nils-herrmann/clean_scirepeval huggingface dataset:
Representations Systems, Perceptual Position, and Presence in Immersive Virtual Environments

This paper discusses factors that may contribute to the participant's sense of presence in immersive virtual environments. We distinguish between external factors, that is those wholly determined by the hardware and software technology employed to generate the environment, and subjective factors, that is how sensory inputs to the human participant are processed internally. The therapeutic technique known as neurolinguistic programming (NLP) is used as a basis for measuring such internal factors. NLP uses the idea of representation systems (visual, auditory, and kinesthetic) and perceptual position (egocentric or exocentric) to code subjective experience. The paper also considers one external factor, that is how the virtual environment represents a participant—either as a complete body, or just an arrow cursor that responds to hand movements. A case-control pilot experiment is described, where the controls have self-representation as an arrow cursor, and the experimental group subjects as a simple virtual body. Measurements of subjects' preferred representation systems and perceptual positions are obtained based on counts of types of predicates and references used in essays written after the experiment. These, together with the control variable (possession/absence of a virtual body), are used as explanatory variables in a regression analysis, with reported sense of presence as the dependent variable. Although tentative and exploratory in nature, the data analysis does suggest a relationship between reported sense of presence, preferred representation system, perceptual position, and an interaction effect between these and the virtual body factor.
Unsupervised feature extraction with autoencoder trees

The autoencoder is a popular neural network model that learns hidden representations of unlabeled data. Typically, singleor multilayer perceptrons are used in constructing an autoencoder, but we use soft decision trees (i.e., hierarchical mixture of experts) instead. Such trees have internal nodes that implement soft multivariate splits through a gating function and all leaves are weighted by the gating values on their path to get the output. The encoder tree converts the input to a lower dimensional representation in its leaves, which it passes to the decoder tree that reconstructs the original input. Because the splits are soft, the encoder and decoder trees can be trained back to back with stochastic gradient-descent to minimize reconstruction error. In our experiments on handwritten digits, newsgroup posts, and images, we observe that the autoencoder trees yield as small and sometimes smaller reconstruction error when compared with autoencoder perceptrons. One advantage of the tree is that it learns a hierarchical representation at different resolutions at its different levels and the leaves specialize at different local regions in the input space. An extension with locally linear mappings in the leaves allows a more flexible model. We also show that the autoencoder tree can be used with multimodal data where a mapping from one modality (i.e., image) to another (i.e., topics) can be learned.
Mechatronic design of innovative fingers for anthropomorphic robot hands

In this paper, a novel design approach for the development of robot hands is presented. This approach, that can be considered alternative to the “classical” one, takes into consideration compliant structures instead of rigid ones. Compliance effects, which were considered in the past as a “defect” to be mechanically eliminated, can be viceversa regarded as desired features and can be properly controlled in order to achieve desired properties from the robotic device. In particular, this is true for robot hands, where the mechanical complexity of “classical” design solutions has always originated complicated structures, often with low reliability and high costs. In this paper, an alternative solution to the design of dexterous robot hand is illustrated, considering a “mechatronic approach” for the integration of the mechanical structure, the sensory and electronic system, the control and the actuation part. Moreover, the preliminary experimental activity on a first prototype is reported and discussed. The results obtained so far, considering also reliability, costs and development time, are very encouraging, and allows to foresee a wider diffusion of dextrous hands for robotic applications.
The acceptability of assistive technology to older people

Assistive technology (AT) is defined in this paper as ‘any device or system that allows an individual to perform a task that they would otherwise be unable to do, or increases the ease and safety with which the task can be performed’ (Cowan and Turner-Smith 1999). Its importance in contributing to older people’s independence and autonomy is increasingly recognised, but there has been little research into the viability of extensive installations of AT. This paper focuses on the acceptability of AT to older people, and reports one component of a multidisciplinary research project that examined the feasibility, acceptability, costs and outcomes of introducing AT into their homes. Sixty-seven people aged 70 or more years were interviewed in-depth during 2001 to find out about their use and experience of a wide range of assistive technologies. The findings suggest a complex model of acceptability, in which a ‘ felt need’ for assistance combines with ‘product quality ’. The paper concludes by considering the tensions that may arise in the delivery of acceptable assistive technology.
Who Should Review this Pull-Request: Reviewer Recommendation to Expedite Crowd Collaboration

