Our goal in this paper is to experimentally investigate whether folk conceptions of explanation are psychologistic. In particular, are people more likely to classify speech acts as explanations when they cause understanding in their recipient? The empirical evidence that we present suggests this is so. Using the side-effect effect as a marker of mental state ascriptions, we argue that lay judgments of explanatory status are mediated by judgments of a speaker’s and/or audience’s mental states. First, we show that attributions of (...) both understanding and explanation exhibit a side-effect effect. Next, we show that when the speaker’s and audience’s level of understanding is stipulated, the explanation side-effect effect goes away entirely. These results not only extend the side-effect effect to attributions of understanding, they also suggest that attributions of explanation exhibit a side-effect effect because they depend upon attributions of understanding, supporting the idea that folk conceptions of explanation are psychologistic. (shrink)

This paper examines Ian Hacking's analysis of the looping effects of psychiatric classifications, focusing on his recent account of interactive and indifferent kinds. After explicating Hacking's distinction between 'interactive kinds' (human kinds) and 'indifferent kinds' (natural kinds), I argue that Hacking cannot claim that there are 'interactive and indifferent kinds,' given the way that he introduces the interactive-indifferent distinction. Hacking is also ambiguous on whether his notion of interactive and indifferent kinds is supposed to offer an account of classifications or (...) objects of classification. I argue that these conceptual difficulties show that Hacking's account of interactive and indifferent kinds cannot be based on - and should be clearly separated from - his distinction between interactive kinds and indifferent kinds. In clarifying Hacking's account, I argue that interactive and indifferent kinds should be regarded as objects of classification (i.e., kinds of people) that can be identified with reference to a law-like biological regularity and are aware of how they are classified. Schizophrenia and depression are discussed as examples. I subsequently offer reasons for resisting Hacking's claim that the objects of classification in the human sciences - as a result of looping effects - are 'moving targets'. (shrink)

BackgroundThe US Food and Drug Administration (FDA) regulates goods accounting for 20% of US consumers’ total expenditure. The agency’s potential susceptibility to corporate lobbying and political influence may adversely affect the its abilities to fulfill its duties as a vital federal agency. This study assesses whether the FDA’s product recall classifications in recall scenarios are influenced by firms’ lobbying activities.MethodsThe universe of all FDA recalls between 2012 and 2019 is obtained from the FDA’s website. Firm names are matched to federal-level (...) lobbying data obtained from the Center for Responsive Politics – a non-profit and nonpartisan organization that tracks lobbying expenditures and campaign contributions. Analyses are conducted using ordinary-least-squares regressions, in which the dependent variable is recall classification and independent variables are three different measures of firms’ lobbying activities in the one year prior to the recall.ResultsFirms that engage in lobbying appear more likely to receive favourable classifications from the FDA. When examining the above results by product type, we find that classification of food recalls seems to be subject to lobbying influence, but the same does not appear to be true for drug and device recalls. Evidence is consistent with the conjecture that the distinction between medical and food firms may be a result of medical firms targeting lobbying efforts at FDA approvals, rather than recalls.ConclusionsBetween 2012 and 2019, the FDA’s product recall classifications seem to be significantly influenced by firms’ lobbying activities. Lobbying firms appear to have received more favorable (i.e., less severe) recall classifications compared to non-lobbying firms. (shrink)

In this article we study the problem of classifying φ-spaces with a fixed basis subspace. In the first part, this problem is treated in a more general topological context. The second part is devoted to effective φ-spaces.

Effective and specifically targeted social and therapeutic responses for antisocial personality disorders and psychopathy are scarce. Some authors maintain that this scarcity should be overcome by revising current syndrome - based classifications of these conditions and devising better biocognitive classifications of antisocial individuals. The inspiration for the latter classifications has been embedded in the Research domain criteria approach (RDoC). RDoC - type approaches to psychiatric research aim at transforming diagnosis, provide valid measures of disorders, aid clinical practice, and improve (...) health outcomes by integrating the data on the genetic, neural, cognitive, and affective systems underlying psychiatric conditions. In the first part of the paper, we discuss the benefits of such approaches in comparison to the dominant syndrome-based ones and review recent attempts at building biocognitive classifications of antisocial individuals. Other researchers, how ever, have objected that biocognitive approaches in psychiatry are committed to an untenable form of explanatory. (shrink)

Sports organizations face various ethical challenges. To tackle these, ethical codes are becoming increasingly popular instruments. However, a lot of questions remain concerning their effectiveness. There is a particular lack of knowledge when it comes to their form and content, and on the influence of these features on the effectiveness of these codes of ethics. Therefore, we developed a framework to analyze ethical codes and used this to assess codes of ethics in sports clubs from six disciplines. The form and (...) content characteristics of the codes were tested for their impact on the ethical climate of sports clubs. Our results show that most codes focus on the themes of integrity, and on solidarity among the members of sports clubs. Deontological phrasing is dominant in ethical codes in sports clubs. The occurrence of comprehension aids, such as examples, and procedural content, such as the mentioning of sanctions, is low. When it comes to code effectiveness, i.e., the influence on the ethical climate, we see a positive significant effect of the use of consequentialist oriented statements, and of the combination of regulatory and inspirational phrasing. Also the presence of explicit guidelines for the members of the board had a positive significant affect. This shows that codes should be a source of inspiration and stimulate moral awareness, rather than merely guide behavior by simple rules. Other content elements do not influence code effectiveness, suggesting that ethical codes merely have a function of raising ethical awareness in sports clubs. These results help to shed light on what it takes for a code of ethics to become a useful and effective instrument to tackle unethical behavior in sports clubs. (shrink)

