dataesr/scientific-paragraphs-categorization huggingface dataset:
g. ,  [47, 48] ). In this paper we propose a convolutional neural network (CNN) architecture, called SCNet, for learning geometrically plausible semantic correspondence (Figure  1 ). Following the proposal flow approach to semantic correspondence of Ham et al. [11] , we use object proposals  [29, 40, 51]  as matching primitives, and explicitly incorporate the geometric consistency of these proposals in our loss function. Unlike  [11]  with its hand-crafted features, however, we train our system in an end-to-end manner using image pairs extracted from the PASCAL VOC 2007 keypoint dataset  [7]. A comparative evaluation on several standard benchmarks demonstrates that the proposed approach substantially outperforms both recent deep architectures and previous methods based on hand-crafted features.
FlowNet  [9]  uses an end-to-end scheme to learn optical flow with a synthetic dataset, and several recent approaches also use supervision from reconstructed 3D scenes and stereo pairs  [12, 46, 47, 48]. MC-CNN  [47]  and its efficient extension  [48]  train CNN models to predict how well two image patches match and use this information to compute the stereo matching cost. DeepCompare  [46]  learns a similarity function for patches directly from images of a 3D scene, which allows for various types of geometric and photometric transformations (e. g. , rotation and illumination changes). These approaches are inherently limited to matching images of the same physical object/scene. In contrast, Long et al. [27]  use CNN features pre-trained for ImageNet classification tasks (due to a lack of available datasets for learning semantic correspondence) with performance comparable to SIFT flow.
Experimental details Dataset. We use the PF-PASCAL dataset that consists of 1300 image pairs selected from PASCAL-Berkeley keypoint annotations 5 of 20 object classes. Each pair of images in PF-PASCAL share the same set of non-occluded keypoints. We divide the dataset into 700 training pairs, 300 validation pairs, and 300 testing pairs. The image pairs for training/validation/testing are distributed proportionally to the number of image pairs of each object class. In training, we augment the data into a total of 1400 pairs by horizontal mirroring. We also test our trained models with the PF-WILLOW dataset  [11] , Caltech-101  [8]  and PASCAL Parts  [49]  to further validate a generalization of the models. Region proposal. Unless stated otherwise, we choose to use the method of Manen et al.
Proposal flow components We use the PF-PASCAL dataset to evaluate region matching performance. This setting allows our method to be tested against three other methods in  [11] : NAM, PHM and LOM. NAM finds correspondences using handcrafted features only. PHM and LOM additionally consider global and local geometric consistency, respectively, between region matchings. We also compare our SCNet-learned feature against whitened HOG  [6] , the best performing handcraft feature of  [11]. Experiments on the PF-WILLOW dataset  [11]  showed similar results with the ones on the PF-PASCAL dataset. For details, refer to our project webpage. Quantitative comparison. Figure  3 (a) compares SCNet methods with the proposal flow methods  [11]  on the PF-PASCAL dataset. Our SCNet models outperform the other methods that use the HOG feature.
25 times smaller than (i) and (ii)) by adding a randomly initialized FC layer on top of (ii). Note that this is exactly equivalent to SCNet-A without training on the target dataset. Results with different object proposals. SCNet can be combined with any region proposal methods. In this experiment, we train and evaluate SCNet-AG+ on PF-PASCAL with four region proposal methods: randomized prim (RP)  [29] , selective search (SS)  [41] , random uniform sampling (US), and sliding window (SW). US and SW are extracted using the work of  [16] , and SW is similar to regular grid sampling used in other popular methods  [20, 26, 33]. Figure  3 (d) compares matching performance in PCR and mIoU@k when using the different proposals. RP performs best, and US performs worst with a large margin. This   shows that the region proposal process is an important factor for matching performance.
