nils-herrmann/clean_scirepeval huggingface dataset:
A systematic mapping study on the open source software development process

There is no globally accepted open source software development process to define how open source software is developed in practice. A process description is important for coordinating all the software development activities involving both people and technology.  The research question that this study sets out to answer is: What activities do open source software process models contain? The activity groups on which it focuses are Concept Exploration, Software Requirements, Design, Maintenance and Evaluation.  We conduct a systematic mapping study (SMS). A SMS is a form of systematic literature review that aims to identify and classify available research papers concerning a particular issue.  We located a total of 29 primary studies, which we categorized by the open source software project that they examine and by activity types (Concept Exploration, Software Requirements, Design, Maintenance and Evaluation). The activities present in most of the open source software development processes were Execute Tests and Conduct Reviews, which belong to the Evaluation activities group. Maintenance is the only group that has primary studies addressing all the activities that it contains.  The primary studies located by the SMS are the starting point for analyzing the open source software development process and proposing a process model for this community. The papers in our paper pool that describe a specific open source software project provide more regarding our research question than the papers that talk about open source software development without referring to a specific open source software project.
Quantitative Evaluation of Style Transfer

Style transfer methods produce a transferred image which is a rendering of a content image in the manner of a style image. There is a rich literature of variant methods. However, evaluation procedures are qualitative, mostly involving user studies. We describe a novel quantitative evaluation procedure. One plots effectiveness (a measure of the extent to which the style was transferred) against coherence (a measure of the extent to which the transferred image decomposes into objects in the same way that the content image does) to obtain an EC plot. We construct EC plots comparing a number of recent style transfer methods. Most methods control within-layer gram matrices, but we also investigate a method that controls cross-layer gram matrices. These EC plots reveal a number of intriguing properties of recent style transfer methods. The style used has a strong effect on the outcome, for all methods. Using large style weights does not necessarily improve effectiveness, and can produce worse results. Cross-layer gram matrices easily beat all other methods, but some styles remain difficult for all methods. Ensemble methods show real promise. It is likely that, for current methods, each style requires a different choice of weights to obtain the best results, so that automated weight setting methods are desirable. Finally, we show evidence comparing our EC evaluations to human evaluations.
Low-power high-speed level shifter design for block-level dynamic voltage scaling environment

Two novel level shifters that are suitable for block-level dynamic voltage scaling environment (namely, V/sub DD/-hopping) are proposed. In order to achieve reduction in power consumption and delay, the first proposed level shifter which is called contention mitigated level shifter (CMLS) uses a contention-reduction technique. The simulation results with 65-nm CMOS model show 24% reduction in power and 50% decrease in delay with 4% area increase compared with the conventional level shifter. The second proposed level shifter which is called bypassing enabled level shifter (BELS) implements a bypass function and it is fabricated using 0.35/spl mu/m CMOS technology. The measurement results show that the power and delay of the proposed BELS are reduced by 50% and 65%, respectively with 60% area overhead over the conventional level shifter.
Design of Cross-Shaped Slot Loaded Wideband Microstrip Patch Antenna for Mobile Communication

A very simple, constrict, cross-shaped slot loaded wideband rectangular microstrip patch antenna is demonstrated in this communication. Rectangular patch with a cross ( ) shaped slot is radiating element. The probe feed technique is used for this shape. The selected antenna is simulated using IE3D simulator, 12.28 version of Zeland. Wideband characteristics of antenna are shown in simulated results. The impedance bandwidth (VSWR ≤ 2) at -10 dB returns loss comes out to be about 35.4%, ranging 1.946 GHz to 2.772 GHz.
Graphical Figure Classification Using Data Fusion for Integrating Text and Image Features

This paper describes a multimodal (image + text) learning approach for automatically identifying three graphical figure types commonly found in biomedical literature, namely, diagrams, statistical figures and flow charts. The goal is to improve retrieval of figures from biomedical journal articles. In this article, we describe a data fusion approach to combine information from both text and image sources, believed to contain complementary information. Text information about the image is extracted from the figure caption. The data fusion process includes a hybrid of evolutionary algorithm (EA) and Binary Particle Swarm Optimization (BPSO) called method applied to find an optimal subset of extracted image features. Chi-square statistic and information gain metric are used to select the optimal subset of extracted text features, which along with image features are input to Multi-Layer Perceptron Neural Network classifiers, whose outputs are characterized as fuzzy sets to determine the final classification result.  figure images extracted from a test subset of Biome Central® journals extracted from U.S. National Library of Medicine's PubMed Central ® repository yielded classification accuracy as high as 96.1%.
Valid items for screening dysphagia risk in patients with stroke: a systematic review.

