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Background: In the digital environment, users' academic reading behaviour has changed, working with many articles simultaneously to search, filter, scan, link, annotate and analyse content fragments. The semantic enhancement environment has been widely set with semantic technologies to offer additional and handy support for users and thus facilitate the reading process. Despite many efforts devoted to developing a semantic enhancement environment, less attention has been paid to its actual effects. Objectives: This study aims to explore the effects of a semantic enhancement environment. Methods: Elaborating on cognitive load theory and focusing on academic reading, this study compared users' cognitive load and reading effect under the semantic enhancement environment and plain text environment and verified the three hypotheses. Results and conclusions: Through experiments and statistical analysis, this study found that, under a semantic enhancement environment, users had their cognitive load reduced and their reading effect improved. Consequently, a semantic enhancement environment promotes academic reading. Takeaways: This study adds to previous literature on semantic publishing by empirically corroborating the conducive effects of semantic enhancement environment in academic reading. Additionally, it justifies semantic publishing and provides a reference for the future design of semantically enhanced reading environments. Lay Description: What is already known about this topic?: The effects of a semantic enhancement environment are open to discuss.Academic reading is fundamental to researches and hence needs promotion.Limited studies investigated the effects of a semantic enhancement environment on academic reading.Publishers require effective reading environment designs. What this paper adds?: This study constructed a semantic enhancement reading environment to conduct a reading experiment.The experiment tested cognitive load, by using WP scale, and reading effects, by answer time and answer score.Semantic enhancement environment reduces users' cognitive load, promotes users' reading effects, and bolsters users' understanding of academic reading materials, especially in microstructure content.The design of semantic enhancement reading environments needs to fully consider the features of reading materials and give users a controllable way of interaction. The implications of study findings for practitioners: Compared to plain text environment, semantic enhancement environment is preferred in academic reading.Publishers are encouraged to add semantic enhancements to their publications.This study discussed how to design an effective learning and reading environment from the perspectives of reading resources, media forms of resources, and interaction modes. [ABSTRACT FROM AUTHOR]

In Wittgenstein's manuscripts dating from 1929 and 1930, there are a number of entries on the notion of probability. In this paper, I explore Wittgenstein's manuscripts between October 1929 and March 1930 and demonstrate, first, that Wittgenstein completely rejects the assumption that probability statements are based on an a priori principle. Second, I argue that the standard interpretation, which claims that Wittgenstein adopts an 'epistemological view' of probability, significantly understates the importance of Wittgenstein's development of the notion of hypotheses as a philosophical term of art. [ABSTRACT FROM AUTHOR]

An etymological proposition is often said to be probable or improbable from the phonetic point of view, and it is not rare for opinions to diverge on which it is. The estimation is typically purely intuitive, based on perceived similarity and no more than a handful of analogous examples. This paper proposes a method for quantifying the phonetic probability of an etymology and comparing it to the alternative hypothesis. It is intended to be used with sizable datasets, to produce a well-supported, objective verdict. [ABSTRACT FROM AUTHOR]

This paper presents two new trajectory probability hypothesis density (TPHD) and trajectory cardinality probability hypothesis density (TCPHD) filters for joint tracking and classification (JTC), namely JTC-TPHD and JTC-TCPHD filters. We first introduce the classified trajectory RFS model to accommodate the motion model-based class information. The adaptation of the TPHD and TCPHD filters to the classified trajectory RFS is then formulated, i.e., JTC-TPHD and JTC-TCPHD, which are capable of jointly estimating the number, class, motion model, and trajectory status of multiple targets. We also develop the linear Gaussian implementation (LGM) as an analytic closed-form solution of the proposed filters. Simulation experiments discuss the validity and superiority of the proposed filters for multi-target JTC. • The classified trajectory variable to accommodate class information. • The proposal of JTC-T(C)PHD filters for joint tracking and classification. • The development of linear Gaussian mixture implementations. • The simulation demonstration of algorithms' validity and superiority. [ABSTRACT FROM AUTHOR]

This paper analyzes the historical context and systematic importance of Kant's hypothetical use of reason. It does so by investigating the role of hypotheses in Kant's philosophy of science. We first situate Kant's account of hypotheses in the context of eighteenth-century German philosophy of science, focusing on the works of Wolff, Meier, and Crusius. We contrast different conceptions of hypotheses of these authors and elucidate the different theories of probability informing them. We then adopt a more systematic perspective to discuss Kant's idea that scientific hypotheses must articulate real possibilities. We argue that Kant's views on the intelligibility of scientific hypotheses constitute a valuable perspective on scientific understanding and the constraints it imposes on scientific rationality. • Analyzes the role of hypotheses in eighteenth-century German philosophy. • Explains the relation between probability and hypotheses in various authors. • Explains how hypotheses paved the way for obtaining certain truths. • Explores the connection between hypotheses and scientific understanding in Kant. [ABSTRACT FROM AUTHOR]

The treatment of uncertainty in climate-change science is dominated by the far-reaching influence of the 'frequentist' tradition in statistics, which interprets uncertainty in terms of sampling statistics and emphasizes p-values and statistical significance. This is the normative standard in the journals where most climate-change science is published. Yet a sampling distribution is not always meaningful (there is only one planet Earth). Moreover, scientific statements about climate change are hypotheses, and the frequentist tradition has no way of expressing the uncertainty of a hypothesis. As a result, in climate-change science, there is generally a disconnect between physical reasoning and statistical practice. This paper explores how the frequentist statistical methods used in climate-change science can be embedded within the more general framework of probability theory, which is based on very simple logical principles. In this way, the physical reasoning represented in scientific hypotheses, which underpins climate-change science, can be brought into statistical practice in a transparent and logically rigorous way. The principles are illustrated through three examples of controversial scientific topics: the alleged global warming hiatus, Arctic-midlatitude linkages, and extreme event attribution. These examples show how the principles can be applied, in order to develop better scientific practice. "La théorie des probabilités n'est que le bon sens reduit au calcul." (Pierre-Simon Laplace, Essai Philosophiques sur les Probabilités, 1819). "It is sometimes considered a paradox that the answer depends not only on the observations but on the question; it should be a platitude." (Harold Jeffreys, Theory of Probability, 1st edition, 1939). [ABSTRACT FROM AUTHOR]

• Knowledge mining on varying sets of compound hypotheses is inspected. • Bayesian learning is equipped with technique allowing to extend the set of hypotheses. • The technique copes with learning with less data than the amount of parameters. • The use of data forecasters as a knowledge source in parameter estimation is refined. • A rich set of interesting related research and application problems is presented. The paper proposes the way how to assign a proper prior probability to a new, generally compound, hypothesis. To this purpose, it uses the minimum relative-entropy principle and a forecaster-based knowledge transfer. Methodologically, it opens a way towards enriching the standard Bayesian framework by the possibility to extend the set of models during learning without the need to restart. The presented use scenarios concern: (a) creating new hypotheses, (b) learning problems with an insufficient amount of data, and (c) sequential Monte Carlo estimation. They indicate a strong application potential of the proposed technique. Related interesting open research problems are listed. [ABSTRACT FROM AUTHOR]

In this paper, we define consistent criteria for hypotheses testing, such that the probability of any kind of error is zero for a given criterion. The necessary and sufficient conditions for the existence of such criteria are given. [ABSTRACT FROM AUTHOR]