a categorical accident with individuals that can be pointed at in the external world. In the Metaphysics of al-Shifā, Ibn Sīnā proposed a solution aimed at eliminating the objections based on the infinite regress against his view. The article tests the applicability of the model built in this solution to the meaning of otherness (al-mughāyara), the results of which reveal the incompatibility of otherness with this model as a problem. When examining the source of this problem, the following findings are noted: The categorical relative (al-muḍāf) and pure relation (iḍāfa) are not the same thing. Pure relation is a general concept upon which the categorical relative is based and to which it cannot be reduced, because the predicate of oneness (wahda) becomes valid for multiplicity (kathra) through pure relation. Otherness is a general predicate that is inherent in and coextensive with pure relation; in this way, otherness is included in the most general class of concepts that explain the order in the existence of all existents including the categorical relative. As Ibn Sīnā’s solution model in Metaphysics aims to explain the result of pure relation in essences, it cannot be applied to pure relation phases that prioritize results and transcend categories and thus cannot be applied to otherness.

Coupled analytical-numerical approach for determining hydrodynamic responses of breakwater with multiple OWCs

A coupled analytical-numerical approach has been developed that allows one to study the hydrodynamic responses of breakwaters of arbitrary geometry that have multiple OWCs with horizontal openings. The approach incorporates the eigenfunction of the OWC fluid domain into the boundary element method, which automatically satisfy the boundary condition at the OWC opening. As a result, the exact geometry of the OWCs no longer needs to be modelled and the number of elements will be largely reduced especially when dealing with 3D breakwaters. With the present approach, a class of 2D OWC-integrated breakwater concepts in existing literature was simulated for validation and parametric study. The approach was then applied to the 3D breakwater with multiple OWCs under slender OWC assumptions, and the numerical results obtained are compared to data from a reduced scale model test. The 2D case studies show that the present approach is able to generate accurate results on reflection and transmission, wave loads, and energy extraction efficiencies with a higher efficiency than conventional boundary element method. Comparison of 3D results with the model test results shows that the prediction of the natural frequency and the wave reflection and transmission coefficients are in good agreement.

VFC-SMOTE: very fast continuous synthetic minority oversampling for evolving data streams

The world is constantly changing, and so are the massive amount of data produced. However, only a few studies deal with online class imbalance learning that combines the challenges of class-imbalanced data streams and concept drift. In this paper, we propose the very fast continuous synthetic minority oversampling technique (VFC-SMOTE). It is a novel meta-strategy to be prepended to any streaming machine learning classification algorithm aiming at oversampling the minority class using a new version of Smote and Borderline-Smote inspired by Data Sketching. We benchmarked VFC-SMOTE pipelines on synthetic and real data streams containing different concept drifts, imbalance levels, and class distributions. We bring statistical evidence that VFC-SMOTE pipelines learn models whose minority class performances are better than state-of-the-art. Moreover, we analyze the time/memory consumption and the concept drift recovery speed.

Algorithms and Lower Bounds for De Morgan Formulas of Low-Communication Leaf Gates

The class FORMULA[s]∘G consists of Boolean functions computable by size- s De Morgan formulas whose leaves are any Boolean functions from a class G. We give lower bounds and (SAT, Learning, and pseudorandom generators ( PRG s )) algorithms for FORMULA[n 1.99 ]∘G, for classes G of functions with low communication complexity . Let R (k) G be the maximum k -party number-on-forehead randomized communication complexity of a function in G. Among other results, we show the following: • The Generalized Inner Product function GIP k n cannot be computed in FORMULA[s]° G on more than 1/2+ε fraction of inputs for s=o(n 2 /k⋅4 k ⋅R (k) (G)⋅log⁡(n/ε)⋅log⁡(1/ε)) 2 ). This significantly extends the lower bounds against bipartite formulas obtained by [62]. As a corollary, we get an average-case lower bound for GIP k n against FORMULA[n 1.99 ]∘PTF k −1 , i.e., sub-quadratic-size De Morgan formulas with degree-k-1) PTF ( polynomial threshold function ) gates at the bottom. Previously, it was open whether a super-linear lower bound holds for AND of PTFs. • There is a PRG of seed length n/2+O(s⋅R (2) (G)⋅log⁡(s/ε)⋅log⁡(1/ε)) that ε-fools FORMULA[s]∘G. For the special case of FORMULA[s]∘LTF, i.e., size- s formulas with LTF ( linear threshold function ) gates at the bottom, we get the better seed length O(n 1/2 ⋅s 1/4 ⋅log⁡(n)⋅log⁡(n/ε)). In particular, this provides the first non-trivial PRG (with seed length o(n)) for intersections of n halfspaces in the regime where ε≤1/n, complementing a recent result of [45]. • There exists a randomized 2 n-t #SAT algorithm for FORMULA[s]∘G, where t=Ω(n\√s⋅log 2 ⁡(s)⋅R (2) (G))/1/2. In particular, this implies a nontrivial #SAT algorithm for FORMULA[n 1.99 ]∘LTF. • The Minimum Circuit Size Problem is not in FORMULA[n 1.99 ]∘XOR; thereby making progress on hardness magnification, in connection with results from [14, 46]. On the algorithmic side, we show that the concept class FORMULA[n 1.99 ]∘XOR can be PAC-learned in time 2 O(n/log n) .

