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This paper proposes a computer model of information behavior to study information security professionals and an architecture, which mimics the way our brain learns new concepts to simulate this behavior computationally. Used to represent and describe any domain of knowledge, ontologies may be used to study human information behavior and show some of the concepts and relationships involved in this field of knowledge. A deep knowledge of the core concepts underpinning this field can provide us with a solid basis for constructing a model. Computer-programming tools can be used not only to capture the ideas that make up this field of Knowledge but can also simulate the human information behavior. The use of computers also allows us to crawl data over the Internet and process large amounts of them in order to find patterns with some specific characteristics. In the paper, we also present the current state of this research and the challenges of the model. [ABSTRACT FROM AUTHOR]

This paper takes college students' ideological and political education ideology construction and values as the research goal, and uses the methods of literature analysis, interdisciplinary research and questionnaires to study the ideology construction of ideological and political education of college students in China and the status quo of their values, it summarizes current problems of diversified ideals and beliefs and complex value orientations in college students' ideological and political education ideology construction and values. This paper analyzes the main causes of the problems, based on the survey results, it follows the laws of brain science and brain activities, makes full use of the relevant research results of cognitive science and social psychology, and explores the ideology construction of college students' ideological and political education and their values from the three aspects of the characteristics, classification and essence of ideological and political education cognition, and provides a theoretical basis for promoting the effectiveness of college students' ideological and political education construction. [ABSTRACT FROM AUTHOR]

It was recently reported that men self-cite >50% more often than women across a wide variety of disciplines in the bibliographic database JSTOR. Here, we replicate this finding in a sample of 1.6 million papers from Author-ity, a version of PubMed with computationally disambiguated author names. More importantly, we show that the gender effect largely disappears when accounting for prior publication count in a multidimensional statistical model. Gender has the weakest effect on the probability of self-citation among an extensive set of features tested, including byline position, affiliation, ethnicity, collaboration size, time lag, subject-matter novelty, reference/citation counts, publication type, language, and venue. We find that self-citation is the hallmark of productive authors, of any gender, who cite their novel journal publications early and in similar venues, and more often cross citation-barriers such as language and indexing. As a result, papers by authors with short, disrupted, or diverse careers miss out on the initial boost in visibility gained from self-citations. Our data further suggest that this disproportionately affects women because of attrition and not because of disciplinary under-specialization. [ABSTRACT FROM AUTHOR]

In their recent paper, Lau, Clark, and Lappin explore the idea that the probability of the occurrence of word strings can form the basis of an adequate theory of grammar (Lau, Jey H., Alexander Clark & 15 Shalom Lappin. 2017. Grammaticality, acceptability, and probability: A prob- abilistic view of linguistic knowledge. Cognitive Science 41(5):1201-1241). To make their case, they present the results of correlating the output of several probabilistic models trained solely on naturally occurring sentences with the gradient acceptability judgments that humans report for ungrammatical sentences derived from roundtrip machine translation errors. In this paper, we first explore the logic of the Lau et al. argument, both in terms of the choice of evaluation metric (gradient acceptability), and in the choice of test data set (machine translation errors on random sentences from a corpus). We then present our own series of studies intended to allow for a better comparison between LCL's models and existing grammatical theories. We evaluate two of LCL's probabilistic models (trigrams and recurrent neural network) against three data sets (taken from journal articles, a textbook, and Chomsky's famous colorless-green-ideas sentence), using three evaluation metrics (LCL's gradience metric, a categorical version of the metric, and the experimental-logic metric used in the syntax literature). Our results suggest there are very real, measurable cost-benefit tradeoffs inherent in LCL's models across the three evaluation metrics. The gain in explanation of gradience (between 13% and 31% of gradience) is offset by losses in the other two metrics: a 43%-49% loss in coverage based on a categorical metric of explaining acceptability, and a loss of 12%-35% in explaining experimentallydefined phenomena. This suggests that anyone wishing to pursue LCL's models as competitors with existing syntactic theories must either be satisfied with this tradeoff, or modify the models to capture the phenomena that are not currently captured. [ABSTRACT FROM AUTHOR]

One of the central questions in cognitive science is to understand how conceptual knowledge is represented in the human mind. The classical answer to this question is the assumption of a separate semantic module in which knowledge is stored through abstract symbolic representations. The semantic module is separate from other systems, such as modules for perception and action. In the last twenty years, an alternative approach, known as embodied or grounded cognition, has been developed and intensively studied, starting from the assumption that conceptual knowledge is in constant interaction with perception and action, and that conceptual knowledge is grounded in them through the mechanism of perceptual simulation. The aim of this paper is to review different theoretical perspectives on embodied cognition and to evaluate them. The theory of perceptual symbol systems, the model of the immersed experiencer and the indexical hypothesis are presented in detail, as well as empirical findings that support or refute these models. Then, critiques of embodied cognition related to the understanding of abstract concepts are presented, as well as responses to these critiques. The idea of degrees or continuum of embodiment is also considered. Finally, directions for future research are outlined, to try to shed light on the question of what exactly is the role of embodiment in the representation of knowledge. [ABSTRACT FROM AUTHOR]

Syntactic priming effects have been investigated for several decades in psycholinguistics and the cognitive sciences to understand the cognitive mechanisms that support language production and comprehension. The question of whether speakers prime themselves is central to adjudicating between two theories of syntactic priming, activation-based theories and expectation-based theories. However, there is a lack of a statistical model to investigate the two different theories when nominal repeated measures are obtained from multiple participants and items. This paper presents a Markov mixed-effect multinomial logistic regression model in which there are fixed and random effects for own-category lags and cross-category lags in a multivariate structure and there are category-specific crossed random effects (random person and item effects). The model is illustrated with experimental data that investigates the average and participant-specific deviations in syntactic self-priming effects. Results of the model suggest that evidence of self-priming is consistent with the predictions of activation-based theories. Accuracy of parameter estimates and precision is evaluated via a simulation study using Bayesian analysis. [ABSTRACT FROM AUTHOR]

Pragmaticism states that general rules of action, or habits, are generalizing tendencies that lead us to action in conceivable situations describable in general terms. As a method of 'putting questions to our minds,' it assigns meanings to signs in terms of conceivable practical consequences for rational conduct. Questions are experiments on various ways of finding solutions in thoughts. This paper proposes pragmaticism as a logical method to study consciousness. In particular, perceptions of relations of differences create a "temporal contract" between states of minds that give rise to experiences. Peirce's "dyadic consciousness" is this drafting of a contract between states of mind, anticipating and occasionally furthering beyond the key notions of 4E cognitive science. [ABSTRACT FROM AUTHOR]

