Miracle-dz/UniFAR-FTUs huggingface dataset:
Subtle Discrimination in the Workplace: A Vicious Cycle

Due to rising pressure to appear egalitarian, subtle discrimination pervades today's workplace. Although its ambiguous nature may make it seem innocuous on the surface, an abundance of empirical evidence suggests subtle discrimination undermines employee and organizational functioning, perhaps even more so than its overt counterpart. In the following article, we argue for a multidimensional and continuous, rather than categorical, framework for discrimination. In doing so, we propose that there exist several related but distinct continuums on which instances of discrimination vary, including subtlety, formality, and intentionality. Next, we argue for organizational scholarship to migrate toward a more developmental, dynamic perspective of subtle discrimination in order to build a more comprehensive understanding of its antecedents, underlying mechanisms, and outcomes. We further contend that everyone plays a part in the process of subtle discrimination at work and, as a result, bears some responsibility in addressing and remediating it. We conclude with a brief overview of research on subtle discrimination in the workplace from each of four stakeholder perspectives—targets, perpetrators, bystanders, and allies—and review promising strategies that can be implemented by each of these stakeholders to remediate subtle discrimination in the workplace.
Notes of the ECML / MLnet Workshop on Empirical Learning of Natural Language Processing Tasks , April 26 , 1997 , Prague , Czech Republic Empirical Learning of Natural Language Processing Tasks

Language learning has thus far not been a hot application for machine-learning (ML) research. This limited attention for work on empirical learning of language knowledge and behaviour from text and speech data seems unjusti ed. After all, it is becoming apparent that empirical learning of Natural Language Processing (NLP) can alleviate NLP's all-time main problem, viz. the knowledge acquisition bottleneck: empirical ML methods such as rule induction, top down induction of decision trees, lazy learning, inductive logic programming, and some types of neural network learning, seem to be excellently suited to automatically induce exactly that knowledge that is hard to gather by hand. In this paper we address the question why NLP is an interesting application for empirical ML, and provide a brief overview of current work in this area. 1 Empirical Learning of Natural Language Looking at the ML literature of the last decade, it is clear that language learning has not been an important application area of ML techniques. Especially the absence of more research on the empirical learning of language knowledge and behaviour from text and speech data is strange. After all, a main problem of the AI discipline of Natural Language Processing (NLP) is the knowledge-acquisition bottleneck : for each new language, domain, theoretical framework, and application, linguistic knowledge bases (lexicons, rule sets, grammars) have to be built basically from scratch. In our opinion, there are at least three reasons why ML researchers should become more interested in NLP as an application area:
Health, Technical Efficiency and Agricultural Production in Indian Districts

We investigate whether better population health may impact economic performance through improvements in technical efficiency in agricultural production. Using district-level data from India, we employ a random-coefficients approach to estimate a Cobb-Douglas production function, computing overall and input-specific technical efficiencies for each district. We then model health (district infant mortality rate) as a determinant of (in)efficiency in a second stage, controlling for a range of other socioeconomic variables. In the preferred specifications, we find that decreases in the infant mortality rate are associated with substantively and statistically significant increases in overall technical efficiency, and that a good portion of this association is likely due to improvements in the efficiency of labor use.
Cultivating Ethical Dispositions in Early Childhood Practice for an Ethic of Care: A Contemplative Approach

The purpose of this paper is to argue that care ethics is part of an ongoing feminist challenge to a narrow kind of epistemology in higher education which separates mind from body and feeling from intellect. Care ethics imagines a self which is embodied and relational. Connecting this philosophical model with psychological research in attachment and moral development, I propose an experiential and contemplative approach to the ethical development of teachers in ECEC.
Leveraging Content and Connections for Scientific Article Recommendation in Social Computing Contexts

Rapid proliferation of information technologies has generated a great volume of information that makes scientific information searching more challenging. Personalized recommendation is a widely used technique to help researchers find relevant information. Researchers involved in a social computing context generate abundant content and form heterogeneous connections. Existing article recommendation techniques fail to perform a deep analysis of this information. This research proposes a novel approach to recommend scientific articles to researchers by leveraging content and connections. In this approach, we first analyze the semantic content of the article by keyword similarity calculation and then extract online users’ connections to support article voting and finally employ a two-stage recommendation process to suggest relevant articles. The proposed method has been implemented in ScholarMate (www.scholarmate.com), an online research social network platform. Two experiments are conducted and the evaluation results indicate that the proposed method is more effective than the baseline methods.
Education for everyone : agenda for education in a democracy

