nils-herrmann/clean_scirepeval huggingface dataset:
Relationship Between the Psychological Attitude Toward Fantasy and Flow Experience and Mental Health
Year: 2010

1  This study examined the relationship between the psychological attitude toward fantasy and fl ow experience and subjective well being. College students vocational school students and university staff (N =299 completed (1 the multiphasic fantasy inventory (2 the fl ow experience checklist and (3 the subjective well being inventory. First a series of tests revealed that the participants scoring high on fl ow experience scored high on wish fulfi llment in fantasy coping with the futureandpositive eff ectand scored low onfantasy activity is useless. The participants scoring high on mental health scored high oncoping with the futureand scored low onfantasy prone indulging in fantasy in unpleasant conditionsandnegative eff ect. Second a model explaining the relationship between the psychological attitude toward fantasy and flow experience and subjective well being was constructed. A covariance structure analysis was used and the goodness of fi t index was not found to be signifi cant.
A broadband seismic metamaterial plate with simple structure and easy realization
Venue: Journal of Applied Physics
Year: 2019

A novel seismic metamaterial plate consisting of two common building materials, which has the advantages of simple structure and easy to realize, is proposed. The seismic metamaterial plate creates a wide bandgap with a relative bandwidth of 1. Using numerical simulation, the bandgap properties of the metamaterial plate both in a free space and on a half space are studied and it is found that the wide bandgap of the free plate is kept even if in half space. Using scaled (1:30) experiments under 1 g conditions and simulations, we study the transmission spectrum under the surface waves incident on the seismic metamaterial plate and verify that it has a good attenuation effect in the corresponding frequency range. This work paves the way to the design of seismic metamaterials allowing an unparalleled control of surface wave propagation.
Mutagenic Effectiveness and Efficiency of Gamma Rays in Sesame (Sesamum indicum L.
Year: 2014

Mutagenic effectiveness and efficiency of gamma rays were studied in the genotypes of sesame TTVS 51 and TTVS 19. The dosage of gamma rays for mutagenic treatments was fixed based on the LD values 50 available in the literature in relation to sesame and allied oilseed crops. The mutagenic effectiveness and efficiency is high at 250Gy in both genotypes. The frequency of viable mutants was high in 450Gy on M2 seedling basis. Mutants with wider spectrum of variation were found at 250Gy and 350Gy of gamma rays. Mutagenic effectiveness and efficiency increased with the decreased in dose or concentration in sesame. Compared to TTVS 19, TTVS 51 produced more economically viable mutations.
An Empirical Analysis on College Students' Employment Pressure
Year: 2011

The college students'employment is not only related to the survival and development of colleges,but also related to the interests of thousands of families,and even related to the stability of the country and the society.Under the fierce competition and the severe employment circumstances,the employment pressure has become an important source of university students' psychological pressure,and gradually become a kind of collective confusion,even crisis.An empirical study was taken to measure students'life caused by the employment pressure in life,career,morality,and society.It was confirmed that the employment pressure has become a bottleneck for college students'growth.The employment is an essential part of the higher education,and the career guidance is urgently needed.
Antibiotics alone for uncomplicated acute appendicitis in high operative risk adult patients: Analytical review of RCTs and proposal of evidence based treatment decision.
Venue: Acta bio medica Atenei Parmensis
Year: 2017

Clinical trials have so far shown controversial results as regards the standard of care for treating uncomplicated acute appendicitis (uC AA) High operational risk adult patients (HORAP) could represent selected patients where primary antibiotic conservative therapy (pACT or A) could be indicated.  We carried a comprehensive search of the PubMed searching engine in the English language scientific literature from 1995 to 2015, using medical subject headings "antibiotics" "uncomplicated appendicitis" "appendicectomy" "conservative treatment" "surgery" and "randomized clinical trial" All RCTs comparing the outcomes of pACT versus primary surgical open or laparoscopic appendectomy (pSOLA or S) as primary treatment options for uC AA were identified. Inclusion criteria for our analytical review were RCTs evaluating outcomes in terms of or related to all of the following four parameters: treatment efficacy, post therapeutic/operative complications, in hospital length of stay (LOS) and recurrence.  The conclusion of all five RCTs considered antibiotics alone in the treatment of AA as an efficient and non inferior therapeutic option respect to surgery. Primary ACT was characterised by a higher LOS, a higher rate of recurrence and a lower rate of postoperative complication than pSOLA.  Based on the current body of evidence, an appropriate pACT could be a rational tailored primary treatment option for CT proven uC AA in HORAP. Accurate diagnoses and surgical risk stratification in patients with uC AA could aid decision making for target therapy. However, results of large sample prospective multicenter RCTs are required to routinely recommend pACT for uC AA in the clinical practice.
WHICH DATABASES SHOULD BE USED TO IDENTIFY STUDIES FOR SYSTEMATIC REVIEWS OF ECONOMIC EVALUATIONS?
Venue: International Journal of Technology Assessment in Health Care
Year: 2018