Multimodal sentiment analysis is one of the most attractive interdisciplinary research topics in artificial intelligence (AI). Different from other classification issues, multimodal sentiment analysis of human is a much finer classification problem. However, most current work accept all multimodalities as the input together and then output final results at one time after fusion and decision processes. Rare models try to divide their models into more than one fusion modules with different fusion strategies for better adaption of different tasks. Additionally, most recent multimodal sentiment analysis methods pay great focuses on binary classification, but the accuracy of multi-classification still remains difficult to improve. Inspired by the emotional processing procedure in cognitive science, both binary and multi-classification abilities are improved in our method by dividing the complicated problem into smaller issues which are easier to be handled. In this paper, we propose a Hierarchal Attention-BiLSTM (Bidirectional Long-Short Term Memory) model based on Cognitive Brain limbic system (HALCB). HALCB splits the multimodal sentiment analysis into two modules responsible for two tasks, the binary classification and the multi-classification. The former module divides the input items into two categories by recognizing their polarity and then sends them to the latter module separately. In this module, Hash algorithm is utilized to improve the retrieve accuracy and speed. Correspondingly, the latter module contains a positive sub-net dedicated for positive inputs and a negative sub-nets dedicated for negative inputs. Each of these binary module and two sub-nets in multi-classification module possesses different fusion strategy and decision layer for matching its respective function. We also add a random forest at the final link to collect outputs from all modules and fuse them at the decision-level at last. Experiments are conducted on three datasets and compare the results with baselines on both binary classification and multi-classification tasks. Our experimental results surpass the state-of-the-art multimodal sentiment analysis methods on both binary and multi-classification by a big margin. [ABSTRACT FROM AUTHOR]

Any complete understanding of human psychology must take into account that a brain's actions in the world are mediated by the body it belongs to. In the process of such interaction the human being creates artificial things, structures and mechanisms, such as technology, relationships, and culture. The subjective world is not simply the interactions between neurons at different systemic levels, but the existence of mental contents, which are determined by specific features of a certain domain of reality with which a cognitive agent interacts by means of her actions. It is possible to understand specific features of the subjective world with the conception of the "embodied" and "inactive" cognition. In this framework, the problems of consciousness of consciousness (introspection), free will, the possibility of "reading" another mind and constructing mental phenomena are analyzed. If cognitive studies are not used for manipulating humans, and are not means of human degradation (such danger exists) they must take into consideration specific features of the human phenomenon: the belonging of the human being to the social world and the world of culture. The development of cognitive studies can lead humans to new levels and contribute to new interpretations of humanism. However, under certain conditions, this development can lead to the death of the human being. This paper shows that some Soviet philosophers and psychologists, during the second half of the twentieth century, anticipated the formulations and solutions of several contemporary key problems in cognitive science before these problems emerged. [ABSTRACT FROM AUTHOR]

Dating back to the 19th century, the discovery of processing stages has been of great interest to researchers in cognitive science. The goal of this paper is to demonstrate the validity of a recently developed method, hidden semi-Markov model multivariate pattern analysis (HsMM-MVPA), for discovering stages directly from EEG data, in contrast to classical reaction-time-based methods. To test the validity of stages discovered with the HsMM-MVPA method, we applied it to two relatively simple tasks where the interpretation of processing stages is straightforward. In these visual discrimination EEG data experiments, perceptual processing and decision difficulty were manipulated. The HsMM-MVPA revealed that participants progressed through five cognitive processing stageswhile performing these tasks. The brain activation of one of those stages was dependent on perceptual processing, whereas the brain activation and the duration of two other stages were dependent on decision difficulty. In addition, evidence accumulation models (EAMs) were used to assess to what extent the results of HsMM-MVPA are comparable to standard reaction-time-based methods. Consistent with the HsMM-MVPA results, EAMs showed that nondecision time varied with perceptual difficulty and drift rate varied with decision difficulty. Moreover, nondecision and decision time of the EAMs correlated highly with the first two and last three stages of the HsMM-MVPA, respectively, indicating that the HsMM-MVPA gives a more detailed description of stages discovered with this more classical method. The results demonstrate that cognitive stages can be robustly inferred with the HsMM-MVPA. [ABSTRACT FROM AUTHOR]

In a society where computers have become ubiquitous, it is necessary to develop a broader understanding of the nature of computing and programming, not just from a technical viewpoint but also from a historical and philosophical perspective. Computers and computer programs do not exist in a vacuum. Instead, they are a part of a rich socio-technological context that provides ways for understanding computers and reasoning about programs. This includes not only formal logic, mathematics, sciences, and technology but also cognitive sciences and sociology. The focus of this special issue is on questions that arise when we consider computing and programming in a wider context. In particular, the papers in this special issue explore the interplay between computing or programming and mathematics, formal logic, sciences, technology, and society. [ABSTRACT FROM AUTHOR]

It is a widely accepted thesis in the cognitive sciences and in naturalistic philosophy of mind that the contents of at least some mental representations are innate. A question that has popped up in discussions concerning innate mental representations is this. Are externalist theories of mental content applicable to the content of innate representations? Views on the matter vary and sometimes conflict. To date, there has been no comprehensive assessment of the relationship between content externalism and content innateness. The aim of this paper is to provide such an assessment. I focus on the notions of innateness that are employed in innateness hypotheses within the cognitive sciences and adjacent fields of philosophy, and on causal externalist theories of content. I distinguish between three accounts of what being innate might amount to in innateness hypotheses within the cognitive sciences, and between three types of causal externalism. I explain what the possibility of innate externalistically individuated representations depends upon given all nine combinations. I explain why causal externalism can be true of innate mental representations, given but one of these combinations. [ABSTRACT FROM AUTHOR]

In this discussion paper, teaching and learning are characterized as being situated, complex, and reciprocally interactive activities. Accordingly, a teacher's pedagogical actions are always action and reaction at the same time. Irrespective of the reciprocally interactive nature of teaching and learning, educational research has sought to identify characteristics of teacher expertise that enable teachers to influence students' learning in a systematic and positive way. In this respect, the contributions to this special issue offer an innovative research paradigm, because they bring together different and originally separated strands of research: (1) research on professional vision in teacher education, (2) research on the structure and development of expertise in cognitive science, (3) research on cognitive processes by means of eye-tracking technologies in psychology, and (4) research on educational effectiveness and instructional quality in educational science. Following this introduction, a detailed discussion of the rewards and challenges of each of the seven contributions is provided. This discussion leads to the following conclusions: (a) The emphasis on the "reacting teacher" in professional vision research undervalues the role of lesson planning for the flexible handling of sudden events during class instruction. (b) The assumption of professional vision as mediator between a teacher's knowledge and pedagogical actions overlooks that teachers can sometimes do more than they can tell. (c) Not all of the gaze behavior that is currently studied needs to be instructed. (d) Research should move beyond explorative expert-novice comparisons to hypotheses-driven designs that investigate how teachers can learn to successfully apply evidence-based pedagogical principles. [ABSTRACT FROM AUTHOR]

