nils-herrmann/clean_scirepeval huggingface dataset:
Etude de la qualite bacteriologique de l'eau utilisee dans l'industrie agroalimentaire dans le Nord du Maroc
Venue: The Pan African Medical Journal
Year: 2017

L'eau est utilisee d'une facon primordiale dans tout le processus de la fabrication des produits alimentaires. Les industries agroalimentaires du Nord du Maroc utilisent differentes sources d'eaux mais l'eau de reseau public et l'eau de puits sont les principales sources d'eau utilisee. Cette eau peut s'averer la source principale des eventuelles contaminations et alterations des aliments. Notre but est d'evaluer la qualite bacteriologique de l'eau utilisee par les industries agroalimentaires dans la region du Nord du Maroc, d'identifier les differents germes responsables de la pollution de ces eaux et de definir les principales causes de cette pollution. Methodes Des echantillons d'eau preleves aux robinets ou des puits ont ete analyses pour la recherche des germes indicateurs de la pollution (coliformes totaux (CT) coliformes fecaux (CF) enterocoques intestinaux (E) microorganismes revivifiables (MOR) anaerobies sulfitoreducteurs) et les germes pathogenes (Salmonelles, Staphylocoques, Pseudomonas aeruginosa) Le denombrement des bacteries a ete fait par la technique de filtration et par incorporation en milieu solide en surfusion. Resultats Les resultats ont montre que les eaux du reseau public ont ete de qualite bacteriologique satisfaisante tandis que 40% des eaux des puits ont ete non conformes aux normes a cause de la presence des indicateurs de pollution CT, CF, E et MOR. En revanche, les germes pathogenes, en particulier les Salmonelles, ont ete absents dans les eaux de tous les puits analyses.  La pollution de ces puits a ete generalement liee au non respect des conditions de puisage hygieniques. La qualite bacteriologique des eaux de ces puits peut etre amelioree par une protection adequate.
Realidad aumentada como apoyo para el aprendizaje en los ninos de inicial dos del Centro de cuidado infantil Amanecer Feliz
Year: 2018

1997 la realidad aumentada es el termino usado para definir un tipo de tecnologia donde la vision de la realidad se amplia con elementos virtuales que anaden informacion digital. El objeto principal de la realidad aumentada es mejorar la percepcion que tienen las personas sobre su entorno y permitir nuevas formas de interaccion mediante la visualizacion de informacion que el usuario no puede percibir con sus sentidos.
HCKBoost: Hybridized composite kernel boosting with extreme learning machines for hyperspectral image classification
Venue: Neurocomputing
Year: 2019

Utilization of contextual information on the hyperspectral image (HSI) analysis is an important fact. On the other hand, multiple kernels (MKs) and hybrid kernels (HKs) in connection with kernel methods have significant impact on the classification process. Activation of spatial information via composite kernels (CKs) and exploiting hidden features of the spectral information via MKs and HKs have been shown great successes on hyperspectral images separately. In this work, it is aimed to aggregate composite and hybrid kernels to obtain high classification success with a boosting based community learner. Spatial and spectral hybrid kernels are constructed using weighted convex combination approach with respect to individual success of the predefined kernels. Composite kernel formation is realized with certain proportions of the obtained spatial and spectral HKs. Computationally fast and effective extreme learning machine (ELM) classification algorithm is adopted. Since, main objective is to obtain optimal kernel during ensemble formation operation, unlike the standard MKL methods, proposed method disposes off the complex optimization processes and allows multi class classification. Pavia University, Indian Pines, and Salinas hyperspectral scenes that have ground truth information are used for simulations. Hybridized composite kernels (HCK) are constructed using Gaussian, polynomial, and logarithmic kernel functions with various parameters and then obtained results are presented comparatively along with the state of the art MKL, CK, sparse representation, and single kernel based methods.
Resiliencia de agroecosistemas citricolas a la variabilidad climatica en el departamento del Meta, Colombia
Year: 2019

