nils-herrmann/clean_scirepeval huggingface dataset:
Opinion on the ethical implications of new health technologies and citizen participation

Recent years have witnessed a wave of innovation in health technologies driven by new medical breakthroughs, novel scientific approaches and the rise of digital health technologies. Pioneering methods of drug development and disease diagnosis, the rise of 'big health data' and new means of providing networked care have led to predictions that European health systems are on the cusp of transformation. While much of the promise held in these technological innovations remains to be fully realised, the rise of new health technologies are accompanied by a profound set of shifts in the way individuals whether as patients, citizens or consumers engage with matters of health.
Methods for churn prediction in the pre paid mobile telecommunications industry
Venue: 2016 International Conference on Communications (COMM)

Recently, due to the global competition companies active in different industries started to be concerned about the customer churn. With a churn rate of 30% the telecommunications sector takes the first place on the list. The telecommunications operators need to identify customers who are at risk of churning by implementing predictive models. In this paper, we present an advanced data mining methodology which predicts customer churn in the pre paid mobile telecommunications industry using a call details records dataset that consists of 3333 customers with 21 attributes each. We first apply the principal component analysis algorithm to reduce the dimensionality of the data and eliminate the problem of multicollinearity. To implement the predictive models, on the resulted principal components and discrete variables we initially propose and then apply three machine learning algorithms: neural networks, support vector machines, and Bayesian networks. To evaluate the models, we use the confusion matrix, the gain measure and the ROC curve.
Churn Prediction in Mobile Social Games: Towards a Complete Assessment Using Survival Ensembles
Venue: 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)

Reducing user attrition, i.e. churn, is a broad challenge faced by several industries. In mobile social games, decreasing churn is decisive to increase player retention and rise revenues. Churn prediction models allow to understand player loyalty and to anticipate when they will stop playing a game. Thanks to these predictions, several initiatives can be taken to retain those players who are more likely to churn. Survival analysis focuses on predicting the time of occurrence of a certain event, churn in our case. Classical methods, like regressions, could be applied only when all players have left the game. The challenge arises for datasets with incomplete churning information for all players, as most of them still connect to the game. This is called a censored data problem and is in the nature of churn. Censoring is commonly dealt with survival analysis techniques, but due to the inflexibility of the survival statistical algorithms, the accuracy achieved is often poor. In contrast, novel ensemble learning techniques, increasingly popular in a variety of scientific fields, provide high class prediction results. In this work, we develop, for the first time in the social games domain, a survival ensemble model which provides a comprehensive analysis together with an accurate prediction of churn. For each player, we predict the probability of churning as function of time, which permits to distinguish various levels of loyalty profiles. Additionally, we assess the risk factors that explain the predicted player survival times. Our results show that churn prediction by survival ensembles significantly improves the accuracy and robustness of traditional analyses, like Cox regression.
Anchored enrichment dataset for true flies (order Diptera) reveals insights into the phylogeny of flower flies (family Syrphidae)
Venue: BMC Evolutionary Biology

BackgroundAnchored hybrid enrichment is a form of next generation sequencing that uses oligonucleotide probes to target conserved regions of the genome flanked by less conserved regions in order to acquire data useful for phylogenetic inference from a broad range of taxa. Once a probe kit is developed, anchored hybrid enrichment is superior to traditional PCR based Sanger sequencing in terms of both the amount of genomic data that can be recovered and effective cost. Due to their incredibly diverse nature, importance as pollinators, and historical instability with regard to subfamilial and tribal classification, Syrphidae (flower flies or hoverflies) are an ideal candidate for anchored hybrid enrichment based phylogenetics, especially since recent molecular phylogenies of the syrphids using only a few markers have resulted in highly unresolved topologies. Over 6200 syrphids are currently known and uncovering their phylogeny will help us to understand how these species have diversified, providing insight into an array of ecological processes, from the development of adult mimicry, the origin of adult migration, to pollination patterns and the evolution of larval resource utilization.ResultsWe present the first use of anchored hybrid enrichment in insect phylogenetics on a dataset containing 30 flower fly species from across all four subfamilies and 11 tribes out of 15. To produce a phylogenetic hypothesis, 559 loci were sampled to produce a final dataset containing 217,702 sites. We recovered a well resolved topology with bootstrap support values that were almost universally >95 The subfamily Eristalinae is recovered as paraphyletic, with the strongest support for this hypothesis to date. The ant predators in the Microdontinae are sister to all other syrphids. Syrphinae and Pipizinae are monophyletic and sister to each other. Larval predation on soft bodied hemipterans evolved only once in this family.ConclusionsAnchored hybrid enrichment was successful in producing a robustly supported phylogenetic hypothesis for the syrphids. Subfamilial reconstruction is concordant with recent phylogenetic hypotheses, but with much higher support values. With the newly designed probe kit this analysis could be rapidly expanded with further sampling, opening the door to more comprehensive analyses targeting problem areas in syrphid phylogenetics and ecology.
Kinetics of anorthite dissolution in basaltic melt

