nils-herrmann/clean_scirepeval huggingface dataset:
Effective DevOps with AWS Second Edition
Year: 2018

Scale and maintain outstanding performance in your AWS based infrastructure using DevOps principles Key Features Implement continuous integration and continuous deployment pipelines on AWS Gain insight from an expert who has worked with Silicon Valley's most high profile companies Implement DevOps principles to take full advantage of the AWS stack and services Book Description The DevOps movement has transformed the way modern tech companies work. Amazon Web Services (AWS) which has been at the forefront of the cloud computing revolution, has also been a key contributor to the DevOps movement, creating a huge range of managed services that help you implement DevOps principles. Effective DevOps with AWS, Second Edition will help you to understand how the most successful tech start ups launch and scale their services on AWS, and will teach you how you can do the same. This book explains how to treat infrastructure as code, meaning you can bring resources online and offline as easily as you control your software. You will also build a continuous integration and continuous deployment pipeline to keep your app up to date. Once you have gotten to grips will all this, we'll move on to how to scale your applications to offer maximum performance to users even when traffic spikes, by using the latest technologies, such as containers. In addition to this, you'll get insights into monitoring and alerting, so you can make sure your users have the best experience when using your service. In the concluding chapters, we'll cover inbuilt AWS tools such as CodeDeploy and CloudFormation, which are used by many AWS administrators to perform DevOps. By the end of this book, you'll have learned how to ensure the security of your platform and data, using the latest and most prominent AWS tools. What you will learn Implement automatic AWS instance provisioning using CloudFormation Deploy your application on a provisioned infrastructure with Ansible Manage infrastructure using Terraform Build and deploy a CI/CD pipeline with Automated Testing on AWS Understand the container journey for a CI/CD pipeline using AWS ECS Monitor and secure your AWS environment Who this book is for Effective DevOps with AWS is for you if you are a developer, DevOps engineer, or you work in a team which wants to build and use AWS for software infrastructure. Basic computer science knowledge is required to get the most out of this book.
Investigation and monitoring the perspective V T I trainees have regarding significant environmental problems aiming to the adults education
Year: 2017

Environmental education is a learning process that enhances citizens 'knowledge and awareness about the environment and associated challenges, develops the necessary skills and experience to meet these challenges, and fosters attitudes, motivations, and commitments so that the well informed citizens are able to make accurate decisions and take responsible action (UNESCO, 1978) This paper aims to analyse and monitor the perspective V.T.I trainees have, regarding significant environmental problems and environmental education in general. Based on this research, the view of respondents gained from previous levels of education is listed, as well as the evaluation of environmental education.
Acute kidney injury after cardiac surgery according to Risk/Injury/Failure/Loss/End stage, Acute Kidney Injury Network, and Kidney Disease: Improving Global Outcomes classifications.
Venue: Journal of critical care
Year: 2013

The epidemiology of acute kidney injury (AKI) after cardiac surgery depends on the definition used. Our aims were to evaluate the Risk/Injury/Failure/Loss/End stage (RIFLE) criteria, the AKI Network (AKIN) classification, and the Kidney Disease: Improving Global Outcomes (KDIGO) classification for AKI post cardiac surgery and to compare the outcome of patients on renal replacement therapy (RRT) with historical data.  Retrospective analysis of 1881 adults who had cardiac surgery between May 2006 and April 2008 and determination of the maximum AKI stage according to the AKIN, RIFLE, and KDIGO classifications.  The incidence of AKI using the AKIN and RIFLE criteria was 25.9% and 24.9% respectively, but individual patients were classified differently. The area under the receiver operating characteristic curve for hospital mortality was significantly higher using the AKIN compared with the RIFLE criteria (0.86 vs 0.78, P .0009) Incidence and outcome of AKI according to the AKIN and KDIGO classification were identical. The percentage of patients who received RRT was 6.2% compared with 2.7% in 1989 to 1990. The associated hospital mortality fell from 82.9% in 1989 to 1990 to 15.6% in 2006 to 2008.  The AKIN classification correlated better with mortality than did the RIFLE criteria. Mortality of patients needing RRT after cardiac surgery has improved significantly during the last 20 years.
A New Learning Algorithm for the Maxq Hierarchical Reinforcement Learning Method
Venue: 2007 International Conference on Information and Communication Technology
Year: 2007

