nils-herrmann/clean_scirepeval huggingface dataset:
An examination of the perceptions of principals and teachers regarding principal leadership style effectiveness and flexibility as it relates to school improvement
Year: 2014

Principals play a critical role in a school's success. This study investigated how principals' leadership styles and teachers' perceptions o f principals' leadership styles related to school performance. The sample for the study included 19 schools, 19 principals, and 139 third, fourth, and fifth grade teachers. Both principals and teachers were administered a Leader Behavior Analysis II 20 item questionnaire. No Child Left Behind (2002) and increased accountability have intensified the need for principals to be effective instructional leaders who move schools forward. Although principals may have an indirect role in student achievement, they have a direct influence on quality teaching and instruction (Beteille, Kalogrides, Loeb, 2009) This investigation highlighted some o f the numerous challenges that many schools face; however, research continues to reveal that no school can have success without an effective leader (Leithwood Mascall, 2008) Effective leaders select the leadership style which is best suited to increase performance (Blanchard, Zigarmi, Zigarmi, 1985) This study further sought to discover if one leadership style was more effective than another in achieving overall school improvement. School leaders who use effective leadership styles may impact student performance through motivation o f the teachers (Christie, Thompson, Whiteley, 2009) Data collected from the Leader Behavior Analysis II were analyzed using independent Tests, Pearson Product Moment Correlation, and Chi Square. The present study revealed no significant difference or no significant relationship between self reported and perceived leadership styles, flexibility and effectiveness, and
The Perceptions of EFL Teachers on the Impact of the Usage of Code Switching in EFL Classroom Management
Year: 2015

This study aims to investigate university level language teachers' views on how their usage of code switching affects their classroom management in EFL classes. This study is conducted in Turkish setting, which is Cyprus and especially at the European University of Lefke. The participants are selected randomly from the same university and they are the members of the English Preparatory School. They were asked to write five daily reports during one week and they were interviewed for 30 minutes. In order to reach a valid and reliable data, two data collection methods were chosen, which were reflective reports and interview; hence, the nature of the current study was qualitative. In other words, cross case analysis was used to examine and identify common themes between cases. This study lasted three months; therefore, it was not a longitudinal work, so this research was limited and needed further inquiry. In Cyprus EFL classes, code switching should be considered and investigated more in classroom management. It is revealed that there is a need to raise EFL teachers' awareness on when and when not to use code switching. There are certain ways to promote code switching in EFL classes and there are by providing more seminars, workshops and organizational meetings weekly or monthly.
Revisiting the linearity in cross lingual embedding mappings: from a perspective of word analogies
Venue: ArXiv
Year: 2020

Most cross lingual embedding mapping algorithms assume the optimised transformation functions to be linear. Recent studies showed that on some occasions, learning a linear mapping does not work, indicating that the commonly used assumption may fail. However, it still remains unclear under which conditions the linearity of cross lingual embedding mappings holds. In this paper, we rigorously explain that the linearity assumption relies on the consistency of analogical relations encoded by multilingual embeddings. We did extensive experiments to validate this claim. Empirical results based on the analogy completion benchmark and the BLI task demonstrate a strong correlation between whether mappings capture analogical information and are linear.
PENGGUNAAN METODE MENJIPLAK DENGAN MEDIA DAUN PEPAYA DALAM MENGEMBANGKAN MOTORIK HALUS ANAK KELOMPOK B DI TK NEGERI PEMBINA SEKAYU TAHUN 2019
Year: 2020

Abstrak: Penelitian ini bertujuan untuk mengetahui ada atau tidaknya pengaruh metode menjiplak dengan media daun pepaya dalam mengembangkan motorik halus anak kelompok B di TK Negeri Pembina Sekayu. Penelitian ini menggunakan penelitian kuantitatif dengan metode penelitian eksperimen. Metode eksperimen yang digunakan dalam penelitian ini menggunakan desai One Group Pretest Posstest Design. Pengumpulan data menggunakan teknik observasi dan dokumentasi. Data yang terkumpul kemudian dianalisis melalui uji hipotesis yang menggunakan ttest dengan tarif signifikan (a) 0,05. Hasil penelitian ini menunjukkan adanya peningkatan sebesar 33,43 dari keadaan pretest dan posttest dengan perolehan rata rata data posttest sebesar 57,60 dengan kriteria "Sesuai" sedangkan rata rata data pretest pengembangan motorik halus anak sebesar 28,9 dengan kriteria "Tidak Sesuai" Pernyataan di atas dapat dibuktikan dari pengujian hipotesis yang diperoleh bahwa thitung>ttabelatau 17,09 1,771 maka H0 ditolakdan Ha diterima. Maka dapat disimpulkan bahwa hipotesis yang menyatakan metode menjiplak dengan media daun pepaya memiliki pengaruh terhadap mengembangkan motorik halus anak kelompok B TK Negeri Pembina Sekayu. Dengan kegiatan menjiplak daun pepaya yang menyenangkan akan membuat anak tidak mudah bosan dan tangan bisa terlatih dengan baik.
Sports participation of children with or without developmental delay: prediction from child and family factors.
Venue: Research in developmental disabilities
Year: 2015

