nils-herrmann/clean_scirepeval huggingface dataset:
Statistical Machine Translation of English Text to API Code Usages
Year: 2018

Statistical Machine Translation of English Text to API Code Usages: A comparison of Word Map, Contextual Graph Ordering, Phrase based, and Neural Network Translations Dharani Kumar Palani Statistical Machine Translation (SMT) has gained enormous popularity in recent years as natural language translations have become increasingly accurate. In this thesis we apply SMT techniques in the context of translating English descriptions of programming tasks to source code. We evaluate four existing approaches: maximum likelihood word maps, ContextualExpansion, phrase based, and neural network translation. As a training and test (i.e. reference translation) data set we clean and align the popular developer discussion forum StackOverflow. Our baseline approach, WordMapK, uses a simple maximum likelihood word map model which is then ordered using existing code usage graphs. The approach is quite effective, with a precision and recall of 20 and 50, respectively. Adding context to the word map model, ContextualExpansion, is able to increase the precision to 25 with a recall of 40. The traditional phrase based translation model, Moses, achieves a similar precision and recall also incorporating the context of the input text by mapping English sequences to code sequences. The final approach is neural network translation, OpenNMT. While the median precision is 100 the recall is only 20. When manually examining the output of the neural translation, the code usages are very small and obvious. Our results represent an application of existing natural language strategies in the context of software engineering. We make our scripts, corpus, and reference translations in the hope that future work will adapt these techniques to further increase the quality of English to code statistical machine translation.
What do we really talk about when we talk about context in pervasive computing: a review and exploratory analysis
Venue: iiWAS
Year: 2017

At the heart of ubiquitous and pervasive computing is the integration of semantically rich contextual information into systems that intelligently adapt their behavior to the context. This paper presents an analysis of the contextual elements considered in the scientific discourse on pervasive computing. To support researchers with positioning their work, this paper explores how well the facets of context are represented and which context elements are particularly important in specific application domains, such as healthcare or traffic. Results suggest that context elements are considered diversely among domains. Context spreads across a long tail of heterogeneous, rather specific context elements. Potential factors explaining this high diversity relate to sensor technology, structure of context information as well as purposes and design of context aware systems.
Tax Avoidance and Geographic Earnings Disclosure
Year: 2013

This study tests the relation between corporate tax avoidance and disclosure of geographic earnings for U.S. multinational companies. We find that after the adoption of Statement of Financial Accounting Standards No. 131 in 1998, firms opting to discontinue disclosure of geographic earnings in their financial reports have lower worldwide effective tax rates. These results are consistent with managers perceiving that non disclosure of geographic earnings helps mask tax avoidance behavior. However, the relation between tax avoidance and non disclosure reduces after implementation of Schedule M 3 in the annual corporate tax filing beginning in 2004. Schedule M 3 requires a detailed reconciliation of book income to tax income and aims to make firms' tax avoidance activities associated with shifting profits to lower tax foreign jurisdictions more apparent to the IRS. This study contributes to our understanding of the relation between financial reporting behavior and tax reporting behavior.
Flexural performance of fire damaged and rehabilitated two span reinforced concrete slabs and beams
Year: 2012

Five two span reinforced concrete (RC) slabs and seven two span RC beams were tested under the ISO 834 standard fire with different durations. CFRP strengthening was then applied to some of the specimens after the damaged concrete was removed from the specimens and replaced with polymer mortar. All the specimens were loaded to failure to investigate the influence of fire damage and the effectiveness of strengthening methods. Test results indicated that the flexural capacities of specimens decrease with the fire duration increases. Moreover, fire exposure had more significant effect on the flexural rigidity than on the bearing capacity of the specimens. After rehabilitation, the bearing capacities of specimens reached or even exceeded that of the reference RC specimen, and the strengthening methods seemed to have limited effect on flexural rigidity recovery. From the analysis of moment redistribution of tested beams, elevated temperature is found having different impacts on sagging moment region and hogging moment region. The damage of RC continuous member is definitely a comprehensive response of different regions.
A Precise Performance Analysis of Learning with Random Features
Venue: ArXiv
Year: 2020

We study the problem of learning an unknown function using random feature models. Our main contribution is an exact asymptotic analysis of such learning problems with Gaussian data. Under mild regularity conditions for the feature matrix, we provide an exact characterization of the asymptotic training and generalization errors, valid in both the under parameterized and over parameterized regimes. The analysis presented in this paper holds for general families of feature matrices, activation functions, and convex loss functions. Numerical results validate our theoretical predictions, showing that our asymptotic findings are in excellent agreement with the actual performance of the considered learning problem, even in moderate dimensions. Moreover, they reveal an important role played by the regularization, the loss function and the activation function in the mitigation of the "double descent phenomenon" in learning.
Leadership Style, Anonymity, and the Discussion of an Ethical Issue in an Electronic Context
Venue: Int. J. e Collab.
Year: 2006