Screening for dysphagia is essential to the implementation of preventive therapies for patients with stroke. A systematic review was undertaken to determine the evidence-based validity of dysphagia screening items using instrumental evaluation as the reference standard.



Four databases from 1985 through March 2011 were searched using the terms cerebrovascular disease, stroke deglutition disorders, and dysphagia.  homogeneous stroke population, comparison to instrumental examination, clinical examination without equipment, outcome measures of dysphagia or aspiration, and validity of screening items reported or able to be calculated. Articles meeting inclusion criteria were evaluated for methodological rigor. Sensitivity, specificity, and predictive capabilities were calculated for each item.



Total source documents numbered 832; 86 were reviewed in full and 16 met inclusion criteria. Study quality was variable. Testing swallowing, generally with water, was the most commonly administered item across studies. Both swallowing and nonswallowing items were identified as predictive of aspiration. Neither swallowing protocols nor validity were consistent across studies.



Numerous behaviors were found to be associated with aspiration. The best combination of nonswallowing and swallowing items as well as the best swallowing protocol remains unclear. Findings of this review will assist in development of valid clinical screening instruments.
A Clinical Study of 35 Cases of Pincer Nails

Pincer nail is a nail deformity characterized by transverse overcurvature of the nail plate. Pincer nail can affect a patient's quality of life due to its chronic, recurrent course; however, there have been no clinical studies on the pincer nail condition in Korean patients.



The purpose of this study was to characterize the clinical findings and treatment of pincer nail. In addition, possible etiological factors were considered, and treatment efficacy was evaluated.



The medical records and clinical photographs of 35 patients (12 males, 23 females) who were diagnosed with pincer nail between August 1, 2005 and July 31, 2009 were studied.



Patient age ranged from 10 to 77 (52.09±17.26) years, and there was a predominance of female (23 out of 35 patients, F:M=2:1). The mean duration of the disorder was 7.45 years (range 0.25~40); 85% had pincer nail for at least 1 year. In addition, 40% had a history of previous treatment and recurrence. There were 82.8% patients with the common type of pincer nails. The most commonly involved nails were both great toenails. Among 35 patients, nail grinding was started in 30 patients, and 25 patients showed clinical improvement with nail grinding. The width index increased and the height index decreased after treatment. The mean follow up period was 8.42 months (range 1~27), and 7 patients showed recurrence after 8.8 months (range 2~20). Among 35 patients, 5 patients were treated with nail extraction with matricectomy, and the symptoms resolved immediately. The mean follow up period was 7.6 months (range 0~19), and recurrence was not observed. Onychomycosis was also present in 37.1% of patients, and itraconazole pulse therapy for 3 months was added.



The results of this study demonstrate the clinical features of pincer nail in Korean patients. The findings show that the common type of pincer nail was most common, and nail grinding as a conservative treatment greatly improved pincer nails despite a risk of recurrence. When onychomycosis was also present, oral antifungal therapy added to nail grinding resulted in a more rapid change in nail thickness and clinical improvement.
Distribution-Specific Hardness of Learning Neural Networks

Although neural networks are routinely and successfully trained in practice using simple gradient-based methods, most existing theoretical results are negative, showing that learning such networks is difficult, in a worst-case sense over all data distributions. In this paper, we take a more nuanced view, and consider whether specific assumptions on the “niceness” of the input distribution, or “niceness” of the target function (e.g. in terms of smoothness, non-degeneracy, incoherence, random choice of parameters etc.), are sufficient to guarantee learnability using gradient-based methods. We provide evidence that neither class of assumptions alone is sufficient: On the one hand, for any member of a class of “nice” target functions, there are difficult input distributions. On the other hand, we identify a family of simple target functions, which are difficult to learn even if the input distribution is “nice”. To prove our results, we develop some tools which may be of independent interest, such as extending Fourier-based hardness techniques developed in the context of statistical queries (Blum et al., 1994), from the Boolean cube to Euclidean space and to more general classes of functions.
Spatial and temporal visualisation techniques for crash analysis.