In 1997, Quassim Cassam published his first authored book I Self and World i . BRIEF SUMMARY OF SELF AND WORLD In I Self and World i , Cassam's overall claim is that 'introspective awareness of one's thinking, experiencing self as a physical object among physical objects, is a I necessary i condition of self-consciousness' (1997, p. 3). After that, I quickly got others on board: Dan Zahavi, whose expertise in phenomenology and philosophy of mind is clearly relevant to the book, and Anil Gomes, since he has been strongly influenced by Cassam and also has his own takes on Kant and philosophy of mind. Before that, I already knew that Cassam himself has moved on to other areas, partly due to the fact that his Oxford Kantian approach - not only I Self and World i but also I The Possibility of Knowledge i and various papers - was not taken serious by many of his peers. [Extracted from the article]

The rapid development and widespread adoption of Artificial Intelligence (AI) technologies have made the development of AI-specific design methods an important topic to advance. In recent decades, the centre of gravity in AI has shifted away from cognitive science and related fields like psychology. However, there is a clear need and potential for added value in returning to stronger interaction. One potential challenge for this interaction may be the lack of common conceptual grounds and design languages. In this article, we aim to contribute to the development of conceptual interfaces for human-based AI-specific design methods through the idea of cognitive mimetics. We begin by introducing basic concepts from mimetic design and interpret them in the context of this thematic area. These provide some of the basic building blocks for a design language and bring to the surface key questions. These in turn provide a ground for explicating cognitive mimetics. In the second part of this paper, we focus on specifying a key aspect in cognitive mimetics: the contents of information processes. Others engaged in this field can derive value from using or developing the basic conceptual machinery to specify their own approaches in this interdisciplinary field that is still shaping itself. Furthermore, those who resonate with the idea of cognitive mimetics, as specified here, can join in taking this particular approach further. [ABSTRACT FROM AUTHOR]

One of the main challenges of cognitive science is to explain the representation of conceptual knowledge and the mechanisms involved in evaluating the similarities between these representations. Theories that attempt to explain this phenomenon should account for the fact that conceptual knowledge is not static. In line with this thinking, many studies suggest that the representation of a concept changes depending on context. Traditionally, concepts have been studied as vectors within a geometric space, sometimes called Semantic-Vector Space Models (S-VSMs). However, S-VSMs have certain limitations in emulating human biases or context effects when the similarity of concepts is judged. Such limitations are related to the use of a classical geometric approach that represents a concept as a point in space. Recently, some theories have proposed the use of sequential projections of subspaces based on Quantum Probability Theory (Busemeyer and Bruza, 2012; Pothos et al., 2013). They argue that this theoretical approach may facilitate accounting for human similarity biases and context effects in a more natural way. More specifically, Pothos and Busemeyer (2011) proposed the Quantum Similarity Model (QSM) to determine expectation in conceptual spaces in a non-monotonic logic frame. To the best of our knowledge, previous data-driven studies have used the QSM subspaces in a unidimensional way. In this paper, we present a data-driven method to generate these conceptual subspaces in a multidimensional manner using a traditional S-VSM. We present an illustration of the method taking Tversky's classical examples to explain the effects of Asymmetry, Triangular Inequality, and the Diagnosticity by means of sequential projections of those conceptual subspaces. • A new method for generating multidimensional subspaces in a Semantic-VSM was created. • We exemplify the violation of symmetry by order effect in a Semantic-VSM. • We exemplify the violation of triangular equality in a Semantic-VSM. • Two new models are proposed to evaluate the diagnosticity effect in Semantic-VSMs. [ABSTRACT FROM AUTHOR]

This paper will discuss whether there are chances for a paradigm shift, i.e., a shift away from the (almost without much thought) taken-for-granted sequential, from piece-to-whole mainstream understanding of a cognitivist, now almost 70-year old information processing perspective to a perspective that takes as its starting point the whole, and hence meaning. The whole may stand for an object as embedded in its immediately salient as well as inconspicuous environment, where parts cannot be made sense of without knowing and understanding their roles in the larger configurations. It may stand for an organism or an organismic collectivity as embedded in its environment, a person or a collectivity of people in their embedded immediate and phenomenal field, which we will also not understand unless we understand their larger environmental, societal and cultural embeddedness. In today's scientific climate where the mainstream information-processing perspective is serving as an unchallenged, often hidden assumption within neuroscience and computer science, I will look for recent, promising developments which might nonetheless be paving the road to a perspective so long ago proposed by the Gestaltists yet somehow "lost in translation". In that sense, Gestalt theory, which till today has been widely distorted as a theory that talks about 'a bunch of grouping principles in (static) vision', is the very first dynamical theory within the psychological sciences that meticulously proposed this with a firm philosophical grounding, something that is ever more missing within theory building in psychology. [ABSTRACT FROM AUTHOR]

The paper presents an overview on some specificities of philosophical and of sociological approaches to religion. Among the chosen philosophical epistemological models are described: religion as universum (F.Schleiermacher, G.W.F.Hegel); the anthropocentric approach; the religion interpreted as a moral phenomenon (I.Kant); religion within the framework of dichotomies of human nature (E.Fromm, P.Tillich). The author presents and comments a few aspects of the J.Beckford’s analysis of problems and trends in the sociology of religion. The latest development in the Cognitive science of religion (CSR) are emphasized. [ABSTRACT FROM AUTHOR]

Events and event prediction are pivotal concepts across much of cognitive science, as demonstrated by the papers in this special issue. We first discuss how the study of events and the predictive processing framework may fruitfully inform each other. We then briefly point to some links to broader philosophical questions about events. Hohwy, Hebblewhite, and Drummond detail links between the contributions in the special issue and the general predictive processing framework and emphasize the contributions' potential to help answer philosophical questions about the nature on events. [ABSTRACT FROM AUTHOR]

