nils-herrmann/clean_scirepeval huggingface dataset:
Factors associated with high job satisfaction among care workers in Swiss nursing homes a cross sectional survey study
Venue: BMC Nursing

BackgroundWhile the relationship between nurses' job satisfaction and their work in hospital environments is well known, it remains unclear, which factors are most influential in the nursing home setting. The purpose of this study was to describe job satisfaction among care workers in Swiss nursing homes and to examine its associations with work environment factors, work stressors, and health issues.MethodsThis cross sectional study used data from a representative national sample of 162 Swiss nursing homes including 4,145 care workers from all educational levels (registered nurses, licensed practical nurses, nursing assistants and aides) Care worker reported job satisfaction was measured with a single item. Explanatory variables were assessed with established scales, as e.g. the Practice Environment Scale Nursing Work Index. Generalized Estimating Equation (GEE) models were used to examine factors related to job satisfaction.ResultsOverall, 36.2 of respondents reported high satisfaction with their workplace, while another 50.4 were rather satisfied. Factors significantly associated with high job satisfaction were supportive leadership (OR 3.76) better teamwork and resident safety climate (OR 2.60) a resonant nursing home administrator (OR 2.30) adequate staffing resources (OR 1.40) fewer workplace conflicts (OR .61) less sense of depletion after work (OR .88) and fewer physical health problems (OR .91).ConclusionsThe quality of nursing home leadership at both the unit supervisor and the executive administrator level was strongly associated with care workers' job satisfaction. Therefore, recruitment strategies addressing specific profiles for nursing home leaders are needed, followed by ongoing leadership training. Future studies should examine the effects of interventions designed to improve nursing home leadership and work environments on outcomes both for care staff and for residents.
Unclipping our wings: Ways forward in qualitative research in sport management

In this review article, we aim to explore and promote dialogue regarding the use of contemporary qualitative research methods being used in sport management. The first section is a snapshot of qualitative research from 2011 to 2013 in the three main sport management journals: Sport Management Review (SMR) the Journal of Sport Management (JSM) and European Sport Management Quarterly (ESMQ) Secondly, we comment on this snapshot, outlining not only how far we have come in qualitative research in sport management but also drawing attention to some of the constraints to its current use. Thirdly, we illustrate what might help us to reflect on our use of qualitative research methods. This leads us to our final section, in which we utilise that reflection to outline some ways forward; how can we contribute to sport management by using qualitative research methods in imaginative and innovative ways?
An Evaluation of Sport Management Student Preparedness: Recommendations for Adapting Curriculum to Meet Industry Needs

Experiential learning has become a driving force of universities around the world, and is a crucial part of many sport management programs. This is particularly true given the competitive nature of the field and the rapid changes the industry continuously faces. This work seeks to reexamine the sport management curricula to ensure a progression and evolution toward a superior level of student preparedness for their internship experiences. Through the use of both quantitative and qualitative methods, our major findings recommend a focus on academic, experiential, and professional development. Theoretical and practical implications are discussed along with limitations and directions for further investigation.
Effects of Internship Satisfaction on the Pursuit of Employment in Sport Management

The internship could have a significant impact upon the student's desire to enter the field after graduation. Despite a substantial amount of research that has been conducted with employees in many fields, relatively little research has been conducted with sport management interns. The purpose of this study, therefore, was twofold: (1) investigate the satisfaction of student interns with characteristics of the internship experience and (2) investigate the effect of students' satisfaction with their internship on their affective occupational commitment for and subsequent intentions to pursue employment in the sport management field. A total of 248 undergraduate students from two universities in the Southeastern United States completed a survey. Participants generally indicated satisfaction with opportunities to develop pertinent skills, engage in meaningful tasks, and build professional networks during the internship. Those who reported satisfaction with the internship were more likely to enter the field a.
Online and Offline Shopping Motivation of Apparel Consumers in Ibadan Metropolis, Nigeria