Flow field Given a sparse region matching result and its corresponding scores, we generate dense semantic flow using a densifying technique in  [11]. In brief, we select out a region match with the highest score, and assign dense correspondences to the pixels within the matched regions by linear interpolation. This process is repeated without replacement of the region match until we assign dense correspondences to all pixels in the source image. The results are evaluated on PF-PASCAL dataset. To evaluate transferability performance of the models, we also test them on other datasets such as PF-WILLOW  [11] , Caltech-101  [8]  and PASCAL Parts  [49]  datasets, and compare with state-of-the-art results on these datasets. In these cases direct comparison between learning-based methods may not be fair in the sense that they are trained on different datasets.
Results on PF-PASCAL. We compare SCNet with Proposal Flow  [11]  and UCN  [5]  on the PF-PASCAL dataset, and summarize the result in Table  1. The UCN is retrained using the code provided by the authors on the PF-PASCAL dataset for fair comparison. Using the raw network of  [5]  trained on a different subset of PASCAL yields as expected lower performance, with a mean PCK of 36. 0 as opposed to the 55. 6 obtained for the retrained network. The three variants of SCNet do consistently better than UCN as well as all methods in  [11] , with a PCK of 66. 3 or above. Among all the methods, SCNet-AG+ performs best with a PCK of 72. 2. Figure  5  presents two examples of dense matching for PF-PASCAL. Ground truth are presented as circles and predicted keypoints are presented as crosses. We observe a better performance of SCNet-AG and SCNet-AG+.
Results on PF-WILLOW. For evaluating transferability, we test (PF-PASCAL trained) SCNet and UCN on the PF-WILLOW dataset  [11]  and compare the results with recent methods in Table  2  where PCK is averaged over all classes. The postfix 'w/SF' and 'w/PF' represent that matching is performed by SIFT Flow  [26]  and Proposal Flow  [11] , respectively. On this dataset where the data has a different distribution, SCNet-AG slightly outperforms the A and AG+ variants (PCK@0. 05). We observe that all SCNet models significantly outperform UCN, which is trained on the same dataset with the SCNet models, as well as other methods Table  2 : Fixed-threshold PCK on PF-WILLOW. Method PCK@0. 05 PCK@0. 1 PCK@0. 15 SIFT Flow  [26]  0. 247 0. 380 0. 504 DAISY w/SF  [43]  0. 324 0. 456 0. 555 DeepC w/SF  [46]  0.
Methods IoU PCK NAMHOG  [11]  0. 35 0. 13 PHMHOG  [11]  0. 39 0. 17 LOMHOG  [11]  0. 41 0. 17 Congealing  [24]  0. 38 0. 11 RASL  [32]  0. 39 0. 16 CollectionFlow  [19]  0. 38 0. 12 DSP  [20]  0. 39 0. 17 FCSS w/SF  [21]  0. 44 0. 28 FCSS w/PF  [21]  0. 46 0. 29 SCNet-A 0. 47 0. 17 SCNet-AG 0. 47 0. 17 SCNet-AG+ 0. 48 0. 18 using hand-crafted features  [26, 43, 22]  and learned features  [35, 46, 12, 45, 36, 21]. Results on Caltech-101. We also evaluate our approach on the Caltech-101 dataset  [8]. Following the experimental protocol in  [20] , we randomly select 15 pairs of images for each object class, and evaluate matching accuracy with three metrics: Label transfer accuracy (LT-ACC)  [25] , the IoU metric, and the localization error (LOC-ERR) of corresponding pixel positions. Table  3  shows that SCNet achieves comparable results with the state of the art.
The best performer, FCSS  [21] , is trained on images from the same Caltech-101 dataset, while SCNet models are not. Results on PASCAL Parts. Following  [11] , we use the dataset provided by  [49]  where the images are sampled from the PASCAL part dataset  [3]. For this experiment, we measure the weighted IoU score between transferred segments and the ground truth, with weights determined by the pixel area of each part. To evaluate alignment accuracy, we measure the PCK metric (α = 0. 05) using keypoint annotations for the PASCAL classes. Following  [11]  once again, we use selective search (SS) to generate proposals for SCNet in this experiment. The results are summarized in Table  4. SCNet models outperform all other results on the dataset in IoU, and SCNet-AG+ performs best among them. FCSS w/PF  [21]  performs better in PCK on this dataset.
