nils-herrmann/clean_scirepeval huggingface dataset:
Students' Reflections on Vocabulary Learning through Synchronous and Asynchronous Games and Activities
Year: 2018

Many learners are now quite digitally skilled. However, this does not entail that they know how to learn through digital technologies. Therefore, establishing an interactive virtual learning platform that connects everyone together in a classroom environment and helping learners become familiar with such media might serve a set of purposes in any educational setting. Today, with the advances in web based learning technologies, a hybrid teaching methodology has become widespread: blended learning. It is a term used to describe the way e learning is being combined with traditional classroom methods and independent study. Educators design online materials and utilize them in synchronous or asynchronous ways to suit the convenience of learners and instructors and program demands. In this study with a group of intermediate level English language learners at the School of Foreign Languages of an English medium state university, the goal was to enhance students' vocabulary learning performance by using synchronous and asynchronous games and activities that will activate and maintain intrinsic motivation in an effort to teach parts of speech and collocations over a period of eight weeks. The data consisted of a survey of students' reflections on their vocabulary learning experiences through digital games and activities. The findings were discussed with respect to the efficiency of incorporating synchronous and asynchronous learning materials.
Video Prediction with Temporal Spatial Attention Mechanism and Deep Perceptual Similarity Branch
Venue: 2019 IEEE International Conference on Multimedia and Expo (ICME)
Year: 2019

Video prediction is a challenging but worth exploring task in computer vision. Different from image analysis, the challenge of video analysis derives from more complicated dependencies in time as well as in space. In this paper, we propose a Temporal Spatial Attention Mechanism (TSAM) to capture not only spatial appearance dependencies but also temporal dynamic dependencies in video sequence. The TSAM is transplantable for existing networks and allows the long range dependency modeling for various video analysis tasks (we take video prediction for specific experiments in this paper) Besides, we propose an additional Deep Perceptual Similarity Branch (DPSB) to encourage a better approximation to the ground truth in high level feature space, laying the foundation for frame generation. Extensive experiments on KTH, Penn Action and UCF 101 datasets demonstrate that our model performs quite competitively across diverse natural visual scenes, even in long term video prediction.
The Construction and Validation of a Sustainable Tourism Development Evaluation Model
Venue: International journal of environmental research and public health
Year: 2020

As climate change, food crises, sustainable development, and ecological conservation gain traction, the revival of traditional fishing villages has become an important governmental policy for Taiwan. To reduce cognitive bias, the choice experiment method was applied to construct an attribute function in fishing village tourism coupled with virtual reality headsets. Conditional logit and random parameter logit models were employed to estimate tourism utility functions. Moreover, a latent class model was employed to determine whether hetxerogeneous preferences regarding fishing village travel existed. The sampling sites were distributed across the Dongshi area. In total, 612 tourists and 170 local residents were interviewed. After incomplete questionnaires were removed, 816 valid questionnaires remained, representing 95.83% of the total questionnaires. Older residents and residents with shorter histories of education were inclined to increase land development and utilization by reducing natural landscapes; tourists preferred preserving landscapes and preventing land development. Residents with more education believed that local landscape imagery was essential. Tourists who were more educated, with high incomes, and those who were older believed that a selling platform incorporating local industries and products within the villages would be attractive for other tourists.
Pedometer steps in primary school aged children: a comparison of school based and out of school activity.
Venue: Journal of science and medicine in sport
Year: 2006

While studies of the physical activity habits of New Zealand children have been carried out, the findings have been restricted by the use of proxy and self report measures and limited to total overall daily activity. Objective measurement of children's in school and out of school physical activity using pedometry is likely to provide more accurate data on habitual daily activity. To date, no such data are available for New Zealand children. In the present study, children from school years 1 6 (girls, n=46; boys, n=45) at a New Zealand primary school wore a Yamax Digiwalker SW 200 pedometer to record school based and out of school steps over a 3 day period. Mean daily steps for the overall sample were 14 333 (S.D.=4110) Boys (X=15 606; S.D.=4601) were significantly more active than girls (X=13 031; S.D.=3079) (p=.00) Mean steps were also significantly higher in older age groups for both boys (p=.03) and in particular, girls (p=.00) Of note, for the overall sample, steps taken out of school made up 52.4% of total daily steps. Girls (53.6% and boys (51.3% took a similar proportion of their overall daily steps outside of the school environment. While a significant difference was found between the most and least active tertiles in steps taken during both during school hours (p=.00) and outside of school hours (p=.00) the most active third of the sample completed significantly more of their daily steps outside of school (55.1% than did their least active (46.7% counterparts (p=.00) These results suggest that physical activity outside of the school environment is a key contributor to a child's overall level of physical activity, reinforcing the need for interventions targeting the family and community as well as the school environment.
A Flexible Multilayered Dry Electrode and Assembly to Single Lead ECG Patch to Monitor Atrial Fibrillation in a Real Life Scenario
Venue: IEEE Sensors Journal
Year: 2020

