nils-herrmann/clean_scirepeval huggingface dataset:
The need for new processes, methodologies and tools to support big data teams and improve big data project effectiveness

As data continues to be produced in massive amounts, with increasing volume, velocity and variety, big data projects are growing in frequency and importance. However, the growth in the use of big data has outstripped the knowledge of how to support teams that need to do big data projects. In fact, while much has been written in terms of the use of algorithms that can help generate insightful analysis, much less has been written about methodologies, tools and frameworks that could enable teams to more effectively and efficiently "do" big data projects. Hence, this paper discusses the key research questions relating methodologies, tools and frameworks to improve big data team effectiveness as well as the potential goals for a big data process methodology. Finally, the paper also discusses related domains, such as software development, operations research and business intelligence, since these fields might provide insight into how to define a big data process methodology.
Thou shalt not hate: Countering Online Hate Speech

Hate content in social media is ever increasing. While Facebook, Twitter, Google have attempted to take several steps to tackle this hate content, they most often risk the violation of freedom of speech. Counterspeech, on the other hand, provides an effective way of tackling the online hate without the loss of freedom of speech. Thus, an alternative strategy for these platforms could be to promote counterspeech as a defense against hate content. However, in order to have a successful promotion of such counterspeech, one has to have a deep understanding of its dynamics in the online world. Lack of carefully curated data largely inhibits such understanding. In this paper, we create and release the first ever dataset for counterspeech using comments from YouTube. The data contains 9438 manually annotated comments where the labels indicate whether a comment is a counterspeech or not. This data allows us to perform a rigorous measurement study characterizing the linguistic structure of counterspeech for the first time. This analysis results in various interesting insights such as: the counterspeech comments receive double the likes received by the non-counterspeech comments, for certain communities majority of the non-counterspeech comments tend to be hate speech, the different types of counterspeech are not all equally effective and the language choice of users posting counterspeech is largely different from those posting noncounterspeech as revealed by a detailed psycholinguistic analysis. Finally, we build a set of machine learning models that are able to automatically detect counterspeech in YouTube videos with an F1-score of 0.73.
Comparison of Two Ultra-Fast Actuator Concepts

In this paper, two different types of ultra-fast electromechanical actuators are compared using a multi-physical finite element simulation model that has been experimentally validated. They are equipped with a single-sided Thomson coil (TC) and a double-sided drive coil (DSC), respectively. The former consists of a spirally-wound flat coil with a copper armature on top, while the latter consists of two mirrored spiral coils that are connected in series. Initially, the geometry and construction of each of the actuating schemes are discussed. Subsequently, the theory behind the two force generation principles are described. Furthermore, the current, magnetic flux densities, accelerations, and induced stresses are analyzed. Moreover, mechanical loadability simulations are performed to study the impact on the requirements of the charging unit, the sensitivity of the parameters, and evaluate the degree of influence on the performance of both drives. Finally, it is confirmed that although the DSC is mechanically more complex, it has a greater efficiency than that of the TC.
MIMO Signal Processing in Offset-QAM Based Filter Bank Multicarrier Systems

Next-generation communication systems have to comply with very strict requirements for increased flexibility in heterogeneous environments, high spectral efficiency, and agility of carrier aggregation. This fact motivates research in advanced multicarrier modulation (MCM) schemes, such as filter bank-based multicarrier (FBMC) modulation. This paper focuses on the offset quadrature amplitude modulation (OQAM)-based FBMC variant, known as FBMC/OQAM, which presents outstanding spectral efficiency and confinement in a number of channels and applications. Its special nature, however, generates a number of new signal processing challenges that are not present in other MCM schemes, notably, in orthogonal-frequency-division multiplexing (OFDM). In multiple-input multiple-output (MIMO) architectures, which are expected to play a primary role in future communication systems, these challenges are intensified, creating new interesting research problems and calling for new ideas and methods that are adapted to the particularities of the MIMO-FBMC/OQAM system. The goal of this paper is to focus on these signal processing problems and provide a concise yet comprehensive overview of the recent advances in this area. Open problems and associated directions for future research are also discussed.
Rivaroxaban versus enoxaparin for thromboprophylaxis after total knee arthroplasty (RECORD4): a randomised trial

Prophylaxis for venous thromboembolism is recommended for at least 10 days after total knee arthroplasty; oral regimens could enable shorter hospital stays. We aimed to test the efficacy and safety of oral rivaroxaban for the prevention of venous thromboembolism after total knee arthroplasty.



