dataesr/scientific-paragraphs-categorization huggingface dataset:
REFERENCE IMPLEMENTATION The OrbisGIS platform has been used to prototype an implementation of the symbology model all along the standardization work by iterations with tests and validations  (Bocher & Petit, 2015). In the long term, this platform might be adopted as a reference implementation at the OGC ("CITE"). OrbisGIS is a Geographical Information System designed by and for research  (Bocher & Petit, 2013)  which is the main advantage for research communities comparing to other GIS. Indeed, OrbisGIS does not intend to reproduce classical GIS functionalities. It is designed to explore new issues or questions in the field of geospatial techniques and methods (such as language issues to query spatial information and issues on cartography about standardization, semantics and user interface design).
To address these challenges, the OrbisGIS architecture (object and data model) and its user interface are frequently redesigned. This approach is fundamental to test the concepts and the ideas related to the ongoing standardization process of symbology standards at OGC. Furthermore, the fact that we have a common set of GIS features organized with the dynamic module system OSGi to access to the geodata, library to use simple features functions, layer model, rendering engine, etc. (OSGi, 2014) , gives flexibility to plug some experimental code without breaking the platform and the user can easily switch from one to another plugin (Fig. 13 ). More importantly, the usage of OSGi technology does offer a way to implement the modularization principles depicted in the above (i. e. , one OSGi bundle per symbology extension).
Another motivation is related to the license. OrbisGIS is an open source software, distributed under the GPL3 license and therefore grants four freedoms (1) to run the program for any purpose, (2) to study how the program works and adapt it to your needs, (3) to redistribute copies so you can help your neighbour, and (4) to improve the program, and to release your improvements to the public, so that the whole community benefits  (Steiniger & Hunter, 2012). This aspect is essential in order to have a reference implementation available for the community of implementers of a standard, guiding them better in the understanding of a specification. Given the core principle of science that having open source code available does enable reproducibility  (Ertz, Rey & Joost, 2014) , we argue that this is also valid for open standards.
On one side, it is easy for other researchers and businesses to verify and re-use new developments and adapt them to their needs  (Steiniger & Hunter, 2012). Furthermore, having the code of the rendering engine, the user interfaces and all the tests fully accessible should facilitate the understanding and the dissemination of standards for portrayal interoperability while minimizing interoperability defects. In the following we describe the main aspects covered by OrbisGIS to implement the proposed redesign of the symbology model.
5 kW transmitted with a 95 % efficiency. Problem (P 1 ) is relatively small: 6 variables (5 real and 1 integer) and 2 inequalities. It is sufficient to test the validity of the proposed design process. It belongs to the Mixed Integer Non-Linear Program (MINLP) problems class. Since the model is purely analytic, each evaluation is very fast (less than 1 millisecond). Then the use of a metaheuristic method with a huge number of evaluations does not require much CPU time. We choose a Particle Swarm Optimization (PSO) algorithm because of its simplicity of implementation  [10]. A modified version of classical PSO accounting for integer variables (called MIPSO) is implemented using MATLAB software. Bounds constraints are handled by projection, and non-linear constraints by a penalty function. Therefore the initial optimization problem is transformed in an equivalent problem without nonlinear constraint as follows: min x∈D (︂ Vm(x) + µp [︀ max (︀ 0 ; 50 -Te(x) )︀ + max (︀ 0 ; R 3 (x) -15 • 10 -2 )︀]︀ )︂ (5) For the following runs, the penalty factor µp has been chosen equal to 100.
Our article presents an interdisciplinary study that combines approaches from linguistics, computer science and GIScience to develop and explore a set of rules for the extraction and classification of FM from a text corpus. The article is structured as follows. In Section 2, we outline the state of art in the area of spatial information annotation and extraction, and provide a theoretical basis for the phenomenon of FM and its types (static and dynamic), followed by the problem statement. The data and general methodology used are discussed in Section 3. Section 4. 1 focuses on the process of developing rules for FM extraction, while Section 4. 2 focuses on its classification by describing spatial language and concepts characteristic of each type of FM. The implementation of rules in Unitex 2 and their evaluation performance on the training and validation data, and types of errors are presented in Section 5, followed by the conclusion in Section 6.