Github facilitates the pull-request mechanism as an outstanding social coding paradigm by integrating with social media. The review process of pull-requests is a typical crowd sourcing job which needs to solicit opinions of the community. Recommending appropriate reviewers can reduce the time between the submission of a pull-request and the actual review of it. In this paper, we firstly extend the traditional Machine Learning (ML) based approach of bug triaging to reviewer recommendation. Furthermore, we analyze social relations between contributors and reviewers, and propose a novel approach to recommend highly relevant reviewers by mining comment networks (CN) of given projects. Finally, we demonstrate the effectiveness of these two approaches with quantitative evaluations. The results show that CN-based approach achieves a significant improvement over the ML-based approach, and on average it reaches a precision of 78% and 67% for top-1 and top-2 recommendation respectively, and a recall of 77% for top-10 recommendation.
Single-Shot Refinement Neural Network for Object Detection

For object detection, the two-stage approach (e.g., Faster R-CNN) has been achieving the highest accuracy, whereas the one-stage approach (e.g., SSD) has the advantage of high efficiency. To inherit the merits of both while overcoming their disadvantages, in this paper, we propose a novel single-shot based detector, called RefineDet, that achieves better accuracy than two-stage methods and maintains comparable efficiency of one-stage methods. RefineDet consists of two inter-connected modules, namely, the anchor refinement module and the object detection module. Specifically, the former aims to (1) filter out negative anchors to reduce search space for the classifier, and (2) coarsely adjust the locations and sizes of anchors to provide better initialization for the subsequent regressor. The latter module takes the refined anchors as the input from the former to further improve the regression accuracy and predict multi-class label. Meanwhile, we design a transfer connection block to transfer the features in the anchor refinement module to predict locations, sizes and class labels of objects in the object detection module. The multitask loss function enables us to train the whole network in an end-to-end way. Extensive experiments on PASCAL VOC 2007, PASCAL VOC 2012, and MS COCO demonstrate that RefineDet achieves state-of-the-art detection accuracy with high efficiency. Code is available at https://github.com/sfzhang15/RefineDet.
Relative Impedance Control for Dual-Arm Robots Performing Asymmetric Bimanual Tasks

This paper presents a method of implementing impedance control (with inertia, damping, and stiffness terms) on a dual-arm system by using the relative Jacobian technique. The proposed method significantly simplifies the control implementation because the dual arm is treated as a single manipulator, whose end-effector motion is defined by the relative motion between the two end effectors. As a result, task description becomes simpler and more intuitive when specifying the desired impedance and the desired trajectories. This is the basis for the relative impedance control. In addition, the use of time-delay estimation enhances ease of implementation of our proposed method to a physical system, which would have been otherwise a very tedious and complicated process.
A Review on Entity Relation Extraction

Because of large amounts of unstructured data generated on the Internet, entity relation extraction is believed to have high commercial value. Entity relation extraction is a case of information extraction and it is based on entity recognition. This paper firstly gives a brief overview of relation extraction. On the basis of reviewing the history of relation extraction, the research status of relation extraction is analyzed. Then the paper divides theses research into three categories: supervised machine learning methods, semi-supervised machine learning methods and unsupervised machine learning method, and toward to the deep learning direction.
Maternal depression and parenting behavior: a meta-analytic review.

The results of 46 observational studies were analyzed to assess the strength of the association between depression and parenting behavior and to identify variables that moderated the effects. The association between depression and parenting was manifest most strongly for negative maternal behavior and was evident to a somewhat lesser degree in disengagement from the child. The association between depression and positive maternal behavior was relatively weak, albeit significant. Effects for negative maternal behavior were moderated by timing of the depression: Current depression was associated with the largest effects. However, residual effects of prior depression were apparent for all behaviors. Socioeconomic status, child age, and methodological variables moderated the effects for positive behavior: Effects were strongest for studies of disadvantaged women and mothers of infants. Studies using diagnostic interviews and self-report measures yielded similar effects, suggesting that deficits are not specific to depressive disorder. Research is needed to identify factors that affect the magnitude of parenting deficits among women who are experiencing depression and other psychological difficulties.
CONNECT: CONtextual NamE disCovery for blockchain-based services in the IoT

