nils-herrmann/clean_scirepeval huggingface dataset:
Sketch-based shape retrieval

We develop a system for 3D object retrieval based on sketched feature lines as input. For objective evaluation, we collect a large number of query sketches from human users that are related to an existing data base of objects. The sketches turn out to be generally quite abstract with large local and global deviations from the original shape. Based on this observation, we decide to use a bag-of-features approach over computer generated line drawings of the objects. We develop a targeted feature transform based on Gabor filters for this system. We can show objectively that this transform is better suited than other approaches from the literature developed for similar tasks. Moreover, we demonstrate how to optimize the parameters of our, as well as other approaches, based on the gathered sketches. In the resulting comparison, our approach is significantly better than any other system described so far.
Examining technology acceptance by school teachers: a longitudinal study

The role of information technology (IT) in education has significantly increased, but resistance to technology by public school teachers worldwide remains high. This study examined public school teachers’ technology acceptance decision-making by using a research model that is based on key findings from relevant prior research and important characteristics of the targeted user acceptance phenomenon. The model was longitudinally tested using responses from more than 130 teachers attending an intensive 4-week training program on Microsoft PowerPoint, a common but important classroom presentation technology. In addition to identifying key acceptance determinants, we examined plausible changes in acceptance drivers over the course of the training, including their influence patterns and magnitudes. Overall, our model showed a reasonably good fit with the data and exhibited satisfactory explanatory power, based on the responses collected from training commencement and completion. Our findings suggest a highly prominent and significant core influence path from job relevance to perceived usefulness and then technology acceptance. Analysis of data collected at the beginning and the end of the training supports most of our hypotheses and sheds light on plausible changes in their influences over time. Specifically, teachers appear to consider a rich set of factors in initial acceptance but concentrate on fundamental determinants (e.g. perceived usefulness and perceived ease of use) in their continued acceptance.
Large-Scale Information Extraction from Textual Definitions through Deep Syntactic and Semantic Analysis

We present DefIE, an approach to large-scale Information Extraction (IE) based on a syntactic-semantic analysis of textual definitions. Given a large corpus of definitions we leverage syntactic dependencies to reduce data sparsity, then disambiguate the arguments and content words of the relation strings, and finally exploit the resulting information to organize the acquired relations hierarchically. The output of DefIE is a high-quality knowledge base consisting of several million automatically acquired semantic relations.
Dual Density Operators and Natural Language Meaning

Density operators allow for representing ambiguity about a vector representation, both in quantum theory and in distributional natural language meaning. For mally equivalently, they allow for discarding part of the description of a composite system, where we co nsider the discarded part to be the context. We introduce dual density operators, which allow f r two independent notions of context. We demonstrate the use of dual density operators within a gra mmatical-compositional distributional framework for natural language meaning. We show that dual de nsity operators can be used to simultaneously represent: (i) ambiguity about word meanings (e. g. queen as a person vs. queen as a band), and (ii) lexical entailment (e.g. tiger ⇒ mammal). We provide a proof-of-concept example.
Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification

Machine reading comprehension (MRC) on real web data usually requires the machine to answer a question by analyzing multiple passages retrieved by search engine. Compared with MRC on a single passage, multi-passage MRC is more challenging, since we are likely to get multiple confusing answer candidates from different passages. To address this problem, we propose an end-to-end neural model that enables those answer candidates from different passages to verify each other based on their content representations. Specifically, we jointly train three modules that can predict the final answer based on three factors: the answer boundary, the answer content and the cross-passage answer verification. The experimental results show that our method outperforms the baseline by a large margin and achieves the state-of-the-art performance on the English MS-MARCO dataset and the Chinese DuReader dataset, both of which are designed for MRC in real-world settings.
Effects of McGill stabilization exercises and conventional physiotherapy on
pain, functional disability and active back range of motion in patients with chronic
non-specific low back pain

