nils-herrmann/clean_scirepeval huggingface dataset:
Finite Element Modeling of Tall Buildings : The Importance of Considering Foundation Systems for Lateral Stiffness

Structural analysis and design is often conducted under the assumption of rigid base boundary conditions, particularly if the foundation system extends to bedrock, though the extent to which the actual flexibility of the soil-foundation system affects the predicted periods of vibration depends on the application. While soil-structure interaction has mostly received attention in seismic applications, lateral flexibility below the ground surface may in some cases influence the dynamic properties of tall, flexible structures, generally greater than 50 stories and dominated by wind loads. This study will explore this issue and develop a hybrid framework within which these effects can be captured and eventually be applied to existing finite element models of two tall buildings in the Chicago Full-Scale Monitoring Program. It is hypothesized that the extent to which the rigid base condition assumption applies in these buildings depends on the relative role of cantilever and frame actions in their structural systems. In this hybrid approach, the lateral and axial flexibility of the foundation systems are first determined in isolation and then introduced to the existing finite element models of the buildings as springs, replacing the rigid boundary conditions assumed by designers in the original finite element model development. The evaluation of the periods predicted by this hybrid framework, validated against companion studies and full-scale data, are used to quantify the sensitivity of foundation modeling to the super-structural system primary deformation mechanisms and soil type. Not only will this study demonstrate the viability of this hybrid approach, but also illustrate situations under which foundation flexibility in various degrees of freedom should be considered in the modeling process.
End-to-end learning of semantic role labeling using recurrent neural networks

Semantic role labeling (SRL) is one of the basic natural language processing (NLP) problems. To this date, most of the successful SRL systems were built on top of some form of parsing results (Koomen et al., 2005; Palmer et al., 2010; Pradhan et al., 2013), where pre-defined feature templates over the syntactic structure are used. The attempts of building an end-to-end SRL learning system without using parsing were less successful (Collobert et al., 2011). In this work, we propose to use deep bi-directional recurrent network as an end-to-end system for SRL. We take only original text information as input feature, without using any syntactic knowledge. The proposed algorithm for semantic role labeling was mainly evaluated on CoNLL-2005 shared task and achieved F1 score of 81.07. This result outperforms the previous state-of-the-art system from the combination of different parsing trees or models. We also obtained the same conclusion with F1 = 81.27 on CoNLL2012 shared task. As a result of simplicity, our model is also computationally efficient that the parsing speed is 6.7k tokens per second. Our analysis shows that our model is better at handling longer sentences than traditional models. And the latent variables of our model implicitly capture the syntactic structure of a sentence.
Matching and Sorting in Online Dating

This paper studies the economics of match formation using a novel dataset obtained from a major online dating service. Online dating takes place in a new market environment that has become a common means to find a date or a marriage partner. According to comScore (2006), 17 percent of all North American and 18 percent of all European Internet users visited an online personals site in July 2006. In the United States, 37 percent of all single Internet users looking for a partner have visited a dating Web site (Mary Madden and Amanda Lenhart 2006). The Web site we study provides detailed information on the users’ attributes and interactions, which we use to estimate a rich model of mate preferences. Based on the preference estimates, we then examine whether an economic matching model can explain the observed online matching patterns, and we evaluate the efficiency of the matches obtained on the Web site. Finally, we explore whether the estimated preferences and a matching model are helpful in understanding sorting patterns observed “offline,” among dating and married couples. Two distinct literatures motivate this study. The first is the market design literature, which focuses on designing and evaluating the performance of market institutions. A significant branch of this literature is devoted to matching markets (Alvin E. Roth and Marilda A. O. Sotomayor 1990), with the goal of understanding the allocation mechanism in a particular market, and assessing whether an alternative mechanism with better theoretical properties (typically in terms Matching and Sorting in Online Dating
Fast and Accurate Sequence Labeling with Iterated Dilated Convolutions

Bi-directional LSTMs have emerged as a standard method for obtaining per-token vector representations serving as input to various token labeling tasks (whether followed by Viterbi prediction or independent classification). This paper proposes an alternative to Bi-LSTMs for this purpose: iterated dilated convolutional neural networks (ID-CNNs), which have better capacity than traditional CNNs for large context and structured prediction. We describe a distinct combination of network structure, parameter sharing and training procedures that is not only more accurate than Bi-LSTM-CRFs, but also 8x faster at test time on long sequences. Moreover, ID-CNNs with independent classification enable a dramatic 14x testtime speedup, while still attaining accuracy comparable to the Bi-LSTM-CRF. We further demonstrate the ability of IDCNNs to combine evidence over long sequences by demonstrating their improved accuracy on whole-document (rather than per-sentence) inference. Unlike LSTMs whose sequential processing on sentences of length N requires O(N) time even in the face of parallelism, IDCNNs permit fixed-depth convolutions to run in parallel across entire documents. Today when many companies run basic NLP on the entire web and large-volume traffic, faster methods are paramount to saving time and energy costs.
PHYSICAL ACTIVITY AND SPORT PREFERENCES OF 10 TO 14-YEAR-OLD CHILDREN : A 5-YEAR PROSPECTIVE STUDY