Understanding the underlying structure of single vehicle crashes (SVCs) is essential for improving safety on the roads. Past research has found that SVCs tend to cluster both spatially and temporally. However, limited research has been conducted to investigate the interaction between the location of SVCs and the time they occur, especially at different levels of scales or spatial extents. This paper applied spatial, temporal and spatio-temporal techniques to investigate patterns of SVCs in Western Australia between 1999 and 2008, at different levels of scale. Spider graphs were adapted to identify temporal patterns of vehicle crashes at two different levels of scales: daily and weekly with respect to their causes. The spatial structures of vehicle crashes were analysed using Kernel Density Estimation analysis at three different scales: West Australia, Metropolitan area, and Perth Local Government Area (LGA). These are illustrated using spatial zooming theory. Comap was then used to demonstrate the spatio-temporal interaction effect on vehicle crashes. The results show significant differences in spatio-temporal patterns of SVCs for various crash causes. The techniques used here have the potential to help decision makers in developing effective road safety strategies.
Application-specific slicing for MVNO and traffic characterization [Invited]

In this paper, we apply the concept of a software-defined data plane to defining new services for mobile virtual network operators (MVNOs) to empower them with the capability of tailoring fine-grained subscription plans that can meet users' demands. For this purpose, we have recently proposed the concept of application-and/ or device-specific slicing that classifies application-and/or device-specific traffic into slices and introduced various applications of our proposed system [Optical Fiber Communication Conf. . This paper reports the prototype implementation of the proposal into a real MVNO connecting customized smartphones so we can identify applications/devices from the given traffic with 100% accuracy. We first characterize the traffic patterns of popular applications to build a detailed understanding of how network resources are utilized for real users and applications and what their demands on different network resources are. Then, we classify traffic according to the devices and find that the flow characteristics of LTE RAN slices are very similar to those of 3G. We discover that most of the bandwidth is consumed by those flows with the duration of several hundreds of seconds, and the flow size is larger than 5 Mbytes in both LTE and 3G MVNO networks.
Twitter vigilance: A multi-user platform for cross-domain Twitter data analytics, NLP and sentiment analysis

The growth and diffusion of online social media have been enormously increased in recent years, as well as the research and commercial interests toward these rising sources of information as a direct public expression of the communities. Moreover, the depth and the quality of data that can be harvested by monitoring and analysis tools have evolved significantly. In particular, Twitter has revealed to be one of the most widespread microblogging services for instantly publishing and sharing opinions, feedbacks, ratings etc., contributing in the development of the emerging role of users as sensors. However, due to the huge amount of data to be collected and analyzed and limitations on data access imposed by Twitter public APIs, more efficient requirements are needed for analytics tools, both in terms of data ingestion and processing, as well as for the computation of analysis metrics, to be provided for deeper statistic insights and further investigations. In this paper, the Twitter Vigilance platform is presented, realized by the DISIT Lab at University of Florence. Twitter Vigilance has been designed as a cross-domain, multi-user tool for collecting and analyzing Twitter data, providing aggregated metrics based on the volume of tweets and retweets, users' influence network, Natural Language Processing and Sentiment Analysis of textual content. The proposed architecture has been validated against a dataset of about 270 million tweets showing a high efficiency in recovering Twitter data. For this reason it has been adopted by a number of researchers as a study platform for social media analysis, early warning, etc.
Ideological Segregation Online and Offline

We use individual and aggregate data to ask how the Internet i s changing the ideological segregation of the American electorate. Focusing on onl ine news consumption, offline news consumption, and face-to-face social interactions, w e define ideological segregation in each domain using standard indices from the literature on ra cial segregation. We find that ideological segregation of online news consumption is low i n absolute terms, higher than the segregation of most offline news consumption, and significan tly lower than the segregation of face-to-face interactions with neighbors, co-workers, or family members. We find no evidence that the Internet is becoming more segregated over time.
Gender differences in narcissism: a meta-analytic review.

Despite the widely held belief that men are more narcissistic than women, there has been no systematic review to establish the magnitude, variability across measures and settings, and stability over time of this gender difference. Drawing on the biosocial approach to social role theory, a meta-analysis performed for Study 1 found that men tended to be more narcissistic than women (d = .26; k = 355 studies; N = 470,846). This gender difference remained stable in U.S. college student cohorts over time (from 1990 to 2013) and across different age groups. Study 1 also investigated gender differences in three facets of the Narcissistic Personality Inventory (NPI) to reveal that the narcissism gender difference is driven by the Exploitative/Entitlement facet (d = .29; k = 44 studies; N = 44,108) and Leadership/Authority facet (d = .20; k = 40 studies; N = 44,739); whereas the gender difference in Grandiose/Exhibitionism (d = .04; k = 39 studies; N = 42,460) was much smaller. We further investigated a less-studied form of narcissism called vulnerable narcissism-which is marked by low self-esteem, neuroticism, and introversion-to find that (in contrast to the more commonly studied form of narcissism found in the DSM and the NPI) men and women did not differ on vulnerable narcissism (d = -.04; k = 42 studies; N = 46,735). Study 2 used item response theory to rule out the possibility that measurement bias accounts for observed gender differences in the three facets of the NPI (N = 19,001). Results revealed that observed gender differences were not explained by measurement bias and thus can be interpreted as true sex differences. Discussion focuses on the implications for the biosocial construction model of gender differences, for the etiology of narcissism, for clinical applications, and for the role of narcissism in helping to explain gender differences in leadership and aggressive behavior. Readers are warned against overapplying small effect sizes to perpetuate gender stereotypes.
Liquid Leakage Thin Film-Tape Sensor