Let K be a family of structures, closed under isomorphism, in a fixed computable language. We consider effective lists of structures from K such that every structure in K is isomorphic to exactly one structure on the list. Such a list is called a computable classification of K, up to isomorphism. Using the technique of Friedberg enumeration, we show that there is a computable classification of the family of computable algebraic fields and that with a 0'-oracle, we (...) can obtain similar classifications of the families of computable equivalence structures and of computable finite-branching trees. However, there is no computable classification of the latter, nor of the family of computable torsion-free abelian groups of rank 1, even though these families are both closely allied with computable algebraic fields. (shrink)

Classification is an important goal in many branches of mathematics. The idea is to describe the members of some class of mathematical objects, up to isomorphism or other important equivalence, in terms of relatively simple invariants. Where this is impossible, it is useful to have concrete results saying so. In model theory and descriptive set theory, there is a large body of work showing that certain classes of mathematical structures admit classification while others do not. In the present (...) paper, we describe some recent work on classification in computable structure theory.Section 1 gives some background from model theory and descriptive set theory. From model theory, we give sample structure and non-structure theorems for classes that include structures of arbitrary cardinality. We also describe the notion of Scott rank, which is useful in the more restricted setting of countable structures. From descriptive set theory, we describe the basic Polish space of structures for a fixed countable language with fixed countable universe. We give sample structure and non-structure theorems based on the complexity of the isomorphism relation, and on Borel embeddings.Section 2 gives some background on computable structures. We describe three approaches to classification for these structures. The approaches are all equivalent. However, one approach, which involves calculating the complexity of the isomorphism relation, has turned out to be more productive than the others. Section 3 describes results on the isomorphism relation for a number of mathematically interesting classes—various kinds of groups and fields. In Section 4, we consider a setting similar to that in descriptive set theory. We describe an effective analogue of Borel embedding which allows us to make distinctions even among classes of finite structures. Section 5 gives results on computable structures of high Scott rank. Some of these results make use of computable embeddings. Finally, in Section 6, we mention some open problems and possible directions for future work. (shrink)

Classification shapes medicine and guides its practice. As clinicians classify symptoms and illnesses, they trigger a range of actions and consequences. The assignment of particular disease labels is linked to both therapeutic and social responses. However, the classifications of medicine, natural though they may seem, contain significant social content, and are arrived at via a number of cultural framing devices (Aronowitz 2008). This article will explore the social intent and construction of classification and their embodiment in medical diagnosis. (...) class='Hi'>Effective classification recognizes difference as well as similarity. By classifying, we are putting items together that have more in common with one another than they .. (shrink)

Sports organizations face various ethical challenges. To tackle these, ethical codes are becoming increasingly popular instruments. However, a lot of questions remain concerning their effectiveness. There is a particular lack of knowledge when it comes to their form and content, and on the influence of these features on the effectiveness of these codes of ethics. Therefore, we developed a framework to analyze ethical codes and used this to assess codes of ethics in sports clubs from six disciplines. The form and (...) content characteristics of the codes were tested for their impact on the ethical climate of sports clubs. Our results show that most codes focus on the themes of integrity, and on solidarity among the members of sports clubs. Deontological phrasing is dominant in ethical codes in sports clubs. The occurrence of comprehension aids, such as examples, and procedural content, such as the mentioning of sanctions, is low. When it comes to code effectiveness, i.e., the influence on the ethical climate, we see a positive significant effect of the use of consequentialist oriented statements, and of the combination of regulatory and inspirational phrasing. Also the presence of explicit guidelines for the members of the board had a positive significant affect. This shows that codes should be a source of inspiration and stimulate moral awareness, rather than merely guide behavior by simple rules. Other content elements do not influence code effectiveness, suggesting that ethical codes merely have a function of raising ethical awareness in sports clubs. These results help to shed light on what it takes for a code of ethics to become a useful and effective instrument to tackle unethical behavior in sports clubs. (shrink)