Preface 1. Schooling for Everyone 2. Agenda for Education in a Democracy 3. The Context of Schooling in a Democracy 4. An Essential Narrative for Schooling 5. Democracy, Education, and the Human Conversation 6. Renewal 7. Leadership for Educational Renewal 8. Experiencing the Agenda Notes Appendix Index.
Effect of membrane stiffness and cytoskeletal element density on mechanical stimuli within cells: an analysis of the consequences of ageing in cells

A finite element model of a single cell was created and used to compute the biophysical stimuli generated within a cell under mechanical loading. Major cellular components were incorporated in the model: the membrane, cytoplasm, nucleus, microtubules, actin filaments, intermediate filaments, nuclear lamina and chromatin. The model used multiple sets of tensegrity structures. Viscoelastic properties were assigned to the continuum components. To corroborate the model, a simulation of atomic force microscopy indentation was performed and results showed a force/indentation simulation with the range of experimental results. A parametric analysis of both increasing membrane stiffness (thereby modelling membrane peroxidation with age) and decreasing density of cytoskeletal elements (thereby modelling reduced actin density with age) was performed. Comparing normal and aged cells under indentation predicts that aged cells have a lower membrane area subjected to high strain as compared with young cells, but the difference, surprisingly, is very small and may not be measurable experimentally. Ageing is predicted to have a more significant effect on strain deep in the nucleus. These results show that computation of biophysical stimuli within cells are achievable with single-cell computational models; correspondence between computed and measured force/displacement behaviours provides a high-level validation of the model. Regarding the effect of ageing, the models suggest only small, although possibly physiologically significant, differences in internal biophysical stimuli between normal and aged cells.
Social Norm Perception in Economic Laboratory Experiments: Inexperienced versus Experienced Participants

We study whether social norm perception in economic laboratory experiments differs between subjects that participate for the first time and subjects that already participated many times. Consistent with previous studies, inexperienced subjects pronounce egalitarianism, while experienced subjects pronounce efficiency and the maximization of their own earnings. Moreover, experienced subjects evaluate exploitation and deception of other individuals in the lab as more appropriate than inexperienced subjects. Field norms also slightly differ between the two groups, but to a lower extent than lab norms. We therefore conclude that learning effects are more important than selection effects for explaining differences between inexperienced and experienced participants. We also conduct exploratory analyses on the relation between lab and field norms and find that behaving unsocial in the lab is considered substantially more appropriate than in the field. This appears inconsistent with the hypothesis that social preferences measured in economic experiments are inflated and indicates a distinction between revealed social preferences and the elicitation of normatively appropriate behavior.
A close look at fractional derivatives approximations

In this work we discuss the accuracy of some approximations for fractional derivatives. We analyze the cases in which the fractional derivatives are defined in a domain different from the real line and show how the order of accuracy can be affected by the presence of boundaries. We observe the accuracy can be easily lost, in particular near the boundary points, unless some conditions of regularity are satisfied or certain boundary conditions are verified.
Dynamic Selection and Distributional Bounds on Search Costs in Dynamic Unit-Demand Models

This paper develops a dynamic model of consumer search that, despite placing very little structure on the dynamic problem faced by consumers, allows us to exploit intertemporal variation in within-period price and search cost distributions to estimate the population distribution from which consumers' search costs are initially drawn. We show that static approaches to estimating this distribution generally suffer from a dynamic sample selection bias because forward-looking consumers with unit demand for a good may delay their purchase in a way that depends on their individual search cost. We analyze identification of the population search cost distribution using only price data and develop estimable nonparametric upper and lower bounds on the distribution function and a nonlinear least squares estimator for parametric models. We also consider the additional identifying power of weak assumptions such as monotonicity of purchase probabilities in search costs. We apply our estimators to analyze the online market for two widely used econometrics textbooks. Our results suggest that static estimates of the search cost distribution are biased upwards, in a distributional sense, relative to the true population distribution. In a small-scale simulation study, we show that this is typical in a dynamic setting where consumers with high search costs are more likely to delay purchase than those with lower search costs.
Assessing Serviceability and Reliability to Affect Customer Satisfaction of Internet Banking

The purpose of the research is to analyze factors of serviceability and reliability that could influence internet banking customer satisfaction in China. Moreover, the paper also tries to explain the relationship among serviceability, reliability and customer satisfaction, and to find some major variables for keeping high level internet banking customer satisfaction. The data demonstrated that serviceability and reliability have direct and significant effect to online banking customer satisfaction in the Chinese banking sector. Finally, it is observed that convenience, comfortable, empathy, privacy, security and assurance are key factors to affect customer satisfaction in the internet banking sector. After the validation of measurement scales, the hypotheses are contrasted through structural modeling. The authors validate the hypotheses and a measurement model. The paper proposes a model for analyzing empirically the link among serviceability, reliability and customer satisfaction in Chinese internet banking industry.
Electrospun Nanostructured Fibers of Collagen-Biomimetic Apatite on Titanium Alloy

