nils-herrmann/clean_scirepeval huggingface dataset:
The Algorithms for FPGA Implementation of Sparse Matrices Multiplication

In comparison to dense matrices multiplication, sparse matrices multiplication real performance for CPU is roughly 5–100 times lower when expressed in GFLOPs. For sparse matrices, microprocessors spend most of the time on comparing matrices indices rather than performing floating-point multiply and add operations. For 16-bit integer operations, like indices comparisons, computational power of the FPGA significantly surpasses that of CPU. Consequently, this paper presents a novel theoretical study how matrices sparsity factor influences the indices comparison to floating-point operation workload ratio. As a result, a novel FPGAs architecture for sparse matrix-matrix multiplication is presented for which indices comparison and floating-point operations are separated. We also verified our idea in practice, and the initial implementations results are very promising. To further decrease hardware resources required by the floating-point multiplier, a reduced width multiplication is proposed in the case when IEEE-754 standard compliance is not required.
Current concepts in the rehabilitation of the overhead throwing athlete.

The overhead throwing motion is an extremely skillful and intricate movement that is very stressful on the shoulder joint complex. The overhead throwing athlete places extraordinary demands on this complex. Excessively high stresses are applied to the shoulder joint because of the tremendous forces generated by the thrower. The thrower's shoulder must be lax enough to allow excessive external rotation, but stable enough to prevent symptomatic humeral head subluxations, thus requiring a delicate balance between mobility and functional stability. We refer to this as the "thrower's paradox." This balance is frequently compromised, which leads to injury. Numerous types of injuries may occur to the surrounding tissues during overhead throwing. Frequently, injuries can be successfully treated with a well-structured and carefully implemented nonoperative rehabilitation program. The key to successful nonoperative treatment is a thorough clinical examination and accurate diagnosis. Athletes often exhibit numerous adaptive changes that develop from the repetitive microtraumatic stresses observed during overhead throwing. Treatment should focus on the restoration of these adaptations during the rehabilitation program. In this article, the typical musculoskeletal profile of the overhead thrower and various rehabilitation programs for specific injuries are discussed. Rehabilitation follows a structured, multiphase approach with emphasis on controlling inflammation, restoring muscle balance, improving soft tissue flexibility, enhancing proprioception and neuromuscular control, and efficiently returning the athlete to competitive throwing.
Intelligent Software Engineering: Synergy between AI and Software Engineering

There has been a long history of applying AI technologies to address software engineering problems especially on tool automation. On the other hand, given the increasing importance and popularity of AI software, recent research efforts have been on exploring software engineering solutions to improve the productivity of developing AI software and the dependability of AI software. The emerging field of intelligent software engineering is to focus on two aspects: (1) instilling intelligence in solutions for software engineering problems; (2) providing software engineering solutions for intelligent software. This extended abstract shares perspectives on these two aspects of intelligent software engineering.
Dynamic walking control by using the liquid level model and the preview of zero-moment-point

This electronic document presents the application of a novel method of bipedal walking pattern generation assured by “the liquid level model” and the preview control of zero-moment-point (ZMP). In this method, the trajectory of the center of mass (CoM) of the robot is generated assured by the preview controller to maintain the ZMP at the desired location knowing that the robot is modeled as a running liquid level model on a tank. The proposed approach combines the preview control theory with simple model “the liquid level model”, to assure a stable dynamic walking. Simulations results show that the proposed pattern generator guarantee not only to walk dynamically stable but also good performance.
Open information extraction as an intermediate semantic structure for Persian text summarization

Semantic applications typically exploit structures such as dependency parse trees, phrase-chunking, semantic role labeling or open information extraction. In this paper, we introduce a novel application of Open IE as an intermediate layer for text summarization. Text summarization is an important method for providing relevant information in large digital libraries. Open IE is referred to the process of extracting machine-understandable structural propositions from text. We use these propositions as a building block to shorten the sentence and generate a summary of the text. The proposed system offers a new form of summarization that is able to break the structure of the sentence and extract the most significant sub-sentential elements. Other advantages include the ability to identify and eliminate less important sections of the sentence (such as adverbs, adjectives, appositions or dependent clauses), or duplicate pieces of sentences which in turn opens up the space for entering more sentences in the summary to enhance the coverage and coherency of it. The proposed system is localized for Persian language; however, it can be adopted to other languages. Experiments performed on a standard data set “Pasokh” with a standard comparison tool showed promising results for the proposed approach. We used summaries produced by the system in a real-world application in the virtual library of Shahid Beheshti University and received good feedbacks from users.
Anchored neighborhood regression based single image super-resolution from self-examples

In this paper, we present a novel self-learning single image super-resolution (SR) method, which restores a high-resolution (HR) image from self-examples extracted from the low-resolution (LR) input image itself without relying on extra external training images. In the proposed method, we directly use sampled image patches as the anchor points, and then learn multiple linear mapping functions based on anchored neighborhood regression to transform LR space into HR space. Moreover, we utilize the flipped and rotated versions of the self-examples to expand the internal patch space. Experimental comparison on standard benchmarks with state-of-the-art methods validates the effectiveness of the proposed approach.
The compassionate sexist? How benevolent sexism promotes and undermines gender equality in the workplace.

