dataesr/scientific-paragraphs-categorization huggingface dataset:
), there are 420 images that remain for testing. To ensure a fair comparison, we use the training set to train an SVM classifier with the HIK and the remaining images for testing; moreover, the parameters are set as recommended by the authors  [59] , i. e. , following the procedure in  [20] , where five-fold cross-validation is performed. The dataset is first randomly split into five equal sets; then, the classifier is trained on four of the sets and evaluated on the held-out set. The average classification accuracy and standard variance are computed over the 200 evaluations.
Classification Result Comparison We extract the 17 local features based on CBPT, and the smallest regions for extracting features are set as 15 pixels. The dictionary size of FV encoding is 100. Thus, the proposed CTS descriptor provides a good classification result. Table  6  illustrates that the CTS descriptor outperforms the state-of-the-art algorithms on the UC Merced dataset. The BOVW (bag of SIFT) algorithm is a high-level feature that encodes SIFT descriptors  [60]. The local descriptor SIFT is very discriminative but not sufficiently semantic. The Bag of colors method simply uses the color information, and the region is not semantic. This demonstrates the advantage of our descriptors, which combine a hierarchical region-based method with color and texture fusion. Furthermore, we compare the CTS descriptor with the latest well-designed method.
Classification Results BOVW  [20]  71. 86 SPM  [38]  74 SC+ Pooling  [61]  81. 67 ˘1. 23 Bag of colors  [62]  83. 46 ˘1. 57 COPD  [63]  91. 33 ˘1. 11 HMFF  [62]  92. 38 ˘0. 62 UFL-SC  [64]  90. 26 ˘1. 51 SAL-LDA  [65]  88. 33 ˘1. 15 CTS 93. 08 ˘1. 13 More precisely, we analyze the confusion matrix of the classification results based on the CTS descriptor. To obtain more stable results, we repeat the classification experiments 200 times. Then, the confusion vector of class i is defined as c i pjq " sum i pjq sum i psamplesq ˆ100% (29) where sum i pjq is the number of images that belong to class i but that are misclassified as class j and sum i psamplesq is the number of testing samples in class i. Figure  5  displays the confusion matrix of the CTS descriptor on the UC Merced dataset. As observed in the confusion matrix, there is some confusion between certain scenes.
Methods Classification Results BOVW  [20]  71. 86 SPM  [38]  74 SC+ Pooling  [61]  81. 67 ± 1. 23 Bag of colors  [62]  83. 46 ± 1. 57 COPD  [63]  91. 33 ± 1. 11 HMFF  [62]  92. 38 ± 0. 62 UFL-SC  [64]  90. 26 ± 1. 51 SAL-LDA  [65]  88. 33 ± 1. 15 CTS 93. 08 ± 1. 13 More precisely, we analyze the confusion matrix of the classification results based on the CTS descriptor. To obtain more stable results, we repeat the classification experiments 200 times. Then, the confusion vector of class i is defined as ( ) (j) 100% (samples) i i i sum j c sum = × (29) where ( ) i sum j is the number of images that belong to class i but that are misclassified as class j and (samples) i sum is the number of testing samples in class i. Figure  5  displays the confusion matrix of the CTS descriptor on the UC Merced dataset.
Experiments on Large Satellite Scenes To further demonstrate the discriminative ability and robustness of the CTS descriptor, we apply our descriptor to two large satellite scene images. 4. 2. 1. Experiments on Scene-TZ Scene-TZ  [56]  is a 4000 ˆ4000-pixel HRS scene that was taken over the Majuqiao Town of southwest Tongzhou District in Beijing. The original image and the actual geographic location are shown in Figure  6. There are 8 semantic classes in Scene-TZ: bare land, low buildings, factories, high buildings, farmland, green land, road and water, where each class has some similar texture and color information. We show one sample per class in Figure  7a , and the hand-labeled ground reference data are shown in Figure  7b. Scene-TZ  [56]  is a 4000 × 4000-pixel HRS scene that was taken over the Majuqiao Town of southwest Tongzhou District in Beijing.