The last decade has seen an explosion of research on the gut microbiota—the trillions of microorganisms that colonize the human gut. It is now clear that interindividual diversity in microbiota composition plays an important role in determining susceptibility to a wide variety of diseases. However, identifying the precise changes in microbiota composition that play causal roles has remained a largely unrealized goal. Here, we propose that functional classifications of microbes based on their interactions with and effects on the host—particularly the (...) host immune system—will illuminate the role of the microbiota in shaping human physiology. We outline the benefits of “functional” classification compared to phylogenetic classifications, and review current efforts at functional classification of the microbiota. Finally, we outline a theoretical framework for classifying host-microbiota interactions. Future advances enabling broader functional classifications of the microbiota promise to revolutionize our understanding of the role of gut microbes in health and disease. Functional classifications of the human microbiota reveal previously hidden patterns and will be key to cracking the microbiota composition code. This figure shows a theoretical comparison between phylogenetic classification and functional classification of a host-associated microbial community. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Classification and Explanation in Aristotle’s Theory of DefinitionGreg Bayer1. introductiona problem lies at the heart of Aristotle’s theory of definition. On the one hand, Aristotle says in Topics VI.4 that “the one who defines well must define by means of genus1 and differentia” (141b25–7); indeed his view of definition most often seems to be confined to its role of picking out the definiendum by indicating the class it (...) belongs to—what can be called its “class definition.”2 But on the other hand, in Posterior Analytics II.2 he makes the pronouncement that “knowing what something is ” is the same as knowing why it is ” (90a31–2), and then goes on in II.2 and 8 to argue that definition is an, “explanation” or “explanatory principle.” How can these two notions of definition be reconciled?Short of giving the full Aristotelian solution to this problem, I hope here to suggest one way in which Aristotle’s notion of class definition—especially the reformed version presented in De Partibus Animalium I.2–4—can be shown to relate to his notion of explanatory definition in APo II. The link is his notion of scientific, “problems,” as discussed in APo II. 14 and elsewhere.3 It will turn out that a class definition in effect presents a scientific problem to the scientist which can best be solved by an explanation—ultimately by an “explanatory definition”—accounting for the attributes that form the basis of [End Page 487] class membership. Or more succinctly: the class definition presents a problem that the explanatory definition solves. Further, although the discussion in PA I is meant to be a reform of the simpler but unworkable method of arriving at the genus-differentia formula based upon, “division,” I intend to argue that an important ancillary benefit of the reformed method is that it is capable of articulating scientific problems in a much clearer way than division can. In fact, it seems very well suited to the task of presenting problems and hence to the task of searching for explanatory definitions.2. scientific problemsIn APo II. 14, Aristotle begins to outline the proper procedure for formulating scientific problems. We are told that “in order to obtain problems, we must pick out the sections 4 and divisions” of the subject we are investigating, about which we are formulating a problem (98a1–3). That is, we must learn to pick out each genus in the hierarchy of genera the subject belongs to by learning to identify the attributes associated with each of these genera. This will enable us to account for each attribute we find in the subject under investigation in terms of the genus the attribute is primarily associated with. As Aristotle explains: “Let A stand for animal, B for [an attribute] of every animal, and C, D, and E some particular animal [species]; it is clear why B belongs to D—because of A” (98a9–11).This, in a sense, shows one way a scientific problem—Why does attribute B belong to D?—can be “solved”: by finding the right “division” of D that accounts for its having B. But what is important to note here is that, in fact, our investigation only begins with this process of finding the right genus. This is simply how we “obtain” the problem, strictly speaking, not the means for ultimately solving it. Indeed the problem of accounting for why an attribute belongs to a given subject is not even completely formulated until we are able to determine the proper “division,” or genus, the attribute universally belongs to. Then we can ask the more fundamental question of why the attribute belongs to this genus, i.e., why B belongs to genus A. Now this question can be analyzed further, in terms of the other attributes of the genus. Any genus (or infima species) is, in effect, designated by a regular concomitance of the many attributes that identify it—i.e., attributes that universally co-occur in it. So the scientific problem we are formulating, regarding why a certain attribute belongs to a certain subject, ultimately should be reformulated in this way: Why does... (shrink)

Electroencephalography and functional near-infrared spectroscopy have potentially complementary characteristics that reflect the electrical and hemodynamic characteristics of neural responses, so EEG-fNIRS-based hybrid brain-computer interface is the research hotspots in recent years. However, current studies lack a comprehensive systematic approach to properly fuse EEG and fNIRS data and exploit their complementary potential, which is critical for improving BCI performance. To address this issue, this study proposes a novel multimodal fusion framework based on multi-level progressive learning with multi-domain features. The framework consists (...) of a multi-domain feature extraction process for EEG and fNIRS, a feature selection process based on atomic search optimization, and a multi-domain feature fusion process based on multi-level progressive machine learning. The proposed method was validated on EEG-fNIRS-based motor imagery and mental arithmetic tasks involving 29 subjects, and the experimental results show that multi-domain features provide better classification performance than single-domain features, and multi-modality provides better classification performance than single-modality. Furthermore, the experimental results and comparison with other methods demonstrated the effectiveness and superiority of the proposed method in EEG and fNIRS information fusion, it can achieve an average classification accuracy of 96.74% in the MI task and 98.42% in the MA task. Our proposed method may provide a general framework for future fusion processing of multimodal brain signals based on EEG-fNIRS. (shrink)

To classify is to organize the particulars in a body of information according to some meaningful scheme. Difficulty recognizing metaphor, synonyms and homonyms, and levels of generalization renders those applications of artificial intelligence that are currently in widespread use at a loss to deal effectively with classification. Indexing conveys nothing about relationships; it pinpoints information on particular topics without reference to anything else. Keyword searching is a form of indexing, and here artificial intelligence excels. Growing reliance on automated means (...) of accessing information brings an increase in indexing and a corresponding decrease in classification. This brings about a shift from the modernist view of the world as permanently and hierarchically structured to the indeterminacy and contingency associated with postmodernism. (shrink)

Taking the idea of waste as an in-use phenomenon, we developed amatrix to explain four categories of waste which result from users’ failure touse a resource properly. These categories were illustrated by examples built onpractical food waste measurements, surveys, theses formatting requirementsand newspaper reports. We have categorized different facets of waste from abusiness perspective; thus, contributed to have improved waste managementpractices. We also showed that parsimony was also a wasteful behaviour.Parsimony was shown to be a waste by its effects on (...) others’ need of fulfilmentand other-worldly consequences for the miser. (shrink)

In order to solve the problem of the influence of feature word position in lyrics on music emotion classification, this paper designs a music classification and detection model in complex noise environment. Firstly, an intelligent detection algorithm for electronic music signals under complex noise scenes is proposed, which can solve the limitations existing in the current electronic music signal detection process. At the same time, denoising technology is introduced to eliminate the noise and extract the features from the (...) signal. Secondly, from the perspective of audio and lyrics of song sentiment analysis and the unique characteristics of lyrics text, a lyric sentiment analysis method based on text title and position weight is proposed. Finally, considering the influence of the weight of feature words in different positions on the classification of lyrics, the analytic hierarchy process is used to calculate the weight of feature words in different positions of text title and lyrics before, in, and after the text. The results show that in the complex noise environment, the accuracy of music classification and detection of the proposed model is more than 90%, which is far beyond the control range of the actual application of music processing. The effect of music classification and detection is better than that of the contrast model, which has a certain practical application value. (shrink)

