dataesr/scientific-paragraphs-categorization huggingface dataset:
The BoF approach is also widely used in a supervised fashion for solving a classification task  [22]. In this case, each class of scenes from a given typology, say {park, boulevard, square}, is modeled by a GMM trained on scenes taken from a training set. In order to predict the class of a given scene s, the likelihood each model given the scene are computed. The scene is then labeled park if the likelihood of the GMM trained on park scenes is higher than all other likelihoods. While BoF has largely been superseded by more sophisticated methods for the task of acoustic scene classification  [22] , it remains the best-performing model for acoustic scene similarity retrieval. Though, this representation was recently shown to perform comparably to direct averaging of the features for a variety of datasets  [12].
Logarithmic compression Many algorithms in pattern recognition, including nearest neighbor classifiers and support vector machines, tend to work best when all features follow a standard normal distribution across all training instances  [34]. Yet the distribution of the scattering coefficients is skewed towards larger values. We can reduce this skewness by applying a pointwise concave transformation to all coefficients. In particular, we find that the logarithm performs particularly well in this respect. Figure  2  shows the distribution of an arbitrarily chosen scattering coefficient over the DCASE 2013 dataset, before and after logarithmic compression. [Fig. 2.
Standardisation Let Sx(γ, n) be a dataset, where γ and n denote feature and sample indices, respectively. Many algorithms operate better on features which have zero mean and unit variance to avoid mismatch in numeric ranges  [34]. To standardise Sx(γ, n), we subtract the sample mean vector µ[Sx(γ)] from Sx(γ, n) and divide the result by the sample standard deviation vector σ[Sx](γ). The vectors µ[Sx(γ)] and σ[Sx](γ) are estimated from the entire dataset.
, c u M } of M clusters. This partition is obtained by minimizing the variance of each cluster and known as a k-means clustering  [35]. Each scene s u is then described by a set of clusters C u. Note that this quantization approach differs from unsupervised learning schemes such as the ones studied in  [36] , where the scene features are projected in a dictionary learned from the entire dataset. Here, with the aim of better balancing the influence of salient sound events and texture-like sounds on the final decision, the similarity between two scenes is computed based on the similarity of their centroids. The similarity between the scene centroids µ u m over the entire dataset, is computed using a radial basis function (RBF) kernel K combined with a local scaling method  [37] : K uv mn = exp - µ u m -µ v n 2 µ u m -µ u m,q µ v n -µ v n,q.
Dataset The experiments in this paper are carried out on the publicly available DCASE 2013 dataset  [22]. Although the dataset was constructed for the task of acoustic scene classification, where the goal is to correctly assign the class of a given recording, we can use the same recordings and class labels for the task of acoustic scene similarity retrieval. The dataset consists of two parts, a public and a private subset, each made up of 100 acoustic scene recordings sampled at 44100 Hz and 30 seconds in duration. The dataset is evenly divided into 10 acoustic scene classes: bus, busy street, office, open air market, park, quiet street, restaurant, supermarket, tube, and tube station. The recordings were made by three different recordists at a wide variety of locations in the Greater London area over a period of several months.
In order to avoid any correlation between recording conditions and label distribution, all recordings were carried out under moderate weather conditions, at varying times of day and week, and each recordist recorded each scene type. As a result, the dataset enjoys significant intra-class diversity while remaining of manageable size, making it suitable for evaluation of algorithmic design choices  [12]. As an illustration, Figure  3  represents the wavelet scalogram of one recording within the DCASE 2013, labeled as park. [Fig. 3  about here. ].
Evaluation and algorithm The evaluation is performed on the private part of the DCASE 2013 dataset. As a metric, we use the precision at rank k (p@k). This number is computed by taking a query item and counting the number of items of the same class within the k closest neighbors, and then averaging over all query items. We determine these neighbors using one of the proposed similarity measures RbQc, RbQ-a, or RbQ-w. We compute p@k for k = {1,. , 9}, since each class only has 10 items. Note that p@1 is equal to the classification accuracy obtained by the nearest-neighbor classifier in a leave-one-out cross-validation setting. The RbQ measures are compared to commonly used early integration approach early, which consists in averaging over time the feature set of each scene, resulting in one feature vector per scene.