Como objetivo principal se proyecto determinar la resiliencia a la variabilidad climatica (VC) en agroecosistemas citricolas de Naranja Valencia, con diferente Estructura Agroecologica Principal (EAP) planteandose como hipotesis que un agroecosistema con una EAP biodiversa y articulada con el paisaje es mas resiliente ante los afectos de la VC. El trabajo de campo se inicio en el ano 2012, donde se caracterizaron, tipificaron y clasificaron los sistemas de produccion citricolas localizados en el departamento del Meta. El estudio evidencio la conformacion de seis grupos de agricultores o dominios de recomendacion. En cada grupo identificado se seleccionaron tres fincas en donde se evaluaron los parametros de la EAP (modificada) relacionandolos con los registros de productividad (PD) y con el numero de controles fitosanitarios (NCF) durante los anos 2013, 2014 y 2015. Este indice se integro al Indice de Resiliencia Agroecosistemico (IRAg) constatandose que los sistemas agricolas mas diversificados son mas resilientes a la VC, que se expresa en mayor PD y en requerir menor NCF. Para el estudio de la caracterizacion climatica se efectuaron analisis estadisticos con las series de las variables climaticas temperatura y precipitacion, de las estaciones del IDEAM localizadas en Villavicencio, Granada, Guamal y Lejanias, municipios en donde se concentra el 89% del area sembrada y el 94,7% de la produccion. El analisis determino que durante escenarios ENOS (Nino, Nina y neutro) la distribucion de la precipitacion y temperatura, estan asociadas al movimiento de masas de aire humedo conocido como el Dipolo del Amazonas (ARH) Los resultados se presentan en cinco capitulos en formato tipo articulo, en el primero se analiza el marco conceptual referido al pensamiento ambiental y a la agroecologia como ciencia, en el segundo se indica el ejercicio desplegado para caracterizar, tipificar y clasificar los sistemas de produccion citricola, en el tercero se presenta la caracterizacion de la variabilidad climatica tomando como referencia la ecofisiologia de los citricos en el tropico bajo y especificamente los requerimientos de naranja Valencia, en el cuarto efectuando algunos ajustes se evalua la EAP, y en quinto se propone la metodologia denominada Indice de Resiliencias Agroecosistemico (IRAg) para evaluar la resiliencia de los agroecosistemas.
Elite Repudiation of the R Word and Public Opinion About Intellectual Disability.
Venue: Intellectual and developmental disabilities
Year: 2015

Part of the motivation for encouraging elite stakeholders like governments, professionals, and advocacy groups to replace the language of "mental retardation" with "intellectual disability" (ID) is the belief that elite endorsement could undermine negative attitudes and influence the public to follow suit. We examine the veracity of this expectation empirically with an experiment that exposed individuals to information about endorsements of the terminology change made by the federal government, Special Olympics, or professional psychologists. We subsequently measured attitudes about persons with ID and the language used to describe ID. Results indicate that exposure to information about elite endorsement of the terminological shift either exacerbated negative attitudes or had no effect, suggesting that other factors may have primacy over "expert" opinion.
Proceedings of the 26th International Conference on World Wide Web
Venue: WWW
Year: 2017