A laboratory experiment was conducted with 42 student groups to evaluate the effects of transactional vs. transformational leadership styles and anonymity when groups supported by an Electronic Meeting System (EMS) discussed the ethical issue of copying copyrighted software. A confederate leader displayed either transformational or transactional behaviors. Transformational leaders motivate effort by raising the awareness of followers to make them aspire to higher order needs and values and by developing them to fulfill their aspirations. Transactional leaders motivate effort by highlighting the contractual exchange involved in a relationship. Participants working with a transformational confederate were more likely to make arguments that challenged the copying of copyrighted software than those working with one who was more transactional. These arguments, in turn, caused groups exposed to such arguments to have greater deviation among its members in intentions to copy the software. Participants working with a transactional confederate were more likely to make arguments in favor of copying copyrighted software. These arguments, in turn, caused groups exposed to such arguments to have a greater mean of intentions to copy the software. Implications for practice and future research on ethics and leadership in the electronic context are discussed.
Object Detection and Instance Segmentation in Chest X rays for Tuberculosis Screening
Year: 2021

Tuberculosis (TB) is a highly contagious disease leading to the deaths of approximately 2 million people annually. TB primarily affects the lungs and is spread through the air when people cough, sneeze, or spit. Providing healthcare professionals with better information, at a faster pace, is essential for combating this disease, especially in Low and Middle Income Countries (LMICs) with resource constrained health systems. In this paper we describe how using convolution neural networks (CNNs) with an object level annotated dataset of chest X rays (CXRs) allows us to identify the location of pulmonary issues indicative of TB. We compare the performance of Faster R CNN, Mask R CNN, Cascade versions of each, and SOLOv2, demonstrating reasonable results with a small dataset. We present a method to reduce the false positive rate by comparing the location of a detected object with the known location of areas where the detected class is likely to occur in the lung. Our results show that object detection and instance segmentation of CXRs can be achieved with a dataset of high quality, object level annotations, and could be used as part of an automated TB screening process. This work has the potential to improve the speed of TB diagnosis in LMICs, if properly integrated into the healthcare system and adapted to existing clinical workflows and local regulations.  Terence Griffin, Yu Cao, Benyuan Liu, Maria J. Brunette, and Xinzi Sun
Deep Clustering for Unsupervised Learning of Visual Features
Venue: ECCV
Year: 2018

Clustering is a class of unsupervised learning methods that has been extensively applied and studied in computer vision. Little work has been done to adapt it to the end to end training of visual features on large scale datasets. In this work, we present DeepCluster, a clustering method that jointly learns the parameters of a neural network and the cluster assignments of the resulting features. DeepCluster iteratively groups the features with a standard clustering algorithm, k means, and uses the subsequent assignments as supervision to update the weights of the network. We apply DeepCluster to the unsupervised training of convolutional neural networks on large datasets like ImageNet and YFCC100M. The resulting model outperforms the current state of the art by a significant margin on all the standard benchmarks.
A GaN SiC hybrid material for high frequency and power electronics
Year: 2018

We demonstrate that 3.5% in plane lattice mismatch between GaN (0001) epitaxial layers and SiC (0001) substrates can be accommodated without triggering extended defects over large areas using a grain boundary free AlN nucleation layer (NL) Defect formation in the initial epitaxial growth phase is thus significantly alleviated, confirmed by various characterization techniques. As a result, a high quality 0.2 mm thin GaN layer can be grown on the AlN NL and directly serve as a channel layer in power devices, like high electron mobility transistors (HEMTs) The channel electrons exhibit a state of the art mobility of >2000 cm2/V s, in the AlGaN/GaN heterostructures without a conventional thick C or Fe doped buffer layer. The highly scaled transistor processed on the heterostructure with a nearly perfect GaN SiC interface shows excellent DC and microwave performances. A peak RF power density of 5.8 W/mm was obtained at VDSQ 40 V and a fundamental frequency of 30 GHz. Moreover, an unpassivated 0.2 mm GaN/AlN/SiC stack shows lateral and vertical breakdowns at 1.5 kV. Perfecting the GaN SiC interface enables a GaN SiC hybrid material that combines the high electron velocity thin GaN with the high breakdown bulk SiC, which promises further advances in a wide spectrum of high frequency and power electronics.We demonstrate that 3.5% in plane lattice mismatch between GaN (0001) epitaxial layers and SiC (0001) substrates can be accommodated without triggering extended defects over large areas using a grain boundary free AlN nucleation layer (NL) Defect formation in the initial epitaxial growth phase is thus significantly alleviated, confirmed by various characterization techniques. As a result, a high quality 0.2 mm thin GaN layer can be grown on the AlN NL and directly serve as a channel layer in power devices, like high electron mobility transistors (HEMTs) The channel electrons exhibit a state of the art mobility of >2000 cm2/V s, in the AlGaN/GaN heterostructures without a conventional thick C or Fe doped buffer layer. The highly scaled transistor processed on the heterostructure with a nearly perfect GaN SiC interface shows excellent DC and microwave performances. A peak RF power density of 5.8 W/mm was obtained at VDSQ 40 V and a fundamental frequency of 30 GHz. Moreover, an unpassivated 0.2 mm GaN/A.
Generative adversarial networks for SAR image realism
Venue: Defense Security
Year: 2018