Levels of Sophistication in Elementary Students’ Understanding of Polygon Concept and Polygons Classes

This paper reports sophistication levels in third grade children’s understanding of polygon concept and polygon classes. We consider how children endow mathematical meaning to parts of figures and reason to identify relationships between polygons. We describe four levels of sophistication in children’s thinking as they consider a figure as an example of a polygon class through spatial structuring (the mental operation of building an organization for a set of figures). These levels are: (i) partial structuring of polygon concept; (ii) global structuring of polygon concept; (iii) partial structuring of polygon classes; and (iv) global structuring of polygon classes. These levels detail how cognitive apprehensions, dimensional deconstruction, and the use of mathematical language intervene in the mental process of spatial structuring in the understanding of the classes of polygons.

Ontological Modelling of the Decision-making Support Intellectual System in Risks Analysis in the Innovation and Investment Sphere

The purpose of the article is to study, analyze and consider general problems and prospects of using ontologies when modelling decision-making processes in intelligent systems when analyzing the risks of the innovation and investment sphere. The research methodology lies in methods of the basic concepts semantic analysis of a given subject area (innovation and investment projects and management processes for the implementation of these projects). The article discusses the existing approaches to modelling decision-making processes in the analysis of risks in the innovation and investment sphere. The novelty of the research is the intellectualization problems solving of processes in the innovation and investment sphere on the basis of formal ontological models. Conclusions. The practical value of the presented results lies in the development and use of knowledge management system components for identifying and predicting problem situations in innovation and investment projects. The proposed integrated ontology can be used in the management of innovation and investment projects in various subject areas since it contains classes of concepts that have the status of a project activity standard. The developed ontological model makes it possible to develop software architecture for an intelligent decision support system, develop metadata and build a set of interrelated thesauri to support the semantics of end-user requests.

Mental Integrations as Functional Wholes

Abstract It is argued that a mental integration is formed only if the result is a functional whole. This idea is then used to clarify the definition of a concept and discuss problems in which an instance may belong to different conceptual classes depending on the context. The same idea is also applied to the rules for dealing with an entity when it is formed out of sub-entities. Specific examples of how such rules are frequently violated in literature as well as colloquially are discussed.

A Determinants-of-Fertility Ontology for Detecting Future Signals of Fertility Issues From Social Media Data: Development of an Ontology.

BackgroundSouth Korea has the lowest fertility rate in the world despite considerable governmental efforts to boost it. Increasing the fertility rate and achieving the desired outcomes of any implemented policies requires reliable data on the ongoing trends in fertility and preparations for the future based on these trends.ObjectiveThe aims of this study were to (1) develop a determinants-of-fertility ontology with terminology for collecting and analyzing social media data; (2) determine the description logics, content coverage, and structural and representational layers of the ontology; and (3) use the ontology to detect future signals of fertility issues.MethodsAn ontology was developed using the Ontology Development 101 methodology. The domain and scope of the ontology were defined by compiling a list of competency questions. The terms were collected from Korean government reports, Korea’s Basic Plan for Low Fertility and Aging Society, a national survey about marriage and childbirth, and social media postings on fertility issues. The classes and their hierarchy were defined using a top-down approach based on an ecological model. The internal structure of classes was defined using the entity-attribute-value model. The description logics of the ontology were evaluated using Protégé (version 5.5.0), and the content coverage was evaluated by comparing concepts extracted from social media posts with the list of ontology classes. The structural and representational layers of the ontology were evaluated by experts. Social media data were collected from 183 online channels between January 1, 2011, and June 30, 2015. To detect future signals of fertility issues, 2 classes of the ontology, the socioeconomic and cultural environment, and public policy, were identified as keywords. A keyword issue map was constructed, and the defined keywords were mapped to identify future signals. R software (version 3.5.2) was used to mine for future signals.ResultsA determinants-of-fertility ontology comprised 236 classes and terminology comprised 1464 synonyms of the 236 classes. Concept classes in the ontology were found to be coherently and consistently defined. The ontology included more than 90% of the concepts that appeared in social media posts on fertility policies. Average scores for all of the criteria for structural and representations layers exceeded 4 on a 5-point scale. Violence and abuse (socioeconomic and cultural factor) and flexible working arrangement (fertility policy) were weak signals, suggesting that they could increase rapidly in the future.ConclusionsThe determinants-of-fertility ontology developed in this study can be used as a framework for collecting and analyzing social media data on fertility issues and detecting future signals of fertility issues. The future signals identified in this study will be useful for policy makers who are developing policy responses to low fertility.

