nils-herrmann/clean_scirepeval huggingface dataset:
Effectiveness of a Web Based Simulation in Improving Nurses' Workplace Practice With Deteriorating Ward Patients: A Pre and Postintervention Study
Venue: Journal of medical Internet research

Nurses play an important role in detecting patients with clinical deterioration. However, the problem of nurses failing to trigger deteriorating ward patients still persists despite the implementation of a patient safety initiative, the Rapid Response System. A Web based simulation was developed to enhance nurses' role in recognizing and responding to deteriorating patients. While studies have evaluated the effectiveness of the Web based simulation on nurses' clinical performance in a simulated environment, no study has examined its impact on nurses' actual practice in the clinical setting.  The objective of this study was to evaluate the impact of Web based simulation on nurses' recognition of and response to deteriorating patients in clinical settings. The outcomes were measured across all levels of Kirkpatrick's 4 level evaluation model with clinical outcome on triggering rates of deteriorating patients as the primary outcome measure.  A before and after study was conducted on two general wards at an acute care tertiary hospital over a 14 month period. All nurses from the two study wards who undertook the Web based simulation as part of their continuing nursing education were invited to complete questionnaires at various time points to measure their motivational reaction, knowledge, and perceived transfer of learning. Clinical records on cases triggered by ward nurses from the two study wards were evaluated for frequency and types of triggers over a period of 6 months pre and 6 months postintervention.  The number of deteriorating patients triggered by ward nurses in a medical general ward increased significantly (P<.001) from pre (84/937, 8.96% to postintervention (91/624, 14.58% The nurses reported positively on the transfer of learning (mean 3.89, SD 0.49) from the Web based simulation to clinical practice. A significant increase (P<.001) on knowledge posttest score from pretest score was also reported. The nurses also perceived positively their motivation (mean 3.78, SD 0.56) to engage in the Web based simulation.  This study provides evidence on the effectiveness of Web based simulation in improving nursing practice when recognizing and responding to deteriorating patients. This educational tool could be implemented by nurse educators worldwide to address the educational needs of a large group of hospital nurses responsible for patients in clinical deterioration.
Comparison of the effect of web based, simulation based, and conventional training on the accuracy of visual estimation of postpartum hemorrhage volume on midwifery students: A randomized clinical trial
Venue: Journal of education and health promotion

Delay in diagnosis of bleeding can be due to underestimation of the actual amount of blood loss during delivery. Therefore, this research aimed to compare the efficacy of web based, simulation based, and conventional training on the accuracy of visual estimation of postpartum hemorrhage volume.  This three group randomized clinical trial study was performed on 105 midwifery students in Mashhad School of Nursing and Midwifery in 2013. The samples were selected by the convenience method and were randomly divided into three groups of web based, simulation based, and conventional training. The three groups participated before and 1 week after the training course in eight station practical tests, then, the students of the web based group were trained on line for 1 week, the students of the simulation based group were trained in the Clinical Skills Centre for 4 h, and the students of the conventional group were trained for 4 h presentation by researchers. The data gathering tool was a demographic questionnaire designed by the researchers and objective structured clinical examination. Data were analyzed by software version 11.5.  The accuracy of visual estimation of postpartum hemorrhage volume after training increased significantly in the three groups at all stations (1, 2, 4, 5, 6 and 7 (P 0.001) 8 (P 0.027) except station 3 (blood loss of 20 cc, P 0.095) but the mean score of blood loss estimation after training did not significantly different between the three groups (P 0.95)  Training increased the accuracy of estimation of postpartum hemorrhage, but no significant difference was found among the three training groups. We can use web based training as a substitute or supplement of training along with two other more common simulation and conventional methods.
Web Based Simulation and Remote Triggered Laboratory for Robots

This paper describes a web based virtual laboratory developed to facilitate learning robot kinematics and control through simulation and online control of some well known robots in real time with vision feedback system. The web based self learning content and tools can be used in engineering education for both graduates and research scholars. Using Java Server Pages (JSPs) WAMP and Python, we provide a standard and robust platform for client environment. The website primarily addresses the delivery of basic concepts in robot kinematics.
The Univariate Flagging Algorithm (UFA) a Fully Automated Approach for Identifying Optimal Thresholds in Data
Venue: ArXiv

In many data classification problems, there is no linear relationship between an explanatory and the dependent variables. Instead, there may be ranges of the input variable for which the observed outcome is signficantly more or less likely. This paper describes an algorithm for automatic detection of such thresholds, called the Univariate Flagging Algorithm (UFA) The algorithm searches for a separation that optimizes the difference between separated areas while providing the maximum support. We evaluate its performance using three examples and demonstrate that thresholds identified by the algorithm align well with visual inspection and subject matter expertise. We also introduce two classification approaches that use UFA and show that the performance attained on unseen test data is equal to or better than that of more traditional classifiers. We demonstrate that the proposed algorithm is robust against missing data and noise, is scalable, and is easy to interpret and visualize. It is also well suited for problems where incidence of the target is low.
ActiveClean: Interactive Data Cleaning For Statistical Modeling
Venue: Proc. VLDB Endow.

