nils-herrmann/clean_scirepeval huggingface dataset:
The development of a new test of agility for rugby league.

Agility requires change of direction speed (CODS) and also perceptual and decision-making skills and reaction speed. The purpose of this study was to develop a reliable and valid agility test for rugby league, which stressed all those dimensions. Players from a subelite rugby league team were tested twice on a sport-specific reactive agility test (RAT) and CODS test. Data were analyzed for reliability. For validity results from the subelite groups, first test was compared with data from an elite group. The RAT required participants to run toward an unpredictable life-size video of an attacking opponent and react to that video by changing direction. The CODS test required the same movement patterns however direction changes were preplanned. The subelite group's mean time to complete the CODS test and RAT on their first test was 1.67 ± 0.15 and 1.98 ± 0.16 seconds, respectively, and 1.62 ± 0.14 and 1.91 ± 0.17 seconds, respectively, on their second test (results are ± σ). Statistical analyses revealed no significant difference in means (p < 0.05) and good correlation (intraclass correlation coefficient = 0.87 and 0.82, respectively). The elite group's mean time to complete the tests was 1.65 ± 0.09 and 1.79 ±0.12 seconds, respectively. Statistical analyses revealed a significant difference in mean RAT time between the elite group and the subelite group (p < 0.05). The RAT was reliable and valid. Performance differences on the RAT were attributed to differences in perceptual skills and/or reaction ability. Testing and training agility should therefore stress those dimensions of agility and not just CODS.
Network Traffic Prediction Using Radial Basis Function Neural Network Optimized by Ant Colony Algorithm

The disadvantages of the traditional radial basis function (RBF) neural network during the network traffic prediction process, such as a slow convergence rate and easy occurrence of local optima, result in low prediction precision. In this study, the ant colony optimization (ACO) algorithm is used to optimize the parameters of the RBF neural network for network traffic prediction. ACO is used to train the width and centre of the basis function of the RBF neural network, simplify the network structure, accelerate the convergence speed, prevent the occurrence of local optima, and improve the generalist ability of the RBF neural network. The experimental results show that compared with the genetic algorithm (GA)-RBF and particle swarm optimization (PSO)-RBF traffic prediction models, the proposed model exhibits higher prediction accuracy and can describe the varying trends in the network traffic well. The model used in this study exhibits strong generalization ability and good stability and therefore has practical value in network traffic prediction.
OpenFst: A General and Efficient Weighted Finite-State Transducer Library

We describe OpenFst, an open-source library for weighted finite-state transducers (WFSTs). OpenFst consists of a C++ template library with efficient WFST representations and over twenty-five operations for constructing, combining, optimizing, and searching them. At the shell-command level, there are corresponding transducer file representations and programs that operate on them. OpenFst is designed to be both very efficient in time and space and to scale to very large problems. This library has key applications speech, image, and natural language processing, pattern and string matching, and machine learning. We give an overview of the library, examples of its use, details of its design that allow customizing the labels, states, and weights and the lazy evaluation of many of its operations. Further information and a download of the OpenFst library can be obtained from http://www.openfst.org.
Coding Facial Expressions with Gabor Wavelets

A method for extracting information about facial expressions from images is presented. Facial expression images are coded using a multi-orientation, multi-resolution set of Gabor filters which are topographically ordered and aligned approximately with the face. The similarity space derived from this representation is compared with one derived from semantic ratings of the images by human observers. The results show that it is possible to construct a facial expression classifier with Gabor coding of the facial images as the input stage. The Gabor representation shows a significant degree of psychological plausibility, a design feature which may be important for human-computer inter-
Predictive Control of a Three-Level Boost Converter and an NPC Inverter for High-Power PMSG-Based Medium Voltage Wind Energy Conversion Systems

In this paper, a new medium voltage power converter topology using a diode rectifier, three-level boost (TLB) converter, and neutral-point-clamped (NPC) inverter is proposed for a high-power permanent magnet synchronous generator-based wind energy conversion system. The generator-side TLB converter performs the maximum power point tracking and balancing of dc-link capacitor voltages, while the grid-side NPC inverter regulates the net dc-bus voltage and reactive power to the grid. A significant improvement in the grid power quality is accomplished as the NPC inverter no longer controls the dc-link neutral point voltage. A model predictive strategy is proposed to control the complete system where the discrete-time models of the proposed power electronic converters are used to predict the future behavior of control variables. These predictions are evaluated using two independent cost functions, and the switching states which minimize these cost functions are selected and applied to the generator- and grid-side converters directly. In order to comply with the high-power application, the switching frequencies of the TLB converter and NPC inverter are minimized and maintained below 1.5 and 1 kHz, respectively. The proposed topology and control strategy are verified through MATLAB simulations on a 3-MW/3000-V/577-A system and dSPACE DS1103-based experiments on 3.6-kW/208-V/10-A prototype.
Parameter estimation for text analysis

