nils-herrmann/clean_scirepeval huggingface dataset:
Did Austerity Cause Brexit Thiemo Fetzer July 22 2018
Year: 2018

Did austerity cause Brexit? This paper shows that the rise of popular support for the UK Independence Party (UKIP) as the single most important correlate of the subsequent Leave vote in the 2016 European Union (EU) referendum, along with broader measures of political dissatisfaction, are strongly and causally associated with an individual's or an area's exposure to austerity since 2010. In addition to exploiting data from the population of all electoral contests in the UK since 2000, I leverage detailed individual level panel data allowing me to exploit within individual variation in exposure to specific rules based welfare reforms as well as broader measures of political preferences. The results suggest that the EU referendum could have resulted in a Remain victory had it not been for a range of austerity induced welfare reforms. These reforms activated existing economic grievances. Further, auxiliary results suggest that the underlying economic grievances have broader origins than what the current literature on Brexit suggests. Up until 2010, the UK's welfare state evened out growing income differences across the skill divide through transfer payments. This pattern markedly stops from 2010 onwards as austerity started to bite.
Novel low band gap polymers based on pyrrolo[32d:45d']bisthiazole PBTz and thienylenevinylene TV For Organic Electronic Applications
Year: 2018

Novel low band gap polymers based on pyrrolo[3,2 d:4,5 d']bisthiazole (PBTz) and thienylenevinylene (TV) For Organic Electronic Applications Dhananjaya Patra?,1 Hassan S. Bazzi,1 Lei Fang!2 and Mohammed Al Hashimi1 1Department of Chemistry, Texas AM Bradley, D. D. C. Brown, A. R. Marks, R. N. Mackay, K. Friend, R. H. Burn, P. L. Holmes, A. B.  1990, 347, 539. 2. Sekitani, T. Zschieschang, U. Klauk, H. Someya, T.  Zhao, W. Li, S. Yao, H. Zhang, S. Zhang, Y. Yang, B. Hou,  2017, 139, 7148. 4. Li, N. Baran, D. Forberich, K. Machui, F. Ameri, T. Turbiez, M. Carrasco O. M. Drees, M. Facchetti, A. Krebse, F.C. Brabec, C. J. Energy Environ. Sci. 2013, 6, 3407. 5. Kim, J. Lim, B. Baeg, K. J. Noh, Y. Y. Khim, D. Jeong, H. G. Yun, J. M. Kim, D. Y.  Fu, B. Y. Baltazar, J. Hu, Z. K. Chien, A. T. Kumar, S. Henderson, C. L. Collard, D. M. Reichmanis, E. Chem. Mater.,2012, 24, 4123 7.Al Hashimi, M. Han, Y. Smith,  Y. A. Watkins, S. E. Anthopoulos, T. D. Heeney, M.  Al Hashimi, M. Baklar, M. A. Colleaux, F. Watkins, S. E. Anthopoulos, T. D. Stingelin, N. Heeney, M. Macromolecules, 2011, 44, 5194. 9. Patra, D. Lee,  2017, 5, 2247.
Deep Learning for Aspect Based Sentiment Analysis: A Comparative Review
Venue: Expert Syst. Appl.
Year: 2019

The increasing volume of user generated content on the web has made sentiment analysis an important tool for the extraction of information about the human emotional state. A current research focus for sentiment analysis is the improvement of granularity at aspect level, representing two distinct aims: aspect extraction and sentiment classification of product reviews and sentiment classification of target dependent tweets. Deep learning approaches have emerged as a prospect for achieving these aims with their ability to capture both syntactic and semantic features of text without requirements for high level feature engineering, as is the case in earlier methods. In this article, we aim to provide a comparative review of deep learning for aspect based sentiment analysis to place different approaches in context.
The Effect of Lutein on Eye and Extra Eye Health
Venue: Nutrients
Year: 2018

Lutein is a carotenoid with reported anti inflammatory properties. A large body of evidence shows that lutein has several beneficial effects, especially on eye health. In particular, lutein is known to improve or even prevent age related macular disease which is the leading cause of blindness and vision impairment. Furthermore, many studies have reported that lutein may also have positive effects in different clinical conditions, thus ameliorating cognitive function, decreasing the risk of cancer, and improving measures of cardiovascular health. At present, the available data have been obtained from both observational studies investigating lutein intake with food, and a few intervention trials assessing the efficacy of lutein supplementation. In general, sustained lutein consumption, either through diet or supplementation, may contribute to reducing the burden of several chronic diseases. However, there are also conflicting data concerning lutein efficacy in inducing favorable effects on human health and there are no univocal data concerning the most appropriate dosage for daily lutein supplementation. Therefore, based on the most recent findings, this review will focus on lutein properties, dietary sources, usual intake, efficacy in human health, and toxicity.
Design Thinking: An Innovative Concept for Developing User Centered Software
Year: 2012

