nils-herrmann/clean_scirepeval huggingface dataset:
Specificity of acceleration, maximum speed, and agility in professional soccer players.

High-speed actions are known to impact soccer performance and can be categorized into actions requiring maximal speed, acceleration, or agility. Contradictory findings have been reported as to the extent of the relationship between the different speed components. This study comprised 106 professional soccer players who were assessed for 10-m sprint (acceleration), flying 20-m sprint (maximum speed), and zigzag agility performance. Although performances in the three tests were all significantly correlated (p < 0.0005), coefficients of determination (r(2)) between the tests were just 39, 12, and 21% for acceleration and maximum speed, acceleration and agility, and maximum speed and agility, respectively. Based on the low coefficients of determination, it was concluded that acceleration, maximum speed, and agility are specific qualities and relatively unrelated to one another. The findings suggest that specific testing and training procedures for each speed component should be utilized when working with elite players.
ANALYSIS OF CROP YIELD PREDICTION USING DATA MINING TECHNIQUES

Agrarian sector in India is facing rigorous problem to maximize the crop productivity. More than 60 percent of the crop still depends on monsoon rainfall. Recent developments in Information Technology for agriculture field has become an interesting research area to predict the crop yield. The problem of yield prediction is a major problem that remains to be solved based on available data.  Mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. This paper presents a brief analysis of crop yield prediction using Multiple Linear Regression (MLR) technique and Density based clustering technique for the selected region i.e. East Godavari district of Andhra Pradesh in India.
Probability of stroke: a risk profile from the Framingham Study.

A health risk appraisal function has been developed for the prediction of stroke using the Framingham Study cohort. The stroke risk factors included in the profile are age, systolic blood pressure, the use of antihypertensive therapy, diabetes mellitus, cigarette smoking, prior cardiovascular disease (coronary heart disease, cardiac failure, or intermittent claudication), atrial fibrillation, and left ventricular hypertrophy by electrocardiogram. Based on 472 stroke events occurring during 10 years' follow-up from biennial examinations 9 and 14, stroke probabilities were computed using the Cox proportional hazards model for each sex based on a point system. On the basis of the risk factors in the profile, which can be readily determined on routine physical examination in a physician's office, stroke risk can be estimated. An individual's risk can be related to the average risk of stroke for persons of the same age and sex. The information that one's risk of stroke is several times higher than average may provide the impetus for risk factor modification. It may also help to identify persons at substantially increased stroke risk resulting from borderline levels of multiple risk factors such as those with mild or borderline hypertension and facilitate multifactorial risk factor modification.
Domain adaptation network based on hypergraph regularized denoising autoencoder

Domain adaptation learning aims to solve the classification problems of unlabeled target domain by using rich labeled samples in source domain, but there are three main problems: negative transfer, under adaptation and under fitting. Aiming at these problems, a domain adaptation network based on hypergraph regularized denoising autoencoder (DAHDA) is proposed in this paper. To better fit the data distribution, the network is built with denoising autoencoder which can extract more robust feature representation. In the last feature and classification layers, the marginal and conditional distribution matching terms between domains are obtained via maximum mean discrepancy measurement to solve the under adaptation problem. To avoid negative transfer, the hypergraph regularization term is introduced to explore the high-order relationships among data. The classification performance of the model can be improved by preserving the statistical property and geometric structure simultaneously. Experimental results of 16 cross-domain transfer tasks verify that DAHDA outperforms other state-of-the-art methods.
Cannabis and Cannabis Extracts: Greater Than the Sum of Their Parts?

A central tenet underlying the use of botanical remedies is that herbs contain many active ingredients. Primary active ingredients may be enhanced by secondary compounds, which act in beneficial synergy. Other herbal constituents may mitigate the side effects of dominant active ingredients. We reviewed the literature concerning medical cannabis and its primary active ingredient, ∆9-tetrahydrocannabinol (THC). Good evidence shows that secondary compounds in cannabis may enhance the beneficial effects of THC. Other cannabinoid and non-cannabinoid compounds in herbal cannabis or its extracts may reduce THC-induced anxiety, cholinergic deficits, and immunosuppression. Cannabis terpenoids and flavonoids may also increase cerebral blood flow, enhance cortical activity, kill respiratory pathogens, and provide anti-inflammatory activity.
Reddit Temporal N-gram Corpus and its Applications on Paraphrase and Semantic Similarity in Social Media using a Topic-based Latent Semantic Analysis

This paper introduces a new large-scale n-gram corpus that is created specifically from social media text. Two distinguishing characteristics of this corpus are its monthly temporal attribute and that it is created from 1.65 billion comments of user-generated text in Reddit. The usefulness of this corpus is exemplified and evaluated by a novel Topic-based Latent Semantic Analysis (TLSA) algorithm. The experimental results show that unsupervised TLSA outperforms all the state-of-the-art unsupervised and semi-supervised methods in SEMEVAL 2015: paraphrase and semantic similarity in Twitter tasks.
Development and test of a model linking safety-specific transformational leadership and occupational safety.

