dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion We have introduced a novel transition system for lexicalized discontinuous parsing. The SR-GAP transition system produces short derivations, compared to SR-SWAP, while being able to derive any discontinuous tree. Our experiments show that it outperforms the best previous transition system  (Maier, 2015)  in similar settings and different datasets. Combined with a span-based feature set, we obtained a very efficient parser with state-of-the-art results. We also provide an efficient C++ implementation of our parser, based on a tree-structured stack. Direct follow-ups to this work consist in switching to a neural scoring model to improve the representations of D and S and alleviate the locality issues in feature extraction  (Kiperwasser and Goldberg, 2016; Cross and Huang, 2016).
Acknowledgments We thank three anonymous reviewers, as well as Héctor  Martínez Alonso ,  Olga Seminck  and  Chloé Braud  for comments and suggestions to improve prior versions of this article. We also thank  Djamé Seddah  for help with the SPMRL dataset. This work was partially funded by the  Agence Nationale de la Recherche  ( ParSiTi  project,  ANR-16-CE33-0021 ).
It can take up to several days of work for a complex shot of only a few seconds, using dedicated tools within video editing softwares like Silhouettefx, Adobe After Effect, Autodesk Flame and The Foundry Nuke. As discussed in  [15] , professional roto artists use mostly tools based on roto-curves, i. e. , parametric closed curves that can be easily defined, moved and edited throughout shots. By contrast, these artists hardly use brush-based tools, even if empowered by local appearance modelling, graphbased regularization and optic flow-based tracking as After Effect's ROTOBRUSH. Due to its massive prevalence in professional workflows, we address here rostoscoping in its closed contour form, which we aim to facilitate. Roto curves being interactively placed in selected keyframes, automation can be sought either at the key-frame level (reducing the number of user's inputs) or at the tracking level (reducing the number of required key-frames).
Bezier roto-curves defined by the artist in the selected key-frames allow the realtime learning of a non-linear low-dimensional shape space based on a Gaussian process latent variable model. Shape tracking between key-frames, as well as subsequent edits, are then constrained within this smooth manifold (up to planar transforms), with substantial gains in work time. Our work is fully complementary to ROTO++: while ROAM does not use a strong shape prior in its current form, it allows to capture the dynamic appearance of the video object, something that ROTO++ does not address. Note that the annotation (ground-truth roto-curves) of the rotoscoping dataset that constitutes another contribution of  [15]  is not yet available, as per submission time. In their seminal rotoscoping work, Agarwala et al.
Results We now report experimental comparisons that focus on the minimum input scenario: an initial object selection (curve or mask, depending on the tool) is provided to the system and automatic object segmentation is produced in the rest of the shot. 6 We do not consider additional user interactions. Datasets. We evaluate our approach on the recent CPC rotoscoping dataset  [16]. It contains 9 videos consisting of 60 to 128 frames which represent typical length of shots for rotoscoping. These sequences were annotated by professional artists using standard post-production tools. We also provide qualitative results on shots from the ROTO++  [15]  dataset (Fig. 10 ) for which the authors have not released the groundtruth yet, as well as from the VIDEO SNAPCUT dataset  [3]  (Fig. 12 ). In addition to that, we use the DAVIS video segmentation dataset  [18]  which comprises 50 challenging sequences with a wide range of difficulties: large occlusions, long-range displacements, non-rigid deformations, camouflaging effects and complex multi-part objects.
Let us note that this dataset is intended to benchmark pixel-based video segmentation methods, not rostocoping tools based on roto-curves. Evaluation Metrics. We use standard video segmentation evaluation metrics and report the average accuracy, i. e. , the proportion of ground-truth pixels that are correctly identified, and the more demanding average intersection-overunion (IoU), i. e. , the area of the intersection of ground-truth and extracted objects over the area of their union. We also report runtimes and evolution of IoU as sequences proceed. Baselines. We compare with several state-of-the-art methods. Our main comparison is with recent approaches that rely on a closed-curve, i. e. , CPC  [16]  and ROTO++  [15]. We initialize all methods with the same object and measure their performance over the rest of each sequence.
