dataesr/scientific-paragraphs-categorization huggingface dataset:
S48 The History and Provenance of Manuscripts Speculum 92/S1 (October 2017) Within nodegoat, the data can be searched and explored in various ways. All the records in the database can be browsed and listed, as shown in Fig. 4. The records can also be filtered to show only those which meet a single criterion or a combination of criteria. Fig. 5  shows the process for filtering records to show those where Alfred Chester Beatty appears as an owner. Fig. 6  shows the process for finding manuscripts that are or were located in Rome at some point in their history. These filters can easily be combined to show those manuscripts once owned by Beatty and also located in Rome. A particularly valuable feature of nodegoat is its capacity to produce geographical and network visualizations. Fig. 7  shows a geographical visualization of the history of a single manuscript (Phillipps 12264).
The other kind of visualization is a network graph, showing the connections between manuscripts and persons, organizations, and places. Fig. 12  shows the network graph for the same group of Phillipps-Beatty manuscripts. The predominant elements are Beatty (who owned all these manuscripts), Sotheby's (which sold most of them), and London (where all of them were owned or sold). A time slider can be used here too, with Fig. 13  showing the network graph for these manuscripts since 1936. The network graph for a single manuscript can also be viewed, as in Fig. 14. Both the geographical visualizations and the network graphs can be explored for an entire data set. Fig. 15  shows the geographical visualization, and Fig. 16  the network graph, for a pilot data set of nearly 1,500 former Phillipps manuscripts.
Research Questions The nodegoat database assembled as part of this project is being used to address a series of provenance-oriented research questions. The first of these, illustrated in many of the figures above, relates to Phillipps manuscripts that were subsequently owned by Alfred Chester Beatty. Where were these manuscripts produced, what are their histories, and who has owned them? Where are they now? This research was presented at a symposium at Trinity College Dublin in 2015 and updates earlier work by Christopher De Hamel. 19 lfred Chester Beatty owned at least sixty manuscripts that had previously belonged to Phillipps. Most of them were bought directly from Phillipps's grandson, Thomas FitzRoy Fenwick, and were acquired in four groups between 1920 and 1925. Beatty also made ten other individual purchases of Phillipps manuscripts.
His final purchase, in 1948, was one of Phillipps's great treasures: the Armenian Gospel Book (Phillipps 15364) with which he was photographed in 1860. Recording and analyzing the provenance of these manuscripts in nodegoat helps to clarify and visualize their histories. The Phillipps-Beatty manuscripts have passed through the hands of most of the great private collectors of the nineteenth and twentieth centuries, for example, giving some indication of their significance and of the overlapping interests of these collectors. The pre-Phillipps provenance of these manuscripts included most of the major collectors of the nineteenth century: Leander van Ess, Luigi Celotti, Henry Drury, Gerard and Johann Meerman, Richard Heber, Joseph-Felix Allard, and Justin MacCarthy-Reagh. Their post-Beatty provenance includes many well-known collectors of the twentieth century: St.
John Hornby, Major J. R. Abbey, Philip Hofer, Eric Millar, William Scheide, Martin Schøyen, Peter Ludwig, Sir Paul Getty, and A. S. Yahuda. A subgroup with a particularly interesting provenance is the six manuscripts now in the Biblioteca Nazionale Centrale di Roma. They were all produced at the abbey  A second research question for which the nodegoat database has provided an analytical environment is an attempt to identify those Phillipps manuscripts which are now located in North American institutions. 20 A significant number of manuscripts moved from Great Britain to North America during the lengthy dispersal of the Phillipps collection. Some of these were acquired before Seymour De Ricci's Census of 1935, while others were acquired before Faye and Bond's Supplement in 1962. 21 Today, those collections contain more than two thousand three hundred Phillipps manuscripts.