Analysts often clean dirty data iteratively cleaning some data, executing the analysis, and then cleaning more data based on the results. We explore the iterative cleaning process in the context of statistical model training, which is an increasingly popular form of data analytics. We propose ActiveClean, which allows for progressive and iterative cleaning in statistical modeling problems while preserving convergence guarantees. ActiveClean supports an important class of models called convex loss models (e.g. linear regression and SVMs) and prioritizes cleaning those records likely to affect the results. We evaluate ActiveClean on five real world datasets UCI Adult, UCI EEG, MNIST, IMDB, and Dollars For Docs with both real and synthetic errors. The results show that our proposed optimizations can improve model accuracy by up to 2.5x for the same amount of data cleaned. Furthermore for a fixed cleaning budget and on all real dirty datasets, ActiveClean returns more accurate models than uniform sampling and Active Learning.
Interactive and Deterministic Data Cleaning
Venue: SIGMOD Conference

We present Falcon, an interactive, deterministic, and declarative data cleaning system, which uses SQL update queries as the language to repair data. Falcon does not rely on the existence of a set of pre defined data quality rules. On the contrary, it encourages users to explore the data, identify possible problems, and make updates to fix them. Bootstrapped by one user update, Falcon guesses a set of possible sql update queries that can be used to repair the data. The main technical challenge addressed in this paper consists in finding a set of sql update queries that is minimal in size and at the same time fixes the largest number of errors in the data. We formalize this problem as a search in a lattice shaped space. To guarantee that the chosen updates are semantically correct, Falcon navigates the lattice by interacting with users to gradually validate the set of sql update queries. Besides using traditional one hop based traverse algorithms (e.g. BFS or DFS) we describe novel multi hop search algorithms such that Falcon can dive over the lattice and conduct the search efficiently. Our novel search strategy is coupled with a number of optimization techniques to further prune the search space and efficiently maintain the lattice. We have conducted extensive experiments using both real world and synthetic datasets to show that Falcon can effectively communicate with users in data repairing.
Data Cleaning: Overview and Emerging Challenges
Venue: SIGMOD Conference

Detecting and repairing dirty data is one of the perennial challenges in data analytics, and failure to do so can result in inaccurate analytics and unreliable decisions. Over the past few years, there has been a surge of interest from both industry and academia on data cleaning problems including new abstractions, interfaces, approaches for scalability, and statistical techniques. To better understand the new advances in the field, we will first present a taxonomy of the data cleaning literature in which we highlight the recent interest in techniques that use constraints, rules, or patterns to detect errors, which we call qualitative data cleaning. We will describe the state of the art techniques and also highlight their limitations with a series of illustrative examples. While traditionally such approaches are distinct from quantitative approaches such as outlier detection, we also discuss recent work that casts such approaches into a statistical estimation framework including: using Machine Learning to improve the efficiency and accuracy of data cleaning and considering the effects of data cleaning on statistical analysis.
PrivateClean: Data Cleaning and Differential Privacy
Venue: SIGMOD Conference

Recent advances in differential privacy make it possible to guarantee user privacy while preserving the main characteristics of the data. However, most differential privacy mechanisms assume that the underlying dataset is clean. This paper explores the link between data cleaning and differential privacy in a framework we call PrivateClean. PrivateClean includes a technique for creating private datasets of numerical and discrete valued attributes, a formalism for privacy preserving data cleaning, and techniques for answering sum, count, and avg queries after cleaning. We show: (1) how the degree of privacy affects subsequent aggregate query accuracy, (2) how privacy potentially amplifies certain types of errors in a dataset, and (3) how this analysis can be used to tune the degree of privacy. The key insight is to maintain a bipartite graph relating dirty values to clean values and use this graph to estimate biases due to the interaction between cleaning and privacy. We validate these results on four datasets with a variety of well studied cleaning techniques including using functional dependencies, outlier filtering, and resolving inconsistent attributes.
A statistical training data cleaning strategy for the PCA based chiller sensor fault detection, diagnosis and data reconstruction method