In a randomised, double-blind, phase III study, 3148 patients undergoing knee arthroplasty received either oral rivaroxaban 10 mg once daily, beginning 6-8 h after surgery, or subcutaneous enoxaparin 30 mg every 12 h, starting 12-24 h after surgery. Patients had mandatory bilateral venography between days 11 and 15. The primary efficacy outcome was the composite of any deep-vein thrombosis, non-fatal pulmonary embolism, or death from any cause up to day 17 after surgery. Efficacy was assessed as non-inferiority of rivaroxaban compared with enoxaparin in the per-protocol population (absolute non-inferiority limit -4%); if non-inferiority was shown, we assessed whether rivaroxaban had superior efficacy in the modified intention-to-treat population. The primary safety outcome was major bleeding. 
The primary efficacy outcome occurred in 67 (6.9%) of 965 patients given rivaroxaban and in 97 (10.1%) of 959 given enoxaparin (absolute risk reduction 3.19%, 95% CI 0.71-5.67; p=0.0118). Ten (0.7%) of 1526 patients given rivaroxaban and four (0.3%) of 1508 given enoxaparin had major bleeding (p=0.1096).



Oral rivaroxaban 10 mg once daily for 10-14 days was significantly superior to subcutaneous enoxaparin 30 mg given every 12 h for the prevention of venous thromboembolism after total knee arthroplasty.



Bayer Schering Pharma AG, Johnson & Johnson Pharmaceutical Research & Development.
Crowdsourcing via Tensor Augmentation and Completion

Nowadays, the rapid proliferation of data makes it possible to build complex models for many real applications. Such models, however, usually require large amount of labeled data, and the labeling process can be both expensive and tedious for domain experts. To address this problem, researchers have resorted to crowdsourcing to collect labels from non-experts with much less cost. The key challenge here is how to infer the true labels from the large number of noisy labels provided by non-experts. Different from most existing work on crowdsourcing, which ignore the structure information in the labeling data provided by non-experts, in this paper, we propose a novel structured approach based on tensor augmentation and completion. It uses tensor representation for the labeled data, augments it with a ground truth layer, and explores two methods to estimate the ground truth layer via low rank tensor completion. Experimental results on 6 real data sets demonstrate the superior performance of the proposed approach over state-of-the-art techniques.
A Probabilistic Approach for Optimizing Spectral Clustering

Spectral clustering enjoys its success in both data clustering and semisupervised learning. But, most spectral clustering algorithms cannot handle multi-class clustering problems directly. Additional strategies are needed to extend spectral clustering algorithms to multi-class clustering problems. Furthermore, most spectral clustering algorithms employ hard cluster membership, which is likely to be trapped by the local optimum. In this paper, we present a new spectral clustering algorithm, named “Soft Cut”. It improves the normalized cut algorithm by introducing soft membership, and can be efficiently computed using a bound optimization algorithm. Our experiments with a variety of datasets have shown the promising performance of the proposed clustering algorithm.
Relationship Between Choice Reaction Time and Expertise in Team and Individualsports : A Gender Differences Approach

Experts in sport benefit from some cognitive mechanisms and strategies which enables them to reduce response times and increase response accuracy.Reaction time is mediated by different factors including type of sport that athlete is participating in and expertise status. The present study aimed to investigate the relationship between CRTs and expertise level in collegiate athletes, as well as evaluating the role of sport and gender differences.44 male and female athletesrecruited from team and individual sports at elite and non-elite levels. The Lafayette multi-choice reaction time was used to collect data.All subjectsperformed a choice reaction time task that required response to visual and auditory stimuli. Results demonstrated a significant overall choice reaction time advantage for maleathletes, as well as faster responses to stimuli in elite participants.Athletes of team sportsdid not showmore accurate performance on the choice reaction time tasks than athletes of individual sports. These findings suggest that there is a relation between choice reaction time and expertise in athletes and this relationship can be mediated by gender differences. Overall, athletes with intrinsic perceptualmotor advantages such as faster reaction times are potentially more equipped for participation in high levels of sport.
Plasma Dicing Fully Integrated Process-Flows Suitable for BEOL Advanced Packaging Fabrications

Comprehensive investigations were conducted on identifying integration efforts needed to adapt plasma dicing technology in BEOL pre-production environments. First, the authors identified the suitable process flows. Within the process flow, laser grooving before plasma dicing was shown to be a key unit process to control resulting die sidewall quality. Significant improvement on laser grooving quality has been demonstrated. Through these efforts, extremely narrow kerfs and near ideal dies strengths were achieved on bare Si dies. Plasma dicing process generates fluorinated polymer residues on both Si die sidewalls and under the topography overhangs on wafer surfaces, such as under the solder balls or microbumps. Certain areas cannot be cleaned by in-chamber post-treatments. Multiple cleaning methods demonstrated process capability and compatibility to singulated dies-on-tape handling. Lastly, although many methods exist commercially for backmetal and DAF separations, the authors' investigation is still inconclusive on one preferred process for post-plasma dicing die separations.
A digital controlled PV-inverter with grid impedance estimation for ENS detection

