dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, past work has highlighted the need to provide a link between HCI/VIS prototype and traditional analysis software (e. g. , Matlab, Python) run on desktop computers [WBG * 19]. On the other hand, research on 2D visualization widgets is not rare (see visual analytics contributions, e. g. , [HS04, KHPA12, YS20]), which suggests that such research is valued in the visualization research community. The lack of focus on 3D visualization widgets could therefore be explained either by the fact that the challenges of manipulating virtual probes, cutting planes, and the like are still not addressed, or the fact that 3D visualization communities might not be interested or aware of the need for 3D visualization widgets beyond these. Either way, we hope that this survey will contribute to highlight this specific need and eventually foster more work that supports the manipulation of visualization widgets for 3D data.
Conclusion In our report we have surveyed interaction techniques designed to assist domain experts who rely on 3D spatial visualization. We have discussed techniques relying on tactile, tangible, gestural input or hybrid combinations of interaction paradigms, and classified each technique based on the 3D visualization task it supports. We used three high-level task groups: (1) Volumetric view and object manipulation, (2) defining, placing, & manipulating visualization widgets, and (3) 3D data selection & annotation. Our classification highlighted the tremendous amount of effort put into two of these tasks. In particular, Volumetric view and object manipulation and 3D data selection have been well covered with all interaction paradigms. A possible explanation lies in the overlap of interest in these tasks between the visualization community and other HCI and VR communities.
Acknowledgements This work was supported in part by the NSF (IIS-1704604) and by MIT/IAS at Linköping University. We wish to thank all our reviewers and editors for their voluntary work and their helpful feedback and comments on several versions of our manuscript. We would like to thank Karin Ivarsson for her help in proof-reading this manuscript. Finally, we thank all the authors we cited who provided open-access version of their papers. Whenever possible, we have linked to such open-access versions in the references.
In this paper, we introduce a symbolic and lazy approach for relation extraction based on Concepts of Neighbours. A first contribution is the representation of sentences by graphs, covering both lexical and syntactic information. Other contributions, compared to previous application of Concepts of Neighbours, are that the instances to be classified are couples of graph nodes, instead of single nodes, and that node labels are organized into a taxonomy (e. g. , word hypernyms). We validate our approach with experiments on TACRED  [20] , a dataset for evaluating relation extraction methods. In the sequel, Section 2 gives preliminaries about Graph-FCA and Concepts of Neighbours. Then, Section 3 introduces the modeling of sentences as graphs. Section 4 details how Concepts of Neighbours can be used for relation extraction.
Modeling Sentences as Graphs The input data of relation extraction is a set of annotated sentences. Each sentence is annotated by two entities, the subject and object of the relation, and by the type of those entities. The input sentences are split in two parts: training sentences for which the relation is known, and test sentences for which the relation is to be predicted. In this section we explain and discuss the modeling of annotated sentences as graphs because this is the required input of Concepts of Neighbours, and more precisely as RDF graphs because this is the expected input of the existing implementation of Concepts of Neighbours.
Type Hierarchies An RDF graph can be enriched with inferred types and edges by declaring domain knowledge. The most common form of domain knowledge is hierarchies of types, based on the RDFS property rdfs:subClassOf. The inference rule is that: if node X has type A and A is a subclass of B, then X also has type B. We use several type hierarchies to increase the generalization power of Concepts of Neighbours. First, the set of POS-tags  [16]  is fine-grained enough to create a hierarchy on a few POS-tags: for example, the gerund of a verb (POS-tag VBG) is a subclass of verb (POS-tag VB ). In total, we have 11 rdfs:subClassOf declarations  5. Second, in order to add semantic knowledge to the modeling, the lexical database WordNet  [14]  is used for creating a lemma hierarchy for nouns and verbs.
Relation Extraction with Concepts of Neighbours Once the modeling is done, for each test example we want to compute which examples of the training corpus are the more similar, and do a prediction from their annotation. In order to do so, an RDF graph regrouping the modeling of all the sentences of the dataset is made, the Concepts of Neighbours method is used to group the examples by similarities, and then a decision method is used to do a prediction from those Concepts of Neighbours. In the following, we first present how the Concepts of Neighbours have been adapted to this specific task. Then, we describe two decision methods for making prediction from Concepts of Neighbours.
In both cases, the pruning strategy presented in Section 3. 2 ensures that the intension focuses on and around the path between the subject and the object. Reduction of the Set of Couples. Another issue is the large number of potential neighbours: as detailed in  [3] , to compute the Concepts of Neighbours for a tuple of k objects in a graph involving n objects, the algorithm has to generate and partition n k tuples. Therefore, for a large graph, the computation of concepts of arity greater than 1 is rapidly intractable. In the present case, if we consider a dataset composed of ten thousands of sentences, there are tens of billions of potential neighbours. However, the use of the Concepts of Neighbours method is in this context for extracting relations by comparing a (subject, object) couple from a test sentence to annotated couples from the training dataset, and it appears that the number of annotated couples is far smaller: only one per example in the training dataset.
