nils-herrmann/clean_scirepeval huggingface dataset:
Fast mass transport through sub-2-nanometer carbon nanotubes.

We report gas and water flow measurements through microfabricated membranes in which aligned carbon nanotubes with diameters of less than 2 nanometers serve as pores. The measured gas flow exceeds predictions of the Knudsen diffusion model by more than an order of magnitude. The measured water flow exceeds values calculated from continuum hydrodynamics models by more than three orders of magnitude and is comparable to flow rates extrapolated from molecular dynamics simulations. The gas and water permeabilities of these nanotube-based membranes are several orders of magnitude higher than those of commercial polycarbonate membranes, despite having pore sizes an order of magnitude smaller. These membranes enable fundamental studies of mass transport in confined environments, as well as more energy-efficient nanoscale filtration.
Modelling of transmission line loaded with BCI probe using circuit concept approach

An estimation method of the induced current of a transmission line under the bulk current injection (BCI) test is presented. In this method, the influence of the current injected by the BCI probe is expressed using the equivalent voltage and current sources. By modelling the transmission line loaded with the BCI probe as a 3-port network, an absolute value of the induced current of transmission line can be estimated analytically without using numerical simulation. To validate the proposed method, the calculated results are shown and compared with the experimental results.
The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions

Received () Revised () Recurrent nets are in principle capable to store past inputs to produce the currently desired output. Because of this property recurrent nets are used in time series prediction and process control. Practical applications involve temporal dependencies spanning many time steps, e.g. between relevant inputs and desired outputs. In this case, however, gradient based learning methods take too much time. The extremely increased learning time arises because the error vanishes as it gets propagated back. In this article the decaying error ow is theoretically analyzed. Then methods trying to overcome vanishing gradients are brieey discussed. Finally, experiments comparing conventional algorithms and alternative methods are presented. With advanced methods long time lag problems can be solved in reasonable time.
A comparison of two learning algorithms for text categorization

This paper examines the use of inductive learning to categorize natural language documents into predeened content categories. Categorization of text is of increasing importance in information retrieval and natural language processing systems. Previous research on automated text categorization has mixed machine learning and knowledge engineering methods, making it diicult to draw conclusions about the performance of particular methods. In this paper we present empirical results on the performance of a Bayesian classiier and a decision tree learning algorithm on two text categorization data sets. We nd that both algorithms achieve reasonable performance and allow controlled tradeoos between false positives and false negatives. The stepwise feature selection in the decision tree algorithm is particularly eeective in dealing with the large feature sets common in text categorization. However, even this algorithm is aided by an initial preeltering of features, connrming the results found by Almuallim and Dietterich on artiicial data sets. We also demonstrate the impact of the time-varying nature of category deenitions.
Co-Existence Study Between Analog TV (PAL-I) and LTE in Digital Dividend Band: South African Case Study

Due to spectrum demand for mobile communication services, many studies has considered the impact of coexistence of two services in the same band. This paper presents a coexistence study between analog television (ATV) frequency using PAL-I systems and Long Term Evolution (LTE) in the digital dividend frequency band using South Africa as a case study. Four different scenarios between ATV and LTE are presented and simulated in SEAMCAT. The ATV as the primary user of the band while DTV is a secondary user. The results obtained from the simulation shows that the ATV and LTE can coexist.
Classifier Ensembles for Dialect and Language Variety Identification

In this paper we present ensemble-based systems for dialect and language variety identification using the datasets made available by the organizers of the VarDial Evaluation Campaign 2018. We present a system developed to discriminate between Flemish and Dutch in subtitles and a system trained to discriminate between four Arabic dialects: Egyptian, Levantine, Gulf, North African, and Modern Standard Arabic in speech broadcasts. Finally, we compare the performance of these two systems with the other systems submitted to the Discriminating between Dutch and Flemish in Subtitles (DFS) and the Arabic Dialect Identification (ADI) shared tasks at VarDial 2018.
A printed antenna array fed by balanced Schiffman shifter used for UWB location system

A double-sided printed bow-tie antenna array fed by balanced Schiffman phase shifter, which used as a reader antenna in ultra-wideband (UWB) location system, is proposed in this paper. The presented antenna has a impedance bandwidth of 3.4-4.4 GHz and a high gain of more than 5dB within the whole bandwidth. Good omnidirectional radiation pattern is obtained in the plane of θ = 15°. Good simulation results are achieved by HFSS.
Escape loneliness by going digital: a quantitative and qualitative evaluation of a Dutch experiment in using ECT to overcome loneliness among older adults.