The nodegoat database makes it possible to identify manuscripts that fall into the category of being owned by American institutions and to analyze their characteristics and their relationships. This kind of research question is nevertheless a relatively small investigation into a specific grouping of manuscripts, rather than an analysis across the entire Phillipps collection. This reflects the stage reached by the project, where only a small percentage of Phillipps manuscripts have been covered and only the section of the network around these manuscripts has been documented. As the database expands its coverage, more quantitative questions should become feasible, particularly those connected to metrics for network analysis. 22 e Future of Provenance Data This project was designed to test new digital approaches to aggregating evidence for the history of manuscripts.
Its results have included a new data model for recording manuscript provenance, an assessment of the difficulties involved in combining data from a variety of heterogeneous sources, and an initial evaluation of the usefulness of geographical and network visualizations of the evidence relating to a pilot group of manuscripts. The project has also demonstrated how this kind of digital environment can be used as the basis for addressing research questions related to the history and provenance of specific groups of manuscripts. The work done by this project is a first step towards a broader approach to the aggregation of data and the use of network analysis for large-scale research questions related to manuscript histories. The eventual goal should be the kind of linked data infrastructure that is being developed in classics and ancient history.
14 Christian-Emil Smith Ore, "CIDOC CRM and Object Provenance," 2009, http://www. tei-c. org /SIG/Ontologies/presentations/CIDOC_CRM_provenance. pdf. 15 Ian Robinson, Jim Webber, and Emil Eifrem, Graph Databases (Sebastopol, CA, 2013). 16 Pim Van Bree and Geert Kessels, "Mapping Memory Landscapes in Nodegoat," in Social Informatics, Proceedings of the 6th International Conference, Barcelona, November 11-13, 2014, ed. L. M. Aiello and D. McFarland, Lecture Notes in Computer Science 8852 (Berlin, 2015), 274-78.
Roughly speaking, those automatic tools can be divided into four categories, including search algorithms implemented from scratch in general purpose programming languages [Mat95, ANE15, BV14, BN11, FJP13, BDF11, DF16, DEM15, Leu13, YZW15, DDS14, SW16], SAT/SMT (satisfiability modulo theory) based methods [CB07, KY10, RS09, MP13, KLT15, QCW16, AJN14, SHY16], mixed-integer linear programming (MILP) based methods [AC11, MWGP12, WW11, SHW + 14, FWG + 16, XZBL16] and methods based on classical constraint programming. To the best of our knowledge, the first application of the classical constraint programming (CP) technique in the field of block cipher cryptanalysis are presented in  [GMS16]. In this work, Gerault et al. use a CP solver called Choco to search for the related-key differential characteristics of the AES, where some previous results  [BN10, FJP13]  are rediscovered in a highly automatic way and some better characteristics are found.
Our Contribution. Based on Gerault et al. 's work We argue that the CP approach enjoys certain advantages over other methods in some aspects. Firstly, compared with the methods implemented from scratch, the CP approach is much easier to implement and more efficient in some cases. Second, the solution of the CP model can be delegated to a wide range of open-source or commercially available solvers. These solvers include dedicated CP solvers, but also SAT, MILP or hybrid solvers. In particular, by using the MiniZinc [NSB + 07] language, one can express CP model in a language that can be interpreted by a wide range of solvers. Therefore, we directly benefit from the advances in resolution techniques. Thirdly, the modeling process of CP is much more straightforward than that of the MILP based method.
-A solution of the CP model is x 0 = 0, x 1 = 1, x 2 = 1, x 3 = 1, x 4 = 1. Note that a constraint can be declared in extension, by defining the valid/invalid tuples, or in intension, by defining a relation between the variables. For instance, c 0 and c 1 are declared in intension, and c 2 is declared in extension. Note that the modelling choices influence the resolution time: in this example, defining c 2 in intention would probably be more efficient. Such models can be solved by CP solvers such as Choco  [PFL14]. To illustrate its ease of use, we give a toy example together with its source code in Appendix A. Other solvers exist, and different solvers may have very different performances for a same problem.