Therefore, in the following, we use this set of couples for the computation of Concepts of Neighbours, as it permits to simultaneously reduce drastically the computation cost and remove noise from the computed concepts while keeping all the knowledge of interest. In addition, as evoked in Section 3, in a relation extraction dataset each subject and object has a type, and these types can be used to reduce the set of potential neighbours further. For example, if an example has for subject a person and for object a location, it can be seen that the relation expressed by this example could be place_of_birth or place_of_living, but can not be age or parent. Therefore, for a given couple (subject_type, object_type), a set of compatible relations can be deduced from the training dataset. If there is only one compatible relation, this relation can be predicted without computing Concepts of Neighbours for this example, and if there are several possible relations, the set of (subject, object) couples from the training dataset that are annotated with compatible relation types can be used as the set of possible neighbours in the algorithm.
Scoring Methods The computation of Concepts of Neighbours of a (subject, object) pair from the test dataset returns a set of concepts, each concept is associated to a set of neighbour couples, and to an extensional distance. In addition, the specialization presented in the previous section ensures that each neighbour couple is annotated with a compatible relation type. From this result, in order to be able to predict a relation, we need to associate a score to each relation type. In the following we present two scoring methods: one based on a weighted votation, and another based on the confidence measure. Exponential-weighted Vote (EV). Each neighbour (s, o) of each concept "votes" for its annotated relation type r(s, o). However, if not weighted this method will only reflect the proportion of each relation among the training examples annotated with a compatible relation.
Experiments In this section we present the experiments conducted on TACRED  [20] , a standard relation extraction benchmark, to evaluate the proposed method. The evaluation of an approach on TACRED is usually made using the microaveraged F1 score. However, as the negative examples have been removed from the dataset, this score is equivalent to accuracy. Therefore, accuracy is the measure we use in this evaluation. The fact that experiments are made on a subset of TACRED causes that direct comparison with existing approaches is no longer possible. In order to evaluate whether our modeling and use of concepts of neighbours are beneficial, we introduce a simple baseline based on named entity types. It predicts the most frequent relation type among the training sentences that have the same subject type and the same object type as the test sentence.
Conclusion We have presented a lazy-learning method for relation extraction, based on the modeling of linguistic data as a graph and on the computation of Concepts of Neighbours on this graph. This approach has been evaluated and validated against a baseline on the subset of positive examples of the TACRED benchmark. In addition, this comes with the adventage that this approach is interpretable as, for each prediction, detailed information about how this prediction has been made is given in the form of graph patterns over the linguistic structure. Several aspects of this contribution lead to tracks for future work. First, this method could be coupled with another method -potentially Deep Learning -in order to be able to distinguish the positive examples from the negative ones. The Concepts of Neighbours method can also be improved in order to provide more flexible and expressive patterns, useful in the case of natural language processing.
Dataset and Baseline TACRED is a dataset made of 106,264 annotated examples, split into a training corpus (68,124 examples), a development corpus (22,631 examples) and a test dataset (15,509 examples). Each example is a sentence with two identified entity mentions (a subject and an object), typed among 23 possible types (the types used by the Stanford NER system [12]), and annotated with a relation type among 41 effective classes and a no_relation class denoting an absence of relation between the two mentions. In order to reflect what can be found in real-world texts, 79. 5% of the examples are in the no_relation class. Several remarks can be made about this dataset. First, as the classification with Concepts of Neighbours is a lazy learning method, there is no validation step, then the development dataset can be merged with the training dataset to form a bigger training dataset.
Second, the negative examples (those in the no_relation class) have no reasons to look like each other but can look like examples in other classes, because they express random situations. Therefore, as our method looks for similarities between a test example and training examples, those negative examples cannot easily be handled in our method. That is why negative examples are removed from the dataset in the experiments, and we focus on discriminating between the 41 relation types rather than discriminating between the presence and absence of a relation. This shrinks the training corpus to 18,446 examples and the test corpus to 3,325 examples.
5. 1 1 Dataset and Baseline TACRED is a dataset made of 106,264 annotated examples, split into a training corpus (68,124 examples), a development corpus (22,631 examples) and a test dataset (15,509 examples). Each example is a sentence with two identified entity mentions (a subject and an object), typed among 23 possible types (the types used by the Stanford NER system [12]), and annotated with a relation type among 41 effective classes and a no_relation class denoting an absence of relation between the two mentions. In order to reflect what can be found in real-world texts, 79. 5% of the examples are in the no_relation class. Several remarks can be made about this dataset. First, as the classification with Concepts of Neighbours is a lazy learning method, there is no validation step, then the development dataset can be merged with the training dataset to form a bigger training dataset.
