nils-herrmann/clean_scirepeval huggingface dataset:
STUDENT MOTIVATION IN PHYSICAL EDUCATION - THE EVIDENCE IN A NUTSHELL

Student motivation plays an important role in the teaching and learning process in general educational settings as well as in physical education (PE). This review should provide a brief and concise overview of the empirical evidence that is available regarding student motivation in PE. The review will organize research findings on student motivation in PE according to its relation to physical activity, motivational factors, barriers to motivation, motivational profiles, and interventions.
Real-time garbage collection for flash-memory storage systems of real-time embedded systems

Flash-memory technology is becoming critical in building embedded systems applications because of its shock-resistant, power economic, and nonvolatile nature. With the recent technology breakthroughs in both capacity and reliability, flash-memory storage systems are now very popular in many types of embedded systems. However, because flash memory is a write-once and bulk-erase medium, we need a translation layer and a garbage-collection mechanism to provide applications a transparent storage service. In the past work, various techniques were introduced to improve the garbage-collection mechanism. These techniques aimed at both performance and endurance issues, but they all failed in providing applications a guaranteed performance. In this paper, we propose a real-time garbage-collection mechanism, which provides a guaranteed performance, for hard real-time systems. On the other hand, the proposed mechanism supports non-real-time tasks so that the potential bandwidth of the storage system can be fully utilized. A wear-leveling method, which is executed as a non-real-time service, is presented to resolve the endurance problem of flash memory. The capability of the proposed mechanism is demonstrated by a series of experiments over our system prototype.
Kinematics analysis of a novel 4-dof stabilized platform

This paper presents a novel stabilized platform with three UPS-type and one RPU-type active limbs based on the shipboard helicopter's landing process and its kinematics are studied systematically. First, a prototype of this stabilized platform is constructed and its dof is analyzed, Second, analytic formulas for solving the inverse displacement kinematics are derived Third, analytic formulas for solving the forward displacement kinematics are derived and verified. Theoretical formulas and results provide a basis for the further optimization, control, and manufacturing.
Motor Bearing Fault Diagnosis Using Trace Ratio Linear Discriminant Analysis

Bearings are critical components in induction motors and brushless direct current motors. Bearing failure is the most common failure mode in these motors. By implementing health monitoring and fault diagnosis of bearings, unscheduled maintenance and economic losses caused by bearing failures can be avoided. This paper introduces trace ratio linear discriminant analysis (TR-LDA) to deal with high-dimensional non-Gaussian fault data for dimension reduction and fault classification. Motor bearing data with single-point faults and generalized-roughness faults are used to validate the effectiveness of the proposed method for fault diagnosis. Comparisons with other conventional methods, such as principal component analysis, local preserving projection, canonical correction analysis, maximum margin criterion, LDA, and marginal Fisher analysis, show the superiority of TR-LDA in fault diagnosis.
Comprehensive Experimental Analyses of Automotive Attack Surfaces

Modern automobiles are pervasively computerized, and hence potentially vulnerable to attack. However, while previous research has shown that the internal networks within some modern cars are insecure, the associated threat model — requiring prior physical access — has justifiably been viewed as unrealistic. Thus, it remains an open question if automobiles can also be susceptible to remote compromise. Our work seeks to put this question to rest by systematically analyzing the external attack surface of a modern automobile. We discover that remote exploitation is feasible via a broad range of attack vectors (including mechanics tools, CD players, Bluetooth and cellular radio), and further, that wireless communications channels allow long distance vehicle control, location tracking, in-cabin audio exfiltration and theft. Finally, we discuss the structural characteristics of the automotive ecosystem that give rise to such problems and highlight the practical challenges in mitigating them.
Developing and Implementing Parametric Human Body Shape Models in Ergonomics Software

Human figure models included with today’s high-end simulation systems are widely used for ergonomic analysis. Male and female figures can be scaled by inputting standard anthropometric dimensions, such as stature, body weight, and limb lengths to represent a large range of body sizes. However, the shapes of these scaled figures are often unrealistic, particularly for extreme body types, because only a relatively few dimensions of the figures are adjusted. Several software packages have the capability of adjusting the figure to match a body surface scan of an individual, but this capability is insufficient when individuals with a wide range of attributes must be simulated. We present a new figure generation capability in the Jack human modeling software based on a statistical analysis of laser scans of hundreds of men and women. A template mesh based on the Jack figure model was fitted to each scan, and a statistical shape model was created using principal component analysis and regression. The model was integrated within the Jack software to create Jack figures with highly realistic body shapes, usable natively with all simulation and analysis features.
Latent Variable Analysis Growth Mixture Modeling and Related Techniques for Longitudinal Data