Transhumanism is a movement that starts from some humanist ideals to reach the posthuman, having as its basic premise the belief that technology can and should be used to control nature. It means, in the midst of technology man leaves being subject to become one of the objects of the new powers. The aim of this paper is to demonstrate that aesthetic medicine is one of the most notorious of the process that requires medicine not only as an activity of healing, but above all as enhancement, which implies a change in the more intimist human condition. In this sense, medicine is integrated into the broader field of technological convergence and bioart. We use Hans Jonas's theory of responsibility to analyze how this process becomes an ethical issue, requiring a responsible attitude on the part of its agents. [ABSTRACT FROM AUTHOR]

Human kinship systems play a central role in social organization, as anthropologists have long demonstrated. Much less is known about how cultural schemas of relatedness are transmitted across generations. How do children learn kinship concepts? To what extent is learning affected by known cross-cultural variation in how humans classify kin? This review draws on research in developmental psychology, linguistics, and anthropology to present our current understanding of the social and cognitive foundations of kinship categorization. Amid growing interest in kinship in the cognitive sciences, the paper aims to stimulate new research on the ontogeny of kinship categorization, a rich domain for studying the nexus of language, culture, and cognition. We introduce an interdisciplinary research toolkit to help streamline future research in this area. [ABSTRACT FROM AUTHOR]

Mobility is integral to mankind, but current cognitive research on migrants is almost nonexistent. Most of the existing knowledge on human cognition is based on data collected from (and by) the wealthiest and least mobile citizens of the world. In turn, most of the migration literature relies on superficial, and at times erroneous, assumptions regarding the cognitive processes underlying migratory phenomenon. However, research conducted to date in reveals striking convergences between core issues relevant to scholars in cognitive science on the one hand, and in migration studies on the other. This current lack on cross‐disciplinary dialog has no scientific grounds for two fields, which are inherently interdisciplinary, and yet it has concerning sociopolitical implications. Mapping out a novel research agenda at the crossroads of the cognitive and social sciences, this article stands as an invitation for researchers to engage in further collaborative work involving joint data collections and joint data analyses. This paper argues that researching migrant cognition would give more breadth to cognitive science and more depth to migration studies, which has the potential to better inform the design of public policies. Though the examples of (a) visual perception, (b) future‐oriented cognition, and (c) language acquisition, this article shows that pairing the discoveries of migration scholars and cognitive scientists can move forward our understanding on the human mind, in an ever‐moving world. This article is categorized under:Economics > Individual Decision‐MakingPsychology > Theory and MethodsPsychology > Comparative PsychologyPsychology > Language [ABSTRACT FROM AUTHOR]

The cognitive science of religion (CSR) is distinctive in its systematic methodology and its interdisciplinary nature. In the field of the study of religion, however, scholars are hardly receptive to CSR because of its "scientific" nature. The reason for this is that CSR is understood as distinct from other studies, nevertheless it has humanistic backgrounds. In order to investigate the intellectual backgrounds of CSR, the paper will describe the initial development of CSR by introducing researches and academic cooperation in the period from the 1990s to the early 2000s. Moreover, by examining academic contexts, what the emergence of CSR brought about to the study of religion will be clarified. CSR emerged as a result of the debate between modernist and postmodernist, connecting various views and resulting in a great shift in the intellectual landscape. This interdisciplinary role can be seen as the most important achievement of CSR. [ABSTRACT FROM AUTHOR]

An important way to develop models in psychology and cognitive science is to express them as computer programs. However, computational modeling is not an easy task. To address this issue, some have proposed using artificial-intelligence (AI) techniques, such as genetic programming (GP) to semiautomatically generate models. In this paper, we establish whether models used to generate data can be recovered when GP evolves models accounting for such data. As an example, we use an experiment from decision-making which addresses a central question in decision-making research, namely, to understand what strategy, or "policy," agents adopt in order to make a choice. In decision-making, this often means understanding the policy that best explains the distribution of choices and/or reaction times of two-alternative forced-choice decisions. We generated data from three models using different psychologically plausible policies and then evaluated the ability and extent of GP to correctly identify the true generating model among the class of virtually infinite candidate models. Our results show that, regardless of the complexity of the policy, GP can correctly identify the true generating process. Given these results, we discuss implications for cognitive science research and computational scientific discovery as well as possible future applications. [ABSTRACT FROM AUTHOR]

Glioma detection and segmentation is a challenging task for radiologists and clinicians. The research reported in this paper seeks to develop a better clinical decision support algorithm for clinicians diagnosis. This paper presents a probabilistic method for detection and segmentation between abnormal tissue regions and brain tumour (tumour core and edema) portions from Magnetic Resonance Imaging (MRI). A framework is constructed to learn structure of undirected graphical models that can represent the spatial relationships among variables and apply it to glioma segmentation. Compared with the pixel of image, the superpixel is more consistent with human visual cognition and contains less redundancy, thus, the superpixels are considered as the basic unit of structure learning and glioma segmentation scheme. ℓ1-regularization techniques are applied to learn the appropriate structure for modeling graphical models. Conditional Random Fields (CRF) are used to model the spatial interactions among image superpixel regions and their measurements. A number of features including statistics features, the combined features from the local binary pattern as well as gray level run length, curve features, and fractal features were extracted from each superpixel. The features are then passed by ℓ1-regularization to ensure a robust classification. The proposed method is compared with support vector machine and Fuzzy c-means to classify each superpixel into normal and abnormal tissue. The proposed system is tested for the presence of low grade as well as high grade glioma tumors on images collected from BRATS2013, BRATS2015 data set and Henan Provincial People’s Hospital (HNPPH) data set. The experiments performed provides similarity between segmented and truth image up to 91.5% by correlation method. [ABSTRACT FROM AUTHOR]

Since 1997 a considerable effort has been spent on the study of the swap (switch) Markov chains on graphic degree sequences. All of these results assume some kind of regularity in the corresponding degree sequences. Recently, Greenhill and Sfragara published a breakthrough paper about irregular normal and directed degree sequences for which rapid mixing of the swap Markov chain is proved. In this paper we present two groups of results. An example from the first group is the following theorem: let be a directed degree sequence on n vertices. Denote by Δ the maximum value among all in- and out-degrees and denote by the number of edges in the realization. Assume furthermore that . Then the swap Markov chain on the realizations of is rapidly mixing. This result is a slight improvement on one of the results of Greenhill and Sfragara. An example from the second group is the following: let d be a bipartite degree sequence on the vertex set U ⊎ V, and let 0 < c1 ≤ c2 < |U| and 0 < d1 ≤ d2 < |V| be integers, where c1 ≤ d(v) ≤ c2: ∀v ∈ V and d1 ≤ d(u) ≤ d2: ∀u ∈ U. Furthermore assume that (c2 − c1 − 1)(d2 − d1 − 1) < max{c1(|V| − d2), d1(|U| − c2)}. Then the swap Markov chain on the realizations of d is rapidly mixing. A straightforward application of this latter result shows that when a random bipartite or directed graph is generated under the Erdős—Rényi G(n, p) model with mild assumptions on n and p then the degree sequence of the generated graph has, with high probability, a rapidly mixing swap Markov chain on its realizations. [ABSTRACT FROM AUTHOR]

