nils-herrmann/clean_scirepeval huggingface dataset:
Pneumatic Reel Actuator: Design, modeling, and implementation

We present the design, modeling, and implementation of a novel pneumatic actuator, the Pneumatic Reel Actuator (PRA). The PRA is highly extensible, lightweight, capable of operating in compression and tension, compliant, and inexpensive. An initial prototype of the PRA can reach extension ratios greater than 16:1, has a force-to-weight ratio over 28:1, reach speeds of 0.87 meters per second, and can be constructed with parts totaling less than $4 USD. We have developed a model describing the actuator and have conducted experiments characterizing the actuator's performance in regards to force, extension, pressure, and speed. We have implemented two parallel robotic applications in the form of a three degree of freedom robot arm and a tetrahedral robot.
An Exploration of Social Media in Extreme Events: Rumor Theory and Twitter during the Haiti Earthquake 2010

Due to its rapid speed of information spread, wide user bases, and extreme mobility, Twitter is drawing attention as a potential emergency reporting tool under extreme events. However, at the same time, Twitter is sometimes despised as a citizen based non-professional social medium for propagating misinformation, rumors, and, in extreme case, propaganda. This study explores the working dynamics of the rumor mill by analyzing Twitter data of the Haiti Earthquake in 2010. For this analysis, two key variables of anxiety and informational uncertainty are derived from rumor theory, and their interactive dynamics are measured by both quantitative and qualitative methods. Our research finds that information with credible sources contribute to suppress the level of anxiety in Twitter community, which leads to rumor control and high information quality.
Object-Based Convolutional Neural Network for High-Resolution Imagery Classification

Timely and accurate classification and interpretation of high-resolution images are very important for urban planning and disaster rescue. However, as spatial resolution gets finer, it is increasingly difficult to recognize complex patterns in high-resolution remote sensing images. Deep learning offers an efficient strategy to fill the gap between complex image patterns and their semantic labels. However, due to the hierarchical abstract nature of deep learning methods, it is difficult to capture the precise outline of different objects at the pixel level. To further reduce this problem, we propose an object-based deep learning method to accurately classify the high-resolution imagery without intensive human involvement. In this study, high-resolution images were used to accurately classify three different urban scenes: Beijing (China), Pavia (Italy), and Vaihingen (Germany). The proposed method is built on a combination of a deep feature learning strategy and an object-based classification for the interpretation of high-resolution images. Specifically, high-level feature representations extracted through the convolutional neural networks framework have been systematically investigated over five different layer configurations. Furthermore, to improve the classification accuracy, an object-based classification method also has been integrated with the deep learning strategy for more efficient image classification. Experimental results indicate that with the combination of deep learning and object-based classification, it is possible to discriminate different building types in Beijing Scene, such as commercial buildings and residential buildings with classification accuracies above 90%.
Self-Critical Sequence Training for Image Captioning

Recently it has been shown that policy-gradient methods for reinforcement learning can be utilized to train deep end-to-end systems directly on non-differentiable metrics for the task at hand. In this paper we consider the problem of optimizing image captioning systems using reinforcement learning, and show that by carefully optimizing our systems using the test metrics of the MSCOCO task, significant gains in performance can be realized. Our systems are built using a new optimization approach that we call self-critical sequence training (SCST). SCST is a form of the popular REINFORCE algorithm that, rather than estimating a baseline to normalize the rewards and reduce variance, utilizes the output of its own test-time inference algorithm to normalize the rewards it experiences. Using this approach, estimating the reward signal (as actor-critic methods must do) and estimating normalization (as REINFORCE algorithms typically do) is avoided, while at the same time harmonizing the model with respect to its test-time inference procedure. Empirically we find that directly optimizing the CIDEr metric with SCST and greedy decoding at test-time is highly effective. Our results on the MSCOCO evaluation sever establish a new state-of-the-art on the task, improving the best result in terms of CIDEr from 104.9 to 114.7.
Remote Sensing Scene Classification by Unsupervised Representation Learning

With the rapid development of the satellite sensor technology, high spatial resolution remote sensing (HSR) data have attracted extensive attention in military and civilian applications. In order to make full use of these data, remote sensing scene classification becomes an important and necessary precedent task. In this paper, an unsupervised representation learning method is proposed to investigate deconvolution networks for remote sensing scene classification. First, a shallow weighted deconvolution network is utilized to learn a set of feature maps and filters for each image by minimizing the reconstruction error between the input image and the convolution result. The learned feature maps can capture the abundant edge and texture information of high spatial resolution images, which is definitely important for remote sensing images. After that, the spatial pyramid model (SPM) is used to aggregate features at different scales to maintain the spatial layout of HSR image scene. A discriminative representation for HSR image is obtained by combining the proposed weighted deconvolution model and SPM. Finally, the representation vector is input into a support vector machine to finish classification. We apply our method on two challenging HSR image data sets: the UCMerced data set with 21 scene categories and the Sydney data set with seven land-use categories. All the experimental results achieved by the proposed method outperform most state of the arts, which demonstrates the effectiveness of the proposed method.
Neural Morphological Tagging from Characters for Morphologically Rich Languages