This chapter gives an overview of recent advances in latent variable analysis. Emphasis is placed on the strength of modeling obtained by using a flexible combination of continuous and categorical latent variables. To focus the discussion and make it manageable in scope, analysis of longitudinal data using growth models will be considered. Continuous latent variables are common in growth modeling in the form of random effects that capture individual variation in development over time. The use of categorical latent variables in growth modeling is, in contrast, perhaps less familiar, and new techniques have recently emerged. The aim of this chapter is to show the usefulness of growth model extensions using categorical latent variables. The discussion also has implications for latent variable analysis of cross-sectional data. The chapter begins with two major parts corresponding to continuous outcomes versus categorical outcomes. Within each part, conventional modeling using continuous latent variables will be described
Tuning of PID Controllers Based on Bode ’ s Ideal Transfer Function

This paper presents a new strategy for tuning PID controllers based on a fractional reference model. The model is represented as an ideal closed-loop system whose open-loop is given by the Bode’s ideal transfer function. The PID controller parameters are determined by the minimization of the integral square error (ISE) between the time responses of the desired fractional reference model and of the system with the PID controller. The resulting closed-loop system (with the PID controller) has the desirable feature of being robust to gain variations with step responses exhibiting an iso-damping property. Several examples are presented that demonstrate the effectiveness and validity of the proposed methodology.
Semi-supervised Log Pattern Detection and Exploration Using Event Concurrence and Contextual Information

Process mining offers a larger variety of techniques for analyzing process execution event logs. Although end-to-end process models can be discovered automatically, often surprisingly many insights can only be obtained and verified by carefully investigating the actual event data. Uncovering such insights is an explorative and iterative process as witnessed in many case studies. Automated pattern detection on event logs can support this. Here, unsupervised techniques generate a multitude of patterns based on statistical properties of the log only (lacking domain context), whereas supervised pattern detection requires domain experts to specify patterns to detect by hand (lacking the event log context). In this paper, we reconcile supervised and unsupervised pattern detection. We let users both, build basic patterns by hand and automatically obtain patterns through unsupervised learning. We visualize pattern matches in the context of the event log (also showing concurrency and other contextual information) and let the user modify earlier patterns based on the domain insights. This enables an iterative approach for identifying more complex patterns than those obtainable by existing techniques. We implemented our approach in the ProM framework and evaluated the tool using both the BPI Challenge 2012 log of a loan application process and an insurance claims log from a major Australian insurance company.
ASR Confidence Estimation with Speaker-Adapted Recurrent Neural Networks

Confidence estimation for automatic speech recognition has been very recently improved by using Recurrent Neural Networks (RNNs), and also by speaker adaptation (on the basis of Conditional Random Fields). In this work, we explore how to obtain further improvements by combining RNNs and speaker adaptation. In particular, we explore different speakerdependent and -independent data representations for Bidirectional Long Short Term Memory RNNs of various topologies. Empirical tests are reported on the LibriSpeech dataset showing that the best results are achieved by the proposed combination of RNNs and speaker adaptation.
Music Emotion Identification from Lyrics

Very large online music databases have recently been created by vendors, but they generally lack content-based retrieval methods. One exception is Allmusic.com which offers browsing by musical emotion, using human experts to classify several thousand songs into 183 moods. In this paper, machine learning techniques are used instead of human experts to extract emotions in Music. The classification is based on a psychological model of emotion that is extended to 23 specific emotion categories. Our results for mining the lyrical text of songs for specific emotion are promising, generate classification models that are human-comprehensible, and generate results that correspond to commonsense intuitions about specific emotions. Mining lyrics focused in this paper is one aspect of research which combines different classifiers of musical emotion such as acoustics and lyrical text.
Automated Scenario Generation - Coupling Planning Techniques with Smart Objects

Serious games allow for adaptive and personalised forms of training; the nature and timing of learning activities can be tailored to the trainee’s needs and interests. Autonomous game-based training requires for the automatic selection of appropriate exercises for an individual trainee. This paper presents a framework for an automated scenario generation system. The underlying notion is that a learning experience is defined by the objects and agents that inhabit the training environment. Our system uses automated planning to assess the behaviour required to achieve the (personalised) training objective. It then generates a scenario by selecting semantically annotated (or ‘smart’) objects and by assigning goals to the virtual characters. The resulting situations trigger the trainee to execute the desired behaviour. To test the framework, a prototype has been developed to train the First Aid treatment of burns. Experienced instructors evaluated scenarios written by three types of authors: the prototype, first-aid experts, and laymen. The prototype produced scenarios that were at least as good as laymen scenarios. First-aid experts seemed the best scenario writers, although differences were not significant. It is concluded that combining automated planning, smart objects, and virtual agent behaviour, is a promising approach to automated scenario generation.
Texture-Consistent Shadow Removal