In a combat environment, synthetic aperture radar (SAR) is attractive for several reasons, including automatic target recognition (ATR) In order to effectively develop ATR algorithms, data from a wide variety of targets in different configurations is necessary. Naturally, collecting all this data is expensive and time consuming. To mitigate the cost, simulated SAR data can be used to supplement real data, but the accuracy and performance is degraded. We investigate the use of generative adversarial networks (GANs) a recent development in the field of machine learning, to make simulated data more realistic and therefore better suited to develop ATR algorithms for real world scenarios. This class of machine learning algorithms has been shown to have good performance in image translation between image domains, making it a promising method for improving the realism of simulated SAR data. We compare the use of two different GAN architectures to perform this task. Data from the publicly available MSTAR dataset is paired with simulated data of the same targets and used to train each GAN. The resulting images are evaluated for realism and the ability to retain target class. We show the results of these tests and make recommendations for using this technique to inexpensively augment SAR data sets.
The impact of physical work environment on employee performance by job satisfaction as mediator
Year: 2020

The purpose of this study is to determine the impact of physical work environment on job satisfaction, the impact of physical work environment on employee performance, the impact of job satisfaction on employee performance, and the role of job satisfaction in mediating the impact of physical work environment on employee performance. The type of this research is a literature review that describes the effect between variables based on existing theories and empirical studies of relevant journals. The results of this study showed that the physical work environment has a positive effect on job satisfaction, the physical work environment has a positive effect on employee performance, job satisfaction has a positive effect on employee performance, the physical work environment has a positive effect on employee performance through job satisfaction.
The Use of Mobile Phone in English Language Lesson: A Shift from Teacher Centred to Student Centred through Mobile Learning
Year: 2018

Most area of life requires changes. One of the things changes from time to time is learning method or strategy. In educational sector, especially in Teaching English to Speaker of Other Language (TESOL) teachers are demanded to have creative and innovative ways to teach language other than their own mother tongue in order to maintain pupils' interest, creativity and learning outcomes. One alternative way to teach English is by integrating technology particularly mobile phone. However, there still questions on what is going on if teachers allow student to access mobile phone during class and how it can be done. This study aims to enrich teachers' knowledge and broaden the paradigm of using mobile learning method serving the digital native generation in TESOL. A reflective analysis was done in collaboration with literature support to provide a clear image of mobile learning done in English class at a particular study program of Universitas Kristen Satya Wacana, Indonesia. The result showed that students' need a transformation of teaching learning process, students can be engaged more actively on learning by using mobile phone and some possible activities are proposed for supporting student centred instruction.
MUHASEBE BILIM DALINDA OZGUN ESERLER: TURK YAZINI INCELEMESI
Year: 2020

Bu calismanin amaci; muhasebe alaninda yazilmis kitaplarin icerik ve yazarlar bakimindan profilini cikarmak, yillar itibariyle hangi konularin onem kazandigini belirlemek ve benzer konularda arastirma yapacak akademisyenlere isik tutmaktir. Kitapyurdu ve Idefix web siteleri ile Gazi, Seckin, Ekin ve Turkmen gibi onde gelen yayinevlerinin internet sitelerinde yer alan, Turkce yazilmis 510 eser incelenmistir. Arastirma kapsaminda bilgisayarli muhasebe, muhasebe denetimi, yonetim ve maliyet muhasebesi, dis ticaret islemleri muhasebesi, banka muhasebesi, finansman yontemleri ve turev araclara yonelik muhasebe islemlerine iliskin konularin guncelliklerini koruduklari saptanmistir. Ayrica guncel, ekonomik ve hukuki gelismelere paralel olarak Enflasyon Muhasebesi, Muhasebe ve Finansal Raporlama Standartlari, Entegre Raporlama konularinin on plana ciktigi gorulmektedir. Muhasebe alaninda yayimlanan kitaplarin en cok (68 adet) Gazi Kitabevinden ciktigi ve yayin yapan kisilerin cogunlukla akademisyen oldugu ise diger bulgulardir.
Domain Name System Security and Privacy: A Contemporary Survey
Venue: Comput. Networks
Year: 2021

