nils-herrmann/clean_scirepeval huggingface dataset:
Evidence for physiotherapy practice: a survey of the Physiotherapy Evidence Database (PEDro).

Evidence-based practice involves the use of evidence from systematic reviews and randomised controlled trials, but the extent of this evidence in physiotherapy has not previously been surveyed. The aim of this survey is to describe the quantity and quality of randomised controlled trials and the quantity of systematic reviews relevant to physiotherapy. The Physiotherapy Evidence Database (PEDro) was searched. The quality of trials was assessed with the PEDro scale. The search identified a total of 2,376 randomised controlled trials and 332 systematic reviews. The first trial was published in 1955 and the first review was published in 1982. Since that time, the number of trials and reviews has grown exponentially. The mean PEDro quality score has increased from 2.8 in trials published between 1955 and 1959 to 5.0 for trials published between 1995 and 1999. There is a substantial body of evidence about the effects of physiotherapy. However, there remains scope for improvements in the quality of the conduct and reporting of clinical trials.
Deep Optical Flow Estimation Via Multi-Scale Correspondence Structure Learning

As an important and challenging problem in computer vision, learning based optical flow estimation aims to discover the intrinsic correspondence structure between two adjacent video frames through statistical learning. Therefore, a key issue to solve in this area is how to effectively model the multi-scale correspondence structure properties in an adaptive end-to-end learning fashion. Motivated by this observation, we propose an endto-end multi-scale correspondence structure learning (MSCSL) approach for optical flow estimation. In principle, the proposed MSCSL approach is capable of effectively capturing the multi-scale inter-image-correlation correspondence structures within a multi-level feature space from deep learning. Moreover, the proposed MSCSL approach builds a spatial Conv-GRU neural network model to adaptively model the intrinsic dependency relationships among these multi-scale correspondence structures. Finally, the above procedures for correspondence structure learning and multi-scale dependency modeling are implemented in a unified end-to-end deep learning framework. Experimental results on several benchmark datasets demonstrate the effectiveness of the proposed approach.
NGUARD: A Game Bot Detection Framework for NetEase MMORPGs

Game bots are automated programs that assist cheating users and enable them to obtain huge superiority, leading to an imbalance in the game ecosystem and the collapse of user interest. Therefore, game bot detection becomes particularly important and urgent. Among many kinds of online games, massively multiplayer online role playing games (MMORPGs), such as World of Warcraft and AION, provide immersive gaming experience and attract many loyal fans. At the same time, however, game bots in MMORPGs have proliferated in volume and method, evolving with the real-world detection methods and showing strong diversity, leaving MMORPG bot detection efforts extremely difficult. To deal with the fast-changing nature of game bots, we here proposed a generalized game bot detection framework for MMORPGs termed NGUARD, denoting NetEase Games' Guard. NGUARD takes charge of automatically differentiating game bots from humans for MMORPGs. In detail, NGUARD exploits a combination of supervised and unsupervised methods. Supervised models are utilized to detect game bots in observed patterns according to the training data. Meanwhile, unsupervised solutions are employed to detect clustered game bots and help discovering new bots. The game bot detection framework NGUARD has been implemented and deployed in multiple MMORPG productions in the NetEase Game portfolio, achieving remarkable performance improvement and acceleration compared to traditional methods. Moreover, the framework reveals outstanding robustness for game bots in mutated patterns and even in completely new patterns on account of the design of the auto-iteration mechanism.
A sample size calculator for SMART pilot studies

