dataesr/scientific-paragraphs-categorization huggingface dataset:
The Future Spaces Solution To enforce the vSpaces, the Future Spaces solution enables the seamless creation of a dynamic connectivity layer, implementing a LAN-like connectivity between components of a vSpace by programming virtualized network functions connecting the corresponding physical devices. We call such slices "Software-Defined LANs" (SD-LANs) and each vSpace definition is ultimately embodied as a SD-LAN. To support this type of connectivity, the solution relies on three core functional components:   Figure  2  presents the simplified architecture for one domain (i. e. , a vPlace), which includes here a single physical home. In our proof of concept prototype, the core functional components are realized as micro-services deployed and orchestrated within a distributed cloud platform (Figure  3 ).
Future Spaces Gateways A Future Spaces Gateway (FSGW) is an important class of Future Spaces instance whose main function is to provide secured and managed fine-grained access control via a set of dedicated network interfaces towards the Future Spaces cloud network and its associated/interconnected devices and services. As for any Future Spaces instance, the FSGW can be implemented as a physical or virtual gateway and provides secured access through a number of physical or virtual network interfaces in a device-agnostic manner (i. e. , without having to modify or install any specific software onto the devices themselves to achieve this secured access). It implements the fine-grained access control mechanisms (e. g. , coming from the SDN-C see Section 5) but also facilitates the onboarding process of the devices onto a vPlace.
Future Spaces Gateways A Future Spaces Gateway (FSGW) is an important class of Future Spaces instance whose main function is to provide secured and managed fine-grained access control via a set of dedicated network interfaces towards the Future Spaces cloud network and its associated/interconnected devices and services. As for any Future Spaces instance, the FSGW can be implemented as a physical or virtual gateway and provides secured access through a number of physical or virtual network interfaces in a device-agnostic manner (i. e. , without having to modify or install any specific software onto the devices themselves to achieve this secured access). It implements the fine-grained access control mechanisms (e. g. , coming from the SDN-C see Section 5) but also facilitates the onboarding process of the devices Sensors 2018, 18, 2986 10 of 30 onto a vPlace.
Acknowledgements The authors greatly appreciate discussions with  P. Ring  and  R. G. T. Zegers. Special thanks to  T. Marketin  for providing a part of the code for pn-RRPA matrix elements. This work is partly supported ICNFP 2017 by  US-NSF  grant  PHY-1404343 ,  NSF   Career grant   PHY-1654379 , by the  Institute for Nuclear Theory under US-DOE Grant   DE-FG02-00ER41132  and by  JINA-CEE  under  US-NSF  Grant  PHY-1430152.
This paper describes a new methodology for noninvasive, objective, and automated assessment of yield in vineyards using image analysis and Boolean model. Image analysis, as an inexpensive and noninvasive procedure, has been studied for this purpose, but the effect of occlusions from the cluster or other organs of the vine has an impact that diminishes the quality of the results. To reduce the influence of the occlusions in the estimation, the number of berries was assessed using the Boolean model. To evaluate the methodology, three different datasets were studied: cluster images, manually acquired vine images, and vine images captured on-the-go using a quad. The proposed algorithm estimated the number of berries in cluster images with a root mean square error (RMSE) of 20 and a coefficient of determination (R 2 ) of 0.
Finally, modified agricultural vehicles can be used to automate the image capture of large datasets  [16, 17]. This approach has to face the limitation introduced by the lack of supervision during the capture, which greatly affects image quality. The segmentation process of images acquired on the field is challenging, because of the uncontrolled scenario characteristics and the unevenness in the berry surface caused by the pruina  [11]. Also, it must be noted that not all the berries in a cluster are visible due to occlusions from other berries or vegetal material from the vine. A method that has resistance to these problems (occlusions and segmentation errors) will greatly improve the prediction reliability. The Boolean model and random set theory was developed by Matheron  [18]  and Serra  [19].
Journal of Sensors The Boolean model can be directly used for berry number estimation, but the ROI must be defined so that the concentration of particles is similar on it. In the case of vine images, particle (berries) concentration is limited to portions in the image (clusters), so a ROI not corresponding to all the image areas must be selected for proper porosity calculation. The ROI was automatically obtained by applying a morphological opening  [24]  (morphological erosion followed by dilation) for all the segmented clusters using a circular kernel of the same radius of the mean berry size. To evaluate the prediction capabilities of the Boolean model, four tests were conducted (each one composed of 100 simulations). The tests were performed by using MATLAB (R2010b, MathWorks, Natick, MA, USA) to generate synthetic images containing randomly placed particles.