Acknowledgments. This work was supported by the  ERC grants VideoWorld and Allegro , the  Institut Universitaire de France , the  National Research Foundation of Korea (NRF)  grant funded by the  Korea government (MSIP)  (No. 2017R1C1B2005584 ) as well as the  MSIT (Ministry of Science and ICT) ,  Korea , under the  ICT Consilience Creative program  ( IITP-2017-R0346-16-1007 ). We gratefully acknowledge the support of  NVIDIA Corporation  with the donation of a Titan X Pascal GPU used for this research. We also thank  JunYoung Gwak  and  Christopher B. Choy  for their help in comparing with UCN.
TomoWarp2 is a Python based code that allows full-field vector displacements to be measured between 2D or 3D image sets, based on a local approach of Digital Image Correlation. These displacements can be used to calculate the complete 2D or 3D strain tensor field to study, for example, heterogeneous deformation responses of materials during loading. The code is provided with a GUI, and can handle different input and output formats. Moreover, new features can be implemented with the flexible code architecture.
This paper outlines a new open-source, Python/C-based code, TomoWarp2, for DVC. TomoWarp2 has its origins in ''TomoWarp'' (a single-threaded C-code), originally developed to analyse 4D seismic images of hydrocarbon reservoirs  [8]  and later adapted to 4D X-ray tomography of deforming materials. The original codebase was used to study localised deformations in geomaterials, including  [6, [8] [9] [10] [11] [12]. However, the desire for greater portability, parallel operation, flexibility in input image format and to facilitate further development prompted TomoWarp2. TomoWarp2 has been used in continuation of previous research topics in geomechanics, (for example:  [13] [14] [15] , and in more diverse research, including charge/discharge of batteries  [16, 17]  and the failure of bones and bone-implant interfaces  [18].
Software description TomoWarp2 belongs to the local variant of DIC techniques (as also implemented by Gates et al. [19]  and in ''CMV3D'' by M. Bornert  [20] ). Local DIC approaches involve independent analysis of points within a reference volume (im1) using local subsets (''correlation windows'') about each point. The displacements of the points are determined by finding the best mapping of the corresponding subset in the reference volume with equivalent subsets in a subsequent volume (im2), by a search over a restricted ''search window'' defined by the user displacement range. The best mapping is determined by a correlation coefficient calculated over the subset. This approach provides displacements with a sensitivity of 1 pixel, but higher accuracy is usually required. Subpixel accuracy can be obtained by either interpolation of the local correlation coefficient field around its maximum in displacement space or by interpolating the image itself (which allows more degrees of freedom).
Software architecture TomoWarp2 is written in Python 2. 7, making use of few external packages (principally numpy  [25] , scipy  [26] , tifffile  [27] ). The program is parallelised using the Python multiprocessing libraries with a specifically written SIMD parallelisation scheme that employs global queues with the necessary features (blocking and read/write semaphores) to manage synchronisation. To speed up the analysis, the pixel search has been written in C and connected to the main part using the Swig package  [28]. The TomoWarp2 program is highly modular and consists of 3 principal processes that run concurrently: a single management process (DIC_setup), a single process for handling data loading and data communication (data_delivery_worker) and a number of identical processes performing the point-by-point correlation (DIC_worker).
This architecture has been adopted for its simplicity and its amenability to running on massively parallel systems (e. g. , easily portable to MPI). DIC_setup calculates the extents of data required in im1 and im2 for each node and communicates the global extent of the required data to data_delivery_worker. The information about the nodes and their extents are individually placed into a global job queue to be read by the DIC_worker(s). The results are collected as they are returned by each DIC_worker. When all nodes have been processed, the output is written to a tab-separated-value (TSV) file containing the number, coordinates, displacement and rotation components, the correlation coefficient, and the potential error code for each node. The management of the image data is handled by the data_delivery_worker.
