dataesr/scientific-paragraphs-categorization huggingface dataset:
II. MODEL DESCRIPTION CPlantBox is currently being developed based on the root model CRootBox  [2]. We took advantage of the object-oriented structure of CRootBox and added new modules to represent the different shoot organs (see Fig. A ). Main additions in CPlantBox are: • The input parameter files are now XML-based (instead of plain text-based in CRootBox). This has the following advantages: increased robustness, flexibility (more parameters for the shoot) and readability. For example, the functiontype (lnf) is added to create different architectures of plants in Fig. B. • As we move from simulating only roots to a full plant, more complex relationships between the different organs have been included in the model. For instance, roots can now be formed by either the seed, other roots or shoot organs (.
Background: Secondary use of data collected in Electronic Health Records opens perspectives for increasing our knowledge of rare diseases. The clinical data warehouse (named Dr. Warehouse) at the Necker-Enfants Malades Children's Hospital contains data collected during normal care for thousands of patients. Dr. Warehouse is oriented toward the exploration of clinical narratives. In this study, we present our method to find phenotypes associated with diseases of interest. Methods: We leveraged the frequency and TF-IDF to explore the association between clinical phenotypes and rare diseases. We applied our method in six use cases: phenotypes associated with the Rett, Lowe, Silver Russell, Bardet-Biedl syndromes, DOCK8 deficiency and Activated PI3-kinase Delta Syndrome (APDS). We asked domain experts to evaluate the relevance of the top-50 (for frequency and TF-IDF) phenotypes identified by Dr.
Background The global trend toward digital health in the US and in Europe has led to an unprecedented adoption of Electronic Health Records (EHRs). By the end of 2014, 83% of US physicians  [1]  and 75% of hospitals  [2]  used some form of EHRs. The increasing number of EHRs opens strong perspectives for the secondary use of data collected during the care process. Many hospitals are now equipped with Clinical Data Warehouses (CDW) integrating all the data produced during the care of the patients for research purposes  [3] [4] [5]. CDWs gather a large variety of information, ranging from structured data (e. g. diagnosis codes, laboratory test results…) to free-text clinical narratives and images. Structured data include coded data using terminologies like the International Classification of Diseases, and questionnaires that provide precise, standardized but somehow limited information.
Conversely free-text reports are produced without constraints and may be used to express nuanced, unexpected, and unexplained signs or symptoms regarding the patient case. Clinical narratives collect information from all aspects of the patient care that might not be collected anywhere else in clinical information system including history of the disease, family history, fine-grained description of all the symptoms, hypothesis of diagnosis or treatment, information from treatments received outside of the hospital, and so forth. Previous studies in different contexts showed the importance of free-text in EHRs. For example  Raghavan et al. identified that unstructured data were essential to solve trial criteria from two studies. [6]. The value of text data is even more important to detect phenotypes in specialized hospitals treating patients with rare diseases and for outpatients, for whom clinical information is barely coded  [7].
Material All data were collected from the Necker Enfants Malades Hospital (Necker Children Hospital), a pediatric University hospital belonging to the Assistance Publique Hôpitaux de Paris group (400 pediatric beds, 200 adult beds). The Necker hospital is a national reference center for rare and undiagnosed diseases. The hospital hosts the Imagine Institute, a research institute focused on genetic diseases. Imagine institute has been developing since 2015 a document-based open-source clinical data warehouse oriented toward free-text: Dr. Warehouse® (DrWH). DrWH includes a full text search engine, and contains, as of August 2017 more than 3. 9 million clinical free-text documents for more than 446,000 patients. In Table  1 , we describe the demographic characteristics of the patients included in DrWH.
We used all the clinical narratives, ranging from hospitalization to outpatient visits reports, available in DrWH to perform this study. The heterogeneity of the records is illustrated in Table  2  with the distribution of these records by hospital departments and type of reports. A demonstration version of DrWH is publicly available at the URL: https://imagine-plateforme-bdd. fr/dwh_pubmed/. Note that for privacy reasons, this demo version has been In brackets lower and upper quartile populated with data from PubMed abstracts and not with patient data. To represent the phenotypes, we used the terminologies from the Unified Medical Language System® (UMLS  [17] ). The UMLS is considered the lingua franca of medical vocabularies. The UMLS has a large coverage of biomedical vocabularies mostly in English.
Comparison to Orphadata For each disease set we compared the phenotypical concepts obtained by our method with those in Orphadata with the following steps. We leveraged HPO to map Orphadata and the UMLS (Orphanet is mapped to HPO, and HPO is integrated in the UMLS). We calculated the number of equivalent phenotypical concepts and the number of phenotypical concepts present in only one of the data sources (i. e. DrWH or Orphadata). The phenotypical concepts were considered equivalent (i) in case of exact mapping (same identifier) or (ii) when a broader phenotype was found (in Orphadata: Arrhythmia, in our extraction: Cardiac flutter). The steps are illustrated with the example of Rett syndrome in Fig. 3.