This paper presents a statistical training data cleaning strategy for PCA based chiller sensor Fault Detection, Diagnosis and Data Reconstruction method. Finding and removing outliers from the original training data set, the training data quality can be improved by the presented data cleaning strategy. This can enhance the efficiency of the fault detection and increase the accuracy of the data reconstruction. Outliers cannot be easily found in the original data set used for training the PCA model. These outliers would severely affect the projection directions of PCA's two orthogonal subspaces (PC subspace and Residual subspace) Therefore, the threshold of Q statistic is changed by the unexpected projection subspaces so that the detection efficiency of the sensor fault is decreased. The Euclidean distance was employed as an index to detect outliers from the original training data. In order to achieve optimal training data for the sensor FDDR, the z scores of each sample's Euclidean Distance were employed as the key to remove the outliers. A field measured data set of a screw water cooled chiller was used to validate the presented strategy. Results demonstrate that the quality of the training data is optimized and sensor fault detection efficiency, as well as the reconstruction data accuracy, is improved when compared to the normal PCA method.
Commonsense Knowledge Base Completion
Venue: ACL

We enrich a curated resource of commonsense knowledge by formulating the problem as one of knowledge base completion (KBC) Most work in KBC focuses on knowledge bases like Freebase that relate entities drawn from a fixed set. However, the tuples in ConceptNet (Speer and Havasi, 2012) define relations between an unbounded set of phrases. We develop neural network models for scoring tuples on arbitrary phrases and evaluate them by their ability to distinguish true held out tuples from false ones. We find strong performance from a bilinear model using a simple additive architecture to model phrases. We manually evaluate our trained model's ability to assign quality scores to novel tuples, finding that it can propose tuples at the same quality level as mediumconfidence tuples from ConceptNet.
Cocktail clustering a new hierarchical agglomerative algorithm for extracting species groups in vegetation databases.

In one approach of formalized vegetation classification, species groups define a vegetation unit as a set of releves, each of which possesses a minimum number of species from that group. Thus, species groups provide unequivocal rules for the assignment of individual vegetation records to vegetation units and can be applied beyond the set of records used for defining them. Here, I present a new method that subjects all species in a vegetation database to clustering that produces such clear membership rules of vegetation records to clusters. More specifically, the algorithm obtains species groups from a species x releve matrix which consist of species that show the highest probability of co occurring with each other and delivers unequivocal rules to assign releves to these groups.  A hierarchical agglomerative clustering algorithm for species is presented that starts with a species x species matrix of the F coefficient of association. After fusing the species with the highest F coefficient, the F association matrix is recalculated for the new group of species. For calculating F association for groups to other species or to the nodes formed by groups of species, the observed frequency distribution of co occurrences of the species in that group is compared to the expected frequency distribution of co occurrence, derived the from the observed number of species occurrences. As a result, for each species group a minimum number of species is obtained that is required to assign a releve to this species group. The resulting Cocktail species groups are partially nested, and with increasing node hierarchy show a tendency of decreasing F correlation to the last joining species to that group.  As the clustering algorithm assigns all of the n species in a data set to groups, the result are n 1 partly nested species groups. These groups correspond to species groups that have been extracted from the same data sets using preconceived start groups. Subsequently, the species groups can be used separately or in logical combinations to classify vegetation releves either by expert systems, Twinspan like classification algorithms or by redefining existing vegetation units with automated brute force match algorithms. Used in this way, Cocktail clustering is able to form the backbone of a consistent large scale vegetation classification system.
Characterization, Anti Inflammatory and Antiproliferative Activities of Natural and Sulfonated Exo Polysaccharides from Streptococcus thermophilus ASCC 1275.
Venue: Journal of food science

Exo polysaccharides (EPS) isolated from Streptococcus thermophilus ASCC 1275 were sulfated (31% High performance liquid chromatography identified that EPS was composed of mannose (30.19% galactose (20.10% glucose (18.05% glucosamine (16.04% galactosamine (9.06% glucuronic acid (3.55% and ribose (3.01% Pro /anti inflammatory cytokine secretion ratios (IL 1b/IL 10, IL 6/IL 10, and TNF a/IL 10) of lipopolysaccharide stimulated RAW 264.7 macrophages were significantly decreased by EPS and S.EPS treatments in a dose dependent manner. Furthermore, anti inflammatory activities of S.EPS improved 49.3% and 24.0% than those of EPS before or after LPS treatment. The reactive oxygen species were inhibited by EPS and S.EPS by 49.6% and 55.1% at 50 mg/mL, respectively. Inhibition activities of S.EPS on nitric oxide production were 12.9% and 55.4% higher than those of EPS at 10 and 50 mg/mL. Additionally, S.EPS exhibited stronger antiproliferative activity on Caco 2 and HepG2 cells. Our results indicated that anti inflammatory and antiproliferative activities of EPS were significantly (P 0.01) improved by sulfonation.
Dynamic simulation of chip formation in the process of cutting

