nils-herrmann/clean_scirepeval huggingface dataset:
Learning Statistically Significant Contrast Sets
Venue: Canadian Conference on AI

Contrast set learning is important to discover control variables that can distinguish different groups in a dataset. Association rule mining has an inherent connection to the contrast set learning problem and has also been used to address it. All of the association rule based contrast set learning techniques use support confidence based methods and inherit their limitations. In recent years statistically significant rule mining has become a viable alternative to address those limitations. We propose a novel contrast set learning approach based on statistically significant rule mining that eliminates the limitations in using traditional rule mining approaches and identifies statistically significant contrast sets. We evaluated our method by building a classifier using the discovered contrast sets. The performance of our classifier, while our method is not for classification per se, reveals the effectiveness of our approach in distinguishing the groups.
Estudio diagnostico de la mortalidad humana en el Ecuador, en la Provincia de Tungurahua, el Canton capital Ambato y los principales cantones con el mayor porcentaje de mortalidad. Republica del Ecuador. 2014.

Se realiza estudio diagnostico de las diez principales causas de mortalidad humana en Ecuador a nivel de pais, la provincia de Tungurahua y principales cantones basada la investigacion en los datos del Instituto Nacional de Estadistica y Censos y Ministerio de Salud Publica correspondientes al ano 2014 en base al Registro Internacional de Mortalidad Humana. Por orden de incidencia predominan enfermedades cardiovasculares (isquemia del corazon, enfermedades cerebrovasculares y enfermedades hipertensivas) y la diabetes mellitus entre las primeras cuatro causas. La neumonia e influenza son la quinta causa, seguida de accidentes por transporte terrestre, cirrosis hepatica, enfermedades urinarias y vias respiratorias bajas, ocupando la neoplasia de estomago la decima causa en el pais. Hay coincidencia en lo reportado por la provincia de Tungurahua y el canton capital Ambato. Entre los cinco cantones principales de la provincia predominan los accidentes por transporte terrestre y enfermedades respiratorias agudas y cronicas. Se analiza predominio de las principales causas de muerte a expensas las enfermedades cronicas no trasmisibles y son comparadas a lo reportado a nivel mundial. Se evalua incidencia de las enfermedades cardiovasculares, diabetes y cancer. La neoplasia de estomago es la causa de cancer mas reportada en la provincia de Tungurahua. Se llama la atencion sobre los accidentes de transporte terrestre, las lesiones auto infligidas y la importancia de las enfermedades cronicas respiratorias bajas en relacion a los efectos de las cenizas del volcan Tungurahua situado en la provincia. Se enfatiza valor del conocimiento sobre las causas mortalidad general humana.
Psoriasis and Obesity
Venue: Dermatology

Psoriasis is a common chronic inflammatory skin disease with a complex pathogenesis consisting of a genetic component, immune dysfunction, and environmental factors. It is associated with numerous comorbidities including psoriatic arthritis, cardiovascular disease, metabolic syndrome, and obesity. Evidence suggests that obesity is a risk factor for incident psoriasis, aggravates existing psoriasis, and that weight reduction may improve the severity of psoriasis in overweight individuals. Excess body weight may interfere with the medical treatment used in psoriasis and adds to the cardiovascular risk profile in these patients, which underscores the importance of effective weight control regimens. In this review we examine the current literature with regard to the association between obesity and psoriasis.
Theoretical Old Age Pension Benefits and Replacement Rates in the Baltic States: A Retrospective Simulation

The author presents a comparative analysis of old age pension systems in Estonia, Latvia and Lithuania using a method of retrospective simulation run on a self developed model. The model baseline case is a person retiring in December 2014 after 40 years of service with nationwide average salary. Other cases include low and high earners, funded schemes participants and simulations for modified notional capital valorisation formulae. Three study countries return very dissimilar results, which is caused by differences in their pension systems' designs. Lack of non contributory element (basic pension) in Latvia leads to a low degree of progressivity, with inexcusably low pensions to low earners and excessively generous pensions to high earners. Participation in funded pillar II schemes has not brought any significant gains to pension plan sharers. Notional capital valorisation rules adopted in different countries that use the NDC system significantly influence pension amount.
Gender differences in benzodiazepines prescription in old age patients
Venue: European Psychiatry

