nils-herrmann/clean_scirepeval huggingface dataset:
Critical pedagogies and language learning : An introduction

Advocates of critical approaches to second language teaching are interested in relationships between language learning and social change. From this perspective, language is not simply a means of expression or communication; rather, it is a practice that constructs, and is constructed by, the ways language learners understand themselves, their social surroundings, their histories, and their possibilities for the future. This collection assembles the work of a variety of scholars interested in critical perspectives on language education in globally diverse sites of practice. All are interested in investigating the ways that social relationships are lived out in language and how issues of power, while often obscured in language research and educational practice (Kubota, this volume), are centrally important in developing critical language education pedagogies. Indeed, as Morgan (this volume) suggests, “politically engaged critiques of power in everyday life, communities, and institutions” are precisely what are needed to develop critical pedagogies in language education. The chapters have varying foci, seeking to better understand the relationships between writers and readers, teachers and students, test makers and test takers, teacher–educators and student teachers, and researchers and researched. The term critical pedagogy is often associated with the work of scholars such as Freire (1968/1970), Giroux (1992), Luke (1988), Luke and Gore (1992), McLaren (1989), and Simon (1992) in the field of education. Aware of myriad political and economic inequities in contemporary societies, advocates have explored the “social visions” that pedagogical practices support (Simon, 1992), and critiques of classroom practices in terms of their social visions have been common and longstanding in critical educational literature.1 Feminist critiques have also considered classroom practice and have identified ways in which the relationships and activities of classrooms contribute to patriarchal, hierarchical, and dominating practices in wider societies (e.g., Davies, 1989;  1989; Gaskell, 1992; Spender, 1982; Walkerdine, 1989). In second language education, critiques of classroom practices in terms of the social visions such practices support are relatively recent but are increasingly being published in major venues.2
Pharmacognostic Study of Euphorbia hirta Linn . : An Hyperglyceamic Plant N ’

To study the preliminary characteristics and pharmacognostic of Euphorbia hirta Linn a ruderal plant, harvested in Ivory Coast.  The pharmacognostic study involved the performance of macroscopic and microscopic examinations of fresh and dried plant. The physico-chemical study involved the determination of moisture content, total ash, ashes sulfuric acid and hydrochloric acid insoluble ashes.  The pharmacognostic study has revealed a small herb with finely toothed leaves, asymmetrical, arranged in pairs, opposite a red vinous stem. The inflorescence is composed of small yellowish flowers unisexual, solitary at each node. The fruits are small capsules hairy three valves. The anatomical and histological section of the limbus showed a protruding cylindrical midrib on the underside and slightly depressed on the other. Medullary parenchyma reduced with large polygonal cells and lower collenchyme are cellulosic. Sclerenchyma sheathing the driver device consists of fibrovascular bundle. Secondary and arranged in radial alignment are wood, having vessels with lignified walls and bast with small cells with cellulose walls. The stem, circular, presented a less developed bark, an epidermis, a collenchyme and parenchyma. The developed central cylinder consists of a main parenchyma are observed wherein a centrifugal primary wood, the primary centripetal bast and sclerenchyma in radial alignment around the cambium and the secondary wood.  pharmacognostic analysis and physico-chemical characteristics can help in the efficient use of this medicinal plant.
SEE: Syntax-Aware Entity Embedding for Neural Relation Extraction

Distant supervised relation extraction is an efficient approach to scale relation extraction to very large corpora, and has been widely used to find novel relational facts from plain text. Recent studies on neural relation extraction have shown great progress on this task via modeling the sentences in low-dimensional spaces, but seldom considered syntax information to model the entities. In this paper, we propose to learn syntax-aware entity embedding for neural relation extraction. First, we encode the context of entities on a dependency tree as sentencelevel entity embedding based on tree-GRU. Then, we utilize both intra-sentence and inter-sentence attentions to obtain sentence set-level entity embedding over all sentences containing the focus entity pair. Finally, we combine both sentence embedding and entity embedding for relation classification. We conduct experiments on a widely used real-world dataset and the experimental results show that our model can make full use of all informative instances and achieve state-of-the-art performance of relation extraction.
The Architectural Implications of Cloud Microservices