We welcome you to the WWW2017 conference, the 26th of the series, and only the second one to be held in Australia. The annual World Wide Web Conference is the premier international forum to present and discuss progress in research, development, standards, and applications related to the Web. This conference is organised under the aegis of the International World Wide Web Conference Committee (IW3C2) in collaboration with local conference organisers in the host city, in this case, the four public universities in Western Australia: Curtin University, Murdoch University, the University of Western Australia and Edith Cowan University. This year, WWW 2017 is offered as the centerpiece of the inaugural Festival of the Web in Perth, a week long celebration. WWW 2017 provides an opportunity to hear from the leaders of the web including three distinguished keynote speeches by world class experts: Mark Pesce, Yoelle Maarek, and Melanie Johnston Hollitt. There is a rich environment of technical activities, including 164 high quality papers in the Research Tracks, 54 papers in the four alternate tracks, over 100 papers in 15 workshops, 13 tutorial sessions, a Ph.D. Symposium track comprising presentations by seven doctoral students, an Industry track consisting of 20 papers focused on applied research, 20 demonstrations, a W3C track examining the latest Web standards and emerging technologies and 64 posters with, for the first time, a number of these offered as e posters to augment the static poster panels. Overall, WWW2017 provides more than 400 high quality presentations on the key research and development issues of the World Wide Web. Co located events in the Festival of the Web 2017 include the 4th Big Data Innovators Gathering (BIG 2017) the Web for All conference (W4A2017) and the 5th Serious Games and Applications for Health conference (SeGaH'17) In addition, several new events include Collaboration Innovation, a one day conference focusing on building smart business innovation through collaboration; the Trust Factory, a curated forum exploring issues of trust and privacy on the web; and Bytes and Rights, a conference focused on issues of web governance, copyright, digital rights, privacy and security on the web. Finally, the Big Day In is a one day IT careers conference designed by students for students, including tips and advice for secondary school students interested in IT and the web. Given Perth's location in one of the world's richest areas of natural resources, DeepSensor, a world class gathering of industry professionals, is being conducted as part of the Festival as an opportunity for professionals to share their real world insights into the continuing development of the internet of things in the mining, oil and gas industries.
CA EHN: Commonsense Word Analogy from E HowNet
Venue: ArXiv
Year: 2019

Word analogy tasks have tended to be handcrafted, involving permutations of hundreds of words with dozens of relations, mostly morphological relations and named entities. Here, we propose modeling commonsense knowledge down to word level analogical reasoning. We present CA EHN, the first commonsense word analogy dataset containing 85K analogies covering 5K words and 6K commonsense relations. This was compiled by leveraging E HowNet, an ontology that annotates 88K Chinese words with their structured sense definitions and English translations. Experiments show that CA EHN stands out as a great indicator of how well word representations embed commonsense structures, which is crucial for future end to end models to generalize inference beyond training corpora. The dataset is publicly available at \url{this https URL}
Pkg2Vec: Hierarchical package embedding for code authorship attribution
Venue: Future Gener. Comput. Syst.
Year: 2021

Authorship attribution of software is the task of identifying the author of a given piece of code. Code attribution is of importance in multiple scenarios, ranging from software plagiarism to cybersecurity. In this paper, we introduce authorship attribution of software packages that better reflect real world scenarios in which code is organized in packages and written by teams. We present a novel approach for software package authorship attribution called Pkg2Vec, based on a hierarchical deep neural network (DNN) architecture, corresponding to the hierarchical nature of software (code) packages. The hierarchical neural network model consists of a token level encoder and an attention mechanism for a function level encoder, together producing package embedding. Beyond package embedding, we use keywords and API calls as resilient features, which reflect the programmer's intention and style. Pkg2Vec is evaluated on a large dataset of public packages and compared to a number of other source code authorship attribution state of the art algorithms. We find that Pkg2Vec significantly outperforms other approaches, achieving a 13% improvement in accuracy
Secure Channel Free Certificate Based Searchable Encryption Withstanding Outside and Inside Keyword Guessing Attacks
Year: 2020

Searchable public key encryption (SPKE) is a useful public key cryptographic primitive that allows a user to perform keyword searches over publicly encrypted messages on an untrusted storage server while guaranteeing the privacy of the original messages as well as the search keywords. However, most of the previously proposed SPKE frameworks suffer from the security vulnerability caused by the keyword guessing attack and some other weaknesses. Inspired by the ideas of certificate based cryptography and signcryption, we present a new SPKE framework called certificate based searchable encryption. The new framework not only provides resistance to the existing known types of keyword guessing attacks, but also enjoys some appealing merits, such as implicit authentication, no key escrow and no secure channel. Under this new framework, we devise a concrete searchable certificate based encryption scheme. In the random oracle model, it is proven to meet the keyword ciphertext indistinguishability, the keyword ciphertext unforgeability and the keyword trapdoor indistinguishability under the adaptive chosen keyword attack. The comparisons indicate that it is secure and practicable.
SUCCESSFUL SURGICAL MANAGEMENT OF BILATERAL INTERDIGITAL FIBROMA OF FORELIMBS IN A GIR COW
Year: 2017