A liquid leakage sensor on adhesive tape with alarming function has been developed. The film sensor tape consists of three layers: a base film layer, a substrate film layer, and a protective film layer. Three conductive lines and one resistive line are printed on the substrate film layer with a nano-sized silver conductive ink by the electronic gravure printing method. The location of liquid leakage has been found to be monitored accurately by applying the pulse voltage to the conductive and resistance lines in a periodic polarity switching mode. The leakage tests for various electrolyte liquids have been performed and the accuracy of leakage error position has been achieved to be about 0.25% on 200 meter long tape length.
Compact structure of band-pass filter integrated with Defected Microstrip Structure (DMS) for wideband applications

This paper presents compact structure of bandpass filter integrated with Defected Microstrip Structure (DMS) to produce band-pass and band reject response simultaneously. The filter design based upon λg0 short-circuited stubs structures of 7th degree. The narrow notch responses produced by DMS was introduced at a frequency of 5.2 GHz in order to reject any undesired signals which may interfere with the wideband communication systems. The design was simulated and fabricated on a Roger Duroid RO4350 with a thickness of 0.508 mm and dielectric constant, εr of 3.48. The group delay in the passband was lower than 0.74 ns. This type of filter is useful in any RF/ microwave communication systems particularly to eliminate any undesired signals in wideband spectrum. While maintaining its excellent performance, the overall physical and cost reduction can also be achieved using this technique.
Finding the K Shortest Loopless Paths in a Network

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Consuming and producing linked open data: the case of OpenCourseWare

The aim of this paper is to present an initiative of application of the Linked Data principles to promote data interoperability between heterogeneous OpenCourseWare (OCW) repositories and to enhance the search and discovery of contents created and shared by the universities.  This paper is a case study of how Linked Data technologies can be applied for the enhancement of open learning contents.  presented under the umbrella of OpenCourseWare Consortium (OCWC) and OCW-Universia consortium, as the integration and access to content from different repositories OCW and the development of a query method to access these data, reveal that Linked Data would offer a solution to filter and select semantically those open educational contents, and automatically are linked to the Linked Open Data Cloud.
Impostor Networks for Fast Fine-Grained Recognition

In this work we introduce impostor networks, an architecture that allows to perform fine-grained recognition with high accuracy and using a light-weight convolutional network, making it particularly suitable for fine-grained applications on low-power and non-GPU enabled platforms. Impostor networks compensate for the lightness of its “backend” network by combining it with a lightweight non-parametric classifier. The combination of a convolutional network and such nonparametric classifier is trained in an end-to-end fashion. Similarly to convolutional neural networks, impostor networks can fit large-scale training datasets very well, while also being able to generalize to new data points. At the same time, the bulk of computations within impostor networks happen through nearest neighbor search in high-dimensions. Such search can be performed efficiently on a variety of architectures including standard CPUs, where deep convolutional networks are inefficient. In a series of experiments with three fine-grained datasets, we show that impostor networks are able to boost the classification accuracy of a moderate-sized convolutional network considerably at a very small computational cost.
Evaluation of Predictive Data Mining Algorithms in Erythemato-Squamous Disease Diagnosis

A lot of time is spent searching for the most performing data mining algorithms applied in clinical diagnosis. The study set out to identify the most performing predictive data mining algorithms applied in the diagnosis of Erythemato-squamous diseases. The study used Naive Bayes, Multilayer Perceptron and J48 decision tree induction to build predictive data mining models on 366 instances of Erythemato-squamous diseases datasets. Also, 10-fold cross-validation and sets of performance metrics were used to evaluate the baseline predictive performance of the classifiers. The comparative analysis shows that the Naive Bayes performed best with accuracy of 97.4%, Multilayer Perceptron came out second with accuracy of 96.6%, and J48 came out the worst with accuracy of 93.5%. The evaluation of these classifiers on clinical datasets, gave an insight into the predictive ability of different data mining algorithms applicable in clinical diagnosis especially in the diagnosis of Erythemato-squamous diseases.