Reviewerly/exHarmony huggingface dataset:
Wheelchair ergometer. Development of a prototype with electronic braking

Abstract A new wheelchair ergometer is described, which compensates for the pulsating character of the work by an automatic control system. This makes it possible to maintain a constant level of power during wheelchair work. Design: An automatic control system has been integrated in an electronically braked bicycle ergometer, and a pedal unit from Rodby Electronic bicycle ergometer RE 820 has been coupled to a modified test wheelchair. With this device, the physical working capacity during submaximal circumstances can be tested in handicapped persons.
Are The Visualization Skills Of First Year Engineering Students Changing?

Michigan Tech has been offering a course in the development of 3-D spatial skills since 1993.Each year since that time, engineering students have been tested with a standardized instrument during orientation and those who failed have been counseled to enroll in the spatial skills course.Instruments regarding student background experience have also been periodically administered.In this study, historical data was examined to determine if there were changes in test results through the years.The items of interest in this study were: changes in the average score on the spatial skills instrument through time, changes in failure rates on the pre-test, the impact of computer games on spatial skills development, gender differences in scores and failure rates through time, and changes in background variables through time.Data trends in this study will inform us regarding changing student backgrounds and will indicate whether the need for a spatial skills intervention course is increasing or decreasing.
Evaluating the Pairwise Event Salience Hypothesis in Indexter

Indexter is a plan-based computational model of narrative discourse which leverages cognitive scientific theories of how events are stored in memory during online comprehension. These discourse models are valuable for static and interactive narrative generation systems because they allow the author to reason about the audience's understanding and attention as they experience a story. A pair of Indexter events can share up to five indices: protagonist, time, space, causality, and intentionality. We present the first in a planned series of evaluations that will explore increasingly nuanced methods of using these indices to predict salience. The Pairwise Event Salience Hypothesis states that when a past event shares one or more indices with the most recently narrated event, that past event is more salient than one which shares no indices with the most recently narrated event. A crowd-sourced (n=200) study of 24 short text stories that control for content, text, and length supports this hypothesis. While this is encouraging, we believe it also motivates the development of a richer model that accounts for intervening events, narrative complexity, and episodic memory decay.
An improved learning algorithm for random neural networks based on particle swarm optimization and input-to-output sensitivity

Extreme learning machine (ELM) for random single-hidden-layer feedforward neural networks (RSLFN) has been widely applied in many fields in the past ten years because of its fast learning speed and good generalization performance. But because traditional ELM randomly selects the input weights and hidden biases, it typically requires high number of hidden neurons and thus decreases its convergence performance. It is necessary to select optimal input weights and hidden biases to improve the convergence performance of the traditional ELM. Generally, the single-hidden-layer feedforward neural networks (SLFN) with low input-to-output sensitivity will cause good robustness of the network, which may further lead into good generalization performance. Moreover, particle swarm optimization (PSO) has no complicated evolutionary operators and fewer parameters need to adjust, and is easy to implement. In this study, an improved ELM based on PSO and input-to-output sensitivity information is proposed to improve RSLFN’s convergence performance. In the improved ELM, PSO encoding the input to output sensitivity information of the SLFN is used to optimize the input weights and hidden biases. The improved ELM could obtain better generalization performance as well as improve the conditioning of the SLFN by decreasing the input-to-output sensitivity of the network. Finally, experiment results on the regression and classification problems verify the improved performance the proposed ELM.
Development of the Predict Observe Explain (POE)-based Thematic Teaching Materials

The learning process cannot be separated from the use of teaching materials that play a role to achieve the competencies that must be achieved by the learners. The purpose of this research is to identify the developmental stages of Predict Observe Explain (POE)-based thematic teaching materials, quality, and effectiveness of teaching materials to improve the learners' learning outcomes. This is a research and development technique. The development of this product is done through the design model of ADDIE learning system, i.e. (1) analysis, (2) design, (3) development, (4) implementation, (5) and evaluation. The results show that the POE-based thematic teaching materials has excellent quality on aspects of graphics and material with the score 4,52 and score 4,75 respectively from the aspects of readability, linguistic, and presentation according to the learners as the users of the product. Furthermore, the effectiveness test results show that the average class score that does not use POE-based thematic materials is 75,72, whereas that using POE thematic materials is 82,08. Therefore, the average score in the experimental class is higher than the control one. The Sig. shows 0,010 <0,05, then H0 is rejected and H1 accepted. It is concluded that there is a significant value difference between the control and experimental class
Primal-dual accelerated gradient descent with line search for convex and nonconvex optimization problems