This study evaluates the outcomes of an Internet-at-home intervention experiment that intended to decrease loneliness among chronically ill and physically handicapped older adults through introducing them to the use of an electronic communication facility.



To determine the effectiveness of the experiment in terms of reducing loneliness, 15 older adults were interviewed three times: shortly before the start, two years later and immediately after termination of the experiment, while their loneliness scores at zero and post-measurement were compared with those of a control group.



Both the participants and the control persons experienced a reduction in loneliness over time. However, the reduction was only significant for the intervention participants. Moreover, the changes in loneliness were significantly greater among the participants compared to the control persons. When looking more in detail, the effect of the experiment was only significant regarding emotional loneliness and among the highest educated. Findings of the qualitative research enabled us to understand the mechanisms through which the intervention helped alleviate loneliness. E-mail was found to facilitate social contact. Furthermore, the computer and Internet were often used to pass the time, taking people's minds off their loneliness. Unexpectedly, the intervention also improved people's self-confidence.



The decline in loneliness is likely to be greater if persons under more favorable circumstances are selected and if more social functions of the Internet are used.
Automatic identification of fruit flies (Diptera: Tephritidae)

Fruit flies are pests of major economic importance in agriculture. blackAmong these pests it is possible to highlight some species of genus Anastrepha, which attack a wide range of fruits, and are widely distributed in the American tropics and subtropics. Researchers seek to identify fruit flies in order to implement management and control programs as well as quarantine restrictions. However, fruit fly identification is manually performed by scarce specialists through analysis of morphological features of the mesonotum, wing, and aculeus. Our objective is to find solid blackknowledge that can serve as a basis for the development of a blacksounding automatic identification system of the Anastrepha fraterculus group, which is of high economic importance in Brazil. Wing and aculeus images datasets from three specimes have been used in this work. The experiments using a classifier multimodal fusion approach blackshows promising e ectiveness results for identification of these fruit flies, with more than black98% classification accuracy, a remarkable result for this di cult problem.
Perceived fit and satisfaction on web learning performance: IS continuance intention and task-technology fit perspectives

Virtual learning system (VLS) is an information system that facilitates e-learning have been widely implemented by higher education institutions to support face-to-face teaching and self-managed learning in the virtual learning and education environment (VLE). This is referred to a blended learning instruction. By adopting the VLS, students are expected to enhance learning by getting access to courserelated information and having full opportunities to interact with instructors and peers. However, there are mixed findings revealed in the literature with respect to the learning outcomes in adopting VLS. In this study, we argue that the link between the precedents of leading students to continue to use VLSs and their impacts on learning effectiveness and productivity are overlooked in the literature. This paper aims to tackle this question by integrating information system (IS) continuance theory with task-technology fit (TTF) to extend our understandings of the precedents of the intention to continue VLS and their impacts on learning. By doing it, factors of technology-acceptance-to-performance, based on TAM (technology acceptance model) and TTF and post-technology-acceptance, based on expectation–confirmation theory, models can be included to test in one study. The results reveal that perceived fit and satisfaction are important precedents of the intention to continue VLS and individual performance. Later, a discussion and conclusions are provided. This study sheds light on learning system design as assisted by IS in VLE and can serve as a basis for promoting VLS in assisting learning. & 2012 Elsevier Ltd. All rights reserved.
Image super-resolution by estimating the enhancement weight of self example and external missing patches

Image super-resolution (SR) is the process of generating a high-resolution (HR) image using one or more low-resolution (LR) inputs. Many SR methods have been proposed, but generating the small-scale structure of an SR image remains a challenging task. We hence propose a single-image SR algorithm that combines the benefits of both internal and external SR methods. First, we estimate the enhancement weights of each LR-HR image patch pair. Next, we multiply each patch by the estimated enhancement weight to generate an initial SR patch. We then employ a method to recover the missing information from the high-resolution patches and create that missing information to generate a final SR image. We then employ iterative back-projection to further enhance visual quality. The method is compared qualitatively and quantitatively with several state-of-the-art methods, and the experimental results indicate that the proposed framework provides high contrast and better visual quality, particularly for non-smooth texture areas.
A Symbolic Approach for Explaining Errors in Image Classification Tasks