Shopping today is much more than just buying, it is an experience itself. Consumers now value convenience and choice as well as getting value for their hard earned money. Motivation is where consumption starts, where it all begins, with the acknowledgement of a need. Online shopping has shown to provide more satisfaction to modern consumers seeking convenience and speed, however, in a country like Nigeria, consumers still buy a lot from shops and malls thereby still making offline shopping very relevant. This research made use of multi stage sampling; using purposive, simple random and convenience sampling techniques. A four point Likert Scale was used to measure consumersi shopping motivation and preferred shopping platform. Most of the research on online shopping focuses on consumers in developed countries with little or none among Nigerian consumers. Consequently, this study provides information on apparel shopping motivation (utilitarian and hedonic) of the average Nigerian; such information is beneficial for online and offline Fashion merchants that seek to retain customers. Consumers of this study were affected by all the utilitarian motivating factors as well as almost all the hedonic shopping motivations measured. It was revealed that the respondents preferred to shop offline than online shopping platforms. In conclusion, the consumers of this study are fashion conscious, utilitarian and hedonic shoppers, however, they prefer the offline shopping platforms.
Scene recognition algorithm based on multi feature and weighted minimum distance classifier for digital hearing aids
Venue: 2016 International Conference on Audio, Language and Image Processing (ICALIP)

The recognition precision of the existing auditory scene recognition algorithms is relatively satisfactory, but they can only be applied to several noise scenarios, and it can't meet the performance requirements of digital hearing aids in complex environment. In order to solve the above problems, scene recognition algorithm based on multi feature and weighted minimum distance classifier is proposed in this paper. In this algorithm, the speech endpoint detection algorithm based on the band partitioning spectral entropy and spectral energy is used to divide the noisy speech into speech segment and noise segment. Then the characteristics such as Critical Band Ratio and band partitioning spectral entropy as well as adaptive short time zero crossing rate of each segment are extracted for the weighted minimum distance classifier to recognize the noise scenario. The experiments result shows that the proposed algorithm has strong robustness and high accuracy. It's suitable to be applied in digital hearing aids.
Dynamic texture and scene classification by transferring deep image features
Venue: Neurocomputing

Dynamic texture and scene classification are two fundamental problems in understanding natural video content. Extracting robust and effective features is a crucial step towards solving these problems. However, the existing approaches suffer from the sensitivity to either varying illumination, or viewpoint changes, or even camera motion, and/or the lack of spatial information. Inspired by the success of deep structures in image classification, we attempt to leverage a deep structure to extract features for dynamic texture and scene classification. To tackle with the challenges in training a deep structure, we propose to transfer some prior knowledge from image domain to video domain. To be more specific, we propose to apply a well trained Convolutional Neural Network (ConvNet) as a feature extractor to extract mid level features from each frame, and then form the video level representation by concatenating the first and the second order statistics over the mid level features. We term this two level feature extraction scheme as a Transferred ConvNet Feature (TCoF) Moreover, we explore two different implementations of the TCoF scheme, i.e. the spatial TCoF and the temporal TCoF. In the spatial TCoF, the mean removed frames are used as the inputs of the ConvNet; whereas in the temporal TCoF, the differences between two adjacent frames are used as the inputs of the ConvNet. We evaluate systematically the proposed spatial TCoF and the temporal TCoF schemes on three benchmark data sets, including DynTex, YUPENN, and Maryland, and demonstrate that the proposed approach yields superior performance.
Using LSTM recurrent neural networks for detecting anomalous behavior of LHC superconducting magnets
Venue: ArXiv

The superconducting LHC magnets are coupled with an electronic monitoring system which records and analyzes voltage time series reflecting their performance. A currently used system is based on a range of preprogrammed triggers which launches protection procedures when a misbehavior of the magnets is detected. All the procedures used in the protection equipment were designed and implemented according to known working scenarios of the system and are updated and monitored by human operators. This paper proposes a novel approach to monitoring and fault protection of the Large Hadron Collider (LHC) superconducting magnets which employs state of the art Deep Learning algorithms. Consequently, the authors of the paper decided to examine the performance of LSTM recurrent neural networks for modeling of voltage time series of the magnets. In order to address this challenging task different network architectures and hyper parameters were used to achieve the best possible performance of the solution. The regression results were measured in terms of RMSE for different number of future steps and history length taken into account for the prediction. The best result of RMSE 0 00104 was obtained for a network of 128 LSTM cells within the internal layer and 16 steps history buffer.
Performance evaluation of open ended schemes of HDFC mutual fund

In this paper we tried to evaluate the performance of HDFC open ended equity schemes with growth option. The period of the study spans from 1st April 2006 till 31st March 2015. To evaluate the performance of the selected mutual fund schemes, monthly returns are compared with Benchmark (BSE National 100 Index) returns. Further, statistical tools like average, standard deviation, beta, coefficient of determination and the risk adjusted performance measures suggested by Sharpe (1966) Treynor (1965) and Jenson (1968) were employed to evaluate the performance for the selected period.
Fast Matching of Binary Descriptors for Large Scale Applications in Robot Vision