To assess the prescription of benzodiazepines (BZD) in elderly patients, and to explore any gender differences.  Six hundred and fifty four patients 65) admitted in an emergency service of a general hospital due to a fall. BZD use information was collected (dose, half life profile) Results BZD are significantly more prescribed to women (47.6% than men (36.1% (X2 8.097, P 0.004) We conducted a logistic regression analysis using as dependent variable taking or not BZD and sex as the independent one, covariating the model by age. We noted that sex remains significant despite enter the age variable in the model (OR 1.5, P 0.013) A total of 21.6% of patients consumed intermediate or long half life BZDs, appearing a greater tendency to prescribe such BZD to women (X2 3.606, P 0.058) In the 58.0% of prescriptions, prescribed dose was higher than the recommended for the elderly. The percentage is significantly higher for men (70.0% than women (53.1% Furthermore, a total of 54 prescriptions (15.8% were even higher than the recommended adult dose, with no significant differences between men and women.  We found evidence of a higher prescription of BZD in women independently of age. Despite not being recommended, prescription of intermediate or long half life BZD continues, in a slightly higher manner in women. BZD are prescribed above the recommended dose for elderly in a large number of patients, especially in men. A considerable proportion of elderly patients (15.8% consume BZD doses even higher than the recommended for adults. Disclosure of interest The authors have not supplied their declaration of competing interest.
Robust exchangeability designs for early phase clinical trials with multiple strata.
Venue: Pharmaceutical statistics

Clinical trials with multiple strata are increasingly used in drug development. They may sometimes be the only option to study a new treatment, for example in small populations and rare diseases. In early phase trials, where data are often sparse, good statistical inference and subsequent decision making can be challenging. Inferences from simple pooling or stratification are known to be inferior to hierarchical modeling methods, which build on exchangeable strata parameters and allow borrowing information across strata. However, the standard exchangeability (EX) assumption bears the risk of too much shrinkage and excessive borrowing for extreme strata. We propose the exchangeability nonexchangeability (EXNEX) approach as a robust mixture extension of the standard EX approach. It allows each stratum specific parameter to be exchangeable with other similar strata parameters or nonexchangeable with any of them. While EXNEX computations can be performed easily with standard Bayesian software, model specifications and prior distributions are more demanding and require a good understanding of the context. Two case studies from phases I and II (with three and four strata) show promising results for EXNEX. Data scenarios reveal tempered degrees of borrowing for extreme strata, and frequentist operating characteristics perform well for estimation (bias, mean squared error) and testing (less type I error inflation)
Definition of a COPD self management intervention: International Expert Group consensus
Venue: European Respiratory Journal

There is an urgent need for consensus on what defines a chronic obstructive pulmonary disease (COPD) self management intervention. We aimed to obtain consensus regarding the conceptual definition of a COPD self management intervention by engaging an international panel of COPD self management experts using Delphi technique features and an additional group meeting. In each consensus round the experts were asked to provide feedback on the proposed definition and to score their level of agreement (1=totally disagree; 5=totally agree) The information provided was used to modify the definition for the next consensus round. Thematic analysis was used for free text responses and descriptive statistics were used for agreement scores. In total, 28 experts participated. The consensus round response rate varied randomly over the five rounds (ranging from 48% (n=13) to 85% (n=23) and mean definition agreement scores increased from 3.8 (round 1) to 4.8 (round 5) with an increasing percentage of experts allocating the highest score of 5 (round 1: 14% (n=3) round 5: 83% (n=19) In this study we reached consensus regarding a conceptual definition of what should be a COPD self management intervention, clarifying the requisites for such an intervention. Operationalisation of this conceptual definition in the near future will be an essential next step. Consensus of a conceptual definition of what should be a COPD self management intervention with its requisites http:/ow.ly/Zfr0F
IncA: A DSL for the definition of incremental program analyses
Venue: 2016 31st IEEE/ACM International Conference on Automated Software Engineering (ASE)

Program analyses support software developers, for example, through error detection, code quality assurance, and by enabling compiler optimizations and refactorings. To provide real time feedback to developers within IDEs, an analysis must run efficiently even if the analyzed code base is large. To achieve this goal, we present a domain specific language called IncA for the definition of efficient incremental program analyses that update their result as the program changes. IncA compiles analyses into graph patterns and relies on existing incremental matching algorithms. To scale IncA analyses to large programs, we describe optimizations that reduce caching and prune change propagation. Using IncA, we have developed incremental control flow and points to analysis for C, well formedness checks for DSLs, and 10 FindBugs checks for Java. Our evaluation demonstrates significant speedups for all analyses compared to their non incremental counterparts.
State Responsibility and Accountability in Managing Big Data in Biobank Research: Tensions and Challenges in the Right of Access to Data