A Gir cow aged between 7 8 years was brought to the Department of Veterinary Surgery and Radiology with history of lameness and tumorous masses between the claws of both forelimbs. Clinical examination revealed that mass had eroded, ulcerated and infected with maggots leading to more swelling and pain. Based on the history and clinical symptoms, the condition was diagnosed as bilateral interdigital fibroma (IDF) which was successfully managed surgically in both forelimbs. Post operative care was done by using Intacef (3 gm) Meloxicam (10ml) Dexona (5 ml) and Tribivet (10ml) alongwith antiseptic dressing with betadine solution and silversulfadiazine ointment for 5 days. Animal showed uneventful recovery. No recurrence of fibroma was reported upto period of 6 months.  Gir cow, Hyperplasia, Interdigital fibroma, Lameness.
Learning based nonlinear model predictive control to improve vision based mobile robot path tracking in challenging outdoor environments
Venue: 2014 IEEE International Conference on Robotics and Automation (ICRA)
Year: 2014

This paper presents a Learning based Nonlinear Model Predictive Control (LB NMPC) algorithm for an autonomous mobile robot to reduce path tracking errors over repeated traverses along a reference path. The LB NMPC algorithm uses a simple a priori vehicle model and a learned disturbance model. Disturbances are modelled as a Gaussian Process (GP) based on experience collected during previous traversals as a function of system state, input and other relevant variables. Modelling the disturbance as a GP enables interpolation and extrapolation of learned disturbances, a key feature of this algorithm. Localization for the controller is provided by an on board, vision based mapping and navigation system enabling operation in large scale, GPS denied environments. The paper presents experimental results including over 1.8 km of travel by a four wheeled, 50 kg robot travelling through challenging terrain (including steep, uneven hills) and by a six wheeled, 160 kg robot learning disturbances caused by unmodelled dynamics at speeds ranging from 0.35 m/s to 1.0 m/s. The speed is scheduled to balance trial time, path tracking errors, and localization reliability based on previous experience. The results show that the system can start from a generic a priori vehicle model and subsequently learn to reduce vehicle and trajectory specific path tracking errors based on experience.
Gated PE NL MA: A multi modal attention based network for video understanding
Venue: Neurocomputing
Year: 2021

In multi modal learning tasks such as video understanding, the most important operations are feature extraction and feature enhancement for single modality and feature aggregation between modalities. In this paper, we present two attention based algorithms, the Position embedding Non local (PE NL) Network and the Multi modal Attention (MA) feature aggregation method. Inspired by Non local Neural Networks and Transformer, our PE NL is a self attention liked feature enhancement operation and it can capture long range dependencies and model relative positions. The MA aggregation method merges visual and audio modalities while reducing feature dimensions and the number of parameters without losing too much accuracy. Both of PE NL and MA blocks can be plugged into many multi modal learning architectures. Our Gated PE NL MA network achieves competitive results on Youtube 8M dataset.
Price discovery and volatility spillover in spot and futures markets: evidences from steel related commodities in China
Year: 2019

The price discovery and spillover effect are significant indicators in futures markets. This study examines the price discovery and spillover effects using vector error correction model and generalized autoregressive conditional heteroskedastic for seven types of steel products in Chinese spot and futures markets. The results show that the price discovery exists in all of steel futures market. It is also confirmed that futures prices in all items are mainly leading spot prices via permanent transitory and information share. In the results of spillover effects, it is found that wire rod, coking coal, coke and silico manganese have the effects between spot and futures market. In rebar market, there is the spillover effect from spot to futures. This information about futures prices can help the market participants to make decisions when they predict the spot prices.
Vocabulary accuracy of statistical machine translation in the legal context
Venue: AMTA
Year: 2014

