nils-herrmann/clean_scirepeval huggingface dataset:
Effect of aquatic exercise on ankylosing spondylitis: a randomized controlled trial

Ankylosing spondylitis (AS) is a chronic systemic inflammatory disease that affects mainly the axial skeleton and causes significant pain and disability. Aquatic (water-based) exercise may have a beneficial effect in various musculoskeletal conditions. The aim of this study was to compare the effectiveness of aquatic exercise interventions with land-based exercises (home-based exercise) in the treatment of AS. Patients with AS were randomly assigned to receive either home-based exercise or aquatic exercise treatment protocol. Home-based exercise program was demonstrated by a physiotherapist on one occasion and then, exercise manual booklet was given to all patients in this group. Aquatic exercise program consisted of 20 sessions, 5× per week for 4 weeks in a swimming pool at 32–33 °C. All the patients in both groups were assessed for pain, spinal mobility, disease activity, disability, and quality of life. Evaluations were performed before treatment (week 0) and after treatment (week 4 and week 12). The baseline and mean values of the percentage changes calculated for both groups were compared using independent sample t test. Paired t test was used for comparison of pre- and posttreatment values within groups. A total of 69 patients with AS were included in this study. We observed significant improvements for all parameters [pain score (VAS) visual analog scale, lumbar flexion/extension, modified Schober test, chest expansion, bath AS functional index, bath AS metrology index, bath AS disease activity index, and short form-36 (SF-36)] in both groups after treatment at week 4 and week 12 (p < 0.05). Comparison of the percentage changes of parameters both at week 4 and week 12 relative to pretreatment values showed that improvement in VAS (p < 0.001) and bodily pain (p < 0.001), general health (p < 0.001), vitality (p < 0.001), social functioning (p < 0.001), role limitations due to emotional problems (p < 0.001), and general mental health (p < 0.001) subparts of SF-36 were better in aquatic exercise group. It is concluded that a water-based exercises produced better improvement in pain score and quality of life of the patients with AS compared with home-based exercise.
A Thematic Analysis of Mothers’ Motivations for Blogging

Various forms of social media are used by many mothers to maintain social ties and manage the stress associated with their parenting roles and responsibilities. ‘Mommy blogging’ as a specific type of social media usage is a common and growing phenomenon, but little is known about mothers’ blogging-related experiences and how these may contribute to their wellbeing. This exploratory study investigated the blogging-related motivations and goals of Australian mothers.  An online survey was emailed to members of an Australian online parenting community. The survey included open-ended questions that invited respondents to discuss their motivations and goals for blogging. A thematic analysis using a grounded approach was used to analyze the qualitative data obtained from 235 mothers.  Five primary motivations for blogging were identified: developing connections with others, experiencing heightened levels of mental stimulation, achieving self-validation, contributing to the welfare of others, and extending skills and abilities.  These motivations are discussed in terms of their various properties and dimensions to illustrate how these mothers appear to use blogging to enhance their psychological wellbeing.
Health care-associated infections: a meta-analysis of costs and financial impact on the US health care system.

Health care-associated infections (HAIs) account for a large proportion of the harms caused by health care and are associated with high costs. Better evaluation of the costs of these infections could help providers and payers to justify investing in prevention.



To estimate costs associated with the most significant and targetable HAIs.



For estimation of attributable costs, we conducted a systematic review of the literature using PubMed for the years 1986 through April 2013. For HAI incidence estimates, we used the National Healthcare Safety Network of the Centers for Disease Control and Prevention (CDC).



Studies performed outside the United States were excluded. Inclusion criteria included a robust method of comparison using a matched control group or an appropriate regression strategy, generalizable populations typical of inpatient wards and critical care units, methodologic consistency with CDC definitions, and soundness of handling economic outcomes.



Three review cycles were completed, with the final iteration carried out from July 2011 to April 2013. Selected publications underwent a secondary review by the research team.



Costs, inflated to 2012 US dollars.



Using Monte Carlo simulation, we generated point estimates and 95% CIs for attributable costs and length of hospital stay. On a per-case basis, central line-associated bloodstream infections were found to be the most costly HAIs at $45,814 (95% CI, $30,919-$65,245), followed by ventilator-associated pneumonia at $40,144 (95% CI, $36,286-$44,220), surgical site infections at $20,785 (95% CI, $18,902-$22,667), Clostridium difficile infection at $11,285 (95% CI, $9118-$13,574), and catheter-associated urinary tract infections at $896 (95% CI, $603-$1189). The total annual costs for the 5 major infections were $9.8 billion (95% CI, $8.3-$11.5 billion), with surgical site infections contributing the most to overall costs (33.7% of the total), followed by ventilator-associated pneumonia (31.6%), central line-associated bloodstream infections (18.9%), C difficile infections (15.4%), and catheter-associated urinary tract infections (<1%).



