nils-herrmann/clean_scirepeval huggingface dataset:
Accounting information systems research: Is it another QWERTY?

This article examines the research within accounting information systems (AIS) as found in articles published in leading accounting, management information systems, and computer science journals from 1982 to 1998. Trend analysis is performed on AIS articles found in the following journals: The Journal of Information Systems; Advances in Accounting Information Systems;  The Accounting Review; Journal of Accounting and Economics, Accounting, Organizations and Society,  A Journal of Practice and Theory; Behavioral Research in Accounting; Journal of Management Accounting Research; Management Science; MIS Quarterly; Decision Sciences; Information Systems Research; and Communications of the ACM, among others. The trend analysis is structured across underlying theory, research method, and information systems lifecycle topics of AIS articles in these journals. This article identifies the extant research streams in AIS, where AIS research has been published from 1982 to 1998, and seeks to provide insight into the question: should AIS exist as its own separate research domain? Is AIS research like the QWERTY keyboard (David, 1985), widely adopted due to popular demand, but ineffective and inefficient, with a better AIS research and teaching model available?
Deep Learning for Detecting Robotic Grasps

We consider the problem of detecting robotic grasps in an RGB-D view of a scene containing objects. In this work, we apply a deep learning approach to solve this problem, which avoids time-consuming hand-design of features. This presents two main challenges. First, we need to evaluate a huge number of candidate grasps. In order to make detection fast, as well as robust, we present a two-step cascaded structure with two deep networks, where the top detections from the first are re-evaluated by the second. The first network has fewer features, is faster to run, and can effectively prune out unlikely candidate grasps. The second, with more features, is slower but has to run only on the top few detections. Second, we need to handle multimodal inputs well, for which we present a method to apply structured regularization on the weights based on multimodal group regularization. We demonstrate that our method outperforms the previous state-of-the-art methods in robotic grasp detection, and can be used to successfully execute grasps on a Baxter robot.
Effect of prebiotic intake on gut microbiota, intestinal permeability and glycemic control in children with type 1 diabetes: study protocol for a randomized controlled trial

The gut microbiome is increasingly recognized as a contributor to disease states. Patients with type 1 diabetes (DM1) have distinct gut microbiota in comparison to non-diabetic individuals, and it has been linked to changes in intestinal permeability, inflammation and insulin resistance. Prebiotics are non-digestible carbohydrates that alter gut microbiota and could potentially improve glycemic control in children with DM1. This pilot study aims to determine the feasibility of a 12-week dietary intervention with prebiotics in children with DM1.



This pilot study is a single-centre, randomized, double-blind, placebo-controlled trial in children aged 8 to 17 years with DM1 for at least one year. Participants will be randomized to receive either placebo (maltodextrin 3.3 g orally/day) or prebiotics (oligofructose-enriched inulin 8 g orally/day; Synergy1, Beneo, Mannheim, Germany). Measures to be assessed at baseline, 3 months and 6 months include: anthropometric measures, insulin doses/regimens, frequency of diabetic ketoacidosis, frequency of severe hypoglycemia, average number of episodes of hypoglycemia per week, serum C-peptide, HbA1c, serum inflammatory markers (IL-6, IFN-gamma, TNF-alpha, and IL-10), GLP-1 and GLP-2, intestinal permeability using urine assessment after ingestion of lactulose, mannitol and 3-O-methylglucose, and stool sample collection for gut microbiota profiling.



This is a novel pilot study designed to test feasibility for a fully powered study. We hypothesize that consumption of prebiotics will alter gut microbiota and intestinal permeability, leading to improved glycemic control. Prebiotics are a potentially novel, inexpensive, low-risk treatment addition for DM1 that may improve glycemic control by changes in gut microbiota, gut permeability and inflammation.
Distant Supervision with Transductive Learning for Adverse Drug Reaction Identification from Electronic Medical Records

Information extraction and knowledge discovery regarding adverse drug reaction (ADR) from large-scale clinical texts are very useful and needy processes. Two major difficulties of this task are the lack of domain experts for labeling examples and intractable processing of unstructured clinical texts. Even though most previous works have been conducted on these issues by applying semisupervised learning for the former and a word-based approach for the latter, they face with complexity in an acquisition of initial labeled data and ignorance of structured sequence of natural language. In this study, we propose automatic data labeling by distant supervision where knowledge bases are exploited to assign an entity-level relation label for each drug-event pair in texts, and then, we use patterns for characterizing ADR relation. The multiple-instance learning with expectation-maximization method is employed to estimate model parameters. The method applies transductive learning to iteratively reassign a probability of unknown drug-event pair at the training time. By investigating experiments with 50,998 discharge summaries, we evaluate our method by varying large number of parameters, that is, pattern types, pattern-weighting models, and initial and iterative weightings of relations for unlabeled data. Based on evaluations, our proposed method outperforms the word-based feature for NB-EM (iEM), MILR, and TSVM with F1 score of 11.3%, 9.3%, and 6.5% improvement, respectively.
Cyberbullying : Labels , Behaviours and Definition in Three European Countries