In this paper a new variant of accelerated gradient descent is proposed. The proposed method does not require any information about the objective function, uses exact line search for the practical accelerations of convergence, converges according to the well-known lower bounds for both convex and non-convex objective functions and possesses primal-dual properties. We also provide a universal version of said method, which converges according to the known lower bounds for both smooth and non-smooth problems.
Indonesian state educational universities’ bibliometric dataset

This data article presents an important bibliometric dataset of ten Indonesian leading educational universities. These ten Indonesian leading educational universities are Universitas Pendidikan Indonesia (UPI), Universitas Negeri Malang (UM), Universitas Negeri Semarang (UNNES), Universitas Negeri Surabaya (Unesa), Universitas Negeri Yogyakarta (UNY), Universitas Negeri Makassar (UNM), Universitas Negeri Jakarta (UNJ), Universitas Negeri Padang (UNP), Universitas Negeri Medan (Unimed), and Universitas Negeri Gorontalo (UNG). Using Scopus database search analysis, this data article collects data on documents per year, subject area of documents, source title of documents, documents type, country/territory, author name and number of documents, affiliation and most cited documents for each universities.
Adaptive Covariance Feedback Cubature Kalman Filtering for Continuous-Discrete Bearings-Only Tracking System

Bearings-only tracking is a continuous-discrete system, whose state of motion is in the continuous-time domain and the measurement is in the discrete-time domain. The unpredicted approximation errors are inevitable due to integration, discretization, and linearization of continuous model in many filtering methods. The adaptive covariance feedback framework is proposed for solving this kind of problem, in which the posterior covariance sequence is proved theoretically to be useful for prior covariance updating. In this framework, the covariance feedback framework is integrated with the continuous-discrete cubature Kalman filtering, and Chebyshev distance is applied to judge the proper condition for the start-up of the feedback channel. The numerical results illustrate the proposed method’s superior performance in accuracy and computational efficiency.
Robust Locally Discriminant Analysis via Capped Norm

Conventional linear discriminant analysis and its extended versions have some potential drawbacks. First, they are sensitive to outliers, noise, and variations in data, which degrades their performances in dimensionality reduction. Second, most of the linear discriminant analysis-based methods only focus on the global structures of data but ignore their local geometric structures, which play important roles in dimensionality reduction. More importantly, the total number of projections obtained by linear discriminant analysis (LDA) based methods are limited by the class number in the training data set. To solve the problems mentioned above, we propose a novel method called robust locally discriminant analysis via capped norm (RLDA), in this paper. By replacing <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{2} $ </tex-math></inline-formula> -norm with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{2,1} $ </tex-math></inline-formula> - norm to construct the robust between-class scatter matrix and using the capped norm to further reduce the negative impact of outliers in constructing the within-class scatter matrix, we can guarantee the robustness of the proposed methods. In addition, we also impose <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{2,1} $ </tex-math></inline-formula> -norm regularized term on projection matrix, so that its joint sparsity can be ensured. Since we redefine the scatter matrices in traditional LDA, the projection numbers we obtain are no longer restricted by the class numbers. The experimental results show the superior performance of RLDA to other compared dimensionality reduction methods.
New links between nonlinearity and differential uniformity

In this paper some new links between the nonlinearity and differential uniformity of some large classes of functions are established. Differentially two-valued functions and quadratic functions are mainly treated. A lower bound for the nonlinearity of monomial δ-uniform permutations is obtained, for any δ, as well as an upper bound for differentially two-valued functions. Concerning quadratic functions, significant relations between nonlinearity and differential uniformity are exhibited. In particular, we show that the quadratic differentially 4-uniform permutations should be differentially two-valued and possess the best known nonlinearity.
Recurrent 3D attentional networks for end-to-end active object recognition

Active vision is inherently attention-driven: an agent actively selects views to attend in order to rapidly perform a vision task while improving its internal representation of the scene being observed. Inspired by the recent success of attention-based models in 2D vision tasks based on single RGB images, we address multi-view depth-based active object recognition using an attention mechanism, by use of an end-to-end recurrent 3D attentional network. The architecture takes advantage of a recurrent neural network to store and update an internal representation. Our model, trained with 3D shape datasets, is able to iteratively attend the best views targeting an object of interest for recognizing it. To realize 3D view selection, we derive a 3D spatial transformer network. It is differentiable, allowing training with backpropagation, and so achieving much faster convergence than the reinforcement learning employed by most existing attention-based models. Experiments show that our method, with only depth input, achieves state-of-the-art next-best-view performance both in terms of time taken and recognition accuracy.
Adapting data management education to support clinical research projects in an academic medical center

