nils-herrmann/clean_scirepeval huggingface dataset:
Facial keypoints detection using Neural Network
Year: 2016

Nowadays, facial keypoints detection has become a very popular topic and its applications include Snapchat, How old are you, have attracted a large number of users. The objective of facial keypoints detection is to find the facial keypoints in a given face, which is very challenging due to very different facial features from person to person. The idea of deep learning has been applied to this problem, such as neural network and cascaded neural network. And the results of these structures are significantly better than stateof the art methods, like feature extraction and dimension reduction algorithms. In our project, we would like to locate the keypoints in a given image using deep architectures to not only obtain lower loss for the detection task but also accelerate the training and testing process for real world applications. We have constructed two basic neural network structures, one hidden layer neural network and convolutional neural network as our baselines. And we have proposed an approach to better locate the coordinates of facial keypoints with introduced features other than raw input. Specifically, we use a block of pretrained Inception Model to extract intermediate features and using different deep structures to compute the final output vector. The experiments results have shown the effectiveness of deep structures for facial keypoints detection tasks, and using the pretrained Inception Model has slightly improved the performance of detection compared to baseline methods.
Apixaban in Comparison With Warfarin in Patients With Atrial Fibrillation and Valvular Heart Disease: Findings From the Apixaban for Reduction in Stroke and Other Thromboembolic Events in Atrial Fibrillation (ARISTOTLE) Trial
Venue: Circulation
Year: 2015

Apixaban is approved for the prevention of stroke and systemic embolism in patients with nonvalvular atrial fibrillation. However, the Apixaban for Reduction in Stroke and Other Thromboembolic Events in Atrial Fibrillation (ARISTOTLE) trial included a substantial number of patients with valvular heart disease and only excluded patients with clinically significant mitral stenosis or mechanical prosthetic heart valves.  We compared the effect of apixaban and warfarin on rates of stroke or systemic embolism, major bleeding, and death in patients with and without moderate or severe valvular heart disease using Cox proportional hazards modeling. Of the 18 201 patients enrolled in ARISTOTLE, 4808 (26.4% had a history of moderate or severe valvular heart disease or previous valve surgery. Patients with valvular heart disease had higher rates of stroke or systemic embolism and bleeding than patients without valvular heart disease. There was no evidence of a differential effect of apixaban over warfarin in patients with and without valvular heart disease in reducing stroke and systemic embolism (hazard ratio [HR] 0.70; 95% confidence interval [CI] 0.51 0.97 and HR, 0.84; 95% CI 0.67 1.04; interaction P=0.38) causing less major bleeding (HR, 0.79; 95% CI, 0.61 1.04 and HR, 0.65; 95% CI, 0.55 0.77; interaction P=0.23) and reducing mortality (HR, 1.01; 95% CI, 0.84 1.22 and HR, 0.84; 95% CI, 0.73 0.96; interaction P=0.10)  More than a quarter of the patients in ARISTOTLE with nonvalvular atrial fibrillation had moderate or severe valvular heart disease. There was no evidence of a differential effect of apixaban over warfarin in reducing stroke or systemic embolism, causing less bleeding, and reducing death in patients with and without valvular heart disease.
Downlink Cellular Network Analysis With LOS/NLOS Propagation and Elevated Base Stations
Venue: IEEE Transactions on Wireless Communications
Year: 2018