The aim of this study is to propose a novel flexible dry electrode and assembly to a wearable single lead ECG patch for continuously monitoring atrial fibrillation (AF) in a real life scenario. Silver ink was screen printed on a flexible polyurethane substrate to fabricate the multilayered dry electrode. A wearable recording patch with three dry electrodes was developed to collect single lead ECG signal, particularly to provide a robust anti artifact capability by employing a driven right leg electrode as reference. The characterization of the dry electrode based patch was quantified by combing the engineering measurement on raw signal quality and the clinical evaluation of automatical AF detection. A total of 50 patients (age 70 11) were enrolled. Each patient wore one or two patches and a standard Holter simultaneously for a continuous 24 hours ECG recording in a real life scenario. The averaged R wave peak amplitude, signal to noise ratio, and signal to artifact ratio were 0.75 1.47 mV, 3 6 dB, and 3 7, respectively. Compared with two different cardiologists' interpretations of the ECGs from dry electrodes, an offline detection algorithm automatically interpreted AF with the overall accuracy 93.57% and 85.94% during the stationary and movement states, respectively. The obtained results demonstrated the ability of using a flexible screen printed dry electrode and assembly to a wearable patch to effectively record ECG signal in patients even disturbed by body motions, which may establish its clinical validity and applicability.
Two factor authentication for voice assistance in digital banking using public cloud services
Venue: 2020 10th International Conference on Cloud Computing, Data Science Engineering (Confluence)
Year: 2020

This paper presents the results of a research on using voice assistants to enable financial and commercial operations which require authentication with increased level of security. Several prototypes of authentication service with two factor authentication for the purpose of voice controlled digital banking and online payments have been developed at the Cyber Security Research Centre of London Metropolitan University. They utilize the power of the cloud technology and the attractiveness of the devices equipped with voice assistant software to provide secure method for two factor authentication, which meets the recent standards for security of the operations in digital banking.
The Effect of Performance Appraisal on Employee Motivation and Productivity
Year: 2020

Performance refers to the degree of accomplishment of the tasks that make up an individual's job it indicates how well an individual's fulfilling the job demands. When properly conducted performance appraisals not only let the employee how well he is performing but should also influence the employee's future level of effort, activities, results and task direction. It provides information about the performance ranks basing on which decisions regarding salary fixation, confirmation, promotion, transfer and demotion are taken. It also provides feedback information about the level of achievement and behavior of subordinate. This information helps to review the performance of the subordinate, rectifying performance deficiencies and to set new standards of
Quality Evaluation and Analysis of Fresh Meat Product Distribution of Supermarkets in Beijing
Year: 2010

The paper presents the result of a field investigation on 79 large scale chain supermarkets in Beijing concerning the safety and quality of fresh meat products in distribution activities.It includes findings on the qualification rate,replenishment,distribution modes,unloading instruments,disinfection and sanitation,and ambient pre refrigeration,etc of the supermarkets and,through analyzing in depth the uncovered hazards,summarizes the four key factors influencing the quality of fresh meat distribution process,i.e.,temperature,duration,disinfection,and inspection.At the end,some suggestions and measures for improvement and correction are given.
A combined Importance Sampling and Kriging reliability method for small failure probabilities with time demanding numerical models
Venue: Reliab. Eng. Syst. Saf.
Year: 2013

Applying reliability methods to a complex structure is often delicate for two main reasons. First, such a structure is fortunately designed with codified rules leading to a large safety margin which means that failure is a small probability event. Such a probability level is difficult to assess efficiently. Second, the structure mechanical behaviour is modelled numerically in an attempt to reproduce the real response and numerical model tends to be more and more time demanding as its complexity is increased to improve accuracy and to consider particular mechanical behaviour. As a consequence, performing a large number of model computations cannot be considered in order to assess the failure probability. To overcome these issues, this paper proposes an original and easily implementable method called AK IS for active learning and Kriging based Importance Sampling. This new method is based on the AK MCS algorithm previously published by Echard et al.  an active learning reliability method combining Kriging and Monte Carlo simulation. Structural Safety 2011;33(2):145 54] It associates the Kriging metamodel and its advantageous stochastic property with the Importance Sampling method to assess small failure probabilities. It enables the correction or validation of the FORM approximation with only a very few mechanical model computations. The efficiency of the method is, first, proved on two academic applications. It is then conducted for assessing the reliability of a challenging aerospace case study submitted to fatigue.
DESIGNING A SECURE BLOCKCHAIN BASED TRADING PLATFORM FOR INTERNET OF THINGS
Year: 2019