The original image and the actual geographic location are shown in Figure  6. There are 8 semantic classes in Scene-TZ: bare land, low buildings, factories, high buildings, farmland, green land, road and water, where each class has some similar texture and color information. We show one sample per class in Figure  7a , and the hand-labeled ground reference data are shown in Figure  7b. First, we divide the large satellite image into non-overlapping sub-images with a size of 100 × 100 pixels. As a result, Scene-TZ is divided into 1600 patches. To assess the classification results, we label each patch with a corresponding semantic category. Because this approach is different from randomly choosing training samples in object categorization, we manually select 10 typical samples for each class as a training set for large satellite image scene classification, because if the training samples are distributed at random, the samples will be uniformly distributed over the entire image.
Discussion HRS image classification plays an important role in understanding remotely sensed imagery. In this paper, we build a multi-scale spatial representation and analyze the color, texture and structure information of an HRS image. Our objective is to design a discriminative color-texture-structure (CTS) descriptor for high-resolution image classification. The experimental results on the UCM-21 dataset and two large satellite images indicate that the proposed CTS descriptor outperforms state-of-the-art methods. The construction of the CBPT plays a vital role in our algorithm. The region model of the CBPT affects the robustness and discrimination of our final descriptor. Moreover, the computational complexity of the CBPT affects the efficiency of our method. As described in Section 2, our CBPT merges the original pixels, and the small regions are not sufficiently semantic.
Color, texture and structure features characterize HRS images from three different aspects. Because they are complementary in terms of image description, descriptors based on an efficient combination of the three cues should be more discriminative. As for certain categories, each feature channel is discriminative, e. g. , the beach in the UC Merced dataset, which results minimal confusion with other categories. However, the city greenland and farmland in Scene-TZ exhibit homogeneity in terms of color and texture but heterogeneity in terms of structure, which is emphasized by the compact rectangle shape. The proposed CTS descriptor achieves good classification results on several HRS image datasets. As an object-based image analysis method, the CBPT representation fully considers the multi-scale property and topological relationships of objects in HRS images.
Conclusions In this paper, a region-based color-texture-structure descriptor, i. e. , the CTS descriptor, has been proposed to classify HRS images via a hierarchical color binary partition tree structure. The main contribution of the CTS descriptor is the use of CBPT to analyze color and texture information, which specifically combines the early and late fusion methods of cues and analyzes the co-occurrence patterns of several objects. The efficiency of the proposed method is substantiated by classification experiments on the 21-class UC Merced dataset and on two large satellite images. Both qualitative and quantitative analyses confirmed the improved performance of the proposed CTS descriptor compared with several other approaches. By defining the initial partition of the merging process on an over-segmentation result, i.
The recent conclusions of the Competiveness Council of the European Union established the roadmap for the transition "to a new way of doing research and science based on openness, big data and cloud computing" 1  in Europe. The overall goal is open science, i. e. a new "friendly regulatory environment for research and innovation (which) breaks down the barriers around universities and ensures that society benefits as much as possible from all scientific insights (maximizing) the input of researchers, universities and knowledge institutions". All scientific publications ought to be freely accessible by 2020. Speed up scientific communication, make scientific publications and results accessible, in particular for industrial R&D -this offensive research policy opens a wide boulevard for information services, such as academic interlending and document supply which pursues similar goals.
Open science shifts the focus from acquisition and access to dissemination, related to activities early in the value chain of scientific communication, and upstream of usual document supply. The Amsterdam Call for Action covers issues like the assessment, evaluation and reward systems, text and data mining, intellectual property and privacy, transparency, research infrastructures, open access and new publishing models. At the same time, the action plan confirms the decreasing role of libraries which are NOT, unlike publishers and "businesses", considered as stakeholders on their own. As a part of "Research Performing Organizations" (p. 3), their role is mainly limited to the stimulation of new publishing models for knowledge transfer (Action Item 8) where they should "act as publishers of open access for their institutions, create a database of open science best practices (and) support discipline-based foundations that help flip subscription journals to FAIR 5 open access by providing funds for APCs and transition by 2020" (p.