This paper presents an approach to shadow removal that preserves texture consistency between the original shadow and lit area. Illumination reduction in the shadow area not only darkens that area, but also changes the texture characteristics there. We achieve textureconsistent shadow removal by constructing a shadow-free and textureconsistent gradient field. First, we estimate an illumination change surface which causes the shadow and remove the gradients it induces. We approximate the illumination change surface with illumination change splines across the shadow boundary. We formulate estimating these splines as an optimization problem which balances the smoothness between the neighboring splines and their fitness to the image data. Second, we sample the shadow effect on the texture characteristics in the umbra and lit area near the shadow boundary, and remove it by transforming the gradients inside the shadow area to be compatible with the lit area. Experiments on photos from Flickr demonstrate the effectiveness of our
Sources of mathematical thinking: behavioral and brain-imaging evidence.

Does the human capacity for mathematical intuition depend on linguistic competence or on visuo-spatial representations? A series of behavioral and brain-imaging experiments provides evidence for both sources. Exact arithmetic is acquired in a language-specific format, transfers poorly to a different language or to novel facts, and recruits networks involved in word-association processes. In contrast, approximate arithmetic shows language independence, relies on a sense of numerical magnitudes, and recruits bilateral areas of the parietal lobes involved in visuo-spatial processing. Mathematical intuition may emerge from the interplay of these brain systems.
TensorFlow: learning functions at scale

TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. Its computational model is based on dataflow graphs with mutable state. Graph nodes may be mapped to different machines in a cluster, and within each machine to CPUs, GPUs, and other devices. TensorFlow supports a variety of applications, but it particularly targets training and inference with deep neural networks. It serves as a platform for research and for deploying machine learning systems across many areas, such as speech recognition, computer vision, robotics, information retrieval, and natural language processing. In this talk, we describe TensorFlow and outline some of its applications. We also discuss the question of what TensorFlow and deep learning may have to do with functional programming. Although TensorFlow is not purely functional, many of its uses are concerned with optimizing functions (during training), then with applying those functions (during inference). These functions are defined as compositions of simple primitives (as is common in functional programming), with internal data representations that are learned rather than manually designed. TensorFlow is joint work with many other people in the Google Brain team and elsewhere. More information is available at tensorflow.org.
Early neurologic assessment in preterm-infants: integration of traditional neurologic examination and observation of general movements.

To evaluate the possible additional benefit in terms of prognostic accuracy of an integrated application of a traditional scorable method of neurologic examination and the Prechtl's method of qualitative assessment of general movements (GMs) in a large population of 903 consecutive preterm infants.



Infants were enrolled from the Intensive Care Unit of the University of Catania. Inclusion criteria were a gestational age below 37 weeks and the absence of genetic disorders. All infants underwent serial ultrasound and at 3 months performed both the GMs assessment and the Hammersmith Infant Neurologic Examination (HINE). Outcome was assessed at 2 years by the Touwen neurologic examination and the Clinical Adaptive Test/Clinical, Linguistic and Auditory Milestone Scale.



The integration of the two methods was shown to be more effective than the single assessments in predicting neurologic outcome. The additional benefit of combining the two approaches was particularly clear for the discrimination between unilateral and bilateral cerebral palsy.



The integrated use of a scorable neurological examination and Prechtl's assessment of GMs can improve early prediction of neurodevelopmental outcome in preterm infants and should complement other clinical and instrumental exams in follow-up programs.
Real-time traffic sign detection using SURF features on FPGA

Drivers' failure to observe traffic signs, especially the stop signs, has led to many serious traffic accidents. Video-based traffic sign detection is an important component of driver-assistance systems. In earlier systems, simple color and shape-based detection methods have been broadly applied. Recently, feature-based traffic sign detection algorithms are proposed to obtain more accurate results, especially when combined with the previous two. The Speeded Up Robust Features (SURF) algorithm is an outstanding feature detector and descriptor with rotation and illumination invariance. Unfortunately, due to its computational complexity, the application of SURF algorithm remains limited in real-time systems. In this paper, we present a real-time SURF-based traffic sign detection system by exploiting parallelism and rich resources in FPGAs. The proposed hardware design is able to accurately process video streams of 800 × 600 resolution at 60 frame per second.
Design and Analysis of a Two Stage Operational Amplifier for High Gain and High Bandwidth