This study investigated which databases and which combinations of databases should be used to identify economic evaluations (EEs) to inform systematic reviews. It also investigated the characteristics of studies not identified in database searches and evaluated the success of MEDLINE search strategies used within typical reviews in retrieving EEs in MEDLINE.  A quasi gold standard (QGS) set of EEs was collected from reviews of EEs. The number of QGS records found in nine databases was calculated and the most efficient combination of databases was determined. The number and characteristics of QGS records not retrieved from the databases were collected. Reproducible MEDLINE strategies from the reviews were rerun to calculate the sensitivity and precision for each strategy in finding QGS records.  The QGS comprised 351 records. Across all databases, 337/351 (96 percent) QGS records were identified. Embase yielded the most records (314; 89 percent) Four databases were needed to retrieve all 337 references: Embase Health Technology Assessment database (MEDLINE or PubMed) Scopus. Four percent (14/351) of records could not be found in any database. Twenty nine of forty one (71 percent) reviews reported a reproducible MEDLINE strategy. Ten of twenty nine (34.5 percent) of the strategies missed at least one QGS record in MEDLINE. Across all twenty nine MEDLINE searches, 25/143 records were missed (17.5 percent) Mean sensitivity was 89 percent and mean precision was 1.6 percent.  Searching beyond key databases for published EEs may be inefficient, providing the search strategies in those key databases are adequately sensitive. Additional search approaches should be used to identify unpublished evidence (grey literature)
A Survey on Semantic Web Search Engine
Year: 2012

The tremendous growth in the volume of data and with the terrific growth of number of web pages, traditional search engines now a days are not appropriate and not suitable anymore. Search engine is the most important tool to discover any information in World Wide Web. Semantic Search Engine is born of traditional search engine to overcome the above problem. The Semantic Web is an extension of the current web in which information is given well defined meaning. Semantic web technologies are playing a crucial role in enhancing traditional web search, as it is working to create machine readable data. but it will not replace traditional search engine. In this paper we made a brief survey on various promising features of some of the best semantic search engines developed so far and we have discussed the various approaches to semantic search. We have summarized the techniques, advantages of some important semantic web search engines that are developed so far.The most prominent part is that how the semantic search engines differ from the traditional searches and their results are shown by giving a sample query as input.
Longitudinal Structural Equation Modeling of Internet Game and Aggression in Children
Year: 2015

This study aimed to identify the effects of Internet game on levels of aggression in children in the context of individual, familial, and social environments. The subjects were 2844 fourth graders participated in the Korean Youth Panel Survey between 2004 and 2008. Internet game use, the levels of aggression, self esteem, self control, stress, depression, attachment to parents, parental control, attachment to teachers, and attachment to peers were measured by 5 point Likert scales. The result showed that gender was the most effective factor influencing the initial level of, the rate of changes, an increase or decrease in the rate of changes in Internet game use and levels of aggression. Boys showed higher levels of Internet game use and aggression than girls. Children with higher self esteem, self control and lower stress and depression showed lower levels of Internet game use and aggression. The levels of Internet game use at fourth grade was positively correlated with later increase in the levels of aggression. Also, the faster increase in Internet game use caused faster increase in the levels of aggression later. Based on the results, preventive measures such as limiting the opportunities to access Internet games might be worth to decrease the aggressive behaviors among children.
Health information technology and digital innovation for national learning health and care systems.
Venue: The Lancet. Digital health
Year: 2021