Cloud services have recently undergone a shift from monolithic applications to microservices, with hundreds or thousands of loosely-coupled microservices comprising the end-to-end application. Microservices present both opportunities and challenges when optimizing for quality of service (QoS) and cloud utilization. In this paper we explore the implications cloud microservices have on system bottlenecks, and datacenter server design. We first present and characterize an end-to-end application built using tens of popular open-source microservices that implements a movie renting and streaming service, and is modular and extensible. We then use the end-to-end service to study the scalability and performance bottlenecks of microservices, and highlight implications they have on the design of datacenter hardware. Specifically, we revisit the long-standing debate of brawny versus wimpy cores in the context of microservices, we quantify the I-cache pressure they introduce, and measure the time spent in computation versus communication between microservices over RPCs. As more cloud applications switch to this new programming model, it is increasingly important to revisit the assumptions we have previously used to build and manage cloud systems.
A Theory of Regret Regulation 1 . 0

We propose a theory of regret regulation that distinguishes regret from related emotions, specifies the conditions under which regret is felt, the aspects of the decision that are regretted, and the behavioral implications. The theory incorporates hitherto scattered findings and ideas from psychology, economics, marketing, and related disciplines. By identifying strategies that consumers may employ to regulate anticipated and experienced regret, the theory identifies gaps in our current knowledge and thereby outlines opportunities for future research.
Customer Relationship Management : Emerging Practice , Process , and Discipline

Customer relationship management (CRM) has once again gained prominence amongst academics and practitioners. However, there is a tremendous amount of confusion regarding its domain and meaning. In this paper, the authors explore the conceptual foundations of CRM by examining the literature on relationship marketing and other disciplines that contribute to the knowledge of CRM. A CRM process framework is proposed that builds on other relationship development process models. CRM implementation challenges as well as CRM's potential to become a distinct discipline of marketing are also discussed in this paper.
The Determinants of Credit Spread Changes

Using dealer’s quotes and transactions prices on straight industrial bonds, we investigate the determinants of credit spread changes. Variables that should in theory determine credit spread changes have rather limited explanatory power. Further, the residuals from this regression are highly cross-correlated, and principal components analysis implies they are mostly driven by a single common factor. Although we consider several macroeconomic and financial variables as candidate proxies, we cannot explain this common systematic component. Our results suggest that monthly credit spread changes are principally driven by local supply0 demand shocks that are independent of both credit-risk factors and standard proxies for liquidity. THE RELATION BETWEEN STOCK AND BOND RETURNS has been widely studied at the aggregate level ~see, e.g., Keim and Stambaugh ~1986!, Fama and French ~1989, 1993!, Campbell and Ammer ~1993!!. Recently, a few studies have investigated that relation at both the individual firm level ~see, e.g., Kwan ~1996!! and portfolio level ~see, e.g., Blume, Keim, and Patel ~1991!, Cornell and Green ~1991!!. These studies focus on corporate bond returns, or yield changes. The main conclusions of these papers are: ~1! high-grade bonds behave like Treasury bonds, and ~2! low-grade bonds are more sensitive to stock returns. The implications of these studies may be limited in many situations of interest, however. For example, hedge funds often take highly levered positions in corporate bonds while hedging away interest rate risk by shorting treasuries. As a consequence, their portfolios become extremely sensitive to changes in credit spreads rather than changes in bond yields. The distinc* Collin-Dufresne is at Carnegie Mellon University. Goldstein is at Washington University in St. Louis. Martin is at Arizona State University. A significant portion of this paper was written while Goldstein and Martin were at The Ohio State University. We thank Rui Albuquerque, Gurdip Bakshi, Greg Bauer, Dave Brown, Francesca Carrieri, Peter Christoffersen, Susan Christoffersen, Greg Duffee, Darrell Duffie, Vihang Errunza, Gifford Fong, Mike Gallmeyer, Laurent Gauthier, Rick Green, John Griffin, Jean Helwege, Kris Jacobs, Chris Jones, Andrew Karolyi, Dilip Madan, David Mauer, Erwan Morellec, Federico Nardari, N.R. Prabhala, Tony Sanders, Sergei Sarkissian, Bill Schwert, Ken Singleton, Chester Spatt, René Stulz ~the editor!, Suresh Sundaresan, Haluk Unal, Karen Wruck, and an anonymous referee for helpful comments. We thank Ahsan Aijaz, John Puleo, and Laura Tuttle for research assistance. We are also grateful to seminar participants at Arizona State University, University of Maryland, McGill University, The Ohio State University, University of Rochester, and Southern Methodist University. THE JOURNAL OF FINANCE • VOL. LVI, NO. 6 • DEC. 2001
FrauDetector: A Graph-Mining-based Framework for Fraudulent Phone Call Detection