The introduction of computationally efficient binary feature descriptors has raised new opportunities for real world robot vision applications. However, brute force feature matching of binary descriptors is only practical for smaller datasets. In the literature, there has therefore been an increasing interest in representing and matching binary descriptors more efficiently. In this article, we follow this trend and present a method for efficiently and dynamically quantizing binary descriptors through a summarized frequency count into compact representations (called fsum) for improved feature matching of binary point features. With the motivation that real world robot applications must adapt to a changing environment, we further present an overview of the field of algorithms, which concerns the efficient matching of binary descriptors and which are able to incorporate changes over time, such as clustered search trees and bag of features improved by vocabulary adaptation. The focus for this article is on evaluation, particularly large scale evaluation, compared to alternatives that exist within the field. Throughout this evaluation it is shown that the fsum approach is both efficient in terms of computational cost and memory requirements, while retaining adequate retrieval accuracy. It is further shown that the presented algorithm is equally suited to binary descriptors of arbitrary type and that the algorithm is therefore a valid option for several types of vision applications.
Fast Localization in Large Scale Environments Using Supervised Indexing of Binary Features
Venue: IEEE Transactions on Image Processing

The essence of image based localization lies in matching 2D key points in the query image and 3D points in the database. State of the art methods mostly employ sophisticated key point detectors and feature descriptors, e.g. Difference of Gaussian (DoG) and Scale Invariant Feature Transform (SIFT) to ensure robust matching. While a high registration rate is attained, the registration speed is impeded by the expensive key point detection and the descriptor extraction. In this paper, we propose to use efficient key point detectors along with binary feature descriptors, since the extraction of such binary features is extremely fast. The naive usage of binary features, however, does not lend itself to significant speedup of localization, since existing indexing approaches, such as hierarchical clustering trees and locality sensitive hashing, are not efficient enough in indexing binary features and matching binary features turns out to be much slower than matching SIFT features. To overcome this, we propose a much more efficient indexing approach for approximate nearest neighbor search of binary features. This approach resorts to randomized trees that are constructed in a supervised training process by exploiting the label information derived from that multiple features correspond to a common 3D point. In the tree construction process, node tests are selected in a way such that trees have uniform leaf sizes and low error rates, which are two desired properties for efficient approximate nearest neighbor search. To further improve the search efficiency, a probabilistic priority search strategy is adopted. Apart from the label information, this strategy also uses non binary pixel intensity differences available in descriptor extraction. By using the proposed indexing approach, matching binary features is no longer much slower but slightly faster than matching SIFT features. Consequently, the overall localization speed is significantly improved due to the much faster key point detection and descriptor extraction. It is empirically demonstrated that the localization speed is improved by an order of magnitude as compared with state of the art methods, while comparable registration rate and localization accuracy are still maintained.
Fast Localization in Large Scale Environments Using Supervised Indexing of Binary Features.
Venue: IEEE transactions on image processing a publication of the IEEE Signal Processing Society

The essence of image based localization lies in matching 2D key points in the query image and 3D points in the database. State of the art methods mostly employ sophisticated key point detectors and feature descriptors, e.g. Difference of Gaussian (DoG) and Scale Invariant Feature Transform (SIFT) to ensure robust matching. While a high registration rate is attained, the registration speed is impeded by the expensive key point detection and the descriptor extraction. In this paper, we propose to use efficient key point detectors along with binary feature descriptors, since the extraction of such binary features is extremely fast. The naive usage of binary features, however, does not lend itself to significant speedup of localization, since existing indexing approaches, such as hierarchical clustering trees and locality sensitive hashing, are not efficient enough in indexing binary features and matching binary features turns out to be much slower than matching SIFT features. To overcome this, we propose a much more efficient indexing approach for approximate nearest neighbor search of binary features. This approach resorts to randomized trees that are constructed in a supervised training process by exploiting the label information derived from that multiple features correspond to a common 3D point. In the tree construction process, node tests are selected in a way such that trees have uniform leaf sizes and low error rates, which are two desired properties for efficient approximate nearest neighbor search. To further improve the search efficiency, a probabilistic priority search strategy is adopted. Apart from the label information, this strategy also uses non binary pixel intensity differences available in descriptor extraction. By using the proposed indexing approach, matching binary features is no longer much slower but slightly faster than matching SIFT features. Consequently, the overall localization speed is significantly improved due to the much faster key point detection and descriptor extraction. It is empirically demonstrated that the localization speed is improved by an order of magnitude as compared with state of the art methods, while comparable registration rate and localization accuracy are still maintained.
Implementation of Mao Zedong's Yan'an "Talks" in the Subei Base Area The Chen Dengke Chen Deng Ke "Phenomenon"