Noun Composition in Mapudungun: Conceptual Patterns in the Naming of Places

This study intended to identify which referent features are more frequently selected in the naming of the concept class of places and, in turn, determine which are the preferred conceptualizations in Mapudungun. This language is mainly spoken by Mapuche people in central and southern areas of Chile and in the Central-West area of Argentina. To identify the features of this conceptualization, a cognitive-semantic analysis of the conceptual patterns of 112 nominal compounds pertaining to the concept subclasses of natural places (intervened and non-intervened) and non-natural places (installations and territorial divisions) was conducted. Results show that, for non-intervened natural places, an entity present in the natural place is preferably selected in the name, whereas for intervened places, an agricultural activity or an animal associated to the place is preferably chosen. Concerning installations, the preferred conceptual pattern specifies, by means of the constituent ruka ‘house’, the man-made nature of the place. Regarding territorial divisions, the preferred naming pattern combines two place concepts. This information is crucial for the creation of neologisms in Mapudungun because it guarantees that newly formed lexical units are coherent with the Mapuche worldview and do not import foreign models.

Remote Patient Monitoring during pandemic caused by COVID-19 using Semantic Web Technologies

Data integration facilities along with decrease in the quantified operational health information occurs due to stuck in hospital admission. Exchanging of information resources has an immense significance due to 3 critical capabilities. These can be enlisted as information swap over through electronic connectivity, interoperability, and compromising under prescribed medication, in which automated systems are involved to solve pathological discrepancies. Along with all these concern about patient safety is taken care of by this startegy. The substantial awareness can be resulted in computational methods and tools which are quite capable for rendering guidance to health care services more resourcefully, which further leads the advancement and execution of intellectual systems within the therapeutic domain. Exchanging of data in the Web can be complies with Resource Description Framework (RDF) and ontology’s. RDF has the capability of capturing information content instead of considering the syntax. Inferential support and transformational approach is feasible by deploying RDF. Facilitation of data by evolution and merging phenomenon can be utilised by the involvement of RDF though schemas variation exists. Semantic interoperability is achieved either by applying standardisation or by translation. Though standardisation is quite acceptable but due to time annihilation transformation in lieu of modernisation is acceptable more. Strategically, realistic approach of semantic interoperability comprises both standards and translations. Conceptual classes, their interrelations, instances and axioms exist in Ontology.

Quantum Hardness of Learning Shallow Classical Circuits

In this paper, we study the quantum learnability of constant-depth classical circuits under the uniform distribution and in the distribution-independent framework of probably approximately correct (PAC) learning. In order to attain our results, we establish connections between quantum learning and quantum-secure cryptosystems. We then achieve the following results. 1. Hardness of PAC learning ${AC}^0$ and ${TC}^0$ under the uniform distribution. Our first result concerns the concept class ${TC}^0$ (resp., ${AC}^0$), the class of constant-depth, polynomial-sized circuits with unbounded fan-in majority gates (resp., ${AND}, {OR}, {NOT}$ gates). We show the following: if there exists no quantum (quasi-)polynomial-time algorithm to solve the ring-learning with errors (${RLWE}$) problem, then there exists no (quasi-)polynomial-time quantum learning algorithm for ${TC}^0$; and if there exists no $2^{O(d^{1/\eta})}$-time quantum algorithm to solve ${RLWE}$ with dimension $d = O(polylog n)$ (for every constant $\eta > 2$), then there exists no $O(n^{ \log^{\nu} n} )$-time quantum learning algorithm for $poly(n)$-sized ${AC}^0$ circuits (for a constant $\nu>0$), matching the classical upper bound of Linial, Mansour and Nisan [J. ACM, 40 (1993), pp. 607--620], where the learning algorithms are under the uniform distribution (even with access to quantum membership queries). The main technique in these results uses an explicit family of pseudorandom functions that are believed to be quantum-secure to construct concept classes that are hard to learn quantumly under the uniform distribution. 2. Hardness of learning ${TC}^0_2$ in the PAC setting. Our second result shows that if there exists no quantum polynomial-time algorithm for the ${LWE}$ problem, then there exists no polynomial-time quantum-PAC learning algorithm for the class ${TC}^0_2$, i.e., depth-2 ${TC}^0$ circuits. The main technique in this result is to establish a connection between the quantum security of public-key encryption schemes and the learnability of a concept class that consists of decryption functions of the cryptosystem. Our results show that quantum resources do not give an exponential improvement to learning constant-depth polynomial-sized neural networks. This also gives a strong (conditional) negative answer to one of the “Ten Semi-Grand Challenges for Quantum Computing Theory" raised by Aaronson https://www.scottaaronson.com/writings/qchallenge.html, 2005.