Comparing Solvers In order to evaluate the performances of the Choco solver further, we picked two problems as benchmarks. The first one is an optimization problem, where the solver must find a differential characteristic with optimal probability. The second one is an enumeration problem, where the solver must list all solutions with predefined properties. It appears that the MILP solver Gurobi outperforms Choco on the optimization problem in our benchmark, and that Choco outperforms MILP for enumerating solutions. In order to try other solvers, we also implemented the first problem in MiniZinc. MiniZinc is a CSP modelling language that is accepted by a wide range of solvers. Using it, we could add the solvers Chuffed and PICAT_SAT  [ZKF15]  to the optimization experiments. It appears that Chuffed is in between Choco and Gurobi, and that PICAT_SAT outperforms Gurobi.
In the optitmization problem, we search for differential characteristics on PRESENT. It appears that Choco does not scale up well for a straightforward implementation of this search. We build a CP model M DSK PRESENTr (∅) for some r, set the objective function to minimize the sum of all probability variables, and try to find the optimal solution (corresponding to the best differential characteristic) by Choco. We also try to find the best differential characteristic of r-round PRESENT by the MILP based method using the Gurobi solver. The comparison of the results are listed in Table  3 , from which we can see that our Choco implementation is not competitive with Gurobi on this problem, and that both approaches are extremely inefficient compared to Matsui's algorithm, which can find the best characteristic of full PRESENT in several seconds  [ANE15].
Also we try to enumerate the characteristics in the same linear hulls by SCIP  [Tob04] , which implements an efficient set of solutions enumeration algorithm based on MILP. The comparison of the two methods are given in Table  4 , from which we can see that Choco dramatically outperforms SCIP in enumerating characteristics. These results confirm how solver dependant the resolution process can be. It appears that there is not a definitive advantage of one method or solver for all purposes, and that different solvers perform differently on different problems. Hence, using the MiniZinc language seems to be the best practice, as it allows to try several solvers without having to translate the model to their respective language.
Accelerating the Search for Integral Distinguishers and Zero-correlation Linear Approximations In this section, we apply the CP approach to search for integral distinguishers and zerocorrelation linear approximations. Experimental results show that when combined with proper search strategies, the CP approach can be very efficient. Using it, we find again and more efficiently the currently known best integral distinguisher of PRESENT and zero-correlation linear approximations of HIGHT (ARX construction). First, we introduce a convenient tool from CP: random restarts. Please take a look at the so-called domain over weighted degree heuristic specified in line 20 and 21 of the Choco code in Appendix A. This heuristic breaks ties at random, using the random seed provided as system time in the example.
end 6 end We implement the Algorithm 2 in the Choco solver combined with random restarts to search for the 9-round integral distinguisher of PRESENT, and the source code can be found in Appendix B. The 9-round distinguisher presented in  [XZBL16]  is rediscovered on an ordinary PC in no more than 36 seconds (the time of the resolution of 64 CP models). By contrast, the same search without using restarts was more than 10 times longer, and the MILP approach needs 3. 4 minutes (roughly 204 seconds)  [XZBL16]  to solve the same problem. Note that only one thread is used in our experiment, but since each of the 64 models is independant, they could be solved in parallel.
A The Choco CP Solver Choco is an open source Java framework dedicated to constraint programming, which is among the fastest CP solvers on the market, and has been awarded two silver medals and three bronze medals at the MiniZinc challenge in 2013 and 2014  [PFL14]. In this section, we give a brief introduction of the relevant parts of Choco 4  by a simple example, and we refer the reader to the Choco documentation  [PFL14]  for more technical information. Let (X , C) be a CP model with X = {x 0 , x 1 , x 2 } and C = {c 0 }, where dom(x 0 ) = dom(x 1 ) = dom(x 2 ) = {0, 1}, vars(c 0 ) = {x 0 , x 2 } and c 0 dictates that (x 0 , x 2 ) ∈ {(1, 1), (0, 0), (0, 1)} ⊆ dom(x 0 ) × dom(x 2 ) = {0, 1} 2 The following code snippet gives all solutions of the CP model. The solver object returned by calling new Solver() in line 4 is a central object of the Choco framework and must be created first.