Presents parameter estimation methods common with discrete probability distributions, which is of particular interest in text modeling. Starting with maximum likelihood, a posteriori and Bayesian estimation, central concepts like conjugate distributions and Bayesian networks are reviewed. As an application, the model of latent Dirichlet allocation (LDA) is explained in detail with a full derivation of an approximate inference algorithm based on Gibbs sampling, including a discussion of Dirichlet hyperparameter estimation.  May 2005,  August 2008.
Supervised Topic Models

We introduce supervised latent Dirichlet allocation (sLDA), a statistical model of labelled documents. The model accommodates a variety of response types. We derive a maximum-likelihood procedure for parameter estimation, which relies on variational approximations to handle intractable posterior expectations. Prediction problems motivate this research: we use the fitted model to predict response values for new documents. We test sLDA on two real-world problems: movie ratings predicted from reviews, and web page popularity predicted from text descriptions. We illustrate the benefits of sLDA versus modern regularized regression, as well as versus an unsupervised LDA analysis followed by a separate regression.
Photorealistic Scene Reconstruction by Voxel Coloring

A novel scene reconstruction technique is presented, different from previous approaches in its ability to cope with large changes in visibility and its modeling of intrinsic scene color and texture information. The method avoids image correspondence problems by working in a discretized scene space whose voxels are traversed in a fixed visibility ordering. This strategy takes full account of occlusions and allows the input cameras to be far apart and widely distributed about the environment. The algorithm identifies a special set of invariant voxels which together form a spatial and photometric reconstruction of the scene, fully consistent with the input images. The approach is evaluated with images from both inward-facing and outward-facing cameras.
Deep Belief Network-Based Approaches for Link Prediction in Signed Social Networks

In some online social network services (SNSs), the members are allowed to label their relationships with others, and such relationships can be represented as the links with signed values (positive or negative). The networks containing such relations are named signed social networks (SSNs), and some real-world complex systems can be also modeled with SSNs. Given the information of the observed structure of an SSN, the link prediction aims to estimate the values of the unobserved links. Noticing that most of the previous approaches for link prediction are based on the members’ similarity and the supervised learning method, however, research work on the investigation of the hidden principles that drive the behaviors of social members are rarely conducted. In this paper, the deep belief network (DBN)-based approaches for link prediction are proposed. Including an unsupervised link prediction model, a feature representation method and a DBN-based link prediction method are introduced. The experiments are done on the datasets from three SNSs (social networking services) in different domains, and the results show that our methods can predict the values of the links with high performance and have a good generalization ability across these datasets.
High speed true random number generator based on open loop structures in FPGAs

Most hardware ‘‘True’’ Random Number Generators (TRNG) take advantage of the thermal agitation around a flip-flop metastable state. In Field Programmable Gate Arrays (FPGA), the classical TRNG structure uses at least two oscillators, build either from PLL or ring oscillators. This creates good TRNG albeit limited in frequency by the interference rate which cannot exceed a few Mbit/s. This article presents an architecture allowing higher bit rates while maintaining provable unconditional security. This speed requirement becomes stringent for secure communication applications such as the cryptographic quantum key distribution protocols. The proposed architecture is very simple and generic as it is based on an open loop structure with no specific component such as PLL.
Structural Design of Convolutional Neural Networks for Steganalysis

Recent studies have indicated that the architectures of convolutional neural networks (CNNs) tailored for computer vision may not be best suited to image steganalysis. In this letter, we report a CNN architecture that takes into account knowledge of steganalysis. In the detailed architecture, we take absolute values of elements in the feature maps generated from the first convolutional layer to facilitate and improve statistical modeling in the subsequent layers; to prevent overfitting, we constrain the range of data values with the saturation regions of hyperbolic tangent (TanH) at early stages of the networks and reduce the strength of modeling using 1×1 convolutions in deeper layers. Although it learns from only one type of noise residual, the proposed CNN is competitive in terms of detection performance compared with the SRM with ensemble classifiers on the BOSSbase for detecting S-UNIWARD and HILL. The results have implied that well-designed CNNs have the potential to provide a better detection performance in the future.
Deep fruit detection in orchards