The tags capture motion verbs, spatial relations in the form of prepositions and spatial named entities. The method combines quantitative and qualitative criteria based on information from the annotated text and data from geographic databases. Both the development of spatial markup languages and the extraction of spatial concepts from text are grounded in spatial semantics, which explores the meaning of spatial language. On the one hand, it equips related research with key spatial semantic concepts such as figure, 5 ground or motion event and their encoding in language(s)  (Langacker 1987 , Talmy 2000). On the other hand, it examines the role that space plays in thinking, which is reflected in the pervasiveness of space-related metaphors  (Lakoff and Johnson 2008) , polysemy of spatial terms  (Zlatev 2007 ) and, generally, figurative use of much of spatial language  (Langacker 2005).
Data and methodology We use two sets of data: a pre-processed (lemma-and part-of-speech-tagged) corpus of alpine texts 'Text + Berg'  (Bubenhofer et al. 2015)  and a corpus of sentences with FM from 'Text + Berg'. Sentences with FM were extracted semi-automatically and classified manually into actual motion, vista and encyclopaedic knowledge FM  (Egorova et al. 2018). We treat this FM corpus as a gold standard. The two sets of data were split into two subcorpora, one for training and one for validation. Texts written between 1969 and 1991 and a corpus of FM from those texts were used in the iterative training process of developing and refining rules for the extraction and classification of FM. Texts written between 1992 and 2008 and a corpus of FM from those texts were used for validation.
g. 'part', 'side', 'edge', 'bottom') for cases like (5a), distance (e. g. 'feet', 'ft', 'mile(s)', 'm', 'meter(s)') for cases like (8c), collection (e. g. 'series', 'succession', 'couple', 'row', 'system', 'maze', 'tangle', 'most', 'several', 'some') for cases like (8d). Spatial entity, spatial part, distance and collection are the four types of subjects encountered in the FM corpus. The search is conducted through lists compiled for each of these types based on the training data and synonyms derived from thesauri. (8) a. A climb along this ledge brought us to the second rock barrier. b. The route on the rock face led diagonally upwards. c. Haifa (sic!) a mile along the ridge brought us to a pleasant clearing. d. Here, a succession of limestone cliffs rises sheer from the sea. Factive motion.
Validation and error discussion We measure the performance of our approach using three commonly used metrics in identification and classification tasks: precision (P), recall (R) and F 1 score (F 1 ). Precision P is defined as the ratio of the number of correctly annotated instances of FM (FM C ) divided by the total number of instances of FM annotated, i. e. both true positives (FM C ) and false positives (FM FP ). P ¼ FM C FM C þ FM FP Recall R is the ratio of the number of correctly annotated instances of FM (FM C ) to the number of FM instances in the collection, i. e. both true positives (FM C ) and false negatives (FM FN ). R ¼ FM C FM C þ FM FN The F 1 score F 1 is the harmonic mean of precision and recall and is defined as F 1 ¼ 2 Â P Â R P þ R The last run of the final set of rules on the training data retrieved 520 instances of FM, corresponding to the recall of 0.
1999 , Zlatev 2007). Summarizing the findings, we first identified a set of concepts uniquely characterizing the types of scenes based on the inspection of data and previous literature. The concepts exhibit various degrees of vagueness. Motion duration is an example of a rather crisp concept, while untravellability, treated as a non-problematic concept in linguistic literature  (Matsumoto 1996, Rojo and Valenzuela 2009) , is an inherently vague concept in the context of alpine space and mountaineering. Secondly, we examined how these concepts are encoded linguistically; again, concepts vary from the perspective of linguistic representation. Some are related to Talmy's closed class  (Talmy 2000) ; a good example is vista frame of reference ('beneath our feet') that can be operationalized exhaustively based on previous literature on spatial prepositions  (Landau and Jackendoff 1993).
Given the richness and complexity of spatial language, the importance of exploring the use of structures like FM and the role of corpora of naturally occurring spatial discourse are being increasingly recognized within the GIScience community  (Stock et al. 2013 , Wallgrün et al. 2018 ). On the one hand, it is an important step towards the spatial parsing of text, which will greatly enrich existing toolboxes, allowing capture of spatial information in a variety of textual data. On the other hand, it enhances our understanding of the way spatial information is represented linguistically in various types of spatial discourses, providing a window into the spatial thinking involved. Overall, our approach provides both conceptual and practical pathways to the development of semantic and database representations of geographical scenes described in natural language at different scales.