In times of economic crisis and rapid technological change, innovation is necessary for competitive advantage and successful business. Design processes and tools are one way to create innovative solutions. This article describes the emergence of design thinking in business and focuses on the four key elements of design thinking in detail: the iterative process, multidisciplinary teams, creative space and designer's mindset. The limitations and potentials of design thinking are also discussed. While design thinking enables creativity, enhances personal development, and prescribes deep immersion into the topic along with empathetic user research, it does not include a business model or blueprint for the implementation. Still, design thinking is a large step toward identifying user centered solutions. The software industry can benefit from the powerful approach in order to create innovative software products.
Analysis of E Commerce (Bukalapak, Shopee, and Tokopedia) Acceptance Models Using TAM2 Method
Venue: 2020 International Seminar on Application for Technology of Information and Communication (iSemantic)
Year: 2020

The high level of public satisfaction with the sale and purchase transactions is a measure of the success of various business programs. Therefore, companies as the main actors in business processes must be supported by the existence of an information technology. The use of E Commerce which is digitally able to facilitate buying and selling online but it is still considered difficult, so the company that runs e commerce needs to know what factors can affect individual interest in using online transactions to improve the existing system to make it better. Based on this, the authors conducted a study to find out what factors could influence the user interest in using e commerce. The author also makes comparisons of three e commerce sites, namely Shopee, Tokopedia, and Bukalapak using TAM2 method. The conclusions are drawn using SEM with PLS as a tool for data analysis. The results of this study are to show that measurement evaluation model has been valid and reliable. Then, obtained structural evaluation model shows the aspects which impacts usage behavior and intention of the users. The evaluation results can provide recommendations of the requirement which have to be considered so that could be developed in further research.
Incentives for Effort or Outputs? A Field Experiment to Improve Student Performance
Year: 2017

One key choice in designing an incentive is whether to reward outcomes directly (outputs) or to reward the actions and behaviors that lead to those outcomes (effort/inputs) I conduct a novel direct test of an input incentive designed to increase student effort against both an output incentive and a control that does not receive an incentive. The interventions were implemented in a classroom level randomized experiment with school children in India. A math software curriculum is implemented in all classrooms regardless of which activity is incentivized. It includes learning modules (the incentivized input) that are completed throughout a unit as well as a test at the end of the unit (the incentivized output) The two incentives are both piecerate and announced at the beginning of each unit. Students who receive an input incentive perform .57 standard deviations better than the control group on a non incentivized outcome test. This performance is statistically significantly larger than the impact of the output incentive (which .24 standard deviations and not significant relative to the control) The input incentive is also almost twice as cost effective as the output incentive. These results provide evidence that there can be large returns to directly inducing student effort in the classroom. The input incentive works better for present biased students along an incentive compatible measure of time preferences collected at baseline, which provides evidence to support the hypothesis that more frequent payments can address time inconsistency. This study also provides direct evidence that piecerate input incentives can be more effective than piecerate output incentives.
A comparative evaluation of immediate versus delayed loading of two implants with mandibular overdenture: An in vivo study
Venue: National journal of maxillofacial surgery
Year: 2019

Implant supported overdenture has been a common treatment for edentulous patients for the past several years and predictably achieves good clinical results, especially in those patients who were uncomfortable with their conventional dentures, as they provide additional retention and support. The concept of immediate loading is more applicable to completely edentulous patients because of their functional and esthetic requirements. Hence, the present study aims to evaluate the outcomes of immediate loading of mandibular two implant retained overdenture and compare it with the conventional delayed loading concept.  This in vivo study was conducted to compare immediate versus delayed loading of two implants with mandibular overdenture. A total of 20 completely edentulous patients (10 delayed loading and 10 immediate loading) were included in the study and certain parameters, i.e. bone loss around implants, periodontal pocket depth, pain and discomfort, implant stability, and microflora around implants, were measured immediately and after healing period of 3 and 6 months.  It can be analyzed from the observations that patients were more satisfied with delayed loading in terms of comfort, speech, function, pain, and chewing efficiency as compared to immediate loading.  It is concluded that implants loaded under delayed protocol seem to have a higher success rate as compared to those that are loaded immediately.
Automatic Epileptic Seizure Detection in EEG Signals Using Multi Domain Feature Extraction and Nonlinear Analysis
Venue: Entropy
Year: 2017