Acknowledgements. The authors would like to thank the anonymous reviewers for their helpful comments and suggestions. The figure of AES in the paper is produced by  TkiZ  [ Jea16 ]. The work of this paper was supported by the  National Key Basic Research Program of China  ( 2013CB834203 ), the  National Natural Science Foundation of China  ( 61402469 ), and the  State Key Laboratory of Information Security. The work of  Siwei Sun  is supported by the  Youth Innovation Promotion Association of Chinese Academy of Sciences , and the  Institute of Information Engineering  ( Qing-Nian-Zhi-Xing  project). This research was conducted with the support of the  FEDER program  of  2014-2020 , the region council of Auvergne, and the  Digital Trust Chair of the University of Auvergne.
To achieve this analysis, we need to run the application within an execution platform. It can be either a platform producing real traces from real physical devices or a simulation platform. For a better reproducibility of the test campaigns, the system under test (SUT) is run in a simulated environment, using a synthetic workload. The SUT may involve both fixed nodes and mobile devices. The movement of the latter ones is managed according to some mobility model, a context manager being in charge of producing location-based data. The network simulator can simulate delays or communication errors on wireless or wired links. Execution traces are collected, including both communication messages and location-based data from which the system spatial configurations can be retrieved. We want to check whether the test trace exhibits some behavior patterns described by scenarios.
The scenario is then decomposed in two files: a graph sequence representing the sequence of spatial configurations, and an automaton representing all event order paths available for this scenario. Our language has been implemented within a UML workshop. For this implementation, we have chosen Eclipse Papyrus workshop through its extension possibilities through the use of UML profiles and the development of Eclipse plugins. A. Scenario modeling 1) UML profile for non-standard elements: A scenario in a mobile setting contains two connected views. These views were integrated into Papyrus with the use of a UML profile in order to allow the representation of non-standard UML elements and some syntactic restrictions to sequence diagrams. The first two views are relevant to the mobile setting, while the syntactic restrictions are relevant to the use of TERMOS for checking execution traces.
The GMP works with two main operations, GroupSplit and GroupMerge. We used specifications of the protocol fully described in  [11]  to write tests scenarios and check key properties of this protocol. One example of property was described using our language in Figure  5. This scenario depicts a property called Membership Agreement. It aims at ensuring that two nodes connected together with a safe link have to be in the same group. To collect traces of execution, we have instrumented the application under test. This instrumentation consists, on each mobile node, to simulate a location device acting as a GPS and to record all communications events with the outside world. To observe how the protocol works, several nodes must be running at the same time. To manage the simulation and coordinate nodes movements between them, a mobility simulator was used.
In other words, the verdict of the execution is accept. • 20 240 executions have not reached the assert block of the scenario. In this scenario, this is due to the lack of one of the two GroupChange messages. • 34 executions stopped inside the assert block. These cases revealed violation of the property. Complete results about tested scenarios are presented Table  I. With these results displayed in the verdict analysis interface presented in the previous section, the user is able to analyse each verdict and detect which events caused it. All the GMP properties were described using TERMOS languages, and verified on traces from the SUT presented above. Each property was violated at least once. The results show the usefulness of our tool which can detect properties violation easily on execution traces.
We had a thorough look at existing semantics for UML Sequence Diagrams  [10]. We also looked at other scenario languages distinguishing potential and mandatory behavior. The most influential work for the TERMOS semantics was work on Live Sequence Charts (LSC)  [16]  [17], as well as work adapting LSC concepts into UML Sequence Diagrams  [18]   [19]. GraphSeq implements an algorithm to match sequences of configurations: the sequence of symbolic configurations of the scenario, and the sequence of concrete configurations traversed during SUT execution. To the best of our knowledge, this is an original contribution. The comparison of sequences of graphs has been much less studied than the comparison of two graphs. The closest work we found is for the analysis of video images. In  [20] , the authors search for sequences of patterns (called pictorial queries) into a sequence of concrete graphs extracted from video images.