Blockchain, a distributed database technology attracts intensive attention recently. It was first described in 1991 by Stuart Haber and W. Scott Stornetta, in order to prevent tampering for document timestamps. After that, Satoshi Nakamoto conceptualized this technology with his work, a peer to peer electronic cash system named as Bitcoin. Due to its authenticity, user anonymity and data immutability; this technology has also been applied to different areas ranging from finance, supply chain managements to social services. In this paper, we propose a blockchain based application for trading data, collected from IoT devices. Our application enables the sellers to commercialize their data in exchange of the currency produced by the application. The application also utilizes smart contracts to establish trust between both sides of the exchange. Furthermore, we discuss the security and reliability of the system components
Lung nodule malignancy classification in chest computed tomography images using transfer learning and convolutional neural networks
Venue: Neural Computing and Applications
Year: 2018

Lung cancer accounts for more than 1.5 million deaths worldwide, and it corresponded to 26% of all deaths due to cancer in 2017. However, lung computer aided diagnosis systems developed to identify lung cancer at early stages are increasing survival rates. This study explores the performance of deep transfer learning from non medical images on lung nodule malignancy classification tasks in order to improve such systems. Initially, the 1018 chest computed tomography (CT) examinations and medical annotations from the LIDC/IDRI were processed. Then, several convolutional neural networks (VGG16, VGG19, MobileNet, Xception, InceptionV3, ResNet50, InceptionResNetV2, DenseNet169, DenseNet201, NASNetMobile and NASNetLarge) were built, trained on the ImageNet dataset, converted into feature extractors and applied on the LIDC/IDRI nodule images. Following this, each set of deep features was submitted to 10 fold cross validations with naive Bayes, multilayer perceptron, support vector machine (SVM) K nearest neighbors KNN and random forest classifiers. Finally, the evaluation metrics accuracy (ACC) area under the curve (AUC) true positive rate (TPR) precision (PPV) and F1 score of each cross validation average result were computed and compared. The results showed that the deep feature extractor based on the ResNet50 and the SVM RBF classifier, achieved an AUC metric of 93.1% (the highest value not only among the evaluated combinations, but also among the related works in the literature evaluated) a TPR of 85.38% an ACC of 88.41% a PPV of 73.48% and an F1 score of 78.83% Based on these results, deep transfer learning proves to be a relevant strategy to extract representative features from lung nodule CT images.
Clinical endodontic management during the COVID 19 Pandemic: A literature review and clinical recommendations.
Venue: International endodontic journal
Year: 2020

The spread of the severe acute respiratory syndrome coronavirus 2 (SARS CoV 2) has resulted in millions of confirmed cases and hundreds of thousands of deaths. Despite all efforts to contain the spread of the disease, the number of infections and deaths continue to rise, particularly in some regions. Given its presence in the salivary secretions of affected patients, and the presence of many reported asymptomatic cases that have tested positive for COVID 19, dental professionals, including Endodontists, are at high risk of becoming infected if they do not take appropriate precautions. As of today, there are no predictable treatments or approved vaccines that can protect the public and health care professionals from the virus; however, there is speculation that a vaccine might be available sometime in 2021. Until then, general dentists and Endodontist will need to be able to treat emergency patients in order to relieve pressure on emergency clinics in hospitals or local community hubs. In addition, as the pandemic continues, strategies to manage patients will need to evolve from a palliative to a more permanent/definitive treatment approach. In this article, an update on the treatment considerations for dental care in general is provided, as well as a discussion on the available endodontic guidelines reported in the literature. Recommendations on clinical management of endodontic emergencies are proposed.
Sedimentology of a "nonactualistic" Middle Ordovician tidal influenced reservoir in the Murzuq Basin (Libya)
Venue: AAPG Bulletin
Year: 2019