Over the past decades, plagiarism has been classified as a multi-layer phenomenon of dishonesty that occurs in higher education. A number of research papers have identified a host of factors such as gender, socialisation, efficiency gain, motivation for study, methodological uncertainties or easy access to electronic information via the Internet and new technologies, as reasons driving plagiarism. The paper at hand examines whether such factors are still effective and if there are any differences between German and Slovene students’ factors influencing plagiarism. A quantitative paper-and-pencil survey was carried out in Germany and Slovenia in 2017/2018 academic year, with a sample of 485 students from higher education institutions. The major findings of this research reveal that easy access to information-communication technologies and the Web is the main reason driving plagiarism. In that regard, there are no significant differences between German and Slovene students in terms of personal factors such as gender, motivation for study, and socialisation. In this sense, digitalisation and the Web outrank national borders. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we argue that awareness comes in degrees, and we propose a novel multi‐factor account that spans both subjective experiences and perceptual representations. At the subjective level, we argue that conscious experiences can be degraded by being fragmented, less salient, too generic, or flash‐like. At the representational level, we identify corresponding features of perceptual representations—their availability for working memory, intensity, precision, and stability—and argue that the mechanisms that affect these features are what ultimately modulate the degree of awareness. We conclude the paper by demonstrating why the original interpretations of certain empirical findings that apparently pose problems for our account are, in fact, flawed. [ABSTRACT FROM AUTHOR]

This paper examines how to maximize contribution in public good dilemmas by arranging people into homogeneous or heterogeneous subgroups. Past studies on the effect of homogeneity of efficacy have exclusively manipulated group composition in their experimental designs, which might have imposed a limit on ecological validity because group membership may not be easily changed in reality. In this study, we maintained the same group composition but varied the subgroup composition. We developed a public good dilemmas paradigm in which participants were assigned to one of the four conditions (high- vs. low-efficacy; homogeneous vs. heterogeneous subgroup) to produce their endowments and then to decide how much to contribute. We found that individuals in homogeneous and heterogeneous subgroups produced a similar amount and proportion of contribution, which was due to the two mediating effects that counteracted each other, namely (a) perceived efficacy relative to subgroup and (b) expectation of contribution of other subgroup members. This paper demonstrates both the pros and cons of arranging people into homogeneous and heterogeneous subgroups of efficacy. [ABSTRACT FROM AUTHOR]

With the convergence of various broadcasting systems, the amount of content available in mobile terminals including IPTV has significantly increased. In this paper, we propose a system that enables users to schedule programs considering both section video clips and full video clips based on the user detection method with similar preference. And, since the system constituting the contents can be classified according to the program, the proposed method can store a program desired by the user, and thus create and schedule a kind of individual channel. Experimental results show that the proposed method has a higher prediction accuracy; this is accomplished by comparing existing channel recommendation methods with the program recommendation methods proposed in this paper. [ABSTRACT FROM AUTHOR]

Background: Due to the disease’s progressive nature, advance care planning (ACP) is recommended for people with early stage dementia. General practitioners (GPs) should initiate ACP because of their longstanding relationships with their patients and their early involvement with the disease, however ACP is seldom applied. Aim: To determine the barriers and facilitators faced by GPs related to ACP with people with dementia. Data sources: We systematically searched the relevant databases for papers published between January 1995 and December 2016, using the terms: primary healthcare, GP, dementia, and ACP. We conducted a systematic integrative review following Whittemore and Knafl’s method. Papers containing empirical data about GP barriers and/or facilitators regarding ACP for people with dementia were included. We evaluated quality using the Mixed-Method-Appraisal-Tool and analyzed data using qualitative content analysis. Results: Ten qualitative, five quantitative, and one mixed-method paper revealed four themes: timely initiation of ACP, stakeholder engagement, important aspects of ACP the conversation, and prerequisites for ACP. Important barriers were: uncertainty about the timing of ACP, how to plan for an uncertain future, lack of knowledge about dementia, difficulties assessing people with dementia’s decisional capacities, and changing preferences. Facilitators for ACP were: an early start when cognitive decline is still mild, inclusion of all stakeholders, and discussing social and medical issues aimed at maintaining normal life. Conclusion: Discussing future care is difficult due to uncertainties about the future and the decisional capacities of people with dementia. Based on the facilitators, we recommend that GPs use a timely and goal-oriented approach and involve all stakeholders. ACP discussions should focus on the ability of people with dementia to maintain normal daily function as well as on their quality of life, instead of end-of-life-discussions only. GPs need training to acquire knowledge and skills to timely initiate collaborative ACP discussions. [ABSTRACT FROM AUTHOR]

This paper studies the drive-response synchronization for quaternion-valued shunting inhibitory cellular neural networks (QVSICNNs) with mixed delays. First, QVSICNN is decomposed into an equivalent real-valued system in order to avoid the non-commutativity of the multiplicity. Then, the existence of almost periodic solutions is obtained based on the Banach fixed point theorem. An novel state-feedback controller is designed to ensure the global exponential almost periodic synchronization. At the end of the paper, an example is given to illustrate the effectiveness of the obtained results. [ABSTRACT FROM AUTHOR]

Aiming to solve the safety problems of high-speed railway operation and management, one new method is urgently needed to construct on the basis of the rough set theory and the uncertainty measurement theory. The method should carefully consider every factor of high-speed railway operation that realizes the measurement indexes of its safety operation. After analyzing the factors that influence high-speed railway safety operation in detail, a rough measurement model is finally constructed to describe the operation process. Based on the above considerations, this paper redistricts the safety influence factors of high-speed railway operation as 16 measurement indexes which include staff index, vehicle index, equipment index and environment. And the paper also provides another reasonable and effective theoretical method to solve the safety problems of multiple attribute measurement in high-speed railway operation. As while as analyzing the operation data of 10 pivotal railway lines in China, this paper respectively uses the rough set-based measurement model and value function model (one model for calculating the safety value) for calculating the operation safety value. The calculation result shows that the curve of safety value with the proposed method has smaller error and greater stability than the value function method’s, which verifies the feasibility and effectiveness. [ABSTRACT FROM AUTHOR]