The MAXQ hierarchical reinforcement learning method is computationally expensive in applications with deep hierarchy. In this paper, we propose a new learning algorithm for MAXQ method to address the open problem of reducing its computational complexity. While the computational cost of the algorithm is considerably decreased, the required storage of new algorithm is less than two times as the original learning algorithm requires storage. Our experimental results in the simple taxi domain problem show satisfactory behavior of the new algorithm.
Curve interpolation based on the canonical arc length parametrization
Venue: Comput. Aided Des.
Year: 2011

We use the canonical equations (CE) of differential geometry, a local Taylor series representation of any smooth curve with parameter the arc length, as a unifying framework for the development of new CNC algorithms, capable of interpolating 2D and 3D curves, represented parametrically, implicitly or as surface intersections, with accurate feedrate control. We use a truncated form of the CE to compute a preliminary point, at an arc distance from the last interpolation point selected to achieve a desired feedrate profile. The next interpolation point is derived by projecting the preliminary point on the curve. The coefficients in the CE involve the curve's curvature, torsion and their arc length derivatives. We provide computing procedures for them for common Cartesian representations, demonstrating the generality of the proposed method. In addition, our algorithms admit corrections, which render them more accurate in terms of the programmed feedrate, compared to existing parametric algorithms of the same order.
Structured FISTA for image restoration
Venue: Numer. Linear Algebra Appl.
Year: 2020

In this paper, we propose an efficient numerical scheme for solving some large scale ill posed linear inverse problems arising from image restoration. In order to accelerate the computation, two different hidden structures are exploited. First, the coefficient matrix is approximated as the sum of a small number of Kronecker products. This procedure not only introduces one more level of parallelism into the computation but also enables the usage of computationally intensive matrix matrix multiplications in the subsequent optimization procedure. We then derive the corresponding Tikhonov regularized minimization model and extend the fast iterative shrinkage thresholding algorithm (FISTA) to solve the resulting optimization problem. Since the matrices appearing in the Kronecker product approximation are all structured matrices (Toeplitz, Hankel, etc. we can further exploit their fast matrix vector multiplication algorithms at each iteration. The proposed algorithm is thus called structured fast iterative shrinkage thresholding algorithm (sFISTA) In particular, we show that the approximation error introduced by sFISTA is well under control and sFISTA can reach the same image restoration accuracy level as FISTA. Finally, both the theoretical complexity analysis and some numerical results are provided to demonstrate the efficiency of sFISTA.
Open Researcher and Contributor ID (ORCID) como identificador de autores en las publicaciones cientificas
Year: 2019

ORCID es un proyecto abierto sin animo de lucro, comunitario, que ofrece un sistema para crear y mantener un registro unico de los investigadores y un metodo claro para vincular las actividades de investigacion y los productos de estos cientificos. En este articulo el autor se propone describir la utilidad del ORCID como identidad digital individual de los investigadores. Este identificador supone la obtencion de un codigo unico para los autores de las publicaciones que las unifica y que, ademas, es interoperable con numerosas bases de datos. Por otra parte, facilita el conocimiento de la actividad investigadora de determinada institucion, la emision de sus informes y la visibilidad de sus resultados. En conclusion, presenta ventajas tanto para el investigador como para la institucion a la que este pertenece. Palabras clave: identificador unico de autores, identidad digital del investigador, comunicacion cientifica, indicadores de produccion cientifica
Dinamika Pengambilan Resiko Pemasaran pada Pengusaha Batik di Laweyan
Year: 2011