Contemporary conceptions of physical education pedagogy stress the importance of considering students’ physical, affective, and cognitive developmental states in developing curricula (Aschebrock, 1999; Crum, 1994; Grineski, 1996; Humel, 2000; Hummel & Balz, 1995; Jones & Ward, 1998;  1995; Siedentop, 1996; Virgilio, 2000). Sport and physical activity preference is one variable that is likely to change with development. Including activities preferred by girls and boys in physical education curricula could produce several benefits, including greater involvement in lessons and increased enjoyment of physical education (Derner, 1994; Greenwood, Stillwell, & Byars, 2000; Knitt et al., 2000; Lee, Fredenburg, Belcher, & Cleveland, 1999; Sass H. & Sass I., 1986; Strand & Scatling, 1994; Volke, Poszony, & Stumpf, 1985). These are significant goals, because preference for physical activity and enjoyment of physical education are important predictors for overall physical activity participation (Sallis et al., 1999a, b). Although physical education curricula should be based on more than simply students’ preferences, student preferences can inform the design of physical education, other schoolbased physical activity programs, and programs sponsored by other agencies. Young people’s physical activity and sport preferences are likely to vary by age, sex, socio-economic status and nationality. Although several studies have been conducted over many years (Greller & Cochran, 1995; Hoffman & Harris, 2000; Kotonski-Immig, 1994; Lamprecht, Ruschetti, & Stamm, 1991; Strand & Scatling, 1994; Taks, Renson, & Vanreusel, 1991; Telama, 1978; Walton et al., 1999), current understanding of children’s preferences in specific sports and movement activities is limited. One of the main limitations is the cross-sectional nature of the data, so the stability of sport and physical activity preferences over time is not known. The main aim of the present research is to describe the levels and trends in the development of sport and physical activity preferences in girls and boys over a period of five years, from the age of 10 to 14. Further, the study aims to establish the stability of preferences over time.
Sentimental analysis on web mining using statistical measures

Opinion mining or sentimental analysis plays important role in the data mining process. In the proposed method, opinions are classified using various statistical measures to provide ratings to help the sentimental analysis of big data. Experimental results demonstrate the efficiency of the proposed method to help in analysis of quality of product, marketers evaluation of success of a new product launched, determine which versions of a product or service are popular and identify demographics like or dislike of product features, etc.
Visual Analogies between Atari Games for Studying Transfer Learning in RL

In this work, we ask the following question: Can visual analogies, learned in an unsupervised way, be used in order to transfer knowledge between pairs of games and even play one game using an agent trained for another game? We attempt to answer this research question by creating visual analogies between a pair of games: a source game and a target game. For example, given a video frame in the target game, we map it to an analogous state in the source game and then attempt to play using a trained policy learned for the source game. We demonstrate convincing visual mapping between four pairs of games (eight mappings), which are used to evaluate three transfer learning approaches.
Best Practices for Measuring Students’ Attitudes toward Learning Science

Science educators often characterize the degree to which tests measure different facets of college students' learning, such as knowing, applying, and problem solving. A casual survey of scholarship of teaching and learning research studies reveals that many educators also measure how students' attitudes influence their learning. Students' science attitudes refer to their positive or negative feelings and predispositions to learn science. Science educators use attitude measures, in conjunction with learning measures, to inform the conclusions they draw about the efficacy of their instructional interventions. The measurement of students' attitudes poses similar but distinct challenges as compared with measurement of learning, such as determining validity and reliability of instruments and selecting appropriate methods for conducting statistical analyses. In this review, we will describe techniques commonly used to quantify students' attitudes toward science. We will also discuss best practices for the analysis and interpretation of attitude data.
Personality as a predictor of Business Social Media Usage: an Empirical Investigation of Xing Usage Patterns