In clinical practice, as well as in other areas where interventions are provided, a sequential individualized approach to treatment is often necessary, whereby each treatment is adapted based on the object ′s response. An adaptive intervention is a sequence of decision rules which formalizes the provision of treatment at critical decision points in the care of an individual. In order to inform the development of an adaptive intervention, scientists are increasingly interested in the use of sequential multiple assignment randomized trials (SMART), which is a type of multistage randomized trial where individuals are randomized repeatedly at critical decision points to a set treatment options. While there is great interest in the use of SMART and in the development of adaptive interventions, both are relatively new to themedical and behavioral sciences. As a result, many clinical researchers will first implement a SMART pilot study (i.e., a small-scale version of a SMART) to examine feasibility and acceptability considerations prior to conducting a full-scale SMART study. A primary aim of this paper is to introduce a new methodology to calculate minimal sample size necessary for conducting a SMART pilot.
USER INTERFACE DESIGN OF VOICE CONTROLLED CONSUMER ELECTRONICS

Today speech recognition of a small vocabulary can be realized so costeffectively that the technology can penetrate into consumer electronics. But, as first applications that failed on the market show, it is by no means obvious how to incorporate voice control in a user interface. This paper addresses the issue of how to design a voice control so that the user perceives it as a benefit. User interface guidelines that are adapted or specific to voice control are presented. Then the process of designing a voice control in the user-centred approach is described. By means of two examples, the car stereo and the telephone answering machine, it is shown how this is turned into practice.
Aerodynamic Design Optimization Studies of a Blended-Wing-Body Aircraft

The blended-wing body is an aircraft configuration that has the potential to be more efficient than conventional large transport aircraft configurations with the same capability. However, the design of the blended-wing is challenging due to the tight coupling between aerodynamic performance, trim, and stability. Other design challenges include the nature and number of the design variables involved, and the transonic flow conditions. To address these issues, we perform a series of aerodynamic shape optimization studies using Reynolds-averaged Navier–Stokes computational fluid dynamics with a Spalart–Allmaras turbulence model. A gradient-based optimization algorithm is used in conjunction with a discrete adjoint method that computes the derivatives of the aerodynamic forces. A total of 273 design variables—twist, airfoil shape, sweep, chord, and span—are considered. The drag coefficient at the cruise condition is minimized subject to lift, trim, static margin, and center plane bending moment constraints. The studies investigate the impact of the various constraints and design variables on optimized blended-wing-body configurations. The lowest drag among the trimmed and stable configurations is obtained by enforcing a 1% static margin constraint, resulting in a nearly elliptical spanwise lift distribution. Trim and static stability are investigated at both onand off-design flight conditions. The single-point designs are relatively robust to the flight conditions, but further robustness is achieved through a multi-point optimization.
Human pharmacology of ayahuasca: subjective and cardiovascular effects, monoamine metabolite excretion, and pharmacokinetics.

The effects of the South American psychotropic beverage ayahuasca on subjective and cardiovascular variables and urine monoamine metabolite excretion were evaluated, together with the drug's pharmacokinetic profile, in a double-blind placebo-controlled clinical trial. This pharmacologically complex tea, commonly obtained from Banisteriopsis caapi and Psychotria viridis, combines N,N-dimethyltryptamine (DMT), an orally labile psychedelic agent showing 5-hydroxytryptamine2A agonist activity, with monoamine oxidase (MAO)-inhibiting beta-carboline alkaloids (harmine, harmaline, and tetrahydroharmine). Eighteen volunteers with prior experience in the use of psychedelics received single oral doses of encapsulated freeze-dried ayahuasca (0.6 and 0.85 mg of DMT/kg of body weight) and placebo. Ayahuasca produced significant subjective effects, peaking between 1.5 and 2 h, involving perceptual modifications and increases in ratings of positive mood and activation. Diastolic blood pressure showed a significant increase at the high dose (9 mm Hg at 75 min), whereas systolic blood pressure and heart rate were moderately and nonsignificantly increased. Cmax values for DMT after the low and high ayahuasca doses were 12.14 ng/ml and 17.44 ng/ml, respectively. Tmax (median) was observed at 1.5 h after both doses. The Tmax for DMT coincided with the peak of subjective effects. Drug administration increased urinary normetanephrine excretion, but, contrary to the typical MAO-inhibitor effect profile, deaminated monoamine metabolite levels were not decreased. This and the negligible harmine plasma levels found suggest a predominantly peripheral (gastrointestinal and liver) site of action for harmine. MAO inhibition at this level would suffice to prevent first-pass metabolism of DMT and allow its access to systemic circulation and the central nervous system.
Social constructivist perspectives on teaching and learning.