Epileptic seizure detection is commonly implemented by expert clinicians with visual observation of electroencephalography (EEG) signals, which tends to be time consuming and sensitive to bias. The epileptic detection in most previous research suffers from low power and unsuitability for processing large datasets. Therefore, a computerized epileptic seizure detection method is highly required to eradicate the aforementioned problems, expedite epilepsy research and aid medical professionals. In this work, we propose an automatic epilepsy diagnosis framework based on the combination of multi domain feature extraction and nonlinear analysis of EEG signals. Firstly, EEG signals are pre processed by using the wavelet threshold method to remove the artifacts. We then extract representative features in the time domain, frequency domain, time frequency domain and nonlinear analysis features based on the information theory. These features are further extracted in five frequency sub bands based on the clinical interest, and the dimension of the original feature space is then reduced by using both a principal component analysis and an analysis of variance. Furthermore, the optimal combination of the extracted features is identified and evaluated via different classifiers for the epileptic seizure detection of EEG signals. Finally, the performance of the proposed method is investigated by using a public EEG database at the University Hospital Bonn, Germany. Experimental results demonstrate that the proposed epileptic seizure detection method can achieve a high average accuracy of 99.25% indicating a powerful method in the detection and classification of epileptic seizures. The proposed seizure detection scheme is thus hoped to eliminate the burden of expert clinicians when they are processing a large number of data by visual observation and to speed up the epilepsy diagnosis.
Drawing Computerization and Intelligent Database Building Algorithm
Venue: 2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)
Year: 2020

In engineering, manufacturing, electronic industry, transportation and other industries, a large number of paper drawings have been accumulated. These paper drawings are an important resource accumulation of the companies. Paper drawings have some disadvantages, such as inconvenient to save, unable to establish database to realize resource reuse and so on. Therefore, this paper proposes a new algorithm of unattended drawing computerization and database intelligent establishment. Firstly, the position of the key text area is located according to the deep learning object detection algorithm, then the text lines in the text area are detected and the text is recognized. Finally, the database is established according to the recognition results. The experimental results show that the average accuracy of the proposed algorithm is 98.6% Compared with the existing drawing retrieval algorithm, the algorithm uses deep learning object detection algorithm to initially locate the text area. It can further improve the accuracy of drawing information extraction, and realize the unattended paper drawing computerization and intelligent establishment of database.
Nonlocal Multipoint Problem for Partial Differential Equations of Even Order with Constant Coefficients
Year: 2020

For a partial differential equation of even order with constant coefficients, in a bounded domain G, we study a problem with boundary conditions in the form of multipoint perturbations of the Neumann conditions by using the Fourier method. The eigenvalues and eigenfunctions of the operator L of the multipoint problem are determined. We establish the conditions of completeness of the system of eigenfunctions V(L) of the operator L in the space L2 (G) In the case of elliptic equation, we establish the conditions under which the system V(L) is a Riesz basis in the space L2 (G) We construct the solution of an inhomogeneous problem with homogeneous multipoint conditions in the form of a Fourier series with respect to the system of eigenfunctions and establish conditions for its existence and uniqueness.
Forecasting low voltage distribution network demand profiles using a pattern recognition based expert system
Year: 2014

The advent of distributed renewable energy supply sources and storage systems has placed a greater degree of focus on the operations of the LV (low voltage) electricity distribution network. However, LV networks are characterised by having much higher variability in time series demand meaning that modelling techniques solely relying on iterative forecasts to produce a next day demand profile forecast are insufficient. To cater for the complexity of LV network demand, a novel hybrid expert system comprised of three modules, namely, correlation clustering, discrete classification neural network, and a post processing procedure was developed. The system operates by classifying a set of key variables associated with a future day and refining a recalled historical demand profile as the forecast. The expert system exhibited high hindcast accuracy when trained with a residential LV transformer's demand data with R2 values ranging from 0.86 to 0.87 and MAPE (mean absolute percentage error) ranging from 11% to 12% across the three phases of the network. Under simulated real world conditions the R2 statistic reduced slightly to 0.81 0.84 and the MAPE increased to 12.5 13.5% Future work will involve integrating the developed expert system for forecasting next day demand in an LV network into a comprehensive distributed energy resource management algorithm.
What Is the Test Retest Reliability of Common Task Functional MRI Measures? New Empirical Evidence and a Meta Analysis
Venue: Psychological science
Year: 2020

Identifying brain biomarkers of disease risk is a growing priority in neuroscience. The ability to identify meaningful biomarkers is limited by measurement reliability; unreliable measures are unsuitable for predicting clinical outcomes. Measuring brain activity using task functional MRI (fMRI) is a major focus of biomarker development; however, the reliability of task fMRI has not been systematically evaluated. We present converging evidence demonstrating poor reliability of task fMRI measures. First, a meta analysis of 90 experiments (N 1,008) revealed poor overall reliability mean intraclass correlation coefficient (ICC) .397. Second, the test retest reliabilities of activity in a priori regions of interest across 11 common fMRI tasks collected by the Human Connectome Project (N 45) and the Dunedin Study (N 20) were poor (ICCs .067 .485) Collectively, these findings demonstrate that common task fMRI measures are not currently suitable for brain biomarker discovery or for individual differences research. We review how this state of affairs came to be and highlight avenues for improving task fMRI reliability.
The EMBL EBI search and sequence analysis tools APIs in 2019
Venue: Nucleic Acids Res.
Year: 2019