Titanium and its alloys are currently the mainly used materials to manufacture orthopaedic implants due to their excellent mechanical properties and corrosion resistance. Although these materials are bioinert, the improvement of biological properties (e.g., bone implant contact) can be obtained by the application of a material that mimics the bone extracellular matrix. To this aim, this work describes a new method to produce nanostructured collagen-apatite composites on titanium alloy substrate, by combining electrospinning and biomimetic mineralization. The characterization results showed that the obtained mineralized scaffolds have morphological, structural, and chemical compositional features similar to natural bone extracellular matrix. Finally, the topographic distribution of the chemical composition in the mineralized matrix evaluated by Fourier Transform Infrared microspectroscopy demonstrated that the apatite nanocrystals cover the collagen fibers assembled by the electrospinning.
Increased discrimination in level set methods with embedded conditional random fields

We propose a novel approach for improving level set segmentation methods by embedding the potential functions from a discriminatively trained conditional random field (CRF) into a level set energy function. The CRF terms can be efficiently estimated and lead to both discriminative local potentials and edge regularizers that take into account interactions among the labels. Unlike discrete CRFs, the use of a continuous level set framework allows the natural use of flexible continuous regularizers such as shape priors. We show promising experimental results for the method on two difficult medical image segmentation tasks.
Academic Emotional Learning: A Critical Component of Self-Regulated Learning in the Emotional Learning Cycle

The aim of this article is to map the academic emotional learning cycle from a theoretical and practical perspective through the lens of self-regulated learning. Focusing on the learner, a further iteration of the emotional dimension of the integrated self-regulated learning model is articulated by considering within-individual components (i.e., the intraindividual system). Academic emotions are considered an important component of learning that, along with intellectual behavioral-cognitive goals, should be explicit targets of learning. An emotional learning cycle is presented, according to which broader affective inclinations––the expectation to experience certain affect or emotion––shape the relations between emotions and self-regulated emotions that occur during a learning episode. Strategies for self-regulated emotions are outlined in light of the need for more research in this field, along with proposed future directions for research and educational implications.
Twin Support Vector Machines for Pattern Classification

We propose twin SVM, a binary SVM classifier that determines two nonparallel planes by solving two related SVM-type problems, each of which is smaller than in a conventional SVM. The twin SVM formulation is in the spirit of proximal SVMs via generalized eigenvalues. On several benchmark data sets, Twin SVM is not only fast, but shows good generalization. Twin SVM is also useful for automatically discovering two-dimensional projections of the data
Metacognitive Mechanisms Underlying Lucid Dreaming

Lucid dreaming is a state of awareness that one is dreaming, without leaving the sleep state. Dream reports show that self-reflection and volitional control are more pronounced in lucid compared with nonlucid dreams. Mostly on these grounds, lucid dreaming has been associated with metacognition. However, the link to lucid dreaming at the neural level has not yet been explored. We sought for relationships between the neural correlates of lucid dreaming and thought monitoring. Human participants completed a questionnaire assessing lucid dreaming ability, and underwent structural and functional MRI. We split participants based on their reported dream lucidity. Participants in the high-lucidity group showed greater gray matter volume in the frontopolar cortex (BA9/10) compared with those in the low-lucidity group. Further, differences in brain structure were mirrored by differences in brain function. The BA9/10 regions identified through structural analyses showed increases in blood oxygen level-dependent signal during thought monitoring in both groups, and more strongly in the high-lucidity group. Our results reveal shared neural systems between lucid dreaming and metacognitive function, in particular in the domain of thought monitoring. This finding contributes to our understanding of the mechanisms enabling higher-order consciousness in dreams.
Biological Control of Meloidogyne incognita by Aspergillus niger F22 Producing Oxalic Acid

Restricted usage of chemical nematicides has led to development of environmentally safe alternatives. A culture filtrate of Aspergillus niger F22 was highly active against Meloidogyne incognita with marked mortality of second-stage juveniles (J2s) and inhibition of egg hatching. The nematicidal component was identified as oxalic acid by organic acid analysis and gas chromatography-mass spectroscopy (GC-MS). Exposure to 2 mmol/L oxalic acid resulted in 100% juvenile mortality at 1 day after treatment and suppressed egg hatching by 95.6% at 7 days after treatment. Oxalic acid showed similar nematicidal activity against M. hapla, but was not highly toxic to Bursaphelenchus xylophilus. The fungus was incubated on solid medium and dried culture was used for preparation of a wettable powder-type (WP) formulation as an active ingredient. Two WP formulations, F22-WP10 (ai 10%) and oxalic acid-WP8 (ai 8%), were prepared using F22 solid culture and oxalic acid. In a field naturally infested with M. incognita, application of a mixture of F22-WP10 + oxalic acid-WP8 at 1,000- and 500-fold dilutions significantly reduced gall formation on the roots of watermelon plants by 58.8 and 70.7%, respectively, compared to the non-treated control. The disease control efficacy of the mixture of F22-WP10 + oxalic acid-WP8 was significantly higher than that of a chemical nematicide, Sunchungtan (ai 30% fosthiazate). These results suggest that A. niger F22 can be used as a microbial nematicide for the control of root-knot nematode disease.
Parallel Clustering Based on Partitions of Local Minimal-Spanning-Trees