Social constructivist perspectives focus on the interdependence of social and individual processes in the co-construction of knowledge. After the impetus for understanding the influence of social and cultural factors on cognition is reviewed, mechanisms hypothesized to account for learning from this perspective are identified, drawing from Piagetian and Vygotskian accounts. The empirical research reviewed illustrates (a) the application of institutional analyses to investigate schooling as a cultural process, (b) the application of interpersonal analyses to examine how interactions promote cognition and learning, and (c) discursive analyses examining and manipulating the patterns and opportunities in instructional conversation. The review concludes with a discussion of the application of this perspective to selected contemporary issues, including: acquiring expertise across domains, assessment, educational equity, and educational reform.
WCDMA direct-conversion receiver front-end comparison in RF-CMOS and SiGe BiCMOS

Wide-band code-division multiple-access direct-conversion receiver front-ends have been implemented in both 0.25-/spl mu/m RF-CMOS and SiGe BiCMOS technologies. These circuits have been designed for the same application, radio architecture, and system specifications, allowing relevant comparisons to be made. The front-ends include a bypassable low-noise amplifier, a quadrature downconverter, baseband variable-gain amplifiers, and a local-oscillator frequency divider with output buffers. At 24.5 mA of total current consumption from a 2.7-3.3-V supply, the CMOS front-end has a noise figure of 5.3 dB, in-band third-order intercept point (IIP3) and second-order intercept point (IIP2) of -14 and +20.7 dBm, respectively, and out-of-band IIP3 and IIP2 of >+1.2 and +69 dBm, respectively. Compared to an SiGe front-end consuming 22 mA, the CMOS circuit has a 2-dB higher noise figure, comparable out-of-band linearity, 3-dB higher in-band IIP3, 12-dB lower in-band IIP2, and 7-dB higher LO-to-RF leakage.
A multi-dimensional meter-adaptive method for automatic segmentation of music

Music structure appears on a wide variety of temporal levels (notes, bars, phrases, etc). Its highest-level expression is therefore dependent on music's lower-level organization, especially beats and bars. We propose a method for automatic structure segmentation that uses musically meaningful information and is content-adaptive. It relies on a meter-adaptive signal representation that prevents from the use of empirical parameters. Moreover, our method is designed to combine multiple signal features to account for various musical dimensions. Finally, it also combines multiple structural principles that yield complementary results. The resulting algorithm proves to already outperform state-of-the-art methods, especially within small tolerance windows, and yet offers several encouraging improvement directions.
The Many Faces of Formative Assessment.

In this research paper we consider formative assessment (FA) and discuss ways in which it has been implemented in four different university courses. We illustrate the different aspects of FA by deconstructing it and then demonstrating effectiveness in improving both teaching and student achievement. It appears that specifically “what is done” was less important since there were positive achievement gains in each study. While positive gains were realized with use of technology, gains were also realized with implementation of nontechnology dependent techniques. Further, gains were independent of class size or subject matter.
Comparing Reward Shaping, Visual Hints, and Curriculum Learning

