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As testing programs transition from paper to online testing, they must study mode comparability to support the exchangeability of scores from different testing modes. To that end, a series of three mode comparability studies was conducted during the 2019–2020 academic year with examinees randomly assigned to take the ACT college admissions exam on paper or online. Unlike many prior mode comparability studies, examinees testing online earned higher scores on average than examinees testing on paper, especially on the English, reading, and writing tests. Much of the observed mode effects were accounted for by items near the ends of the tests. Subsequent examination of item omit rates, differential item functioning by mode, and change‐point analyses supported the notion that the observed mode effects were related to differential speededness. Specifically, examinees testing online were slightly less speeded, and were therefore able to perform better on items near the ends of the tests. Practical implications for linking across modes, test design, and test administration conditions are discussed. [ABSTRACT FROM AUTHOR]

The use of process-based modelling is increasing in most disciplines of plant science, but criteria for what is acceptable modelling work have rarely been made explicit. Unlike empirical work, modelling does not provide direct information on nature, and the goal is generally to explain natural phenomena, or predict their consequences, rather than reveal new phenomena or test specific hypotheses. Goals, methods and results of modelling and empirical work thus differ and they should be judged by different criteria. This note shows how the four criteria of explanation, testability, novelty and good modelling practice can be applied to modelling papers in the plant sciences. The practical implementation of these criteria is discussed, and a checklist of eight questions is derived with which papers can be evaluated. To illustrate the use of the criteria, they are applied to a classic publication from the crop modelling literature [Simulation of Assimilation, Respiration and Transpiration of Crops. Pudoc, Wageningen]. [Copyright &y& Elsevier]

Thematic analysis (TA) is one of the most popular methods in social science. There are several different approaches to TA that hold different ontological commitments, ranging from positivistic coding reliability TA to constructivist reflexive TA. However, there has been less focus on developing an approach that is informed by critical realism, with the notable exception of Wiltshire and Ronkainen. The first part of this paper proposes a five-step critical realist approach to TA. This approach aims to produce nuanced causal explanations of events, countering the mistaken assumption that qualitative research cannot produce causal knowledge. The second part of the paper brings this critical realist approach to TA into conversation with three alternatives: coding reliability, reflexive, and Wiltshire and Ronkainen's approach. The approach to TA in this paper builds on the strengths of these alternatives, offering an accessible way to adopt a critical realist philosophical grounding when doing TA. [ABSTRACT FROM AUTHOR]

Generative adversarial networks (or GANs) are a specific deep learning architecture often used for different usages, such as data generation or image-to-image translation. In recent years, this structure has gained increased popularity and has been used in different fields. One area of expertise currently in vogue is the use of GANs to produce synthetic aperture radar (SAR) data, and especially expand training datasets for SAR automatic target recognition (ATR). In effect, the complex SAR image formation makes these kind of data rich in information, leading to the use of deep networks in deep learning-based methods. Yet, deep networks also require sufficient data for training. However, contrary to optical images, we generally do not have a substantial number of available SAR images because of their acquisition and labelling cost; GANs are then an interesting tool. Concurrently, how to improve explainability for SAR ATR deep neural networks and how to make their reasoning more transparent have been increasingly explored as model opacity deteriorates trust of users. This paper aims at reviewing how GANs are used with SAR images, but also giving perspectives on how GANs could be used to improve interpretability and explainability of SAR classifiers. [ABSTRACT FROM AUTHOR]

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According to the research conducted so far, a crisp value is obtained by defuzzifying the fuzzy value using any defuzzification approach and this value is then used to analyze the entire problem. It is possible, however, that the crisp value generated using existing approaches may not exactly be matched with the fuzzy values. It is probable that any figure slightly early or slightly later than the obtained crisp value will produce the better outcome. So, the purpose of this research work is to provide a technique that can defuzzify fuzzy parameter values so that there are more than one possible values following a small adjustment to the technique. In this paper, the cost, demand and availability of transportation problems are taken in the form of trapezoidal fuzzy number. The advantage of the methodology developed is that the any value close to the crisp value obtained from the existing methods of trapezoidal fuzzy number or a value in the range of the trapezoidal fuzzy number as desired by the decision maker can be obtained. This method has the benefit of allowing the decision makers to receive as much crisp cost as desired in the fuzzy transportation problem. Which facilitated more accurate analysis of fuzzy transportation models. The numerical issue provides an explanation of the recommended strategy. [ABSTRACT FROM AUTHOR]