The authors developed, tested, and replicated a model in which safety-specific transformational leadership predicted occupational injuries in 2 separate studies. Data from 174 restaurant workers (M age = 26.75 years, range = 15-64) were analyzed using structural equation modeling (LISREL 8; K. G. Jöreskog & D. Sörbom, 1993) and provided strong support for a model whereby safety-specific transformational leadership predicted occupational injuries through the effects of perceived safety climate, safety consciousness, and safety-related events. Study 2 replicated and extended this model with data from 164 young workers from diverse jobs (M age = 19.54 years, range = 14-24). Safety-specific transformational leadership and role overload were related to occupational injuries through the effects of perceived safety climate, safety consciousness, and safety-related events.
Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations

Deep neural networks have become the stateof-the-art models in numerous machine learning tasks. However, general guidance to network architecture design is still missing. In our work, we bridge deep neural network design with numerical differential equations. We show that many effective networks, such as ResNet, PolyNet, FractalNet and RevNet, can be interpreted as different numerical discretizations of differential equations. This finding brings us a brand new perspective on the design of effective deep architectures. We can take advantage of the rich knowledge in numerical analysis to guide us in designing new and potentially more effective deep networks. As an example, we propose a linear multi-step architecture (LM-architecture) which is inspired by the linear multi-step method solving ordinary differential equations. The LMarchitecture is an effective structure that can be used on any ResNet-like networks. In particular, we demonstrate that LM-ResNet and LMResNeXt (i.e. the networks obtained by applying the LM-architecture on ResNet and ResNeXt respectively) can achieve noticeably higher accuracy than ResNet and ResNeXt on both CIFAR and ImageNet with comparable numbers of trainable parameters. In particular, on both CIFAR and ImageNet, LM-ResNet/LM-ResNeXt can significantly compress the original networkSchool of Mathematical Sciences, Peking University, Beijing, China MGH/BWH Center for Clinical Data Science, Masschusetts General Hospital, Harvard Medical School Center for Data Science in Health and Medicine, Peking University Laboratory for Biomedical Image Analysis, Beijing Institute of Big Data Research Beijing International Center for Mathematical Research, Peking University Center for Data Science, Peking University.  Bin Dong <dongbin@math.pku.edu.cn>, Quanzheng Li <Li.Quanzheng@mgh.harvard.edu>. Proceedings of the 35 th International Conference on Machine Learning, Stockholm, Sweden, PMLR 80, 2018.  s while maintaining a similar performance. This can be explained mathematically using the concept of modified equation from numerical analysis. Last but not least, we also establish a connection between stochastic control and noise injection in the training process which helps to improve generalization of the networks. Furthermore, by relating stochastic training strategy with stochastic dynamic system, we can easily apply stochastic training to the networks with the LM-architecture. As an example, we introduced stochastic depth to LM-ResNet and achieve significant improvement over the original LM-ResNet on CIFAR10.
Rational Recurrences

Despite the tremendous empirical success of neural models in natural language processing, many of them lack the strong intuitions that accompany classical machine learning approaches. Recently, connections have been shown between convolutional neural networks (CNNs) and weighted finite state automata (WFSAs), leading to new interpretations and insights. In this work, we show that some recurrent neural networks also share this connection to WFSAs. We characterize this connection formally, defining rational recurrences to be recurrent hidden state update functions that can be written as the Forward calculation of a finite set of WFSAs. We show that several recent neural models use rational recurrences. Our analysis provides a fresh view of these models and facilitates devising new neural architectures that draw inspiration from WFSAs. We present one such model, which performs better than two recent baselines on language modeling and text classification. Our results demonstrate that transferring intuitions from classical models like WFSAs can be an effective approach to designing and understanding neural models.
Prevalence of low back pain and its risk factors among secondary school teachers at Bentong, Pahang