This process was repeated for every image because of the measurement variation depending on the distance between the camera and the cluster. (ii) Vine Images. An average radius was set (manually extracted in one image as in the cluster dataset) and applied to all images from the same grapevine variety. The algorithm for image analysis was implemented in MATLAB and process batches of images in a fully automated way. The cluster segmentation procedure was based on a Mahalanobis distance classifier and is defined in the following section. For the on-the-go images, misclassification between the pixels corresponding to clusters and the metal wires used for the vine support were observed. An additional filtering step is described in Section 2. 5 and the benchmarking and validation process of the classification in Section 2.
Validation. To evaluate the developed algorithms, yield estimation has to be compared with real data. Also, due to the especial characteristics of the on-the-go images, the segmentation was ranked before and after the filtering MPMs were applied. The ground truth for every data set was obtained as follows: (i) Manually Acquired Cluster Images. All the photographed clusters were picked and introduced into pretagged plastic bags to allow their conservation during their transport to the laboratory. Then, they were destemmed, and the berries were detached, counted, and weighted. The number of berries per cluster and their weight was used to obtain the average berry weight. (ii) Manually Acquired Vine Images. After the image capturing process, all the vines were harvested, and the clusters were weighted together to obtain the final yield per vine.
Acknowledgments We would like to thank  Vitis Navarra  for their help with the field measurements. We also thank  Dr. Jesús Angulo  for his comments and suggestions. Borja Millan  would like to thank the  Center of Mathematical Morphology  for the support, guidance, and laboratory facilities during his 6-month research stay conducting to this publication. Borja Millán  was funded by the  FPI grant  [ 536/2014 ] by the  University of La Rioja.
, 2011; Solomon et al. , 2013) , the role of wind and heat exchange in lake physics  (Read et al. , 2012) , the impact of climate change  (Adrian et al. , 2009) , response and recovery of lakes to extreme events  (Jennings et al. , 2012; Klug et al. , 2012) , incorporation of high frequency data for model validation  (Hamilton et al. , 2015)  and assisted in development of models  (Staehr et al. , 2010; Read et al. , 2011; Kara et al. , 2012; Hipsey et al. , 2017). Further interrogation of the emerging multi-lake datasets offers the potential to advance our understanding of how lakes respond to pressures such as climate or land use change from the individual to global scales. The collaborative network also creates opportunities for developing and testing modelling tools. Aquatic ecosystem models are recognized as essential instruments to improve understanding of processes, analyse relationships, test hypotheses and predict the state of a system  (Trolle et al.
Study site selection Lakes were not chosen a priori based on their attributes, but rather AEMON and GLEON members were invited to participate in the MLCP by volunteering details of their candidate lake to the group (shared via open access spreadsheet). The requirement for inclusion of a lake was based on the following three conditions: 1. Sufficient temperature data were available for validation (at least 2 years of monthly/regular thermistor chain and/or profile data); 2. High-resolution meteorological forcing data from an on-lake buoy or local terrestrial based station were available; and 3. Gauged or well-estimated inflows and outflows were available over the simulation period to form a reliable lake water balance. Participants were also required to have a basic knowledge of lake modelling.
Instructions as to how to set-up the GLM test cases, and a common binary executable (GLM v2. 2. 0) were made available for download from the Aquatic EcoDynamics (AED) website (https://github. com/AquaticEcoDynamics/GLM). Pre-and postprocessing MATLAB scripts were provided to all participants to ensure a common model setup and assessment approach (https:// github. com/AquaticEcoDynamics/GLMm), and all GLM lake setups were available to other members via a cloud-based, shared folder. A total of 32 lakes was chosen for the analysis, with an alphabetic listing of the lakes and their physical characteristics in Table  1. Each lake is associated with a two letter abbreviated code, and for brevity when presenting model results, the lakes are frequently referred to by this code. To illustrate the range of sizes in the lakes included in this study, lake outlines have been drawn to scale in Fig.
While an effort was made to use lakes with high quality input data, lakes where input data had to be estimated were still selected for the MLCP in order to ensure a sufficient variation in lake characteristics. For seven lakes either inflow, outflow or both were estimated (Bourget, Emaiksoun, Feeagh, Mendota, NamCo, Stechlin and Woods) and the parameter of light attenuation (K w ) was estimated for three lakes (Alexandrina, Muggelsee and Woods). Meteorological data for short wave radiation, air temperature, relative humidity, wind speed and precipitation were supplied either from an on lake station or the closest meteorological station to the lake. Long wave radiation was either measured directly (net or incident) or calculated by GLM using cloud cover data. In an attempt to assess the errors associated with input data limitations, a qualitative weighting system was used to assess each input variable or constant, where a minimum score is associated with the best available input or observation data (Table  A1 ).
gleon. org/), an open source software tool that computes indices of mixing and stratification for lakes and reservoirs  (Read et al. , 2011). The comparison of thermD calculations was included in the analysis as it is a simple, widely-used metric of mixed layer depth, while acknowledging the calculation of thermD can be challenging for weakly stratified and polymictic lakes. Also, the approach used in Lake Analyzer identifies the strongest thermal gradient, and may miss important thermal structure. S T represents resistance to mechanical mixing due to the potential energy inherent in the stratification of the water column, calculated as: S T ¼ g A S Z zD 0 ðz À z v Þr z A z dz (2-6) where g is the acceleration due to gravity, A s is the surface area of the lake, A z is the area of the lake at depth z, z D is the maximum depth of the lake, and z v is the depth to the centre of volume of the lake, and r z is the water density at depth z.