We report convection free anorthite dissolution experiments in a basaltic melt at 1280 1500 degC and 0.5 GPa on two different crystallographic surfaces, 1 2 1 and 3 0 2 to investigate dissolution kinetics. The anisotropy of the anorthite dissolution rate along these two surfaces is negligible. Time series experiments at ~1280 degC show that anorthite dissolution is mainly controlled by diffusion in the melt within experimental uncertainty. Analytical solutions were used to model the dissolution and diffusion processes, and to obtain the diffusivities and the saturation concentrations of the equilibrium determining component (Al2O3) for anorthite dissolution into the basaltic melt. For the first time, we are able to show the physical and chemical characteristics of quench growth effect on the near interface melt using high spatial resolution (0.3 mm) EDS analyses. For anorthite (An# 90) saturation in a melt with 39 53 wt% SiO2 and 0.4 wt% H2O, the concentration of Al2O3 in wt% depends on temperature as follows: ln C s,Al 2 O 3 8.032 0.192 7882 287 T where T is temperature in K, and errors are given at 1s level. Al2O3 diffusivity in basaltic melt during plagioclase dissolution can be expressed as: ln D Al 2 O 3 EBD,plag diss 13.69 1.57 19 313 2485 T where D is in m2/s, and the activation energy is 161 21 kJ/mol. These results are applied to model the convective dissolution of anorthite in basaltic melts. The model indicates that though anorthite crystals can survive for a longer time compared to olivine crystals of the same size in the same melt, the former would rise for a much smaller distance compared to the olivine sinking distance, due to the smaller density contrast between anorthite and melt.
Ca(2+ and OH( release of ceramsites containing anorthite and gehlenite prepared from waste lime mud.
Venue: Journal of environmental sciences

Lime mud is a kind of solid waste in the papermaking industry, which has been a source of serious environmental pollution. Ceramsites containing anorthite and gehlenite were prepared from lime mud and fly ash through the solid state reaction method at 1050degC. The objective of this study was to explore the efficiency of Ca(2+ and OH( release and assess the phosphorus and copper ion removal performance of the ceramsites via batch experiments, X ray diffraction (XRD) and scanning electron microscopy (SEM) The results show that Ca(2+ and OH( were released from the ceramsites due to the dissolution of anorthite, gehlenite and available lime. It is also concluded that gehlenite had stronger capacity for Ca(2+ and OH( release compared with anorthite. The Ca(2+ release could be fit well by the Avrami kinetic model. Increases of porosity, dosage and temperature were associated with increases in the concentrations of Ca(2+ and OH( released. Under different conditions, the ceramsites could maintain aqueous solutions in alkaline conditions (pH=9.3 10.9) and the release of Ca(2+ was not affected. The removal rates of phosphorus and copper ions were as high as 96.88% and 96.81% respectively. The final pH values of both phosphorus and copper ions solutions changed slightly. The reuse of lime mud in the form of ceramsites is an effective strategy.
Iron and Vitamin D Deficiency in Healthy Young Children in Western Europe Despite Current Nutritional Recommendations
Venue: Journal of pediatric gastroenterology and nutrition

Iron deficiency (ID) and vitamin D deficiency (VDD) are the 2 most common micronutrient deficiencies in young children worldwide and may lead to impaired neurodevelopment and rickets, respectively. Risk factors for ID and VDD differ between populations. The objective of this study was to determine the prevalence of and risk factors for ID and VDD in 12 to 36 month old children in Western Europe.  This study took place in Germany, the Netherlands, and the United Kingdom from 2012 to 2014. A venous blood sample was taken to establish iron and vitamin D status. ID was defined as serum ferritin <12 &mgr;g/L in the absence of infection (high sensitivity C reactive protein <10 mg/L) VDD was defined as serum 25 hydroxyvitamin D <50 nmol/L (20 ng/mL) Furthermore, parents were asked to fill out a questionnaire regarding their child's demographic and socioeconomic characteristics, food intake, sun exposure, and medical history.  In 325 children (white race 95% boys 56% mean age 20.7 months) the overall prevalence of ID and VDD was 11.8% and 22.8% respectively. The use of primarily cow's milk as major type of milk was associated with ID (odds ratio [OR] 3.20, 95% confidence interval [CI] 1.12 8.53) and VDD (OR 7.17, 95% CI 3.10 16.57) The use of vitamin D supplements (OR 0.20, 95% CI 0.07 0.56) was associated with a lower prevalence of VDD.  Despite current nutritional recommendations, ID and VDD are common in healthy young white children. Health programs focusing on adequate iron and vitamin D intake at an early age should be implemented to prevent deficiencies.
Can You "Work Your Way Up?" Ability Grouping and the Development of Academic Engagement