Machine learning algorithms, despite their increasing success in handling object recognition tasks, still seldom perform without error. Often the process of understanding why the algorithm has failed is the task of the human who, using domain knowledge and contextual information, can discover systematic shortcomings in either the data or the algorithm. This paper presents an approach where the process of reasoning about errors emerging from a machine learning framework is automated using symbolic techniques. By utilizing spatial and geometrical reasoning between objects in a scene, the system is able to describe misclassified regions in relation to its context. The system is demonstrated in the remote sensing domain where objects and entities are detected in satellite images.
AdaScale: Towards Real-time Video Object Detection Using Adaptive Scaling

In vision-enabled autonomous systems such as robots and autonomous cars, video object detection plays a crucial role, and both its speed and accuracy are important factors to provide reliable operation. The key insight we show in this paper is that speed and accuracy are not necessarily a trade-off when it comes to image scaling. Our results show that re-scaling the image to a lower resolution will sometimes produce better accuracy. Based on this observation, we propose a novel approach, dubbed AdaScale, which adaptively selects the input image scale that improves both accuracy and speed for video object detection. To this end, our results on ImageNet VID and mini YouTube-BoundingBoxes datasets demonstrate 1.3 points and 2.7 points mAP improvement with 1.6× and 1.8× speedup, respectively. Additionally, we improve state-of-the-art video acceleration work by an extra 1.25× speedup with slightly better mAP on ImageNet VID dataset.
Stressful work, psychological job strain, and turnover: a 2-year prospective cohort study of truck drivers.

Based on a model that combines existing organizational stress theory and job transition theory, this 2-year longitudinal study examined antecedents and consequences of turnover among Dutch truck drivers. For this purpose, self-reported data on stressful work (job demands and control), psychological strain (need for recovery after work and fatigue), and turnover were obtained from 820 drivers in 1998 and 2000. In agreement with the model, the results showed that strain mediates the influence of stressful work on voluntary turnover. Also in conformity with the model, job movement to any job outside the trucking industry (i.e., interoccupational turnover) resulted in a larger strain reduction as compared to job movement within the trucking industry (intraoccupational turnover). Finally, strain was found to stimulate interoccupational turnover more strongly than it stimulated intraoccupational turnover. These findings provide a thorough validation of existing turnover theory and give new insights into the turnover (decision) process.
Coreference in Spoken vs. Written Texts: a Corpus-based Analysis

This paper describes an empirical study of coreference in spoken vs. written text. We focus on the comparison of two particular text types, interviews and popular science texts, as instances of spoken and written texts since they display quite different discourse structures. We believe in fact, that the correlation of difficulties in coreference resolution and varying discourse structures requires a deeper analysis that accounts for the diversity of coreference strategies or their sub-phenomena as indicators of text type or genre. In this work, we therefore aim at defining specific parameters that classify differences in genres of spoken and written texts such as the preferred segmentation strategy, the maximal allowed distance in or the length and size of coreference chains as well as the correlation of structural and syntactic features of coreferring expressions. We argue that a characterization of such genre dependent parameters might improve the performance of current state-of-art coreference resolution technology.
Changes in adult attachment styles in American college students over time: a meta-analysis.

The current article examines changes over time in a commonly used measure of adult attachment style. A cross-temporal meta-analysis was conducted on 94 samples of American college students (total N = 25,243, between 1988 and 2011) who chose the most representative description of four possible attachment styles (Secure, Dismissing, Preoccupied, and Fearful) on the Relationship Questionnaire. The percentage of students with Secure attachment styles has decreased in recent years (1988: 48.98%; 2011: 41.62%), whereas the percentage of students with Insecure attachment styles (sum of Dismissing, Preoccupied, Fearful) has increased in recent years (1988: 51.02%; 2011: 58.38%). The percentage of students with Dismissing attachment styles has increased over time (1988: 11.93%; 2011: 18.62%), even after controlling for age, gender, race, and publication status. Positive views of others have declined across the same time period. We discuss possible implications and explanations for these changes.
Exposure-response relationships for transportation noise.