The subsurface of the highly productive Murzuq Basin in southwest Libya remains poorly understood. As a consequence, a need exists for detailed sedimentological studies of both the oil prone Mamuniyat Formation and Hawaz Formation reservoirs in this area. Of particular interest in this case is the Middle Ordovician Hawaz Formation, interpreted as an excellent example of a "nonactualistic," tidally influenced clastic reservoir that appears to extend hundreds of kilometers across much of the North African or Saharan craton. The Hawaz Formation comprises 15 characteristic lithofacies grouped into 7 correlatable facies associations distributed in broad and laterally extensive facies belts deposited in a shallow marine, intertidal to subtidal environment. Three main depositional sequences and their respective systems tracts have also been identified. On this basis, a genetic based stratigraphic zonation scheme has been proposed as a tool to improve subsurface management of this reservoir unit. A nonactualistic sedimentary model is proposed in this work with new ideas presented for marginal to shallow marine depositional environments during the Middle Ordovician in the northern margin of Gondwana.
Hierarchical Deep Hashing for Fast Large Scale Image Retrieval
Venue: 2020 25th International Conference on Pattern Recognition (ICPR)
Year: 2021

Fast image retrieval is of great importance in many computer vision tasks and especially practical applications. Deep hashing, the state of the art fast image retrieval scheme, introduces deep learning to learn the hash functions and generate binary hash codes, and outperforms the other image retrieval methods in terms of accuracy. However, all the existing deep hashing methods could only generate one level hash codes and require a linear traversal of all the hash codes to figure out the closest one when a new query arrives, which is very time consuming and even intractable for large scale applications. In this work, we propose a Hierarchical Deep Hashing(HDHash) scheme to speed up the state of the art deep hashing methods. More specifically, hierarchical deep hash codes of multiple levels can be generated and indexed with tree structures rather than linear ones, and pruning irrelevant branches can sharply decrease the retrieval time. To our best knowledge, this is the first work to introduce hierarchical indexed deep hashing for fast large scale image retrieval. Extensive experimental results on three benchmark datasets demonstrate that the proposed HDHash scheme achieves better or comparable accuracy with significantly improved efficiency and reduced memory as compared to state of the art fast image retrieval schemes.
Revealing the Structure of Deep Neural Networks via Convex Duality
Venue: ICML
Year: 2021

We study regularized deep neural networks (DNNs) and introduce a convex analytic framework to characterize the structure of the hidden layers. We show that a set of optimal hidden layer weights for a norm regularized DNN training problem can be explicitly found as the extreme points of a convex set. For the special case of deep linear networks with $K$ outputs, we prove that each optimal weight matrix is rank $K$ and aligns with the previous layers via duality. More importantly, we apply the same characterization to deep ReLU networks with whitened data and prove the same weight alignment holds. As a corollary, we prove that norm regularized deep ReLU networks yield spline interpolation for one dimensional datasets which was previously known only for two layer networks. Furthermore, we provide closed form solutions for the optimal layer weights when data is rank one or whitened. We then verify our theory via numerical experiments.
Workload Assessment for a Sustainable Manufacturing Paradigm Using Social Network Analysis Method
Year: 2018

In this paper, focus is on the sustainable manufacturing systems functionalities, i.e. process planning and scheduling for effective and efficient performance of the system to achieve the desired objectives. The desired objectives for this research work have been considered according to the above mentioned situation. Hence, makespan, throughput time, and energy consumption were identified as the most appropriate performance measures in line with the context of the problem. Mathematical model will be formulated for the performance measures by considering the realistic constraints. Unpredictable events such as machine breakdown or scheduled maintenance are most common in any manufacturing unit. Workload assignment with these disruptions is a challenge, and therefore, a new methodology has been developed for effective and efficient solutions. In this paper, a new social network analysis based method is being proposed to identify the key machines that should not be disturbed due to its contribution toward achieving the best system's performance. Moreover, an illustrative example along with three different configurations will be presented to demonstrate the feasibility of the proposed approach. For execution, a Flexsim based simulation approach will be followed, and with different instances, the proposed methodology can be executed. The validation of the proposed approach and its effectiveness will be evaluated through comparison with different instances, and finally the efficiency of the proposed approach will be confirmed with the results.
Utopia: Marge Piercy as a Social Critic in Woman on the Edge of Time
Venue: English Language and Literature Studies
Year: 2019

In her utopian novel Woman on the Edge of Time (1976) Marge Piercy critically addresses class, gender, and race as political and social issues in the contemporary society. These matters cause struggle and wars among social classes and individuals as well. People who suffer the effects of these problems struggle for survival and consider themselves in wars with their oppressors as Bee, one of Piercy's characters in the novel, states, "We're all at war" (Piercy, p. 328) Race, gender and class are related in sense that all of them bring suffering to people and are used by any social domineering authority to persecute and dominate those oppressed people. In the following pages, I will discuss the concepts of class, gender, and race, and how these issues are used in Piercy's masterpiece Woman on the Edge of Time to set light on some atrocities of her contemporary world, and at the same time, to provide a utopian future alternative she intentionally urges her readers to think about and if possible to work for.