Many traditional clustering algorithms have the scalability problem while dealing with large data sets. One common strategy to handle the problem is to parallelize the algorithms and execute them along with the input data on high-performance computers. However, many graph-based clustering algorithms are hard to be parallelized since they need to calculate the similarity of all-pairs of all data nodes. In this paper, we propose a new parallel clustering algorithm, called the Para-CPLM (Parallel Clustering based on Partitions of Local Minimal-spanning-trees), which is based on three strategies - graph-based clustering, granular computing, and partition-and-merge. The Para-CPLM partitions the data domain into several regions for parallel execution, and then establishes a local minimal spanning tree in each region. After being established, the Para-CPLM combines those local minimal spanning trees and applies a method, namely the GBC method, to determine the best number of clusters. After the first phase of clustering, it repeatedly finds better pairs (edges) of the inter-clusters to reform the merged tree structure, such that the tree becomes closer to a global minimal spanning tree. Consequently, it uses the GBC method again to find the best number of clusters. From our experimental results, the Para-CPLM achieves significantly shorter execution time and better scalability while compared with the sequential GBC method. In addition, the clustering results are almost identical to those produced by the sequential GBC method.
Learning Phenotype Structure Using Sequence Model

Advanced microarray technologies have enabled to simultaneously monitor the expression levels of all genes. An important problem in microarray data analysis is to discover phenotype structures. The goal is to 1) find groups of samples corresponding to different phenotypes (such as disease or normal), and 2) for each group of samples, find the representative expression pattern or signature that distinguishes this group from others. Some methods have been proposed for this issue, however, a common drawback is that the identified signatures often include a large number of genes but with low discriminative power. In this paper, we propose a g*-sequence model to address this limitation, where the ordered expression values among genes are profitably utilized. Compared with the existing methods, the proposed sequence model is more robust to noise and allows to discover the signatures with more discriminative power using fewer genes. This is important for the subsequent analysis by the biologists. We prove that the problem of phenotype structure discovery is NP-complete. An efficient algorithm, FINDER, is developed, which includes three steps: 1) trivial g*-sequences identifying, 2) phenotype structure discovery, and 3) refinement. Effective pruning strategies are developed to further improve the efficiency. We evaluate the performance of FINDER and the existing methods using both synthetic and real gene expression data sets. Extensive experimental results show that FINDER dramatically improves the accuracy of the phenotype structures discovered (in terms of both statistical and biological significance) and detects signatures with high discriminative power. Moreover, it is orders of magnitude faster than other alternatives.
When groups help and when groups harm: Origins, developments, and future directions of the “Social Cure” perspective of group dynamics

A substantial literature supports the important role that social group memberships play in enhancing health. While the processes through which group memberships constitute a ‘Social Cure’ are becoming increasingly well-defined, the mechanisms through which these groups contribute to vulnerability and act as a ‘Social Curse’ are less understood. We present an overview of the Social Cure literature, and then go beyond this to show how the processes underpinning the health benefits of group membership can also negatively affect individuals through their absence. First, we provide an overview of early Social Cure research. We then describe later research concerning the potential health benefits of identifying with multiple groups, before moving on to consider the ‘darker side’ of the Social Cure by exploring how intra-group dynamics can foster Curse processes. Finally, we synthesise evidence from both the Cure and Curse literatures to highlight the complex interplay between these phenomena, and how they are influenced by both intra- and inter-group processes. We conclude by considering areas we deem vital for future investigation within the discipline.
Estimation of the maximal moment exponent with censored data

Heavy-tailed distributions have been used to model phenomena in which extreme events occur with high probability. In these type of occurrences, it is likely that extreme events are not observable after a certain threshold. Appropriate estimators are needed to deal with this type of censored data. We show that the well-known Hill-Hall estimator is unable to deal with censored data and yields highly biased estimates. We propose and study an unbiased modified maximum likelihood estimator, as well as a truncated tail regression estimator. We assess the expected value and the variance of these estimators in the cases of stable- and Pareto-distributed data.