Schools are not only tasked with communicating academic knowledge to students, but also preparing and training them to assume adult roles, including participation in the work force. Educators attempt to foster engagement with learning and cultivate student behaviors such as punctual attendance and preparation for classes, attention to lectures and directions, and timely completion of assigned tasks. These learning behaviors are crucial to students' success in school and their preparation for the labor market. However, the widespread practice of grouping students by perceived ability means that students in the same school, or even the same classroom, can have very different training experiences. While there is a large literature on how ability grouping can affect student achievement, we have comparatively little empirical evidence on how it affects the development of students' academic engagement. I consider two different types of grouping within class grouping in the early elementary grades, and between class grouping (tracking) during high school using data from the Early Childhood Longitudinal Study Kindergarten Cohort of 2002 and the National Education Longitudinal Study of 1988. I describe predictors of students' assignments to ability groups and examine opportunities for advancement. I also use propensity score stratification to estimate the effects of group assignments on math and reading achievement as well as students' academic engagement through its attendant learning behaviors. Findings indicate that there are substantial opportunities for students to move up to higher groups as they progress through school and students can improve their group assignments by demonstrating positive academic engagement. However, group assignments do tend to "stick" to students over time, even after controlling for prior achievement, learning behaviors, and other observable characteristics. Moreover, assignment to a higher group improves students' math and reading achievement outcomes and improves students' academic engagement, while assignment to a lower group depresses both of these, making upward mobility more difficult. These effects diminish in size as students age. Following high school, students who graduated from an academic track were more likely to attend traditional four year colleges (though less likely to attend other postsecondary education programs) and differences in labor market outcomes were modest.
microRNAs in breast cancer: regulatory roles governing the hallmarks of cancer
Venue: Biological reviews of the Cambridge Philosophical Society

A large number of etiological factors and the complexity of breast cancers present challenges for prevention and treatment. Recently, the emergence of microRNAs (miRNAs) as cancer biomarkers has added an extra dimension to the 'molecular signatures' of breast cancer. Bioinformatic analyses indicate that each miRNA can regulate hundreds of target genes and could serve functionally as 'oncogenes' or 'tumour suppressor' genes, and co ordinate multiple cellular processes relevant to cancer progression. A number of studies have shown that miRNAs play important roles in breast tumorigenesis, metastasis, proliferation and differentiation of breast cancer cells. This review provides a comprehensive overview of miRNAs with established functional relevance in breast cancer, their established target genes and resulting cellular phenotype. The role and application of circulating miRNAs in breast cancer is also discussed. Furthermore, we summarize the role of miRNAs in the hallmarks of breast cancer, as well as the possibility of using miRNAs as potential biomarkers for detection of breast cancer.
DBpedia entities expansion in automatically building dataset for Indonesian NER
Venue: 2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS)

Named Entity Recognition (NER) plays a significant role in Information Extraction (IE) In English, the NER systems have achieved excellent performance, but for the Indonesian language, the systems still need a lot of improvement. To create a reliable NER system using machine learning approach, a massive dataset to train the classifier is a must. Several studies have proposed methods in automatically building dataset for Indonesian NER using Indonesian Wikipedia articles as the source of the dataset and DBpedia as the reference in determining entity types automatically. The objective of our research is to improve the quality of the automatically tagged dataset. We proposed a new method in using DBpedia as the referenced named entities. We have created some rules in expanding DBpedia entities corpus for category person, place, and organization. The resulting training dataset is trained using Stanford NER tool to build an Indonesian NER classifier. The evaluation shows that our method improves recall significantly but has lower precision compared to the previous research.
Morphological awareness and bilingual word learning: a longitudinal structural equation modeling study

This longitudinal study examined the contribution of morphological awareness to bilingual word learning of Malay English bilingual children in Singapore where English is the medium of instruction. Participants took morphological awareness and lexical inference tasks in both English and Malay twice with an interval of about half a year, the first time at the end of Grade 3 (Time 1) and the second time at the end of the first semester of Grade 4 (Time 2) Structural equation modeling (SEM) analyses revealed that within both languages, morphological awareness significantly predicted lexical inference at Time 1 as well as Time 2, and the contribution also became strengthened over time. Cross linguistic SEM analyses showed that concurrently at both Time 1 and Time 2, English morphological awareness only had a significant indirect effect on Malay lexical inference. The exact indirect relationships, however, varied between Time 1 and Time 2. In addition, an indirect effect of Time 1 English morphological awareness on Time 2 Malay lexical inference also surfaced. These findings suggest concurrent as well as longitudinal cross linguistic transfer of morphological awareness from English to Malay.
Derivational Morphology in Reading Comprehension of Chinese speaking Learners of English: A Longitudinal Structural Equation Modeling Study