A Pattern Language for Class Responsibility Assignment for Business Applications

Assigning class responsibility is a design decision to be made early in the design phase in software development, which bridges requirements and an analysis model. In general, assigning class responsibility relies heavily on the expertise and experience of the developer, and it is often ad-hoc. Class responsibility assignment rules are hard to be uniformly defined across the various domains of systems. Thus, the existing work describes general stepwise guidelines without concrete methods, which imposes the limit in deriving an analysis model from requirements specification without any loss of information and providing sufficient quality of the analysis model. This study tried to grasp the commonality and variations in analyzing the business application domain. By narrowing the subject of the solution, the presented patterns can help identify and assign class responsibilities for a system belonging to the business application domain. The presented pattern language consists of six segmented patterns, including 19 variations of relationship type among conceptual classes. Each sequence of a use case specification could be analyzed as the result of weaving a set of the six segmented patterns. A case study with a payroll system is presented to prove the patterns' feasibility, explaining how the proposed patterns can develop an analysis model. The coverage of the proposing CRA patterns and enhancement of implementation code quality is discussed as the benefit.

The COVID-19 Ontology.

MotivationThe COVID-19 pandemic has prompted an impressive, worldwide response by the academic community. In order to support text mining approaches as well as data description, linking and harmonization in the context of COVID-19, we have developed an ontology representing major novel coronavirus (SARS-CoV-2) entities. The ontology has a strong scope on chemical entities suited for drug repurposing, as this is a major target of ongoing COVID-19 therapeutic development.ResultsThe ontology comprises 2270 classes of concepts and 38 987 axioms (2622 logical axioms and 2434 declaration axioms). It depicts the roles of molecular and cellular entities in virus-host interactions and in the virus life cycle, as well as a wide spectrum of medical and epidemiological concepts linked to COVID-19. The performance of the ontology has been tested on Medline and the COVID-19 corpus provided by the Allen Institute.Availabilityand implementationCOVID-19 Ontology is released under a Creative Commons 4.0 License and shared via https://github.com/covid-19-ontology/covid-19. The ontology is also deposited in BioPortal at https://bioportal.bioontology.org/ontologies/COVID-19.Supplementary information Supplementary data are available at Bioinformatics online.

Learning Design and Learning Analytics: Snapshot 2020

“Learning design” belongs to that interesting class of concepts that appear on the surface to be simple and self-explanatory, but which are actually definitionally vague and contested in practice. Like “learning analytics,” the field of learning design aspires to improve teaching practice, the learning experience, and learning outcomes. And like learning analytics, this interdisciplinary field also lacks a shared language, common vocabulary, or agreement over its definition and purpose, resulting in uncertainty even about who its practitioners are — Educators? Designers? Researchers? All of these? (Law, Li, Farias Herrera, Chan & Pong, 2017). Almost a decade ago, however, learning analytics researchers pointed to the rich potential for synergies between learning analytics and learning design (Lockyer & Dawson, 2011). These authors (and others since, as cited below) argued that effective alignment of learning analytics and learning design would benefit both fields, and would offer educators and investigators the evidence they need that their efforts and innovations in learning design are “worth it” in terms of improving teaching practice and learning: "The integration of research related to both learning design and learning analytics provides the necessary contextual overlay to better understand observed student behavior and provide the necessary pedagogical recommendations where learning behavior deviates from pedagogical intention" (Lockyer & Dawson, 2011, p. 155).