Sports participation is beneficial to health and socioemotional adjustment in youth across development. While there is some evidence indicating lower sports participation for children with developmental delays (DD) as compared with their typically developing (TD) peers, little is known as to the predictors of this differential participation. Given the increased risk of physical and mental health difficulties for children with DD, understanding more about this disparity is important. We examined sports participation in elementary school aged children with or without DD and examined child and family predictors of three indices of sports participation: number of sports and highest relational sport at ages 6 and 8, and consistent sports from 6 to 8. Children with TD were significantly higher on all three indicators. Mother and child factors related significantly to sports participation indices. The number of sports related positively to mother education and positive perceptions and negatively to mother employment. Relational sports were higher in boys, children with higher social skills, and lower behavior problems. In regression analyses at child age 8 that included these other variables, delay status (DD or TD) did not have a significant effect. Perspectives on varying influences on sports participation and implications for intervention are discussed.
Three Mechanisms of Weight Decay Regularization
Venue: ICLR
Year: 2019

Weight decay is one of the standard tricks in the neural network toolbox, but the reasons for its regularization effect are poorly understood, and recent results have cast doubt on the traditional interpretation in terms of $L_2$ regularization. Literal weight decay has been shown to outperform $L_2$ regularization for optimizers for which they differ. We empirically investigate weight decay for three optimization algorithms (SGD, Adam, and K FAC) and a variety of network architectures. We identify three distinct mechanisms by which weight decay exerts a regularization effect, depending on the particular optimization algorithm and architecture: (1) increasing the effective learning rate, (2) approximately regularizing the input output Jacobian norm, and (3) reducing the effective damping coefficient for second order optimization. Our results provide insight into how to improve the regularization of neural networks.
Detect Rumors Using Time Series of Social Context Information on Microblogging Websites
Venue: CIKM
Year: 2015

Automatically identifying rumors from online social media especially microblogging websites is an important research issue. Most of existing work for rumor detection focuses on modeling features related to microblog contents, users and propagation patterns, but ignore the importance of the variation of these social context features during the message propagation over time. In this study, we propose a novel approach to capture the temporal characteristics of these features based on the time series of rumor's lifecycle, for which time series modeling technique is applied to incorporate various social context information. Our experiments using the events in two microblog datasets confirm that the method outperforms state of the art rumor detection approaches by large margins. Moreover, our model demonstrates strong performance on detecting rumors at early stage after their initial broadcast.
Ground Moving Target Imaging and Motion Parameter Estimation With Airborne Dual Channel CSSAR
Venue: IEEE Transactions on Geoscience and Remote Sensing
Year: 2017

This paper deals with the issue of ground moving target imaging and motion parameter estimation with an airborne dual channel circular stripmap synthetic aperture radar (CSSAR) system. Although several methods of ground moving target motion parameter estimation have been proposed for the conventional airborne linear stripmap SAR, they cannot be applied to airborne CSSAR because the range history of a ground moving target for airborne CSSAR is different than that for airborne linear stripmap SAR. In this paper, the moving target's range history for airborne dual channel CSSAR and the target signal model after the displaced phase center antenna processing are derived, and a new ground moving target imaging and motion parameter estimation algorithm is developed. In this algorithm, the estimation of baseband Doppler centroid and its compensation are first performed. Then focusing is implemented in the 2 D frequency domain via phase multiplication, and the target is focused in the SAR image without azimuth displacement due to the compensation of the Doppler shift caused by its motion. Finally, the target's motion parameters are estimated with its Doppler parameters and its position in the SAR image. Numerical simulations are conducted to validate the derived range history and the performance of the proposed algorithm.
Restricted modes: Social media, content classification and LGBTQ sexual citizenship
Venue: New Media Soc.
Year: 2021

In the context of recent controversies surrounding the censorship of lesbian, gay, bisexual, transgender and queer online content, specifically on YouTube and Tumblr, we interrogate the relationship between normative understandings of sexual citizenship and the content classification regimes. We argue that these content classification systems and the platforms' responses to public criticism both operate as norm producing technologies, in which the complexities of sexuality and desire are obscured in order to cultivate notions of a 'good' lesbian, gay, bisexual, transgender or queer sexual citizen. However, despite normative work of classification seeking to distinguish between sexuality and sex, we argue that the high profile failures of these classification systems create the conditions for users to draw attention to, rather than firm, these messy boundaries.
A Loose Coupled Fusion of Inertial and UWB Assisted by a Decision Making Algorithm for Localization of Emergency Responders
Year: 2019