One open challenge for Web services is to increment their reliability  [4]  a particular feature of dependability which considers many more aspects like: reliability, availability, security, maintainability among others  [10]. In this paper we propose to tackle the MAPE loop of autonomic computing in a distributed and efficient manner by using communication-induced checkpointing (CiC), attacking the dependability problem for Web services. CiC has proved being worthy and has gain a great level of maturity specially for distributed systems, solving issues like rollback recovery, software debugging and software verification among others  [11]. In general, CiC aims at building consistent global snapshots (CGS) or simply checkpoints (one from each process) avoiding dangerous patterns: like zigzag paths and zigzag cycles  [12]  as stipulated by the authors.
MAPE Cycle Business processes must be dependable so they are available when requested; solutions that suggest augmenting Web services dependability must also be scalable and even autonomic  [22]. In this work we propose an approach which follows the autonomic computing MAPE cycle based on CiC protocols (as shown in Fig. 3 ). As illustrated in Fig. 3   anomaly or fault. In other words, identifies patterns in the logs looking for specific problems that occurred  [20]. If the Web service presents normal parameters then it is immediately returned from Analysis module to the Web service invoking a service. However, the message is forward to the analysis process that checks the new aggregate values with the aim of predicting the immediate future state of the system, based on Hidden Markov Model (HMM)  [24] , Bayesian Networks  [25, 26]  or any other method that supports prediction.
Motivation: Non-coding RNAs (ncRNAs) play important roles in many biological processes and are involved in many diseases. Their identification is an important task, and many tools exist in the literature for this purpose. However, almost all of them are focused on the discrimination of coding and ncRNAs without giving more biological insight. In this paper, we propose a new reliable method called IRSOM, based on a supervised Self-Organizing Map (SOM) with a rejection option, that overcomes these limitations. The rejection option in IRSOM improves the accuracy of the method and also allows identifing the ambiguous transcripts. Furthermore, with the visualization of the SOM, we analyze the rejected predictions and highlight the ambiguity of the transcripts. Results: IRSOM was tested on datasets of several species from different reigns, and shown better results compared to state-of-art.
The most popular tool, named CPC  (Kong et al. , 2007) , is based on this idea. The authors built a model based on an SVM algorithm using several sequence features like ORFs quality, and on BLASTX results against a protein database. But this approach has an important drawback of time consuming. Recently, a new version of CPC, that is alignmentfree, has been proposed by the same group. The new method, called CPC2  (Kang et al. , 2017) , uses SVM technique to build a model from four features: the Fickett score, the length, integrity and isoelectric point of the longest ORF. CPAT  (Wang et al. , 2013) , CNCI  (Sun et al. , 2013)  and PLEK  (Li et al. , 2014)  are three other existing methods that are alignment-free. CPAT uses four sequence features: the length and coverage of the maximal ORF, the Fickett score (representing the bias of base position in the codons and the percentage composition of each base) and the Hexamer score (log-likelihood ratio between the frequencies of each Hexamer in the sequence with a model computed on coding sequences).
Bioinformatics, 34, 2018 , i620-i628 doi: 10. 1093 /bioinformatics/bty572 ECCB 2018  frequencies are weighted by the k-mer length. Note that among these existing tools, only CPAT can be retrained. The sources of CNCI and CPC2 cannot be used to train a model. The available models for CNCI are built only for vertebrates and plants, and the ones for CPC2 are built from a mix of data (Human data for the coding and data coming from GENCODE for the non-coding). PLEK offers a script to train a new model but it is very slow (at least 2 days for a training on a dataset of 7500 sequences with 10 threads). In this paper, we present IRSOM, a new alignment-free method for discriminating non-coding and coding RNAs. IRSOM is a supervised classifier composed of a Self-Organizing Map (SOM) and a perceptron layer which is fully connected to the SOM.
