nils-herrmann/clean_scirepeval huggingface dataset:
High Cycle Fatigue Damage Mechanisms of MAR M 247 Superalloy at High Temperatures
Venue: Transactions of the Indian Institute of Metals

High cycle fatigue tests of the MAR M 247 superalloy under constant load were conducted with the aim to determine S N curves at temperatures 650, 800, 900 and 950 degC. It has been found that the slope of the S N curves reflects changes of the cyclic deformation mechanism with temperature. At temperatures 650 and partly at 800 degC, fatigue crack propagation exhibits crystallographic character (propagation along the {111} system) in early stages of the fatigue life. The crystallographic mode of the fatigue crack propagation was found to disappear at temperatures 900 and 950 degC.
Stage I fatigue cracking in MAR M 247 superalloy at elevated temperatures

Nickel base superalloys exhibit fatigue fracture behavior with features of brittle like cleavage cracking under high cycle fatigue loading at temperatures up to approximately 800 degC. This specific fracture behavior was already documented in several studies, but a possible mechanism of fatigue crack propagation under this mode has not been made completely clear yet. The aim of this paper is to put more light on the phenomenon by using advanced electron microscopy techniques like electron back scattered diffraction (EBSD) and focused ion beam (FIB) sectioning. Fractured specimens after high cycle fatigue tests were thoroughly examined with the aim to localize the fatigue crack initiation sites and accompanying features of the fatigue crack propagation. Several specimens were cross sectioned in order to characterize active slip systems, cyclic plastic deformation localization and fatigue crack propagation. Dislocation structures were studied by transmission electron microscopy (TEM)
Microbiological Safety of Street Vended Foods in Jigjiga City, Eastern Ethiopia
Venue: Ethiopian journal of health sciences

Food safety problems are particularly becoming an increasingly serious threat to public health in developing countries. This study was conducted to assess microbiological safety of street vended foods from May to November, 2014 in Jigjiga City.  A cross sectional design was used to answer questions concerning the current status of food hygiene and sanitation practire of street food vending sites. Interview and observational assessments were used to collect socio demographic data about street food venders. One hundred thirty two samples of street foods were aseptically collected from four 'kebeles' of Jigjiga City. Both descriptive and analytical statistical methods were applied.  The majority of the street food vendors were women, 120(90.9% with the average age group of 23 49 years, (42.85% and 99(66.7% them were illiterate. The study revealed that 95(72% of the food samples had pathogenic bacterial contaminations. Three different bacterial species were isolated: E. coli 68(51.5% S. aureus 85(64.4% and 26(19.7% Salmonella species. The highest incidence of S. aureus 23/33(69% was seen in 'Sambusa' the highest incidence of E. coli 24/33(73.5% was observed in 'Pasta' while the highest Salmonella incidence was observed in 'Ades'  This study revealed that there is a reasonable gap on food safety knowledge among street food venders. The microbial profile was also higher compared to standards set by the World Health Organization. Due attention should be given by the government to improve knowledge about food safety and the quality standard of street foods sold in the City.
YouTube 8M: A Large Scale Video Classification Benchmark
Venue: ArXiv

Many recent advancements in Computer Vision are attributed to large datasets. Open source software packages for Machine Learning and inexpensive commodity hardware have reduced the barrier of entry for exploring novel approaches at scale. It is possible to train models over millions of examples within a few days. Although large scale datasets exist for image understanding, such as ImageNet, there are no comparable size video classification datasets. In this paper, we introduce YouTube 8M, the largest multi label video classification dataset, composed of ~8 million videos (500K hours of video) annotated with a vocabulary of 4800 visual entities. To get the videos and their labels, we used a YouTube video annotation system, which labels videos with their main topics. While the labels are machine generated, they have high precision and are derived from a variety of human based signals including metadata and query click signals. We filtered the video labels (Knowledge Graph entities) using both automated and manual curation strategies, including asking human raters if the labels are visually recognizable. Then, we decoded each video at one frame per second, and used a Deep CNN pre trained on ImageNet to extract the hidden representation immediately prior to the classification layer. Finally, we compressed the frame features and make both the features and video level labels available for download. We trained various (modest) classification models on the dataset, evaluated them using popular evaluation metrics, and report them as baselines. Despite the size of the dataset, some of our models train to convergence in less than a day on a single machine using TensorFlow. We plan to release code for training a TensorFlow model and for computing metrics.
Polymer Based Nanocomposite Coatings for Anticorrosion Applications