This study aims to examine students’ perception of the term used to label cyberbullying, the perception of different forms and behaviours (written, verbal, visual, exclusion and impersonation) and the perception of the criteria used for its definition (imbalance of power, intention, repetition, anonymity and publicity) in three different European countries: Italy, Spain and Germany. Seventy adolescents took part in nine focus groups, using the same interview guide across countries. Thematic analysis focused on three main themes related to: (1) the term used to label cyberbullying, (2) the different behaviours representing cyberbullying, (3) the three traditional criteria of intentionality, imbalance of power and repetition and the two new criteria of anonymity and publicity. Results showed that the best word to label cyberbullying is ‘cybermobbing’ (in Germany), ‘virtual’ or ‘cyber-bullying’ (in Italy), and ‘harassment’ or ‘harassment via Internet or mobile phone’ (in Spain). Impersonation cannot be considered wholly as cyberbullying behaviour. In order to define a cyberbullying act, adolescents need to know whether the action was done intentionally to harm the victim, the effect on the victim and the repetition of the action (this latter criterion evaluated simultaneously with the publicity). Information about the anonymity and publicity contributes to better understand the nature and the severity of the act, the potential effects on the victim and the intentionality.
Cognitive Task Analysis

This chapter presents an overview of the current state of cognitive task analysis (CTA) in research and practice. CTA uses a variety of interview and observation strategies to capture a description of the explicit and implicit knowledge that experts use to perform complex tasks. The captured knowledge is most often transferred to training or the development of expert systems. The first section presents descriptions of a variety of CTA techniques, their common characteristics, and the typical strategies used to elicit knowledge from experts and other sources. The second section describes research on the impact of CTA and synthesizes a number of studies and reviews pertinent to issues underlying knowledge elicitation. In the third section, we discuss the integration of CTA with training design. Finally, in the fourth section, we present a number of recommendations for future research and conclude with general comments.
Modular Multitask Reinforcement Learning with Policy Sketches

We describe a framework for multitask deep reinforcement learning guided by policy sketches. Sketches annotate tasks with sequences of named subtasks, providing information about high-level structural relationships among tasks but not how to implement them—specifically not providing the detailed guidance used by much previous work on learning policy abstractions for RL (e.g. intermediate rewards, subtask completion signals, or intrinsic motivations). To learn from sketches, we present a model that associates every subtask with a modular subpolicy, and jointly maximizes reward over full task-specific policies by tying parameters across shared subpolicies. Optimization is accomplished via a decoupled actor–critic training objective that facilitates learning common behaviors from multiple dissimilar reward functions. We evaluate the effectiveness of our approach in three environments featuring both discrete and continuous control, and with sparse rewards that can be obtained only after completing a number of high-level subgoals. Experiments show that using our approach to learn policies guided by sketches gives better performance than existing techniques for learning task-specific or shared policies, while naturally inducing a library of interpretable primitive behaviors that can be recombined to rapidly adapt to new tasks.
Enhanced Characterness for Text Detection in the Wild

Text spotting is an interesting research problem as text may appear at any random place and may occur in various forms. Moreover, ability to detect text opens the horizons for improving many advanced computer vision problems. In this paper, we propose a novel language agnostic text detection method utilizing edge enhanced Maximally Stable Extremal Regions in natural scenes by defining strong characterness measures. We show that a simple combination of characterness cues help in rejecting the non text regions. These regions are further fine-tuned for rejecting the non-textual neighbor regions. Comprehensive evaluation of the proposed scheme shows that it provides comparative to better generalization performance to the traditional methods for this task.
House Prices , Borrowing Constraints and Monetary Policy in the Business Cycle