This article presents synthesis curves for the relationship between DNL and percentage highly annoyed for three transportation noise sources. The results are based on all 21 datasets examined by Schultz [ (1978)] and Fidell et al.  for which acceptable DNL and percentage highly annoyed measure could be derived, augmented with 34 datasets. Separate, nonidentical curves were found for aircraft, road traffic, and railway noise. A difference between sources was found using data for all studies combined and for only those studies in which respondents evaluated two sources. The latter outcome strengthens the conclusion that the differences between sources cannot be explained by differences in study methodology.
Stock Market Classification Model Using Sentiment Analysis on Twitter Based on Hybrid Naive Bayes Classifiers

Sentiment analysis has become one of the most popular process to predict stock market behaviour based on consumer reactions. Concurrently, the availability of data from Twitter has also attracted researchers towards this research area. Most of the models related to sentiment analysis are still suffering from inaccuracies. The low accuracy in classification has a direct effect on the reliability of stock market indicators. The study primarily focuses on the analysis of the Twitter dataset. Moreover, an improved model is proposed in this study; it is designed to enhance the classification accuracy. The first phase of this model is data collection, and the second involves the filtration and transformation, which are conducted to get only relevant data. The most crucial phase is labelling, in which polarity of data is determined and negative, positive or neutral values are assigned to people opinion. The fourth phase is the classification phase in which suitable patterns of the stock market are identified by hybridizing Naïve Bayes Classifiers (NBCs), and the final phase is the performance and evaluation. This study proposes Hybrid Naïve Bayes Classifiers (HNBCs) as a machine learning method for stock market classification. The outcome is instrumental for investors, companies, and researchers whereby it will enable them to formulate their plans according to the sentiments of people. The proposed method has produced a significant result; it has achieved accuracy equals 90.38%.
Robotics, Vision and Control - Fundamental Algorithms in MATLAB®

Interestingly, robotics vision and control fundamental algorithms in matlab springer tracts in advanced robotics that you really wait for now is coming. It's significant to wait for the representative and beneficial books to read. Every book that is provided in better way and utterance will be expected by many peoples. Even you are a good reader or not, feeling to read this book will always appear when you find it. But, when you feel hard to find it as yours, what to do? Borrow to your friends and don't know when to give back it to her or him.
IEEE 802.15.7r1 Reference Channel Models for Visible Light Communications

The IEEE has established the standardization group 802.15.7r1 “Short Range Optical Wireless Communications”, which is currently in the process of developing a standard for visible light communication (VLC). As with any other communication system, realistic channel models are of critical importance for VLC system design, performance evaluation, and testing. This article presents the reference channel models that were endorsed by the IEEE 802.15.7r1 Task Group for evaluation of VLC system proposals. These were developed for typical indoor environments, including home, office, and manufacturing cells. While highlighting the channel models, we further discuss physical layer techniques potentially considered for IEEE 802.15.7r1.
A Fast KNN Algorithm for Text Categorization

The KNN algorithm applied to text categorization is a simple, valid and non-parameter method. The traditional KNN has a fatal defect that the time of similarity computing is huge. The practicality will be lost when the KNN algorithm is applied to text categorization with the high dimension and huge samples. In this paper, a method called TFKNN(Tree-Fast-K-Nearest-Neighbor) is presented, which can search the exact k nearest neighbors quickly. In the method, a SSR tree for searching K nearest neighbors is created, in which all child nodes of each non-leaf node are ranked according to the distances between their central points and the central point of their parent. Then the searching scope is reduced based on the tree. Subsequently , the time of similarity computing is decreased largely.
Risk factors for shoulder and elbow injuries in adolescent baseball pitchers.

There is little evidence supporting current safety recommendations for adolescent pitchers.



Pitching practices of adolescent pitchers without history of arm injury will be significantly different from those of adolescent pitchers who required shoulder or elbow surgery.



Case control study; 
Ninety-five adolescent pitchers who had shoulder or elbow surgery and 45 adolescent pitchers who never had a significant pitching-related injury completed a survey. Responses were compared between the 2 groups using t tests and chi(2) analyses. Multivariable logistic regression models were developed to identify the risk factors.



The injured group pitched significantly more months per year, games per year, innings per game, pitches per game, pitches per year, and warm-up pitches before a game. These pitchers were more frequently starting pitchers, pitched in more showcases, pitched with higher velocity, and pitched more often with arm pain and fatigue. They also used anti-inflammatory drugs and ice more frequently to prevent an injury. Although the groups were age matched, the injured group was taller and heavier. There were no significant differences regarding private pitching instruction, coach's chief concern, pitcher's self-rating, exercise programs, stretching practices, relieving frequency, pitch type frequency, or age at which pitch types were first thrown.



Pitching practices were significantly different between the groups. The factors with the strongest associations with injury were overuse and fatigue. High pitch velocity and participation in showcases were also associated with increased risk for injury.



New recommendations were made based on these results. Adherence to the recommendations may reduce the incidence of significant injury to adolescent pitchers.