This paper investigates neural characterbased morphological tagging for languages with complex morphology and large tag sets. We systematically explore a variety of neural architectures (DNN, CNN, CNNHighway, LSTM, BLSTM) to obtain character-based word vectors combined with bidirectional LSTMs to model across-word context in an end-to-end setting. We explore supplementary use of word-based vectors trained on large amounts of unlabeled data. Our experiments for morphological tagging suggest that for ”simple” model configurations, the choice of the network architecture (CNN vs. CNNHighway vs. LSTM vs. BLSTM) or the augmentation with pre-trained word embeddings can be important and clearly impact the accuracy. Increasing the model capacity by adding depth, for example, and carefully optimizing the neural networks can lead to substantial improvements, and the differences in accuracy (but not training time) become much smaller or even negligible. Overall, our best morphological taggers for German and Czech outperform the best results reported in the literature by a large margin.
Antennas and propagation for body centric communications

Body centric wireless communication is now accepted as an important part of 4th generation (and beyond) mobile communications systems, taking the form of human to human networking incorporating wearable sensors and communications. There are also a number of body centric communication systems for specialized occupations, such as paramedics and fire-fighters, military personnel and medical sensing and support. To support these developments there is considerable ongoing research into antennas and propagation for body centric communications systems, and this paper will summarise some of it, including the characterisation of the channel on the body, the optimisation of antennas for these channels, and communications to medical implants where advanced antenna design and characterisation and modelling of the internal body channel are important research needs. In all of these areas both measurement and simulation pose very different and challenging issues to be faced by the researcher.
An Empirical Study on Recommendations of Similar Bugs

The work to be performed on open source systems, whether feature developments or defects, is typically described as an issue (or bug). Developers self-select bugs from the many open bugs in a repository when they wish to perform work on the system. This paper evaluates a recommender, called NextBug, that considers the textual similarity of bug descriptions to predict bugs that require handling of similar code fragments. First, we evaluate this recommender using 69 projects in the Mozilla ecosystem. We show that for detecting similar bugs, a technique that considers just the bug components and short descriptions perform just as well as a more complex technique that considers other features. Second, we report a field study where we monitored the bugs fixed for Mozilla during a week. We sent mails to the developers who fixed these bugs, asking whether they would consider working on the recommendations provided by NextBug, 39 developers (59%) stated that they would consider working on these recommendations, 44 developers (67%) also expressed interest in seeing the recommendations in their bug tracking system.
Three-year follow-up and clinical implications of a mindfulness meditation-based stress reduction intervention in the treatment of anxiety disorders.

A previous study of 22 medical patients with DSM-III-R-defined anxiety disorders showed clinically and statistically significant improvements in subjective and objective symptoms of anxiety and panic following an 8-week outpatient physician-referred group stress reduction intervention based on mindfulness meditation. Twenty subjects demonstrated significant reductions in Hamilton and Beck Anxiety and Depression scores postintervention and at 3-month follow-up. In this study, 3-year follow-up data were obtained and analyzed on 18 of the original 22 subjects to probe long-term effects. Repeated measures analysis showed maintenance of the gains obtained in the original study on the Hamilton [F(2,32) = 13.22; p < 0.001] and Beck [F(2,32) = 9.83; p < 0.001] anxiety scales as well as on their respective depression scales, on the Hamilton panic score, the number and severity of panic attacks, and on the Mobility Index-Accompanied and the Fear Survey. A 3-year follow-up comparison of this cohort with a larger group of subjects from the intervention who had met criteria for screening for the original study suggests generalizability of the results obtained with the smaller, more intensively studied cohort. Ongoing compliance with the meditation practice was also demonstrated in the majority of subjects at 3 years. We conclude that an intensive but time-limited group stress reduction intervention based on mindfulness meditation can have long-term beneficial effects in the treatment of people diagnosed with anxiety disorders.
A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings

Recent work has managed to learn crosslingual word embeddings without parallel data by mapping monolingual embeddings to a shared space through adversarial training. However, their evaluation has focused on favorable conditions, using comparable corpora or closely-related languages, and we show that they often fail in more realistic scenarios. This work proposes an alternative approach based on a fully unsupervised initialization that explicitly exploits the structural similarity of the embeddings, and a robust self-learning algorithm that iteratively improves this solution. Our method succeeds in all tested scenarios and obtains the best published results in standard datasets, even surpassing previous supervised systems. Our implementation is released as an open source project at https://github. com/artetxem/vecmap.
Measurement of functional activities in older adults in the community.