I develop and estimate a monetary business cycle model with nominal loans and collateral constraints tied to housing values. Demand shocks move together housing and nominal prices, and are amplified and propagated over time. The financial accelerator is not uniform: nominal debt dampens supply shocks, stabilizing the economy under interest rate control. Structural estimation supports two key model features: collateral effects dramatically improve the response of aggregate demand to house prices shocks; nominal debt improves the sluggish response of output to inflation surprises. Finally, policy evaluation considers the role of house prices and debt indexation in affecting monetary policy trade-offs.  Their desired portfolios contain more capital than their net worth [...]. Household debtors are frequently young families acquiring homes and furnishings before they earn incomes to pay for them outright; given the difficulty of borrowing against future wages, they are liquidity-constrained and have a high marginal propensity to consume” James Tobin (1980), Asset Accumulation and Economic Activity : Reflections on Contemporary Macroeconomic Theory A long tradition in economics, starting with Irving Fisher’s (1933) debt-deflation explanation of the Great Depression, considers financial factors as key elements of business cycles. In this view, deteriorating credit market conditions, like growing debt burdens and falling asset prices, are not just passive reflections of a declining economy, but are themselves a major factor depressing economic activity. Although this “credit view” has a long history, most of theoretical work on this subject has been partial equilibrium in nature until the late 1980s, when Ben Bernanke and Mark Gertler (1989) formalized these ideas in a general equilibrium framework. Following their work, various authors have presented dynamic models in which financing frictions on the firm side may amplify or propagate output fluctuations in response to aggregate disturbances: examples include the real models of Nobuhiro Kiyotaki and John Moore (1997) and Charles Carlstrom and Timothy Fuerst (1997), and the sticky-price model of Bernanke, Gertler and Simon Gilchrist (1999). Empirically, various studies have shown that firms’ investment decisions are sensitive to various measures of firms’ net worth (see Glenn Hubbard, 1998, for a review). At the same time, evidence of financing constraints at the household level has been widely documented by Stephen Zeldes (1989), Tullio Jappelli and Marco Pagano (1989), John Campbell and Gregory Mankiw
Factors Influencing Customer Loyalty Toward Online Shopping

—The significant growth of online shopping makes the competition in this industry become more intense. Maintaining customer loyalty has been recognized as one of the essential factor for business survival and growth. The purpose of this study is to examine empirically the influence of satisfaction, trust and commitment on customer loyalty in online shopping. This paper describes a theoretical model for investigating the influence of satisfaction, trust and commitment on customer loyalty toward online shopping. Based on the theoretical model, hypotheses were formulated. The primary data were collected from the respondents which consists of 300 students. Multiple regression and qualitative analysis were used to test the study hypotheses. The empirical study results revealed that satisfaction, trust and commitment have significant impact on student loyalty toward online shopping.
PDFX: fully-automated PDF-to-XML conversion of scientific literature

PDFX is a rule-based system designed to reconstruct the logical structure of scholarly articles in PDF form, regardless of their formatting style. The system's output is an XML document that describes the input article's logical structure in terms of title, sections, tables, references, etc. and also links it to geometrical typesetting markers in the original PDF, such as paragraph and column breaks. The key aspect of the presented approach is that the rule set used relies on relative parameters derived from font and layout specifics of each article, rather than on a template-matching paradigm. The system thus obviates the need for domain- or layout-specific tuning or prior training, exploiting only typographical conventions inherent in scientific literature. Evaluated against a significantly varied corpus of articles from nearly 2000 different journals, PDFX gives a 77.45 F1 measure for top-level heading identification and 74.03 for extracting individual bibliographic items. The service is freely available for use at
Robust Principal Component Analysis with Side Information

The robust principal component analysis (robust PCA) problem has been considered in many machine learning applications, where the goal is to decompose the data matrix to a low rank part plus a sparse residual. While current approaches are developed by only considering the low rank plus sparse structure, in many applications, side information of row and/or column entities may also be given, and it is still unclear to what extent could such information help robust PCA. Thus, in this paper, we study the problem of robust PCA with side information, where both prior structure and features of entities are exploited for recovery. We propose a convex problem to incorporate side information in robust PCA and show that the low rank matrix can be exactly recovered via the proposed method under certain conditions. In particular, our guarantee suggests that a substantial amount of low rank matrices, which cannot be recovered by standard robust PCA, become recoverable by our proposed method. The result theoretically justifies the effectiveness of features in robust PCA. In addition, we conduct synthetic experiments as well as a real application on noisy image classification to show that our method also improves the performance in practice by exploiting side information.
Bonding to Zirconia ( A Systematic Review ) J Dental Sci Bonding to Zirconia ( A Systematic Review )

Zirconium oxide (ZrO2), or zirconia, is a metal oxide that was identified as a reaction product of heating the gem, zircon, by the German chemist Martin Heinrich Klaproth in 1789 . Developments over the last 10–15 years in ceramic materials science for dental applications have led to a class of high strength materials (i.e. zirconiabased ceramics) which potentially provide better fracture resistance and long-term viability when compared to porcelain and other inorganic, non-metallic alternatives. There is a wealth of information in the scientific literature regarding the use of zirconia (ZrO2) in dental applications [2-4].
Language Evolution by Iterated Learning With Bayesian Agents