In this paper, we investigate the downlink performance of dense cellular networks with elevated base stations (BSs) using a channel model that incorporates line of sight (LOS)/non line of sight (NLOS) propagation into both small scale and large scale fading. Modeling LOS fading with Nakagami <inline formula> <tex math notation="LaTeX"$m$ /tex math>/inline formula> fading, we provide a unified framework based on stochastic geometry that encompasses both closest and strongest BS association. This paper is particularized to two distance dependent LOS/NLOS models of practical interest. Considering the effect of LOS propagation alone, we derive closed form expressions for the coverage probability with Nakagami <inline formula> <tex math notation="LaTeX"$m$ /tex math>/inline formula> fading, showing that the performance for strongest BS association is the same as in the case of Rayleigh fading, whereas for closest BS association it monotonically increases with the shape parameter <inline formula> <tex math notation="LaTeX"$m$ /tex math>/inline formula> Then, focusing on the effect of elevated BSs, we show that network densification eventually leads to near universal outage even for moderately low BS densities: in particular, the maximum area spectral efficiency is proportional to the inverse of the square of the BS height.
Reducing Transformer Depth on Demand with Structured Dropout
Venue: ICLR
Year: 2020

Overparameterized transformer networks have obtained state of the art results in various natural language processing tasks, such as machine translation, language modeling, and question answering. These models contain hundreds of millions of parameters, necessitating a large amount of computation and making them prone to overfitting. In this work, we explore LayerDrop, a form of structured dropout, which has a regularization effect during training and allows for efficient pruning at inference time. In particular, we show that it is possible to select sub networks of any depth from one large network without having to finetune them and with limited impact on performance. We demonstrate the effectiveness of our approach by improving the state of the art on machine translation, language modeling, summarization, question answering, and language understanding benchmarks. Moreover, we show that our approach leads to small BERT like models of higher quality compared to training from scratch or using distillation.
Practices and Outcomes of Communicative Language Teaching in Higher Secondary Schools in Rural Bangladesh
Venue: Qualitative Research in Education
Year: 2019

This paper aims to provide insights into the challenges of Communicative Language Teaching (CLT) practice at higher secondary level in Bangladeshi rural settings. Employing qualitative approach, in depth interviews were conducted with 24 rural English language teachers to explore the problems they face in CLT implementation. The interview data were scrutinized using thematic analysis. Three major themes emerge from the analysis which are: (i) pedagogical factors; (ii) contextual factors; and (iii) personal factors that obstruct CLT implementation to reach at its expected outcome. The paper reveals the gap between the objectives of the present ELT curriculum and teachers' practices. The teachers are optimistic with CLT approach for improving students' English skill, but they need viable support to overcome the factors working as the barriers of its proper implementation. Based on the teachers' suggestion, the study recommends the aligning of curriculum and test format, and also the training and logistic support for the teachers to overcome the issues surrounding the CLT implementation in the rural context of Bangladesh.
Major, Minor and Trace Elements Existence in Surface Sediments from Gwadar to Jiwani Coastal Areas of Pakistan
Year: 2020

06 June, 2020   This study was carried out along the western coastal parts of Balochistan from Gwadar east Bay to Jiwani. The local anomaly of major, minor and trace elements were studied in this area. Zone I (Gwadar east and west Bay) indicates that calcium oxide, potassium oxide and titanium dioxide have higher or average concentrations, while ZoneII indicates higher or average concentrations of calcium oxide, titanium dioxide and ferric oxide. Trace elements zirconium, chromium, europium, strontium, and copper are in higher or average concentrations in both zones. Gwadar west Bay and Jiwani are intermediate in carbonate, which show a high degree of maturity, suggesting the high rate of weathering in the source area. Sediment samples from Ganz, Jiwani, and Gwadar east Bay represent the quartz rich fields in this region. Gwadar east Bay has a positive correlation between aluminium oxide and potassium oxide which suggests that abundance of potassium oxide is controlled by variation in K feldspar contents. A positive correlation between strontium and calcium oxide suggests that strontium is associated with calcium oxide in biogenic carbonate material in Gwadar west Bay. The positive correlation between aluminium oxide, copper, and zinc in Pishukan indicates that the abundance of these elements is due to limited silt and clay fractions present in sediments. Enrichment factor shows that chromium and strontium are probably of anthropogenic origin in this region.
A Visible Infrared Compatible Camouflage Photonic Crystal with Enhanced Emission in 5~8 mm
Year: 2021