Background: Librarians and researchers alike have long identified research data management (RDM) training as a need in biomedical research. Despite the wealth of libraries offering RDM education to their communities, clinical research is an area that has not been targeted. Clinical RDM (CRDM) is seen by its community as an essential part of the research process where established guidelines exist, yet educational initiatives in this area are unknown.Case Presentation: Leveraging the author’s academic library’s experience supporting CRDM through informationist grants and REDCap training in our medical center, we developed a 1.5 hour CRDM workshop. This workshop was designed to use established CRDM guidelines in clinical research and address common questions asked by our community through the library’s existing data support program. The workshop was offered to the entire medical center 4 times between November 2017 and July 2018. This case study describes the development, implementation, and evaluation of this workshop.Conclusions: The 4 workshops were well attended and well received by the medical center community, with 99% stating that they would recommend the class to others and 98% stating that they would use what they learned in their work. Attendees also articulated how they would implement the main competencies they learned from the workshop into their work. For the library, the effort to support CRDM has led to the coordination of a larger institutional collaborative training series to educate researchers on best practices with data, as well as the formation of institution-wide policy groups to address researcher challenges with CRDM, data transfer, and data sharing.
Algorithm Design for Edge Detection of High-Speed Moving Target Image under Noisy Environment

For some measurement and detection applications based on video (sequence images), if the exposure time of camera is not suitable with the motion speed of the photographed target, fuzzy edges will be produced in the image, and some poor lighting condition will aggravate this edge blur phenomena. Especially, the existence of noise in industrial field environment makes the extraction of fuzzy edges become a more difficult problem when analyzing the posture of a high-speed moving target. Because noise and edge are always both the kind of high-frequency information, it is difficult to make trade-offs only by frequency bands. In this paper, a noise-tolerant edge detection method based on the correlation relationship between layers of wavelet transform coefficients is proposed. The goal of the paper is not to recover a clean image from a noisy observation, but to make a trade-off judgment for noise and edge signal directly according to the characteristics of wavelet transform coefficients, to realize the extraction of edge information from a noisy image directly. According to the wavelet coefficients tree and the Lipschitz exponent property of noise, the idea of neural network activation function is adopted to design the activation judgment method of wavelet coefficients. Then the significant wavelet coefficients can be retained. At the same time, the non-significant coefficients were removed according to the method of judgment of isolated coefficients. On the other hand, based on the design of Daubechies orthogonal compactly-supported wavelet filter, rational coefficients wavelet filters can be designed by increasing free variables. By reducing the vanishing moments of wavelet filters, more high-frequency information can be retained in the wavelet transform fields, which is benefit to the application of edge detection. For a noisy image of high-speed moving targets with fuzzy edges, by using the length 8-4 rational coefficients biorthogonal wavelet filters and the algorithm proposed in this paper, edge information could be detected clearly. Results of multiple groups of comparative experiments have shown that the edge detection effect of the proposed algorithm in this paper has the obvious superiority.
A Comparison of the Homomorphic Encryption Libraries HElib, SEAL and FV-NFLlib

Fully homomorphic encryption has considerably evolved during the past 10 years. In particular, the discovery of more efficient schemes has brought the computational complexity down to acceptable levels for some applications. Several implementations of these schemes have been publicly released, enabling researchers and practitioners to better understand the performance properties of the schemes. This improved understanding of the performance has led to the discovery of new potential applications of homomorphic encryption, fuelling further research on all fronts. In this work, we provide a comparative benchmark of the leading homomorphic encryption libraries HElib, FV-NFLlib, and SEAL for large plaintext moduli of up to 2048 bits, and analyze their relative performance.
The effect of Z-Score standardization (normalization) on binary input due the speed of learning in back-propagation neural network

The speed of learning in neural network environment is considered as the most effective parameter spatially in large data sets. This paper tries to minimize the time required for the neural network to fully understand and learn about the data by standardize input data. The paper showed that the Z-Score standardization of input data significantly decreased the number of epoochs required for the network to learn. This paper also proved that the binary dataset is a serious limitation for the convergence of neural network, so the standardization is a must in such case where the 0’s inputs simply neglect the connections in the neural network. The data set used in this paper are features extracted from gel electrophoresis images and that open the door for using artificial intelligence in such areas.
Experimental comparison of tomography and self-testing in certifying entanglement