While quality improvement initiatives have decreased HAI incidence and costs, much more remains to be done. As hospitals realize savings from prevention of these complications under payment reforms, they may be more likely to invest in such strategies.
Hierarchical Attention-Based Recurrent Highway Networks for Time Series Prediction

Time series prediction has been studied in a variety of domains. However, it is still challenging to predict future series given historical observations and past exogenous data. Existing methods either fail to consider the interactions among different components of exogenous variables which may affect the prediction accuracy, or cannot model the correlations between exogenous data and target data. Besides, the inherent temporal dynamics of exogenous data are also related to the target series prediction, and thus should be considered as well. To address these issues, we propose an end-to-end deep learning model, i.e., Hierarchical attention-based Recurrent Highway Network (HRHN), which incorporates spatio-temporal feature extraction of exogenous variables and temporal dynamics modeling of target variables into a single framework. Moreover, by introducing the hierarchical attention mechanism, HRHN can adaptively select the relevant exogenous features in different semantic levels. We carry out comprehensive empirical evaluations with various methods over several datasets, and show that HRHN outperforms the state of the arts in time series prediction, especially in capturing sudden changes and sudden oscillations of time series.
Need ful fi llment and experiences on social media : A case on Facebook and

With an increasing inflow and outflow of users from social media, understanding the factors the drive their adoption becomes even more pressing. This paper reports on a study with 494 users of Facebook and WhatsApp. Different from traditional uses & gratifications studies that probe into typical uses of social media, we sampled users' single recent, outstanding (either satisfying or unsatisfying) experiences, based on a contemporary theoretical and methodological framework of 10 universal human needs. Using quantitative and qualitative analyses, we found WhatsApp to unlock new opportunities for intimate communications, Facebook to be characterized by primarily non-social uses, and both media to be powerful lifelogging tools. Unsatisfying experiences were primarily rooted in the tools' breach of offline social norms, as well in content fatigue and exposure to undesirable content in the case of Facebook. We discuss the implications of the findings for the design of social media.
A Survey of Collaborative Filtering-Based Recommender Systems: From Traditional Methods to Hybrid Methods Based on Social Networks

In the era of big data, recommender system (RS) has become an effective information filtering tool that alleviates information overload for Web users. Collaborative filtering (CF), as one of the most successful recommendation techniques, has been widely studied by various research institutions and industries and has been applied in practice. CF makes recommendations for the current active user using lots of users’ historical rating information without analyzing the content of the information resource. However, in recent years, data sparsity and high dimensionality brought by big data have negatively affected the efficiency of the traditional CF-based recommendation approaches. In CF, the context information, such as time information and trust relationships among the friends, is introduced into RS to construct a training model to further improve the recommendation accuracy and user’s satisfaction, and therefore, a variety of hybrid CF-based recommendation algorithms have emerged. In this paper, we mainly review and summarize the traditional CF-based approaches and techniques used in RS and study some recent hybrid CF-based recommendation approaches and techniques, including the latest hybrid memory-based and model-based CF recommendation algorithms. Finally, we discuss the potential impact that may improve the RS and future direction. In this paper, we aim at introducing the recent hybrid CF-based recommendation techniques fusing social networks to solve data sparsity and high dimensionality and provide a novel point of view to improve the performance of RS, thereby presenting a useful resource in the state-of-the-art research result for future researchers.
Transcatheter tricuspid valve-in-valve in patients with transvalvular device leads.

To describe the effects of percutaneously implanted valve-in-valve in the tricuspid position for patients with pre-existing transvalvular device leads.



In this case series, we describe implantation of the Melody valve and SAPIEN XT valve within dysfunctional bioprosthetic tricuspid valves in three patients with transvalvular device leads.



In all cases, the valve was successfully deployed and device lead function remained unchanged. In 1/3 cases with 6-month follow-up, device lead parameters remain unchanged and transcatheter valve-in-valve function remains satisfactory.



Transcatheter tricuspid valve-in-valve is feasible in patients with pre-existing transvalvular devices leads. Further study is required to determine the long-term clinical implications of this treatment approach.
Indoor Segmentation and Support Inference from RGBD Images

We present an approach to interpret the major surfaces, objects, and support relations of an indoor scene from an RGBD image. Most existing work ignores physical interactions or is applied only to tidy rooms and hallways. Our goal is to parse typical, often messy, indoor scenes into floor, walls, supporting surfaces, and object regions, and to recover support relationships. One of our main interests is to better understand how 3D cues can best inform a structured 3D interpretation. We also contribute a novel integer programming formulation to infer physical support relations. We offer a new dataset of 1449 RGBD images, capturing 464 diverse indoor scenes, with detailed annotations. Our experiments demonstrate our ability to infer support relations in complex scenes and verify that our 3D scene cues and inferred support lead to better object segmentation.
Effects of culture and education on neuropsychological testing: a preliminary study with indigenous and nonindigenous population.