Zedong's 1942 "Talks at the Yan'an Forum on Literature and Art" were officially established as the foundation of national policy on culture after the founding of the People's Republic, but from the very outset they had direct implications for writers and artists in the Communist base areas of the time. As a case study of the implementation of the spirit of Mao's "Talks" prior to 1949, this paper will discuss how illiterate peasant soldier Chen Dengke (1919 98) was educated by Party cultural cadres in the North Jiangsu (Subei) Base Area, enabling him within the space of just four years to produce a novella and a novel. In order to critically examine the auto/biographical and "slice of life" writings on which this paper relies, brief discussion will be provided of temporal considerations and genre boundaries of this class of writing in the context of the ever changing political orthodoxy with which writers were required to comply during the Maoist period. The creation of what has been called the Chen Dengke "phenomenon" is not only a fascinating story, but also illustrates the operation of Communist Party cultural policy during the Sino Japanese and Civil Wars.
"Self Assessment of Hearing Disabilities in Cochlear Implant Users Using the SSQ and the Reduced SSQ5 Version. Otology Neurotology 2013;34: 1622 9"
Venue: Otology neurotology official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology

We thank you for giving us the opportunity to add some comments and trends raised after the publication of our article 'Self Assessment of Hearing Disabilities in Cochlear Implant Users Using the SSQ and the Reduced SSQ5 Version. The five item Speech Spatial and Qualities of Hearing Scale (SSQ5) introduced by Demeester et al. (1) was used as a starting point to investigate whether this questionnaire is robust to effectively measure evolutions in hearing (dis)abilities in Cochlear Implant (CI) users. Recently, Noble et al. (2) developed and evaluated a 12 item version of the Speech Spatial and Qualities of Hearing Scale (SSQ) They concluded that the SSQ12 provides similar results to SSQ49 in a large clinical research sample. To examine which reduced SSQ version had the strongest degree of agreement with the original SSQ49 in a CI population, Spearman correlations coefficients were calculated in our CI study group.  1 shows that the 12 item version provides more similar results to the SSQ49 (r1/4 0.90) compared with the 5 item version (r1/4 0.62) of Demeester et al. (1) In conclusion, the SSQ5 is a robust questionnaire to effectively measure the evolutions in hearing (dis)abilities in CI users. However, the degree of
Mergers of accreting stellar mass black holes

We present post Newtonian $N$ body simulations on mergers of accreting stellar mass black holes (BHs) where such general relativistic effects as the pericenter shift and gravitational wave (GW) emission are taken into consideration. The attention is concentrated on the effects of the dynamical friction and the Hoyle Lyttleton mass accretion by ambient gas. We consider a system composed of ten BHs with initial mass of $30~M_\odot$ As a result, we show that mergers of accreting stellar mass BHs are classified into four types: a gas drag driven, an interplay driven, a three body driven, or an accretion driven merger. We find that BH mergers proceed before significant mass accretion, even if the accretion rate is \sim10$ Eddington accretion rate, and then all BHs can merge into one heavy BH. Using the simulation results for a wide range of parameters, we derive a critical accretion rate \dot{m}_{\rm c} below which the BH growth is promoted faster by mergers. Also, it is found that the effect of the recoil by the GW emission can reduce \dot{m}_{\rm c} especially in gas number density higher than $10^8~\rm cm} 3} and enhance the escape probability of merged BHs. Very recently, a gravitational wave event, GW150914, as a result of the merger of a \sim 30~M_\odot$ BH binary has been detected (Abbott et al. 2016) Based on the present simulations, the BH merger in GW150914 is likely to be driven by three body encounters accompanied by a few $M_\odot$ of gas accretion, in high density environments like dense interstellar clouds or galactic nuclei.
Efficacy and safety of anticoagulant therapy in three specific populations with sepsis: a meta analysis of randomized controlled trials: reply
Venue: Journal of thrombosis and haemostasis JTH

See also Umemura Y, Yamakawa K, Ogura H, Yuhara H, Fujimi S. Efficacy and safety of anticoagulant therapy in three specific populations with sepsis: a meta analysis of randomized controlled trials.  Haemost 2016; 14: 518 30 and Wada H, Matsumoto T, Aota T, Imai H, Suzuki K, Katayama N. Efficacy and safety of anticoagulant therapy in three specific populations with sepsis: a meta analysis of randomized controlled trials: comment. This issue, pp 2308 9.