Prevalence of low back pain and its risk factors among secondary school teachers at Bentong,  The purpose of this study is to determine the prevalence of low back pain among Secondary School Teachers and to investigate the associated risk factors at Bentong,   A self-administered questionnaire was distributed to 260 subjects through random sampling in 5 secondary schools. Seven female teaches were excluded because they never meet with the inclusion criteria, where at the end of the study only 253 subjects was included.  In the study, I found that prevalence of low back pain is high among secondary school teachers. Female teachers reported a significantly higher prevalence of low back pain when compared to male teachers. And the middle age group of teachers has reported high prevalence of pain compare to the younger and older age group. The highest risk factor for the low back pain among teachers is prolong standing, followed by prolong sitting and working with computer.  We found a high prevalence of low back pain among school teachers with most female and middle age group people affected and they are related with highest risk factor. There is a need to develop specific strategies on ergonomics educate, regular physical exercises and occupational stress in the schools to reduce the occurrence of Work-related Musculoskeletal Disorders (WMSDs) of the low back pain among teachers.
Mindfulness-based approaches: are they all the same?

Mindfulness-based approaches are increasingly employed as interventions for treating a variety of psychological, psychiatric and physical problems. Such approaches include ancient Buddhist mindfulness meditations such as Vipassana and Zen meditations, modern group-based standardized meditations, such as mindfulness-based stress reduction and mindfulness-based cognitive therapy, and further psychological interventions, such as dialectical behavioral therapy and acceptance and commitment therapy. We review commonalities and differences of these interventions regarding philosophical background, main techniques, aims, outcomes, neurobiology and psychological mechanisms. In sum, the currently applied mindfulness-based interventions show large differences in the way mindfulness is conceptualized and practiced. The decision to consider such practices as unitary or as distinct phenomena will probably influence the direction of future research.
Unsupervised Deep Context Prediction for Background Foreground Separation

In many high level vision applications such as tracking and surveillance, background estimation is a fundamental step. In the past, background estimation was usually based on low level hand-crafted features such as raw color components, gradients, or local binary patterns. These existing algorithms observe performance degradation in the presence of various challenges such as dynamic backgrounds, photo-metric variations, camera jitter, and shadows. To handle these challenges for the purpose of accurate background estimation, we propose a unified method based on Generative Adversarial Network (GAN) and image inpainting. It is an unsupervised visual feature learning hybrid GAN based on context prediction. It is followed by a semantic inpainting network for texture optimization. We also propose a solution of arbitrary region inpainting by using center region inpainting and Poisson blending. The proposed algorithm is compared with the existing algorithms for background estimation on SBM.net dataset and for foreground segmentation on Maryam Sultana, Soon Ki Jung School of Computer Science and Engineering Virtual Reality Laboratory, Kyungpook National University, Daegu, Republic of Korea, E-mail: maryam@vr.knu.ac.kr, skjung@knu.ac.kr Arif Mahmood Department of Computer Science and Engineering Qatar University Doha, Qatar E-mail: rfmahmood@gmail.com Sajid Javed Department of Computer Science Tissue Image Analytics Laboratory University of Warwick United Kingdom, E-mail: s.javed.1@warwick.ac.uk CDnet 2014 dataset. The proposed algorithm has outperformed the compared methods with significant mar-
Revit-Based Automation Modeling for Intermediate Railway Station

The railway station is the basic unit of transportation networks. It contains cluster of data which should be organized appropriately to effectively extract useful information. Building Information Modeling (BIM) process revolves around a virtual information-rich 3D model. Once a model has been developed in a BIM software such as Revit, third-party add-ins application can be used to further leverage the data. In this paper, authors develop a Revit-based extension tool which enables railway engineer to design and represent the railway station simultaneously. It has three major components: (1) Access database that includes the standard design rules, (2) 3D models of station elements represented separately as Revit families, and (3) C# program code is used for performing the design process. The model has been verified and applied to a real case study of XiaMen station, China. As a result, this model highlights any possible conflicts before the real construction starts. By the enhancement of databases linked to the developed tool, the BIM techniques could be considered as a potential direction for modeling conceptualization of intermediate railway stations.
Deaf-Mute Communication Interpreter

Communications between deaf-mute and a normal person have always been a challenging task. The project aims to facilitate people by means of a glove based deaf-mute communication interpreter system. The glove is internally equipped with five flex sensors, tactile sensors and accelerometer. For each specific gesture, the flex sensor produces a proportional change in resistance and accelerometer measures the orientation of hand. The processing of these hand gestures is in Arduino. The glove includes two modes of operation – training mode to benefit every user and an operational mode. The concatenation of letters to form words is also done in Arduino. In addition, the system also includes a text to speech conversion (TTS) block which translates the matched gestures i.e. text to voice output. Deaf-Mute, communication, gesture, Flex sensor, tactile sensors, accelerometer, TTS.
Gesture recognition with a Wii controller