Although sexist attitudes are generally thought to undermine support for employment equity (EE) policies supporting women, we argue that the effects of benevolent sexism are more complex. Across 4 studies, we extend the ambivalent sexism literature by examining both the positive and the negative effects benevolent sexism has for the support of gender-based EE policies. On the positive side, we show that individuals who endorse benevolent sexist attitudes on trait measures of sexism (Study 1) and individuals primed with benevolent sexist attitudes (Study 2) are more likely to support an EE policy, and that this effect is mediated by feelings of compassion. On the negative side, we find that this support extends only to EE policies that promote the hiring of women in feminine, and not in masculine, positions (Study 3 and 4). Thus, while benevolent sexism may appear to promote gender equality, it subtly undermines it by contributing to occupational gender segregation and leading to inaction in promoting women in positions in which they are underrepresented (i.e., masculine positions).
Coreference Resolution : Current Trends and Future Directions

Coreference resolution seeks to find the mentions in text that refer to the same real-world entity. This task has been well-studied in NLP, but until recent years, empirical results have been disappointing. Recent research has greatly improved the state-of-the-art. In this review, we focus on five papers that represent the current state-ofthe-art and discuss how they relate to each other and how these advances will influence future work in this area.
Conditional End-to-End Audio Transforms

We present an end-to-end method for transforming audio from one style to another. For the case of speech, by conditioning on speaker identities, we can train a single model to transform words spoken by multiple people into multiple target voices. For the case of music, we can specify musical instruments and achieve the same result. Architecturally, our method is a fullydifferentiable sequence-to-sequence model based on convolutional and hierarchical recurrent neural networks. It is designed to capture long-term acoustic dependencies, requires minimal post-processing, and produces realistic audio transforms. Ablation studies confirm that our model can separate speaker and instrument properties from acoustic content at different context sizes. Empirically, our method achieves competitive performance on community-standard datasets.
A Model-driven Role-based Access Control for SQL Databases

Nowadays security has become an important aspect in information systems engineering. A mainstream method for information system security is Role-based Access Control (RBAC), which restricts system access to authorised users. While the benefits of RBAC are widely acknowledged, the implementation and administration of RBAC policies remains a human intensive activity, typically postponed until the implementation and maintenance phases of system development. This deferred security engineering approach makes it difficult for security requirements to be accurately captured and for the system’s implementation to be kept aligned with these requirements as the system evolves. In this paper we propose a model-driven approach to manage SQL database access under the RBAC paradigm. The starting point of the approach is an RBAC model captured in SecureUML. This model is automatically translated to Oracle Database views and instead-of triggers code, which implements the security constraints. The approach has been fully instrumented as a prototype and its effectiveness has been validated by means of a case study.
NRZ versus RZ in 10-40-Gb/s dispersion-managed WDM transmission systems

We compare nonreturn-to-zero (NRZ) with return-to-zero (RZ) modulation format for wavelength-division-multiplexed systems operating at data rates up to 40 Gb/s. We find that in 10-40-Gb/s dispersion-managed systems (single-mode fiber alternating with dispersion compensating fiber), NRZ is more adversely affected by nonlinearities, whereas RZ is more affected by dispersion. In this dispersion map, 10- and 20-Gb/s systems operate better using RZ modulation format because nonlinearity dominates. However, 40-Gb/s systems favor the usage of NRZ because dispersion becomes the key limiting factor at 40 Gb/s.
Sinogram-consistency learning in CT for metal artifact reduction

This paper proposes a sinogram consistency learning method to deal with beam-hardening related artifacts in polychromatic computerized tomography (CT). The presence of highly attenuating materials in the scan field causes an inconsistent sinogram, that does not match the range space of the Radon transform. When the mismatched data are entered into the range space during CT reconstruction, streaking and shading artifacts are generated owing to the inherent nature of the inverse Radon transform. The proposed learning method aims to repair inconsistent sinograms by removing the primary metal-induced beam-hardening factors along the metal trace in the sinogram. Taking account of the fundamental difficulty in obtaining sufficient training data in a medical environment, the learning method is designed to use simulated training data. We use a patient-type specific learning model to simplify the learning process. The quality of sinogram repair was established through data inconsistency-evaluation and acceptance checking, which were conducted using a specially designed inconsistencyevaluation function that identifies the degree and structure of mismatch in terms of projection angles. The results show that our method successfully corrects sinogram inconsistency by extracting beam-hardening sources by means of deep learning.
Design of low-phase-noise CMOS ring oscillators