The steady increase in photovoltaic (PV) installations calls for new and better control methods in respect to the utility grid connection. Limiting the harmonic distortion is essential to the power quality, but other requirements also contribute to a more safe grid-operation, especially in dispersed power generation networks. For instance, the knowledge of the utility impedance at the fundamental frequency can be used to detect a utility failure. A PV-inverter with this feature can anticipate a possible network problem and decouple it in time. This paper describes the digital implementation of a PV-inverter with different advanced, robust control strategies and an embedded online technique to determine the utility grid impedance. By injecting an interharmonic current and measuring the voltage response it is possible to estimate the grid impedance at the fundamental frequency. The presented technique, which is implemented with the existing sensors and the CPU of the PV-inverter, provides a fast and low cost approach for online impedance measurement, which may be used for detection of islanding operation. Practical tests on an existing PV-inverter validate the control methods, the impedance measurement, and the islanding detection.
MgNet: A Unified Framework of Multigrid and Convolutional Neural Network

We develop a unified model, known as MgNet, that simultaneously recovers some convolutional neural networks (CNN) for image classification and multigrid (MG) methods for solving discretized partial different equations (PDEs). This model is based on close connections that we have observed and uncovered between the CNN and MG methodologies. For example, pooling operation and feature extraction in CNN correspond directly to restriction operation and iterative smoothers in MG, respectively. As the solution space is often the dual of the data space in PDEs, the analogous concept of feature space and data space (which are dual to each other) is introduced in CNN. With such connections and new concept in the unified model, the function of various convolution operations and pooling used in CNN can be better understood. As a result, modified CNN models (with fewer weights and hyper parameters) are developed that exhibit competitive and sometimes better performance in comparison with existing CNN models when applied to both CIFAR-10 and CIFAR-100 data sets.
Social-Emotional Competence: An Essential Factor for Promoting Positive Adjustment and Reducing Risk in School Children.

Social-emotional competence is a critical factor to target with universal preventive interventions that are conducted in schools because the construct (a) associates with social, behavioral, and academic outcomes that are important for healthy development; (b) predicts important life outcomes in adulthood; (c) can be improved with feasible and cost-effective interventions; and (d) plays a critical role in the behavior change process. This article reviews this research and what is known about effective intervention approaches. Based on that, an intervention model is proposed for how schools should enhance the social and emotional learning of students in order to promote resilience. Suggestions are also offered for how to support implementation of this intervention model at scale.
Motion of the shoulder complex during multiplanar humeral elevation.

Many prior studies have evaluated shoulder motion, yet no three-dimensional analysis comparing the combined clavicular, scapular, and humeral motion during arm elevation has been done. We aimed to describe and compare dynamic three-dimensional motion of the shoulder complex during raising and lowering the arm across three distinct elevation planes (flexion, scapular plane abduction, and coronal plane abduction).



Twelve subjects without a shoulder abnormality were enrolled. Transcortical pin placement into the clavicle, scapula, and humerus allowed electromagnetic motion sensors to be rigidly fixed. The subjects completed two repetitions of raising and lowering the arm in flexion, scapular, and abduction planes. Three-dimensional angles were calculated for sternoclavicular, acromioclavicular, scapulothoracic, and glenohumeral joint motions. Joint angles between humeral elevation planes and between raising and lowering of the arm were compared.



General patterns of shoulder motion observed during humeral elevation were clavicular elevation, retraction, and posterior axial rotation; scapular internal rotation, upward rotation, and posterior tilting relative to the clavicle; and glenohumeral elevation and external rotation. Clavicular posterior rotation predominated at the sternoclavicular joint (average, 31 degrees). Scapular posterior tilting predominated at the acromioclavicular joint (average, 19 degrees). Differences between flexion and abduction planes of humerothoracic elevation were largest for the glenohumeral joint plane of elevation (average, 46 degrees).



Overall shoulder motion consists of substantial angular rotations at each of the four shoulder joints, enabling the multiple-joint interaction required to elevate the arm overhead.
Author ' s personal copy Review of power curve modelling for wind turbines

Currently, variable speed wind turbine generators (VSWTs) are the type of wind turbines most widely installed. For wind energy studies, they are usually modelled by means the approximation of the manufacturer power curve using a generic equation. In literature, several expressions to do this approximation can be found; nevertheless, there is not much information about which is the most appropriate to represent the energy produced by a VSWT. For this reason, in this paper, it is carried out a review of the equations commonly used to represent the power curves of VSWTs: polynomial power curve, exponential power curve, cubic power curve and approximate cubic power curve. They have been compared to manufacturer power curves by using the coefficients of determination, as fitness indicators, and by using the estimation of energy production. Data gathered from nearly 200 commercial VSWTs, ranging from 225 to 7500 kW, has been used for this analysis. Results of the analysis presented in the paper show that exponential and cubic approximations give the higher R values and the lower error in energy estimation. With the approximate cubic power curve quite high values of R and low errors in energy estimation are achieved, which makes this kind of approximation very interesting due to its simplicity. Finally, the polynomial power curve shows the worst results mainly due to its sensitivity to the data given by the manufacturer.
Transportation Modes Classification Using Sensors on Smartphones