Because of surface structural constraint and thermal management requirement, visible infrared compatible camouflage is still a great challenge. In this study, we introduce a 2D periodic aperture array into ZnO/Ag/ZnO film to realize visible infrared compatible camouflage with a performance of thermal management by utilizing the extraordinary optical transmission in a dielectric/metal/dielectric (D/M/D) structure. Because of the high visible transmittance of the D/M/D structure, when applied on a visible camouflage coating, the beneath coating can be observed, realizing arbitrary visible camouflage. Due to the perforated Ag layer, both low emittances in 3~5 mm, 8~14 mm for infrared camouflage and high emittance in 5~8 mm for heat dissipation by radiation are achieved theoretically and experimentally. The fabricated photonic crystal exhibits high temperature infrared camouflage in two atmospheric windows. With the same heating power of 0.40 W/cm2, this photonic crystal is 12.2 cooler than a sample with a low emittance surface. The proposed visible infrared compatible camouflage photonic crystal with the performance of thermal management provides a guideline on coordinated control of light and heat, indicating a potential application in energy thermal technologies.
Forensic analysis in social networking applications
Year: 2021

In digital forensics, social media is promptly evolving as a frontier technology. The digital information may explore the magazine contents on social media to offer incredible sustenance in the process of criminal investigations. Conversely, discovering the potential evidence using social media is not presented as a proof in any court of law. The forensic process involves social media to collect the evidence of appropriate legal, scientific, and discretion rights of the individuals. The legal process challenges practitioners and investigators to deal with the vibrant and dissimilar nature of social media. The forensic investigators engage in the service of effective investigation to collect, conduct, and manage the legal social contents provided with sophisticated tools. This chapter conducts the current state of existence, namely, acquisition of legal evidence, case admissibility, jurisdiction, and services in social network forensics. It also provides a part of direct challenges to collect, analyze, present, and validate the evidences of social media in lawful actions. This work studies on the current state of the art approaches to highlight the open issues that deal with the mandatory challenges of social networks.
Policy cognition of potential consumers of new energy vehicles and its sensitivity to purchase willingness
Year: 2020

The implementation effect of the marketing policies of new energy vehicles is based on a high policy cognition of potential consumers. By using questionnaire survey and experimental research and dividing the consumption promotion policies of new energy vehicles into two categories: "supply" and "demand," this study analyzes potential consumers' policy cognition and its sensitivity to purchase willingness. Results indicate that the policy cognition and purchase willingness of the potential consumers of new energy vehicles are currently at a low level. Policy announcements can also help increase consumer policy cognition and purchase willingness. The sensitivity to the purchase willingness of potential consumers' policy cognition of either "supply" or "demand" side is very significant, and the sensitivity of the "demand side" policy cognition is greater than that of the "supply side" policy. The "purchase subsidy" policy in the "demand side" has the highest sensitivity, whereas the "tax reduction" policy has the lowest sensitivity. The "infrastructure" policy in the "supply side" has the highest sensitivity, whereas the "financial support" and "regulatory improvement" policies are less sensitive. The policy cognition of "purchase subsidy" and "priority right" in the "demand side" and the policy cognition of "infrastructure" in the "supply side" do not match the policy sensitivity. To this end, comprehensively improving the policy cognition of potential consumers in various policies of new energy vehicles and effectively changing the current situation in which the number of the marketing policies of new energy vehicles is promulgated are necessary. However, the policy cognition is low, and the policy "hanging" phenomenon is relatively common. Changing the one sidedness and incompleteness of the current "subsidy retreat" policy propaganda process and guide consumers to correctly recognize that "subsidy subsidence" can evidently increase new energy vehicle consumers' "gain of acquisition." To change the mismatch status of the policy cognition in and policy sensitivity to new energy vehicles, the policy must focus on improving the policy cognition of "priority right" in certain non limited cities and intensify the promotion and implementation of incentive policies for the construction and optimization of "infrastructures" such as charging piles of new energy vehicles.
Demand Side Management and the Participation in Consecutive Energy Markets A Multistage Stochastic Optimization Approach
Venue: 2018 15th International Conference on the European Energy Market (EEM)
Year: 2018