EXPERIMENTAL SETUP 3. 1 Benchmark Dataset We consider the set of bi-objective problems proposed in  [34]. For each MO-ICOP, the set of seeds S is randomly generated in the variable space X = [-5, 5] d. The objective vector of each seed is first assigned to a subset of non-dominated seeds S nd , ensuring that no seed in S nd dominates another seed from S nd. Objective values are then assigned to the remaining dominated seeds S d , ensuring that each seed in S d does not dominate any seed in S nd. Using this method, 50 problems were generated in 4 problem dimensions d ∈ {5, 10, 20, 30} and 6 values for k ∈ {1, 2, 3, 4, 5, 6}, creating a total of 1 200 problems covering a wide range of MO-ICOPs. We emphasise that, although the problem dimension is typically given, the seeds and the power of interpolation are unknown for unseen (black-box) problems.
Importance of Features for Search Performance We now investigate the combined effect of the considered features on search performance. For this purpose, we train a random forest regression model  [3]  that predicts the expected (relative) hypervolume based on input features, separately for each algorithm. We perform 50 independent trainings on the benchmark dataset using the randomForest R package  [20]  with default parameters, and we report average values below. The coefficients of determination (R 2 ) of the models on training data are 0. 88, 0. 89, 0. 86, and 0. 88 respectively, for NSGA-II, GDE3, MOEA/D-DE-DRA, and DECMO2++. This means than more than 85% of the variance in the hypervolume of each algorithm between MO-ICOPs is explained by the features. In addition to prediction accuracy, random forest models yield the relative importance of each feature for prediction.
Comparison with GloptiPoly and other NLP solvers Next, we compare the optimal value of global minimum found using the proposed B-spline algorithms ( & ) with CENSO, GloptiPoly  [16, 20]  and BARON, LINDO Global and SCIP NLP solvers. For the current tests, we consider the above set of 11 test functions. The idea is to investigate where same optimal value of global minimum can be found with the considered methods. The CENSO solver implements the algorithm in  [13]  and is available on Github: https://github. com/bgrimstad/censo. The GAMS and GloptiPoly source code for these problems are available at [https://bit. ly/2YHonrh]. The GAMS interface for BARON, LINDO Global and SCIP is available through the NEOS server  [28]. Notes. ( ⋆) Indicates that the algorithm did not give the result even after one hour and therefore terminated.
As the dimension and number of constraints in problem increases, to obtain convergence the relaxation order is gradually increased to a value greater than or equal to the dimension of the problem. For medium dimension problem (like 7 with 11 constraints) GloptiPoly exhausts with memory even with small relaxation order. Whereas other NLP solvers may be able to solve large dimension problems with non-optimal value of global minimum. Here, we want to mention that proposed algorithms are implemented in MATLAB, whereas except GloptiPoly all other NLP solvers mentioned above are implemented in different languages and therefore comparison based on computation time between proposed algorithms and these NLP solvers is not carried out.
Global DTM from UAV images As the project is focused on the ruined buildings and galleries, it is also necessary to survey the quarry area to have a global point cloud in which to integrate the different datasets. The goal would be to eventually obtain a digital terrain model (DTM) of the granite quarry by following the path with its close surroundings from the three underground galleries to the ruins. Thus, we carried out a photogrammetric campaign with a DJI Phantom 3 drone equipped with a 12-Megapixel camera. With an interval of 3 seconds between each shot, nearly 2000 photos were captured at an altitude of about 30 m above the site. 32 targets were placed on the ground on the cleared areas, to allow georeferencing thanks to their measured 3D positions, with enough spacing to cover the nearly 700 meters of path travelled by the UAV.
These photogrammetric targets were georeferenced by GNSS determination in the RGF93-Lambert 93 coordinate system. After acquiring the images, the photogrammetric processing was mainly performed on Metashape. Agisoft Metashape has the advantage of providing a wide range of automatic tools. We have for this project almost 2. 000 photos that we had to sort and assemble in chunks to lighten and optimize the calculations of the software. Several steps were therefore carried out, starting with the identification, and marking of targets on the ground. It was necessary to manually reposition more precisely the marking of the numerous targets detected approximately by the software. Then, after the alignment of the photos for each chunk, we were able to associate the coordinates, taken on the ground with the GNSS in Lambert-93, to the various targets to geo-reference the project using the transformation calculated from the control points.
