nils-herrmann/clean_scirepeval huggingface dataset:
High performance work systems: the gap between policy and practice in health care reform.

Studies of high-performing organisations have consistently reported a positive relationship between high performance work systems (HPWS) and performance outcomes. Although many of these studies have been conducted in manufacturing, similar findings of a positive correlation between aspects of HPWS and improved care delivery and patient outcomes have been reported in international health care studies. The purpose of this paper is to bring together the results from a series of studies conducted within Australian health care organisations. First, the authors seek to demonstrate the link found between high performance work systems and organisational performance, including the perceived quality of patient care. Second, the paper aims to show that the hospitals studied do not have the necessary aspects of HPWS in place and that there has been little consideration of HPWS in health system reform.



The paper draws on a series of correlation studies using survey data from hospitals in Australia, supplemented by qualitative data collection and analysis. To demonstrate the link between HPWS and perceived quality of care delivery the authors conducted regression analysis with tests of mediation and moderation to analyse survey responses of 201 nurses in a large regional Australian health service and explored HRM and HPWS in detail in three casestudy organisations. To achieve the second aim, the authors surveyed human resource and other senior managers in all Victorian health sector organisations and reviewed policy documents related to health system reform planned for Australia.



The findings suggest that there is a relationship between HPWS and the perceived quality of care that is mediated by human resource management (HRM) outcomes, such as psychological empowerment. It is also found that health care organisations in Australia generally do not have the necessary aspects of HPWS in place, creating a policy and practice gap. Although the chief executive officers of health service organisations reported high levels of strategic HRM, the human resource and other managers reported a distinct lack of HPWS from their perspectives. The authors discuss why health care organisations may have difficulty in achieving HPWS.



Leaders in health care organisations should focus on ensuring human resource management systems, structures and processes that support HPWS. Policy makers need to consider HPWS as a necessary component of health system reform. There is a strong need to reorient organisational human resource management policies and procedures in public health care organisations towards high performing work systems.
Adolescent self-compassion : Associations with narcissism , self-esteem , aggression , and internalizing symptoms in at-risk males

Self-compassion is an attitude toward oneself that involves perceiving one’s experiences as an opportunity for self-awareness and improvement, as well as limited self-judgment after failure. Self-compassion has not been extensively studied in adolescence, a time when self-perception and self-appraisals regarding success and failure take on notable importance. This study considered the connection between self-compassion, narcissism, self-esteem, aggression, and internalizing problems in a sample of 251 male adolescents, ages 16–18, attending a residential program. Self-compassion was negatively correlated with aggression and vulnerable narcissism and positively correlated with self-esteem. In general, selfcompassion did not exhibit the hypothesized protective effect on the relation between narcissism and aggression. Findings indicate that, as expected, self-compassion is indicative of a relatively secure, positive sense of self in adolescents.
Automatic Intra-Register Vectorization for the Intel® Architecture

Recent extensions to the Intel® Architecture feature the SIMD technique to enhance the performance of computational intensive applications that perform the same operation on different elements in a data set. To date, much of the code that exploits these extensions has been hand-coded. The task of the programmer is substantially simplified, however, if a compiler does this exploitation automatically. The high-performance Intel® C++/Fortran compiler supports automatic translation of serial loops into code that uses the SIMD extensions to the Intel® Architecture. This paper provides a detailed overview of the automatic vectorization methods used by this compiler together with an experimental validation of their effectiveness.
Validation of electronic medical record-based phenotyping algorithms: results and lessons learned from the eMERGE network.

Genetic studies require precise phenotype definitions, but electronic medical record (EMR) phenotype data are recorded inconsistently and in a variety of formats.



To present lessons learned about validation of EMR-based phenotypes from the Electronic Medical Records and Genomics (eMERGE) studies.



The eMERGE network created and validated 13 EMR-derived phenotype algorithms. Network sites are Group Health, Marshfield Clinic, Mayo Clinic, Northwestern University, and Vanderbilt University.



By validating EMR-derived phenotypes we learned that: (1) multisite validation improves phenotype algorithm accuracy; (2) targets for validation should be carefully considered and defined; (3) specifying time frames for review of variables eases validation time and improves accuracy; (4) using repeated measures requires defining the relevant time period and specifying the most meaningful value to be studied; (5) patient movement in and out of the health plan (transience) can result in incomplete or fragmented data; (6) the review scope should be defined carefully; (7) particular care is required in combining EMR and research data; (8) medication data can be assessed using claims, medications dispensed, or medications prescribed; (9) algorithm development and validation work best as an iterative process; and (10) validation by content experts or structured chart review can provide accurate results.