This article examines some of the ways that Muslim and evangelical Christian schools in England and The Netherlands deal with religious education. Various schools take different views about how aspects of religious belief should be taught and how Christian or Muslim belief should be related to the wider curriculum of the school. While some of the schools have attempted to integrate, for example, evangelical Christianity throughout the whole of the curriculum, others have been content to have the religious teaching as (...) a separate component of the curriculum. This paper uses the work of Basil Bernstein to describe and understand the nature of the religious curriculum in these schools. Through a series of case studies of schools and curricula it examines the nature of the curricula and their possible effects on children. (shrink)

Accurate prediction of battery quality using early-cycle data is critical for battery, especially lithium battery in microgrid networks. To effectively predict the lifetime of lithium-ion batteries, a time series classification method is proposed that classifies batteries into high-lifetime and low-lifetime groups using features extracted from early-cycle charge-discharge data. The proposed method is based on a smooth localized complex exponential model that can extract battery features from time-frequency maps and self-adaptively select the time-frequency resolution to maximize the discrepancy of data (...) from the two groups. A smooth localized complex exponential periodogram is then calculated to obtain the time-frequency decomposition of the whole time series data for further classification. The experimental results show that, by using battery features extracted from the first 128 charge-discharge processes, the proposed method can accurately classify batteries into high-lifetime and low-lifetime groups, with classification accuracy and specificity as high as 95.12% and 92.5%, respectively. (shrink)

This paper addresses philosophical issues concerning whether mental disorders are natural kinds and how the DSM should classify mental disorders. I argue that some mental disorders (e.g., schizophrenia, depression) are natural kinds in the sense that they are natural classes constituted by a set of stable biological mechanisms. I subsequently argue that a theoretical and causal approach to classification would provide a superior method for classifying natural kinds than the purely descriptive approach adopted by the DSM since DSM-III. My (...) argument suggests that the DSM should classify natural kinds in order to provide predictively useful (i.e., projectable) diagnostic categories and that a causal approach to classification would provide a more promising method for formulating valid diagnostic categories. (shrink)

The investigation of computational properties of discontinuous functions is an important concern in computable analysis. One method to deal with this subject is to consider effective variants of Borel measurable functions. We introduce such a notion of Borel computability for single-valued as well as for multi-valued functions by a direct effectivization of the classical definition. On Baire space the finite levels of the resulting hierarchy of functions can be characterized using a notion of reducibility for functions and corresponding complete (...) functions. We use this classification and an effective version of a Selection Theorem of Bhattacharya-Srivastava in order to prove a generalization of the Representation Theorem of Kreitz-Weihrauch for Borel measurable functions on computable metric spaces: such functions are Borel measurable on a certain finite level, if and only if they admit a realizer on Baire space of the same quality. This Representation Theorem enables us to introduce a realizer reducibility for functions on metric spaces and we can extend the completeness result to this reducibility. Besides being very useful by itself, this reducibility leads to a new and effective proof of the Banach-Hausdorff-Lebesgue Theorem which connects Borel measurable functions with the Baire functions. Hence, for certain metric spaces the class of Borel computable functions on a certain level is exactly the class of functions which can be expressed as a limit of a pointwise convergent and computable sequence of functions of the next lower level. (shrink)

Four experiments demonstrate category congruency priming by subliminal prime words that were never seen as targets in a valence-classification task and a gender-classification task . In Experiment 1, overlap in terms of word fragments of one or more letters between primes and targets of different valences was larger than between primes and targets of the same valence. In Experiments 2 and 3, the sets of prime words and target words were completely disjoint in terms of used letters. In (...) Experiment 4, pictures served as targets. The observed subliminal priming effects for novel primes cannot be driven by partial analysis of primes at the word-fragment level; they suggest instead that primes were processed semantically as whole words contingent upon prime duration. (shrink)

Classification learning is a preeminent human ability within the animal kingdom but the key mechanisms of brain networks regulating learning remain mostly elusive. Recent neuroimaging advancements have depicted human brain as a complex graph machinery where brain regions are nodes and coherent activities among them represent the functional connections. While long-term motor memories have been found to alter functional connectivity in the resting human brain, a graph topological investigation of the short-time effects of learning are still not widely investigated. (...) For instance, classification learning is known to orchestrate rapid modulation of diverse memory systems like short-term and visual working memories but how the brain functional connectome accommodates such modulations is unclear. We used publicly available repositories (openfmri.org) selecting three experiments, two focused on short-term classification learning along two consecutive runs where learning was promoted by trial-by-trial feedback errors, while a further experiment was used as supplementary control. We analyzed the functional connectivity extracted from BOLD fMRI signals, and estimated the graph information processing in the cerebral networks. The information processing capability, characterized by complex network statistics, significantly improved over runs, together with the subject classification accuracy. Instead, null-learning experiments, where feedbacks came with poor consistency, did not provoke any significant change in the functional connectivity over runs. We propose that learning induces fast modifications in the overall brain network dynamics, definitely ameliorating the short-term potential of the brain to process and integrate information, a dynamic consistently orchestrated by modulations of the functional connections among specific brain regions. (shrink)