Laser-scanner data acquisition: The goal was to survey the two industrial ruined buildings: the depot and the forge. These buildings were surveyed by indirect georeferencing with a FARO Focus3D X330 TLS: it is a three-step field method. First, the positions of the future scanning stations are roughly determined to ensure the whole recording of the building remains. Then spheres are installed visible from several stations to be able to register the project. Finally, the scans are carried out by taking care to respect a certain overlap. This overflow of information leads to an over-determined system for the resolution of the unknowns which are here the XYZ positions of the stations. Thanks to this redundancy, it is possible to obtain better results afterwards and it will be possible to discard a sphere if it presents too large deviations.
For lasergrammetry as for photogrammetry, the processing of raw data is much more complex than for point surveying. Processing was performed on the Faro Scene software, optimized for use with Faro TLS. The method being analogous for the different buildings a global presentation will be proposed. Following the detection of targets, the import of their georeferencing and their matching, Scene calculates the registration based on these targets. It seeks to optimize the location of the stations using least squares. The observation equations used are defined by the matching of the spheres. Finally, we obtain the registered point cloud corresponding to the unification and georeferencing of the elementary clouds inherent to each station. The aim is then to evaluate the generated results. To do so, two aspects will be addressed: a qualitative (visual) evaluation and a quantitative evaluation (supporting figures).
Qualitative analysis: Visually, on each of the different point clouds it is important to notice that the walls overlap well. In fact, there are no noticeable discrepancies and there is no lack of information. No error is noticeable on our clouds, so the quantitative analysis is to be done. The error report generated by Faro Scene matches our expectations with errors that do not exceed 2 cm. Now, cleaning the point cloud of each building is needed. Indeed, it still contains elements to be removed (spheres, vegetation, noise) and needs to be sampled. The CloudCompare software was used for these steps. Spheres, vegetation, and noise are segmented by hand and then removed from the point cloud while building avatars are spatially sampled at 1 cm. In this way, a good compromise between an attractive visual rendering for a virtual tour and a reduced volume of data without significant loss of information was achieved.
The methodology for the digital reconstruction of these WWII concentration camp's buildings consists in assembling the reality-based techniques (laser scanning, photogrammetry, topographic surveying) with computer aided architectural design methods, here using Autodesk Maya. At this stage of the project, this step is not over yet but trials on the Autodesk Maya software have begun. The still existing portions can be more easily modelled with the help of the dense clouds' meshing. Indeed, we have the exact dimensions of the foundations and we can either digitize on the meshes or measure then draw directly. Drawing on Maya 3D modeling application, requires the use of edge loops. Using lines or polylines is not possible and all the created faces must have four or less vertices. For the missing information such as textures, upper floors' heights, roofs, windows, and doors' aspects, we refer to the archive images we have or to the archaeologists who worked on this site for a few years.
The only data we could obtain on the pictures were the number and position of the windows, doors, and chimneys, and we could also see the texture's change between the floors on the two bigger buildings (depot and forge). With a photogrammetric project using a single photo on PhotoModeler software, we could determine the height of the wiped-out floors. We used control points with known coordinates from the point clouds to process the photogrammetric project. There were however a lot of information that was not available on the pictures. For example, there are faces of the buildings we cannot see so the missing data was coordinated with the archaeologists through logical reasoning and in some cases by interpretation. A colour code will be applied on the models to show the different sources of modelling with their respective levels of precision  (Landes et al.
The exterior of each gallery and its entrance, almost filled for one of them, was also recorded by TLS. The various rocky spurs are particularly visible. After this acquisition phase with TLS, the resulting points clouds were processed on Faro Scene software. Positioning significant numbers of spheres enabled registration and geo-referencing. The resulting dense cloud could not be coloured because of the difficult (or lack of) lightning inside the galleries. The three dense clouds were then sampled at a 1-mm distance and resulted in point clouds of approximately 200 million points (Table  1 ). For the end use of the modeling, a resampling of the point cloud at 1-cm distance has been performed. After the obtention of the caves' 3D geometry, the purpose was to obtain its textures through a photogrammetric campaign.
From a methodological point of view, we have proceeded by side. Once all the images with targets have been acquired, we have taken a third (S3) set of images, this time over the entire gallery and after removing the targets. This time we proceeded by profile, spaced 2 m. apart and including images of the walls and ceiling, preserving an overlapping of at least 60% in both directions. The soil, partially water covered was not subject of a particular recording. In the last series (S3) we also added oblique images. The acquisition of the images here was quite tricky because of the low light. We used LED plates that offer powerful but diffuse lighting to illuminate the different scenes, while trying to avoid shadows due to the raw nature of extracting the walls or ceiling of the gallery. For processing, we used Metashape software to align the various photos (S3) that were geo-referenced and then consolidated with the targets detected in the S1 and S2 series, respectively.