nils-herrmann/clean_scirepeval huggingface dataset:
Offline provisioning and Synchronization of Content for Mobile Webapps
Venue: WEBIST
Year: 2015

Despite progress in the development of cross platform frameworks, today's apps often are realized either as native apps or as Webapps. The latter are easy to implement and rely on robust and future proof technologies such as HTML5, Cascading Style Sheets (CSS) and JavaScript. However, Webapps commonly require a stable Internet connection. Recent developments facilitate offline enabled Webapps. If an app is able to store data locally, it should also be enabled to synchronize with a server backend. We assess current HTML5 storage capabilities and discuss their possibilities along with considerations of mobile computing synchronization strategies. Our work includes the compilation of requirements that Webapps have regarding their offline capabilities. Moreover, we discuss the status quo of offline storage and give an outlook based on a proof ofconcept implementation.
A review on the state of art of Internet of Things
Year: 2016

The Internet of Things is fast growing technology with business opportunities and risks. It is confluence of wireless networks, internet and computing. IoT connects the physical objects like vehicles, buildings and other devices with embedded intelligent sensors and enables these objects to exchange and collect data. The domains where IoT is becoming popular are smart cities, e health, smart grids, e commerce, smart transportation, and e commerce etc. The embedded and wearable computing will have greater impact in providing services in wide range of applications by 2020[1] The architecture of IoT is incorporated with the latest technologies of communication protocols, intelligent sensor and RFID. The security and privacy issues of IoT are crucial as it connects large number devices. In this article we analyze a state of art review of IoT with regard to technologies, protocols, application issues.
Abortion: US "global gag rule" is killing women and girls, says report
Venue: BMJ
Year: 2019

A US government policy that blocks federal funding for non governmental organisations that provide or support abortion is killing women, a new report says, because they are being excluded from a range of essential sexual and reproductive health services and information and turning to unsafe abortions. The "global gag rule" rule prohibits foreign non governmental organisations that receive US global health funding from providing legal abortion services or referrals and also bars advocacy for abortion law reform. The rule, first implemented by the Reagan administration and rescinded by subsequent Democratic presidents, was reinstated and expanded by Donald Trump when he took office in 2017. It now affects an estimated $9bn (PS7bn; EUR8bn) of US global health assistance. Two years on, an assessment of services in Kenya, Nepal, Nigeria, and South Africa has found "irrefutable evidence" that the gag rule was "depriving women and girls and other marginalised populations of essential information and services," with devastating  Vanesa Rios, who wrote the report for the US based International Women's Health Coalition, told a press briefing at the Women
Cyber Hate Speech on Twitter: An Application of Machine Classification and Statistical Modeling for Policy and Decision Making
Year: 2015

The use of "Big Data" in policy and decision making is a current topic of debate. The 2013 murder of Drummer Lee Rigby in Woolwich, London, UK led to an extensive public reaction on social media, providing the opportunity to study the spread of online hate speech (cyber hate) on Twitter. Human annotated Twitter data was collected in the immediate aftermath of Rigby's murder to train and test a supervised machine learning text classifier that distinguishes between hateful and/or antagonistic responses with a focus on race, ethnicity, or religion; and more general responses. Classification features were derived from the content of each tweet, including grammatical dependencies between words to recognize "othering" phrases, incitement to respond with antagonistic action, and claims of well founded or justified discrimination against social groups. The results of the classifier were optimal using a combination of probabilistic, rule based, and spatial based classifiers with a voted ensemble meta classifier. We demonstrate how the results of the classifier can be robustly utilized in a statistical model used to forecast the likely spread of cyber hate in a sample of Twitter data. The applications to policy and decision making are discussed.
Structural, mechanical, electronic and magnetic properties of a new series of quaternary Heusler alloys CoFeMnZ (Z=Si, As, Sb) A first principle study
Year: 2015

The structural, mechanical, electronic and magnetic properties of the series of Heusler alloys CoFeMnZ (Z=Si, As, and Sb) have been investigated theoretically. The objective is to seek for stable half metallic ferromagnets materials with Curie temperatures higher than room temperature. The series of CoFeMnZ (Z=Si, As and Sb) is found to exhibit half metallic ferromagnetism with high magnetic moment and the localized moment in these magnetic compounds resides at the Mn atom. It has been observed that all our compounds have high Curie temperatures with high spin polarizations.
Aplikasi Cloud Computing untuk server berbasis django dengan google app engine
Year: 2018