108 ROTO++(2 keyframes)  [15]. 974. 691 --0. 156 CPC  [16]. Ablation study. To evaluate the importance of each part of our model, we consider 4 different configurations: • Baseline: negative gradient with 2 -regularizer; • Lean: baseline + local appearance model; • Medium: lean + landmarks; • Full: medium + automatic re-parametrization and global appearance model; For all configurations, we used cross-validation (maximizing the mean IoU) on the training fold of the DAVIS dataset to set the parameters and kept them fixed for all experiments. Quantitative results. The quantitative results for the CPC dataset are summarized in Table  2. While average accuracy is quite similar and saturated for all methods, all configurations of ROAM outperform all baselines. In terms of IoU all versions of ROAM, with the full configuration being the best, outperforms significantly all others.
The reason why landmarks ("medium conf. ") do not add much to ROAM is that the CPC dataset does not exhibit many large displacements. The CPC method  [16]  was evaluated only on the first ten frames of each sequence since their authors have released results only on these frames and not yet their code. As a consequence, the average accuracy and average IoU scores that we computed and reported in Table  2  for CPC are based on partial videos, as opposed to the scores for all the other methods (including our approach). When similarly restricted to 10 first frames, ROAM performs on par with CPC for all the sequences of the dataset except "drop". The latter sequence, which shows a water drop falling down, is of a very particular kind: the object of interest is transparent, making color models (both local and global) useless if not harmful, and exhibits a very smooth round shape.
For this sequence, the CPC method  [16] , which makes use of Bézier curves and relies solely on the strength of the image gradients, is clearly more adapted and indeed delivers better results. Results for the DAVIS dataset are reported in Table  1 , and qualitatively illustrated in Figs. 4  and 11. While our method is on par with JUMPCUT (pixel-wise labelling), we again significantly outperform ROTO++ by almost 25 IoU points (note that using ROTO++ with only two keyframes is not a typical scenario, however, this shows how complementary our approaches are). Comparing the different configurations of ROAM-local appearance models add 3 points, landmarks 15 and global model with re-parametrization another 5 points-demonstrates the importance of all components of our framework. Since it is difficult to understand the behavior of each method from average scores, we also report IoU score for each frame in Fig.
7 , with in addition the effect of varying the size of the displacement space in ROAM (from windows of 3 × 3 to 13 × 13 pixels) represented with a blue shadow. It can be seen that ROAM is more robust in time, not experiencing sudden performance drops as others. Note, that we could not provide more quantitative comparison since results/implementations of other methods were not available from the authors. In particular, comparison with the CPC method  [16]  would be interesting since the DAVIS dataset  [18]  exhibits many large displacements and major topology changes. Importance of landmarks and warping. Using alternating optimization has one more benefit. We can use the predicted position of landmarks in the next frame to estimate the transformation between the two and "warp" the contour to the next frame.
1 and qualitative results on the surfer sequence from VIDEO SNAPCUT dataset are provided in Figs. 5  and 6. Observe that the local color models are a powerful way to capture local appearance complexities of an object through a video sequence. However, self-occlusions and articulated motion can cause these models to fail (right arm crossing the right leg of the surfer). Our contour reparametrization allows the efficient handling of this situation. Furthermore, the beneficial effect of the global color models can be observed in Fig. 6 , where the right foot of the surfer is successfully tracked along the whole sequence. Timing breakdown. Table  4  provides detailed timing breakdown for our algorithm. These timings were obtained on an Intel Xeon 32@3. 1GHz CPU machine with 8GB RAM and Nvidia GeForce Titan X GPU.