Despite the diverse structure of the five EMRs of the eMERGE sites, we developed, validated, and successfully deployed 13 electronic phenotype algorithms. Validation is a worthwhile process that not only measures phenotype performance but also strengthens phenotype algorithm definitions and enhances their inter-institutional sharing.
Dynamic Impact of Online Word-of-Mouth and Advertising on Supply Chain Performance

Cooperative (co-op) advertising investments benefit brand goodwill and further improve supply chain performance. Meanwhile, online word-of-mouth (OWOM) can also play an important role in supply chain performance. On the basis of co-op advertising, this paper considers a single supply chain structure led by a manufacturer and examines a fundamental issue concerning the impact of OWOM on supply chain performance. Firstly, by the method of differential game, this paper analyzes the dynamic impact of OWOM and advertising on supply chain performance (i.e., brand goodwill, sales, and profits) under three different supply chain decisions (i.e., only advertising, and manufacturers with and without sharing cost of OWOM with retailers). We compare and analyze the optimal strategies of advertising and OWOM under the above different supply chain decisions. Secondly, the system dynamics model is established to reflect the dynamic impact of OWOM and advertising on supply chain performance. Finally, three supply chain decisions under two scenarios, strong brand and weak brand, are analyzed through the system dynamics simulation. The results show that the input of OWOM can enhance brand goodwill and improve earnings. It further promotes the OWOM reputation and improves the supply chain performance if manufacturers share the cost of OWOM with retailers. Then, in order to eliminate the retailers from word-of-mouth fraud and establish a fair competition mechanism, the third parties (i.e., regulators or e-commerce platforms) should take appropriate punitive measures against retailers. Furthermore, the effect of OWOM on supply chain performance under a strong brand differed from those under a weak brand. Last but not least, if OWOM is improved, there would be more remarkable performance for the weak brand than that for the strong brand in the supply chain.
SIGKDD demo: sensors and software to allow computational entomology, an emerging application of data mining

The history of humankind is intimately connected to insects. Insect borne diseases kill a million people and destroy tens of billions of dollars worth of crops annually. However, at the same time, beneficial insects pollinate the majority of crop species, and it has been estimated that approximately one third of all food consumed by humans is directly pollinated by bees alone. Given the importance of insects in human affairs, it is somewhat surprising that computer science has not had a larger impact in entomology. We believe that recent advances in sensor technology are beginning change this, and a new field of Computational Entomology will emerge. We will demonstrate an inexpensive sensor that allows us to capture data from flying insects, and the software that allows us to analyze the data. Moreover, we will distribute both the sensors and software for free, to parties willing to take part in a crowdsourcing project on insect classification.
A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems

Recent online services rely heavily on automatic personalization to recommend relevant content to a large number of users. This requires systems to scale promptly to accommodate the stream of new users visiting the online services for the first time. In this work, we propose a content-based recommendation system to address both the recommendation quality and the system scalability. We propose to use a rich feature set to represent users, according to their web browsing history and search queries. We use a Deep Learning approach to map users and items to a latent space where the similarity between users and their preferred items is maximized. We extend the model to jointly learn from features of items from different domains and user features by introducing a multi-view Deep Learning model. We show how to make this rich-feature based user representation scalable by reducing the dimension of the inputs and the amount of training data. The rich user feature representation allows the model to learn relevant user behavior patterns and give useful recommendations for users who do not have any interaction with the service, given that they have adequate search and browsing history. The combination of different domains into a single model for learning helps improve the recommendation quality across all the domains, as well as having a more compact and a semantically richer user latent feature vector. We experiment with our approach on three real-world recommendation systems acquired from different sources of Microsoft products: Windows Apps recommendation, News recommendation, and Movie/TV recommendation. Results indicate that our approach is significantly better than the state-of-the-art algorithms (up to 49% enhancement on existing users and 115% enhancement on new users). In addition, experiments on a publicly open data set also indicate the superiority of our method in comparison with transitional generative topic models, for modeling cross-domain recommender systems. Scalability analysis show that our multi-view DNN model can easily scale to encompass millions of users and billions of item entries. Experimental results also confirm that combining features from all domains produces much better performance than building separate models for each domain.
On Modeling and Predicting Individual Paper Citation Count over Time