This article takes the issue of epistemology in writing for (performance) art to ask: 'What is the value of using "fictional" - as in "novelistic" - writing in reflective discourse on creative practice generally?' Using Susan Sontag's seminal essay 'Against Interpretation' as a starting point, the article argues that much writing on art assumes art's 'will-to-signify' - its value as a form of meaning - and consequently 'explanation' as the purpose of art writing. The problems with this reflex are discussed, including its suppression of alternative responses, which may include acknowledging that art is an affective entity: it has a function (if, in Kant's phrase, it is 'without purpose') and it has an ontology that may be more than its identity as signification. Extending, or restoring, the scope of art's reflective discourse in this way, the paper also notes, via reference to George Steiner, that a reciprocal extension for the media of this discourse is also possible, and it seeks to map the two extensions as the axes of a grid that offers varied combinations of the content-form dimensions of art writing. One of these conjunctions produces 'fictional writing' as a possible response to art. Seeming to dispel the problem of reductionism in explanatory discourse, the article then goes on to argue that the use of fiction in the spaces of art writing - 'Situational Fiction' - may be valuable in other ways as well. Hence, this is an argument for knowledge of creative practice in creative form. But 'Situational Fiction' may pursue this ethos of 'creative knowledge' in another way as well: as its reflexive dimension implicates the reader in deciding whether any aspect of this academic paper designates this work as 'fictional', as the paper understands this. [ABSTRACT FROM AUTHOR]

This paper presents arguments and evidence from psychology and neuroscience supporting Lipton's 2004 claim that scientists create knowledge through an abductive process that he calls "Inference to the Best Explanation". The paper develops two conclusions. Conclusion 1 is that without conscious effort on our part, our brains use a process very similar to abduction as a powerful way of interpreting sensory information. To support Conclusion 1, evidence from psychology and neuroscience is presented that suggests that what we humans perceive through our senses is not reality, but rather, our 'brain's "best guess" of the causes of its sensory input. The implication of this best guessing is that our brains use a process very similar to abduction throughout our lives to inform us of what is happening in the world around us. In addition, an argument based on Darwinian evolution is presented claiming that our brains do an excellent job of interpreting sensory information from the outside world. (If they did not, we, as a species, could hardly have survived.) Combining these two claims leads to Conclusion 1. Building on Conclusion 1, Conclusion 2 is that Lipton and others are correct in claiming that scientists use abduction when creating scientific theories. Abduction must be strong, because Nature chose abduction for its own sensemaking purposes. This paper's contribution to knowledge is in pointing out that recent psychological and neuroscientific research has major implications for the philosophical world's confidence in the probable validity of abductive inference. The punchline is simple: Nature chose abduction! [ABSTRACT FROM AUTHOR]

In this paper, we present the envisioned style and scope of the new topic "Explanation Paradigms Leveraging Analytic Intuition" (ExPLAIn) with the International Journal on Software Tools for Technology Transfer (STTT). Intention behind this new topic is to (1) explicitly address all aspects and issues that arise when trying to, if possible, reveal and then confirm hidden properties of black-box systems, or (2) to enforce vital properties by embedding them into appropriate system contexts. Machine-learned systems, such as Deep Neural Networks, are particularly challenging black-box systems, and there is a wealth of formal methods for analysis and verification waiting to be adapted and applied. The selection of papers of this first Special Section of ExPLAIn, most of which were co-authored by editorial board members, is an illustrative example of the style and scope envisioned: In addition to methodological papers on verification, explanation, and their scalability, case studies, tool papers, literature reviews, and position papers are also welcome. [ABSTRACT FROM AUTHOR]