This paper examines the accuracy of free online SMT output provided by Google Translate (GT) in the difficult context of legal translation. The paper analyzes English machine translations produced by GT for a large sample of Spanish legal vocabulary items that originate from a voluminous text of judgment summaries produced by the Supreme Court of Spain. Prior to this study, this same text was translated into English but without MT and it was found that the majority of the translation solutions that were chosen for the said vocabulary items could be hand selected from mostly EU databases with versions in English and Spanish. The paper argues that MT in the legal translation context should be worthwhile if the output can consistently provide a reasonable amount of accurate translations of the types of vocabulary items translators in this context often have to do research on before being able to effectively translate them. Much of the currently available translated text used to train SMT comes from international organizations, such as the EU and the UN which often write about legal matters. Moreover, SMT can use the immediate co text of vocabulary items as a way of attempting to identify correct translations in its database.
Amodular approach to integrating multiple data sources 1 into real time clinical prediction for pediatric diarrhea 2
Year: 2021

Traditional clinical prediction models focus on parameters of the individual patient. For 22 infectious diseases, sources external to the patient, including characteristics of prior patients and 23 seasonal factors, may improve predictive performance. We describe the development of a 24 predictive model that integrates multiple sources of data in a principled statistical framework using 25 a post test odds formulation. Our method enables electronic real time updating and flexibility, 26 such that components can be included or excluded according to data availability. We apply this 27 method to the prediction of etiology of pediatric diarrhea, where "pre test" epidemiologic data may 28 be highly informative. Diarrhea has a high burden in low resource settings, and antibiotics are 29 often over prescribed. We demonstrate that our integrative method outperforms traditional 30 prediction in accurately identifying cases with a viral etiology, and show that its clinical application, 31 especially when used with an additional diagnostic test, could result in a 61% reduction in 32 inappropriately prescribed antibiotics.
Predominance of Gastrointestinal Protozoan Parasites in Children: A Brief Review
Year: 2016

Intestinal parasitic infections are among the major diseases of concern to public health throughout the world  About 25% of world's population suffers from one or more kinds of intestinal parasitic infections (helminthes/protozoa) Children because of their complex nutritional requirements and less developed immune systems are observed to be the principal sufferers of these parasitic infections  Moreover, they have an extremely delicate physiology which can result in severe upsetting of their biochemical and physiological processes associated with these infections. Intestinal protozoan parasites can affect children in a variety of ways; they cause mal absorption, reduced growth, increased risk for protein energy malnutrition, reduced psychomotor development and anemia. This report addresses Cryptosporidium, Giardia, and Entamoeba as the main parasitic protozoa of concern among children worldwide.
A review on time lapse seismic data processing and interpretation
Venue: Geosciences Journal
Year: 2015

Time lapse (TL) seismic or 4D seismic has great potential in monitoring and interpreting time varying variations in reservoir fluid properties during hydrocarbon production. The TL surveys interpret dynamic changes in reservoir parameters by detecting differences in seismic responses from different vintages, which are obtained by repeated seismic surveys over the same area. For a proper and more precise quantitative interpretation of the TL seismic responses, TL seismic processing enhances repeatability of the TL data sets, and thus reduces artificial differences that come from unavoidable differences in repeated seismic survey environments. This study aims to bring an insight to the state of the art in both TL seismic data processing and interpretation by reviewing previously published researches. TL data processing is a sequential process conducting 4D binning and simultaneous 4D pre stack processing; 4D binning improves spatial repeatability while "simultaneous 4D pre stack processing" derives common processing operators employing cross equalization (XEQ) as a core technique. TL seismic interpretation is categorized by the characteristics of data being interpreted into two strategies: interpretation of data sets from all TL vintages and interpretation of differences in seismic data between vintages.