To complete the monitoring part, a PC is used as an industrial network using OPC-server ensuring communication between the PLC and LabView. In this configuration, the PLC and the LabView application are the OPC-clients. A LabView interface was also designed for human-machine interface. Moreover, LabView allows the interaction between the PLC and Matlab & Simulink software for simulation tests. Figure  2  shows the automation structure of the process. This includes the human-machine interface at the top (LabView), the control part (PLC), sensors, drivers and the actuators at the bottom. With this layout, it is possible to measure and collect data from the system and manipulate the various actuators. A command board was designed and assembled to keep the equipment safe, to protect the laboratory power network and for possible separate interruption of the reactors.
Data Logging The data logging has two structures: (i) OPC connection between the Lab-View application and the PLC that records the data from the process every second; (ii) data logging that records data every 5 mins. The data files can be downloaded by accessing the PLC memory via a web-browser using the PLC address in the network (The PLC has 15 days of autonomy). Before any data acquisition and process operation, instrument calibration was executed. An optical densitometer was used to measure the total suspended solids (TSS) concentration. It was calibrated by taking samples from the pipe between the denitrification and nitrification tanks, from the nitrification tank and a dilution of 1:2 from the latter. The samples were dried in an oven set to 105 and then the dry solid particles were weighed.
The superscript T denotes the matrix transpose. The optimization is performed in this study using the Nelder-Mead algorithm as implemented by the fminsearch function in Matlab. A lower bound of the covariance matrix P of the estimated parameters is obtained by the inverse of the Fisher Information Matrix (FIM): P = F -1 ( θ, Ω) (8) The FIM is computed by: F ( θ) = nt i=1 ∂Y m ∂θ T (ti, θ) Ω -1 ∂Y m ∂θ (9) where Y m is the vector of the process outputs, Ω is the estimated covariance matrix Ω = J( θ) (n t -p) Ξ ( 10 ) and p is the number of parameters to be estimated. The square root σ j of the j th diagonal element of P is an estimate of the standard deviation of θj , which can be used to compute parameter confidence intervals. For instance, an interval of ± 1. 96σ corresponds to a probability of 95%.
Finally, we have developed a simple prototype of PICN and made several experiments and simulations to evaluate its performance. The code for prototype and the scripts of simulation are available at GitHub 1. The main contributions of this article are: (1) deploying an ICN at the edge of the legacy network that is compatible with all the network and application layer protocols including HTTP. (2) Leveraging clients resources for caching instead of pervasive caching to ease the network cost to deploy ICN while benefiting from lower latency and lower uplink traffic. (3) Proposing a content-based security as one of the main ICN primitives; the solution uses self-certifying content instead of self-certifying name in order to be compatible with HTTP URIs (Uniform Resource Identifier). (  4 ) Suggesting some privacy and incentive algorithms; since we use the collaboration of the clients in providing the cache storage, we need to deal with privacy and incentive issues.
B. Experimental Results In this section, a local implementation of PICN is examined. For this purpose, we developed the seeker and client softwares in the QT cross-platform development framework. The seeker framework is executed on an arbitrary machine in a LAN. Any other machine on the LAN runs the client framework (given the seeker machine IP address) to share its own cache and use other clients' cache storage. The client framework plays the role of a proxy server for the browser, so a proxy setting configuration is required for the browser to use it. Accordingly, every HTTP request is forwarded to the client framework and is replied through it. We determine a set of content types for PICN that supports all the images, videos and flash files. The contents of this set are cached and retrieved by PICN.
Hit ratio and the average latency for retrieving the contents are computed by simulation. Latency is the time spent between requesting an HTTP content and receiving the last byte of its reply. For calculating the latency, the bandwidth on each link is shared between all the simultaneous content flows on that link. Transferring content between two clients on the same local network is called local client retrieval and between two clients of neighbor local networks is called remote client retrieval. 1) Berkeley trace files: This dataset includes 18 days of HTTP traces gathered from the Home IP service offered by UC Berkeley to students, faculty, and staff. There are 8559556 requests from 8374 clients for 2230517 different URLs in this trace file. Table  III  describes trace file information. Given that the number of different URLs is only about a quarter of the number of requests, there are many redundant requests for the same contents.