Two measures of social function designed for community studies of normal aging and mild senile dementia were evaluated in 195 older adults who underwent neurological, cognitive, and affective assessment. An examining and a reviewing neurologist and a neurologically trained nurse independently rated each on a Scale of Functional Capacity. Interrater reliability was high (examining vs. reviewing neurologist, r = .97; examining neurologist vs. nurse, tau b = .802; p less than .001 for both comparisons). Estimates correlated well with an established measure of social function and with results of cognitive tests. Alternate informants evaluated participants on the Functional Activities Questionnaire and the Instrumental Activities of Daily Living Scale. The Functional Activities Questionnaire was superior to the Instrumental Activities of Daily scores. Used alone as a diagnostic tool, the Functional Activities Questionnaire was more sensitive than distinguishing between normal and demented individuals.
Employees' Compliance with BYOD Security Policy: Insights from Reactance, Organizational Justice, and Protection Motivation Theory

The trend of bring your own device (BYOD) has been rapidly adopted by organizations. Despite the pros and cons of BYOD adoption, this trend is expected to inevitably keep increasing. Yet, BYOD has raised significant concerns about information system security as employees use their personal devices to access organizational resources. This study aims to examine employees' intention to comply with an organization’s IS security policy in the context of BYOD. We derived our research model from reactance, protection motivation and organizational justice theories. The results of this study demonstrate that an employee’s perceived response efficacy and perceived justice positively affect an employee’s intention to comply with BYOD security policy. Perceived security threat appraisal was found to marginally promote the intention to comply. Conversely, perceived freedom threat due to imposed security policy negatively affects an employee’s intention to comply with the security policy. We also found that an employee’s perceived cost associated with compliance behavior positively affects an employee’s perceptions of threat to an individual freedom. An interesting double-edged sword effect of a security awareness program was confirmed by the results. BYOD security awareness program increases an employee’s response efficacy (a positive effect) and response cost (a negative effect). The study also demonstrates the importance of having an IT support team for BYOD, as it increases an employee’s response-efficacy and perceived justice.
A high-resolution 3D dynamic facial expression database

Face information processing relies on the quality of data resource. From the data modality point of view, a face database can be 2D or 3D, and static or dynamic. From the task point of view, the data can be used for research of computer based automatic face recognition, face expression recognition, face detection, or cognitive and psychological investigation. With the advancement of 3D imaging technologies, 3D dynamic facial sequences (called 4D data) have been used for face information analysis. In this paper, we focus on the modality of 3D dynamic data for the task of facial expression recognition. We present a newly created high-resolution 3D dynamic facial expression database, which is made available to the scientific research community. The database contains 606 3D facial expression sequences captured from 101 subjects of various ethnic backgrounds. The database has been validated through our facial expression recognition experiment using an HMM based 3D spatio-temporal facial descriptor. It is expected that such a database shall be used to facilitate the facial expression analysis from a static 3D space to a dynamic 3D space, with a goal of scrutinizing facial behavior at a higher level of detail in a real 3D spatio-temporal domain.
Interventions for promoting smoking cessation during pregnancy.

Smoking remains one of the few potentially preventable factors associated with low birthweight, preterm birth and perinatal death.



To assess the effects of smoking cessation programs implemented during pregnancy on the health of the fetus, infant, mother, and family.


SEARCH STRATEGY
We searched the Cochrane Pregnancy and Childbirth Group trials register and the Cochrane Tobacco Addiction Group trials register (July 2003), MEDLINE (January 2002 to July 2003), EMBASE (January 2002 to July 2003), PsychLIT (January 2002 to July 2003), CINAHL (January 2002 to July 2003), and AUSTHEALTH (January 2002 to 2003). We contacted trial authors to locate additional unpublished data. We handsearched references of identified trials and recent obstetric journals.



Randomised and quasi-randomised trials of smoking cessation programs implemented during pregnancy.



Four reviewers assessed trial quality and extracted data independently.



