Reviewerly/exHarmony huggingface dataset:
Valid Arguments and Proofs

In this chapter, we take into account Prawitz’s semantics of valid arguments and proofs. Together with the more recent theory of grounds, these semantics belong to the field of proof-theoretic semantics, and have two main sources of inspiration: on the one hand, some pioneering research by Gentzen; on the other, the fundamental results that Prawitz himself has obtained with reference to some of Gentzen’s theories. Along with other ideas stemming from the related intuitionistic tradition, these two suggestions merge in almost all Prawitz’s semantic investigations. Thus, the next section is dedicated to the introduction of some technical—and arguably indispensable—concepts and results.
Languages of Grounding

In the first part, we discussed the reasons that have led Prawitz to the adoption of the theory of grounds, as well as how this theory appears in the writings that Prawitz has so far devoted to his ground-theoretic approach.
Completeness and Recognizability

In this chapter we deal with two topics: completeness of intuitionistic first-order logic with respect to the formal ground-theoretic framework outlined in Chaps. 4 , 5 and 6 , and the recognizability problem, discussed in Chaps. 3 and 4 , read through the lens of the formal framework of the last two chapters. Let us begin with completeness.
FPC‐Net: Learning to detect face forgery by adaptive feature fusion of patch correlation with CG‐Loss

Abstract With the rapid development of manipulation technologies, the generation of Deep Fake videos is more accessible than ever. As a result, face forgery detection becomes a challenging task, attracting a significant amount of attention from researchers worldwide. However, most previous work, consisting of convolutional neural networks (CNN), is not sufficiently discriminative and cannot fully utilise subtle clues and similar textures during the process of facial forgery detection. Moreover, these methods cannot simultaneously consider accuracy and time efficiency. To address such problems, we propose a novel framework named FPC‐Net to extract some meaningful and unnatural expressions in local regions. This framework utilises CNN, long short‐term memory (LSTM), channel groups loss (CG‐Loss) and adaptive feature fusion to detect face forgery videos. First, the proposed method exploits spatial features by CNN, and a channel‐wise attention mechanism is employed to separate channels. Specifically, with the help of channel groups loss, the channels are divided into two groups, each representing a specific class. Second, LSTM is applied to learn the correlation of spatial features. Finally, the correlation of features is mapped into other latent spaces. Through a lot of experiments, the results are that the detection speed of the proposed method reaches 420 FPS and the auc scores achieve best performance of 99.7%, 99.9%, 94.7%, and 82.0% on Raw Celeb‐DF, Raw Face Forensics++, F2F and NT datasets respectively. The experimental results demonstrate that the proposed framework has great time efficiency performance while improving the detection performance compared with other frame‐level methods in most cases.
Multi-scale self-attention-based feature enhancement for detection of targets with small image sizes

In this paper, we propose a feature enhancement method based on multi-scale self-attention, mainly including a multi-scale feature combination module and a self-attention module. The multi-scale feature combination module integrates the multi-layers’ features extracted from the backbone network in both the top-down and bottom-up directions. Then, the shallow and deep features are combined. The self-attention module enhances the feature representation by assigning attention weights to the features that have intrinsic connection to the features of the target. The multi-scale self-attention-based feature enhancement method improves the performance for detecting targets with small image sizes in complex scenes by mutual combination between deep and shallow features and between local and global features. The experimental results show the effectiveness of the proposed feature enhancement method.
Capture and control content discrepancies via normalised flow transfer

Unsupervised Style Transfer (UST) has recently been a hot topic in Computer Vision, and this type of work has been exemplified by CycleGAN. Although the existing UST methods have proven to be useful, in many circumstances, we want to be able to manage not just the transformation of a single piece of instance, but also the morphological features of the two data sets’ underlying distributions. To this end, we propose a novel framework called Normalised Flow Transfer (NFT), where a reversible probability transform using the normalised flow method is developed to transfer the data in the first domain to the second, so as to exhibit their probabilities under both domains in the mapping process. A particularly interesting application under this framework is that when the data sets in two domains contain numerous clusters based on their finite class labels, we can control the distribution pattern across the two domains and apply any constraints on the underlying distributions of the same class. The experimental results show that not only can we devise many new complex style transfer functions, but also our framework has better image generation capabilities in terms of evaluation metrics, including mean square error, inception score and Frechet inception distance.
Development of lightning-leader extraction algorithm for digital photographs applying quartile and deep learning