When considering how to reduce the learning effort required for Reinforcement Learning (RL) agents on complex tasks, designers can apply several common approaches. Reward shaping boosts the immediate reward provided by the environment, effectively encouraging (or discouraging) specific actions. Curriculum learning (Bengio et al. 2009) aims to help an agent learn a complex task by learning a sequence of simpler tasks. Hints may also be provided (e.g., a yellow brick road), which fall outside the notion of shaping or a curricula. Despite the prevalence of these approaches, few studies examine how they compare to (or complement) each other or when an approach is better. As a first step in this direction, we analyze shaping, hints, and curricula for a Deep RL agent in Malmo (Johnson et al. 2016), a research platform for Minecraft. Figure 1 (left) shows the layouts used in our study, which are distinguished by the number of rooms, the placement of the target, and whether color is included. For all rooms, the starting position of the agent is selected from five blocks at the bottom of the room (highlighted gray). In one-room situations and the right-most two-room situation, the target is always chosen from the five blocks at the top of the room (highlighted gray). In the left-most two-room situation, the target is set just beyond the doorway. Visual hints are provided in some situations by coloring some of the floor blocks blue. We seek to answer whether shaping, hints, or the curricula have the most impact on performance, which we measure as the time to reach the target, the distance from the target, the cumulative reward, or the number of actions taken. For this task, performance is most impacted by the curriculum used and hints while shaping had less impact, suggesting that designing an effective curriculum and providing appropriate hints deserve more attention for similar navigation tasks with Deep RL agents. Our methodology provides an evaluation protocol, serving as a foundation for further studies that tease apart when (and why) methods excel or fail.
K-Means-Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality

The K-means algorithm is a commonly used technique in cluster analysis. In this paper, several questions about the algorithm are addressed. The clustering problem is first cast as a nonconvex mathematical program. Then, a rigorous proof of the finite convergence of the K-means-type algorithm is given for any metric. It is shown that under certain conditions the algorithm may fail to converge to a local minimum, and that it converges under differentiability conditions to a Kuhn-Tucker point. Finally, a method for obtaining a local-minimum solution is given.
Effects of myofascial release techniques on pain, physical function, and postural stability in patients with fibromyalgia: a randomized controlled trial.

To determine the effect of myofascial release techniques on pain symptoms, postural stability and physical function in fibromyalgia syndrome.



A randomized, placebo-controlled trial was undertaken.



Eighty-six patients with fibromyalgia syndrome were randomly assigned to an experimental group and a placebo group.



Patients received treatments for 20 weeks. The experimental group underwent 10 myofascial release modalities and the placebo group received sham short-wave and ultrasound electrotherapy.



Outcome variables were number of tender points, pain, postural stability, physical function, clinical severity and global clinical assessment of improvement. Outcome measures were assessed before and immediately after, at six months and one year after the last session of the corresponding intervention.



After 20 weeks of myofascial therapy, the experimental group showed a significant improvement (P  <  0.05) in painful tender points, McGill Pain Score (20.6 ± 6.3, P < 0.032), physical function (56.10 ± 17.3, P < 0.029), and clinical severity (5.08 ± 1.03, P < 0.039). At six months post intervention, the experimental group had a significantly lower mean number of painful points, pain score (8.25 ± 1.13, P < 0.048), physical function (58.60 ± 16.30, P < 0.049) and clinical severity (5.28 ± 0.97, P < 0.043). At one year post intervention, the only significant improvements were in painful points at second left rib and left gluteal muscle, affective dimension, number of days feeling good and clinical severity.



The results suggest that myofascial release techniques can be a complementary therapy for pain symptoms, physical function and clinical severity but do not improve postural stability in patients with fibromyalgia syndrome.
Accurate Multiple View 3D Reconstruction Using Patch-Based Stereo for Large-Scale Scenes

In this paper, we propose a depth-map merging based multiple view stereo method for large-scale scenes which takes both accuracy and efficiency into account. In the proposed method, an efficient patch-based stereo matching process is used to generate depth-map at each image with acceptable errors, followed by a depth-map refinement process to enforce consistency over neighboring views. Compared to state-of-the-art methods, the proposed method can reconstruct quite accurate and dense point clouds with high computational efficiency. Besides, the proposed method could be easily parallelized at image level, i.e., each depth-map is computed individually, which makes it suitable for large-scale scene reconstruction with high resolution images. The accuracy and efficiency of the proposed method are evaluated quantitatively on benchmark data and qualitatively on large data sets.
Snooping and Sexting: Digital Media as a Context for Dating Aggression and Abuse Among College Students.