Industri batik Laweyan mengalami fase modernisasi sejak munculnya pengusaha batik pada awal abad 20 akibat ditemukannya alat pembatik cap yang menggantikan canting dan mencapai puncak kejayan pada tahun 1960an. Setelah mencapai kejayaan, pengusaha batik mengalami penurunan, banyak diantara mereka yang bangkrut bahkan gulung tikar yang disebabkan tidak adanya proses regenerasi, belum adanya sistem menejemen perusahaan yang baik, kurang adanya inovasi atau kreatifitas dalam hal menciptakan model atau motif baru sebagai antisipasi munculnya batik printing dan sablon, kurangnya dukungan dari kegiatan promosi yang ada. Namun, di tahun 2011 ini pengusaha batik semakin berkembang dan mampu mengembalikan kejayaan usaha batik yang pernah diraih pada tahun 1960an. Perkembangan usaha yang terjadi sangat dipengaruhi oleh pemasaran yang dilakukan karena pemasaran merupakan jiwa dari sebuah usaha. Informan dalam penelitian ini diambil secara purposive sampling sebanyak tiga orang. Sumber data berupa kata kata yang dikumpulkan menggunakan metode wawancara dan observasi. Analisis data yang digunakan adalah analisis diskriptif yaitu berupa paparan, uraian dan gambaran. Hasil penelitian, setiap pengusaha mempunyai kemauan dan kesiapan melanjutkan usaha, dan keyakinan adanya peluang. Pemahaman tentang batik juga terus dikembangkan. Hal tersebut mendorong pengusaha untuk mencapai tujuan mendirikan usahanya yaitu mengembangkan dan mendapatkan keuntungan, maka pengusaha berani mengambil resiko terutama dalam bidang inovasi dan kreativitas dengan tetap mengutamakan kualitas dan mempertahankan keaslian batik. Batik yang diproduksi mempunyai ciri khas tersendiri dengan desain terbatas dan disesuaikan dengan permintaan pasar. Faktor yang mempengaruhi pengambilan resiko pemasarannya adalah kemampuan atau pengetahuan, keyakinan, persaingan, perang harga, promosi dan pelanggan.
FinBERT: A Pretrained Language Model for Financial Communications
Venue: ArXiv
Year: 2020

Contextual pretrained language models, such as BERT (Devlin et al. 2019) have made significant breakthrough in various NLP tasks by training on large scale of unlabeled text re sources.Financial sector also accumulates large amount of financial communication text.However, there is no pretrained finance specific language models available. In this work,we address the need by pretraining a financial domain specific BERT models, FinBERT, using a large scale of financial communication corpora. Experiments on three financial sentiment classification tasks confirm the advantage of FinBERT over generic domain BERT model. The code and pretrained models are available at this https URL. We hope this will be useful for practitioners and researchers working on financial NLP tasks.
A Survey on Distributed Denial of Service (DDoS) Attacks in SDN and Cloud Computing Environments
Venue: IEEE Access
Year: 2019

Recently, software defined networks (SDNs) and cloud computing have been widely adopted by researchers and industry. However, widespread acceptance of these novel networking paradigms has been hampered by the security threats. Advances in the processing technologies have helped attackers in increasing the attacks too, for instance, the development of Denial of Service (DoS) attacks to distributed DoS (DDoS) attacks which are seldom identified by conventional firewalls. In this paper, we present the state of art of the DDoS attacks in SDN and cloud computing scenarios. Especially, we focus on the analysis of SDN and cloud computing architecture. Besides, we also overview the research works and open problems in identifying and tackling the DDoS attacks.
School Based Peer Related Social Competence Interventions for Children with Autism Spectrum Disorder: A Meta Analysis and Descriptive Review of Single Case Research Design Studies
Venue: Journal of autism and developmental disorders
Year: 2015