The successful use of polymeric coatings for corrosion prevention or mitigation is often hindered by their inherently porous microstructure that fails to resist the ingress of detrimental species and/or by their vulnerability to damage by surface abrasion, wear, or scratches. Incorporation of nanomaterials in polymeric coatings can greatly improve their barrier performance. While the last decade has seen a substantial amount of research on polymeric nanocomposite coatings, the knowledge underlying the critical roles nanomaterials play remains scattered. This chapter discusses the utilization of nanotechnology to greatly enhance the properties of polymer based coatings for anticorrosion applications, by modifying the microstructure of the coating bulk or endowing it with additional functionality. It starts with a brief discussion of the relevant knowledge base, including: microstructure of polymer nanocomposites, influence of nanomodification on properties of polymeric coatings, fabrication approaches, and the use of polymeric nanocoating as a carrier for corrosion inhibitors. It also provides a review of technological advances in the use of nanotechnology to produce high performance polymeric coatings with outstanding corrosion resistance and other relevant properties. The chapter concludes with a snapshot review of the advanced characterization of nanocomposite coatings for corrosion protection.
Winning Faces Vary by Ideology: How Nonverbal Source Cues Influence Election and Communication Success in Politics

Not just the content of a communication but also the source of the communication shapes its persuasiveness. Recent research in political communication suggests that important source cues are nonverbal and relate to the physical traits of the source such that attractive and competent looking sources have better success in attracting votes and policy support. Yet, are all nonverbal source cues similarly received irrespective of audience, or does their reception vary across audiences? Specifically, we ask whether some physical traits are received positively by some audiences but backfire for others. Utilizing research on ideological stereotypes and the determinants of facial preferences, we focus on the relationship between the facial dominance of the source and the ideology of the receiver. Across five studies, we demonstrate that a dominant face is a winning face when the audience is conservative but backfires and decreases success when the audience is liberal. On the other hand, a non dominant face constitutes a winning face among liberal audiences but backfires among conservatives. These effects seemingly stem from deep seated psychological responses and shape both the election and communication success of real world politicians. If the faces of politicians do not match the ideology of their constituency, they are more likely to lose in the competition for votes and policy support.
An adaptive model selection strategy for surrogate assisted particle swarm optimization algorithm
Venue: 2016 IEEE Symposium Series on Computational Intelligence (SSCI)

Computationally expensive problems pose a serious challenge to the successful application of evolutionary algorithms to complex engineering optimization. To address this challenge, surrogate models, also known as metamodels, are commonly used in lieu of the expensive fitness function for the computational cost of optimization. However, it is nontrivial to choose an appropriate metamodel for properly replacing the expensive fitness function. In this paper, an adaptive model selection strategy based on fitness landscape analysis is proposed for a surrogate assisted particle swarm optimization algorithm. The structure of the sampling space is learned, based on which the more suited surrogate model, either a polynomial regression model or a radial basis function network, will be chosen to estimate the fitness value. Simulation results on seven widely used benchmark functions demonstrate the efficacy of the proposed algorithm.
Arginine Patch Predicts the RNA Annealing Activity of Hfq from Gram Negative and Gram Positive Bacteria.
Venue: Journal of molecular biology

The Sm protein Hfq facilitates interactions between small non coding RNA (sRNA) and target mRNAs. In enteric Gram negative bacteria, Hfq is required for sRNA regulation, and hfq deletion results in stress intolerance and reduced virulence. By contrast, the role of Hfq in Gram positive is less established and varies among species. The RNA binding and RNA annealing activity of Hfq from Escherichia coli, Pseudomonas aeruginosa, Listeria monocytogenes, Bacillus subtilis, and Staphylococcus aureus were compared using minimal RNAs and fluorescence spectroscopy. The results show that RNA annealing activity increases with the number of arginines in a semi conserved patch on the rim of the Hfq hexamer and correlates with the previously reported requirement for Hfq in sRNA regulation. Thus, the amino acid sequence of the arginine patch can predict the chaperone function of Hfq in sRNA regulation in different organisms.
The Impact of Social Media on Student Academic Life in Higher Education