This review included 64 trials. Fifty-one randomised controlled trials (20,931 women) and six cluster-randomised trials (over 7500 women) provided data on smoking cessation and/or perinatal outcomes. Despite substantial variation in the intensity of the intervention and the extent of reminders and reinforcement through pregnancy, there was an increase in the median intensity of both 'usual care' and interventions over time. There was a significant reduction in smoking in the intervention groups of the 48 trials included: (relative risk (RR) 0.94, 95% confidence interval (CI) 0.93 to 0.95), an absolute difference of six in 100 women continuing to smoke. The 36 trials with validated smoking cessation had a similar reduction (RR 0.94, 95% CI 0.92 to 0.95). Smoking cessation interventions reduced low birthweight (RR 0.81, 95% CI 0.70 to 0.94) and preterm birth (RR 0.84, 95% CI 0.72 to 0.98), and there was a 33 g (95% CI 11 g to 55 g) increase in mean birthweight. There were no statistically significant differences in very low birthweight, stillbirths, perinatal or neonatal mortality but these analyses had very limited power. One intervention strategy, rewards plus social support (two trials), resulted in a significantly greater smoking reduction than other strategies (RR 0.77, 95% CI 0.72 to 0.82). Five trials of smoking relapse prevention (over 800 women) showed no statistically significant reduction in relapse.



Smoking cessation programs in pregnancy reduce the proportion of women who continue to smoke, and reduce low birthweight and preterm birth. The pooled trials have inadequate power to detect reductions in perinatal mortality or very low birthweight.
Permanent-Magnet Synchronous Motor Magnet Designs With Skewing for Torque Ripple and Cogging Torque Reduction

This paper examines the torque ripple and cogging torque variation in surface-mounted permanent-magnet synchronous motors (PMSMs) with skewed rotor. The effect of slot/pole combinations and magnet shapes on the magnitude and harmonic content of torque waveforms in a PMSM drive has been studied. Finite element analysis and experimental results show that the skewing with steps does not necessarily reduce the torque ripple but may cause it to increase for certain magnet designs and configurations. The electromagnetic torque waveforms, including cogging torque, have been analyzed for four different PMSM configurations having the same envelop dimensions and output requirements.
Discovering Information Propagation Patterns in Microblogging Services

During the last decade, microblog has become an important social networking service with billions of users all over the world, acting as a novel and efficient platform for the creation and dissemination of real-time information. Modeling and revealing the information propagation patterns in microblogging services cannot only lead to more accurate understanding of user behaviors and provide insights into the underlying sociology, but also enable useful applications such as trending prediction, recommendation and filtering, spam detection and viral marketing. In this article, we aim to reveal the information propagation patterns in Sina Weibo, the biggest microblogging service in China. First, the cascade of each message is represented as a tree based on its retweeting process. Afterwards, we divide the information propagation pattern into two levels, that is, the macro level and the micro level. On one hand, the macro propagation patterns refer to general propagation modes that are extracted by grouping propagation trees based on hierarchical clustering. On the other hand, the micro propagation patterns are frequent information flow patterns that are discovered using tree-based mining techniques. Experimental results show that several interesting patterns are extracted, such as popular message propagation, artificial propagation, and typical information flows between different types of users.
Detailed screening for fetal anomalies and cardiac defects at the 11-13-week scan.

To assess the diagnostic efficacy of the first-trimester anomaly scan including first-trimester fetal echocardiography as a screening procedure in a 'medium-risk' population.



In a prospective study, we evaluated 3094 consecutive fetuses with a crown-rump length (CRL) of 45-84 mm and gestational age between 11 + 0 and 13 + 6 weeks, using transabdominal and transvaginal ultrasonography. The majority of patients were referred without prior abnormal scan or increased nuchal translucency (NT) thickness, the median maternal age was, however, 35 (range, 15-46) years, and 53.8% of the mothers (1580/2936) were 35 years or older. This was therefore a self-selected population reflecting an increased percentage of older mothers opting for prenatal diagnosis. The follow-up rate was 92.7% (3117/3363).



The prevalence of major abnormalities in 3094 fetuses was 2.8% (86/3094). The detection rate of major anomalies at the 11 + 0 to 13 + 6-week scan was 83.7% (72/86), 51.9% (14/27) for NT < 2.5 mm and 98.3% (58/59) for NT >or= 2.5 mm. The prevalence of major congenital heart defects (CHD) was 1.2% (38/3094). The detection rate of major CHD at the 11 to 13 + 6-week scan was 84.2% (32/38), 37.5% (3/8) for NT < 2.5 mm and 96.7% (29/30) for NT >or= 2.5 mm.



The overall detection rate of fetal anomalies including fetal cardiac defects following a specialist scan at 11 + 0 to 13 + 6 weeks' gestation is about 84% and is increased when NT >or= 2.5 mm. This extends the possibilities of a first-trimester scan beyond risk assessment for fetal chromosomal defects. In experienced hands with adequate equipment, the majority of severe malformations as well as major CHD may be detected at the end of the first trimester, which offers parents the option of deciding early in pregnancy how to deal with fetuses affected by genetic or structural abnormalities without pressure of time.