This paper discusses situated problem solving accomplished in the three constituent domains of Kashmiri carpet weaving practice viz. designing, coding and weaving. While, the designers’ problem relates to carving spatial regions on graph-sheets, the coders’ problem relates to cognitively demarcating relevant coding area in those graphs while generating code. Likewise, the weavers’ problem lies in demarcating weaving-territory on the loom, while the manufacturers’ problem relates to determining a particular spatial area for quality assessment. The paper discusses how the actors create fixed, transient and negotiable boundaries on the graph, the loom and the carpet to solve this problem of area determination and demarcation. Section 1 introduces this practice, its task domains and the problem of area determination and demarcation, Section 2 discusses the methodology, Section 3 discusses in detail the problem of area-determination in each task domain and the problem solving strategies employed by the actors using material available in their task-environments itself. In addition, the problem and resolution of area determination as encountered by the manufacturers is discussed. The paper concludes with a discussion on findings of this study with larger findings in cognitive science and accentuates the role played by situated and distributed cognition in unearthing the findings reported in this paper. [ABSTRACT FROM AUTHOR]

Geographers are increasingly interested in understanding the significance of developments in neuroscience, psychology and the behavioural sciences. Indeed, consideration of these disciplines has arguably shaped the trajectories of human geography since at least the 1960s, but its “neural turn” has only recently been acknowledged. This paper provides an original analysis of the intersections of research on neuroscience and geography. With reference to qualitative interviews with cognitive scientists and neuroscientists based in the UK, it identifies how geographical concepts have been approached within contemporary neuroscience while also identifying the broad trajectories of geographers’ engagements with neuroscience. The discussion demonstrates the political implications of these disciplinary trends for a geographical account of brain culture and brain‐based explanations in policy and practice. Specifically it proposes the development of a “critical neuro‐geography” capable of providing an overarching analysis of these phenomena. The paper's novel synthesis of hitherto disconnected engagements between geography, cognitive science and neuroscience establishes the rationale for a more sustained and critical engagement between neuroscience and geography sensitive to issues of situated subjectivity, power, inequality and difference. [ABSTRACT FROM AUTHOR]

In business replenishment, discount offers of multi-item may either provide different discount schedules with a single discount type, or provide schedules with multiple discount types. The paper investigates the joint effects of multiple discount schemes on the decisions of multi-item joint replenishment. In this paper, a joint replenishment problem (JRP) model, considering three discount (all-unit discount, incremental discount, total volume discount) offers simultaneously, is constructed to determine the basic cycle time and joint replenishment frequencies of multi-item. To solve the proposed problem, a heuristic algorithm is proposed to find the optimal solutions and the corresponding total cost of the JRP model. Numerical experiment is performed to test the algorithm and the computational results of JRPs under different discount combinations show different significance in the replenishment cost reduction. [ABSTRACT FROM AUTHOR]

This paper argues that deciding on whether the cognitive sciences need a Representational Theory of Mind matters. Far from being merely semantic or inconsequential, the answer we give to the RTM-question makes a difference to how we conceive of minds. How we answer determines which theoretical framework the sciences of mind ought to embrace. The structure of this paper is as follows. Section 1 outlines Rowlands’s (2017) argument that the RTM-question is a bad question and that attempts to answer it, one way or another, have neither practical nor theoretical import. Rowlands concludes this because, on his analysis, there is no non-arbitrary fact of the matter about which properties something must possess in order to qualify as a mental representation. By way of reply, we admit that Rowlands’s analysis succeeds in revealing why attempts to answer the RTM-question simpliciter are pointless. Nevertheless, we show that if specific formulations of the RTM-question are stipulated, then it is possible, conduct substantive RTM debates that do not collapse into merely verbal disagreements. Combined, Sections 2 and 3 demonstrate how, by employing specifying stipulations, we can get around Rowlands’s arbitrariness challenge. Section 2 reveals why RTM, as canonically construed in terms of mental states exhibiting intensional (with-an-s) properties, has been deemed a valuable explanatory hypothesis in the cognitive sciences. Targeting the canonical notion of mental representations, Section 3 articulates a rival nonrepresentational hypothesis that, we propose, can do all the relevant explanatory work at much lower theoretical cost. Taken together, Sections 2 and 3 show what can be at stake in the RTM debate when it is framed by appeal to the canonical notion of mental representation and why engaging in it matters. Section 4 extends the argument for thinking that RTM debates matter. It provides reasons for thinking that, far from making no practical or theoretical difference to the sciences of the mind, deciding to abandon RTM would constitute a revolutionary conceptual shift in those sciences. [ABSTRACT FROM AUTHOR]

China’s fast economic growth contributes to the rapid development of its urbanization process, and also renders a series of industrial accidents, which often cause loss of life, damage to property and environment, thus requiring the associated risk analysis and safety control measures to be implemented in advance. However, incompleteness of historical failure data before the occurrence of accidents makes it difficult to use traditional risk analysis approaches such as probabilistic risk analysis in many cases. This paper aims to develop a new methodology capable of assessing regional industrial safety (RIS) in an uncertain environment. A hierarchical structure for modelling the risks influencing RIS is first constructed. The hybrid of evidential reasoning (ER) and Analytical Hierarchy Process (AHP) is then used to assess the risks in a complementary way, in which AHP is hired to evaluate the weight of each risk factor and ER is employed to synthesise the safety evaluations of the investigated region(s) against the risk factors from the bottom to the top level in the hierarchy. The successful application of the hybrid approach in a real case analysis of RIS in several major districts of Beijing (capital of China) demonstrates its feasibility as well as provides risk analysts and safety engineers with useful insights on effective solutions to comprehensive risk assessment of RIS in metropolitan cities. The contribution of this paper is made by the findings on the comparison of risk levels of RIS at different regions against various risk factors so that best practices from the good performer(s) can be used to improve the safety of the others. [ABSTRACT FROM AUTHOR]