The integration of fluctuating renewable energies leads to higher price fluctuations in day ahead markets, consequently the incentives for the activation of flexible loads increase. Even if relative forecasting errors decrease, the absolute forecasting error of renewable power production is expected to increase, therefore the demand for reserve power will rise. To fully exploit the economic potential of evolving energy markets through the utilization of production process flexibility, multiple markets need to be considered at the same time. Production planning and the participation in the reserve markets can be formulated as a multistage Stochastic Mixed Integer Linear Programming (SMILP) problem that minimizes the expected total costs, which consists of cost for purchasing power subtracted by the revenues from offering reserve energy. The presented approach incorporates a production process model which considers uncertainties of spot and reserve market prices in terms of a stochastic process.
A Tutorial on Modeling fMRI Data using a General Linear Model
Year: 2017

Functional magnetic resonance imaging (fMRI) has quickly become the most commonly used method among scientists in many disciplines for examining brain function. However, there are challenges in translating the underlying statistics from specialized fMRI analysis tools to a more general statistical language such as R. This paper translates key terminology and is a self contained tutorial of how to model a single voxel of fMRI data using the nlme mixed effects framework in R. This translation is not only interesting pedagogically, but many advanced statistical modeling techniques of interest in psychology, for example, growth models that are implemented using structural equation modeling, are unavailable for fMRI. This need for more modeling flexibility to advance the study of developmental cognitive neuroscience, and similarly the trajectories of aging and neurodegenerative change, also motivates this tutorial as a first step to understanding how to script more complicated analyses. Finally, this tutorial is written using Rmarkdown (see Supplemental Materials) so that it is itself an example for writing reproducible analysis.
Understanding the emotions behind social images: Inferring with user demographics
Venue: 2015 IEEE International Conference on Multimedia and Expo (ICME)
Year: 2015

Understanding the essential emotions behind social images is of vital importance: it can benefit many applications such as image retrieval and personalized recommendation. While previous related research mostly focuses on the image visual features, in this paper, we aim to tackle this problem by "linking inferring with users' demographics" Specifically, we propose a partially labeled factor graph model named D FGM, to predict the emotions embedded in social images not only by the image visual features, but also by the information of users' demographics. We investigate whether users' demographics like gender, marital status and occupation are related to emotions of social images, and then leverage the uncovered patterns into modeling as different factors. Experiments on a data set from the world's largest image sharing website Flickr1 confirm the accuracy of the proposed model. The effectiveness of the users' demographics factors is also verified by the factor contribution analysis, which reveals some interesting behavioral phenomena as well.
Induction of neutralising antibodies and cellular immune responses against SARS coronavirus by recombinant measles viruses
Venue: Vaccine
Year: 2008

Live attenuated recombinant measles viruses (rMV) expressing a codon optimised spike glycoprotein (S) or nucleocapsid protein (N) of severe acute respiratory syndrome associated coronavirus (SARS CoV) were generated (rMV S and rMV N) Both recombinant viruses stably expressed the corresponding SARS CoV proteins, grew to similar end titres as the parental strain and induced high antibody titres against MV and the vectored SARS CoV antigens (S and N) in transgenic mice susceptible to measles infection. The antibodies induced by rMV S had a high neutralising effect on SARS CoV as well as on MV. Moreover, significant N specific cellular immune responses were measured by IFN g ELISPOT assays. The pre existence of anti MV antibodies induced by the initial immunisation dose did not inhibit boost of anti S and anti N antibodies. Immunisations comprising a mixture of rMV S and rMV N induced immune responses similar in magnitude to that of vaccine components administered separately. These data support the suitability of MV as a bivalent candidate vaccine vector against MV and emerging viruses such as SARS CoV.
Does the Dark Triad of Personality Predict Corrupt Intention? The Mediating Role of Belief in Good Luck
Venue: Front. Psychol.
Year: 2016