A Relevance-Theoretic Classification of Jokes Relevance Theory pictures communication as an inferential activity that adjusts, in parallel, the explicit content of utterances, the implicated premises and conclusions that can be derived, and the right amount of contextual information needed to obtain them. When applied to jokes, a relevance-theoretic classification may be proposed depending on whether the humorist plays with the audience's inferential activity aimed at an explicit interpretation, with the audience's inference devoted to deriving implications or with their (...) access to the right amount and quality of contextual information needed to obtain relevant interpretations. In this paper three types of jokes are proposed which focus on these aspects. A fourth type is also added, but this time referred to broad contextual assumptions on social or cultural values of society that are targeted by humorists. (shrink)

The articles comprising this dissertation concern classification and concept formation in the social and behavioral sciences. In particular, the emphasis in the study is on the philosophical analysis of interdisciplinary settings created by the recent intellectual developments on the interfaces between the social sciences, psychology, and neuroscience. The need for a systematic examination of the problems of conceptual coordination and integration across disciplinary boundaries is illustrated by focusing on phenomena whose satisfactory explanation requires drawing together the theoretical resources from (...) a variety of disciplines. In philosophy, questions regarding the nature of scientific concepts have often been framed in terms of theories of natural kinds. For this reason, analysis of the notion of natural kind as well as examination of how theories of natural kinds should be connected to recent philosophical accounts of scientific explanation and mechanisms form the core of the study. Building on contemporary discussions on these topics in the philosophy of biology, the philosophy of cognitive science, and the philosophy of the social sciences, the articles develop a mechanistic theory of natural kinds in the social and behavioral sciences, and scrutinize its applicability and usefulness as a theory of conceptual change in interdisciplinary settings. The study suggests that, although the mechanistic theory cannot account for the functioning of the whole range of scientific concepts, interweaving biological, cognitive, and social mechanisms in the manner suggested by the mechanistic theory offers a naturalistic and non-reductionist basis for conceptualizing epistemic coordination across disciplinary boundaries. (shrink)

In this paper we study a new approach to classify mathematical theorems according to their computational content. Basically, we are asking the question which theorems can be continuously or computably transferred into each other? For this purpose theorems are considered via their realizers which are operations with certain input and output data. The technical tool to express continuous or computable relations between such operations is Weihrauch reducibility and the partially ordered degree structure induced by it. We have identified certain choice (...) principles such as co-finite choice, discrete choice, interval choice, compact choice and closed choice, which are cornerstones among Weihrauch degrees and it turns out that certain core theorems in analysis can be classified naturally in this structure. In particular, we study theorems such as the Intermediate Value Theorem, the Baire Category Theorem, the Banach Inverse Mapping Theorem, the Closed Graph Theorem and the Uniform Boundedness Theorem. We also explore how existing classifications of the Hahn—Banach Theorem and Weak Kőnig's Lemma fit into this picture. Well-known omniscience principles from constructive mathematics such as LPO and LLPO can also naturally be considered as Weihrauch degrees and they play an important role in our classification. Based on this we compare the results of our classification with existing classifications in constructive and reverse mathematics and we claim that in a certain sense our classification is finer and sheds some new light on the computational content of the respective theorems. Our classification scheme does not require any particular logical framework or axiomatic setting, but it can be carried out in the framework of classical mathematics using tools of topology, computability theory and computable analysis. We develop a number of separation techniques based on a new parallelization principle, on certain invariance properties of Weihrauch reducibility, on the Low Basis Theorem of Jockusch and Soare and based on the Baire Category Theorem. Finally, we present a number of metatheorems that allow to derive upper bounds for the classification of the Weihrauch degree of many theorems and we discuss the Brouwer Fixed Point Theorem as an example. (shrink)

It has been argued that a biomarker-informed classification system for antisocial individuals has the potential to overcome many obstacles in current conceptualizations of forensic and psychiatric constructs and promises better targeted treatments. However, some have expressed ethical worries about the social impact of the use of biological information for classification. Many have discussed the ethical and legal issues related to possibilities of using biomarkers for predicting antisocial behaviour. We argue that prediction should not raise the most pressing ethical (...) worries. Instead, issues connected with ‘biologisation’, such as stigmatization and negative effects on self-image, need more consideration. However, we conclude that also in this respect there are no principled ethical objections against the use of biomarkers to guide classification and treatment of adult antisocial individuals. (shrink)

As one of the most widely used methods in deep learning technology, convolutional neural networks have powerful feature extraction capabilities and nonlinear data fitting capabilities. However, the convolutional neural network method still has disadvantages such as complex network model, too long training time and excessive consumption of computing resources, slow convergence speed, network overfitting, and classification accuracy that needs to be improved. Therefore, this article proposes a dense convolutional neural network classification algorithm based on texture features for images (...) in virtual reality videos. First, the texture feature of the image is introduced as a priori information to reflect the spatial relationship between pixels and the unique characteristics of different types of ground features. Second, the grey level cooccurrence matrix is used to extract the grey level correlation features of the image in space. Then, Gauss Markov Random Field is used to establish the statistical correlation characteristics between neighbouring pixels, and the extracted GLCM-GMRF texture feature and image intensity vector are combined. Finally, based on DenseNet, an improved shallow layer dense convolutional neural network is proposed, which can compress network parameters and improve the feature extraction ability of the network. The experimental results show that compared with the current classification method, this method can effectively suppress the influence of coherent speckle noise and obtain better classification results. (shrink)