As a demand-driven service, it should move toward a kind of scientific information commons, able to provide all kinds of information on demand -research results, publications and also information about research and outcomes -to anybody interested and involved in research, in-or outside of academia, and in any format and with any license requested for specific usage. Some aspects:  Innovation: efficient discovery and search technology, user experience design of the front office.  Rich and structured formats compliant with content mining tools.  Legal compliance and customized licensing.  More and other content, including datasets, multimedia files, print material, learning objects, metadata, from trustworthy sources.  Knowledge extracted from documents, data and metadata.
C. Statistical Approach Rule based approach adopted above does not take into account French words used in ALG which are pronounced as in French language. This issue takes us to choose a statistical approach in order to consider this feature. We use statistical machine translation system where source language is a text (a set of graphemes) and target language is its phonetic representation (a set of phonemes). This system uses Moses package  [17] , Giza++  [18]  for alignment and SRILM  [19]  for language model training. The main motivation of using a statistical approach is that we can include French phonemes in the training data. For building this system, the first component is a parallel corpus including a text and its phonetic representation. Actually, this resource is not available, so we created it by using the rule based converter described above.
We proceed as follows: we used the rule based system to convert Arabic words and French words phonologically altered (category 1 and 2) to Arabic phonemes. Whereas for French words realized with French phonemes (category 3), we began by identifying them and we transliterated them to their original form in Latin script, then converted them to French phonemes (using a free French G2P converter), all these operations were done by hand. For example the word is transliterated to connexion then converted to /kOnnEksjÕ/. This system operates at grapheme and phoneme level, we split the parallel corpus into individual graphemes and phonemes including a special character as word separator in order to restore the word after conversion process (see Table  XVI ). Experiment: For evaluating the statistical approach, we split the parallel corpus into three datasets: training data (80%) tuning data (10%) and testing data (10%).
3) Function words We deleted all function words that do not exist in ALG like relative pronouns and personal pronouns related to the dual and feminine plural, then we translated remaining ones to ALG. Note that we introduced dialect stems with non Arabic letters G, V, and P in stems table and we modified BAMA code to consider words containing these letters. Also, since every stem entry in BAMA contains an English glossary, when creating a dialect entry, we added the Arabic word to English glossary, so for each dialect entry is associated an English and Arabic glossary. After creating affixes and stems tables for ALG, compatibility tables of BAMA were updated according to the data included in these tables.
We combined a rule based approach to a statistical appraoch. The level of correctness for the G2P converter is about 85%. In terms of corpus resources, this task enabled us to transcribe the ALG corpus to a phonetic form. We also proposed a morphological analyser for AlG that we adapted from the well known BAMA dedicated for MSA. We reached an accuracy rate of 69% when evaluating it on a dataset extracted from ALG corpus. Our future work before developing a statistical machine translation system, is to extend the corpus we created to other Algerian Arabic dialects, and to adapt all tools dedicated to ALG to these dialects.
Knowledge bases such as Wikidata, DBpedia, YAGO, or the Google Knowledge Vault collect a vast number of facts about the world. But while quite some facts are known about the world, little is known about how much is unknown. For example, while the knowledge base may tell us that Barack Obama is the father of Malia Obama and Sasha Obama, it does not tell us whether these are all of his children. This is not just an epistemic challenge, but also a practical problem for data producers and consumers. We envision that KBs become annotated with information about their recall on specific topics. We show what such annotations could look like, how they could be obtained, and survey related work.