The aim of this study was to compare the effects of "McGill stabilization exercises" and "conventional physiotherapy" on pain, functional disability and active back flexion and extension range of motion in patients with chronic non-specific low back pain.  Thirty four patients with chronic non-specific low back pain were randomly assigned to McGill stabilization exercises group (n=17) and conventional physiotherapy group (n=17). In both groups, patients performed the corresponding exercises for six weeks. The visual analog scale (VAS), Quebec Low Back Pain Disability Scale Questionnaire and inclinometer were used to measure pain, functional disability, and active back flexion and extension range of motion, respectively.  Statistically significant improvements were observed in pain, functional disability, and active back extension range of motion in McGill stabilization exercises group. However, active back flexion range of motion was the only clinical symptom that statistically increased in patients who performed conventional physiotherapy. There was no significant difference between the clinical characteristics while compared these two groups of patients.  The results of this study indicated that McGill stabilization exercises and conventional physiotherapy provided approximately similar improvement in pain, functional disability, and active back range of motion in patients with chronic non-specific low back pain. However, it appears that McGill stabilization exercises provide an additional benefit to patients with chronic non-specific low back, especially in pain and functional disability improvement.
Semiology of Graphics - Diagrams, Networks, Maps

The semiology of graphics diagrams networks maps that we provide for you will be ultimate to give preference. This reading book is your chosen book to accompany you when in your free time, in your lonely. This kind of book can help you to heal the lonely and get or add the inspirations to be more inoperative. Yeah, book as the widow of the world can be very inspiring manners. As here, this book is also created by an inspiring author that can make influences of you to do more.
Learning by Playing - Solving Sparse Reward Tasks from Scratch

We propose Scheduled Auxiliary Control (SACX), a new learning paradigm in the context of Reinforcement Learning (RL). SAC-X enables learning of complex behaviors – from scratch – in the presence of multiple sparse reward signals. To this end, the agent is equipped with a set of general auxiliary tasks, that it attempts to learn simultaneously via off-policy RL. The key idea behind our method is that active (learned) scheduling and execution of auxiliary policies allows the agent to efficiently explore its environment – enabling it to excel at sparse reward RL. Our experiments in several challenging robotic manipulation settings demonstrate the power of our approach. A video of the rich set of learned behaviours can be found at https://youtu.be/mPKyvocNe M.
Essential elements of questionnaire design and development.

The aims of this paper were (1) to raise awareness of the issues in questionnaire development and subsequent psychometric evaluation, and (2) to provide strategies to enable nurse researchers to design and develop their own measure and evaluate the quality of existing nursing measures.



The number of questionnaires developed by nurses has increased in recent years. While the rigour applied to the questionnaire development process may be improving, we know that nurses are still not generally adept at the psychometric evaluation of new measures. This paper explores the process by which a reliable and valid questionnaire can be developed.



We critically evaluate the theoretical and methodological issues associated with questionnaire design and development and present a series of heuristic decision-making strategies at each stage of such development. The range of available scales is presented and we discuss strategies to enable item generation and development. The importance of stating a priori the number of factors expected in a prototypic measure is emphasized. Issues of reliability and validity are explored using item analysis and exploratory factor analysis and illustrated using examples from recent nursing research literature.



Questionnaire design and development must be supported by a logical, systematic and structured approach. To aid this process we present a framework that supports this and suggest strategies to demonstrate the reliability and validity of the new and developing measure.


 TO CLINICAL PRACTICE
In developing the evidence base of nursing practice using this method of data collection, it is vital that questionnaire design incorporates preplanned methods to establish reliability and validity. Failure to develop a questionnaire sufficiently may lead to difficulty interpreting results, and this may impact upon clinical or educational practice. This paper presents a critical evaluation of the questionnaire design and development process and demonstrates good practice at each stage of this process.
Fraud Detection Using Data Mining Techniques

Data mining technology is applied to fraud detection to establish the fraud detection model, describe the process of creating the fraud detection model, then establish data model with ID3 decision tree, and establish example of fraud detection model by using this model. As e-commerce sales continue to grow, the associated online fraud remains an attractive source of revenue for fraudsters. These fraudulent activities impose a considerable financial loss to merchants, making online fraud detection a necessity. The problem of fraud detection is concerned with not only capturing the fraudulent activities, but also capturing them as quickly as possible. This timeliness is crucial to decrease financial losses.
Blockchain Technology and Decentralized Governance : Is the State Still Necessary ?