Combining different technologies is gaining significant popularity among researchers and industry for the development of indoor positioning systems (IPSs) These hybrid IPSs emerge as a robust solution for indoor localization as the drawbacks of each technology can be mitigated or even eliminated by using complementary technologies. However, fusing position estimates from different technologies is still very challenging and, therefore, a hot research topic. In this work, we pose fusing the ultrawideband (UWB) position estimates with the estimates provided by a pedestrian dead reckoning (PDR) by using a Kalman filter. To improve the IPS accuracy, a decision making algorithm was developed that aims to assess the usability of UWB measurements based on the identification of non line of sight (NLOS) conditions. Three different data fusion algorithms are tested, based on three different time of arrival positioning algorithms, and experimental results show a localization accuracy of below 1.5 m for a 99th percentile.
Emotional intelligence dimensions as predictors of coping reactions to stress in nursing practitioners.
Venue: Fukushima journal of medical science
Year: 2019

Stress is an inevitable part of nursing life. Nurses need to respond to their stressful environment contents to reduce negative consequences. We examined the role of dimensions of emotional intelligence in predicting coping reactions to stress in 201 college nursing practitioners in this cross sectional study between November 2017 and January 2018. The nurses appraised their own emotions, but they were not sure that appraised others' emotions, regulated their and others' emotions. They could cope with new ideas and faced obstacles. Most of the nurses were moderately stressed. Those nurses were not able to regulate their own emotions were determined to have a higher level of perceived stress. Active coping, positive reframing and acceptance were the most prevalent coping mechanisms. The investigation showed that others' emotion appraisal was a predictor for self blame reaction, and emotion utilization was the predictor for positive reframing and self blame.
HIV HCV co infection and HCV mono infection are associated with subcortical and cortical atrophy
Year: 2011

Co infection with the Human Immunodeficiency Virus (HIV) and Hepatitis C virus (HCV) is a significant problem with almost 30% of HIV infected individuals also HCV co infected  Both viruses have similar routes of transmission and are capable of crossing the blood brain barrier [2] resulting in neuropathological changes and brain inflammation. Cognitive impairment was previously thought to result from cirrhosis associated hepatic encephalopathy. Recent evidence, however, suggests that approximately one third of people with chronic HCV experience cognitive impairment  even in the absence of cirrhosis and that its occurrence is unrelated to other indices of liver function. While the use of neuroimaging techniques such as Magnetic Resonance Spectroscopy have helped to identify metabolite abnormalities in regions such as the basal ganglia, little is known about how the co infection affects other brain regions, including which cortical regions are differentially affected. The current study sought to investigate cortical thickness and subcortical structure volume across a group of HCV/HIV co infected, HCV monoinfected and healthy adults employing an automated method for regional parcellation that uses curvature landmarks and gray matter (GM)/white matter (WM) surface boundary information. We conducted a cortical surface based analysis of the whole cortical mantle obtained from volumetric magnetic resonance imaging (MRI) data. The main objective of the study is to compare measures of cortical thickness and subcortical structure volume between HCV mono infected, HIV/HCV co infected, and healthy controls.
A Short Review on Image Caption Generation with Deep Learning
Year: 2019

Methodologies that utilize Deep Learning offer great potential for applications that automatically attempt to generate captions or descriptions about images. Image captioning is considered to be one of the intellectually challenging problems in imaging science. The application domains include: automatic caption (or description) generation for images for people who suffer from various degrees of visual impairment; the automatic creation of metadata for images (indexing) for use by search engines; general purpose robot vision systems; and many others. Each of these application domains can positively and significantly impact many other task specific applications. This paper is not meant to be a comprehensive review of image captioning; rather, it is a concise review of image captioning methodologies based on deep learning, strengths and limitations, the datasets and the evaluation metrics used in automatic image captioning. Finally, a quick discussion about the software and hardware requirements for implementing an image captioning method is presented.
An Analysis of Prerequisite Skills for Reading Comprehension
Year: 2016

In this paper, we focus on the synthetic understanding of documents, specifically reading comprehension (RC) A current problem with RC is the need for a method of analyzing the RC system performance to realize further development. We propose a methodology for examining RC systems from multiple viewpoints. Our methodology consists of three steps: define a set of basic skills used for RC, manually annotate questions of an existing RC task, and show the performances for each skill of existing systems that have been proposed for the task. We demonstrated the proposed methodology by annotating MCTest, a freely available dataset for testing RC. The results of the annotation showed that answering RC questions requires combinations of multiple skills. In addition, our defined RC skills were found to be useful and promising for decomposing and analyzing the RC process. Finally, we discuss ways to improve our approach based on the results of two extra annotations.
Agricultural Monitoring and Controlling System Using Wireless Sensor Network
Year: 2019