Referring to recent research calls regarding the role of individual differences on technology adoption and use, this paper reports on an empirical investigation of the influence of a user’s personality on the usage of the European career-oriented social network XING and its usage intensity (n = 760). Using structural equation modeling, a significant influence of personality on the intensity of XING usage was found (R2 = 12.4%,α = 0.758). More specifically, results indicated the major role played by the personality traits Extraversion, Emotional Stability and Openness to Experience as proper predictors for XING usage. Contrary to prior research on private-oriented social media, I discovered a significant positive Emotional Stability–XING usage intensity relationship instead of a negative relationship which is explained by Goffman’s Self
Natural Language Interface for Databases Using a Dual-Encoder Model

We propose a sketch-based two-step neural model for generating structured queries (SQL) based on a user’s request in natural language. The sketch is obtained by using placeholders for specific entities in the SQL query, such as column names, table names, aliases and variables, in a process similar to semantic parsing. The first step is to apply a sequence-to-sequence (SEQ2SEQ) model to determine the most probable SQL sketch based on the request in natural language. Then, a second network designed as a dual-encoder SEQ2SEQ model using both the text query and the previously obtained sketch is employed to generate the final SQL query. Our approach shows improvements over previous approaches on two recent large datasets (WikiSQL and SENLIDB) suitable for data-driven solutions for natural language interfaces for databases.
Extreme Network Compression via Filter Group Approximation

In this paper we propose a novel decomposition method based on filter group approximation, which can significantly reduce the redundancy of deep convolutional neural networks (CNNs) while maintaining the majority of feature representation. Unlike other low-rank decomposition algorithms which operate on spatial or channel dimension of filters, our proposed method mainly focuses on exploiting the filter group structure for each layer. For several commonly used CNN models, including VGG and ResNet, our method can reduce over 80% floatingpoint operations (FLOPs) with less accuracy drop than state-of-the-art methods on various image classification datasets. Besides, experiments demonstrate that our method is conducive to alleviating degeneracy of the compressed network, which hurts the convergence and performance of the network.
Brand communication through digital influencers: Leveraging blogger engagement

The growing power of bloggers to influence their connected network has emerged as a new communication venue for brands. This study elaborates upon the role of bloggers in brand communication, and reveals how brands can engage with bloggers, currently considered as online opinion leaders, from the perspective of the two-step flow theory. Following clarification of the aims of the study, we report on in-depth interviews with 17 brand and digital agency representatives, selected because they regard communication with bloggers as an important strategy in increasing the influence of their brands among online communities. This exploratory study reflects current blogger communication implementations, and concludes with a discussion of seven major issues arising from the literature review and interviews (definition of bloggers, blogger selection criteria, digital integration, power of bloggers, long-term relationship building with bloggers, measurement, and budgetary issues in blogger communication). These areas represent relatively unexplored areas of blogger engagement from both an academic and managerial perspective. Based on the findings of the interviews, we propose a model which traces the influencer role of bloggers from the two-step flow theory perspective. This model is named as the brand communication through digital influencers model.
On Evaluation of Embodied Navigation Agents

Skillful mobile operation in three-dimensional environments is a primary topic of study in Artificial Intelligence. The past two years have seen a surge of creative work on navigation. This creative output has produced a plethora of sometimes incompatible task definitions and evaluation protocols. To coordinate ongoing and future research in this area, we have convened a working group to study empirical methodology in navigation research. The present document summarizes the consensus recommendations of this working group. We discuss different problem statements and the role of generalization, present evaluation measures, and provide standard scenarios that can be used for benchmarking.
PERCEPTION TOWARDS ONLINE SHOPPING : AN EMPIRICAL STUDY OF INDIAN CONSUMERS

Consumers are playing an important role in online shopping. The increasing use of Internet by the younger generation in India provides an emerging prospect for online retailers. If online retailers know the factors affecting Indian consumers’ buying behaviour, and the associations between these factors and type of online buyers, then they can further develop their marketing strategies to convert potential customers into active ones. In this study four key dimensions of online shopping as perceived by consumers in India are identified and the different demographic factors are also studied which are the primary basis of market segmentation for retailers. It was discovered that overall website quality, commitment factor, customer service and security are the four key factors which influence consumers’ perceptions of online shopping. the study revealed that the perception of online shoppers is independent of their age and gender but not independent of their education & gender and income & gender Finally, the recommendations presented in this research may help foster growth of Indian online retailing in future.
Deep-6DPose: Recovering 6D Object Pose from a Single RGB Image