Prior fields are input as TSV files in the same format as the output from the program, i. e. , they can be generated by a previous run of the code or by some external process. The correlation is generally performed over a regular rectangular grid of nodes. However, prior fields can be used to provide coordinates of arbitrarily distributed node points (in addition to providing the prior estimate of displacements). Reprocessing can also be performed for a subset of nodes that did not give satisfactory results in a previous correlation. The nodes to be reprocessed can be selected based on the output error or a threshold on the correlation coefficient in a given prior. Post-processing of the results by median filtering and outlier removal is provided. In interactive mode, i. e. , when the GUI is used, these filters can be applied several times and the result displayed and saved in a new TSV file.
Impact One of the main focuses of this project was to obtain user friendly and flexible code. In this perspective, a big effort has been made to simplify the input setup through both a text input file and a GUI. Moreover, the number of required input parameters has been minimised and an automatic detection of image format has been implemented, while the possibility to specify a number of parameters allows the refinement and optimisation of the computation. The main output of the code is a TSV file, but all the results, including those from post-processing, can be saved in common image analysis software formats, which avoids the need for further conversion. The GUI also allows measured displacements (and associated histograms) to be displayed, which facilitates filtering and post-processing.
Another key aim is to enable continuous development of the code, e. g. , to provide new features that can help users answer their specific research questions. This is facilitated by the architecture of the code, and the simplicity and independence of the single processes. Furthermore, the open nature allows users to, themselves, add-to or adapt the code to suite their needs. Moreover, the parallel computation and the possibility to run on server farms can significantly reduce computation times (and opens the door to more detailed analysis of larger image volumes). TomoWarp2 is currently used by more than 15 research groups, situated in France, Sweden, UK, Spain, Chile, Norway, USA and Japan, in research ranging from geomechanics to biomechanics and Li-ion batteries.
Conclusions TomoWarp2 is a Python based code for full-field vector displacement measurement between 2D or 3D image sets, based on local digital image correlation. Complete strain tensor fields can be computed from the displacements, e. g. , for detailed characterisation of mechanical behaviour of a specimen in a deformation test. The simple structure of the input file, together with the GUI and the possibility to use different formats of input and output images, make the code straight forward and quick to use, while the code architecture enables new features to be implemented easily. Future developments will include discrete correlation in the style of  [6]  (to track displacements and rotation of grains) as well as an MPI implementation.
Acknowledgments. We thank  Peter Lindqvist  for his kind interest in this work. This manuscript has been finalized while the first author was visiting the  KTH (Stockholm)  in February 2017. He wishes to thank  Erik Lindgren  for the kind invitation. The authors are members of the  Gruppo Nazionale per l'Analisi Matematica, la Probabilità e le loro Applicazioni (GNAMPA) of the Istituto Nazionale di Alta Matematica (INdAM).
Until recently, such tools have proved to be valid allies for spambots timely detection. Unfortunately, the characteristics of the new wave of social spambots are such that standard classification approaches, where a single account is evaluated according to a set of established features tested over known datasets, are no longer successful. In this work, we demonstrate this claim by investigating the performances of several state-of-the-art tools techniques when struggling against the latest wave of social spambots. The unsatisfactory results of the surveyed techniques call for new approaches capable of turning the tide of this long-lasting fight. Interestingly, we assist to a paradigm-shift in modeling and analyzing online accounts. Independently from each other, new research efforts were born, which leverage characteristics of groups of accounts -rather than those of a single account -as a red flag for anomalous behaviors.
We provide a review of these prominent research directions, highlighting the new dimensions to sound out for successfully fighting  against this novel generation of spambots. Contributions. Our main contributions are: • We provide empirical evidence of the existence of a novel wave of Twitter spambots, which, up to now, has been just theorized  [15]. • We evaluate if, and to which extent, state-of-the-art detection techniques succeed in spotting such new spambots. • We critically revise an emerging stream of research, which adopt features tied to groups of accounts rather than individual accounts features. • We leverage results of a crowdsourcing spambot detection campaign for drawing new guidelines for the annotation of datasets comprising social spambots. • Finally, we publicly release to the scientific community an annotated dataset 1 , consisting of genuine accounts, traditional spambots, and -for the first time-the novel social spambots.