This paper presents a framework for modeling the phase noise in complementary metal–oxide–semiconductor (CMOS) ring oscillators. The analysis considers both linear and nonlinear operations, and it includes both device noise and digital switching noise coupled through the power supply and substrate. In this paper, we show that fast rail-to-rail switching is required in order to achieve low phase noise. Further, flicker noise from the bias circuit can potentially dominate the phase noise at low offset frequencies. We define the effective factor for ring oscillators with large and nonlinear voltage swings and predict its increase for CMOS processes with smaller feature sizes. Our phase-noise analysis is validated via simulation and measurement results for ring oscillators fabricated in a number of CMOS processes.
Skill-based Exception Handling and Error Recovery for Collaborative Industrial Robots

Gualzru’s path to the Advertisement World . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 Fernando Fernández, Moisés Mart́ınez, Ismael Garćıa-Varea, Jesús Mart́ınez-Gómez,  Raquel Viciana, Pablo Bustos, Luis J. Manso, Luis Calderita, Marco Antonio Gutiérrez Giraldo, Pedro Núñez, Antonio Bandera, Adrián Romero-Garcés, Juan Bandera and Rebeca Marfil
Unsupervised Training for 3D Morphable Model Regression

We present a method for training a regression network from image pixels to 3D morphable model coordinates using only unlabeled photographs. The training loss is based on features from a facial recognition network, computed on-the-fly by rendering the predicted faces with a differentiable renderer. To make training from features feasible and avoid network fooling effects, we introduce three objectives: a batch distribution loss that encourages the output distribution to match the distribution of the morphable model, a loopback loss that ensures the network can correctly reinterpret its own output, and a multi-view identity loss that compares the features of the predicted 3D face and the input photograph from multiple viewing angles. We train a regression network using these objectives, a set of unlabeled photographs, and the morphable model itself, and demonstrate state-of-the-art results.
Future Frame Prediction for Anomaly Detection - A New Baseline

Anomaly detection in videos refers to the identification of events that do not conform to expected behavior. However, almost all existing methods tackle the problem by minimizing the reconstruction errors of training data, which cannot guarantee a larger reconstruction error for an abnormal event. In this paper, we propose to tackle the anomaly detection problem within a video prediction framework. To the best of our knowledge, this is the first work that leverages the difference between a predicted future frame and its ground truth to detect an abnormal event. To predict a future frame with higher quality for normal events, other than the commonly used appearance (spatial) constraints on intensity and gradient, we also introduce a motion (temporal) constraint in video prediction by enforcing the optical flow between predicted frames and ground truth frames to be consistent, and this is the first work that introduces a temporal constraint into the video prediction task. Such spatial and motion constraints facilitate the future frame prediction for normal events, and consequently facilitate to identify those abnormal events that do not conform the expectation. Extensive experiments on both a toy dataset and some publicly available datasets validate the effectiveness of our method in terms of robustness to the uncertainty in normal events and the sensitivity to abnormal events. All codes are released in https://github.com/StevenLiuWen/ano_pred_cvpr2018.
The control volume radial basis function method CV-RBF with Richardson extrapolation in geochemical problems

The aim of this paper is to present how to implement a control volume approach improved by Hermite radial basis functions (CV-RBF) for geochemical problems. A multi-step strategy based on Richardson extrapolation is proposed as an alternative to the conventional dual step sequential non-iterative approach (SNIA) for coupling the transport equations with the chemical model. Additionally, this paper illustrates how to use PHREEQC to add geochemical reaction capabilities to CV-RBF transport methods. Several problems with different degrees of complexity were solved including cases of cation exchange, dissolution, dissociation, equilibrium and kinetics at different rates for mineral species. The results show that the solution and strategies presented here are effective and in good agreement with other methods presented in the literature for the same cases. & 2015
Neural Network Classifiers Estimate Bayesian a posteriori Probabilities