Public transport (PT) is a key element in most major cities around the world. With the development of smartphones, available journey planning information is becoming an integral part of the PT system. Each traveler has specific preferences when undertaking a trip, and these preferences can also be reflected on the smartphone. This paper considers transit assignment in urban public-transport networks in which the passengers receive smartphone-based information containing elements that might influence the travel decisions in relation to line loads, as well as passenger benefits, and the paper discusses the transition from the current widespread choosing approach to a personalized decision-making approach based on smartphone information. The approach associated with smartphone guidance that considers passengers’ preference on travel time, waiting time and transfer is proposed in the process of obtaining his/her preferred route from the potential travel routes generated by the Deep First Search (DFS) method. Two other approaches, based on the scenarios reflecting reality, include passengers with access to no real time information, and passengers that only have access to the arrival time at the platform are used as comparisons. For illustration, the same network proposed by Spiess and Florian is utilized on the experiments in an agent-based model. Two experiments are conducted respectively according to whether each passenger’s choosing method is consistent. As expected, the results in the first experiment showed that the travel for consistent passengers with smartphone guidance was clearly shorter and that it can reduce travel time exceeding 15% and weighted cost exceeding 20%, and the average saved time approximated 3.88 minutes per passenger. The second experiment presented that travel cost, as well as cost savings, gradually decreased by employing smartphone guidance, and the maximum cost savings accounted for 14.2% of the total weighted cost. [ABSTRACT FROM AUTHOR]

Abstract: People use commonsense science knowledge to flexibly explain, predict, and manipulate the world around them, yet we lack computational models of how this commonsense science knowledge is represented, acquired, utilized, and revised. This is an important challenge for cognitive science: Building higher order computational models in this area will help characterize one of the hallmarks of human reasoning, and it will allow us to build more robust reasoning systems. This paper presents a novel assembled coherence (AC) theory of human conceptual change, whereby people revise beliefs and mental models by constructing and evaluating explanations using fragmentary, globally inconsistent knowledge. We implement AC theory with Timber, a computational model of conceptual change that revises its beliefs and generates human‐like explanations in commonsense science. Timber represents domain knowledge using predicate calculus and qualitative model fragments, and uses an abductive model formulation algorithm to construct competing explanations for phenomena. Timber then (a) scores competing explanations with respect to previously accepted beliefs, using a cost function based on simplicity and credibility, (b) identifies a low‐cost, preferred explanation and accepts its constituent beliefs, and then (c) greedily alters previous explanation preferences to reduce global cost and thereby revise beliefs. Consistency is a soft constraint in Timber; it is biased to select explanations that share consistent beliefs, assumptions, and causal structure with its other, preferred explanations. In this paper, we use Timber to simulate the belief changes of students during clinical interviews about how the seasons change. We show that Timber produces and revises a sequence of explanations similar to those of the students, which supports the psychological plausibility of AC theory. [ABSTRACT FROM AUTHOR]

Drawing on job design theories and a conceptual framework of work-related goals and motivation in later adulthood, the aim of this paper is to explore how work-related and individual factors are separately and jointly related to psychological work ability and bridge employment intentions via late job mobility. The cross-sectional study is based on a sample of 171 older Spanish workers aged 45–65 and beyond. We differentiated between groups of older workers in mid career (45–55 years of age) and in their later careers (56 years and beyond). Our results confirm that task characteristics and, secondarily, knowledge characteristics are the most relevant factors in perceptions of psychological work ability among aged workers. Both age groups display a very marked personal mastery trait, which mediates the relationships between job characteristics and both psychological work ability and late job mobility intentions. The paper concludes with a discussion of theoretical and practical implications and suggestions for future research on the issues implied in the psychological adjustment of older workers in their mid and late careers. [ABSTRACT FROM AUTHOR]

Background: In practical research, it was found that most people made health-related decisions not based on numerical data but on perceptions. Examples include the perceptions and their corresponding linguistic values of health risks such as, smoking, syringe sharing, eating energy-dense food, drinking sugar-sweetened beverages etc. For the sake of understanding the mechanisms that affect the implementations of health-related interventions, we employ fuzzy variables to quantify linguistic variable in healthcare modeling where we employ an integrated system dynamics and agent-based model. Methodology: In a nonlinear causal-driven simulation environment driven by feedback loops, we mathematically demonstrate how interventions at an aggregate level affect the dynamics of linguistic variables that are captured by fuzzy agents and how interactions among fuzzy agents, at the same time, affect the formation of different clusters(groups) that are targeted by specific interventions. Results: In this paper, we provide an innovative framework to capture multi-stage fuzzy uncertainties manifested among interacting heterogeneous agents (individuals) and intervention decisions that affect homogeneous agents (groups of individuals) in a hybrid model that combines an agent-based simulation model (ABM) and a system dynamics models (SDM). Having built the platform to incorporate high-dimension data in a hybrid ABM/SDM model, this paper demonstrates how one can obtain the state variable behaviors in the SDM and the corresponding values of linguistic variables in the ABM. Conclusions: This research provides a way to incorporate high-dimension data in a hybrid ABM/SDM model. This research not only enriches the application of fuzzy set theory by capturing the dynamics of variables associated with interacting fuzzy agents that lead to aggregate behaviors but also informs implementation research by enabling the incorporation of linguistic variables at both individual and institutional levels, which makes unstructured linguistic data meaningful and quantifiable in a simulation environment. This research can help practitioners and decision makers to gain better understanding on the dynamics and complexities of precision intervention in healthcare. It can aid the improvement of the optimal allocation of resources for targeted group (s) and the achievement of maximum utility. As this technology becomes more mature, one can design policy flight simulators by which policy/intervention designers can test a variety of assumptions when they evaluate different alternatives interventions. [ABSTRACT FROM AUTHOR]

Background: Routine Data Quality Assessments (RDQAs) were developed to measure and improve facility-level electronic medical record (EMR) data quality. We assessed if RDQAs were associated with improvements in data quality in KenyaEMR, an HIV care and treatment EMR used at 341 facilities in Kenya. Methods: RDQAs assess data quality by comparing information recorded in paper records to KenyaEMR. RDQAs are conducted during a one-day site visit, where approximately 100 records are randomly selected and 24 data elements are reviewed to assess data completeness and concordance. Results are immediately provided to facility staff and action plans are developed for data quality improvement. For facilities that had received more than one RDQA (baseline and follow-up), we used generalized estimating equation models to determine if data completeness or concordance improved from the baseline to the follow-up RDQAs. Results: 27 facilities received two RDQAs and were included in the analysis, with 2369 and 2355 records reviewed from baseline and follow-up RDQAs, respectively. The frequency of missing data in KenyaEMR declined from the baseline (31% missing) to the follow-up (13% missing) RDQAs. After adjusting for facility characteristics, records from follow-up RDQAs had 0.43-times the risk (95% CI: 0.32–0.58) of having at least one missing value among nine required data elements compared to records from baseline RDQAs. Using a scale with one point awarded for each of 20 data elements with concordant values in paper records and KenyaEMR, we found that data concordance improved from baseline (11.9/20) to follow-up (13.6/20) RDQAs, with the mean concordance score increasing by 1.79 (95% CI: 0.25–3.33). Conclusions: This manuscript demonstrates that RDQAs can be implemented on a large scale and used to identify EMR data quality problems. RDQAs were associated with meaningful improvements in data quality and could be adapted for implementation in other settings. [ABSTRACT FROM AUTHOR]

