dataesr/scientific-paragraphs-categorization huggingface dataset:
). The observers identify archaeological features in the available set of data using a shared or individual observation protocol. While this can induce biases (see part 1), we argue that even if they appear inconsistent in the first instance, these different The second phase consists of analyzing these observed datasets, considering the way they have been recognized and interpreted from the point of view of the movement process, in order to propose a schema of the concepts used and the links established by the observers to relate various concepts. This is precisely what we attempted to do by applying an ontological approach to each of the case studies analyzed in this article. This exercise makes it possible to select all the characteristics regardless of their name or morphology and to associate them in a generic domain of "potential path".
Some Conclusions and Implications of This Approach for Practices of Archaeological Knowledge Creation in the Contemporary Context In this paper, we presented an approach to archaeologically studying the diverse expressions in the physical landscape of phenomena, such as movement, through a process of semantic modeling of domain literature and observation-based interpretation using fieldwork and remote sensing data. We focused on movement in three contexts, highlighting the variability in archaeologically recognizable physical evidence for movement and in the concepts and language used to describe movement and its infrastructure. The increasing use of extensive remote sensing datasets and, in particular, the uptake of machine learning to scale up identifications of archaeological features in the landscape motivated the development of this approach, which aims to guard against uncritically reinforcing standardized and strongly codified ideas about how complex phenomena appear in these data.
Despite these widely acknowledged problems, in practice the standardization of recording and reporting for both excavation and survey has increased. This latest step-change is propelled by improved methods and decreasing costs of collecting extensive observational data, notably through remote sensing methods including satellite imagery, UAV-based sensing, and geophysical prospection. While these datasets have been large by archaeological standards for some time, their scale has grown exponentially in recent years. More importantly, while until recently the archaeological use of these data resources has been constrained by the pace of manual interpretation, improvements in machine learning and automated feature detection, notably since the 2010s (see, e. g. ,  [4, 5, 73] ), are speeding their interpretation.
We demonstrated that by creating an ontology of movement practices based on text analysis we can attempt to disentangle, structure, and clarify some of the semantic biases involved in the practice of identifying and interpreting features in large observational datasets as carried out by archaeologists whose knowledge base is inevitably embedded in the literature representing the current state of disciplinary knowledge. In this context, developing ontologies can serve as a useful heuristic exercise, aiding in understanding the reasoning behind largely implicit frames of reference and inference, and supporting comparing diverse situations. The breakdown of implicit conceptual references into explicit, logical chains of reasoning which describe basic entities and their relationships enables the use of constituent elements to reconstruct, analyze, and compare practices, such as those related to movement, from the bottom up.
Then, we introduced the concept of the track graph as a possible analytical tool for exploring and comparing pathway systems and movement practices at multiple spatial and temporal scales. The track graph is defined as a set of nodes and edges representing all observed features in a study region that are related to movement. Based on graph theory, it offers possibilities for applying well-established analytical approaches such as network analysis and agent-based modeling. At the same time, it allows for a richer description and understanding of observed features related to movement through the conceptualization, modeling, and connecting of informal pathway systems, as exemplified by the concepts of wayfaring and meshworks. While the practical application of this approach to new datasets is still on the horizon, we tried to illustrate the potential and necessity of synthesizing data from various sources using a formalized, but not standardized, approach based on ontological reasoning and basic graph theoretical concepts.
Acknowledgments: The research for this paper was partly made possible through the  Movescape  project thanks to a PICS grant awarded to  Laure Nuninger  by  CNRS , a  CLUE+ grant  to  Philip Verhagen  by  Vrije Universiteit Amsterdam  and funds from  ZRC-SAZU  to  Žiga Kokalj  for organizing TRAIL2018 (Postojna, Slovenia). It was also supported by one month invited professor positions granted to  Philip Verhagen  and  Rachel Opitz  by the  University of Franche-Comté. Travel funds were also made available by the  University of Glasgow  and by the  University of Tours  (thanks to the  SoLidar  project, co-organiser). Movescape workshops were organised in Besançon (MSHE C. N. Ledoux ,  CNRS ), in Tours ( MSH Val de Loire and CITERES, University of Tours), in Amsterdam (Vrije Universiteit Amsterdam), in Glasgow (University of Glasgow ), and opened to a larger public in Atlanta ( CAA2017 ) and in Postojna ( TRAIL2018 ).
Supplier Impersonation Fraud (SIF) is a rising issue for Business-to-Business companies. The use of remote and quick digital transactions has made the task of identifying fraudsters more difficult. In this paper, we propose a data-driven fraud detection system whose goal is to provide an accurate estimation of financial transaction legitimacy by using the knowledge contained in the network of transactions created by the interaction of a company with its suppliers. We consider the real dataset collected by SIS-ID for this work. We propose to use a graph-based approach to design an Anomaly Detection System (ADS) based on a Self-Organizing Map (SOM) allowing us to label a suspicious transaction as either legitimate or fraudulent based on its similarity with frequently occurring transactions for a given company.
This approach might not be the most suitable to handle the increasing volume of digital transactions. Thus, the need for automated, data-driven SIF detection system arises. In this work, we present GraphSIF, a SIF detection system that uses a Business-To-Business (B2B) transactions dataset to construct a graph modeling the relationships between client and supplier companies in a B2B ecosystem, and describe how these relationships can be used to derive useful knowledge in order to assert the legitimacy of transactions. We use the transaction network to create a time-evolving behavior sequence summing up the evolution of the graph through time. We then compare the new graph created by adding a suspicious transaction to the behavior sequence and investigate the potential discrepancy it introduces.