So the hit ratio in the caches is high with this dataset. We also assume 10 local networks and 10 seekers for the underlying overlay topology. We map 8374 clients uniformly to the machines of all local networks. So for each local network, there are about 837 clients that are connected to one seeker. A cache storage of 100 M B is assigned to each client. So the total cache storage of all the clients is about 83 GB which is comparable to the total size of the supported contents for caching in PICN which is about 57 GB. Simulation results for the trace file show that the hit ratio is 37. 4% in the local machine caches, 24. 2% in local clients and 10% in remote clients; so total hit ratio is 71. 6%. According to this simulation, 2. 9% of external traffic is reduced using PICN with 100 M B of cache size for each client.
2% and up to 96. 3%. 2) IRCache trace files: This dataset includes cache access logs from the IRCache proxies during day 9th of January 2007 for 8 root cache servers located in cities throughout the US (Table  IV ). We performed two simulations for each day because the clients IP addresses have been anonymized by changing the IP addresses and the mapping of real-toanonymous IP addresses are different from day to day. Some IP addresses in the trace files are NAT addresses, so there are many requests from these addresses for the same contents. Therefore we consider 10 most repeated client addresses in each trace file as NAT and assign uniformly one of the other client addresses to the correspondent requests. In this simulation, there are 816 clients, each one has a cache storage of only 100 M B.
Acknowledgements P. E. acknowledges financial support from a  PET methodology programme grant  from  MRCUK  (ref no. G1100809/1 ). P. E. and T. T. acknowledge financial support for reciprocal visits from a  DAIWA Small Grant  from the DAIWA foundation. This work was partly supported by  Bilateral Joint Research Project between JSPS, Japan , and  F. R. S. FNRS, Belgium.
The Fietsgame The Fietsgame has been designed by a consortium of physiotherapists, game designers, researchers, and an information technology company with the goal of improving the rehabilitation process. The specific aim was to increase the mobility of the joints and surrounding soft tissues and to increase muscular strength as well as endurance. The system has the following two components: the exercise games and Community Care 360 (CC360) with a therapist control interface and the patient's medical record. The exercise games and CC360 are connected by the Internet of Things (IoT) server from Consultants to Government and Industries (CGI)  [33]. The games were run on a PC with Windows 10 software and displayed on a 48-inch TV. A Raspberry Pi device was used to connect the PC to the IoT platform.
In the patient application (Figure  8  bottom, originally in Dutch; see also Multimedia Appendix 2), the patient can see the exercises assigned to him/her by his/her physiotherapist and play the exercise games by performing the required therapeutic movements. The game software assesses the performance of the patient by analyzing the data captured by Kinect v2. The final results, such as the number of successfully accomplished movements, knee angle, hip angle, and start and end time of the exercises, will be recorded and sent to the IoT platform and can be accessed through the therapist and patient applications in CC360. The exercises can also be assigned through a local configuration file on a PC when the patient receives no assignment from the physiotherapist through CC360.
Data Analyses All the behavioral data and self-reported scores of the questionnaires were analyzed with SPSS statistics package version 24. To answer our research questions, the measured data, including behavioral and self-reported data, were analyzed using descriptive statistics for all the 6 exercise games, and 2 trained researchers coded the qualitative feedback from patients and physiotherapists separately. Under the broad question, themes emerged from the coded data. The researchers discussed and refined the codes, that is, codes with similar meanings were grouped together, and the more frequently a code appeared, the more the theme was strengthened. We then analyzed the codes addressing gaming experience, game design, system operation, usefulness, and intention to use the exercise games.