The distance on this average feature vector is then used to determine p@k. For the BoF approach of Aucouturier et al. [9] , GMMs are estimated for each scene using the expectation-maximization (EM) algorithm  [39, 40]. The similarity between a given pair of scene GMMs is then calculated through Monte Carlo sampling approximation. To ensure convergence of the EM algorithm for the scattering features, we reduce their dimension from 1367 to 30 by projecting the features onto the top 30 principal components of the dataset. the number of Gaussians is optimized for each type of features by grid search in the range  [2, 20]. Best p@5 is reached with 8 and 4 Gaussians, respectively for MFCCs and scattering features. Recommended number of Gaussians for MFCCs given in  [9]  is 10. The scaling parameter q of the RBF kernels (see Eq.
about here. ] 8 Conclusion This paper presents a new approach for modeling acoustic scenes based on scattering transforms at small scales and cluster-based representations at large scales. Compared to traditional BoF and summary statistics models, this representation allows for the characterization of distinct sound events superimposed on a stationary texture, a concept which has strong grounding in the cognitive psychology literature. To adequately capture such distinct events, we develop a cluster-based model and validate it using experiments on acoustic scene similarity retrieval. For this task, we show significant improvements over the traditional BoF and summary statistics models based on both standard MFCCs and scattering features. These outcomes shall be studied further in future work by considering larger databases and emerging tasks in ecoacoustics  [1, 41].
Availability of data and materials The dataset supporting the conclusions of this article is available in the dcase2013 repository, http://c4dm. eecs. qmul. ac. uk/ sceneseventschallenge/description. html. The software supporting the conclusions of this article is available in -Companion website: https://github. com/mathieulagrange/ paperRelevanceBasedSimilarity -Programming language: Matlab -License: GNU GPL -Any restrictions to use by non-academics: license needed.
The EDAM Browser is a client-side web-based visualization javascript widget for the navigation of the EDAM ontology  (Ison et al. 2013). EDAM is a structured and controlled vocabulary describing bioinformatics and computational biology concepts. It is used to annotate resource descriptions (tools, web services) for discovery  (Ison et al. 2015)  and integration in platforms such as galaxy  (Afgan et al. 2016). The EDAM Browser provides users a simple and performant interface to explore EDAM when annotating or searching for bioinformatics resources.
User interface and functionalities The user interface (see Fig. 1 ) is divided in two main panels, a tree representation of the ontology on the left, and a properties card on the right. The tree on the left represents the different parts of the ontology (formats, data, operations and topics). By expanding or closing the nodes, users explore the hierarchy of the EDAM concepts. The selected concept is displayed in bold, and all the path(s) leading from the root to it are highlighted. The properties card panel on the right displays the main properties of the concept, such as its definition, comments, synonyms, and its non-hierarchical relations to other concepts (e. g. an operation has inputs, outputs, and belongs to a given topic). It also includes counts and links to entries in various databases that use this term, such as bio.
Conclusion The EDAM Browser is publicly available at https://ifb-elixirfr. github. io/edam-browser/. The interface displays all of the information necessary to users, and avoids ontology development jargon. The EDAM Browser is not a generic ontology navigation and/or edition platform, like other systems such as AberOWL  (Hoehndorf et al. 2015) , BioPortal  (Whetzel et al. 2011) , OLS -Ontology Lookup Service  (Jupp et al. 2015) , Ontobee  (Xiang et al. 2011)  and WebProtégé  (Tudorache et al. 2013). Rather, it is tailored to the structure and properties of EDAM, and targets both contributors and users who might not be ontology experts. With this interface, users can both explore quickly and efficiently EDAM, and contribute their own expertise to the ontology.