This longitudinal study examined the contribution of morphological awareness to English as a Second Language (ESL) reading comprehension. Young Chinese speaking ESL learners completed twice with a one year interval the same set of tasks that measured derivational awareness, vocabulary breadth, fluency of reading derived words, and passage comprehension. Structural Equation Modeling analyses revealed that at both Time 1 and Time 2, morphological awareness had a significant direct effect on reading comprehension over and above vocabulary knowledge, and such an effect became stronger over time. A significant indirect effect of morphological awareness on reading comprehension was also observed at both times. However, how the indirect effect was achieved varied across times. Longitudinally, Time 1 morphological awareness failed to significantly predict Time 2 reading comprehension over and above Time 1 reading comprehension and vocabulary knowledge; however, an indirect effect through the latter two variables was significant. This study indicates an increasingly important role of morphology in ESL reading comprehension, and highlights a need of explicit teaching of morphology to facilitate ESL learners' reading development.
Study Bacteria Host Interactions Using Intestinal Organoids.
Venue: Methods in molecular biology

The intestinal epithelial cells function to gain nutrients, retain water and electrolytes, and form an efficient barrier against foreign microbes and antigens. Researchers employed cell culture lines derived from human or animal cancer cells as experimental models in vitro for understanding of intestinal infections. However, most in vitro models used to investigate interactions between bacteria and intestinal epithelial cells fail to recreate the differentiated tissue components and structure observed in the normal intestine. The in vitro analysis of host bacteria interactions in the intestine has been hampered by a lack of suitable intestinal epithelium culture systems. Here, we present a new experimental model using an organoid culture system to study bacterial infection.
A hybrid cuckoo harmony search algorithm for optimal design of water distribution systems

Meta heuristic algorithms have been broadly used to deal with a range of water resources optimization problems over the past decades. One issue that exists in the use of these algorithms is the requirement of large computational resources, especially when handling real world problems. To overcome this challenge, this paper develops a hybrid optimization method, the so called CSHS, in which a cuckoo search (CS) algorithm is combined with a harmony search (HS) scheme. Within this hybrid framework, the CS is employed to find the promising regions of the search space within the initial explorative stages of the search, followed by a thorough exploitation phase using the combined CS and HS algorithms. The utility of the proposed CSHS is demonstrated using four water distribution system design problems with increased scales and complexity. The obtained results reveal that the CSHS method outperforms the standard CS, as well as the majority of other meta heuristics that have previously been applied to the case studies investigated, in terms of efficiently seeking optimal solutions. Furthermore, the CSHS has two control parameters that need to be fine tuned compared to many other algorithms, which is appealing for its practical application as an extensive parameter calibration process is typically computationally very demanding.
Multivariate data mining for estimating the rate of discolouration material accumulation in drinking water distribution systems

Particulate material accumulates over time as cohesive layers on internal pipeline surfaces in water distribution systems (WDS) When mobilised, this material can cause discolouration. This paper explores factors expected to be involved in this accumulation process. Two complementary machine learning methodologies are applied to significant amounts of real world field data from both a qualitative and a quantitative perspective. First, Kohonen self organising maps were used for integrative and interpretative multivariate data mining of potential factors affecting accumulation. Second, evolutionary polynomial regression (EPR) a hybrid data driven technique, was applied that combines genetic algorithms with numerical regression for developing easily interpretable mathematical model expressions. EPR was used to explore producing novel simple expressions to highlight important accumulation factors. Three case studies are presented: UK national and two Dutch local studies. The results highlight bulk water iron concentration, pipe material and looped network areas as key descriptive parameters for the UK study. At the local level, a significantly increased third data set allowed K fold cross validation. The mean cross validation coefficient of determination was 0.945 for training data and 0.930 for testing data for an equation utilising amount of material mobilised and soil temperature for estimating daily regeneration rate. The approach shows promise for developing transferable expressions usable for pro active WDS management.
Implementation of EEG based driver's attention tracking and habitats monitoring system
Venue: 2016 International Conference on Communication and Electronics Systems (ICCES)

Distracted driving leads to many catastrophic consequences. Developing a countermeasure to track driver's focus of attention is necessary. In recent years driver distraction had received considerable attention in the research literature and in the popular media. Nowadays more accidents occurred due to inattentiveness and rash driving or due to the drowsiness of the driver. This paper implements a driver monitoring system using EEG. Brain wave sensors are useful to check the driver attentive levels. This system will monitor the different parameters of driver like attention, alcohol consumption, accident detection and if the steering is hold or not. If the driver is not attentive and any value which exceeds sensor ranges the ignition unit will be controlled. And the location will be indicated. This system will be helpful in preventing many accidents, and consequently save money and reduce personal suffering.