In this paper a design and comparison between a fully differential RC Miller compensated CMOS op-amp and conventional op-amp is presented. High gain enables the circuit to operate efficiently in a closed loop feedback system, whereas high bandwidth makes it suitable for high speed applications. A novel RC Miller compensation technique is used to optimize the parameters of gain and bandwidth for high speed applications are illustrated in this research work. The design is also able to address any fluctuation in supply or dc input voltages and stabilizes the operation by nullifying. The design is implemented on TSMC 0.18 m CMOS process at 3.3 V as supply voltage under room temperature 27 C. The simulated result shows that a unity gain bandwidth of 136.8 MHz with a high gain of 92.27 dB is achieved for the proposed op-amp circuit. The total areas of the layouts are 0.000158 mm and 0.000532 mm for conventional and proposed respectively.  Two-Stage Op-amp, fully differential, high gain, high bandwidth
GP-GAN: Gender Preserving GAN for Synthesizing Faces from Landmarks

Facial landmarks constitute the most compressed representation of faces and are known to preserve information such as pose, gender and facial structure present in the faces. Several works exist that attempt to perform high-level face-related analysis tasks based on landmarks alone without the aid of face images. In contrast, in this work, an attempt is made to tackle the inverse problem of synthesizing faces from their respective landmarks. The primary aim of this work is to demonstrate that information preserved by landmarks (gender in particular) can be further accentuated by leveraging generative models to synthesize corresponding faces. Though the problem is particularly challenging due to its ill-posed nature, we believe that successful synthesis will enable several applications such as boosting performance of high-level face related tasks using landmark points and performing dataset augmentation. To this end, a novel face-synthesis method known as Gender Preserving Generative Adversarial Network (GP-GAN) that is guided by adversarial loss, perceptual loss and a gender preserving loss is presented. Further, we propose a novel generator sub-network UDeNet for GP-GAN that leverages advantages of U-Net and DenseNet architectures. Extensive experiments and comparison with recent methods are performed to verify the effectiveness of the proposed method. Our code is available at: https://github.com/DetionDXlGP-GAN-Gender-Preserving-GAN-for-Synthesizing-Faces-from-Landmarks
A new neonatal cortical and subcortical brain atlas: the Melbourne Children's Regional Infant Brain (M-CRIB) atlas

Investigating neonatal brain structure and function can offer valuable insights into behaviour and cognition in healthy and clinical populations; both at term age, and longitudinally in comparison with later time points. Parcellated brain atlases for adult populations are readily available, however warping infant data to adult template space is not ideal due to morphological and tissue differences between these groups. Several parcellated neonatal atlases have been developed, although there remains strong demand for manually parcellated ground truth data with detailed cortical definition. Additionally, compatibility with existing adult atlases is favourable for use in longitudinal investigations. We aimed to address these needs by replicating the widely-used Desikan-Killiany (2006) adult cortical atlas in neonates. We also aimed to extend brain coverage by complementing this cortical scheme with basal ganglia, thalamus, cerebellum and other subcortical segmentations. Thus, we have manually parcellated these areas volumetrically using high-resolution neonatal T2-weighted MRI scans, and initial automated and manually edited tissue classification, providing 100 regions in all. Linear and nonlinear T2-weighted structural templates were also generated. In this paper we provide manual parcellation protocols, and present the parcellated probability maps and structural templates together as the Melbourne Children's Regional Infant Brain (M-CRIB) atlas.
The least mean fourth (LMF) adaptive algorithm and its family

New steepest descent algorithms for adaptive filtering and have been devised which allow error minimization in the mean fourth and mean sixth, etc., sense. During adaptation, the weights undergo exponential relaxation toward their optimal solutions. T ime constants have been derived, and surprisingly they turn out to be proportional to the time constants that would have been obtained if the steepest descent least mean square (LMS) algorithm of Widrow and Hoff had been used. The new gradient algorithms are insignificantly more complicated to program and to compute than the LMS algorithm. Their general form is W J+l = w, t 2plqK-lx,, where W, is the present weight vector, W, + 1 is the next weight vector, r, is the present error, X, is the present input vector, u is a constant controlling stability and rate of convergence, and 2 K is the exponent of the error being minimized. Conditions have been derived for weight-vector convergence of the mean and of the variance for the new gradient algorithms. The behavior of the least mean fourth (LMF) algorithm is of special interest. In comparing this algorithm to the LMS algorithm, when both are set to have exactly the same time constants for the weight relaxation process, the LMF algorithm, under some circumstances, will have a substantially lower weight noise than the LMS algorithm. It is possible, therefore, that a min imum mean fourth error algorithm can do a better job of least squares estimation than a mean square error algorithm. This intriguing concept has implications for all forms of adaptive algorithms, whether they are based on steepest descent or otherwise.