Within the European context the Data Protection Directive (Directive 95/46/EC) maintains an important role in current legal debates on the rights and obligations different stakeholders have in the processing of personal data. Biobanking and data sharing infrastructures pose new ethical and legal dilemmas in the interpretations we uphold with regard to the processing of personal data. This chapter examines the challenges associated with the data subject's right of access to data in Finnish biobanking. The Data Protection Directive provides provisions for individuals to confirm "as to whether or not data relating to him are being processed" Finland's recent Biobank Act (688/2012) has raised concerns since it also requires biobanks to provide, upon request, information regarding data which may have clinical (actionable) relevance for the individual's personal health. There is, however, no governance mechanism in place through which common standards and practices could be implemented. As a result the extension of the right to access data and mandating biobanks to relate significance of results to personal health has become a major concern for biobankers in Finland. The management of data, research results and incidental findings in biobanks is becoming, however, an increasingly significant challenge for all biobanks and the countries which are in the process of drafting policy and regulatory frameworks for the management and governance of big data, public health genomics and personalised medicine. The Finnish case highlights the challenges that many states are increasingly facing across Europe and elsewhere in terms of how to govern and coordinate the management of biomedical big data.
Selecting appropriate cloud solution for managing big data projects using hybrid AHP entropy based assessment
Venue: 2016 International Conference on Innovation and Challenges in Cyber Security (ICICCS INBUSH)

Today technology that learns from data to forecast future behavior of individuals, organizations, government and country as a whole, is playing a crucial role in the advancement of human race. In fact, the strategic advantage most of the companies today strive for are use of new available technologies like cloud computing and big data. However, today's dynamic business environment poses severe challenges in front of companies as to how to make use of the power of big data with the technical flexibility that cloud computing provides? Therefore, evaluating, ranking and selecting the most appropriate cloud solution to manage big data project is a complex concern which requires multi criteria decision environment. In this paper we propose a hybrid entropy method combined with Analytical Hierarchical Process (AHP) to select appropriate cloud solution to manage big data projects in group decision making environment. In order to collate individual opinions of decision makers for rating the importance of various criteria and alternatives, we employed usability analysis using the proposed hybrid AHP Entropy method.
Managing big data integrity
Venue: 2016 International Conference on Engineering MIS (ICEMIS)

Big data becomes a real opportunity and a serious worry for data managers, data scientists, researchers and even for business managers. An opportunity as a set of powerful technologies and a bunch of interesting concepts that aim resolving business problems of an organization. A worry because big data implies big challenges and big resources that make harder the task of mining value from all the available data. The data, even in context of big data, need to have a value to help on making good business decisions. In fact, sound decision requires confidence on data that must draw the real world, at any stage of data processing. In other words, the data have to keep their integrity properties from origination until the final destination in analysis reports or in a business indicator. In this paper, we will throw light on integrity issues for big data and we introduce a new model about how preserving integrity in the context of big data.
Managing big data in coal fired power plants: a business intelligence framework
Venue: Ind. Manag. Data Syst.

Many power producers are looking for ways to develop smarter energy capabilities to tackle challenges in the sophisticated, non linear dynamic processes due to the complicated operating conditions. One prominent strategy is to deploy advanced intelligence systems and analytics to monitor key performance indicators, capture insights about the behavior of the electricity generation processes, and identify factors affecting combustion efficiency. Thus, the purpose of this paper is to outline a way to incorporate a business intelligence framework into existing coal fired power plant data to transform the data into insights and deliver analytical solutions to power producers.  The proposed ten step business intelligence framework combines the architectures of database management, business analytics, business performance management, and data visualization to manage existing enterprise data in a coal fired power plant.  The results of this study provide plant wide signals of any unusual operational and coal quality factors that impact the level of NOx and consequently explain and predict the leading causes of variation in the emission of NOx in the combustion process.  Once the framework is integrated into the power generation process, it is important to ensure that the top management and the data analysts at the plants have the same perceptions of the benefits of big data and analytics in the long run and continue to provide support and awareness of the use of business intelligence technology and infrastructure in operational decision making.  The key finding of this study helps the power plant prioritize the important factors associated with the emission of NOx; closer attention to those factors can be promptly initiated in order to improve the performance of the plant.  The use of big data is not just about implementing new technologies to store and manage bigger databases but rather about extracting value and creating insights from large volumes of data. The challenge is to strategically and operationally reconsider the entire process not only to prepare, integrate, and manage big data but also to make proper decisions as to which data to select for the analysis and how to apply analytical techniques to create value from the data that is in line with the strategic direction of the enterprise. This study seeks to fill this gap by outlining how to implement the proposed business intelligence framework to provide plant wide signals of any unusual operational and coal quality factors that impact the level of NOx and to explain and predict the leading causes of variation in the emission of NOx in the combustion process.
DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