The core technology of Bitcoin, the blockchain, has recently emerged as a disruptive innovation with a wide range of applications, potentially able to redesign our interactions in business, politics and society at large. Although scholarly interest in this subject is growing, a comprehensive analysis of blockchain applications from a political perspective is severely lacking to date. This paper aims to fill this gap and it discusses the key points of blockchain-based decentralized governance, which challenges to varying degrees the traditional mechanisms of State authority, citizenship and democracy. In particular, the paper verifies to which extent blockchain and decentralization platforms can be considered as hyper-political tools, capable to manage social interactions on large scale and dismiss traditional central authorities. The analysis highlights risks related to a dominant position of private powers in distributed ecosystems, which may lead to a general disempowerment of citizens and to the emergence of a stateless global society. While technological utopians urge the demise of any centralized institution, this paper advocates the role of the State as a necessary central point of coordination in society, showing that decentralization through algorithm-based consensus is an organizational theory, not a stand-alone political theory.
Evaluation of a new VMAT QA device, or the “X” and “O” array geometries

We introduce a logical process of three distinct phases to begin the evaluation of a new 3D dosimetry array. The array under investigation is a hollow cylinder phantom with diode detectors fixed in a helical shell forming an "O" axial detector cross section (ArcCHECK), with comparisons drawn to a previously studied 3D array with diodes fixed in two crossing planes forming an "X" axial cross section (Delta⁴). Phase I testing of the ArcCHECK establishes: robust relative calibration (response equalization) of the individual detectors, minor field size dependency of response not present in a 2D predecessor, and uncorrected angular response dependence in the axial plane. Phase II testing reveals vast differences between the two devices when studying fixed-width full circle arcs. These differences are primarily due to arc discretization by the TPS that produces low passing rates for the peripheral detectors of the ArcCHECK, but high passing rates for the Delta⁴. Similar, although less pronounced, effects are seen for the test VMAT plans modeled after the AAPM TG119 report. The very different 3D detector locations of the two devices, along with the knock-on effect of different percent normalization strategies, prove that the analysis results from the devices are distinct and noninterchangeable; they are truly measuring different things. The value of what each device measures, namely their correlation with--or ability to predict--clinically relevant errors in calculation and/or delivery of dose is the subject of future Phase III work.
To Trust Or Not To Trust A Classifier

Knowing when a classifier’s prediction can be trusted is useful in many applications and critical for safely using AI. While the bulk of the effort in machine learning research has been towards improving classifier performance, understanding when a classifier’s predictions should and should not be trusted has received far less attention. The standard approach is to use the classifier’s discriminant or confidence score; however, we show there exists an alternative that is more effective in many situations. We propose a new score, called the trust score, which measures the agreement between the classifier and a modified nearest-neighbor classifier on the testing example. We show empirically that high (low) trust scores produce surprisingly high precision at identifying correctly (incorrectly) classified examples, consistently outperforming the classifier’s confidence score as well as many other baselines. Further, under some mild distributional assumptions, we show that if the trust score for an example is high (low), the classifier will likely agree (disagree) with the Bayes-optimal classifier. Our guarantees consist of non-asymptotic rates of statistical consistency under various nonparametric settings and build on recent developments in topological data analysis.
Semi-supervised Ensemble Learning with Weak Supervision for Biomedical Relationship Extraction

Natural language understanding research has recently shifted towards complex Machine Learning and Deep Learning algorithms. Such models often outperform significantly their simpler counterparts. However, their performance relies on the availability of large amounts of labeled data, which are rarely available. To tackle this problem, we propose a methodology for extending training datasets to arbitrarily big sizes and training complex, data-hungry models using weak supervision. We apply this methodology on biomedical relationship extraction, a task where training datasets are excessively time-consuming and expensive to create, yet has a major impact on downstream applications such as drug discovery. We demonstrate in a small-scale controlled experiment that our method consistently enhances the performance of an LSTM network, with performance improvements comparable to hand-labeled training data. Finally, we discuss the optimal setting for applying weak supervision using this methodology.
Chainspace: A Sharded Smart Contracts Platform

Chainspace is a decentralized infrastructure, known as a distributed ledger, that supports user defined smart contracts and executes user-supplied transactions on their objects. The correct execution of smart contract transactions is verifiable by all. The system is scalable, by sharding state and the execution of transactions, and using S-BAC, a distributed commit protocol, to guarantee consistency. Chainspace is secure against subsets of nodes trying to compromise its integrity or availability properties through Byzantine Fault Tolerance (BFT), and extremely highauditability, non-repudiation and ‘blockchain’ techniques. Even when BFT fails, auditing mechanisms are in place to trace malicious participants. We present the design, rationale, and details of Chainspace; we argue through evaluating an implementation of the system about its scaling and other features; we illustrate a number of privacy-friendly smart contracts for smart metering, polling and banking and measure their performance.
Eyeball model-based iris center localization for visible image-based eye-gaze tracking systems