Languages are transmitted from person to person and generation to generation via a process of iterated learning: people learn a language from other people who once learned that language themselves. We analyze the consequences of iterated learning for learning algorithms based on the principles of Bayesian inference, assuming that learners compute a posterior distribution over languages by combining a prior (representing their inductive biases) with the evidence provided by linguistic data. We show that when learners sample languages from this posterior distribution, iterated learning converges to a distribution over languages that is determined entirely by the prior. Under these conditions, iterated learning is a form of Gibbs sampling, a widely-used Markov chain Monte Carlo algorithm. The consequences of iterated learning are more complicated when learners choose the language with maximum posterior probability, being affected by both the prior of the learners and the amount of information transmitted between generations. We show that in this case, iterated learning corresponds to another statistical inference algorithm, a variant of the expectation-maximization (EM) algorithm. These results clarify the role of iterated learning in explanations of linguistic universals and provide a formal connection between constraints on language acquisition and the languages that come to be spoken, suggesting that information transmitted via iterated learning will ultimately come to mirror the minds of the learners.
ON SCHIZOPHRENIC EXPERIENCES OF THE NEUTRON OR WHY WE SHOULD BELIEVE IN THE MANY-WORLDS INTERPRETATION OF QUANTUM THEORY

There are many interpretations of quantum mechanics, and new ones continue to appear. The Many-Worlds Interpretation (MWI) introduced by Everett (1957) impresses me as the best candidate for the interpretation of quantum theory. My belief is not based on a philosophical affinity for the idea of plurality of worlds as in Lewis (1986), but on a judgment that the physical difficulties of other interpretations are more serious. However, the scope of this paper does not allow a comparative analysis of all alternatives, and my main purpose here is to present my version of MWI, to explain why I believe it is true, and to answer some common criticisms of MWI. The MWI is not a theory about many objective “worlds”. A mathematical formalism by itself does not define the concept of a “world”. The “world” is a subjective concept of a sentient observer. All (subjective) worlds are incorporated in one objective Universe. I think, however, that the name Many-Worlds Interpretation does represent this theory fairly well. Indeed, according to MWI (and contrary to the standard approach) there are many worlds of the sort we call in everyday life “the world”. And although MWI is not just an interpretation of quantum theory – it differs from the standard quantum theory in certain experimental predictions – interpretation is an essential part of MWI; it explains the tremendous gap between what we experience as our world and what appears in the formalism of the quantum state of the Universe. Schrödinger’s equation (the basic equation of quantum theory) predicts very accurately the results of experiments performed on microscopic systems. I shall argue in what follows that it also implies the existence of many worlds. The purpose of addition of the collapse postulate, which represents the difference between MWI and the standard approach, is to escape the implications of Schrödinger’s equation for the existence of many worlds. Today’s technology does not allow us to test the existence of the “other” worlds. So only God or “superman” (i.e., a superintelligence equipped with supertechnology) can take full
Identification through Visual Clustering

Phishing is a form of online fraud, where criminals exploit the implicit trust placed in an organisation’s website branding to acquire sensitive information from the organisation’s customers. Major browser vendors race through hundreds of thousands of spam emails a day, assessing each URL to find Phishing websites to add to their blacklists. The quantity of suspect sites is so large that automated classification is essential. Creators of Phishing sites are aware of the popular text-based classifiers and implement a variety of countermeasures to disguise the site from software while remaining recognisable to humans. Consequently researchers are exploring using computer vision to better recognise Phishing sites. We present Distinctive Region Classifier a novel approach to identify the targets of potential Phishing attacks from screenshots. It is a multi-class classifier that combines computer vision techniques with density-based clustering to identify a page by elements that are indicative of a single class in our training set. Our system performs comparably with published approaches in this space. Additionally, we present resilient distributed implementation of a visual classifier and blacklist. We find our cluster accelerates classification to a rate equal to commercial anti-Phishing measures.
Knowledge-Based Question Answering as Machine Translation

A typical knowledge-based question answering (KB-QA) system faces two challenges: one is to transform natural language questions into their meaning representations (MRs); the other is to retrieve answers from knowledge bases (KBs) using generated MRs. Unlike previous methods which treat them in a cascaded manner, we present a translation-based approach to solve these two tasks in one unified framework. We translate questions to answers based on CYK parsing. Answers as translations of the span covered by each CYK cell are obtained by a question translation method, which first generates formal triple queries as MRs for the span based on question patterns and relation expressions, and then retrieves answers from a given KB based on triple queries generated. A linear model is defined over derivations, and minimum error rate training is used to tune feature weights based on a set of question-answer pairs. Compared to a KB-QA system using a state-of-the-art semantic parser, our method achieves better results.