VII. CONCLUSION This paper presented a tooling support for TERMOS, a UML-based scenario language for the testing of mobile computing systems. Its formal scemantics allows an automated analysis of test traces. A grammar for predicates has been created to accommodate richer descriptions of scenarios. Several checks during the verification process from the depiction of requirements scenarios to trace analysis have been implemented to ensure the correctness of our method. A manmachine interface was integrated to help the user to analyze the test verdict. The full integration of our main tools, GraphSeq and TERMOS, led us to develop a complete tool chain for the automated checking of test traces. The integration has been done in UML workshop called Papyrus. In order to validate our test framework, we led experiments on a group membership protocol.
, 2017)  all rely directly on threat assessments based on IUCN criteria. In parallel, there is also an urgency in listing threatened species in the near future. This is, for example, the case of Target 2 of the Global Strategy for Plant Conservation (GSPC) of the United Nation's Convention on Biological Diversity, which calls for assessing the conservation status of all known plant species by 2020 (https://www. cbd. int/gspc/targets. shtml). However, as of 2016, the Red List included assessments of just 21,898 plant species (IUCN Standards and Petitions Subcommittee, 2016), ca. 6. 2% of the estimated global total (~352,000 flowering plant species;  Paton et al. , 2008). The ThreatSearch database (www. bgci. org/threat_search. php) documents the conservation assessments of ca. 150,000 taxa, including assessments at the species and infraspecific levels based on both older or current IUCN criteria; preliminary, global or regional assessments; and assessments based on other non-IUCN criteria.
Thus, ThreatSearch represents an uncritical, highend estimate of the total number of plant taxa assessed to date. Hence, over the last three decades, progress toward this target has been slow largely because the process of performing and publishing full Red List assessments is time-consuming. Accelerating global conservation assessments is urgently needed  (Krupnick, Kress, & Wagner, 2009; Miller et al. , 2012). While alternative methods have been developed to streamline and simplify large-scale conservation assessments (e. g. ,  Krupnick et al. , 2009; Miller et al. , 2012; Ocampo-Peñuela, Jenkins, Vijay, Li, & Pimm, 2016; Ter Steege et al. , 2015) , none are based on the theoretical framework provided by IUCN, and they thus have little immediate impact for concrete conservation actions. The International Union for Conservation of Nature employs five complementary criteria (A, B, C, D and E) under which a species can be evaluated, and, when not already extinct, assessments assign species to three threatened categories (Critically Endangered (CR); Endangered (E); VU (Vulnerable)), or otherwise to LC (Least Concerned), NT  (Near Threatened)  or DD (Data Deficient, when insufficient data are available).
and preliminary assessments in a spreadsheet; and (4) provides a visualization of the results by generating maps suitable for the submission of full assessments to the IUCN Red List. ConR can be used for any living organism for which reliable georeferenced distribution data are available. As distributional data for taxa become increasingly available via large open access datasets, ConR provides a novel, timely tool to guide and accelerate the work of the conservation and taxonomic communities by enabling practitioners to conduct preliminary assessments simultaneously for hundreds or even thousands of species in an efficient and time-saving way. K E Y W O R D S area of occupancy, criterion B, distribution range, extent of occurrence, IUCN, location, preliminary status, threatened taxa.
| Subpopulations "Subpopulations" are defined as "geographically or otherwise distinct groups in the population between which there is little demographic or genetic exchange  (IUCN, 2012; Rivers, Bachman, Meagher, Lughadha, & Brummitt, 2010). " Although the number of subpopulations is not directly taken into account for assessments based on criterion B, this information is requested during the submission process to the IUCN Red List. Below, we describe ConR, an R package to generate batch preliminary assessments of conservation status following the IUCN guidelines using multiple species datasets based on Criterion B. ConR makes it possible to: (1) calculate or estimate the key parameters needed for an assessment under criterion B; (2) generate preliminary assessments of multiple species using a batch process; and (3) summarize the estimated parameters and preliminary assessments in a spreadsheet and spatially visualize the results on generated maps.