The purpose of this review was to critically examine and summarize the impact of school based interventions designed to facilitate the peer related social competence of children with autism spectrum disorder (ASD) Reviewed studies employed a single case experimental design, targeted peer related social competence, included children 3 12 years old with an ASD, and took place in school settings. Articles were analyzed descriptively and using the evaluative method to determine study quality. Additionally, effect size estimates were calculated using nonoverlap of all pairs method and Tau U. A total of 37 studies including 105 children were reviewed. Overall, ES estimates ranged from weak to strong, but on average, the reviewed interventions produced a moderate to strong effect, and quality ratings were generally in the acceptable to high range. Findings suggest that children with ASD can benefit from social skill interventions implemented with peers in school settings.
Generation of an induced pluripotent stem cell (iPSC) line from a patient with maturity onset diabetes of the young type 3 (MODY3) carrying a hepatocyte nuclear factor 1 alpha (HNF1A) mutation.
Venue: Stem cell research
Year: 2018

Heterozygous non synonymous (p.S142F) mutation in HNF1A leads to maturity onset diabetes of the young (MODY) type 3, which is a subtype of dominant inherited young onset non autoimmune diabetes due to the defect of insulin secretion from pancreatic beta cells. We generated induced pluripotent stem cells (iPSCs) from a patient with HNF1A p.S142F mutation. Cells from this patient, which were reprogrammed by non integrative viral transduction had normal karyotype, harboured the HNF1A p.S142F mutation, expressed pluripotency hallmarks.
A brain specific microRNA regulates dendritic spine development
Venue: Nature
Year: 2006

MicroRNAs are small, non coding RNAs that control the translation of target messenger RNAs, thereby regulating critical aspects of plant and animal development. In the mammalian nervous system, the spatiotemporal control of mRNA translation has an important role in synaptic development and plasticity. Although a number of microRNAs have been isolated from the mammalian brain, neither the specific microRNAs that regulate synapse function nor their target mRNAs have been identified. Here we show that a brain specific microRNA, miR 134> is localized to the synapto dendritic compartment of rat hippocampal neurons and negatively regulates the size of dendritic spines postsynaptic sites of excitatory synaptic transmission. This effect is mediated by miR 134 inhibition of the translation of an mRNA encoding a protein kinase, Limk1, that controls spine development. Exposure of neurons to extracellular stimuli such as brain derived neurotrophic factor relieves miR 134 inhibition of Limk1 translation and in this way may contribute to synaptic development, maturation and/or plasticity.
Improving Multi turn Response Selection Models with Complementary Last Utterance Selection by Instance Weighting
Venue: PAKDD
Year: 2020

Open domain retrieval based dialogue systems require a considerable amount of training data to learn their parameters. However, in practice, the negative samples of training data are usually selected from an unannotated conversation data set at random. The generated training data is likely to contain noise and affect the performance of the response selection models. To address this difficulty, we consider utilizing the underlying correlation in the data resource itself to derive different kinds of supervision signals and reduce the influence of noisy data. More specially, we consider a main complementary task pair. The main task (i.e. our focus) selects the correct response given the last utterance and context, and the complementary task selects the last utterance given the response and context. The key point is that the output of the complementary task is used to set instance weights for the main task. We conduct extensive experiments in two public datasets and obtain significant improvement in both datasets. We also investigate the variant of our approach in multiple aspects, and the results have verified the effectiveness of our approach.
Facebook as A Learning Media in TISOL (Teaching Indonesian to Speakers of Other Languages) Learning to Support The Independency of Foreign Students in Indonesia
Venue: Journal of Physics: Conference Series
Year: 2019

TISOL process is not merely conducted face to face, but also by using media, both printed media and non printed, computer/internet, radio and television broadcast. By using this method the foreign students can independently adjust the suitability of time and place in studying the material. Learning and teaching process as a form of interaction between tutors and tutorial participants (foreign students) as well as between participants related to teaching and learning activities, forms a social network using a social media as a means of interaction to enable interaction in many directions (active participation of members) and flexible. One of the most popular social media in the world is Facebook. Facebook features as a social media can be utilized to support the implementation of teaching and learning process, especially online tutorials, among other facilities of various documents modules with interactive discussions among participants arranged chronologically per topic. The purpose of this paper is to review the features of Facebook and simulate learning Tutorial Online by using Facebook so features can be used with existing advantages and disadvantages as well as suggestions how to use Facebook to improve the quality of Online Tutorials in TISOL learning.
Digital Entrepreneurship: Toward a Digital Technology Perspective of Entrepreneurship
Year: 2017