KBs should know what they know. Defining Completeness. In line with work in databases  [12, 8, 14] , we define completeness by help of a hypothetical ideal KB K *. The ideal KB contains all facts of the real world. We say that a KB K is correct, if K ⊆ K *. We say that K is complete for a query Q, if Q(K) ⊇ Q(K * ). For example, we could say that a KB K is complete for the children of Obama by saying K is complete for SELECT ?x WHERE {Obama hasChild ?x. } This means that evaluating this query on K will return at least the two children that we would expect as an answer in the real world. Completeness is always bound to a particular query, because we do not expect that we can ever construct a KB K = K *. A query can represent the completeness of simple triples about a subject (as in the example), but also for complex constellations, such as "This KB is complete for all rivers longer than 100km in Europe".
Growth Patterns over Time. The growth of data over time, and especially the end of such a growth, might indicate completeness. For instance, we can imagine that once a new congress is established, its members are added to a KB until eventually all are inside. The fact that the number then remains constant could indicate completeness. Interrelation. The completeness of a certain class expression could be learned from the completeness of other class expressions. For instance, it might be that if parents of a person are complete, then also the children are complete with a higher probability. Popularity of Entities. It might be that completeness correlates with the number of facts about an entity. For example, if a personality has numerous and very detailed facts in YAGO, then it could be more likely that some basic facts such as his children are complete.
Combining Completeness Information Once information about the recall of KBs for individual classes exists, methods need to be found to present this information in a meaningful way. Several techniques can annotate query answers with completeness information  [8, 14, 15] , but only if the underlying database is annotated with such information, and only for crisp boolean completeness information. Techniques from the domain of query answering over probabilistic databases  [2]  could possibly be extended to handle non-crisp completeness assertions, while techniques from data profiling can help understand distributions and skew  [13]. Also, one would need to apply these techniques to state-of-the-art KBs in order to finally know how much we currently know about the world (see Fig. 1 ). The community would then have to develop benchmarks for comparing the performance of completeness estimators, and for the completeness of KBs themselves, and would face the classic challenge of KB alignment, because information may be differently presented in different KBs.
An example of computed quantity of interest C P is plotted in Figure  9  for V 0 = 2. 25 m/s, ' = 0. 29 and several test values of the tip speed ratio l for the turbine rotor: the power coefficient of the isolated turbine is found to reach a maximum value C Pmax = 1. 059 for the optimal value of TSR l* = 2. 3. The single BEM-RANS calculation yields an optimal value of the power coefficient which is very close to the optimal reference value obtained from a series of expensive URANS simulation. Note the considered flow conditions (inflow velocity and blockage ratio) were not part of the extensive database used to calibrate the expressions of the hydrodynamic coefficients C D , C L or C X , C Y used in the BEM-RANS model which makes this flow problem a convincing validation of the BEM-RANS model.
Activity Models in CVET In the SecuriVi project  (Chevaillier et al. , 2012)  and the 3D Virtual Operating Room project  (Sanselone et al. , 2014)  the activities that the agent and the trainee can perform are described respectively thanks to the HAVE activity meta-model and Business Process Model Notation diagrams. In both projects, agents are assigned to a role and have specific tasks to do for which they must be synchronized. Yet we want the trainee to adapt her behavior to her teammates on activities where no roles are pre-attributed to team members. The scenario language LORA (Language for Object-Relation Application)  (Gerbaud et al. , 2007)  supports the description of collective activity: roles can be associated with actions and domain experts can specify collective actions that several agents have to do simultaneously.
ACKNOWLEDGEMENTS This work was carried out in the framework of the  VICTEAMS  project ( ANR-14-CE24-0027 , funded by the  National Agency for Research)  and funded by both the  Direction Générale de l'Armement (DGA)  and the  Labex   MS2T , which is supported by the  French Government , through the program "Investments for the future" managed by the  National Agency for Research (Reference   ANR-11-IDEX-0004-02 ).
In order to perform adequate statistical and spectral analyses, it is necessary to know the nature of the considered time series. For this purpose, the stationarity of the series and the occurrence of unit-root are addressed with the Augmented-Dickey Fuller tests. As an example, the harmonic analysis is not relevant for temperature, turbidity and salinity due to the nonstationary condition, except for the nearly stationary sea level datasets. In order to consider the dominant frequencies associated to the dynamics, the large number of data provided by the sensors should enable the estimation of Fourier spectral analysis. Different powerspectra show a complex variability and reveal an influence of environmental factors such as tides. However, the previous classical spectral analysis, namely the Blackman-Tukey method, requires not only linear and stationary data but also evenly-spaced data.