We analyzed the influence of education and of culture on the neuropsychological profile of an indigenous and a nonindigenous population. The sample included 27 individuals divided into four groups: (a) seven illiterate Maya indigenous participants, (b) six illiterate Pame indigenous participants, (c) seven nonindigenous participants with no education, and (d) seven Maya indigenous participants with 1 to 4 years of education . A brief neuropsychological test battery developed and standardized in Mexico was individually administered. Results demonstrated differential effects for both variables. Both groups of indigenous participants (Maya and Pame) obtained higher scores in visuospatial tasks, and the level of education had significant effects on working and verbal memory. Our data suggested that culture dictates what it is important for survival and that education could be considered as a type of subculture that facilitates the development of certain skills.
Fatal butane inhalation from gas cartridges: a case report and literature review

Volatile substances are commonly misused with easy-to-obtain commercial products, such as glue, shoe polish, nail polish remover, butane lighter fluid, gasoline and computer duster spray. This report describes a case of sudden death of a 29-year-old woman after presumably inhaling gas cartridge butane from a plastic bag. Autopsy, pathological and toxicological analyses were performed in order to determine the cause of death. Pulmonary edema was observed pathologically, and the toxicological study revealed 2.1μL/mL of butane from the blood. The causes of death from inhalation of volatile substances have been explained by four mechanisms; cardiac arrhythmia, anoxia, respiratory depression, and vagal inhibition. In this case, the cause of death was determined to be asphyxia from anoxia. Additionally, we have gathered fatal butane inhalation cases with quantitative analyses of butane concentrations, and reviewed other reports describing volatile substance abuse worldwide.
Obsessive-compulsive disorder across developmental trajectory: cognitive processing of threat in children, adolescents and adults.

While the cognitive theory of obsessive-compulsive disorder (OCD) is one of the most widely accepted accounts of the maintenance of the disorder in adults, no study to date has systematically evaluated the theory across children, adolescence and adults with OCD.



This paper investigated developmental differences in the cognitive processing of threat in a sample of children, adolescents and adults with OCD. Using an idiographic assessment approach, as well as self-report questionnaires, this study evaluated cognitive appraisals of responsibility, probability, severity, thought-action fusion (TAF), thought-suppression, self-doubt and cognitive control. It was hypothesised that there would be age related differences in reported responsibility for harm, probability of harm, severity of harm, thought suppression, TAF, self-doubt and cognitive control.



Results of this study demonstrated that children with OCD reported experiencing fewer intrusive thoughts, which were less distressing and less uncontrollable than those experienced by adolescents and adults with OCD. Furthermore, responsibility attitudes, probability biases and thought suppression strategies were higher in adolescents and adults with OCD. Cognitive processes of TAF, perceived severity of harm, self-doubt and cognitive control were found to be comparable across age groups.



These results suggest that the current cognitive theory of OCD needs to address developmental differences in the cognitive processing of threat. Furthermore, for a developmentally sensitive theory of OCD, further investigation is warranted into other possible age related maintenance factors. Implications of this investigation and directions for future research are discussed.
Verification of Workflow Nets

Workflow management systems will change the architecture of future information systems dramatically. The explicit representation of business procedures is one of the main issues when introducing a workflow management system. In this paper we focus on a class of Petri nets suitable for the representation, validation and verification of these procedures. We will show that the correctness of a procedure represented by such a Petri net can be verified by using standard Petri-net-based techniques. Based on this result we provide a comprehensive set of transformation rules which can be used to construct and modify correct procedures.
An fMRI Investigation of Spontaneous Mental State Inference for Moral Judgment

Human moral judgment depends critically on “theory of mind,” the capacity to represent the mental states of agents. Recent studies suggest that the right TPJ (RTPJ) and, to lesser extent, the left TPJ (LTPJ), the precuneus (PC), and the medial pFC (MPFC) are robustly recruited when participants read explicit statements of an agent's beliefs and then judge the moral status of the agent's action. Real-world interactions, by contrast, often require social partners to infer each other's mental states. The current study uses fMRI to probe the role of these brain regions in supporting spontaneous mental state inference in the service of moral judgment. Participants read descriptions of a protagonist's action and then either (i) “moral” facts about the action's effect on another person or (ii) “nonmoral” facts about the situation. The RTPJ, PC, and MPFC were recruited selectively for moral over nonmoral facts, suggesting that processing moral stimuli elicits spontaneous mental state inference. In a second experiment, participants read the same scenarios, but explicit statements of belief preceded the facts: Protagonists believed their actions would cause harm or not. The response in the RTPJ, PC, and LTPJ was again higher for moral facts but also distinguished between neutral and negative outcomes. Together, the results illuminate two aspects of theory of mind in moral judgment: (1) spontaneous belief inference and (2) stimulus-driven belief integration.
Evaluation Measures for Models Assessment over Imbalanced Data Sets