A critical comparative analysis of howfarmers and scientists classify and manage soilsreveals fundamental differences as well assimilarities. In the past, the study of local soilknowledge has been predominantly targeted atdocumenting how farmers classified their soils incontrast to understanding how such classificatoryknowledge was made use of in actually managing soilsfor sustaining production. Often, classificatorydesigns – being cognitive and linguistic in nature –do not reflect the day-to-day actions in farming.Instead of merely describing local soil classificationin relation to scientific criteria, understanding howdifferent types (...) of ethnolinguistic soil categories arerelated to crops, climate, subsistence needs,fertility, market demand, and cultural norms of thesociety would be far more effective in focusingresearch and development efforts. (shrink)

Brain tumor classification plays a niche role in medical prognosis and effective treatment process. We have proposed a combined feature and image-based classifier for brain tumor image classification in this study. Carious deep neural network and deep convolutional neural networks -based architectures are proposed for image classification, namely, actual image feature-based classifier, segmented image feature-based classifier, actual and segmented image feature-based classifier, actual image-based classifier, segmented image-based classifier, actual and segmented image-based classifier, and finally, CFIC. The (...) Kaggle Brain Tumor Detection 2020 dataset has been used to train and test the proposed classifiers. Among the various classifiers proposed, the CFIC performs better than all other proposed methods. The proposed CFIC method gives significantly better results in terms of sensitivity, specificity, and accuracy with 98.86, 97.14, and 98.97%, respectively, compared with the existing classification methods. (shrink)

Transfer learning can improve the robustness of deep learning in the case of small samples. However, when the semantic difference between the source domain data and the target domain data is large, transfer learning easily introduces redundant features and leads to negative transfer. According the mechanism of the human brain focusing on effective features while ignoring redundant features in recognition tasks, a brain-like classification method based on adaptive feature matching dual-source domain heterogeneous transfer learning is proposed for the (...) preoperative aided diagnosis of lung granuloma and lung adenocarcinoma for patients with solitary pulmonary solid nodule in the case of small samples. The method includes two parts: (1) feature extraction and (2) feature classification. In the feature extraction part, first, By simulating the feature selection mechanism of the human brain in the process of drawing inferences about other cases from one instance, an adaptive selected-based dual-source domain feature matching network is proposed to determine the matching weight of each pair of feature maps and each pair of convolution layers between the two source networks and the target network, respectively. These two weights can, respectively, adaptive select the features in the source network that are conducive to the learning of the target task, and the destination of feature transfer to improve the robustness of the target network. Meanwhile, a target network based on diverse branch block is proposed, which made the target network have different receptive fields and complex paths to further improve the feature expression ability of the target network. Second, the convolution kernel of the target network is used as the feature extractor to extract features. In the feature classification part, an ensemble classifier based on sparse Bayesian extreme learning machine is proposed that can automatically decide how to combine the output of base classifiers to improve the classification performance. Finally, the experimental results (the AUCs were 0.9542 and 0.9356, respectively) on the data of two center data show that this method can provide a better diagnostic reference for doctors. (shrink)

C.S. Peirce, the American philosopher and a principal figure in the development of the modern study of semiotics, struggled, mostly during his later years, to work out a systematic method for classifying sciences. By doing this, he hoped to define more clearly the various tasks of these sciences by showing how their individual effects are interrelated and how these effects, considered in their interrelations, establish pragmatic meanings for each individual science. Much of his work was centered on the meaning and (...) function of logic in relation to other areas of human knowledge. By rightly defining the work of logic, Peirce argued, the work of the other sciences could be pursued with more rigorous reasoning. Beverley Kent closely examines the published and unpublished writings of Peirce and carefully attends to the chronological development of his systems of classification; she thereby shows for the first time in the scholarly literature how seeming contradictions in Peirce's evolving classification are really part of an increasingly clear position. Logic, Peirce came to understand, is actually dependent on ethics and aesthetics for its principles. Kent shows how Peirce's working out of the classification of logic in relation to other sciences is a clue to the significant differences between his early and late philosophy and, perhaps even more important, to a reading of his more general claims for a philosophy of "pragmaticism." This work will be of interest to readers of Peirce and American philosophy, to historians of logic and semiotics, and to those more generally interested in the history of systems of knowledge-classification. (shrink)

Identifying the necessary and sufficient conditions for individuating and classifying diseases is a matter of great importance in the fields of law, ethics, epidemiology, and of course, medicine. In this paper, I first propose a means of achieving this goal, ensuring that no two distinct disease-types could correctly be ascribed to the same disease-token. I then posit a metaphysical ontology of diseases—that is, I give an account of what a disease is. This is essential to providing the most effective (...) means of interfering with disease processes. Following existing work in the philosophy of medicine and epidemiology (primarily Christopher Boorse; Caroline Whitbeck; Alexander Broadbent), philosophy of biology (Joseph LaPorte; D.L. Hull), conditional analyses of causation (J.L. Mackie; David Lewis), and recent literature on dispositional essentialism (Stephen Mumford and Rani Anjum; Alexander Bird), I endorse a dispositional conception of disease. Following discussion of various conceptions of disease-identity, their relations to the clinical and pathological effects of the diseases in question, and how diseases are treated, I conclude (i) that diseases should be individuated by their causes, and (ii) that diseases are causal processes best seen as simultaneously acting sequences of mutually manifesting dispositions. (shrink)