In many applications today user interaction is moving away from mouse and pens and is becoming pervasive and much more physical and tangible. New emerging interaction technologies allow developing and experimenting with new interaction methods on the long way to providing intuitive human computer interaction. In this paper, we aim at recognizing gestures to interact with an application and present the design and evaluation of our sensor-based gesture recognition. As input device we employ the Wii-controller (Wiimote) which recently gained much attention world wide. We use the Wiimote's acceleration sensor independent of the gaming console for gesture recognition. The system allows the training of arbitrary gestures by users which can then be recalled for interacting with systems like photo browsing on a home TV. The developed library exploits Wii-sensor data and employs a hidden Markov model for training and recognizing user-chosen gestures. Our evaluation shows that we can already recognize gestures with a small number of training samples. In addition to the gesture recognition we also present our experiences with the Wii-controller and the implementation of the gesture recognition. The system forms the basis for our ongoing work on multimodal intuitive media browsing and are available to other researchers in the field.
Graph Attention Networks

We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations. By stacking layers in which nodes are able to attend over their neighborhoods’ features, we enable (implicitly) specifying different weights to different nodes in a neighborhood, without requiring any kind of computationally intensive matrix operation (such as inversion) or depending on knowing the graph structure upfront. In this way, we address several key challenges of spectral-based graph neural networks simultaneously, and make our model readily applicable to inductive as well as transductive problems. Our GAT models have achieved or matched state-of-the-art results across four established transductive and inductive graph benchmarks: the Cora, Citeseer and Pubmed citation network datasets, as well as a protein-protein interaction dataset (wherein test graphs remain unseen during training).
Exploring Fault-Tolerant Network-on-Chip Architectures

The advent of deep sub-micron technology has exacerbated reliability issues in on-chip interconnects. In particular, single event upsets, such as soft errors, and hard faults are rapidly becoming a force to be reckoned with. This spiraling trend highlights the importance of detailed analysis of these reliability hazards and the incorporation of comprehensive protection measures into all network-on-chip (NoC) designs. In this paper, we examine the impact of transient failures on the reliability of on-chip interconnects and develop comprehensive counter-measures to either prevent or recover from them. In this regard, we propose several novel schemes to remedy various kinds of soft error symptoms, while keeping area and power overhead at a minimum. Our proposed solutions are architected to fully exploit the available infrastructures in an NoC and enable versatile reuse of valuable resources. The effectiveness of the proposed techniques has been validated using a cycle-accurate simulator
Basics of Oracle Text Retrieval

Most current information management systems can be classifi ed into text retrieval systems, relational/object database systems, or semistructured/XML database systems . However, in practice, many applications data sets involve a combination of free text, structured dat a, nd semistructured data. Hence, integration of different types of information management systems has be en, and continues to be, an active research topic. In this paper, we present a short survey of prior work o n integrating and inter-operating between text, structured, and semistructured database systems. We classify existing literature based on the kinds of systems being integrated and the approach to integration . Based on this classification, we identify the challenges and the key themes underlying existing work in th is area.
Pattern of global cyber war and crime : A conceptual framework

The flourishing synergy arising between organized crimes and the Internet has increased the insecurity of the digital world. How hackers frame their actions? What factors encourage and energize their behavior? These are very important but highly underresearched questions. We draw upon literatures on psychology, economics, international relation and warfare to propose a framework that addresses these questions. We found that countries across the world differ in terms of regulative, normative and cognitive legitimacy to different types of web attacks. Cyber wars and crimes are also functions of the stocks of hacking skills relative to the availability of economic opportunities. An attacking unit’s selection criteria for the target network include symbolic significance and criticalness, degree of digitization of values and weakness in defense mechanisms. Managerial and policy implications are discussed and directions for future research are suggested.
Geometric error measurement and compensation of machines — An update

For measuring machines and machine tools, geometrical accuracy is a key performance criterion. While numerical compensation is well established for CMMs, it is increasingly used on machine tools in addition to mechanical accuracy. This paper is an update on the CIRP keynote paper by Sartori and Zhang from 1995 [Sartori S, Zhang GX (1995) Geometric error measurement and compensation of machines, ]. Since then, numerical error compensation has gained immense importance for precision machining. This paper reviews the fundamentals of numerical error compensation and the available methods for measuring the geometrical errors of a machine. It discusses the uncertainties involved in different mapping methods and their application characteristics. Furthermore, the challenges for the use of numerical compensation for manufacturing machines are specified. Based on technology and market development, this work aims at giving a perspective for the role of numerical compensation in the future.