Scoping searches Scoping searches on the review topic were conducted in August 2016 and indicate a dearth of review materials available relating to cholera diagnosis. Searches included PROSPERO, the Cochrane Library and MEDLINE; the former databases were searched using the term "cholera"; in MEDLINE, the terms "diagnosis" or "detection" were added. PROSPERO indicated five systematic reviews on cholera are on-going  [15] [16] [17] [18] [19] ; topics ranged from the safety and effectiveness of antibacterial treatment  [15]  to the effectiveness of oral cholera vaccine when used reactively  [18]. The Cochrane Library held records of eight Cochrane  [20] [21] [22] [23] [24] [25] [26] [27]  and four Centre for Reviews and Dissemination (CRD) reviews of similar focus to PROS-PERO protocols  [28] [29] [30] [31] ; documents explored the effects and impact of vaccines and chemoprophylaxis  [26, 27, 29, 31] , antimicrobial and antibiotic treatment  [25, 31]  and oral rehydration salts  [23, 28].
Search strategy and information sources We will search MEDLINE, CINAHL, Embase, IndMed, Scopus, Proquest, the Campbell collaboration and the WHO libraries (WHOLIS) (see Fig. 1 ). Tables  2  and 3  illustrate search strings to be used for MEDLINE and CINAHL; similar searches will be conducted in the remaining databases listed and studies selected as per criteria outlined below. The Cochrane Library has been excluded from searches as no trials or articles of relevance on cholera diagnostic products or methods were identified during scoping searches. In addition to the above sources, we will search reference lists from relevant articles and include the studies identified by Dick et al. [12]  We will also search grey literature (e. g. OpenGrey).
Study and data management KD will act as main reviewer and data custodian. The study will be conducted in line with the CRD's guidance on diagnostic accuracy systematic reviews  [32]. Two reviewers (JF, KD) will screen all titles and abstracts for inclusion. Articles identified will be downloaded directly into EndNote/Zotero for screening. Data extraction will be conducted by two reviewers, in duplicate and blinded as per standard systematic review methodology. Information on pre-specified items will be extracted into a Microsoft Excel document (see the "Data extraction" section below and Additional file 1). Following resolution of disagreements, a collated version of extracted data will be made available to the review team for analysis. Disagreements between reviewers will be resolved by consultation of a third reviewer (FO).
Study selection Figure  2  outlines the study selection and data extraction procedures employed. We will include studies from any global settings reporting on any diagnostic technologies targeting cholera O1 or O139, of both classical and El Tor biotypes and Ogawa and Inaba serotypes  [33]. We will include primary diagnostic accuracy studies (laboratory or field based) that focus on the evaluation of methods or products used in cholera diagnosis in human stool or detection in water. Prospective studies and studies retrospectively evaluating diagnostic or detection products will be included. We acknowledge that culture methods are considered the gold standard for cholera diagnosis and detection; however, we will include any studies reporting on the performance of index tests against a specified comparator test.
Data extraction Full texts of included abstracts will be retrieved and data extracted as per the pre-specified template (Additional file 1). The template has been tailored to the current study and closely mirrors the Cochrane group's data extraction template for systematic reviews  [34]. We will extract data on the following domains and items among others: Study characteristics: study design, duration and aims, populations included and sample characteristics, products evaluated, outcomes noted and statistical methods used; Diagnostic product or method characteristics: index and/or reference product name, technical specifications (e. g. test format, turn-around time, disease target and stability of test under transport and storage conditions), cost (e. g. price per test and cost per use of test), product design and use (e.
Narrative and quantitative synthesis All studies, irrespective of quality, will be included in a narrative synthesis. As per Popay et al. [32, 35] , we will synthesise insights from the reviewed literature in order to provide a theory of how cholera diagnostic products are used, including an account of deployment challenges Fig. 2  Selection criteria applied to abstracts retrieved via literature searches. This figure is a decision tree outlining how study selection criteria will be applied during abstract screening and best practices. We will group, describe and critically appraise the studies and products noted therein according to study type, setting and population characteristics, and type of diagnostic/detection product or method. Should meta-analyses not be appropriate (see the below section), we will synthesise quantitative data and provide a descriptive account of diagnostic method and accuracy metrics.