The lack of hosting services that provide hosting with python programming language makes it difficult for website developers using python programming to upload it. With its made this research wil be able to make it easier for python website to upload and save it so it can be access from everywhere. Many conveniences offered in google app engine one of them is a free trial feature where no need to pay anything if you want this platform. Of course there are also weakness on of which there are still to many restrictions on data access. The method used in the preparation of this application is a exam method that was built using django python. The result of this application is where a website with python language that's get website address that will be access by everyone
Solar radiation forecasting using artificial neural network and random forest methods: Application to normal beam, horizontal diffuse and global components
Venue: Renewable Energy
Year: 2019

Three methods, smart persistence, artificial neural network and random forest, are compared to forecast the three components of solar irradiation (global horizontal, beam normal and diffuse horizontal) measured on the site of Odeillo, France, characterized by a high meteorological variability. The objective is to predict hourly solar irradiations for time horizons from h+1 to h+6. The random forest (RF) method is the most efficient and forecasts the three components with a nRMSE from 19.65% for h+1 27.78% for h+6 for the global horizontal irradiation (GHI) a nRMSE from 34.11% for h+1 49.08% for h+6 for the beam normal irradiation (BNI) a nRMSE from 35.08% for h+1 49.14% for h+6 for diffuse horizontal irradiation (DHI) The improvement brought by the use of RF compared to Artificial Neural Network (ANN) and smart persistence (SP) increases with the forecasting horizon. A seasonal study is realized and shows that the forecasting of solar irradiation during spring and autumn is less reliable than during winter and summer because during these periods the meteorological variability is more important.
A New Framework of Intelligent Public Transportation System Based on the Internet of Things
Venue: IEEE Access
Year: 2019

As a new paradigm of information technology, the Internet of Things (IoT) is attracting increasing attention from various industrial fields. It is foreseeable that the applications of IoT will be prevalent in the public transportation system and bring changes to the system in the near future. In this paper, we analyze the impact of IoT environment on the public transportation system, propose a new framework of the intelligent public transportation system based on IoT, and present the deployment of the elements, the communication network, and the three tier architecture of the system in detail. We also present the information flow, technical scheme, optimization model, and algorithm of the main modules of dynamic optimization of the system. The innovative points of this paper lie in: (1) a new framework for public transport system based on IoT, which integrates the scheduling problems of subway, bus, and shared taxi, is proposed for better coordinated transfer solutions; (2) transport flow prediction methods based on periodic patterns mining is proposed for road flow analysis and passenger flow analysis, and; (3) mathematical model and DSS based evolutionary computation algorithm are proposed for solving the dynamic bus scheduling and controlling problems. The proposed intelligent transport system based on IoT can assist the decision makers to increase the utilization rate of the transport resources, improve the efficiency of scheduling, and reduce passengers' traveling time.
Testing Contextualized Word Embeddings to Improve NER in Spanish Clinical Case Narratives
Year: 2020

In the Big Data era there is an increasing need to fully exploit and analyse the huge quantity of information available about health. Natural Language Processing (NLP) technologies can contribute to extract relevant information from unstructured data contained in Electronic Health Records (EHR) such as clinical notes, patient's discharge summaries and radiology reports among others. Extracted information could help in health related decision making processes. Named entity recognition (NER) devoted to detect important concepts in texts (diseases, symptoms, drugs, etc. is a crucial task in information extraction processes especially in languages other than English. In this work, we develop a deep learning based NLP pipeline for biomedical entity extraction in Spanish clinical narrative.  We explore the use of contextualized word embeddings to enhance named entity recognition in Spanish language clinical text, particularly of pharmacological substances, compounds, and proteins. Various combinations of word and sense embeddings were tested on the evaluation corpus of the PharmacoNER 2019 task, the Spanish Clinical Case Corpus (SPACCC) This data set consists of clinical case sections derived from open access Spanish language medical publications.  NER system integrates in domain pre trained Flair and FastText word embeddings, byte pairwise encoded and the biLSTM based character word embeddings. The system yielded the best performance measure with F score of 90.84% Error analysis showed that the main source of errors for the best model is the newly detected false positive entities with the half of that amount of errors belonged to longer than the actual ones detected entities.  Our study shows that our deep learning based system with domainspecific contextualized embeddings coupled with stacking of complementary embeddings yields superior performance over the system with integrated standard and general domain word embeddings. With this system, we achieve performance competitive with the state of the art.  Liliya Akhtyamova, MCs Technological University Dublin IRELAND Corresponding Author E Mail: akhtyamova@phystech.edu Corresponding Author Secondary Information: Corresponding Author's Institution: Technological University Dublin Corresponding Author's Secondary Institution: First Author: Liliya Akhtyamova, MSc First Author Secondary Information:  Liliya Akhtyamova, MSc Paloma Martinez, PhD
Investigation of Online Learning Satisfaction During COVID 19: In Relation to Academic Achievement
Year: 2020