In political campaigns, perceived candidate credibility influences the persuasiveness of messages. In campaigns aiming to influence people’s beliefs, micro-targeted campaigns (MTCs) that target specific voters using their psychological profile have become increasingly prevalent. It remains open how effective MTCs are, notably in comparison to population-targeted campaign strategies. Using an agent-based model, the paper applies recent insights from cognitive models of persuasion, extending them to the societal level in a novel framework for exploring political campaigning. The paper provides an initial treatment of the complex dynamics of population level political campaigning in a psychologically informed manner. Model simulations show that MTCs can take advantage of the psychology of the electorate by targeting voters favourable disposed towards the candidate. Relative to broad campaigning, MTCs allow for efficient and adaptive management of complex campaigns. Findings show that disliked MTC candidates can beat liked population-targeting candidates, pointing to societal questions concerning campaign regulations. [ABSTRACT FROM AUTHOR]

Pruning is applied in order to combat over-fitting problem where the tree is pruned back with the goal of identifying decision tree with the lowest error rate on previously unobserved instances, breaking ties in favour of smaller trees with high accuracy. In this paper, pruning with Bayes minimum risk is introduced for estimating the risk-rate. This method proceeds in a bottom-up fashion converting a parent node of a subtree to a leaf node if the estimated risk-rate of the parent node for that subtree is less than the risk-rates of its leaf. This paper proposes a post-pruning method that considers various evaluation standards such as attribute selection, accuracy, tree complexity, and time taken to prune the tree, precision/recall scores, TP/FN rates and area under ROC. The experimental results show that the proposed method produces better classification accuracy and its complexity is not much different than the complexities of reduced-error pruning and minimum-error pruning approaches. The experiments also demonstrate that the proposed method shows satisfactory performance in terms of precision score, recall score, TP rate, FP rate and area under ROC. [ABSTRACT FROM AUTHOR]

Designing an efficient association rule mining (ARM) algorithm for multilevel knowledge-based transactional databases that is appropriate for real-world deployments is of paramount concern. However, dynamic decision making that needs to modify the threshold either to minimize or maximize the output knowledge certainly necessitates the extant state-of-the-art algorithms to rescan the entire database. Subsequently, the process incurs heavy computation cost and is not feasible for real-time applications. The paper addresses efficiently the problem of threshold dynamic updation for a given purpose. The paper contributes by presenting a novel ARM approach that creates an intermediate itemset and applies a threshold to extract categorical frequent itemsets with diverse threshold values. Thus, improving the overall efficiency as we no longer needs to scan the whole database. After the entire itemset is built, we are able to obtain real support without the need of rebuilding the itemset (e.g. Itemset list is intersected to obtain the actual support). Moreover, the algorithm supports to extract many frequent itemsets according to a pre-determined minimum support with an independent purpose. Additionally, the experimental results of our proposed approach demonstrate the capability to be deployed in any mining system in a fully parallel mode; consequently, increasing the efficiency of the real-time association rules discovery process. The proposed approach outperforms the extant state-of-the-art and shows promising results that reduce computation cost, increase accuracy, and produce all possible itemsets. [ABSTRACT FROM AUTHOR]

Traffic surveillance systems are interesting to many researchers to improve the traffic control and reduce the risk caused by accidents. In this area, many published works are only concerned about vehicle detection in normal conditions. The camera may vibrate due to wind or bridge movement. Detection and tracking of vehicles is a very difficult task when we have bad weather conditions in winter (snowy, rainy, windy, etc.), dusty weather in arid and semi-arid regions, at night, etc. Also, it is very important to consider speed of vehicles in the complicated weather condition. In this paper, we improved our method to track and count vehicles in dusty weather with vibrating camera. For this purpose, we used a background subtraction based strategy mixed with an extra processing to segment vehicles. In this paper, the extra processing included the analysis of the headlight size, location, and area. In our work, tracking was done between consecutive frames via a generalized particle filter to detect the vehicle and pair the headlights using the connected component analysis. So, vehicle counting was performed based on the pairing result, with Centroid of each blob we calculated distance between two frames by simple formula and hence dividing it by the time between two frames obtained from the video. Our proposed method was tested on several video surveillance records in different conditions such as dusty or foggy weather, vibrating camera, and in roads with medium-level traffic volumes. The results showed that the new proposed method performed better than our previously published method and other methods, including the Kalman filter or Gaussian model, in different traffic conditions. [ABSTRACT FROM AUTHOR]

In developing nations, many expanding cities are facing challenges that result from the overwhelming numbers of people and vehicles. Collecting real-time, reliable and precise traffic flow information is crucial for urban traffic management. The main purpose of this paper is to develop an adaptive model that can assess the real-time vehicle counts on urban roads using computer vision technologies. This paper proposes an automatic real-time background update algorithm for vehicle detection and an adaptive pattern for vehicle counting based on the virtual loop and detection line methods. In addition, a new robust detection method is introduced to monitor the real-time traffic congestion state of road section. A prototype system has been developed and installed on an urban road for testing. The results show that the system is robust, with a real-time counting accuracy exceeding 99% in most field scenarios. [ABSTRACT FROM AUTHOR]

Discussions of extended cognition have increasingly engaged with the empirical and methodological practices of cognitive science and psychology. One topic that has received increased attention from those interested in the extended mind is music cognition. A number of authors have argued that music not only shapes emotional and cognitive processes, but also that it extends those processes beyond the bodily envelope. The aim of this paper is to evaluate the case for extended music cognition. Two accounts are examined in detail: Joel Krueger’s “musically extended emotional mind” and Tom Cochrane’s “expression and extended cognition.” Each account is evaluated using three “anti-extension” arguments. I argue that Krueger and Cochrane’s accounts offer important steps toward extended music cognition, but that each account remains underdeveloped in various ways. To supplement existing approaches, I propose a complementaryextended computational approach to music cognition(ECMC). The claim is that music cognition forms part of an extended system in virtue of involving computational processes that range across environmental and in-the-head elements. The paper concludes by showing how the ECMC deals with each of the three anti-extension challenges and responds to objections. [ABSTRACT FROM AUTHOR]