Evaluating a scientist’s past and future potential impact is key in decision making concerning with recruitment and funding, and is increasingly linked to publication citation count. Meanwhile, timely identifying those valuable work with great potential before they receive wide recognition and become highly cited papers is both useful for readers and authors in many regards. We propose a method for predicting the citation counts of individual publications, over an arbitrary time period. Our approach explores paper-specific covariates, and a point process model to account for the aging effect and triggering role of recent citations, through which papers lose and gain their popularity, respectively. Empirical results on the Microsoft Academic Graph data suggests that our model can be useful for both prediction and interpretability.
Comparison between proton boron fusion therapy (PBFT) and boron neutron capture therapy (BNCT): a Monte Carlo study

The aim of this study is to compare between proton boron fusion therapy (PBFT) and boron neutron capture therapy (BNCT) and to analyze dose escalation using a Monte Carlo simulation. We simulated a proton beam passing through the water with a boron uptake region (BUR) in MCNPX. To estimate the interaction between neutrons/protons and borons by the alpha particle, the simulation yielded with a variation of the center of the BUR location and proton energies. The variation and influence about the alpha particle were observed from the percent depth dose (PDD) and cross-plane dose profile of both the neutron and proton beams. The peak value of the maximum dose level when the boron particle was accurately labeled at the region was 192.4% among the energies. In all, we confirmed that prompt gamma rays of 478 keV and 719 keV were generated by the nuclear reactions in PBFT and BNCT, respectively. We validated the dramatic effectiveness of the alpha particle, especially in PBFT. The utility of PBFT was verified using the simulation and it has a potential for application in radiotherapy.
Research on data augmentation for image classification based on convolution neural networks

The performance of deep convolution neural networks will be further enhanced with the expansion of the training data set. For the image classification tasks, it is necessary to expand the insufficient training image samples through various data augmentation methods. This paper explores the impact of various data augmentation methods on image classification tasks with deep convolution Neural network, in which Alexnet is employed as the pre-training network model and a subset of CIFAR10 and ImageNet (10 categories) are selected as the original data set. The data augmentation methods used in this paper include: GAN/WGAN, Flipping, Cropping, Shifting, PCA jittering, Color jittering, Noise, Rotation, and some combinations. Experimental results show that, under the same condition of multiple increasing, the performance evaluation on small-scale data sets is more obvious, the four individual methods (Cropping, Flipping, WGAN, Rotation) perform generally better than others, and some appropriate combination methods are slightly more effective than the individuals.
A Taxonomy of Attacks in RPL-based Internet of Things

The growing interest for the Internet of Things is contributing to the large-scale deployment of Low power and Lossy Networks (LLN). These networks support communications amongst objects from the real world, such as home automation devices and embedded sensors, and their interconnection to the Internet. An open standard routing protocol, called RPL, has been specified by the IETF in order to address the specific properties and constraints of these networks. However, this protocol is exposed to a large variety of attacks. Their consequences can be quite significant in terms of network performance and resources. In this paper, we propose to establish a taxonomy of the attacks against this protocol, considering three main categories including attacks targeting network resources, attacks modifying the network topology and attacks related to network traffic. We describe these attacks, analyze and compare their properties, discuss existing counter-measures and their usage from a risk management perspective.
Financial Time Series Forecasting - A Machine Learning Approach

The Stock Market is known for its volatile and unstable nature. A particular stock could be thriving in one period and declining in the next. Stock traders make money from buying equity when they are at their lowest and selling when they are at their highest. The logical question would be: "What Causes Stock Prices To Change?". At the most fundamental level, the answer to this would be the demand and supply. In reality, there are many theories as to why stock prices fluctuate, but there is no generic theory that explains all, simply because not all stocks are identical, and one theory that may apply for today, may not necessarily apply for tomorrow. This paper covers various approaches taken to attempt to predict the stock market without extensive prior knowledge or experience in the subject area, highlighting the advantages and limitations of the different techniques such as regression and classification. We formulate both short term and long term predictions. Through experimentation we achieve 81% accuracy for future trend direction using classification, 0.0117 RMSE for next day price and 0.0613 RMSE for next day change in price using regression techniques. The results obtained in this paper are achieved using only historic prices and technical indicators. Various methods, tools and evaluation techniques will be assessed throughout the course of this paper, the result of this contributes as to which techniques will be selected and enhanced in the final artefact of a stock prediction model. Further work will be conducted utilising deep learning techniques to approach the problem. This paper will serve as a preliminary guide to researchers wishing to expose themselves to this area.
Asymptomatic vulvar pigmentation.