In recent years, fraud is increasing rapidly with the development of modern technology and global communication. Although many literatures have addressed the fraud detection problem, these existing works focus only on formulating the fraud detection problem as a binary classification problem. Due to limitation of information provided by telecommunication records, such classifier-based approaches for fraudulent phone call detection normally do not work well. In this paper, we develop a graph-mining-based fraudulent phone call detection framework for a mobile application to automatically annotate fraudulent phone numbers with a "fraud" tag, which is a crucial prerequisite for distinguishing fraudulent phone calls from normal phone calls. Our detection approach performs a weighted HITS algorithm to learn the trust value of a remote phone number. Based on telecommunication records, we build two kinds of directed bipartite graph: i) CPG and ii) UPG to represent telecommunication behavior of users. To weight the edges of CPG and UPG, we extract features for each pair of user and remote phone number in two different yet complementary aspects: 1) duration relatedness (DR) between user and phone number; and 2) frequency relatedness (FR) between user and phone number. Upon weighted CPG and UPG, we determine a trust value for each remote phone number. Finally, we conduct a comprehensive experimental study based on a dataset collected through an anti-fraud mobile application, Whoscall. The results demonstrate the effectiveness of our weighted HITS-based approach and show the strength of taking both DR and FR into account in feature extraction.
THE SOCIAL APPROACH OF KNOWLEDGE MANAGEMENT: THE EFFECT ON THE ORGANIZATIONAL LEARNING CAPACITY. ACADEMIC TRACK

The quest to stay competitive in the global and dynamic economy is increasingly turning organizations towards learning capacity as the main source of competitive advantage. This is due to the widespread recognition of knowledge as the major factor that will determine future success of organizations. Accordingly, knowledge management has become a central topic for academics and practitioners as the most important initiative to enhance learning capacity in organizations. This initiative embraces the government of a set of conditions that are necessary for the processes of generation, accumulation and utilization of knowledge. Specifically, behavioral and interpersonal skills are often cited as essential enablers for successful knowledge management. The present study focuses on the issue of establishing behavioral initiatives of knowledge management for facilitating the development of a learning capacity in organizations. The analysis is accomplished through a validated questionnaire that surveyed 111 Spanish companies. The research findings show the positive relations between behavioral elements of knowledge management and learning capacity in organizations. So, these results validate that by creating and nurturing a trusting, creative and innovative climate, an organization can sustain its competitive advantages.
Exploring Mobile eCommerce in Graphical Bound Retailing

In the last couple of years, the interest of Mobile IT has arisen tremendously and future directions point towards an explosive expansive area. The objective with this paper is to explore how mobile eCommerce services map customers' requirements in geographical bound retailing. This is done through WineGuide, a geographically bound recommendation service for wine and food adapted to mobile phones. The service addresses well-known problems within the area of shopping, by: (1) offering expert recommendations; (2) notifying the user where products are available; (3) distributing information in appropriate situations; (4) letting the user search for products. The findings of the study indicate that the full potential of mobile eCommerce services can only be established through a complete eCommerce transaction implementation. The mobile phone gets the role of a remote controller, where products are ordered, paid for and home delivered through a few pressings on the
Gold nanoparticles: assembly, supramolecular chemistry, quantum-size-related properties, and applications toward biology, catalysis, and nanotechnology.

Although gold is the subject of one of the most ancient themes of investigation in science, its renaissance now leads to an exponentially increasing number of publications, especially in the context of emerging nanoscience and nanotechnology with nanoparticles and self-assembled monolayers (SAMs). We will limit the present review to gold nanoparticles (AuNPs), also called gold colloids. AuNPs are the most stable metal nanoparticles, and they present fascinating aspects such as their assembly of multiple types involving materials science, the behavior of the individual particles, size-related electronic, magnetic and optical properties (quantum size effect), and their applications to catalysis and biology. Their promises are in these fields as well as in the bottom-up approach of nanotechnology, and they will be key materials and building block in the 21st century. Whereas the extraction of gold started in the 5th millennium B.C. near Varna (Bulgaria) and reached 10 tons per year in Egypt around 1200-1300 B.C. when the marvelous statue of Touthankamon was constructed, it is probable that “soluble” gold appeared around the 5th or 4th century B.C. in Egypt and China. In antiquity, materials were used in an ecological sense for both aesthetic and curative purposes. Colloidal gold was used to make ruby glass 293 Chem. Rev. 2004, 104, 293−346
Virtual Reality in Scientific Visualization

VIRTUAL reality, also called virtual environments, is a new interface paradigm that uses computers and human-computer interfaces to create the effect of a three-dimensional world in which the user interacts directly with virtual objects. The term “virtual reality” has received quite a lot of attention in the last few years, so we feel it is important to be clear about its meaning. For the purposes of this article, we adopt the following definition: Virtual reality is the use of computers and human-computer interfaces to create the effect of a three-dimensional world containing interactive objects with a strong sense of threedimensional presence. Steve Bryson Virtual Reality in Scientific Visualization
DeepID-Net: Deformable deep convolutional neural networks for object detection