Mobile app stores provide an extremely rich source of information on app descriptions, characteristics, and usage, and analyzing these data provides insights and a deeper understanding of the nature of apps. However, manual analysis of this vast amount of information on mobile apps is not a simple and straightforward task; it is costly in terms of human effort and time. Computational methods such as topic modeling can provide an efficient and satisfactory approach to mobile app information analysis. Topic modeling is (...) a type of statistical modeling technique for discovering abstract topics that occur in a set of documents. This study explores the relationship between features of Arabic apps and investigates how well the current predefined Google Play app categories represent the type and genre of Arabic mobile apps. Based on the textual app description analysis, we aim to design and develop a sustainable classification system using the Latent Dirichlet Allocation method of topic modeling in order to cover the Arabic apps classification in Google Play app store. Our study supports the hypothesis that the textual app descriptions are effective in suggesting new categories for Arabic mobile apps in Google Play app store. Also, the results indicated that the current classification on Google Play app store is not suitable for our case study “Arabic apps,” as well as it is not sustainable, as it can not cover the new app types including Arabic apps. This study offers an important contribution to Arabic app analysis and design, to improve app search and exploration in several domains such as business, marketing, and technical development. Furthermore, it provides insights for the future of Arabic app research and provides guidance for the development of an Arabic app dashboard that will support users on how to select an app based on their specific needs. (shrink)

This dissertation examines psychiatry from a philosophy of science perspective, focusing on issues of realism and classification. Questions addressed in the dissertation include: What evidence is there for the reality of mental disorders? Are any mental disorders natural kinds? When are disease explanations of abnormality warranted? How should mental disorders be classified? -/- In addressing issues concerning the reality of mental disorders, I draw on the accounts of realism defended by Ian Hacking and William Wimsatt, arguing that biological research (...) on mental disorders supports the inference that some mental disorders (e.g., schizophrenia, mood disorders, and anxiety disorders) are real theoretical entities, and that the evidence supporting this inference is causal and abductive. In explicating the nature of such entities, I argue that real mental disorders are natural kinds insofar as they are natural classes of abnormal behavior whose members share the same causal structure. I present this position in terms of Richard Boyd’s homeostatic cluster property theory of natural kinds, and argue that this perspective reveals limitations of Hacking’s account on the looping effects of human kinds, which suggests that the objects classified by psychiatrists are unstable entities. I subsequently argue that a subset of mental disorders (e.g., schizophrenia and Down syndrome) are mental illnesses insofar as they are disorders caused by a dysfunctional biological process that leads to harmful consequences for individuals. I present this analysis against Thomas Szasz’s argument that mental illness is a myth. -/- In addressing issues of psychiatric classification, my analysis focuses on the Diagnostic and Statistical Manual of Mental Disorders (DSM), which has been published regularly by the American Psychiatric Association since 1952, and is currently in its fourth edition. After examining the history of DSM in the twentieth century, and in particular, DSM’s shift to an atheoretical and purely descriptive system in the 1980s, I consider the relative merits of descriptive versus causal systems of classification. Drawing on Carl Hempel’s analysis of taxonomic systems in psychiatry, I argue that a causal classification system would provide a superior approach to psychiatric classification than the descriptive system currently favored by DSM. (shrink)

Technological change is widely studied in economic discourse, dominated by an “industrial perspective” in which scientific and engineering advances are categorized by, and analyzed in the context of, industrial classifications. The present study compares this perspective with the alternative approach of studying the effects of scientific advances onproducts (goods and services) in the context of the functions those products serve. Using a function-based classification scheme, data on the economic effects of scientific advances are developed from articles appearing in business (...) journals. These data present a different picture of evolutionary economic change, which may shed additional light on its causality. (shrink)

Research on sequence effects on learning visual categories has shown that interleaving (i.e., studying the categories in a mixed manner) facilitates category induction as compared to blocking (i.e., studying the categories one by one), but learners are unaware of the interleaving effect and prefer blocking. However, little attention has been paid to sequence effects in perceptual learning across further sensory modalities. The present (preregistered) research addresses this shortcoming by using auditory (birdcalls), olfactory (tealeaves), gustatory (ingredient mixtures), and tactile (stones) stimuli (...) across four analog experiments. The number of participants per experiment was determined based on a medium effect size of interleaving. Participants studied six categories (with six exemplars, respectively) either interleaved or blocked. No single experiment showed a significant effect of interleaving. We ran a comprehensive meta‐analysis based on the data from all experiments, which revealed a significant small effect of interleaving, demonstrating its applicability to perceptual learning across all sensory modalities. Learners in the interleaved condition underestimated their classification performance. Overall, learners did not rate interleaving as less effective than blocking, which is at odds with previous studies that consistently demonstrated a metacognitive preference of blocking. Our findings suggest that learners rely less on conventional beliefs about the effectivity of study sequence when dealing with unfamiliar (blindfolded) perceptual learning tasks. (shrink)

Cardiac disease is the leading cause of death worldwide. Cardiovascular diseases can be prevented if an effective diagnostic is made at the initial stages. The ECG test is referred to as the diagnostic assistant tool for screening of cardiac disorder. The research purposes of a cardiac disorder detection system from 12-lead-based ECG Images. The healthcare institutes used various ECG equipment that present results in nonuniform formats of ECG images. The research study proposes a generalized methodology to process all formats (...) of ECG. Single Shoot Detection MobileNet v2-based Deep Neural Network architecture was used to detect cardiovascular disease detection. The study focused on detecting the four major cardiac abnormalities with 98% accuracy results were calculated. The work is relatively rare based on their dataset; a collection of 11,148 standard 12-lead-based ECG images used in this study were manually collected from health care institutes and annotated by the domain experts. The study achieved high accuracy results to differentiate and detect four major cardiac abnormalities. Several cardiologists manually verified the proposed system’s accuracy result and recommended that the proposed system can be used to screen for a cardiac disorder. (shrink)