Many neural network classifiers provide outputs which estimate Bayesian a posteriori probabilities. When the estimation is accurate, network outputs can be treated as probabilities and sum to one. Simple proofs show that Bayesian probabilities are estimated when desired network outputs are 2 of M (one output unity, all others zero) and a squarederror or cross-entropy cost function is used. Results of Monte Carlo simulations performed using multilayer perceptron (MLP) networks trained with backpropagation, radial basis function (RBF) networks, and high-order polynomial networks graphically demonstrate that network outputs provide good estimates of Bayesian probabilities. Estimation accuracy depends on network complexity, the amount of training data, and the degree to which training data reflect true likelihood distributions and u priori class probabilities. Interpretation of network outputs as Bayesian probabilities allows outputs from multiple networks to be combined for higher level decision making, simplifies creation of rejection thresholds, makes it possible to compensate for differences between pattern class probabilities in training and test data, allows outputs to be used to minimize alternative risk functions, and suggests alternative measures of network performance.
Permanent magnet generator design and control for large wind turbines

Direct drive permanent magnet generators(PMGs) are increasingly capturing the global wind market in large onshore and offshore applications. The aim of this paper is to provide a quick overview of permanent magnet generator design and related control issues for large wind turbines. Generator systems commonly used in wind turbines, the permanent magnet generator types, and control methods are reviewed in the paper. The current commercial PMG wind turbine on market is surveyed. The design of a 5 MW axial flux permanent magnet (AFPM) generator for large wind turbines is discussed and presented in detail.
Judgments of Azimuth and Elevation as a Function of Monoscopic and Binocular Depth Cues Using a Perspective Display

The purpose of this study was to investigate the use of three-dimensional display formats for judgments of spatial information using an exocentric frame of reference. Eight subjects judged the azimuth and elevation that separated two computer-generated objects using either a perspective or stereoscopic display. Errors, which consisted of the difference in absolute value between the estimated and actual azimuth or elevation, were analyzed as the response variable. The data indicated that the stereoscopic display resulted in more accurate estimates of elevation, especially for images aligned approximately orthogonally to the viewing vector. However, estimates of relative azimuth direction were not improved by use of the stereoscopic display. Furthermore, it was shown that the effect of compression resulting from a 45-deg computer graphics eye point elevation produced a response bias that was symmetrical around the horizontal plane of the reference cube, and that the depth cue of binocular disparity provided by the stereoscopic display reduced the magnitude of the compression errors. Implications of the results for the design of spatial displays are discussed.
Experiential Robot Learning with Accelerated Neuroevolution

Derivative-based optimization techniques such as Stochastic Gradient Descent has been wildly successful in training deep neural networks. However, it has constraints such as end-to-end network differentiability. As an alternative, we present the Accelerated Neuroevolution algorithm. The new algorithm is aimed towards physical robotic learning tasks following the Experiential Robot Learning method. We test our algorithm first on a simulated task of playing the game Flappy Bird, then on a physical NAO robot in a static Object Centering task. The agents successfully navigate the given tasks, in a relatively low number of generations. Based on our results, we propose to use the algorithm in more complex tasks.
Information Security Policy Compliance: An Empirical Study of Ethical Ideology

Information security policy compliance (ISP) is one of the key concerns that face organizations today. Although technical and procedural measures help improve information security, there is an increased need to accommodate human, social and organizational factors. Despite the plethora of studies that attempt to identify the factors that motivate compliance behavior or discourage abuse and misuse behaviors, there is a lack of studies that investigate the role of ethical ideology per se in explaining compliance behavior. The purpose of this research is to investigate the role of ethics in explaining Information Security Policy (ISP) compliance. In that regard, a model that integrates behavioral and ethical theoretical perspectives is developed and tested. Overall, analyses indicate strong support for the validation of the proposed theoretical model.
Evaluation of Non-Intrusive Approaches for Wiener-Askey Generalized Polynomial Chaos

Polynomial chaos expansions (PCE) are an attractive technique for uncertainty quantification (UQ) due to their strong mathematical basis and ability to produce functional representations of stochastic variability. When tailoring the orthogonal polynomial bases to match the forms of the input uncertainties in a Wiener-Askey scheme, excellent convergence properties can be achieved for general probabilistic analysis problems. Non-intrusive PCE methods allow the use of simulations as black boxes within UQ studies, and involve the calculation of chaos expansion coefficients based on a set of response function evaluations. These methods may be characterized as being either Galerkin projection methods, using sampling or numerical integration, or regression approaches (also known as point collocation or stochastic response surfaces), using linear least squares. Numerical integration methods may be further categorized as either tensor product quadrature or sparse grid Smolyak cubature and as either isotropic or anisotropic. Experience with these approaches is presented for algebraic and PDE-based benchmark test problems, demonstrating the need for accurate, efficient coefficient estimation approaches that scale for problems with significant numbers of random variables.