Detailed expert evaluation The description of the data available in each cohort and the evaluation by the experts are detailed in Table  4. The Fig. 4  is a screenshot of the graphical user interface of Dr. Warehouse for Rett syndrome. The automated phenotyping identified an average of 768 phenotypical concepts associated to each disease. In contrast, the number of UMLS concepts found in Orphadata ranges from 16 for the Silver-Russell syndrome to 120 for the Lowe syndrome. APDS was not documented in Orphanet at the time of redaction of this article. Overall, the experts classified between 11 (SILVER-RUSSELL set, ranked by TF-IDF) and 41 (LOWE set, ranked by TF-IDF) of the top-50 results as relevant to the disease. The number of phenotypical concepts identified by the union of results obtained through ranking by frequency and ranking by TF-IDF ranges from 16 (SILVER-RUSSELL set) to 52 (DOCK8 deficiency and APDS sets).
Comparison with Orphadata The comparison with Orphadata is detailed in Table  5. The limitation to French terms resulted in a reduction of an average of 16 phenotypes, corresponding to an average of 39% of the UMLS concepts (max: 63%, min 21%). We obtained the best coverage for the SILVER RUSSEL set with 100% of the Orphadata phenotypes present in the phenotypes of the patient set. The lowest coverage was.
Average precision Fig. 3  Evaluation procedure for the RETT set found for the LOWE set with 66% of the 76 Orphadata phenotypes present in the phenotypes of the patient set. The average coverage for all the patient sets was 78%. In the six diseases studied, 2. 8% of Orphadata concepts do not belong to the semantic types used for the automated phenotyping. For example, "Dislocated hips" (HP:0002827) is part of the description of Lowe syndrome in Orphadata and is assigned to the semantic type "Injury or Poisoning" in the UMLS. Among the phenotypical concepts in Orphadata, 41 are not represented in the patient sets phenotypical concepts.
Findings and practical significance Our method of automated extraction of phenotypes from narrative reports in a clinical data warehouse can be useful even in the context of rare disease with a low number of patients. Indeed, 4 of the 6 examples showed results above 83% in terms of Average Precision in the top-50 phenotypes based on the experts' evaluation. It was above 49% for the 2 remaining examples. It means that the extracted phenotypes are meaningful and can be used as baseline in diverse situations: enrichment of the phenotypic description of diseases, rapid exploration of the phenotypes in a population, or assisting the experts in the identification of phenotypes of interest. Our approach can be used to enrich existing phenotypic description of rare diseases. For example, osteoporosis was significantly associated with Rett syndrome in the Necker data warehouse.
Limitations Comparison to a gold standard and, interoperability issues It was complex to perform an automated evaluation of phenotypes found by DrWH by comparison to a gold standard (e. g. Orphanet with Orphadata). (1) The extraction of the phenotypical concepts was based on the French terms from the UMLS. However, the coverage of French term is limited compared to the extent of the English counterpart, knowing in particular that the French version of HPO was not available in the UMLS 2017AA. For example, in Orphadata the Rett syndrome is associated with 39 phenotypical concepts, of which only 31 exist in French in the UMLS (Table  5 ). The  The limited coverage of French terms compared to English limits our ability to identify concepts in free-text, but is also a limitation in our evaluation (which tends to underestimate the performance of the method).
The integration of new terminologies (with a French translation) provided with mapping to UMLS, or integrated in the UMLS will reduce the gap between English and non-English terms. The recent increase of interest for non-English Natural Language Processing is a step forward in that direction. (2) The granularity between phenotypical concepts extracted from Orphadata and DrWH may differ (e. g. a very precise term can be identified in DrWH whereas a more general term is present in Orphadata). This issue cannot be addressed by a simple hierarchical reasoning given that phenotypical concepts may be related semantically, but not identical nor hierarchically linked (e. g. Hypotonia (C0026827) vs muscle weakness (C0151786)). (3) We solely used an exact match strategy (with text normalization) to recognize phenotypical concepts in the reports.
Related work Information extraction Several approaches have been developed to recognize UMLS concepts, or terminology terms from free-text records. Savova et al. [30]  developed cTAKES, an open source modular system of pipelined components combining rule-based and machine learning techniques. cTAKES aims at the extraction of information from the clinical narratives. Despite development in other languages  [31, 32] , most of the open source clinical Natural Language Processing systems have been developed for the English language (MedLee  [33] , MetaMap  [34] , HITex  [35] ). Many challenges have helped to test and assess the different tools and methodologies. In non-English languages, less out-of-the-box tools and less learning datasets are available to work with text. More recently a challenge was dedicated to the extraction of information in multiple language medical documents (including French)  [36].