Acknowledgements and contributions GLM development and funding support for LCB, BDB, CB and MRH was provided by the  Australian Research Council (ARC)  (grants  DP130104078 & LP130100756 ). Additional contributions from individuals and organisations as well as sources of data, provided from a variety of organisations are summarised in Appendix D. This study was made possible through the sharing of ideas, data and models across the AEMON and  GLEON  networks as well as discussions and working groups held during AEMON workshops and GLEON meetings.
Introduction Initially, comics were printed on paper books, but nowadays, born-digital comic books have become more and more popular. Physical museums such as the "Internationale de la Bande Dessinée et de l'image" (http://collections. citebd. org) in France and the Kyoto International Manga Museum (http://kyotomm. jp), online museums or archives such as The Digital Comic Museum (http://digitalcomicmuseum. com), Comic Book Plus (http://comicbookplus. com) and the Grand Comics Database (http://comics. org) have already digitized several thousands of comic albums and some are in the public domain. There are also online libraries selling digital and physical comic books such as DC Comics from Warner Bros (http://www. dccomics. com), comiXology (https://www. comixology. com), Sequencity (http://sequencity.
We also propose some improvement techniques for some tasks such as balloon segmentation or text recognition. In summary, the main contributions of this article are: • An indexing system from global image analysis to precise content extraction and encoding. • Deep learning adaptation for comic elements (panels, faces, and characters) for the detection task. • Improvement techniques for balloon segmentation and text recognition. • Comparison of traditional computer vision techniques versus deep learning approaches with intensive experimentation. • A new public dataset with manual annotation of spatial coordinates for panels and comic characters' bodies and faces. This paper is organized as follows. First, we review previous works about comic book image analysis and related approaches using deep learning in Section 2.
Encoding and Indexing To our knowledge, there has been little work on the conceptualization of comic language. In addition to the web schema proposal Periodical Comics  [74]  and the platform Grand Comics Database  [75]  which are both focused on the bibliographic organization of the work, only a few initiatives describing their content have been developed, but not systematically as academic publications. In 2001 an XML formalization was proposed, called ComicsML, allowing to describe the content of a board  [76]. It has been developed in the hope of becoming a framework for the publication of then nascent webcomics. Indeed, the publication of a webcomic is often done directly on the web page of the author and the frequency of update can be very heterogeneous depending on the series. The ambition of ComicsML was therefore to become a formalism that, if adopted by a large number of authors, would allow emerging new uses of reading webcomics, especially through tools exploiting the specificities of the language.
ComicsML allows describing a series of webcomics published on the web, from its most general bibliographic aspects to the structure of the page: layout, type of bubbles or the form of the text used in a box. The syntax, based on tags from the XML language, implies a hierarchical approach in the description of elements composing the image. An approach sharing a number of common points was observed by Morozumi et al. [77]  (applied to manga). A second initiative, called Comic Book Markup Language (CBML), was launched a few years later by John A. Walsh  [78]. In addition, based on XML, it is quite similar in its philosophy to the proposal of  [76] , although it is not reserved for Text Encoding Initiative P5: Guidelines for Electronic Text Encoding and Interchange  [79]  (TEI). The TEI is a framework, proposed by the consortium of the same name, for encoding illustrated textual documents, to facilitate the search for information in large databases of encoded works.
System Overview In this paper, we propose an indexing system for digital comic book images which combines traditional methods and deep learning approaches. The system's objective is to make a processing pipeline of comic book page images, from global images analysis to precise content extraction. The latter is used to index the visual and textual content of comic book images for content-based retrieval systems such as search engines. In the proposed architecture (Figure  2 ), the entire system consists of offline and online procedures that exchange information. In the offline procedure (blue blocks), we analyze and extract information from comic book images to produce a content description text file for each image. All analysis computations in this procedure can be processed offline over all comic books already present in the digital comics database, or on-demand to any new comic book image.
Strategy Used to Build the Proposed System The offline part of Figure  2  shows the complete indexing/analysis pipeline. In the first step, comic book images are processed and then panels are extracted. Panel extraction can be considered as an object detection process. In this work, we have compared the algorithms using image processing techniques with the state-of-the-art object detection methods using deep learning models which are trained on available datasets. In the next step, we have studied text detection/recognition, character/face detection, and balloons segmentation from detected panels in the first step. We use a state-of-the-art algorithm to detect (localize) texts in panels and then an incremental model based on a recurrent neural network with Long Short Term Memory (LSTM) is proposed to recognize texts.