A 29-year-old woman presented with a 3-month history of a macular lesion on the vaginal introit and labia minor, which was causing concern because of its crescent-shaped enlargement. There was no itching or vaginal discharge. The patient’s medical history was unremarkable, particularly for dermatological diseases such as lichen planus or other inflammatory disorders. There was no family history of skin diseases or skin cancer, including melanoma. On physical examination, a dark-brown macule with irregular borders, measuring around 20 · 30 mm, was seen, localized to the vaginal introit and labia minora . On palpation, the lesion was flat, having the contour of the normal vaginal mucosa, and was not indurated or tender. The patient had no oral mucosal, perioral or facial hyperpigmentation, and the remaining physical examination was normal. Dermatoscopy was performed, revealing diffuse pigmentation with a parallel pattern of partially linear and partially curvilinear brown streaks across the lesion.
Learning Neural Models for End-to-End Clustering

We propose a novel end-to-end neural network architecture that, once trained, directly outputs a probabilistic clustering of a batch of input examples in one pass. It estimates a distribution over the number of clusters k, and for each 1 ≤ k ≤ kmax, a distribution over the individual cluster assignment for each data point. The network is trained in advance in a supervised fashion on separate data to learn grouping by any perceptual similarity criterion based on pairwise labels (same/different group). It can then be applied to different data containing different groups. We demonstrate promising performance on high-dimensional data like images (COIL-100) and speech (TIMIT). We call this “learning to cluster” and show its conceptual difference to deep metric learning, semi-supervise clustering and other related approaches while having the advantage of performing learnable clustering fully end-to-end.
A Concept Analysis of Systems Thinking.

This concept analysis, written by the National Quality and Safety Education for Nurses (QSEN) RN-BSN Task Force, defines systems thinking in relation to healthcare delivery.



A review of the literature was conducted using five databases with the keywords "systems thinking" as well as "nursing education," "nursing curriculum," "online," "capstone," "practicum," "RN-BSN/RN to BSN," "healthcare organizations," "hospitals," and "clinical agencies." Only articles that focused on systems thinking in health care were used. The authors identified defining attributes, antecedents, consequences, and empirical referents of systems thinking.



Systems thinking was defined as a process applied to individuals, teams, and organizations to impact cause and effect where solutions to complex problems are accomplished through collaborative effort according to personal ability with respect to improving components and the greater whole. Four primary attributes characterized systems thinking: dynamic system, holistic perspective, pattern identification, and transformation.



Using the platform provided in this concept analysis, interprofessional practice has the ability to embrace planned efforts to improve critically needed quality and safety initiatives across patients' lifespans and all healthcare settings.
6-DoF object pose from semantic keypoints

This paper presents a novel approach to estimating the continuous six degree of freedom (6-DoF) pose (3D translation and rotation) of an object from a single RGB image. The approach combines semantic keypoints predicted by a convolutional network (convnet) with a deformable shape model. Unlike prior work, we are agnostic to whether the object is textured or textureless, as the convnet learns the optimal representation from the available training image data. Furthermore, the approach can be applied to instance- and class-based pose recovery. Empirically, we show that the proposed approach can accurately recover the 6-DoF object pose for both instance- and class-based scenarios with a cluttered background. For class-based object pose estimation, state-of-the-art accuracy is shown on the large-scale PASCAL3D+ dataset.
A review of mangrove rehabilitation in the Philippines: successes, failures and future prospects

From half a million hectares at the turn of the century, Philippine mangroves have declined to only 120,000 ha while fish/shrimp culture ponds have increased to 232,000 ha. Mangrove replanting programs have thus been popular, from community initiatives (1930s–1950s) to government-sponsored projects (1970s) to large-scale international development assistance programs (1980s to present). Planting costs escalated from less than US$100 to over $500/ha, with half of the latter amount allocated to administration, supervision and project management. Despite heavy funds for massive rehabilitation of mangrove forests over the last two decades, the long-term survival rates of mangroves are generally low at 10–20%. Poor survival can be mainly traced to two factors: inappropriate species and site selection. The favored but unsuitable Rhizophora are planted in sandy substrates of exposed coastlines instead of the natural colonizers Avicennia and Sonneratia. More significantly, planting sites are generally in the lower intertidal to subtidal zones where mangroves do not thrive rather than the optimal middle to upper intertidal levels, for a simple reason. Such ideal sites have long been converted to brackishwater fishponds whereas the former are open access areas with no ownership problems. The issue of pond ownership may be complex and difficult, but such should not outweigh ecological requirements: mangroves should be planted where fishponds are, not on seagrass beds and tidal flats where they never existed. This paper reviews eight mangrove initiatives in the Philippines and evaluates the biophysical and institutional factors behind success or failure. The authors recommend specific protocols (among them pushing for a 4:1 mangrove to pond ratio recommended for a healthy ecosystem) and wider policy directions to make mangrove rehabilitation in the country more effective.