Narrative reports versus coded data We have shown that text exploration of clinical reports can provide phenotypes of interest. Whereas structured databases are particularly adapted for the collection of data regarding well documented diseases, clinical report based exploration enables the secondary use of data collected during care. Such approaches allow the development of learning health systems in which there is a bidirectional relation between routine care data and research. In addition, patient generated data could be integrated and mined along with the EHRs  [37]. We plan to conduct additional studies by comparing our results with the French national rare diseases registry  [38].
Any video watermarking scheme dedicated to copyright protection should be robust against several attacks and especially against malicious and dangerous attacks such as camcording. Indeed, this attack has become a real problem for cinematographic production companies. However, until now the researchers don't evaluate the robustness of their video watermarking approaches against this attack or they consider it as a combination of some usual attacks. To resolve this problem, several studies proposed camcording simulators which encourage and help researchers in video watermarking domain to include the camcording in the robustness evaluation. In this paper, a dataset of camcorder videos dedicated to an efficient robustness evaluation of watermarking schemes is proposed which can help researches on camcording simulators' creation.
Despite of their performance, these simulators are insufficient to obtain a real comcorded video. In this paper, we present the efficient methodology used to collect a dataset of Camcorded videos captured in realistic scenarios in a cinema using different capturing devices placed in various positions. In addition, some videos of this dataset are marked by three video watermarking schemes in order to evaluate their robustness against camcording attack. The proposed dataset can be used in order to help researchers to develop efficient camcording simulations or to compare their video watermarking schemes with existing techniques used in the proposed dataset. This paper is organized as follows: the first section presents a survey of camcording attack where we present the existing watermarking methods dedicated to this attack, the usual robustness evaluation method and the developed simulators for camcording attack.
They initially model the flicker signal and demonstrate that its parameters are related to such internal characteristics of the capture devices as the back-light frequency of the LCD screen and the readout time of the camcorder. Then, they introduce an estimation strategy to recuperate these hidden parameters directly from camcorded recordings and demonstrate that such forensic cues could provide intelligence on the pirate devices. They additionally discuss on how to recuperate the shape of the low power flicker signal and demonstrate that it could be used to infer which back-light technology employed in the pirate LCD screen. The authors set out the prospects to better understand the applicability of flicker forensics which will involve large scale validation experiments with a wide diversity of devices, hence the utility of our proposed dataset.
PROPOSED DATASET FOR CAMCORDING VIDEOS In this paper, we propose a video dataset dedicated to evaluate the robustness of watermarking approaches against camcording attack. This dataset can help researchers to build their own camcording simulators by studying the distortion caused by camcorded videos. In more, it gives the researchers the possibility to compare their approaches of video watermarking with the robustness of tested techniques. In fact, the proposed dataset contains different videos (marked and not marked) recorded from different positions in a movie theater using different capturing devices.
Chosen Benchmark Videos The first step in the dataset building is the choice of test videos. Indeed, we have chosen five color free right video (fig2) which present the most sensitive scenes for a video watermarking. The first video is the sequence Stephan which is a benchmark for video watermarking algorithms evaluation and researches on video processing. The second video is a sequence of the famous cartoon HD "Big Buck Bunny". The third one is a sequence of a HD cartoon "Sintel" which presents complex scenes with a fast luminance changing. The fourth video is a video clip "Varoshsequence" which presents a rock concert. The last one is an Italian advertising video "Segugio". The characteristics of these videos are presented in Table  1. Subsequently, in addition of the original versions, we have marked these videos by using three different video watermarking algorithms: multi-frequency insertion (DWT-SVD-DCT) and feature region based approach proposed by  Kerbiche et al.
(A. Kerbiche, 2012) , the algorithm proposed by  Agilandeeswari et al. (L. Agilandeeswari, 2013)  and based on the two transforms DWT-SVD, and spatial algorithm proposed by  Datta et al. (S. Datta, 2014)  which is based on the LSB method. We choose to test three robust and efficient video watermarking algorithms which embed signature in different domain of insertion (multi-frequency and spatial) in order to study their robustness against a realistic case of camcording attack. In addition, these camcorded marked videos can be used by researches to compare their proposals with these videos. Moreover, the usefulness of these camcorded watermarking versions, was to evaluate the proposed dataset and compare it with existing camcording methods, and this by testing their robustness.