The Internet of Things is gaining momentum thanks to the provided vision of seamlessly interconnected devices. However, a unified way to discover and to interact with the surrounding smart environment is missing. As an outcome, we have been assisting to the development of heterogeneous ecosystems, where each service provider adopts its own protocol— thus preventing IoT devices from interacting when belonging to different providers. And, the same is happening again for the blockchain technology which provides a robust and trusted way to accomplish tasks —unfortunately not providing interoperability thus creating the same heterogeneous ecosystems above highlighted. In this context, the fundamental research question we address is how do we find things or services in the Internet of Things. In this paper, we propose the first IoT discovery approach which provides an answer to the above question by exploiting hierarchical and universal multi-layered blockchains. Our approach does neither define new standards nor force service providers to change their own protocol. On the contrary, it leverages the existing and publicly available information obtained from each single blockchain to have a better knowledge of the surrounding environment. The proposed approach is detailed and discussed with the support of relevant use cases.
SELF-EFFICACY DEVELOPMENT IN ADOLESCENCES

Stacie and Meg—juniors at Atlas High School—soon must submit their course requests for next year. They have completed 3 years of science as mandated by the school system and must decide whether to take additional courses. Physics is an option, and although it is not required they believe that taking it may help with college admission. To date they have received similar grades (As and Bs) in science courses. The night before the class sign-up date they discuss the situation with their parents. Meg's dad feels that she should take physics since it will help her understand how the world works. Meg notes that Ms. Blakely (the physics teacher) is not very good. After further discussion, however, Meg concludes that she feels confident about learning physics because she always has been able to learn science in the past and that if she does not understand something she will ask the teacher. So Meg decides to sign up for it. Stacie, on the other hand, tells her parents that she just does not feel smart enough to learn or do well in physics and that because Ms. Blakely is not a good teacher Stacie would not receive much help from her. Stacie also tells her parents that few girls take the course. Under no pressure from her parents, Stacie decides she will not sign up for physics.
Error modeling schemes for fading channels in wireless communications: A survey

Network system designers need to understand the error performance of wireless mobile channels in order to improve the quality of communications by deploying better modulation and coding schemes, and better network architectures. It is also desirable to have an accurate and thoroughly reproducible error model, which would allow network designers to evaluate a protocol or algorithm and its variations in a controlled and repeatable way. However, the physical properties of radio propagation, and the diversities of error environments in a wireless medium, lead to complexity in modeling the error performance of wireless channels. This article surveys the error modeling methods of fading channels in wireless communications, and provides a novel user-requirement (researchers and designers) based approach to classify the existing wireless error models.
AI-Based Game Design : Enabling New Playable Experiences

With the current set of design tools and methods available to game designers, vast portions of the space of possible games are not currently reachable. In the past, technological advances such as improved graphics and new controllers have driven the creation of new forms of gameplay, but games have still not made great strides into new gameplay experiences. We argue that the development of innovative artificial intelligence (AI) systems plays a crucial role in the exploration of currently unreachable spaces. To aid in exploration, we suggest a practice called AI-based game design, an iterative design process that deeply integrates the affordances of an AI system within the context of game design. We have applied this process in our own projects, and in this paper we present how it has pushed the boundaries of current game genres and experiences, as well as discuss the future AI-based game design.
Autonomous motorcycles for agile maneuvers, part I: Dynamic modeling

Single-track vehicles, such as motorcycles, provide an agile mobile platform. Modeling and control of motorcycles for agile maneuvers, such as those by professional racing riders, are challenging due to motorcycle's unstable platform and complex tire/road interaction. As a first step attempting to understand how racing riders drive a motorcycle, this twopart paper presents a modeling and tracking control design of an autonomous motorcycle. In this first-part paper, we discuss a new dynamics model for the autonomous motorcycle. We consider the existence of lateral sliding velocity at each wheel contact point. Because of the importance of the tire/road interaction for vehicle stability and maneuverability, the dynamic modeling scheme also includes the motorcycle tire models. The new nonlinear dynamic models are used for control systems design in the companion paper.
Acute Effects of Deep Tissue Foam Rolling and Dynamic Stretching on Muscular Strength, Power, and Flexibility in Division I Linemen.