Health information technology can support the development of national learning health and care systems, which can be defined as health and care systems that continuously use data enabled infrastructure to support policy and planning, public health, and personalisation of care. The COVID 19 pandemic has offered an opportunity to assess how well equipped the UK is to leverage health information technology and apply the principles of a national learning health and care system in response to a major public health shock. With the experience acquired during the pandemic, each country within the UK should now re evaluate their digital health and care strategies. After leaving the EU, UK countries now need to decide to what extent they wish to engage with European efforts to promote interoperability between electronic health records. Major priorities for strengthening health information technology in the UK include achieving the optimal balance between top down and bottom up implementation, improving usability and interoperability, developing capacity for handling, processing, and analysing data, addressing privacy and security concerns, and encouraging digital inclusivity. Current and future opportunities include integrating electronic health records across health and care providers, investing in health data science research, generating real world data, developing artificial intelligence and robotics, and facilitating public private partnerships. Many ethical challenges and unintended consequences of implementation of health information technology exist. To address these, there is a need to develop regulatory frameworks for the development, management, and procurement of artificial intelligence and health information technology systems, create public private partnerships, and ethically and safely apply artificial intelligence in the National Health Service.
Research on the Efficiency of China's Marine Industry Financial Support Based on DEA
Venue: Journal of Coastal Research
Year: 2020

Cao, Y.L. 2020. Research on the efficiency of china's marine industry financial support based on DEA.  Bai, X. and Zhou, H. (eds. Advances in Water Resources, Environmental Protection, and Sustainable Development. . Financial support is the driving force and means for the development of marine industry. Improving the efficiency of financial support is the key to financial support. With the increasingly fierce market competition, only by relying on high quality products and high customer satisfaction can enterprises survive in the fierce competition. Among many evaluation methods, Data Envelopment Analysis (DEA) as a non parametric statistical method for effectively dealing with multi input and multi output problems, has received more and more attention in evaluating the relative efficiency of decision making units. The adjustment and optimization of financial structure have been proved by theory and practice to promote economic growth, and financial support plays a decisive role in the development of marine industry. China's financial structure is still in the stage of scale expansion to promote the marine economy. In order to promote the improvement of marine industry, we should turn to the intensive mode of efficiency improvement. This paper extends from financial support to the study of financial support efficiency, and puts forward the method of using DEA model to evaluate and analyze the financial support efficiency of marine industry.
A dynamic neural network model for predicting risk of Zika in real time
Venue: BMC Medicine
Year: 2019

In 2015, the Zika virus spread from Brazil throughout the Americas, posing an unprecedented challenge to the public health community. During the epidemic, international public health officials lacked reliable predictions of the outbreak's expected geographic scale and prevalence of cases, and were therefore unable to plan and allocate surveillance resources in a timely and effective manner. In this work, we present a dynamic neural network model to predict the geographic spread of outbreaks in real time. The modeling framework is flexible in three main dimensions (i) selection of the chosen risk indicator, i.e. case counts or incidence rate; (ii) risk classification scheme, which defines the high risk group based on a relative or absolute threshold; and (iii) prediction forecast window (1 up to 12 weeks) The proposed model can be applied dynamically throughout the course of an outbreak to identify the regions expected to be at greatest risk in the future. The model is applied to the recent Zika epidemic in the Americas at a weekly temporal resolution and country spatial resolution, using epidemiological data, passenger air travel volumes, and vector habitat suitability, socioeconomic, and population data for all affected countries and territories in the Americas. The model performance is quantitatively evaluated based on the predictive accuracy of the model. We show that the model can accurately predict the geographic expansion of Zika in the Americas with the overall average accuracy remaining above 85% even for prediction windows of up to 12 weeks. Sensitivity analysis illustrated the model performance to be robust across a range of features. Critically, the model performed consistently well at various stages throughout the course of the outbreak, indicating its potential value at any time during an epidemic. The predictive capability was superior for shorter forecast windows and geographically isolated locations that are predominantly connected via air travel. The highly flexible nature of the proposed modeling framework enables policy makers to develop and plan vector control programs and case surveillance strategies which can be tailored to a range of objectives and resource constraints.
Layout analysis of historical Tibetan documents
Venue: 2019 2nd International Conference on Artificial Intelligence and Big Data (ICAIBD)
Year: 2019

Layout analysis of historical Tibetan document is very important task in the processing of document rebuilding. The result of layout analysis can be used to segment text regions. The task includes skew correction, region segmentation, page layout and structure. Skew correction process uses Tibetan baseline feature and Hough transform to get baseline, so as to get the skew angle of the document. After skew correction, getting the position of border is to realize region segmentation. To obtain the border, we have adopted a series of treatments such as median filtering, Gaussian smoothing, Sobel edge and edge smoothing, removing small area regions, and obtaining the border position. we determine the location of other regions according to the position relationship between border and other regions, e.g. text region, left comments, right comments. Finally, we use XML format to save page information such as author, date time and page information and region positions. Our method is simple and realize batch image layout analysis.
Decoding surface code with a distributed neural network based decoder
Venue: Quantum Mach. Intell.
Year: 2020