Acknowledgments This study is supported by the  COMMIT  project "Virtual worlds for well-being. "The authors would like to thank the other project team members, namely,  Arjan Egges  for his input on design of this study;  Jan Kooijman ,  Eline van Vliet , and other physiotherapists from Aafje rehabilitation center for their suggestions in the design of the game;  Cho Lie Tam ,  Jacob Mulder ,  Marten Eisma , and  Pieter Goossen  from  CGI  for their support to the project and their efforts to connect the games to the CC360 system; and  Maarten Stevens  and  Meint Span  from  8D  games for designing and developing the exercise games.
This paper reports on an in-depth analysis of ISO 5436 part 2 type F2 reference software for the calculation of profile surface texture parameters that has been performed on the input, implementation and output results of the reference software developed by the National Physical Laboratory (NPL), the National Institute of Standards and Technology (NIST) and Physikalisch-Technische Bundesanstalt (PTB). Surface texture parameters have been calculated for a selection of seventeen test data files obtained from the type F1 reference data sets on offer from NPL and NIST. The surface texture parameter calculation results show some disagreements between the software methods of the National Metrology Institutes. These disagreements have been investigated further, and some potential explanations are given.
Software measurement standards come in two types: type F1 reference datasets with certified results, and type F2 reference software which calculate surface texture parameters for any input file. These software measurement standards are produced by National Metrology Institutes (NMIs). Some of the most well regarded software measurement standards for surface texture are the following: 'softgauges version 1. 01' produced by the National Physical Laboratory (NPL), UK, available at http://resource. npl. co. uk/softgauges/Ref_Algo_Overview. htm [11, 12] ; 'SMATS version 1. 0' produced by the National Institute of Standards and Technology (NIST), USA, available at http://physics. nist. gov/VSC/jsp/index. jsp [13, 14, 15] ; and 'RPTB version 2. 06' produced by Physikalisch-Technische Bundesanstalt (PTB), Germany, available at https://www.
For example, Li et al. highlighted that NPL uses interpolation of the input data points to create a continuous profile, whereas PTB and NIST work with a discrete profile. Li et al. performed their comparison with six type F1 reference data sets, again finding generally good agreement between the NMIs. The software measurement standard comparison used the PTB software 'RTPB v1. 05', which has since been updated, bringing the values obtained by PTB much closer to those obtained by NPL and NIST, particularly for the average ordinate parameters (Ra, Rq, etc. ). Li et al. 's work also highlighted the increased disagreement for RSm values and the need for a better definition for this parameter, along with the Rc parameter. More recently, Paricio et al. [23]  performed a similar experiment, comparing the software on offer from NPL, PTB and NIST.
Implementation of software 2. 1 Input file requirements All three NMIs accept the. SMD file format, as defined in ISO 5436-2. NIST and PTB also accept. SDF files, which is the ISO-defined file-type for areal software measurement standards  [25]. The. SDF format is a useful addition, as it allows profile and areal measurements to be pre-processed for parameter calculation in the same way, saving time for users of the software that use both measurement types. Additionally, the NIST software accepts the. TXT format, a common text file type, and PTB accepts the. PR filetype, a simplified version of the. SMD from an old Breitmeier UBM instrument. Although the NPL software is restricted to the SMD format, to facilitate a wider use of the software NPL provide a "data file converter" at http://161.
112. 232. 32/softgauges/Converter. aspx to include. SDF,. PRF and. TXT file formats. All three NMIs assume the height value points within the input file are uniformly spaced along the X -axis. Uniform spacing is part of the. SMD definition in ISO 5436-2, and allows for a much simpler analysis of the file. On top of uniform spacing, NPL assumes some initial processing has already been carried out. In line with the definitions in ISO 5436-2, the NPL software requires an input of a primary profile. Using a primary profile means the input file must already have had the form removed, and must be λs filtered, so that any wavelengths shorter than those associated with roughness are removed. NIST and PTB, however, have no such restrictions and include options for form removal and λs filtering, allowing them to work with files with no prior processing undertaken.