We also compare the traditional algorithms of balloon segmentation with some state-of-the-art segmentation deep learning models. Then we propose a simple technique to boost the performance of balloon segmentation by combining the two approaches. To detect comic characters and faces, we have performed empirical experimentation using state-of-the-art object detection models. Finally, the results from previous steps are used to extract the tail of balloon and to analyze the relationship between balloons and characters. In the last stage, all extracted information is stored in a description file (CBML) to support the online procedure (e. g. , search engines). As all the best object detection deep learning models require a significant amount of labeled data for training, we introduce a new comic book image dataset including panels, characters, and faces positions.
YOLO Model YOLOv2  [11]  is the improved version of the model YOLOv1  [80]  of the same author. While YOLOv1 predicts the coordinates of bounding boxes directly using fully connected layers on top of the convolutional feature extractor, YOLOv2 predicts bounding boxes using hand-picked priors as introduced in Faster-RCNN  [61]. Instead of predicting offsets of bounding boxes as Faster-RCNN, the YOLOv2 model predicts location coordinates relative to the location of the grid cells as YOLOv1. Besides, the YOLOv1 model uses fully connected layers together with convolutional layers, while YOLOv2 uses only convolutional layers. The generic model YOLOv2 is proved to have good results for generic datasets  [11]. However, for the task of object detection in comic book images, there are some shortcomings related to the domain knowledge and the limited labeled data.
Anchor Boxes Learning for Comic Images YOLOv2 model uses anchor boxes, a technique presented in Faster R-CNN  [61]. Instead of predicting coordinates directly, Faster R-CNN predicts bounding boxes using hand-picked priors  [61]. Hand-picked priors are size pre-defined anchor boxes. The network starts the prediction from the anchor boxes and learns to adjust the coordinates to move and resize the anchor boxes to the right position. However, anchor boxes in Faster R-CNN are chosen for general use (not database specific), and anchor boxes in YOLOv2 are learned from VOC  [81]  and COCO [82] datasets. These anchor boxes have not been designed for face, panel and character detection in comic book images. It makes the network harder to predict good detections because the size and aspect ratio (width/height) of faces, panels or characters in comic book images are different from general objects in VOC  [81]  or COCO [82] datasets.
For example, if the anchor boxes are chosen very big, it is difficult for the network to detect small objects; especially faces which have the width often smaller than the height. Thus, we should choose anchor boxes appropriately. Using the same idea in YOLOv2, we find the right anchor boxes for a comic book dataset by using k-means clustering on the bounding boxes of the ground-truth. We apply this clustering to find one set of anchor boxes for each element: face, character, and panel on the DCM772 dataset (see Section 7. 1. 3). In Figure  3 , we show the anchor boxes for characters on the DCM772 dataset. In our experiments, we have used k = 5 in the k-means clustering algorithm to learn five anchor boxes. The five anchor boxes represent the whole objects bounding boxes in the dataset. We use the average Intersection Over Union (IOU) metric to evaluate how well the anchor boxes represent the objects.
Representation Learning One of the problems we have when working on the comic page analysis is that the number of labeled data is limited. It is hard to have significant labeled training data because the cost in human resources is very high. This limitation usually leads to the lower performance of the detector or classifier even if we have a robust learning algorithm/model. In a convolutional neural network such as YOLOv1, the first set of convolutional layers aims at learning the representations of the dataset images for a target task, then the last fully connected layer set is a classifier or detector. If the number of training data is limited, convolutional layers cannot learn good representations for the target task. Some techniques can tackle this problem, for example, "unsupervised representation learning" and "transfer representation learning", which can learn good representations from unlabeled data.
The unsupervised representation learning profits from massive amounts of unlabeled data to find the relation between input data and output by using models such as Stacked Autoencoders  [83] , Deep Belief Network  [84]  and Deep Boltzmann Machine [85]. In the transfer representation learning, a simpler supervised or unsupervised task with more raw data is used to find good representations of the data, then these representations are fine-tuned to deal with the more difficult task using fewer labeled data. While unsupervised representation learning has been largely abandoned except in the field of natural language processing because of its unstable advantage [9], transfer representation learning for convolutional networks is popular  [86]. In this section, we present an approach for transfer learning with comic book image datasets.
32% of the tails from the eBDtheque dataset  [88]. In this approach, we build a geometric graph in a Euclidean plane within the panel where vertices are spatial positions of tail and comic character body (or faces if detected) centroids. Edges are straight-lines segments (associations). We formulated an optimization problem where we search for the best pairs (2-tuples) of tail and face or body corresponding to associations in the story. The main optimization criterion is the Euclidean distance between each entry of the 2-tuples.