State of the art deep neural networks have achieved impressive results on many image classification tasks. However, these same architectures have been shown to be unstable to small, well sought, perturbations of the images. Despite the importance of this phenomenon, no effective methods have been proposed to accurately compute the robustness of state of the art deep classifiers to such perturbations on large scale datasets. In this paper, we fill this gap and propose the DeepFool algorithm to efficiently compute perturbations that fool deep networks, and thus reliably quantify the robustness of these classifiers. Extensive experimental results show that our approach outperforms recent methods in the task of computing adversarial perturbations and making classifiers more robust.
Efficient Algorithms for Convolutional Sparse Representations
Venue: IEEE Transactions on Image Processing

When applying sparse representation techniques to images, the standard approach is to independently compute the representations for a set of overlapping image patches. This method performs very well in a variety of applications, but results in a representation that is multi valued and not optimized with respect to the entire image. An alternative representation structure is provided by a convolutional sparse representation, in which a sparse representation of an entire image is computed by replacing the linear combination of a set of dictionary vectors by the sum of a set of convolutions with dictionary filters. The resulting representation is both single valued and jointly optimized over the entire image. While this form of a sparse representation has been applied to a variety of problems in signal and image processing and computer vision, the computational expense of the corresponding optimization problems has restricted application to relatively small signals and images. This paper presents new, efficient algorithms that substantially improve on the performance of other recent methods, contributing to the development of this type of representation as a practical tool for a wider range of problems.
The Effect Of The Inquiry Based Learning Approach On Student's Critical Thinking Skills

The purpose of this study is to determine the effect of an activity set developed according to the inquiry based learning approach in the unit "Particulate Structure of Matter" on students' critical thinking skills in science and technology courses. The study was conducted with 90 sixth grade students attending four sixth grade classes of a secondary school.Within the framework of the study, in order to evaluate the effects of inquiry based learning approach on the students' critical thinking skills in science and technology courses, the guided activity set was developed by the researchers in line with the inquiry based learning approach. In this study, pre test and posttest control group experimental designs were used.The findings of the study revealed that science and technology learning supported with the guided activities developed in line with the inquiry based learning approach have significant effects on students' critical thinking skills in science and technology courses.The critical thinking level of the experimental group students taught with the inquiry based learning approach was found to be higher than that of the control group students taught with the traditional lecturing method.the critical thinking level of the experimental group students showed a positive increase in all the dimensions.
Comparing the effects of problem based learning and the traditional lecture method on critical thinking skills and metacognitive awareness in nursing students in a critical care nursing course.
Venue: Nurse education today

Problem based learning (PBL) is a method used to develop cognitive and metacognitive skills in nursing students.  The present study was conducted to compare the effects of PBL and the traditional lecture method on critical thinking skills and metacognitive awareness in nursing students in a critical care nursing course.  The present study was conducted with a quasi experimental, single group, pretest posttest design.  A group of third year nursing students (n=40) were recruited from Khorramabad School of Nursing and Midwifery in the west of Iran. The lecture method was used in one group over the first eight weeks of the first semester and PBL was adopted in the second eight weeks. Standardized self report questionnaires including The California Critical Thinking Skills Test B (CCTST B) and the Metacognitive Awareness Inventory (MAI) were administered before and after the use of each of the instruction methods. Data were analyzed in SPSS using the paired t test.  No significant changes were observed in the students' critical thinking skills and metacognitive awareness after performing the lecture method. However, a significant increase was observed in the overall critical thinking score (P<0.01) and its sub scales of evaluation and deduction (P<0.05) and in the overall metacognitive awareness score (P<0.001) after performing the PBL method.