Behara, B and Jacobson, BH. Acute effects of deep tissue foam rolling and dynamic stretching on muscular strength, power, and flexibility in Division I linemen.  2017-A recent strategy to increase sports performance is a self-massage technique called myofascial release using foam rollers. Myofascial restrictions are believed to be brought on by injuries, muscle imbalances, overrecruitment, and/or inflammation, all of which can decrease sports performance. The purpose of this study was to compare the acute effects of a single-bout of lower extremity self-myofascial release using a custom deep tissue roller (DTR) and a dynamic stretch protocol. Subjects consisted of NCAA Division 1 offensive linemen (n = 14) at a Midwestern university. All players were briefed on the objectives of the study and subsequently signed an approved IRB consent document. A randomized crossover design was used to assess each dependent variable (vertical jump [VJ] power and velocity, knee isometric torque, and hip range of motion was assessed before and after: [a] no treatment, [b] deep tissue foam rolling, and [c] dynamic stretching). Results of repeated-measures analysis of variance yielded no pretest to posttest significant differences (p > 0.05) among the groups for VJ peak power (p = 0.45), VJ average power (p = 0.16), VJ peak velocity (p = 0.25), VJ average velocity (p = 0.23), peak knee extension torque (p = 0.63), average knee extension torque (p = 0.11), peak knee flexion torque (p = 0.63), or average knee flexion torque (p = 0.22). However, hip flexibility was statistically significant when tested after both dynamic stretching and foam rolling (p = 0.0001). Although no changes in strength or power was evident, increased flexibility after DTR may be used interchangeably with traditional stretching exercises.
25-Gbps×4 optical transmitter with adjustable asymmetric pre-emphasis in 65-nm CMOS

This paper describes the design and experiment results of 25 Gbps, 4 channels optical transmitter which consist of a vertical-cavity surface emitting laser (VCSEL) driver with an asymmetric pre-emphasis circuit and an electrical receiver. To make data transfers faster in directly modulated a VCSEL-based optical communications, the driver circuit requires an asymmetric pre-emphasis signal to compensate for the nonlinear characteristics of VCSEL. An asymmetric pre-emphasis signal can be created by the adjusting a duty ratio with a delay circuit. A test chip was fabricated in the 65-nm standard CMOS process and demonstrated. An experimental evaluation showed that this transmitter enlarged the eye opening of a 25 Gbps, PRBS=29-1 test signal by 8.8% and achieve four channels fully optical link with an optical receiver at a power of 10.3 mW=Gbps=ch at 25 Gbps.
Structure of Concentrated-Flux-Type Interior Permanent-Magnet Synchronous Motors Using Ferrite Permanent Magnets

Interior permanent magnet synchronous motors (IPMs) usually use NdFeB for achieving high-torque and high-efficiency performance. This paper reviews the performance of concentrated-flux-type ferrite magnet motors (CFFMs) that use ferrite, as an alternative to the general type of IPMs that use NdFeB. A structure to reduce the motor volume and improve the torque density of CFFMs is suggested in this paper. The characteristics of CFFMs for different rotor types are analyzed. The motor parameters are analyzed. Using 2-D finite element analysis (FEA), the efficiency and size of the CFFMs are compared with each other. This paper discusses the results of stress analysis for each rotor type using 2-D FEA. Finally, it suggests a CFFM structure to replace the general type of IPMs that use NdFeB. The proposed LC-type has the highest torque density among all the ferrite rotor types. It is seen that the proposed LC-type ferrite-model replacement for the Nd model is the best choice of all the types.
A Tight Bound of Hard Thresholding

This paper is concerned with the hard thresholding operator which sets all but the k largest absolute elements of a vector to zero. We establish a tight bound to quantitatively characterize the deviation of the thresholded solution from a given signal. Our theoretical result is universal in the sense that it holds for all choices of parameters, and the underlying analysis depends only on fundamental arguments in mathematical optimization. We discuss the implications for two domains: Compressed Sensing. On account of the crucial estimate, we bridge the connection between the restricted isometry property (RIP) and the sparsity parameter for a vast volume of hard thresholding based algorithms, which renders an improvement on the RIP condition especially when the true sparsity is unknown. This suggests that in essence, many more kinds of sensing matrices or fewer measurements are admissible for the data acquisition procedure. Machine Learning. In terms of large-scale machine learning, a significant yet challenging problem is learning accurate sparse models in an efficient manner. In stark contrast to prior work that attempted the `1-relaxation for promoting sparsity, we present a novel stochastic algorithm which performs hard thresholding in each iteration, hence ensuring such parsimonious solutions. Equipped with the developed bound, we prove the global linear convergence for a number of prevalent statistical models under mild assumptions, even though the problem turns out to be non-convex.