The distance rejection rejects the predictions that are far from the predicted classes while the ambiguity rejection rejects the predictions that are in overlapping areas between two or more classes. The distance rejection option rejects a prediction if the largest value provided by the output neurons is lower than a certain threshold. The ambiguity rejection option rejects a prediction if the difference between the two largest values provided by output neurons is lower than a certain threshold. In IRSOM, we use the ambiguity rejection in order to identify the ambiguous transcripts that are on the boundaries between the coding and the ncRNAs. Moreover, by combining the rejection option with the SOM, we are able to visualize and analyze the rejected transcripts. For example, analyzing the ORF features profiles in the SOM allows to highlight the known differences between the coding and the ncRNAs and also shows the ambiguous characteristics of the rejected transcripts.
IRSOM was tested on datasets of several and different species (Human, Mouse, Arabidopsis thaliana, Zebrafish, Escherichia coli, Saccharomyces cerevisae and Drosophila), and compared to different existing tools: CPC2  (Kang et al. , 2017) ,  CPAT Wang et al. , 2013) , CNCI  (Sun et al. , 2013)  and PLEK  (Li et al. , 2014). It shows better or equivalent performances than the other tools in prediction results and comparable time consumption with the fastest tools (CPAT and CPC2). It gives an accuracy greater than 0. 95 for almost all species, and reaches for some of them more than 0. 99. It also demonstrates its capacity to handle very large datasets thanks to its low running time in prediction as well as in training (respectively, less than 2 min and less than 4 min on a Human dataset). In addition, IRSOM is able to visualize the data repartition into clusters for both the coding and the ncRNA classes and to analyze the rejected classifications.
The cluster profiles for each feature can also be visualized and analyzed thanks to IRSOM. The paper is organized as follows. In the next section, we describe our tool IRSOM as well as the used features. After that we describe the datasets and the protocol used to compare our method to the state-of-art tools. Finally, before concluding, we present and discuss in the Results Section the cross-validation and prediction results, as well the running time we obtained with IRSOM and the different tested tools. In the same section, we show also the interest of the rejection option in a study case.
Materials and methods In this section, we describe our algorithm IRSOM for discriminating coding and ncRNAs. IRSOM is a improvement of our previous work in  (Platon et al. , 2017). IRSOM is based on an original supervised SOM approach including a reject option, that we propose in order to well classify the transcripts that are clearly defined as coding or ncRNAs and to reject (for further analysis) the ones that are ambiguous. In the following, we present first the SOM approach and then we describe in details our algorithm. 2. 1 Self-organizing map SOM  (Kohonen, 2001 ) is a neural network that is able to cluster and visualize high dimensional data. By using an unsupervised competitive learning algorithm, SOM is able to produce a map representing the input space. The produced map can have different structures but the most common one is a grid.
Topology of the input space is preserved by using a neighbourhood function during the learning phase of the SOM. Let be an input dataset X such that x i 2 X is the feature vector of the ith input data. We can define a SOM composed of U neuron units such that each neuron unit u is a cluster represented by a weight vector w u 2 R m such that w u ¼ w u;1 ; w u;2 ; :::; w u;m Â Ã. The learning algorithm of SOM is composed of two steps which are: • Assignation: the Best Matching Unit (BMU) of a given input x i is computed as follows: BMU x i ð Þ ¼ argmin u2U k w u À x i k (1) where k : k represents the L2-norm. • Update: the BMU and its neighbours are updated toward x i by: w u t þ 1 ð Þ¼w u t ð Þ þ a t ð Þh t BMU x i ð Þ; u ð Þx i À w u ð Þ (2) where a t ð Þ is the learning rate and h t BMU x i ð Þ; u ð Þ ¼ exp À d BMU xi ð Þ;u ð Þ 2 rÂ 1À t T ð Þ is the neighbourhood function (r is the radius of the map and T is the maximal number of iterations) with d BMU x i ð Þ; u ð Þ the Manhattan distance between the winning neuron and the neuron u in the SOM structure.