In recent years, the automation in agricultural has become a significant issue. The parameters like soil pH, soil moisture, light intensity, temperature, and humidity play a crucial role to increase the productivity of the crops. The continuous monitoring of these environmental and soil parameters helps in taking profitable decisions. For effective utilization of resources like irrigation water and fertilizers, sprinkling of water can be done according to the soil moisture levels in the soil and spraying of the fertilizers can be done according to the soil pH value. Health monitoring of the plants is crucial for sustainable agriculture. Camera module is used to acquire the images of the plants, and image processing techniques can be applied on the acquired images for pest's detection and disease detection. The system gathers the camera data and sensor data and analyzes continuously in a feedback loop which actuates the control devices according to the threshold values.
Block SDoTCloud: Enhancing Security of Cloud Storage through Blockchain based SDN in IoT Network
Venue: 2020 2nd International Conference on Sustainable Technologies for Industry 4.0 (STI)
Year: 2020

Software Defined Networking (SDN) and Blockchain (BC) are the most recent research trends in emerging technology. These technologies have currently been used in the IoT networking environment for various purposes, such as protection, stability, scalability, confidentiality, and so on. Such techniques can be used in this regard to effectively provide security, privacy, and confidentiality to cloud storage. In this paper, the authors proposed Block SDoTCloud architecture to significantly enhance security within the cloud storage network. In order to give the cloud storage environment the requisite facilities, two leading technologies, i.e. SDN and Blockchain were applied. The centralized SDN controller provided the proposed model with tremendous reliability, versatility, and load balancing in one side while on the other hand, a distributed Blockchain created a deep trust between the transaction levels resulting into effective protection for the presented scheme. The efficiency of the proposed architecture is justified through close observation of simulation with a careful selection of relevant parameters.
Self Control in Decision Making Involves Modulation of the vmPFC Valuation System
Venue: Science
Year: 2009

Dieter's Dilemma The ability to exercise self control is central to human success and well being. However, little is known about the neurobiological underpinnings of self control and how or why these neural mechanisms might differ between successful and unsuccessful decision makers. Hare et al. (p. 646) used brain imaging in a dieting population undergoing real life decisions between a healthy or a tempting, yet nutritionally inferior, choice of food. Activity in the ventromedial prefrontal cortex correlated with the value of the stimulus, termed goal value. Importantly, this activity integrated both health and taste values in individuals who were able to exert self control in their choices, while reflecting only taste in those unable to exert self control. The neural circuitry underlying choice and self control is revealed by modeling and brain imaging. Every day, individuals make dozens of choices between an alternative with higher overall value and a more tempting but ultimately inferior option. Optimal decision making requires self control. We propose two hypotheses about the neurobiology of self control: (i) Goal directed decisions have their basis in a common value signal encoded in ventromedial prefrontal cortex (vmPFC) and (ii) exercising self control involves the modulation of this value signal by dorsolateral prefrontal cortex (DLPFC) We used functional magnetic resonance imaging to monitor brain activity while dieters engaged in real decisions about food consumption. Activity in vmPFC was correlated with goal values regardless of the amount of self control. It incorporated both taste and health in self controllers but only taste in non self controllers. Activity in DLPFC increased when subjects exercised self control and correlated with activity in vmPFC.
Nexus approaches to global sustainable development
Venue: Nature Sustainability
Year: 2018

Many global challenges, though interconnected, have been addressed singly, at times reducing one problem while exacerbating others. Nexus approaches simultaneously examine interactions among multiple sectors. Recent quantitative studies have revealed that nexus approaches can uncover synergies and detect trade offs among sectors. If well implemented, nexus approaches have the potential to reduce negative surprises and promote integrated planning, management and governance. However, application and implementation of nexus approaches are in their infancy. No studies have explicitly quantified the contributions of nexus approaches to progress toward meeting the Sustainable Development Goals. To further implement nexus approaches and realize their potential, we propose a systematic procedure and provide perspectives on future directions. These include expanding nexus frameworks that consider interactions among more sectors, across scales, between adjacent and distant places, and linkages with Sustainable Development Goals; incorporating overlooked drivers and regions; diversifying nexus toolboxes; and making these strategies central in policy making and governance for integrated Sustainable Development Goal implementation.Sustainability challenges, such as feeding people with fewer resources, involve challenges at the nexus of multiple issues, such as food, water and energy. This Review explores such nexus approaches, surveying their use towards sustainable development challenges, discussing examples, and proposing a systematic procedure and future directions.