In this paper, we propose deformable deep convolutional neural networks for generic object detection. This new deep learning object detection framework has innovations in multiple aspects. In the proposed new deep architecture, a new deformation constrained pooling (def-pooling) layer models the deformation of object parts with geometric constraint and penalty. A new pre-training strategy is proposed to learn feature representations more suitable for the object detection task and with good generalization capability. By changing the net structures, training strategies, adding and removing some key components in the detection pipeline, a set of models with large diversity are obtained, which significantly improves the effectiveness of model averaging. The proposed approach improves the mean averaged precision obtained by RCNN , which was the state-of-the-art, from 31% to 50.3% on the ILSVRC2014 detection test set. It also outperforms the winner of ILSVRC2014, GoogLeNet, by 6.1%. Detailed component-wise analysis is also provided through extensive experimental evaluation, which provide a global view for people to understand the deep learning object detection pipeline.
Screen Time Tantrums: How Families Manage Screen Media Experiences for Toddlers and Preschoolers

Prior work shows that setting limits on young children's screen time is conducive to healthy development but can be a challenge for families. We investigate children's (age 1 - 5) transitions to and from screen-based activities to understand the boundaries families have set and their experiences living within them. We report on interviews with 27 parents and a diary study with a separate 28 families examining these transitions. These families turn on screens primarily to facilitate parents' independent activities. Parents feel this is appropriate but self-audit and express hesitation, as they feel they are benefiting from an activity that can be detrimental to their child's well-being. We found that families turn off screens when parents are ready to give their child their full attention and technology presents a natural stopping point. Transitioning away from screens is often painful, and predictive factors determine the pain of a transition. Technology-mediated transitions are significantly more successful than parent-mediated transitions, suggesting that the design community has the power to make this experience better for parents and children by creating technologies that facilitate boundary-setting and respect families' self-defined limits.
A Span Selection Model for Semantic Role Labeling

We present a simple and accurate span-based model for semantic role labeling (SRL). Our model directly takes into account all possible argument spans and scores them for each label. At decoding time, we greedily select higher scoring labeled spans. One advantage of our model is to allow us to design and use spanlevel features, that are difficult to use in tokenbased BIO tagging approaches. Experimental results demonstrate that our ensemble model achieves the state-of-the-art results, 87.4 F1 and 87.0 F1 on the CoNLL-2005 and 2012 datasets, respectively.
End-to-End Entity and Event Extraction with Generative Adversarial Imitation Learning

We propose a new framework for entity and event extraction based on generative adversarial imitation learning – an inverse reinforcement learning method using generative adversarial network (GAN). We assume that instances and labels yield to various extents of difficulty and the gains and penalties (rewards) are expected to be diverse. We utilize discriminators to estimate proper rewards according to the difference between the labels committed by ground-truth (expert) and the extractor (agent). Experiments also demonstrate that the proposed framework outperforms state-of-the-art methods.
Deconvolutional Paragraph Representation Learning

Learning latent representations from long text sequences is an important first step in many natural language processing applications. Recurrent Neural Networks (RNNs) have become a cornerstone for this challenging task. However, the quality of sentences during RNN-based decoding (reconstruction) decreases with the length of the text. We propose a sequence-to-sequence, purely convolutional and deconvolutional autoencoding framework that is free of the above issue, while also being computationally efficient. The proposed method is simple, easy to implement and can be leveraged as a building block for many applications. We show empirically that compared to RNNs, our framework is better at reconstructing and correcting long paragraphs. Quantitative evaluation on semi-supervised text classification and summarization tasks demonstrate the potential for better utilization of long unlabeled text data.
A Corpus for Modeling Word Importance in Spoken Dialogue Transcripts

Motivated by a project to create a system for people who are deaf or hard-of-hearing that would use automatic speech recognition (ASR) to produce real-time text captions of spoken English during in-person meetings with hearing individuals, we have augmented a transcript of the Switchboard conversational dialogue corpus with an overlay of word-importance annotations, with a numeric score for each word, to indicate its importance to the meaning of each dialogue turn. Further, we demonstrate the utility of this corpus by training an automatic word importance labeling model; our best performing model has an F-score of 0.60 in an ordinal 6-class word-importance classification task with an agreement (concordance correlation coefficient) of 0.839 with the human annotators (agreement score between annotators is 0.89). Finally, we discuss our intended future applications of this resource, particularly for the task of evaluating ASR performance, i.e. creating metrics that predict ASR-output caption text usability for DHH users better than Word Error Rate (WER).