Detecting objects and their 6D poses from only RGB images is an important task for many robotic applications. While deep learning methods have made significant progress in visual object detection and segmentation, the object pose estimation task is still challenging. In this paper, we introduce an end-toend deep learning framework, named Deep-6DPose, that jointly detects, segments, and most importantly recovers 6D poses of object instances from a single RGB image. In particular, we extend the recent state-of-the-art instance segmentation network Mask R-CNN with a novel pose estimation branch to directly regress 6D object poses without any post-refinements. Our key technical contribution is the decoupling of pose parameters into translation and rotation so that the rotation can be regressed via a Lie algebra representation. The resulting pose regression loss is differential and unconstrained, making the training tractable. The experiments on two standard pose benchmarking datasets show that our proposed approach compares favorably with the state-of-the-art RGB-based multi-stage pose estimation methods. Importantly, due to the end-to-end architecture, Deep-6DPose is considerably faster than competing multi-stage methods, offers an inference speed of 10 fps that is well suited for robotic applications.
ECG Statistics , Noise , Artifacts , and Missing Data

Chapter 1 presented a description of the ECG in terms of its etiology and clinical features, and Chapter 2 an overview of the possible sources of error introduced in the hardware collection and data archiving stages. With this groundwork in mind, this chapter is intended to introduce the reader to the ECG using a signal processing approach. The ECG typically exhibits both persistent features (such as the average PQRS-T morphology and the short-term average heart rate, or average RR interval), and nonstationary features (such as the individual RR and QT intervals, and longterm heart rate trends). Since changes in the ECG are quasi-periodic (on a beatto-beat, daily, and perhaps even monthly basis), the frequency can be quantified in both statistical terms (mean, variance) and via spectral estimation methods. In essence, all these statistics quantify the power or degree to which an oscillation is present in a particular frequency band (or at a particular scale), often expressed as a ratio to power in another band. Even for scale-free approaches (such as wavelets), the process of feature extraction tends to have a bias for a particular scale which is appropriate for the particular data set being analyzed. ECG statistics can be evaluated directly on the ECG signal, or on features extracted from the ECG. The latter category can be broken down into either morphology-based features (such as ST level) or timing-based statistics (such as heart rate variability). Before discussing these derived statistics, an overview of the ECG itself is given.
Soil Resistivity Data Computations ; Single and Two-Layer Soil Resistivity Structure and Its Implication on Earthing Design

Performing High Voltage (HV) tasks with a multi craft work force create a special set of safety circumstances. This paper aims to present vital information relating to when it is acceptable to use a single or a two-layer soil structure. Also it discusses the implication of the high voltage infrastructure on the earth grid and the safety of this implication under a single or a two-layer soil structure. A multiple case study is investigated to show the importance of using the right soil resistivity structure during the earthing system design. Earth Grid, EPR, High Voltage, Soil Resistivity Structure, Step Voltage, Touch Voltage.
Acoustic emission-based fatigue crack growth prediction

Acoustic Emission (AE) is a non-destructive testing (NDT) with potential applications for locating and monitoring fatigue cracks during structural health management and prognosis. To do this, a correlation between acoustic emission signal characteristics and crack growth behavior should be established. In this paper, a probabilistic model of fatigue crack length distribution based on acoustic emission is validated. Using the results from AE-based fatigue experiments the relationship between AE count rates and crack growth rates is reviewed for the effect of loading ratio. Predictions of crack growth rates based on AE count rates model show reasonable agreement with the actual crack growth rates from the test results. Bayesian regression analysis is performed to estimate the marginal distribution of the unknown parameters in the model. The results of the Bayesian regression analysis shows that the results are consistent with respect to changes in loading ratio. Additional experimental evidence might be required to further reduce the uncertainties of the proposed model and further study the effects of the changes in loading frequency, sample geometry and material.
Comparison of Machine Learning Approaches for Prediction of Advanced Liver Fibrosis in Chronic Hepatitis C Patients

Using machine learning approaches as non-invasive methods have been used recently as an alternative method in staging chronic liver diseases for avoiding the drawbacks of biopsy. This study aims to evaluate different machine learning techniques in prediction of advanced fibrosis by combining the serum bio-markers and clinical information to develop the classification models.  A prospective cohort of 39,567 patients with chronic hepatitis C was divided into two sets—one categorized as mild to moderate fibrosis F0-F2, and the other categorized as advanced fibrosis F3-F4 according to METAVIR score. Decision tree, genetic algorithm, particle swarm optimization, and multi-linear regression models for advanced fibrosis risk prediction were developed. Receiver operating characteristic curve analysis was performed to evaluate the performance of the proposed models.  Age, platelet count, AST, and albumin were found to be statistically significant to advanced fibrosis. The machine learning algorithms under study were able to predict advanced fibrosis in patients with HCC with AUROC ranging between 0.73 and 0.76 and accuracy between 66.3 and 84.4 percent.  Machine-learning approaches could be used as alternative methods in prediction of the risk of advanced liver fibrosis due to chronic hepatitis C.