DATASETS We describe the different Twitter datasets that constitute the real-world data used in our experiments. Table  1  reports the name of the datasets, their brief description, and the number of accounts and tweets they feature. The year represents the average of the creation years of the accounts that belong to the dataset. The genuine accounts dataset is a random sample of genuine (human-operated) accounts. We randomly contacted Twitter users by asking a simple question in natural language. All the replies to our questions were manually verified and all the 3,474 accounts that answered were certified as humans. The accounts that did not answer to our question were discarded and are not used in this study. The social spambots #1 dataset was created after observing the activities of a novel group of social bots that we discovered on Twitter during the last Mayoral election in Rome, in 2014.
One of the runners-up employed a social media marketing firm for his electoral campaign, which made use of almost 1,000 automated accounts on Twitter to publicize his policies. Surprisingly, we found such automated 1 http://mib. projects. iit. cnr. it/dataset. html accounts to be similar to genuine ones in every way. Every profile was accurately filled in with detailed -yet fakepersonal information such as a (stolen) photo, (fake) shortbio, (fake) location, etc. Those accounts also represented credible sources of information since they all had thousands of followers and friends, the majority of which were genuine users 2. Furthermore, the accounts showed a tweeting behavior that was apparently similar to those of genuine accounts, with a few tweets posted every day, mainly quotes from popular people, songs, and YouTube videos.
The vast majority of tweets were harmless messages, occasionally interspersed by tweets mentioning a specific genuine (human) account and suggesting him to buy the VIP version of the app from a Web store. Further, we uncovered a third group of social bots, social spambots #3, which advertise products on sale on Amazon. com. The deceitful activity was carried out by spamming URLs pointing to the advertised products. Similarly to the retweeters of the Italian political candidate, also this family of spambots interleaved spam tweets with harmless and genuine ones. We exploited a Twitter crawler to collect data about all the accounts we suspected to belong to the three groups of social spambots. All the accounts collected in this process have then undergone an internal manual verification phase to certify their automated nature.
Among all the distinct retweeters of the Italian political candidate, 50. 05% (991 accounts) were certified as spambots. Similarly, 94. 50%  (   accounts) of the accounts who tweeted the #TALNTS hashtag resulted as spambots. Finally, 89. 29% (464 accounts) of the accounts that tweeted suspicious Amazon. com URLs were also certified as spambots. The three sets of accounts represent our ground truth of novel social spambots. Our internal manual annotation has been carried out by comparing every account to all the others, in order to highlight possible similarities and common behaviors. This is in contrast with the typical annotation process where accounts are labeled one-by-one and by solely exploiting the characteristics of the account under investigation. In addition to genuine users and social spambots, we also collected several datasets of traditional spambots.
Such datasets are used throughout the paper as a strong baseline. The traditional spambots #1 dataset is the training set used in  [43] , kindly provided to us by the authors of that work. In  [43] , the dataset has been used to train a machine learning classifier for the detection of evolving Twitter spambots. Accounts belonging to the traditional spambots #2 dataset are rather simplistic bots that repeatedly mention other users in tweets containing scam URLs. To lure users into clicking the malicious links, the content of their tweets invite the mentioned users to claim a monetary prize. The traditional spambots #3 and traditional spambots #4 datasets are related to 2 different groups of bots that repeatedly tweet about open job positions and job offers. Fake followers are another kind of malicious accounts that recently gained interest both from platform administrators and from the scientific world  [12].
Given that fake followers are rather simplistic in their design and functioning, they can serve as a weak baseline against which to compare social spambots. In April, 2013, we bought 3,351 fake accounts from three different Twitter online markets, namely fastfollowerz. com, intertwitter. com, and twittertechnology. com. All the accounts acquired in this way have been merged in order to obtain the fake followers dataset used in this study. By considering a diverse set of spammer accounts we have captured many of the different dimensions currently exploited by spambots and tamperers to perpetrate their illicit activities. In detail, we have considered (i) fake follower frauds, (ii) retweet frauds, (iii) hashtag promotion, (iv) URL spamming, (v) scamming, and (vi) spam of generic messages.