The aim of this research is; 1) investigating the level of online learning satisfaction among students during COVID 19; 2) analyzing the influence of differences in gender, years of study, major in determining online learning satisfaction among students during COVID 19; 3) to analyze the relationship between online learning satisfaction and student academic achievement during COVID 19. The population was 656 students at STKIP Singkawang, and then a sample of 357 students (87 males and 270 females) was taken using a simple random sampling technique. The instrument in this study was adapted from Aman's Satisfaction instrument, which was then used to collect research data. Data analysis using SPSS with descriptive statistical techniques, MANOVA, and correlation. The results showed that online learning satisfaction was at a high level, meaning that students were satisfied with the online learning that had been implemented. The major differences have a significant effect on determining online learning satisfaction. Intercorrelation shows that there is a significant relationship on each indicator of online learning satisfaction with academic achievement, meaning that the higher the satisfaction felt by students in online learning, the student's academic achievement will increase.
Entity Skeletons for Visual Storytelling
Year: 2020

We are enveloped by stories of visual interpretations in our everyday lives. Story narration often comprises of two stages, which are, forming a central mind map of entities and then weaving a story around them. In this paper, we address these two stages of introducing the right entities at seemingly reasonable junctures and also referring them coherently in the context of visual storytelling. The building blocks of the central mind map, also known as entity skeleton are entity chains including nominal and coreference expressions. We establish a strong baseline for skeleton informed generation and propose a glocal hierarchical attention model that attends to the skeleton both at the sentence (local) and the story (global) levels. We observe that our proposed models outperform the baseline in terms of automatic evaluation metric, METEOR. We also conduct human evaluation from which it is concluded that the visual stories generated by our model are preferred 82% of the times.
Lack of an Association between the TGFBR1*6A Variant and Colorectal Cancer Risk
Venue: Clinical Cancer Research
Year: 2007

Recently a common variant of the TGFBR1 gene, TGFBR1*6A, has been proposed to act as a low penetrance tumor susceptibility allele for colorectal cancer, but data from published studies with individually low statistical power are conflicting. To further evaluate the relationship between TGFBR1*6A and colorectal cancer risk, we have conducted a large case control study and a meta analysis of previously published studies.  A total of 1,042 colorectal cancer cases and 856 population controls were genotyped for the TGFBR1*6A polymorphism. Previously published case control studies of the relationship between TGFBR1*6A and colorectal cancer were identified, and a meta analysis was conducted.  We found no evidence that homozygosity, heterozygosity or carrier status for the TGFBR1*6A allele confers an increased risk of colorectal cancer; respective odds ratios (OR) were 1.05 [95% confidence interval (95% CI) 0.83 1.32] 0.82 (95% CI, 0.34 1.99) and 0.92 (95% CI, 0.74 1.15) respectively. A meta analysis of our case control study and seven other studies that provided data on 2,627 colorectal cancer cases and 3,387 controls also yielded no evidence that possession of the TGFBR1*6A allele is associated with an elevated risk of colorectal cancer; pooled estimate of the OR were 1.20 (95% CI, 0.64 2.24) for homozygosity, 1.11 (95% CI, 0.96 1.29) for heterozygosity, and 1.13 (95% CI, 0.98 1.30) for carriers of TGFBR1*6A.  Current data provide limited support for the hypothesis that sequence variation in TGFBR1 defined by the TGFBR1*6A allele confers an elevated risk of colorectal cancer.
VIZAG GAS LEAK A CASE STUDY ON THE UNCONTROLLED STYRENE VAPOUR RELEASE FOR THE FIRST TIME IN INDIA
Year: 2020

Industrial emergencies involving hazardous chemicals are not uncommon in India. During the last several decades there has been a growing awareness of the expanding risks and consequences of major industrial disasters. Industrial hazards can occur at any stage in the production process, including extraction, processing, manufacture, transportation, storage, use, and disposal. Losses generally involve the release of damaging substances (e.g. chemicals, radioacvity) or damaging levels of energy from industrial facilities or equipment into surrounding environments. This usually occurs in the form of explosions, fires, spills, leaks, or wastes. In a recent occurrence on the wee hours of 7th May 2020, an accident of uncontrolled release of Styrene vapor occurred at LG Polymers India Pvt. Ltd. (LG Polymers) in RR Venkatapuram Village, Visakhapatnam from one of the Styrene storage tanks. The hazardous Styrene vapors spread beyond the factory premises, affecting the populace of five villages habitations. The officials from the District administration along with Police Department, GVMC, National Disaster Response Force (NDRF) and local people rushed to the spot for rescue operations. However, the grave accident led to 12 citizens losing their lives and 585 citizens being hospitalized. In this regard let's take a brief look into the causes of the accident which led to this
Explanation Generation as Model Reconciliation in Multi Model Planning
Venue: ArXiv
Year: 2017