Methodologies To assist in assessing the safety and efficacy of a proposed decontaminating agent of foods of animal origin, EFSA issued in 2010 a revised guidance document titled 'Revision of the joint AFC/ BIOHAZ guidance document on the submission of data for the evaluation of the safety and efficacy of substances for the removal of microbial surface contamination of foods of animal origin intended for human consumption' (EFSA BIOHAZ Panel, 2010a). The assessment was conducted in line with the principles described in this guidance document. 2. 2. 1. Toxicological safety, potential emergence of resistance to biocides and/or to therapeutic antimicrobials and environmental risk assessment (ToRs 1, 3 and 4) The questions as specified in the ToRs 1, 3 and 4 have been addressed by evaluating the information provided by the applicant supplemented with relevant studies identified by the Working Group and Panel members through a literature review.
The achieved reduction in contamination should be expected to provide benefits to public health. This could be supported by reference to existing scientific data, such as epidemiological studies or risk assessments demonstrating public health benefits associated with similar reductions in extent of microbiological contamination'. The benefits to public health will be assessed in this opinion but the satisfactory level of this benefit is a risk management decision. In this assessment, the comparison was made with the untreated control if the water treatment control was not included in the experimental design. The EFSA guidance document on the assessment of the biological relevance of data in scientific assessments provides a general framework for establishing the biological relevance of observations at various stages of the assessment (EFSA Scientific Committee, 2017).
Search for studies The applicant stated that appropriate studies were identified by two literature searches (O  'Connor, 2015; Bassett, 2016)  and by additional searches using literature search engines (AGRICOLA) at the USDA National Agricultural Library. Studies that contained pertinent data (i. e. relevant records identified by the applicant) or that appeared to contain pertinent data (i. e. potentially relevant records identified by the applicant) were listed by the applicant. No study identified in the searches was omitted. The applicant stated that 'While it is probable that there are additional studies relevant to the efficacy of these organic acids, the references assembled here should represent a fairly complete compilation of the available studies'. The applicant identified 41 relevant records, of which 29 considered lactic acid, 8 considered acetic acid and 4 included data on the individual treatment with both substances.
The bibliographic databases Web of Science and CABI were searched on 14 February 2018, using the strategy reported in Table  2 , adjusted from O'  Connor (2015)  by removing the search terms related to other decontamination treatments (e. g. peroxyacetic acid or steam). The language was restricted to English and the time span established was from 2015 onwards. Review papers were excluded, but used for hand-searching their reference lists. In total, 160 unique records were retrieved this way (97 retrieved using Web of Science and 137 using CABI). These records were complemented with 14 records on efficacy prescreened for relevance by handsearching the reference lists of three relevant review papers published since 2015  (Belluco et al. , 2015; Totton et al. , 2016; Young et al. , 2016). The reference list of the review paper by  Totton et al.
Study selection process and identification of relevant experiments Applying the eligibility criteria illustrated in Table  1 , the records were screened using the software DistillerSR for relevance to the review question in two steps: • Screening of titles and abstracts Screening of title and abstracts was done to identify records containing: a) obviously irrelevant studies, to be excluded from the assessment; b) potentially relevant studies or studies with unclear scope, to be moved to full-text screening. The screening was done based on the following criteria: (1) primary research study, (2) at least one surface decontamination experiment on pork carcasses or cuts, and (3) individual application of lactic acid or acetic acid. If negative for one of these criteria, the record was excluded.