Interpolating the time series introduces numerous artifacts to the data. The Lomb-Scargle algorithm is adapted to unevenly-spaced data and is used as an alternative. The limits of the method are also set out. It was found that beyond 50% of missing measures, few significant frequencies are detected, several seasonalities are no more visible, and even a whole range of high frequency disappears progressively. Furhermore, two time-frequency decomposition methods, namely wavelets and Hilbert-Huang Transformation (HHT), are applied for the analysis of the entire dataset. Using the Continuous Wavelet Transform (CWT), some properties of the time series are determined. Then, the inertial wave and several low-frequency tidal waves are identified by the Please note that this is an author-produced.
Introduction The birth of observatories contributes to improve the environmental knowledge. Indeed, long-term research infrastructures such as NEPTUNE Canada and in Europe: EMSO, FIXO3 and JERICO are providing longer and longer time series with high frequency records in open sea and coastal areas. The analysis of such time series requires the implementation of procedures and methods suitable to this increasing data flow towards an automatisation. Consequently, the objective of this paper is to explore adequate analysis methods, assess advantages and inconvenients and compare classical techniques with more recent ones. Coastal ecosystems, especially those of transitional waters, display high variability over a large range of spatial and temporal scales. To study their dynamics and estimate their variations at all scales, high frequency measurements are needed  (Chang and Dickey, 2001; Chavez et al.
, 1997; Dickey, 1991; Dickey et al. , 1993)  together with appropriate analysis techniques. We present here the data recorded every twenty minutes by an autonomous monitoring device, settled in coastal waters of Boulogne-sur-Mer (eastern English Channel, France), recorded from 2005 to 2009. The fixed buoy station can record various biogeochemical parameters simultaneously. The time series of sea level, temperature, salinity and turbidity are considered. The fluctuations of the four quantities are important for ecosystem studies. The paper is structured in the seven following sections. After the description of the background and objectives in section 1, section 2 is dedicated to the presentation of the data. Then, stationarity issues are addressed in section 3. A preliminary analysis of stationarity focuses on the graphs and the correlograms.
The tidal response is modelled as: k A k  k B k  where N constituents are used. Each constituent has a frequency σ k which is known from the development of the potential, and complex amplitudes (A k and B k ) which are not known. A possible offset and (optional) linear drift is handled by the first two terms α and β. One widely-used program for performing harmonic analysis of oceanic tides is is the set of programs in MATLAB created by  Pawlowicz et al. (2002)  to perform classical harmonic analysis for periods of about 1 year or shorter. They have rewritten the FORTRAN package created by Mike  Foreman (1977) , incorporating many useful extras. Confidence intervals for the amplitudes and phases of the tidal constituents are calculated from the estimated variance. Confidence intervals were computed from the average of power spectral density within frequency bands centered on 0; 1; 2;.
cycles/day, ±0. 1 cycles/day (M 0 and M 1 ), ±0. 2 cycles/day (M 2 to M 5 ), ±0. 21 cycles/day (M 6 ), 0. 26 -0. 29 cycles/day (M 7 ), and 0. 3 -0. 5 cycles/day (M 8 )  (Pawlowicz et al. , 2002). We have performed harmonic analysis of the data sets presented in section 2 "Presentation of the experimental database". For the sea level, the harmonic analysis is able to predict 99. 7% of the variance of the original signal (see Fig. 9 ). The highest tidal amplitudes and the corresponding waves can be found in Table  2. Thus, the semidiurnal wave 2 M is the most energetic one. For the salinity, the harmonic analysis is able to predict 18. 4% of the variance of the original signal. This is due to freshwater contents (received from the rivers) and a seasonal cycle with higher variability linked to the regime of rivers and residuel (subtidal) circulation.