When AI systems interact with humans in the loop, they are often called on to provide explanations for their plans and behavior. Past work on plan explanations primarily involved the AI system explaining the correctness of its plan and the rationale for its decision in terms of its own model. Such soliloquy is wholly inadequate in most realistic scenarios where the humans have domain and task models that differ significantly from that used by the AI system. We posit that the explanations are best studied in light of these differing models. In particular, we show how explanation can be seen as a "model reconciliation problem" (MRP) where the AI system in effect suggests changes to the human's model, so as to make its plan be optimal with respect to that changed human model. We will study the properties of such explanations, present algorithms for automatically computing them, and evaluate the performance of the algorithms.
Experience modulates an insect's response to anthropogenic noise
Venue: Behavioral ecology official journal of the International Society for Behavioral Ecology
Year: 2020

In response to anthropogenic noise, vertebrates express modified acoustic communication signals either through individual plasticity or local population adaptation. In contrast, how insects respond to this stressor is poorly studied. Field crickets Gryllus bimaculatus use acoustic signals to attract and locate mates and are commonly found in noisy roadside environments, offering a powerful system to study the effects of anthropogenic noise on insect communication. Rapid repetition of sexual calls (chirps) is essential to attract females, but calling incurs energetic costs and attracts predators. As a result, males are predicted to reduce calling rates when background noise is high. Here, we combine observations and experimental playbacks to show that the responses of field cricket males to anthropogenic noise also depend on their previous experience with passing cars. First, we show that males living on highway edges decrease their chirp rate in response to passing cars. To assess whether this behavioral response depends on previous exposure to car noise, we then broadcast recordings of car noise to males located at different distances from the road and, therefore, with different previous exposure to car noise. Although all tested individuals responded to broadcasted traffic noise, males closest to the road decreased their chirp rate less than individuals calling further from the road. These results suggest that regular exposure to anthropogenic noise may decrease individuals' sensitivity and behavioral responses to noise, allowing them to maintain effective signaling rates. Behavioral plasticity modulated by experience may thus allow some insect species to cope with human induced environmental stressors.
Numerical simulation and analysis of heat leakage reduction in loop heat pipe with carbon fiber capillary wick
Year: 2019

In this paper, a loop heat pipe (LHP) with a carbon fiber capillary wick was taken as the research object to study the steady state inside the LHP and validate the effectiveness of proposed improvements through CFD numerical simulations. A 3D model of the LHP was built and numerical simulations of steady state operation were conducted by ANSYS software. Through comparison of experiment and simulation results, heat leakage from the heated surface to the compensation chamber (CC) was identified as the major cause of failure in the feasibility experiment. The addition of a heat insulation layer on the contact surface between shells of the evaporator and the compensator was validated as an improvement in reducing heat leaking through the metal shells. The substitute of the copper plating by an alumina copper coating on surface of the carbon fiber capillary wick was proposed as an improvement to decrease the heat transferring through the capillary wick. The simulation shows that a silicon sheet with low thermal conductivity can increase the liquid volume fraction in the CC to 23% while an yttria stabilized zirconia (YSZ) heat insulation layer can raise the figure to 74% According to simulation results, the combination of YSZ layer and alumina copper coating can realize a temperature drop of 5.7 K comparing with the combination of silicon sheet and copper plating. The optimized experiment indicates that the use of the heat insulation layers improves stability of operation and increases bearing heat load of the LHP. The dependability of simulation results and the rationality of experimental error were proved by comparing results of experiment and simulation under the same operation conditions.
Experimental and numerical study of SO2 removal from a CO2/SO2 gas mixture in a Cu BTC metal organic framework.
Venue: Journal of molecular graphics modelling
Year: 2020

The mechanism of SO2 removal from a CO2/SO2 gas mixture in a copper benzene 1, 3, 5 tricarboxylate (Cu BTC) material is investigated at the molecular level by the grand canonical Monte Carlo method. The effects of seven kinds of force field relationships among CO2, SO2 and Cu BTC on the selectivity for a SO2/CO2 gas mixture at different temperatures are studied in detail. The accuracy of the simulation model is validated by the experimental data. The results show that more SO2 molecules are adsorbed than CO2, and the electrostatic interactions involving SO2 are more sensitive to temperature than CO2 is. The multilayer desorption for SO2 and CO2 occurs in large square channels. The effect of the electrostatic interactions involving SO2 is stronger than the interactions of CO2. The forms of CO2 and SO2 adsorption in Cu BTC with electrostatic interactions are Cu2+OCO and Cu2+OSO, respectively.