An accurate and reliable image based fruit detection system is critical for supporting higher level agriculture tasks such as yield mapping and robotic harvesting. This paper presents the use of a state-of-the-art object detection framework, Faster R-CNN, in the context of fruit detection in orchards, including mangoes, almonds and apples. Ablation studies are presented to better understand the practical deployment of the detection network, including how much training data is required to capture variability in the dataset. Data augmentation techniques are shown to yield significant performance gains, resulting in a greater than two-fold reduction in the number of training images required. In contrast, transferring knowledge between orchards contributed to negligible performance gain over initialising the Deep Convolutional Neural Network directly from ImageNet features. Finally, to operate over orchard data containing between 100–1000 fruit per image, a tiling approach is introduced for the Faster R-CNN framework. The study has resulted in the best yet detection performance for these orchards relative to previous works, with an F1-score of > 0.9 achieved for apples and mangoes.
Work in Progress: K-Nearest Neighbors Techniques for ABAC Policies Clustering

In this paper, we present an approach based on the K-Nearest Neighbors algorithms for policies clustering that aims to reduce the ABAC policies dimensionality for high scale systems. Since ABAC considers a very large set of attributes for access decisions, it turns out that using such model for large scale systems might be very complicated. To date, researchers have proposed to use data mining techniques to discover roles for RBAC system construction. In this work in progress, we consider the usage of KNN-based techniques for the classification of ABAC policies based on similarity computations of rules in order to enhance the ABAC flexibility and to reduce the number of policy rules.
Internet: Diameter of the World-Wide Web

Despite its increasing role in communication, the World-Wide Web remains uncontrolled: any individual or institution can create a website with any number of documents and links. This unregulated growth leads to a huge and complex web, which becomes a large directed graph whose vertices are documents and whose edges are links (URLs) that point from one document to another. The topology of this graph determines the web's connectivity and consequently how effectively we can locate information on it. But its enormous size (estimated to be at least 8×108 documents) and the continual changing of documents and links make it impossible to catalogue all the vertices and edges.
Biometrics: privacy's foe or privacy's friend?

From the INS to ATM’s, both the public and private sectors are making extensive use of biometrics for human recognition. As this technology becomes more economically viable and technically perfected, and thus more commonplace, the field of biometrics will spark legal and policy concerns. Critics inevitably compare biometrics to Big Brother and the loss of individual privacy. The probiometric lobby generally stresses the greater security and improved service that the technology provides. Is biometrics privacy’s friend or privacy’s foe? This paper explores the various arguments for and against biometrics and contends that while biometrics may pose legitimate privacy concerns, these issues can be adequately addressed. In the final analysis, biometrics emerges as privacy’s friend.
A User's Guide to Compressed Sensing for Communications Systems

This survey provides a brief introduction to compressed sensing as well as several major algorithms to solve it and its various applications to communications systems. We firstly review linear simultaneous equations as ill-posed inverse problems, since the idea of compressed sensing could be best understood in the context of the linear equations. Then, we consider the problem of compressed sensing as an underdetermined linear system with a prior information that the true solution is sparse, and explain the sparse signal recovery based on 1 optimization, which plays the central role in compressed sensing, with some intuitive explanations on the optimization problem. Moreover, we introduce some important properties of the sensing matrix in order to establish the guarantee of the exact recovery of sparse signals from the underdetermined system. After summarizing several major algorithms to obtain a sparse solution focusing on the 1 optimization and the greedy approaches, we introduce applications of compressed sensing to communications systems, such as wireless channel estimation, wireless sensor network, network tomography, cognitive radio, array signal processing, multiple access scheme, and networked control. key words: compressed sensing, sparse signal, compressible signal, 1norm, underdetermined system
PZT-Actuated and -Sensed Resonant Micromirrors with Large Scan Angles Applying Mechanical Leverage Amplification for Biaxial Scanning

This article presents design, fabrication and characterization of lead zirconate titanate (PZT)-actuated micromirrors, which enable extremely large scan angle of up to 106° and high frequency of 45 kHz simultaneously. Besides the high driving torque delivered by PZT actuators, mechanical leverage amplification has been applied for the micromirrors in this work to reach large displacements consuming low power. Additionally, fracture strength and failure behavior of poly-Si, which is the basic material of the micromirrors, have been studied to optimize the designs and prevent the device from breaking due to high mechanical stress. Since comparing to using biaxial micromirror, realization of biaxial scanning using two independent single-axial micromirrors shows considerable advantages, a setup combining two single-axial micromirrors for biaxial scanning and the results will also be presented in this work. Moreover, integrated piezoelectric position sensors are implemented within the micromirrors, based on which closed-loop control has been developed and studied.
Extracting 3D urban models from oblique aerial images