This paper investigates the transportation and vehicular modes classification by using big data from smartphone sensors. The three types of sensors used in this paper include the accelerometer, magnetometer, and gyroscope. This study proposes improved features and uses three machine learning algorithms including decision trees, K-nearest neighbor, and support vector machine to classify the user's transportation and vehicular modes. In the experiments, we discussed and compared the performance from different perspectives including the accuracy for both modes, the executive time, and the model size. Results show that the proposed features enhance the accuracy, in which the support vector machine provides the best performance in classification accuracy whereas it consumes the largest prediction time. This paper also investigates the vehicle classification mode and compares the results with that of the transportation modes.
Right paratracheal air cyst (tracheal diverticulum).

Paratracheal air cysts are collections of air adjacent to the trachea. These lesions are usually an incidental finding at routine chest computed tomography (CT) scan, and their frequency is probably underestimated, because almost all patients are asymptomatic.1 Differential diagnosis of paratracheal air cysts includes tracheal diverticulum, pharyngocele, laryngocele, Zenker diverticulum, apical lung hernia, blebs and bulla, and pneumomediastinum.1-4 We present here a case of chronic obstructive pulmonary disease (COPD) with incidental finding of a large tracheal diverticulum.
Yoga for control of epilepsy

Yoga is an age-old traditional Indian psycho-philosophical-cultural method of leading one's life, that alleviates stress, induces relaxation and provides multiple health benefits to the person following its system. It is a method of controlling the mind through the union of an individual's dormant energy with the universal energy. Commonly practiced yoga methods are 'Pranayama' (controlled deep breathing), 'Asanas' (physical postures) and 'Dhyana' (meditation) admixed in varying proportions with differing philosophic ideas. A review of yoga in relation to epilepsy encompasses not only seizure control but also many factors dealing with overall quality-of-life issues (QOL). This paper reviews articles related to yoga and epilepsy, seizures, EEG, autonomic changes, neuro-psychology, limbic system, arousal, sleep, brain plasticity, motor performance, brain imaging studies, and rehabilitation. There is a dearth of randomized, blinded, controlled studies related to yoga and seizure control. A multi-centre, cross-cultural, preferably blinded (difficult for yoga), well-randomized controlled trial, especially using a single yogic technique in a homogeneous population such as Juvenile myoclonic epilepsy is justified to find out how yoga affects seizure control and QOL of the person with epilepsy.
The effects of overlearning on long-term retention

Once material has been learned to a criterion of one perfect trial, further study within the same session constitutes overlearning. Although overlearning is a popular learning strategy, its effect on long-term retention is unclear. In two experiments presented here, 218 college students learned geography facts (Experiment 1) or word definitions (Experiment 2). The degree of learning was manipulated and measured via multiple test-with-feedback trials, and participants returned for a final cued recall test between one and nine weeks later. The overlearners recalled far more than the low learners at the one-week test, but this difference decreased dramatically thereafter. These data suggest that overlearning (and its concomitant demand for additional study time) is an inefficient strategy for learning material for meaningfully long periods of time.
End-to-End Deep Knowledge Tracing by Learning Binary Question-Embedding

Recent advancements in computer-assisted learning systems have increased research in the are of knowledge tracing, which estimates student proficiency based on their past interaction with the learning systems. In this context, the method called Deep Knowledge Tracing (DKT), which leverages recurrent neural networks, shows remarkable performance; however, existing knowledge tracing methods, including DKT, require human-predefined skill-tags that show what skill is required to solve a question. This limits the optimization of modeling student proficiency and the application to real-world data, which are often not well-organized by skill-tags. In this paper, we propose the first end-to-end DKT model (E2E-DKT), which does not depend on any human-predefined skill-tags. Regarding the correspondence between questions and tags as a binary embedding matrix, we introduce a new Q-Embedding Model, which learns the embedding matrix to help predict student proficiency from the student question-answer logs only. We provided two techniques for learning a better matrix, one is the reconstruction regularization of question-space and tag-space, and the other is the sparse regularization of the question-embedding matrix. Using two open datasets, we empirically validated that the learned tags show the same or better performance on DKT and have an information-efficient structure and more hierarchical relations among each tag than the human-predefined tags. These results show the potential of our proposed method to enhance the applicable scope and effectiveness of DKT, which could help improve the learning experience of students in more diverse environments.