Dataset Characteristics The proposed dataset contains 180 camcordred watermarked videos. Figure  7  presents some of those  The whole dataset will be soon available on our website http://camcordingvideos. github. io/ dataset. html for download or you can drop an email to the authors. The dataset website allows users to search videos by watermarking algorithm (not watermarked version, algo1, algo2, algo3) and capturing device. The users may choose to download all resulting videos, or select a subset from them. Finally, for the third type of camcording, we have camcorded the same videos and positions used for the test of the proposed dataset using the smartphone Samsung note 2 and a LCD screen monitor. The obtained robustness results are shown in the table 5. According to this comparative study, we can notice that despite the performance proved by the au-thors of the tested watermarking algorithms and the good robustness results obtained with usual attacks, some algorithms have not resist to camcording attack and we have failed to detect the presence of the mark on several tested videos whatever the camcording techniques used.
In fact, the algorithm proposed by  Datta et al. (S. Datta, 2014) , which is based on the insertion in spatial domain, doesn't succeed to detect the mark on any video while for the algorithm proposed by  Kerbiche et al. (A. Kerbiche, 2012)  we were able to detect the presence of the mark on the most of selected videos. Moreover, we can also notice that for each camcording technique we have obtained different results. In fact, we success to detect the inserted mark on some videos camcordred by the CamMark simulator and by using the standard capturing scenario (Camcording on a LCD screen monitor) but we fail to detect it on the dataset's video. In fact, for the algorithm proposed by  Datta et al. (S. Datta, 2014)  the detector fails to extract the mark for the five dataset's videos but successes for the CamMark simulated video and the camcorded video on a LCD screen.
POTENTIAL APPLICATIONS Among applications that can use the proposed dataset and benefit from the availability of such a dataset, we can enumerate: • Evaluation protocols of video watermarking algorithms: The proposed dataset can be used in order to compare the efficiency of video watermarking algorithms against the Camcording attacks. In fact, the captured watermarked videos can be used directly by video watermarking researches to compare their algorithms directly with the watermarked videos in the proposed dataset or after applying combination of attacks on them. • Camcorder's simulators development: This dataset can greatly helps the studies on camcorder simulators. In fact, these videos are captured with several capture devices and with different disposition scenarios of these tools, which will allows researchers in this field to study the effects and impacts caused by camcorder process and try to simulate them.
CONCLUSION This paper presents a survey of Camcording attack and proposes a camcordred videos dataset which contains recorded videos from a movie theater with several capture devices and from different shooting. This dataset can be useful for the comparative study of video watermarking algorithms. In fact, it will allow researchers comparing their approaches with the techniques tested in the proposed dataset. Moreover, experimental results show that the robustness evaluation based on the proposed dataset is more realist and efficient than the evaluation based on Camcording simulators thanks to the diversity of the used capturing devices and the real conditions of videos recording. For this reason, the proposed dataset is useful, specially, to improve the camcorder's simulators development by helping researches studding the impact of Camcording and the caused distortion using several capture devices on videos.
INTRODUCTION Software Defined Networks (SDNs) enable logically centralized control over network devices through a controller that operates independently of the network hardware, and can be viewed as the network's operating system  (Sezer et al. , 2013). As a result, SDN provides a flexible controllability and programmability by separating the control and data planes via introducing an open interface between these planes. The improvements in the control and management of networks and networking services do not, however, remove the need to address thoroughly the risk of misconfigurations or software bugs that can lead to network failures  (Gill et al. , 2011). Moreover, due to the flexibility provided by SDNs, it is essential to guarantee the correct (re-)configuration of the paths for network flows.
An SDN controller has a 'global view' of the network and several packet forwarding devices are controlled and configured remotely and dynamically through interfaces using protocols such as the Open Flow (OF) protocol  (McKeown et al. , 2008). SDNenabled switches contain one or more forwarding (flow) tables which are ultimately managed by a controller or a cluster of them, logically representing a single controller; SDN switches receive the forwarding rules from controllers to steer network packets in the data plane. An example of an SDN architecture is depicted in Fig. 1. In particular, Fig. 1  presents an example of a network topology consisting of two controllers, four switches and four hosts. Each switch is connected to a single host as well as to one of the SDN controllers. As mentioned before, in order to assure the quality of the SDN architectures, one of the possibilities is to define methods and techniques for testing such complex composite systems.
Notations In this paper, we refer to the data plane as the resource network connectivity topology (RNCT 2 ), depicting the SDN elements in the resource network. An RNCT is represented by an undirected (network links are assumed to be bidirectional) and k-colored graph G = (V, E, c), where the set V of nodes represents network devices (switches, hosts, etc. ). Edges of the graph (the set E) are unordered pairs (x, y), x, y ∈ V , representing connections between two nodes (links) in a network; and c is a coloring function c : V → N ∪ {0} such that given a node in the network, a corresponding color is assigned to it as a hashed integer. Note that the colors of adjacent nodes can be the same, differently from the common graph coloring functions. As an example, the previously depicted model can accurately represent the data plane (RNCT) shown in Fig.