Oblique airborne cameras are increasingly used for area covering image collection of building facades in urban environments. Until recently, these images were mainly used to improve the visual appearance of relatively simple 3D building models by providing suitable facade texture. Meanwhile, oblique airborne images are also used to generate dense 3D point clouds using state-of-the-art pixel-based multi-stereo image matching. Subsequently, these point clouds can then enhance the amount of geometric and semantic detail of 3D urban models especially for the depicted building facades. However, occlusions and large view-point changes are especially demanding during dense image matching can be very challenging for oblique imagery in complex urban environment. As described in the paper, our matching pipeline tackles these problems by a coarse-to-fine modification of the SGM method. Furthermore the raw 3D point clouds are efficiently analyzed and filtered in subsequent steps, thus 3D data capture from oblique airborne imagery while aiming at the extraction of 3D urban models becomes feasible.
Health App Use Among US Mobile Phone Users: Analysis of Trends by Chronic Disease Status

Mobile apps hold promise for serving as a lifestyle intervention in public health to promote wellness and attenuate chronic conditions, yet little is known about how individuals with chronic illness use or perceive mobile apps.



The objective of this study was to explore behaviors and perceptions about mobile phone-based apps for health among individuals with chronic conditions.



Data were collected from a national cross-sectional survey of 1604 mobile phone users in the United States that assessed mHealth use, beliefs, and preferences. This study examined health app use, reason for download, and perceived efficacy by chronic condition.



Among participants, having between 1 and 5 apps was reported by 38.9% (314/807) of respondents without a condition and by 6.6% (24/364) of respondents with hypertension. Use of health apps was reported 2 times or more per day by 21.3% (172/807) of respondents without a condition, 2.7% (10/364) with hypertension, 13.1% (26/198) with obesity, 12.3% (20/163) with diabetes, 12.0% (32/267) with depression, and 16.6% (53/319) with high cholesterol. Results of the logistic regression did not indicate a significant difference in health app download between individuals with and without chronic conditions (P>.05). Compared with individuals with poor health, health app download was more likely among those with self-reported very good health (odds ratio [OR] 3.80, 95% CI 2.38-6.09, P<.001) and excellent health (OR 4.77, 95% CI 2.70-8.42, P<.001). Similarly, compared with individuals who report never or rarely engaging in physical activity, health app download was more likely among those who report exercise 1 day per week (OR 2.47, 95% CI 1.6-3.83, P<.001), 2 days per week (OR 4.77, 95% CI 3.27-6.94, P<.001), 3 to 4 days per week (OR 5.00, 95% CI 3.52-7.10, P<.001), and 5 to 7 days per week (OR 4.64, 95% CI 3.11-6.92, P<.001). All logistic regression results controlled for age, sex, and race or ethnicity.



Results from this study suggest that individuals with poor self-reported health and low rates of physical activity, arguably those who stand to benefit most from health apps, were least likely to report download and use these health tools.
Stable feature selection via dense feature groups

Many feature selection algorithms have been proposed in the past focusing on improving classification accuracy. In this work, we point out the importance of stable feature selection for knowledge discovery from high-dimensional data, and identify two causes of instability of feature selection algorithms: selection of a minimum subset without redundant features and small sample size. We propose a general framework for stable feature selection which emphasizes both good generalization and stability of feature selection results. The framework identifies dense feature groups based on kernel density estimation and treats features in each dense group as a coherent entity for feature selection. An efficient algorithm DRAGS (Dense Relevant Attribute Group Selector) is developed under this framework. We also introduce a general measure for assessing the stability of feature selection algorithms. Our empirical study based on microarray data verifies that dense feature groups remain stable under random sample hold out, and the DRAGS algorithm is effective in identifying a set of feature groups which exhibit both high classification accuracy and stability.
Progress on a cognitive-motivational-relational theory of emotion.

The 2 main tasks of this article are 1st, to examine what a theory of emotion must do and basic issues that it must address. These include definitional issues, whether or not physiological activity should be a defining attribute, categorical versus dimensional strategies, the reconciliation of biological universals with sociocultural sources of variability, and a classification of the emotions. The 2nd main task is to apply an analysis of appraisal patterns and the core relational themes that they produce to a number of commonly identified emotions. Anger, anxiety, sadness, and pride (to include 1 positive emotion) are used as illustrations. The purpose is to show the capability of a cognitive-motivational-relational theory to explain and predict the emotions. The role of coping in emotion is also discussed, and the article ends with a response to criticisms of a phenomenological, folk-theory outlook.