There has been a rise in decoding quantum error correction codes with neural network based decoders, due to the good decoding performance achieved and adaptability to any noise model. However, the main challenge is scalability to larger code distances due to an exponential increase of the error syndrome space. Note that successfully decoding the surface code under realistic noise assumptions will limit the size of the code to less than 100 qubits with current neural network based decoders. Such a problem can be tackled by a distributed way of decoding, similar to the renormalization group (RG) decoders. In this paper, we introduce a decoding algorithm that combines the concept of RG decoding and neural network based decoders. We tested the decoding performance under depolarizing noise with noiseless error syndrome measurements for the rotated surface code and compared against the blossom algorithm and a neural network based decoder. We show that a similar level of decoding performance can be achieved between all tested decoders while providing a solution to the scalability issues of neural network based decoders.
Layer Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks
Venue: NeurIPS
Year: 2019

Graph convolutional networks (GCNs) have recently received wide attentions, due to their successful applications in different graph tasks and different domains. Training GCNs for a large graph, however, is still a challenge. Original full batch GCN training requires calculating the representation of all the nodes in the graph per GCN layer, which brings in high computation and memory costs. To alleviate this issue, several sampling based methods are proposed to train GCNs on a subset of nodes. Among them, the node wise neighbor sampling method recursively samples a fixed number of neighbor nodes, and thus its computation cost suffers from exponential growing neighbor size across layers; while the layer wise importance sampling method discards the neighbor dependent constraints, and thus the nodes sampled across layer suffer from sparse connection problem. To deal with the above two problems, we propose a new effective sampling algorithm called LAyer Dependent  Sampling (LADIES) Based on the sampled nodes in the upper layer, LADIES selects nodes that are in the neighborhood of these nodes and uses the constructed bipartite graph to compute the importance probability. Then, it samples a fixed number of nodes according to the probability for the whole layer, and recursively conducts such procedure per layer to construct the whole computation graph. We prove theoretically and experimentally, that our proposed sampling algorithm outperforms the previous sampling methods regarding both time and memory. Furthermore, LADIES is shown to have better generalization accuracy than original full batch GCN, due to its stochastic nature.
Process Drama in the Japanese University EFL Classroom: The Emigration Project
Year: 2014

This paper examines the impact of using a process drama project in a Japanese university EFL class focusing on the social issue of emigration from a historical perspective while simultaneously developing English communicative skills. Speaking in English is a skill that many Japanese people find challenging. There are a number of cultural reasons for this, for example the enormous linguistic gap between Japanese and English communicative patterns in terms of explicitness/implicitness, hierarchy, gender, and the role of silence. Therefore, the overt aim was to help students develop English language skills while learning about Japanese historical emigration through the medium of English with Japanese scaffolding. This is in keeping with the needs of the average Japanese university EFL student, who has had six years of accuracy based study for the short term target of the university entrance examination and who sees English in terms of mathematical code, rather than as a communicative tool. The narrative arc of the paper follows the Noh theatre JO HAKYU, Enticement Elaboration Consolidation trajectory to take the reader through the emigration process drama project held in the spring semester of 2009 at the School of Human Welfare Studies (HWS) Kwansei Gakuin University (KGU) Japan. The research approach was a mixed method one and data was collected through digital recording of role plays, student self critical reflection by writing in role and writing out of role in an online class group, qualitative and quantitative questionnaires and teacher observation. Results indicated that process drama projects can have a positive influence on Japanese university EFL learners from the perspective of both linguistic and intercultural communicative competence.
Optimization of sentiment analysis using machine learning classifiers
Venue: Human centric Computing and Information Sciences
Year: 2017

Words and phrases bespeak the perspectives of people about products, services, governments and events on social media. Extricating positive or negative polarities from social media text denominates task of sentiment analysis in the field of natural language processing. The exponential growth of demands for business organizations and governments, impel researchers to accomplish their research in sentiment analysis. This paper leverages four state of the art machine learning classifiers viz. Naive Bayes, J48, BFTree and OneR for optimization of sentiment analysis. The experiments are performed using three manually compiled datasets; two of them are captured from Amazon and one dataset is assembled from IMDB movie reviews. The efficacies of these four classification techniques are examined and compared. The Naive Bayes found to be quite fast in learning whereas OneR seems more promising in generating the accuracy of 91.3% in precision, 97% in F measure and 92.34% in correctly classified instances.