Pseudo-dimension of quantum circuits

We characterize the expressive power of quantum circuits with the pseudo-dimension, a measure of complexity for probabilistic concept classes. We prove pseudo-dimension bounds on the output probability distributions of quantum circuits; the upper bounds are polynomial in circuit depth and number of gates. Using these bounds, we exhibit a class of circuit output states out of which at least one has exponential gate complexity of state preparation, and moreover demonstrate that quantum circuits of known polynomial size and depth are PAC-learnable.

Structured (De)composable Representations Trained with Neural Networks

This paper proposes a novel technique for representing templates and instances of concept classes. A template representation refers to the generic representation that captures the characteristics of an entire class. The proposed technique uses end-to-end deep learning to learn structured and composable representations from input images and discrete labels. The obtained representations are based on distance estimates between the distributions given by the class label and those given by contextual information, which are modeled as environments. We prove that the representations have a clear structure allowing decomposing the representation into factors that represent classes and environments. We evaluate our novel technique on classification and retrieval tasks involving different modalities (visual and language data). In various experiments, we show how the representations can be compressed and how different hyperparameters impact performance.

Learning Privately with Labeled and Unlabeled Examples

A private learner is an algorithm that given a sample of labeled individual examples outputs a generalizing hypothesis while preserving the privacy of each individual. In 2008, Kasiviswanathan et al. (FOCS 2008) gave a generic construction of private learners, in which the sample complexity is (generally) higher than what is needed for non-private learners. This gap in the sample complexity was then further studied in several followup papers, showing that (at least in some cases) this gap is unavoidable. Moreover, those papers considered ways to overcome the gap, by relaxing either the privacy or the learning guarantees of the learner. We suggest an alternative approach, inspired by the (non-private) models of semi-supervised learning and active-learning, where the focus is on the sample complexity of labeled examples whereas unlabeled examples are of a significantly lower cost. We consider private semi-supervised learners that operate on a random sample, where only a (hopefully small) portion of this sample is labeled. The learners have no control over which of the sample elements are labeled. Our main result is that the labeled sample complexity of private learners is characterized by the VC dimension. We present two generic constructions of private semi-supervised learners. The first construction is of learners where the labeled sample complexity is proportional to the VC dimension of the concept class, however, the unlabeled sample complexity of the algorithm is as big as the representation length of domain elements. Our second construction presents a new technique for decreasing the labeled sample complexity of a given private learner, while roughly maintaining its unlabeled sample complexity. In addition, we show that in some settings the labeled sample complexity does not depend on the privacy parameters of the learner.

A Robust Systematic Approach for Ensuring Optimal Telecom Service Delivery

Ensuring a high degree of customer satisfaction is critical to the success of telecom companies in maintaining or increasing market share. Using churn prediction as an indicator of customer satisfaction, we present a robust system that allows telecom companies to gauge the perceived quality of service delivery. The system employs a novel approach known as the churn drift detection method (CDDM), which can effectively predict customer churn by simultaneously addressing the interdependent problems of concept drift and class imbalance in raw data. We have applied CDDM to the call detail record (CDR) of a south Asian telecom company for churn prediction. By measuring criteria like prediction error and detection delay, experiments have shown that on average, CDDM gives 3 percent less prediction error, 50 percent less detection delay, and almost 1-2 percent better evaluation time than existing techniques.

A Multi-label Classification System to Distinguish among Fake, Satirical, Objective and Legitimate News in Brazilian Portuguese

Currently, there has been a significant increase in the diffusion of fake news worldwide, especially the political class, where the possible misinformation that can be propagated, appearing at the elections debates around the world. However, news with a recreational purpose, such as satirical news, is often confused with objective fake news. In this work, we decided to address the differences between objectivity and legitimacy of news documents, where each article is treated as belonging to two conceptual classes: objective/satirical and legitimate/fake. Therefore, we propose a DSS (Decision Support System) based on a Text Mining (TM) pipeline with a set of novel textual features using multi-label methods for classifying news articles on these two domains. For this, a set of multi-label methods was evaluated with a combination of different base classifiers and then compared with a multi-class approach. Also, a set of real-life news data was collected from several Brazilian news portals for these experiments. Results obtained reported our DSS as adequate (0.80 f1-score) when addressing the scenario of misleading news, challenging the multi-label perspective, where the multi-class methods (0.01 f1-score) overcome by the proposed method. Moreover, it was analyzed how each stylometric features group used in the experiments influences the result aiming to discover if a particular group is more relevant than others. As a result, it was noted that the complexity group of features could be more relevant than others.