The EMBL EBI provides free access to popular bioinformatics sequence analysis applications as well as to a full featured text search engine with powerful cross referencing and data retrieval capabilities. Access to these services is provided via user friendly web interfaces and via established RESTful and SOAP Web Services APIs (https:/www.ebi.ac.uk/seqdb/confluence/display/JDSAT/EMBL EBI+Web+Services+APIs+ +Data+Retrieval) Both systems have been developed with the same core principles that allow them to integrate an ever increasing volume of biological data, making them an integral part of many popular data resources provided at the EMBL EBI. Here, we describe the latest improvements made to the frameworks which enhance the interconnectivity between public EMBL EBI resources and ultimately enhance biological data discoverability, accessibility, interoperability and reusability.
A review on computer vision systems in monitoring of poultry: A welfare perspective
Year: 2020

Monitoring of poultry welfare related bio processes and bio responses is vital in welfare assessment and management of welfare related factors. With the current development in information technologies, computer vision has become a promising tool in the real time automation of poultry monitoring systems due to its non intrusive and non invasive properties, and its ability to present a wide range of information. Hence, it can be applied to monitor several bio processes and bio responses. This review summarizes the current advances in poultry monitoring techniques based on computer vision systems, i.e. conventional machine learning based and deep learning based systems. A detailed presentation on the machine learning based system was presented, i.e. pre processing, segmentation, feature extraction, feature selection, and dimension reduction, and modeling. Similarly, deep learning approaches in poultry monitoring were also presented. Lastly, the challenges and possible solutions presented by researches in poultry monitoring, such as variable illumination conditions, occlusion problems, and lack of augmented and labeled poultry datasets, were discussed.
Trails of Data: Three Cases for Collecting Web Information for Social Science Research
Venue: Social Science Computer Review
Year: 2019

As the availability of online data grows rapidly, researchers are confronted with a pressing question: How should social scientists collect Internet data for research? This study focuses on one of the most commonly used data collection techniques: web scraping. Going beyond canned approaches by leveraging a general framework of data communication, this study illustrates how online information can be systematically queried and fetched for reproducible research. To generalize our approaches, we additionally explore the variations in site security and architecture that analysts may encounter during the scraping process before they are given access to the desired data. The approaches we introduce do not rely on any proprietary software and can be easily implemented on any computing platform with programming languages such as Python or R. The methodological discussion in this study is meant to be applicable to current web based research efforts. We include three examples with complete Python implementation. We also present an integrated workflow that enables researchers to produce analytical data sets that are traceable and thus verifiable for analysis or replication. Lastly, options related to the validity and efficiency of data are discussed, and we highlight the ongoing debate surrounding the ethics of online data collection, ultimately advocating for the fair use of online data.
3D Slicer as an image computing platform for the Quantitative Imaging Network.
Venue: Magnetic resonance imaging
Year: 2012

Quantitative analysis has tremendous but mostly unrealized potential in healthcare to support objective and accurate interpretation of the clinical imaging. In 2008, the National Cancer Institute began building the Quantitative Imaging Network (QIN) initiative with the goal of advancing quantitative imaging in the context of personalized therapy and evaluation of treatment response. Computerized analysis is an important component contributing to reproducibility and efficiency of the quantitative imaging techniques. The success of quantitative imaging is contingent on robust analysis methods and software tools to bring these methods from bench to bedside. 3D Slicer is a free open source software application for medical image computing. As a clinical research tool, 3D Slicer is similar to a radiology workstation that supports versatile visualizations but also provides advanced functionality such as automated segmentation and registration for a variety of application domains. Unlike a typical radiology workstation, 3D Slicer is free and is not tied to specific hardware. As a programming platform, 3D Slicer facilitates translation and evaluation of the new quantitative methods by allowing the biomedical researcher to focus on the implementation of the algorithm and providing abstractions for the common tasks of data communication, visualization and user interface development. Compared to other tools that provide aspects of this functionality, 3D Slicer is fully open source and can be readily extended and redistributed. In addition, 3D Slicer is designed to facilitate the development of new functionality in the form of 3D Slicer extensions. In this paper, we present an overview of 3D Slicer as a platform for prototyping, development and evaluation of image analysis tools for clinical research applications. To illustrate the utility of the platform in the scope of QIN, we discuss several use cases of 3D Slicer by the existing QIN teams, and we elaborate on the future directions that can further facilitate development and validation of imaging biomarkers using 3D Slicer.