New digital technologies have transformed the nature of uncertainty inherent in entrepreneurial processes and outcomes as well as the ways of dealing with such uncertainty. This has raised important questions at the intersection of digital technologies and entrepreneurship on digital entrepreneurship. We consider two broad implications less bounded entrepreneurial processes and outcomes and less predefined locus of entrepreneurial agency and advance a research agenda that calls for the explicit theorizing of concepts related to digital technologies. In articulating the promise and value of such a digital technology perspective, we consider how it would build on and enrich existing entrepreneurship theories.
IN SITU SHEAR MODULUS REDUCTION COMPUTATION USING SEISMIC INTERFEROMETRY BY DECONVOLUTION FROM BOREHOLE AND SURFACE DATA: THEORY AND EXAMPLES
Year: 2016

We use seismic interferometry by deconvolution to estimate the travel time between two receivers located at the surface and at the bottom of a soil column having strong motion recordings. Since the distance between sensors is known, the computed travel time can be used to estimate the velocity of propagating waves in the medium. In previous studies, several records having different PGA's have been used to estimate the material degradation by computing the lag time either by cross correlation or deconvolution techniques. In this study we show that it is possible to obtain reliable results using a single couple of records if nonlinear effects are triggered. First, some numerical tests are conducted to validate the method with a monolayer soil column, with well known mechanical properties. As a borehole input, we use the record of the Northridge earthquake of 1994 (Sylmar station rock site) while the ground motion at the surface is computed using a 1D SH nonlinear wave propagation code. Furthermore, the code allows to set constant/variable shear velocity profile for each layer and controlling the hysteretic damping following a prescribed damping ratio curve. The results show the capability of the method to compute the G/G0 curve that is compatible with the theoretical one. In addition, we assess the effect of hysteretic damping control during wave propagation. We found that this introduces artificial degradation of the backbone curve, which in turn produces permanent fictitious material degradation. Finally, we use borehole and surface accelerograms from the Mw9 Tohoku earthquake in 2011 recorded at the KiK net IBRH16 station. We estimate the plastic degradation of the first 5 m of loosely packed soil layers and monitor the temporal changes of shear modulus at this site.
A Dictionary based Oversampling Approach to Clinical Document Classification on Small and Imbalanced Dataset
Venue: 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI IAT)
Year: 2020

Medical document classification is one of the prominent research problems in document classification domain. As medical discharge notes are collected from real patients, they are often imbalanced. Moreover, these datasets are usually too small for data hungry models (specially in rare disease cases) Both of these issues can lead to poor classification performance. In this work a new probabilistic dictionary based data augmentation approach is proposed to address these issues by oversampling on the minority class. This method works by creating new documents with high variety by using the extracted synonyms from WordNet with awareness of synonyms' similarities with the original word. To verify the effectiveness of the proposed oversampling approach, three different machine learning methods are used to learn classifiers from the augmented clinical text datasets generated by the oversampling approach. The experimental results show that the proposed method not only provides better classification accuracy than the imbalanced dataset case, but also can outperform some existing augmentation methods on the dataset of 2008 Integrating Informatics with Biology and the Bedside (I2B2) obesity challenge.
A concept architecture model with autonomous coordination for flying formation
Venue: Proceedings of the 10th World Congress on Intelligent Control and Automation
Year: 2012

Flying formations are an attractive prospect for improved science return as they provide a natural capability for temporal, spatial and signal separation with further engineering and economic advantages. As satellite numbers increase, the traditional "remote control" approach begins to break down. It is therefore essential to push control into space; to make spacecraft more autonomous. This article analyses some available typical autonomous coordination paradigms and then a hybrid autonomous coordination hierarchy for formations is presented, which is designed to minimize the traffic of intersatellite links and aid scalability to larger numbers (hundreds) of small satellites for tasks allocation.