We assess the quality of a source of allegedly pure two-qubit states using both standard tomography and methods inspired by device-independent self-testing. Even when the detection and locality loopholes are open, the latter methods can dispense with modeling of the system and the measurements. However, due to finite sample fluctuations, the estimated probability distribution usually does not satisfy the no-signaling conditions exactly. We implement data analysis that is robust against these fluctuations. We demonstrate a high ratio ${f}_{s}/{f}_{t}\ensuremath{\approx}0.988$ between the fidelity estimated from self-testing and that estimated from full tomography, proving the high performance of self-testing methods.
Students’ Perspectives towards Using Youtube in Improving EFL Learners’ Motivation to Speak

&lt;p&gt;&lt;em&gt;It is beneficial for educators to apply the use of modeling strategies to assist English language learners (ELLs) and their ability to improve their understanding of word diction and the movements that are needed to create English sounds using their mouths and tongues. The purpose of this study is to determine whether Youtube, as a media source that is able to model language practice in an engaging manner, can be used to help ELLs gain improve word pronunciation skills. A questionnaire was administered to students to assess the attitudes that students had towards using Youtube to improve their motivation to speak and practice language in class. The results demonstrated that the use of Youtube in the English language classroom represents an engaging method, and this motivates students to participate in these lessons. Furthermore, participation in English speaking lessons using Youtube videos increased student confidence about their English-speaking abilities. It is therefore reasonable for educators to continue to use Youtube and other video media as a part of instruction in the ELL classroom because this motivates students to learn and to practice their speaking skills aloud with confidence, enabling them to achieve their language attainment goals.&lt;/em&gt;&lt;/p&gt;
Cryptanalysis and improvement of dynamic quantum secret sharing protocol based on two-particle transform of Bell states

Recently, Du and Bao proposed a quantum secret sharing protocol based on two-particle transform of Bell states. We study the security of the proposed protocol and find that it is not secure, that is, the two dishonest agents, Bob and Zach, can collude to obtain Alice’s secret messages without the help of the other agents. Finally, we give a possible improvement of the proposed protocol.
Smooth neighborhood recommender systems

Recommender systems predict users’ preferences over a large number of items by pooling similar information from other users and/or items in the presence of sparse observations. One major challenge is how to utilize user-item speciﬁc covariates and networks describing user-item interactions in a high-dimensional situation, for accurate personalized prediction. In this article, we propose a smooth neighborhood recommender in the framework of the latent factor models. A similarity kernel is utilized to borrow neighborhood information from continuous covariates over a user-item speciﬁc network, such as a user’s social network, where the grouping information deﬁned by discrete covariates is also integrated through the network. Consequently, user-item speciﬁc information is built into the recommender to battle the ‘cold-start” issue in the absence of observations in collaborative and contentbased ﬁltering. Moreover, we utilize a “divide-and-conquer” version of the alternating least squares algorithm to achieve scalable computation, and establish asymptotic results for the proposed method, demonstrating that it achieves superior prediction accuracy. Finally, we illustrate that the proposed method improves substantially over its competitors in simulated examples and real benchmark data–Last.fm music data.
The problem of proof identity, and why computer scientists should care about Hilbert's 24th problem

In this short overview article, I will discuss the problem of proof identity and explain how it is related to Hilbert's 24th problem. I will also argue that not knowing when two proofs are ‘the same’ has embarrassing consequences not only for proof theory but also for certain areas of computer science where formal proofs play a fundamental role, in particular, the formal verification of software. Then I will formulate a set of four objectives that a satisfactory notion of proof identity should obey. And finally, I discuss Hughes' combinatorial proofs and argue that they can be seen as a first step towards a possible solution to the problem of proof identity. This article is part of the theme issue ‘The notion of ‘simple proof’ - Hilbert's 24th problem’.
Advancing Clinical Research Through Natural Language Processing on Electronic Health Records: Traditional Machine Learning Meets Deep Learning

Electronic health records (EHR) capture “real-world” disease and care processes and hence offer richer and more generalizable data for comparative effectiveness research than traditional randomized clinical trial studies. With the increasingly broadening adoption of EHR worldwide, there is a growing need to widen the use of EHR data to support clinical research. A big barrier to this goal is that much of the information in EHR is still narrative. This chapter describes the foundation of biomedical language processing and explains how traditional machine learning and the state-of-the-art deep learning techniques can be employed in the context of extracting and transforming narrative information in EHR to support clinical research.