In this study, a lightning extraction algorithm based on quartile filtering and deep learning (DL) was developed. In lightning photographs, many luminous objects (LOs) are found with lightning leaders. To analyze the lightning leaders, they must be extracted. In the algorithm, outliers derived from the quartile filtering were defined as LOs. The obtained LOs contain lightning leaders and others, such as artificial light sources (ALSs) and non-luminous objects (NLOs). This misextraction was improved using the DL. From the extraction results, it was found that the algorithm successfully extracts lightning leads. The lightning extraction algorithm can be used as the preprocess for lightning photometry.
eXplainable Ensemble Strategy using distinct and restrict learning biases: A case study on the Brazilian Forest

Supervised learning algorithms consider different learning biases from the universe of all admissible functions to induce classifiers. When using ensembles, one takes advantage of different biases typically built from the same algorithm to combine complementary classifiers into a single model, such as Random Forest, that builds up several trees from different attributes and examples. This paper innovates ensemble strategies by explaining and exploring distinct, restrict, and complementary biases from different algorithms. Multi-bias classifiers are combined using Fuzzy rules to execute symbolic reasoning and explain how each learning bias contributes to the final classification results. The contributions of our work are twofold: first, the proposed approach looks for the most suitable learner by individually analyzing the attributes of each new instance, and second, the process used to perform such a search is based on inferences run on fuzzy rules, that uses IF–THEN structures, which are interpretable, thus allowing to explain the process used to select the best learner. Finally, it is worth emphasizing that our approach was applied to the Brazilian biodiversity dataset to corroborate that, even working on hundreds of examples, results are promising, thus stimulating studies on biodiversity and the design of sustainable economic solutions.
Abductive Inference and C. S. Peirce: 150 Years Later

This paper is about two things: (i) Charles Sanders Peirce (1837–1914)—an iconoclastic philosopher and polymath who is among the greatest of American minds. (ii) Abductive inference—a term coined by C. S. Peirce, which he defined as “the process of forming explanatory hypotheses. It is the only logical operation which introduces any new idea.” 1. Abductive inference and quantitative economics. Abductive inference plays a fundamental role in empirical scientific research as a tool for discovery and data analysis. Heckman and Singer (2017) strongly advocated “Economists should abduct.” Arnold Zellner (2007) stressed that “much greater emphasis on reductive [abductive] inference in teaching econometrics, statistics, and economics would be desirable.” But currently, there are no established theory or practical tools that can allow an empirical analyst to abduct. This paper attempts to fill this gap by introducing new principles and concrete procedures to the Economics and Statistics community. I termed the proposed approach as Abductive Inference Machine (AIM). 2. The historical Peirce’s experiment. In 1872, Peirce conducted a series of experiments to determine the distribution of response times to an auditory stimulus, which is widely regarded as one of the most significant statistical investigations in the history of nineteenth-century American mathematical research (Stigler in Ann Stat 239–265, 1978). On the 150th anniversary of this historical experiment, we look back at the Peircean-style abductive inference through a modern statistical lens. Using Peirce’s data, it is shown how empirical analysts can abduct in a systematic and automated manner using AIM.
AI and the expert; a blueprint for the ethical use of opaque AI

The increasing demand for transparency in AI has recently come under scrutiny. The question is often posted in terms of "epistemic double standards", and whether the standards for transparency in AI ought to be higher than, or equivalent to, our standards for ordinary human reasoners. I agree that the push for increased transparency in AI deserves closer examination, and that comparing these standards to our standards of transparency for other opaque systems is an appropriate starting point. I suggest that a more fruitful exploration of this question will involve a different comparison class. We routinely treat judgments made by highly trained experts in specialized fields as fair or well grounded even though—by the nature of expert/layperson division of epistemic labor—an expert will not be able to provide an explanation of the reasoning behind these judgments that makes sense to most other people. Regardless, laypeople are thought to be acting reasonably—and ethically—in deferring to the judgments of experts that concern their areas of specialization. I suggest that we reframe our question regarding the appropriate standards of transparency in AI as one that asks when, why, and to what degree it would be ethical to accept opacity in AI. I argue that our epistemic relation to certain opaque AI technology may be relevantly similar to the layperson's epistemic relation to the expert in certain respects, such that the successful expert/layperson division of epistemic labor can serve as a blueprint for the ethical use of opaque AI.
Culturally Significant Presence