In general, the visible image-based eye-gaze tracking system is heavily dependent on the accuracy of the iris center (IC) localization. In this paper, we propose a novel IC localization method based on the fact that the elliptical shape (ES) of the iris varies according to the rotation of the eyeball. We use the spherical model of the human eyeball and estimate the radius of the iris from the frontal and upright-view image of the eye. By projecting the eyeball rotated in pitch and yaw onto the 2-D plane, a certain number of the ESs of the iris and their corresponding IC locations are generated and registered as a database (DB). Finally, the location of IC is detected by matching the ES of the iris of the input eye image with the ES candidates in the DB. Moreover, combined with facial landmark points-based image rectification, the proposed IC localization method can successfully operate under natural head movement. Experimental results in terms of the IC localization and gaze tracking show that the proposed method achieves superior performance compared with conventional ones.
Octonions , E 6 , and Particle Physics

In 1934, Jordan et al. gave a necessary algebraic condition, the Jordan identity, for a sensible theory of quantum mechanics. All but one of the algebras that satisfy this condition can be described by Hermitian matrices over the complexes or quaternions. The remaining, exceptional Jordan algebra can be described by 3 × 3 Hermitian matrices over the octonions. We first review properties of the octonions and the exceptional Jordan algebra, including our previous work on the octonionic Jordan eigenvalue problem. We then examine a particular real, noncompact form of the Lie group E6, which preserves determinants in the exceptional Jordan algebra. Finally, we describe a possible symmetry-breaking scenario within E6: first choose one of the octonionic directions to be special, then choose one of the 2× 2 submatrices inside the 3× 3 matrices to be special. Making only these two choices, we are able to describe many properties of leptons in a natural way. We further speculate on the ways in which quarks might be similarly encoded.
Socioeconomic differences in attitudes and beliefs about healthy lifestyles.

s: The factors underlying socioeconomic status differences in smoking, leisure time physical activity, and dietary choice are poorly understood. This study investigated attitudes and beliefs that might underlie behavioural choices, including health locus of control, future salience, subjective life expectancy, and health consciousness, in a nationally representative sample.



Data were collected as part of the monthly Omnibus survey of the Office of National Statistics in Britain.



A stratified, probability sample of 2728 households was selected by random sampling of addresses. One adult from each household was interviewed.



Higher SES respondents were less likely to smoke and more likely to exercise and eat fruit and vegetables daily. Lower SES was associated with less health consciousness (thinking about things to do to keep healthy), stronger beliefs in the influence of chance on health, less thinking about the future, and lower life expectancies. These attitudinal factors were in turn associated with unhealthy behavioural choices, independently of age, sex, and self rated health.



Socioeconomic differences in healthy lifestyles are associated with differences in attitudes to health that may themselves arise through variations in life opportunities and exposure to material hardship and ill health over the life course.
Scale-free Networks Well Done

Ivan Voitalov, 2 Pim van der Hoorn, 2 Remco van der Hofstad, and Dmitri Krioukov 2, 4, 5 Department of Physics, Northeastern University, Boston, Massachusetts 02115, USA Network Science Institute, Northeastern University, Boston, Massachusetts 02115, USA Department of Mathematics and Computer Science, Eindhoven University of Technology, Postbus 513, 5600 MB Eindhoven, Netherlands Department of Mathematics, Northeastern University, Boston, Massachusetts 02115, USA Department of Electrical & Computer Engineering, Northeastern University, Boston, Massachusetts 02115, USA
Enhancing Recurrent Neural Networks with Positional Attention for Question Answering

Attention based recurrent neural networks (RNN) have shown a great success for question answering (QA) in recent years. Although significant improvements have been achieved over the non-attentive models, the position information is not well studied within the attention-based framework. Motivated by the effectiveness of using the word positional context to enhance information retrieval, we assume that if a word in the question (i.e., question word) occurs in an answer sentence, the neighboring words should be given more attention since they intuitively contain more valuable information for question answering than those far away. Based on this assumption, we propose a positional attention based RNN model, which incorporates the positional context of the question words into the answers' attentive representations. Experiments on two benchmark datasets show the great advantages of our proposed model. Specifically, we achieve a maximum improvement of 8.83% over the classical attention based RNN model in terms of mean average precision. Furthermore, our model is comparable to if not better than the state-of-the-art approaches for question answering.