Digital dating abuse (DDA) is a pattern of behaviors that control, pressure, or threaten a dating partner using a cell phone or the Internet. A survey of 365 college students was conducted, finding that digital monitoring behaviors were especially common. There were no gender differences in number of DDA behaviors experienced, but women reported more negative hypothetical reactions to sexual messaging than men. DDA was associated with measures of physical, sexual, and psychological dating violence. Results suggest that digital media are a context for potentially harmful dating behaviors, and the experience of DDA may differ by gender for sexual behaviors.
Periodic Leaky-Wave Antenna for Millimeter Wave Applications Based on Substrate Integrated Waveguide

Substrate integrated waveguides (SIW) are built up of periodically arranged metallic via-holes or via-slots. The leakage loss of SIW structures increases with the distance between the via-holes or via-slots. An open periodic waveguide with a large via distance supports the propagation of leaky-wave modes and can thus be used for the design of a leaky-wave antenna. In this paper, this leakage loss is studied in detail and used to design a periodic leaky-wave antenna. The proposed concept represents an excellent choice for applications in the millimeter-wave band. Due to its versatility, the finite difference frequency domain method for periodic guided-wave or leaky-wave structures is used to analyze the characteristics of the proposed periodic leaky-wave antenna. Two modes (TE10 and TE20) are investigated and their different leaky-wave properties are analyzed. Based on the proposed leaky-mode analysis method, a novel periodic leaky-wave antenna at 28-34 GHz is designed and fabricated.
Mobile application for Indonesian medicinal plants identification using Fuzzy Local Binary Pattern and Fuzzy Color Histogram

This research proposed a new mobile application based on Android operating system for identifying Indonesian medicinal plant images based on texture and color features of digital leaf images. In the experiments we used 51 species of Indonesian medicinal plants and each species consists of 48 images, so the total images used in this research are 2,448 images. This research investigates effectiveness of the fusion between the Fuzzy Local Binary Pattern (FLBP) and the Fuzzy Color Histogram (FCH) in order to identify medicinal plants. The FLBP method is used for extracting leaf image texture. The FCH method is used for extracting leaf image color. The fusion of FLBP and FCH is done by using Product Decision Rules (PDR) method. This research used Probabilistic Neural Network (PNN) classifier for classifying medicinal plant species. The experimental results show that the fusion between FLBP and FCH can improve the average accuracy of medicinal plants identification. The accuracy of identification using fusion of FLBP and FCH is 74.51%. This application is very important to help people identifying and finding information about Indonesian medicinal plant.
GENERATION AND TESTING OF RANDOM NUMBERS FOR CRYPTOGRAPHIC APPLICATIONS

Random number sequences are of crucial importance in almost every aspect of modern digital cryptography, having a significant impact on the strength of cryptographic primitives in securing secret information by rendering it unknown, unguessable, unpredictable and irreproducible for an adversary. Although the major importance of high performance and high quality randomness generators in cryptography attracts an increasing interest from the research community, unfortunately this tendency is not always shared among security application developers. Thus many available cryptographic systems continue to be compromised due to the utilization of inadequate randomness generators. In this context, the present paper aims to accentuate the crucial importance of random numbers in cryptography and to suggest a set of efficient and practical methods for the generation and testing of random number sequences intended for cryptographic applications.
A high efficiency DC-DC Converter using a new in-package structure of Bonding-Wire inductor

In this paper, a new in-package high efficiency voltage down converter (VDC) is presented. Usually, quality factor of inductors is the main problem for generating high efficiency DC-DC Converters. In order to mitigate this problem, Bonding Wires with a new form are exploited in which four mutual inductions of bonding wire are used to increase the total inductance. This technique provides a high quality and low space in-package inductor with negligible parasitic elements. The peak efficiency for the proposed circuit at 50 MHz switching frequency is around 86% with 150 mA loading current.