IRSOM algorithm IRSOM is a three layers neural network (Fig. 1 ) composed of an input layer that represents the input data, a hidden layer which corresponds to a SOM  (Kohonen, 2001)  and an output layer (supervised layer), that consists of two perceptrons which are fully connected to the neurons of the SOM with forward connections. The SOM computes a new representation of the transcripts using a map of neurons. The perceptron layer is able to assign correctly a class to a transcript by using its new representation. A backpropagation modifies the SOM organization, in order to match with the classes of the transcripts. Moreover, we extend the perceptron layer with a rejection option where the ambiguous predictions are rejected. This rejection option highlight the transcripts which are between the coding and the ncRNAs.
Implementation IRSOM is composed of two major parts, the computation of the features and the neural network. The neural network is implemented using TensorFLow which is an open-source software library for machine learning supported by Google (https://www. tensorflow. org/). This library is available for the three major operating systems (Windows, Ubuntu and macOS) and also for other systems like Android or iOS. The strength of TensorFlow is its capacity to use CPU or GPU without change in the source code, its scalability to big data and the possibility to use HPC clusters. The computation of the features is achieved by a workflow API that we developed in Cþþ. The workflow (Fig. 2 ) is composed of five worker pools which are pools of threads. This workflow is designed to be scalable to big data by using a system of buffer.
Datasets We evaluated our method with coding and ncRNAs coming from several species. In order to cover a large spectrum of species from different reigns, we selected RNAs from Human, Mouse, Oryza sativa, A. thaliana, Zebrafish, E. coli, S. cerevisae and Drosophila. The number of transcripts and their origins are given in Table  1  and the distribution of the transcripts length is shown in Figure  3. The sequences of coding transcripts were extracted from the Ensembl databases  (Zerbino et al. , 2018). We selected only the transcripts available in the Swiss-Prot database  (Consortium, 2017b)  in order to have manually curated transcripts except for the Zebrafish and the Drosophila. Due to their low amount of transcripts we select the transcripts with ID in the UniParc. The non-coding transcripts come from the RNAcentral database  (Consortium, 2017a).
RNAcentral combines the information of multiple ncRNA databases [Ensembl  (Zerbino et al. , 2018) , Rfam  (Kalvari et al. , 2017) , RefSeq (O'  Leary et al. , 2016) , GENCODE  (Derrien et al. , 2012) ]. In the case of the Human and Mouse datasets, we selected the ncRNAs coming from the GENCODE database due to their manual curation. For the Drosophila dataset, we selected the ncRNAs available in the Flybase database which is a reference database for the Drosophila. For the A. thaliana, we got transcripts from three databases in order to have enough data: Rfam, RefSeq and TAIR  (Berardini et al. , 2015 ; a database specialized on A. thaliana). Finally, for the other datasets, we selected the transcripts coming from the Rfam database.
Experimental protocol We show the performance of our tool by comparing it to four classical ncRNA identification tools which are CPAT  (Wang et al. , 2013) , CPC2  (Kang et al. , 2017) , CNCI  (Sun et al. , 2013)  and PLEK  (Li et al. , 2014). The benchmark was executed on a 50 cores virtual machine (VM) under debian with 128 Gb available memory. Each tool was launched separately on the VM with the sequences in fasta format. We performed two types of performance analysis: a crossvalidation analysis and a prediction analysis. For this purpose, each of the different datasets presented above, noted D, has been divided into two subsets (of same size) D sub1 and D sub2 ; D sub1 used in the cross-validation and D sub2 in the prediction. In the cross-validation, we evaluate our tool IRSOM in order to measure the impact of the rejection threshold on the different datasets.
We therefore performed a 10-fold cross-validation with IRSOM, and then a prediction with the different tools, CPAT, CPC2, CNCI, PLEK and IRSOM. Among the tested tools, only CPAT was retrained on all datasets. Unfortunately, the sources to build a new model for CPC2 and CNCI are not available and PLEK is too slow to create new models (more than 2 days for a training on a dataset of 7500 sequences with 10 threads). We did not run CNCI on the datasets of the Drosophila, E. coli and S. cerevisae because the available models were designed for vertebrate or plant organisms only. PLEK authors built their model with a Human dataset but in their article  (Li et al. , 2014) , they show results on vertebrate species. We then run PLEK on the vertebrates datasets. Finally, IRSOM and CPAT are trained on each of the considered species, as well as on all species together (cross-species model).