Social media is rapidly changing the communication setting of todayaEUR(tm)s social world. The emergent of social media is significantly influencing the academic life of students. Institutions and academician are continually trying with social media technologies hoping to excite critical thinking skills, collaboration, and knowledge construction. Today social media has been accepted by higher institution making it a platform where students connect with their instructors, fellow students and other higher authorities across the board. This therefore called for the study to explore and examine how social media has impacted on studentsaEUR(tm) academic life. The study implored a qualitative approach in assessing these impacts. Ten (10) participants were conveniently sampled and interviewed with a period of two weeks. After studying the phenomena that were of interest to the study, and transcribing the various responses of the participants the results reveals that social media is widely used by students of higher institution and that participant are in support of the idea that social media contribute a significant quota to the development of their academic life.
Semi Martingales Et Grossissement Dune Filtration

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BigCloneEval: A Clone Detection Tool Evaluation Framework with BigCloneBench
Venue: 2016 IEEE International Conference on Software Maintenance and Evolution (ICSME)

Many clone detection tools have been proposed in the literature. However, our knowledge of their performance in real software systems is limited, particularly their recall. We previously introduced our BigCloneBench, a big clone benchmark of over 8 million clones within a large inter project Java repository containing 25,000 open source Java systems. In this paper we present BigCloneEval, a framework for evaluating clone detection tools with BigCloneBench. BigCloneEval makes it very easy for clone detection researchers to evaluate and compare clone detection tools. It automates the execution and evaluation of clone detection tools against the reference clones of BigCloneBench, and summarizes recall performance from a variety of perspectives, including per clone type, and per syntactical similarity regions.
Examining the Coherence of the Top Ranked Tweet Topics
Venue: SIGIR

Topic modelling approaches help scholars to examine the topics discussed in a corpus. Due to the popularity of Twitter, two distinct methods have been proposed to accommodate the brevity of tweets: the tweet pooling method and Twitter LDA. Both of these methods demonstrate a higher performance in producing more interpretable topics than the standard Latent Dirichlet Allocation (LDA) when applied on tweets. However, while various metrics have been proposed to estimate the coherence of the generated topics from tweets, the coherence of the top ranked topics, those that are most likely to be examined by users, has not been investigated. In addition, the effect of the number of generated topics K on the topic coherence scores has not been studied. In this paper, we conduct large scale experiments using three topic modelling approaches over two Twitter datasets, and apply a state of the art coherence metric to study the coherence of the top ranked topics and how K affects such coherence. Inspired by ranking metrics such as precision at n, we use coherence at n to assess the coherence of a topic model. To verify our results, we conduct a pairwise user study to obtain human preferences over topics. Our findings are threefold: we find evidence that Twitter LDA outperforms both LDA and the tweet pooling method because the top ranked topics it generates have more coherence; we demonstrate that a larger number of topics (K) helps to generate topics with more coherence; and finally, we show that coherence at n is more effective when evaluating the coherence of a topic model than the average coherence score.
VERSE: Event and Relation Extraction in the BioNLP 2016 Shared Task
Venue: BioNLP

We present the Vancouver Event and Relation System for Extraction (VERSE)1 as a competing system for three subtasks of the BioNLP Shared Task 2016. VERSE performs full event extraction including entity, relation and modification extraction using a feature based approach. It achieved the highest F1 score in the Bacteria Biotope (BB3) event subtask and the third highest F1 score in the Seed Development (SeeDev) binary subtask.
Incremental Global Event Extraction
Venue: COLING

Event extraction is a difficult information extraction task. Li et al. (2014) explore the benefits of modeling event extraction and two related tasks, entity mention and relation extraction, jointly. This joint system achieves state of the art performance in all tasks. However, as a system operating only at the sentence level, it misses valuable information from other parts of the document. In this paper, we present an incremental easy first approach to make the global context of the entire document available to the intra sentential, state of the art event extractor. We show that our method robustly increases performance on two datasets, namely ACE 2005 and TAC 2015.
Automatic Detection of Similarity of Programs in Karel Programming Language based on Natural Language Processing Techniques
Venue: Computacion y Sistemas

In this paper, we present a method for calculating similarity between programs (source codes) One of the applications of the task is detection of code reuse, for example, in the case of plagiarism. The Karel programming language is used for experiments. In order to determine similarity between Karel programs and/or similar software solutions, we make use of techniques from the fields of natural language processing and information retrieval. These techniques use representations of documents as vectors of features and their values. Usually, the features are n grams of words or n grams of characters. In addition, we consider application of the latent semantic analysis for reduction of the number of dimensions of the vector space. Finally, we use a supervised machine learning approach for classification of texts (or programs, which are texts as well) based on their similarity. For evaluation of the proposed method, two corpora were developed: the first corpus is composed of 100 different programs with a total of 9,341 source codes. The second corpus consists of 34 tasks with a total of 374 codes, which are grouped by the proposed solution. Our experiments showed that for the first corpus, the best results were obtained using trigrams of terms (words) accompanied with application of latent semantic analysis, while for the second corpus, the best representation was achieved using character trigrams.
Monitoring migrants or making migrants 'misfit' Data protection and human rights perspectives on Dutch identity management practices regarding migrants
Venue: Comput. Law Secur. Rev.