Humans excel in categorization. Yet from a computational standpoint, learning a novel probabilistic classification task involves severe computational challenges. The present paper investigates one way to address these challenges: assuming class-conditional independence of features. This feature independence assumption simplifies the inference problem, allows for informed inferences about novel feature combinations, and performs robustly across different statistical environments. We designed a new Bayesian classification learning model that incorporates varying degrees of prior belief in class-conditional independence, learns whether or not (...) independence holds, and adapts its behavior accordingly. Theoretical results from two simulation studies demonstrate that classification behavior can appear to start simple, yet adapt effectively to unexpected task structures. Two experiments—designed using optimal experimental design principles—were conducted with human learners. Classification decisions of the majority of participants were best accounted for by a version of the model with very high initial prior belief in class-conditional independence, before adapting to the true environmental structure. Class-conditional independence may be a strong and useful default assumption in category learning tasks. (shrink)

Systems with human-centered artificial intelligence are always as good as their ability to consider their users’ context when making decisions. Research on identifying people’s everyday activities has evolved rapidly, but little attention has been paid to recognizing both the activities themselves and the motions they make during those tasks. Automated monitoring, human-to-computer interaction, and sports analysis all benefit from Web 4.0. Every sport has gotten its move, and every move is not known to everyone. In ice hockey, every move cannot (...) be monitored by the referee. Here, Convolution Neural Network-based Real-Time Image Processing Framework (CNN-RTIPF) is introduced to classify every move in Ice Hockey. CNN-RTIPF can reduce the challenges in monitoring the player’s move individually. The image of every move is captured and compared with the trained data in CNN. These real-time captured images are processed using a human-centered artificial intelligence system. They compared images predicted by probability calculation of the trained set of images for effective classification. Simulation analysis shows that the proposed CNN-RTIPF can classify real-time images with improved classification ratio, sensitivity, and error rate. The proposed CNN-RTIPF has been validated based on the optimization parameter for reliability. To improve the algorithm for movement identification and train the system for many other everyday activities, human-centered artificial intelligence-based Web 4.0 will continue to develop. (shrink)

The enjoyment and pleasure derived from sad music has sparked fascination among researchers due to its seemingly paradoxical nature in producing positive affect. Research is yet to develop a comprehensive understanding of this “paradox.” Contradictory findings have resulted in a great variability within the literature, meaning results and interpretations can be difficult to derive. Consequently, this review collated the current literature, seeking to utilize the variability in the findings to propose a model of differential sad states, providing a means for (...) past and future findings to be interpreted. The proposed model is based on theoretical understanding, as such it requires full empirical support. Comparisons to alternative models, theoretical, clinical, and cognitive implications, as well as future directions are discussed. (shrink)

Six experiments investigated variables predicted to influence subjects’ tendency to classify items by a single property instead of overall similarity, following the paradigm of Norenzayan et al., who found that European Americans tended to give more “logical” rule-based responses. However, in five experiments with Mechanical Turk subjects and undergraduates at an American university, we found a consistent preference for similarity-based responding. A sixth experiment with Korean undergraduates revealed an effect of instructions, also reported by Norenzayan et al., in which (...) class='Hi'>classification instructions led to majority rule-based responding but similarity instructions led to overall similarity grouping. Our American subjects showed no such difference and used similarity more overall. We conclude that Americans do not have a preference for rule responding in classification and discuss the differences between tasks that reliably show strong rule or unidimensional preferences in contrast to this classification paradigm. (shrink)

Using emotional film clips is one of the most popular and effective methods of emotion elicitation. The main goal of the present study was to develop and test the effectiveness of a new and comprehensive set of emotional film excerpts. Fifty film experts were asked to remember specific film scenes that elicited fear, anger, sadness, disgust, amusement, tenderness, as well as emotionally neutral scenes. For each emotion, the 10 most frequently mentioned scenes were selected and cut into film clips. (...) Next, 364 participants viewed the film clips in individual laboratory sessions and rated each film on multiple dimensions. Results showed that the film clips were effective with regard to several criteria such as emotional discreteness, arousal, positive and negative affect. Finally, ranking scores were computed for 24 classification criteria: Subjective arousal, positive and negative affect (derived from the PANAS; Watson & Tellegen, 1988), a positive and a negative affect scores derived from the Differential Emotions Scale (DES; Izard et al., 1974), six emotional discreteness scores (for anger, disgust, sadness, fear, amusement and tenderness), and 15 “mixed feelings” scores assessing the effectiveness of each film excerpt to produce blends of specific emotions. In addition, a number of emotionally neutral film clips were also validated. The database and editing instructions to construct the film clips have been made freely available in a website. (shrink)

Neuromarketing has gained attention to bridge the gap between conventional marketing studies and electroencephalography -based brain-computer interface research. It determines what customers actually want through preference prediction. The performance of EEG-based preference detection systems depends on a suitable selection of feature extraction techniques and machine learning algorithms. In this study, We examined preference detection of neuromarketing dataset using different feature combinations of EEG indices and different algorithms for feature extraction and classification. For EEG feature extraction, we employed discrete wavelet (...) transform and power spectral density, which were utilized to measure the EEG-based preference indices that enhance the accuracy of preference detection. Moreover, we compared deep learning with other traditional classifiers, such as k-nearest neighbor, support vector machine, and random forest. We also studied the effect of preference indicators on the performance of classification algorithms. Through rigorous offline analysis, we investigated the computational intelligence for preference detection and classification. The performance of the proposed deep neural network outperforms KNN and SVM in accuracy, precision, and recall; however, RF achieved results similar to those of the DNN for the same dataset. (shrink)