Unfortunately, the academic communities that respectively research virtual heritage and virtual presence do not often converge. One group has tended to ignore the individual differences and experiential requirements of participants, and the other has tended to conflate culture with society. This chapter highlights problems in the research community’s definitions of presence, and specifically distinguishesPresencecultural presence Cultural PresenceCulturecultural presence from Social PresencePresencesocial. Although the distinction may initially sound academic, this chapter argues that it is of great importance to participants’ cultural learning, and that it affects the perceived authenticity of their virtual environment experience. Having suggested that the specific cultural aspects of virtual environments require more careful attention from the academic community, this chapter then discusses the usefulness and danger of adherence to photo-realismRealismphoto-realism. This chapter is not an outright attack on realism, but an attempt to show where attempts at realism may obscure other important issues, especially in education.
What Have We Learnt from Game–Style Interaction?

Creating a descriptive and to some degree prescriptive taxonomy of place and describing the confusing but vital concept of culture only takes us so far. We also need to explore game-style interactionGame-style interaction and how virtual environments can learn from its fundamental components in order to make them both more engaging and more educational. This chapter discusses related how we can apply game-style interactionGame-style interaction for virtual environments and provides examples and prototypes that aim to break down the ‘magic circle’, immerse participants spatially, and thematically incorporate peripherals and interfaces into the learning environment. Using these ideas we can also incorporate more effective feedback mechanisms and evaluation strategies.
Evaluating Virtual Heritage in the Future

This chapter is an overview of evaluation studies and techniques into virtual environments in general, and those that evaluate presence in particular. It then critiques academic papers in virtual heritage over the last decade and argues that while those who design virtual heritage environments can still learn from the presence researchPresencepresence research community, that these findings and techniques cannot be applied immediately and directly. This chapter also covers an evaluation into cultural learning in virtual environments and discusses some of the pitfalls and potential uncovered by the Palenque case study. For example results indicate that incorporating game–style interaction while increasing engagement may impede cultural learning. The findings may be of particular interest to researchers involved in virtual heritage and cultural tourism. The chapter also presents lessons learnt from applying various evaluation methods, and the complex issues of experimental design.
An Open Conclusion

In the conclusion, the previous eight chapters are summarized, the three-part theory defining three types of virtual environments is returned to, and I mention some possible reasons why there has so far been little research undertaken on the relationship between interaction and cultural learningCulturecultural learning in virtual environments. For cultural presence is not the same as social presencePresencesocial, and the ways in which we learn culture are typically interactive and hybrid; they are not easily simulated. I then return to my working definition of virtual heritage but now align it to international heritage charters and with these in mind I propose five aims to fulfill when designing virtual heritage environments.
Virtual Environments: Constraints and Possibilities

How can we increase awareness and understanding of other cultures using interactive online digital visualizations of past civilizations? In order to answer the above question, this chapter first questions the success of current virtual environments and asks whether they are capable of producing a platform that supports the experience and understanding of place-inscribed culture. To answer this question, a typology of successful virtual environment applications is suggested. These include online communities, virtual realityVirtual environmentVirtual Reality (VR) travel websites, game engines, virtual reality used for training, and virtual reality used as part of therapeutic means to either relax or divert patients, or to cure phobiasPhobias. Impeding the success of virtual environments in terms of technological constraints, lack of evaluation techniques and results, and a lack of content-specific applications that best utilize desktop computing capabilities and respond to user needs. It suggests that much more work needs to be done on not just usable but also useful content. Specifically, there is a large gap in knowledge on what might constitute useful and appropriate virtual travel environments, and how contextual interaction may affect our cultural understanding in these environments.
Virtual Travel: Being Not Quite “There”

This chapter explores issues and consequences associated with travel and tourism. I examine the disastrous effects of tourism on sites around the world, from England to South America, the issue of air travel, and missing opportunities for learning once one reaches a heritage site. So there are possible advantages to using virtual realityVirtual environmentVirtual Reality (VR) technology to create virtual travel, savings in cost and time, safety, minimizing pollution, for the pre-planning of intended visits, or for providing situated information not currently available at the site. The interactive opportunities of digital media may help personalizationPersonalization and customization of the user experience. However, we have four major issues to contend with. How do we create a sense of place in a virtual environment? How do we provide for a feeling of cultural presence that appears to inhabit the virtual site? How realistic and historically accurate does the environment and interaction need to be? And how do we balance the value of entertainment with the need for demonstrably effective learning?
Using Corpus Tools for Academic Writing in EFL Settings: A Data-Driven Learning Approach