Record numbers of migrants and refugees fleeing violence and poverty in parts of Africa and the Middle East present the European Union with unprecedented challenges, including in determining their identity as well as status. In recent years problems of identifying immigrants have been addressed in order to fight identity fraud and illegal entry of migrants. As a result, a wide variety of digital systems have been introduced to orchestrate an effective, preventative modus of identification of migrants. Digital systems are in particular geared towards spotting those migrants who (are about to) commit identity fraud or who enter the territory of EU member states illegally. Although the key aim of the digital systems is framed to protect the administrative, geographic and legal borders of the member state and the safety of its population, empirically based studies demonstrate that these systems bring new risks for migrants themselves. This article intends to contribute to the discussion on the use of digital systems for managing the movement of migrants by analysing identification and risk assessment systems from the perspective of the new European data protection regime and the European Convention on Human Rights. For this purpose, two identification systems the so called INS console within the Dutch immigration and border sector, and the PROGIS console within the law enforcement sector are analysed. A third is the Advanced Passenger Information system operated at Schiphol Airport by border control and immigration services. Against the background of the position of many migrants finding themselves at risk in their home country and of the two legislative frameworks mentioned above, this article addresses two issues. First, the analysis focuses on how migrants are perceived by digital monitoring practices: are they themselves at risk, non risk or do they pose a risk? In the EU, migrants must prove that their case is worthy of asylum status because they are 'at risk' from political unrest or other life threatening circumstances in their home country. Yet, empirical data gathered through semi structured interviews show that simply abiding by the standards during an enrolment process of the INS console, rather than being 'at risk' a migrant can easily be categorised as 'posing a risk' (La Fors Owczynik Van der Ploeg, 2015) Second, this article aims to investigate what the capacity of the new data protection regime is in protecting migrants from being framed as 'a risk' or a 'misfit' stemming from the use of digital systems. Given this second aim, the following discussion also intends to explore the extent to which the European Convention on Human Rights can provide an additional legal remedy for migrants being digitally categorised in a manner that is detrimental to them.
City "Green" Contributions: The Role of Urban Greenspaces as Reservoirs for Biodiversity

Urbanization poses important environmental, social, and ecological pressures, representing a major threat to biodiversity. However, urban areas are highly heterogeneous, with some greenspaces (e.g. urban forests, parks, private gardens) providing resources and a refuge for wildlife communities. In this study we surveyed 10 taxonomic groups to assess their species richness and composition in six greenspaces that differ in size, location, management, and human activities. Species richness differed among taxonomic groups, but not all differed statistically among the studied greenspaces (i.e. sac fungi, bats) Plants, basidiomycetous and sac fungi, and birds showed intermediate assemblage composition similarity <54% The composition of assemblages of copro necrophagous beetles, grasshoppers, amphibians, and bats was related to the specific traits of greenspaces, mainly size and location. The species richness contribution of each greenspace considering all studied taxonomic groups was highest in the largest greenspace that is located at the southeastern border of the city, while the lowest contribution was recorded in the smallest ones, all of them closer to the city's center. Our results shed some light on the way in which different taxonomic groups respond to an array of neotropical urban greenspaces, providing an important basis for future studies.
Combining background subtraction and three frame difference to detect moving object from underwater video
Venue: OCEANS 2016 Shanghai

Moving object detection is the basis of object tracking and classification in computer vision, which has been applied to underwater robots to execute underwater missions and do marine ecological research. The complicated scene and poor lighting condition in underwater environment usually make moving object detection difficult. To solve above problems and to detect moving object from underwater video, we propose an approach combining background subtraction and three frame difference. In this method, firstly we detect moving object pixels by background subtraction and three frame difference perspectively. Next, we perform "AND" operation on the results of background subtraction and three frame difference, background subtraction provides the object information to supplement the incomplete information detected from three frame difference. Lastly, morphology processing is utilized on the result to remove the noise caused by non static objects in background. This method shows an effective and reliable performance in detecting moving object from underwater video.