The current study is the first attempt to examine the association between the Dark Triad of personality (i.e. Machiavellianism, narcissism, and psychopathy) and corruption through a mediator belief in good luck. Based on Ajzen's theory of planned behavior, we assumed that individuals with Dark Triad would be more likely to engage in corruption as a result of belief in good luck. In Study 1, a set of hypothetical scenarios was used to assess the bribe offering intention and the corresponding belief in good luck. Results indicated that while the Dark Triad of personality positively predicted bribe offering intention, it was mediated by the belief in good luck in gain seeking. In Study 2, we presented participants with some hypothetical scenarios of bribe taking and the corresponding belief in good luck. Findings revealed that the Dark Triad of personality was positively related to bribe taking intention; the relationship between narcissism and bribe taking intention, and that between psychopathy and bribe taking intention was mediated by the belief in good luck in penalty avoidance. However, this belief in good luck did not mediate the relationship between Machiavellianism and bribe taking intention. These results hold while controlling for demographic variables, dispositional optimism, and self efficacy. Taken together, this study extended previous research by providing evidence that belief in good luck may be one of the reasons explaining why people with Dark Triad are more likely to engage in corruption regardless of the potential outcomes. Theoretical and practical implications were discussed.
The Meaning, Antecedents and Outcomes of Employee Engagement: A Narrative Synthesis
Year: 2017

The claim that high levels of engagement can enhance organizational performance and individual well being has not previously been tested through a systematic review of the evidence. To bring coherence to the diffuse body of literature on engagement, the authors conducted a systematic synthesis of narrative evidence involving 214 studies focused on the meaning, antecedents and outcomes of engagement. The authors identified six distinct conceptualizations of engagement, with the field dominated by the Utrecht Group's 'work engagement' construct and measure, and by the theorization of engagement within the 'job demands resources' framework. Five groups of factors served as antecedents to engagement: psychological states; job design; leadership; organizational and team factors; and organizational interventions. Engagement was found to be positively associated with individual morale, task performance, extra role performance and organizational performance, and the evidence was most robust in relation to task performance. However, there was an over reliance on quantitative, cross sectional and self report studies within the field, which limited claims of causality. To address controversies over the commonly used measures and concepts in the field and gaps in the evidence base, the authors set out an agenda for future research that integrates emerging critical sociological perspectives on engagement with the psychological perspectives that currently dominate the field.
Can recurrent neural network enhanced EEGNet improve the accuracy of ERP classification task? An exploration and a discussion
Year: 2020

Autism Spectrum Disorder (ASD) is one of the most common developmental conditions with one in 160 children worldwide being diagnosed. Both Virtual Reality (VR) and Brain Computer Interfaces (BCI) are believed to be beneficial to enhance communication for people with ASD. However, BCI solutions for ASD are not yet commercially available. This is partly due to the current challenge with long and fatiguing calibration sessions with conventional gel based BCI. BCI using active electrodes hold the potentials to resolve part of this issue but might increase the challenges to classification of tasks due to reduced signal quality. The dataset considered in this paper, available from the IFMBE Scientific Challenge (IFMBE SC) of 15 participants with ASD, contained data captured using electroencephalogram (EEG) headsets, from g.Nautilus system, with active electrodes in a VR environment. Known approaches, such as the Support Vector Machine (SVM) Linear Discriminant Analysis (LDA) Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) have demonstrated potential solutions to enhancing current algorithms. Nevertheless, in this paper, a novel Recurrent Neural Network (RNN) solution with several pre processing methods was introduced. Our results show that our novel RNN solution achieved 92.59% accuracy, an improvement with 0.61 percentage point from the previously best reported algorithm during the IFMBE SC. Furthermore, with a standard 80% 20% initial separation strategy, our solution also generated a compatible accuracy at 89.92%