Previous research studies indicate that developing writing skills is a challenging process particularly for EFL learners. Academic writing puts an additional burden on the learners’ shoulders as it requires some further advanced skills, such as genre awareness, lexical flexibility, and complex syntactic knowledge, to name but a few. Corpus Linguistic Approaches to language analysis (i.e., Data-Driven Learning) has the potential to guide L2 writers in their attempt to follow the academic genre and learn the required writing skills inductively. Corpora can be exploited in three stages: observation of concordance evidence, classification of salient features and generalization of rules. Learners as the discoverers of language in this approach can benefit from the versatile features of corpora and learn from the patterns they observe through the concordance lines. In the light of the given approach and its potential to create more autonomous EFL learners, this chapter attempts to (a) explain what data-driven learning is and how it may shape the learning experience in an EFL context, (b) elaborate on how corpora can guide EFL learners in academic writing and (c) provide some hands-on uses of corpora in teaching/learning (academic) writing.
Dynamical Systems

Dynamical systems are formally defined—may they be classical or quantum. We introduce important concepts for the analysis of classical nonlinear systems. In order to focus on the essential questions, this chapter restricts to one-dimensional discrete maps as relatively simple examples of dynamical systems. Chapter 4 will extend the discussion of dissipative dynamical systems, in particular, to more physical applications in two or more dimensions.
Cross-language lipreading by reconstructing Spatio-Temporal relations in 3D convolution

While deep learning (DL) has been making enormous strides in lipreading areas, it is still massively underused in learning, understanding, and producing human language content. Current DL lipreading methods rely on single-channel processing and monolingual datasets, which have a limited ability to adapt to cross-language applications. Here, we propose a novel lipreading driven deep learning framework to create cross-language learning patterns. To evaluate the algorithm’s cross-language learning ability, we present a dataset CELR-200 for both Chinese and English in lipreading, containing 200-word classes with more than 80,000 samples. We also propose two Spatio-Temporal Reconstructed 3D convolutional kernels to reconstruct the 3D convolutional Spatio-Temporal relations. By using two STR-3D convolutional kernels, we present two new lipreading models, Serial-STRNet and Parallel-STRNet. These improvements reduce the number of 3D convolutional kernel parameters and improve performance, showing good performance in CELR-200 with 65.68% and 66.35%, respectively. They outperform other lipreading models, achieving an absolute improvement of 2.56% over the state-of-the-art model. Our results identify targets for future investigations and demonstrate that STR-3D convolutional kernels can provide critical insights into lipreading tasks.
Intelligence Perception from Text: Accuracy and Cues

The present study deals with the first impression about a person’s intelligence, based solely on their text. We attempted to calculate the accuracy of a reader’s judgment, and to determine which features of the text are associated with both the reader’s impression as well as the author’s intelligence test scores (Brunswik lens model). The study involved 116 participants aged 16 to 23 years. Each of them read and evaluated 4 out of 20 texts that were written by other participants in an earlier study. Participants evaluated both the general impression of the author’s intelligence and of the text itself, scoring responses to questions such as “How smart is the author of this text?” and to other text features that may be associated with intelligence judgement, including “vocabulary richness” and “grammar complexity”. In addition, objective indicators such as readability (Flesch-Kincaid score), length, and relative errors were calculated for the texts. The accuracy of reader judgments, estimated in a text-based analysis as the Spearman correlation coefficient between the “how smart is the author” score and the actual author test scores, did not reach statistical significance. However, the data indicate the presence of a nonlinear relationship. As revealed by a participant-based analysis, authors from the lower quartile (based on test scores) tend to be rated lower by participants as well. Readability, vocabulary richness, grammar complexity, and the relative number of words longer than six letters all were utilized by the reader participants in their judgments (i.e., correlated with impression scores) and at the same time were valid (i.e., correlated with test scores). We discuss the problem of the narrowness of scientific and psychological impressions on intelligence, compared to everyday perceptions
Modelling of the backside pattern transfer in magic mirror (Makyoh) imaging

Abstract A model of magic-mirror (Makyoh) imaging with the mirror backside relief as the input is described. The mechanical pattern transfer is modelled by a convolution approach while the optical imaging using a nonlinear geometrical optical model, more general than previous approaches. Characteristic features of the imaging is analysed and the linear approximation is examined. Diffraction effects are also treated. Characteristic features for the ancient mirror and applications in semiconductor wafer inspection are discussed.