The domain name system (DNS) is one of the most important components of today's Internet, and is the standard naming convention between human readable domain names and machine routable IP addresses of Internet resources. However, due to the vulnerability of DNS to various threats, its security and functionality have been continuously challenged over the course of time. Although, researchers have addressed various aspects of the DNS in the literature, there are still many challenges yet to be addressed. In order to comprehensively understand the root causes of the vulnerabilities of DNS, it is mandatory to review the various activities in the research community on DNS landscape. To this end, this paper surveys more than 170 peer reviewed papers, which are published in both top conferences and journals in the last ten years, and summarizes vulnerabilities in DNS and corresponding countermeasures. This paper not only focuses on the DNS threat landscape and existing challenges, but also discusses the utilized data analysis methods, which are frequently used to address DNS threat vulnerabilities. Furthermore, we looked into the DNSthreat landscape from the viewpoint of the involved entities in the DNS infrastructure in an attempt to point out more vulnerable entities in the system.
Machine Learning with Generative Adverserial Network
Venue: 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA)
Year: 2020

Generative Adversarial Networks have been presented to train generative models as they have pose a certain level of exertion and difficulty in the past years. Comparatively, GAN has received less attention. This model shows that it can generate MNIST (Modified National Institute of Standards and Technology database) digits conditioned on class labels. Translating an image from a certain source image or the original image to a clearer and higher resolution target domain is the approach introduced in this paper. This work presents a non conventional way to resolve the problem presented in digitizing handwritten digits. This approach is supposed to be easier to implement and understand while having a high efficiency and accuracy as compared to the existing models. It is shown that MNIST digits conditioned on class labels can be generated by this model. Both the generator and discriminator are to be conditioned. The idea is to nurture both the generator and discriminator together and gradually. Beginning from a low resolution image, more layers are added so that the model can increase the definite finer details and progress towards a clearer image as the training progresses.
Effectively improve the quality of camellia oil by the combination of supercritical fluid extraction and molecular distillation (SFE MD)
Venue: LWT
Year: 2019

Abstraction In this study, the combination of supercritical fluid extraction and molecular distillation (SFE MD) was used to improve the quality of camellia oil. A series of important process parameters were investigated to obtain high quality camellia oil. Compared with the camellia oil produced by supercritical fluid extraction (SFE) the acid value of camellia oil obtained by SFE MD was significantly decreased to 0.63 0.01 from 2.93 0.01 mg KOH/g oil, without any change of their fatty acid compositions. Furthermore, unsaponifiable matters of camellia oil such as g tocopherol, b sitosterol and squalene could be effectively preserved and there were no residual harmful substances. The technology provided in this study is effective, economical and environment friendly and could be replicated in an industrial scale.
Monitoring system for manufacturing process: Application to a cardboard factory
Venue: 2015 3rd International Conference on Control, Engineering Information Technology (CEIT)
Year: 2015

In this paper we present two approaches for designing a monitoring system for a manufacturing process of industrial glue in a corrugated board factory. A graphical model to generate fault indicators allowing checking detectability and localisability is used to considered process. These indicators are obtained by applying different steps of structural analysis. Firstly, a bipartite graph is used as the basic modeling theory whereas SIMATIC PCS7 software tool is used to simulate various faults and observe reactions of the designed supervision system. Secondly, SYMBOLS 2000 software tool based on bond graph model is introduced for performance evaluation.
Metabolic regulation of T lymphocytes.
Venue: Annual review of immunology
Year: 2013

T cell activation leads to dramatic shifts in cell metabolism to protect against pathogens and to orchestrate the action of other immune cells. Quiescent T cells require predominantly ATP generating processes, whereas proliferating effector T cells require high metabolic flux through growth promoting pathways. Further, functionally distinct T cell subsets require distinct energetic and biosynthetic pathways to support their specific functional needs. Pathways that control immune cell function and metabolism are intimately linked, and changes in cell metabolism at both the cell and system levels have been shown to enhance or suppress specific T cell functions. As a result of these findings, cell metabolism is now appreciated as a key regulator of T cell function specification and fate. This review discusses the role of cellular metabolism in T cell development, activation, differentiation, and function to highlight the clinical relevance and opportunities for therapeutic interventions that may be used to disrupt immune pathogenesis.