Imbalanced data learning is one of the challenging problems in data mining; among this matter, founding the right model assessment measures is almost a primary research issue. Skewed class distribution causes a misreading of common evaluation measures as well it lead a biased classification. This article presents a set of alternative for imbalanced data learning assessment, using a combined measures (G-means, likelihood ratios, Discriminant power, F-Measure Balanced Accuracy, Youden index, Matthews correlation coefficient), and graphical performance assessment (ROC curve, Area Under Curve, Partial AUC, Weighted AUC, Cumulative Gains Curve and lift chart, Area Under Lift AUL), that aim to provide a more credible evaluation. We analyze the applications of these measures in churn prediction models evaluation, a well known application of imbalanced data
Hepatocellular carcinoma

Hepatocellular carcinoma is the sixth most prevalent cancer and the third most frequent cause of cancer-related death. Patients with cirrhosis are at highest risk of developing this malignant disease, and ultrasonography every 6 months is recommended. Surveillance with ultrasonography allows diagnosis at early stages when the tumour might be curable by resection, liver transplantation, or ablation, and 5-year survival higher than 50% can be achieved. Patients with small solitary tumours and very well preserved liver function are the best candidates for surgical resection. Liver transplantation is most beneficial for individuals who are not good candidates for resection, especially those within Milano criteria (solitary tumour ≤5 cm and up to three nodules ≤3 cm). Donor shortage greatly limits its applicability. Percutaneous ablation is the most frequently used treatment but its effectiveness is limited by tumour size and localisation. In asymptomatic patients with multifocal disease without vascular invasion or extrahepatic spread not amenable to curative treatments, chemoembolisation can provide survival benefit. Findings of randomised trials of sorafenib have shown survival benefits for individuals with advanced hepatocellular carcinoma, suggesting that molecular-targeted therapies could be effective in this chemoresistant cancer. Research is active in the area of pathogenesis and treatment of hepatocellular carcinoma.
A risk-metric framework for enterprise risk management

A risk-metric framework that supports Enterprise Risk Management is described. At the heart of the framework is the notion of a risk profile that provides risk measurement for risk elements. By providing a generic template in which metrics can be codified in terms of metric space operators, risk profiles can be used to construct a variety of risk measures for different business contexts. These measures can vary from conventional economic risk calculations to the kinds of metrics that are used by decision support systems, such as those supporting inexact reasoning and which are considered to closely match how humans combine information.
A semi-supervised domain adaptation assembling approach for image classification

Automatic annotation of images is one of the fundamental problems in computer vision applications. With the increasing amount of freely available images, it is quite possible that the training data used to learn a classifier has different distribution from the data which is used for testing. This results in degradation of the classifier performance and highlights the problem known as domain adaptation. Framework for domain adaptation typically requires a classification model which can utilize several classifiers by combining their results to get the desired accuracy. This work proposes depth-based and iterative depth-based fusion methods which are basically rank-based fusion methods and utilize rank of the predicted labels from different classifiers. Two frameworks are also proposed for domain adaptation. The first framework uses traditional machine learning algorithms, while the other works with metric learning as well as transfer learning algorithm. Motivated from ImageCLEF’s 2014 domain adaptation task, these frameworks with the proposed fusion methods are validated and verified by conducting experiments on the images from five domains having varied distributions. Bing, Caltech, ImageNet, and PASCAL are used as source domains and the target domain is SUN. Twelve object categories are chosen from these domains. The experimental results show the performance improvement not only over the baseline system, but also over the winner of the ImageCLEF’s 2014 domain adaptation challenge.
Methods of privacy preserving in collaborative filtering

Privacy considerations of individuals becomes more and more popular issue in recommender systems due to the increasing need for protecting confidential data. Even though users of recommender systems enjoy with personalized productions, they behave timidly about sharing their private data due to the some privacy concerns about price discrimination, unsolicited marketing, govern-ment surveillance and etc. Thus, preserving confidential data of users while producing accurate predictions is one of the extremely important directions of the researches about recommendation systems. In this paper, we gather the most known studies and recently published ones about producing accurately predictions without endangering privacy in order to guide researchers interested with privacy concerns in recommender systems. Moreover, we give a brief discussion about utilized methods.