Data extraction from included experiments The list of parameters to be extracted from the records included in the assessment was predefined using DistillerSR ® software. Information expressed only in graphs/curves/figures was identified as such. Numerical values were used with the unit as presented in the records. The data set was created for all time points investigated in the studies selected and was checked by a different reviewer to correct any mistakes. Three hierarchical Distiller forms were used for data extraction. The data extracted can be found in Annex 1. • The overarching form contained the experiment-defining variables, i. e. the experimental setting (laboratory scale, pilot-scale representative of industrial process and industrial scale), the type of contamination (natural or artificial), the substance (lactic acid or acetic acid), the application method (spraying or dipping), the product category (pork carcasses pre-chill or pork meat cuts post-chill) and the product subcategory (product as described in the record) • The subform captured information related to: À the treatment characteristics: the concentration, temperature and pH of the decontamination solution, the duration of treatment and pressure of the application À the contamination characteristics: the bacterial group (Salmonella spp.
The log 10 reduction is the difference between the means of the log 10 concentration of control group and treated group. When the levels of reported values were below the detection or quantification limit, that limit was used (i. e. the most conservative estimate). The R code for statistical analysis can be found at https://doi. org/10. 5281/zenodo. 1479671. There are three situations for reporting the enumeration outcomes: • Case 1: The mean concentration is reported for both the treatment group and control group. The data extracted referred to control group as the water treated group or the untreated control group. In case both controls were reported, the water treated group was taken as control. The mean value of the log 10 reduction was calculated as the difference between the mean value of the log 10 concentration for the control group (M c ) and the mean value of the log 10 concentration for the treatment group (M T ), where concentrations are expressed in the record for each group per g or cm 2 of product depending on how it was reported in the records.
The log 10 reduction (difference of means DM) and the 95% CI were calculated as follows: DM ¼ M c À M t CI ¼ DM À T 0:975;nu Â ffiffiffi ffi D p ; where D ¼ SD 2 T nt þ SD 2 C nc and nu ¼ D 2 SD 4 T nt 2 ðntÀ1Þ þ SD 4 C nc 2 ðncÀ1Þ ; • Case 2: Only the log 10 reduction (DM) is directly reported comparing the treatment group and control group with a number of samples (n). No further calculation was necessary. Also here, the data extracted referred to control group as the water treated group or the untreated control group. In case both controls were reported, the water treated group was taken as control. The corresponding 95% CI is either calculated, directly reported or this information is missing. The 95% CI was calculated as follows: CI ¼ DM AE T 0:975;number of trials Â ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 SD dif n r When variation was expressed as standard error, the SD was calculated as follows: SD ¼ SE ffiffiffi n p • Case 3: The following two values for mean log 10 reductions were reported: the log 10 concentration for the untreated control group minus the log 10 concentration for the treatment group and the log 10 concentration for the untreated control group minus the log 10 concentration for the water treated group.
Appraisal of individual experiments The strength of each experiment included in the assessment was appraised taking into consideration elements related to relevance and reliability. • Data relevance refers to the appropriateness of the data for the intended purpose of the assessment (adapted from  Klimisch et al. , 1997; Vermeire et al. , 2013). Relevance refers to the extent to which available data address the objectives of the assessment (e. g. the right target population, hazard of concern, geographical area, etc. )  (EFSA, 2015). • Reliability refers to: (i) precision, i. e. the extent to which random error is minimised and the outcome of the process is reproducible over time  (IPCS, 2009) ; and (ii) accuracy, i. e. the extent to which systematic error (bias) is minimised. Risk of bias (RoB) refers to the extent to which the design and conduct of a study are likely to have prevented bias, i.
Data synthesis and interpretation of results in light of identified uncertainties Each individual experiment included in the assessment was reported in tabular format in the scientific opinion. Each table illustrated the experiment characteristics, population, methods, intervention, outcome(s) and the appraised reliability. The data were summarised using plots of the log 10 reductions of each bacterial group by application of either lactic or acetic acid on pork carcasses pre-chill by spray and on pork meat cuts post-chill by spray or dip. Tables were used to present the proportion of positive samples. An overview of the potential sources of uncertainty identified in the efficacy assessment and the impact that these uncertainties could have on the direction of the effect on prevalence reduction and log 10 reductions is provided in tabular format.