This work examines the simulation of chip formation during the process of cutting. An analysis of complex rheological models enabled a set of assumptions to be considered, thereby substantiating a hypothesis relating to the chip formation process. The studies demonstrated that the chip formation process, taking into account the plastic deformation and destruction of metal in the local zone, is most appropriately represented by a rheological model in the form of a series connection of elastic ductile plastic relaxing medium of Ishlinskiy (reflecting the process of primary deformation of metal from the cut off layer) and the medium of Voigt with two elastic dissipative elements (representing the process of deformation and frictions from the convergent shaving) The attained complex rheological model served as the basis for constructing a representative dynamic model for chip formation process.
Fast Extraction of 3D Fourier Moments via Multiple Integral Images: An Application to Antitank Mine Detection in GPR C Scans
Venue: ICCVG

Automatic landmine detection is a challenging problem in post conflict areas. This paper demonstrates experiments on antitank mine detection in subsurface 3D images generated by Ground Penetrating Radar. In the algorithmic part, we study piecewise Fourier approximations of low orders which are applied to successive 3D windows of an image. We take coefficients moments of these approximations as features for machine learning. The main contribution of the paper is a technique to calculate the moments fast. We construct a set of suitable integral images, which allows later to calculate each moment in constant time. The technique is akin to, but significantly different from the known idea of Viola and Jones, namely: we calculate Fourier moments rather than simple differential features; secondly, we use multiple integral images, not a single one; thirdly, our integral images are cumulative inner products between input image and suitable trigonometric terms. Conducted tests involve boosted decision trees as detectors.
G500(P) An examination of the safety and efficacy of high flow nasal cannula therapy for acute severe asthma and viral induced wheeze in paediatric critical care

The purpose of this abstract is to examine the efficacy and safety of high flow nasal cannula (HFNC) therapy in children over 1 year old admitted to paediatric critical care with a diagnosis of acute severe asthma or viral induced wheeze (VIW)  A retrospective study of all children over 1 year old admitted to our paediatric critical care unit with a diagnosis of acute asthma or VIW who received HFNC therapy between March 2011 and June 2015 was made. Children who were already intubated and children who did not receive HFNC therapy were excluded. All children with a diagnosis of bronchiolitis or pneumonia were excluded. The medical records and the critical care database were examined retrospectively for patient demographics, details of illness and complications.  58 children were identified as fitting our inclusion criteria. The data showed that HFNC use was not limited to any particular age group but was used more in those less than 5 years old: Figure 1.Abstract G500(P) Figure 1 Age of children admitted with Aacute Aasthma/VIW who received HFNC Therapy The HFNC therapy was given for between 1 and 220 h with a median duration of 28  In regards to complications none of the 58 children who received HFNC therapy suffered hypoxia, or developed a pneumothorax. Four required subsequent intubation. There were no fatalities: Fig.3.Abstract G500(P) Figure 3 Rate of Intubation following HFNC Therapy Conclusions HFNC therapy is increasingly used for acute respiratory failure in children. Although it has been studied in a variety of paediatric respiratory conditions, up until now there has been no study of its safety or efficacy specifically in children with acute asthma or VIW. In combination with standard treatment for acute severe asthma, HFNC therapy appears to be safe and efficacious. 4 of 58 patients <7% required subsequent intubation and ventilation. No patients suffered complications. We suggest that HFNC therapy may be safely combined with standard therapy in acute severe asthma or viral induced wheeze in the critical care and emergency setting. A randomised control trial will be necessary to determine if HFNC therapy reduces intubation rates.
Cotard Delusion in the Context of Schizophrenia: A Case Report and Review of the Literature
Venue: Front. Psychol.

The Cotard delusion (CD) is one of a variety of narrowly defined monothematic delusions characterized by nihilistic beliefs about the body's existence or life itself. The presence of CD within the context of schizophrenia is rare <1% and remains understudied. Case: 'Mr. C' is a 58 year old veteran with a prior diagnosis of schizophrenia, who presented with CD in the context of significant depression, suicidal ideation, violence, and self harm behavior. He perseverated in his belief that he was physically dead and possessed by demons for several weeks. This delusion was reinforced by his religious belief that life was an attribute of God, and by inference, he as a human, was dead. His condition gradually improved over the course of treatment with Divalproex and quetiapine with discussions about the rationale for his belief. Upon discharge, Mr. C. demonstrated awareness of his fixation on death and an ability to redirect himself.  This case highlights the need to better understand the co occurrence of CD in schizophrenia, their differentiation, the increased risk of violence and self harm behavior in this presentation, and how specific events and religious factors can influence delusional themes of CD. Pharmacotherapy and aspects of cognitive behavioral therapy may be effective in ameliorating these symptoms in CD.