The application of Artificial Intelligence (AI) and Machine Learning (ML) models is increasingly leveraged to automate and optimize Data Centre (DC) operations. However, the interpretability and transparency of these complex models pose critical challenges. Hence, this paper explores the Shapley Additive exPlanations (SHAP) values model explainability method for addressing and enhancing the critical interpretability and transparency challenges of predictive maintenance models. This method computes and assigns Shapley values for each feature, then quantifies and assesses their impact on the model's output. By quantifying the contribution of each feature, SHAP values can assist DC operators in understanding the underlying reasoning behind the model's output in order to make proactive decisions. As DC operations are dynamically changing, we additionally investigate how SHAP can capture the temporal behaviors of feature importance in the dynamic DC environment over time. We validate our approach with selected predictive models using an actual dataset from a High-Performance Computing (HPC) DC sourced from the Enea CRESCO6 cluster in Italy. The experimental analyses are formalized using summary, waterfall, force, and dependency explanations. We delve into temporal feature importance analysis to capture the features' impact on model output over time. The results demonstrate that model explainability can improve model transparency and facilitate collaboration between DC operators and AI systems, which can enhance the operational efficiency and reliability of DCs by providing a quantitative assessment of each feature's impact on the model's output. [ABSTRACT FROM AUTHOR]

Sober and Huemer have independently argued that simplicity has no place in evaluating philosophical views. In particular, they have argued that the best rationales for scientists to appeal to simplicity in judging between rival theories fail to carry over to philosophers judging between rival philosophical accounts. This paper disagrees with Sober and Huemer. It argues that two rationales for scientific appeals to simplicity equally well underwrite appeals to simplicity when philosophers evaluate rival rational reconstructions of some social normative practice. These two rationales are shown to apply to two philosophical appeals to simplicity: in Quine's argument against analyticity and in an argument against pluralism in accounts of scientific explanation. Some factors are identified that influence how much weight simplicity should carry in these and other philosophical cases. Simplicity's legitimate role in evaluating rival rational reconstructions suggests that simplicity will also turn out to be justly relevant to ontological investigations. [ABSTRACT FROM AUTHOR]

In developing a theory of consciousness, one of the main problems has to do with determining what distinguishes conscious states from non-conscious ones—the delimitation problem. This paper explores the possibility of solving this problem in terms of self-awareness. That self-awareness is essential to understanding the nature of our conscious experience is perhaps the most widely discussed hypothesis in the study of consciousness throughout the history of philosophy. Its plausibility hinges on how the notion of self-awareness is unpacked. The idea that consciousness involves self-awareness has been understood in two different ways: either as awareness of oneself—the subject of experience—or as awareness of the conscious episode itself. In this paper I argue (i) that every experience concerns the subject in a very specific way, involving what I will call 'perspectival de se representation', and (ii) that there is no need to appeal to the experience itself in order to characterize the awareness within that experience. The view I articulate explains the subjective nature of experience without over-intellectualizing it, accommodates the phenomenology of experience, and dispels any doubt about the need to find the self in introspection. [ABSTRACT FROM AUTHOR]

In the explainable artificial intelligence (XAI) field, an algorithm or a tool can help people understand how a model makes a decision. And this can help to select important features to reduce computational costs to realize high-performance computing. But existing methods are usually used to visualize important features or highlight active neurons, and few of them show the importance of relationships between features. In recent years, some methods based on a white-box approach have taken relationships between features into account, but most of them can only work on some specific models. Although methods based on a black-box approach can solve the above problems, most of them can only be applied to tabular data or text data instead of image data. To solve these problems, we propose a local interpretable model-agnostic explanation approach based on feature relationships. This approach combines the relationships between features into the interpretation process and then visualizes the interpretation results. Finally, this paper conducts a lot of experiments to evaluate the correctness of relationships between features and evaluates this XAI method in terms of accuracy, fidelity, and consistency. [ABSTRACT FROM AUTHOR]

This paper considers Minimal Yield, which is a condition on MERGE, and removes the redundancy between Minimal Search and the Phase Impenetrability Condition in warranting the condition when internal MERGE applies. By proposing that Transfer applies freely, the paper claims that Minimal Yield is guaranteed without reference to Minimal Search, providing empirical evidence for the proposed approach and demonstrating its superiority over Minimal Search. As a consequence of the proposal, A-movement is reconsidered. I claim that A-movement is not due to internal MERGE but to external MERGE, arguing for uniform association of phase-internal/cross-phasal relations with external/internal MERGE. This paper concludes that genuine explanation is possible for language. [ABSTRACT FROM AUTHOR]