History dataset The SiS-id SIF detection system uses a set of historical transactions performed by SiS-id's clients. In this section, we describe this dataset in detail. The set of B2B transactions used by the SiS-id detection system is an aggregation of the payments performed by SiS-id's client companies between July 2016 and July 2019. These transactions consists of a feature vector of 4 features : client identification number, supplier identification number, target account identification number, and date of the payment. For the sake of storage, all of the transactions involving the same client, supplier and destination account during a single month are aggregated, resulting in the creation of a fifth feature representing the number of similar transactions issued during the month. The time granularity of the transaction is thus a month.
While this mitigates the risks of damage in case of data leak, it also means that the same company will have a different identifier whether it has issued a transaction, or received a transaction. At the time of writing, 950,929 transaction records are available in the History dataset. These transactions are issued by 6,063 unique companies. This number is more than the number of SiS-id's client companies. This is explained by the fact that SiS-id's client companies can represent a group of several branches such as a multinational group. In this case, each firm possesses its own identification number, but only a global entity will be SiS-id's client. 215,056 unique supplier companies are also found in the dataset, along with 262,157 unique bank accounts to which a payment was transferred. The fact that more bank accounts than supplier companies exist indicates that some suppliers use more than one bank account to be paid.
This dataset represents all the transactions performed by all of SiS-id clients for two years. However, there is no available information about the legitimacy of these data, and thus no knowledge of which transactions are fraudulent and which are legitimate. Due to the economic sensitivity of the data for the companies (as discussed in "SiS-id fraud detection platform" section, no sample can be made publicly available at the moment. However, a request for data samples can be submitted directly to SiS-id through their website 2  , or by contacting Laurent Sarrat, co-author of this paper.
Audit dataset A second set of transactions is available thanks to SiS-id. It consists of the list of transactions that were analyzed using the expert system in the past 2 years (July 2017 -July 2019). The dataset, called the "Audit" dataset, is composed of 218,325 suspicious transactions submitted by 317 unique client companies. The transactions underwent the fraud detection process devised by SiS-id, and were labeled with a legitimacy label : "high" indicate that the transaction have a high chance of being legitimate, "medium" meaning that the rule engine lacks the necessary information to assert if the transaction is legitimate or not, and "low" means that the transaction's legitimacy is low, which can be the case if the transaction is either invalid or fraudulent. This dataset possesses the following properties: 1 It contains real life queries for transaction validation made by companies.
2 Each of the transaction of this dataset contains a target variable corresponding to the classification done by the rule engine. 3 The transactions were cross-validated by the clients for consistency. The legitimacy label found in the dataset might tempt us to use this dataset to perform supervised learning. However, this approach has a major drawback: by using data analysis on a dataset that is the result of the rule-engine system (described in the next section), we will only manage to "rediscover" the rules. However, this dataset might be used as a validation set for other fraud detection systems, in order to compare their performance with SiS-id's expert system and investigate the potential convergence of their results.
For a set of window sizes ws 1 , ws 2 ,. , ws k , the following process is repeated: First, the identifier of C is used to gather all transactions involving C (Hist(C)) from the History dataset (that contains the list of all the transactions occurring between all the companies of the B2B ecosystem). Then, this list of transactions is ordered by date of occurrence and split in N fixed-size windows of size ws i where i ∈ 1,. , k. The sequence of transactions (dubbed "behavior sequence") is then sent to the next phase. An the same time, the oldest transaction of the most recent window of the sequence (w N ) is removed and t is added as its most recent transaction, creating the "test window" w t. This step allows us to isolate the transaction relevant to the specific client and suppliers potentially victims of the supplier impersonation fraud.
In the next step, the transactions found in each window w 1 ,. , w N and w t are used to create a graph that represents the relationships between C and all of its suppliers. Each window is complemented by a set of transactions from the History dataset Hist(S wi ), where S wi is the set of supplier involved with C during w i found in the History Dataset. Similarly, Hist(S wt ) is used to create the test graph corresponding to w t. The graphs are then transformed into a histogram that uses relationships between the accounts paid by C and used by its supplier as characterizing features of the graphs. Each of the graphs is converted into its corresponding histogram H i , thus creating the sequence H 1 ,. , H N and H t the histogram corresponding to the test graph. This step allows us to transform a sequence of transaction into a feature vector representing the relationship between a client and its suppliers, taking into account the historical behavior of the client.
Company local profile In this phase of the fraud detection system, all the transactions involving the client C involved in the tested transaction t =[ C, A, S, d] are gathered from the History dataset. The History dataset contains all the N H transactions made by all clients in the studied B2B environment. By isolating only the transactions issued by C, we create a local profile of C. As shown by  Bolton et al. (2001) , the use of local profile allows to detect anomalous behavior that would have been deemed legitimate when using a global profile. This local profile is dubbed Hist(C).
This approach exploits several properties of the transaction graph in order to construct the embedding. The graph histograms corresponding to the behavior sequence are then used to train our anomaly detection system, while the graph histogram corresponding to the test windows is investigated. The role of graph theory in GraphSIF is to transform the four attributes found in the History dataset into a set of graph-theoretic features allowing to perform fraud detection by taking into account the underlying relationships between companies and bank accounts found in the dataset. In the original form of the History dataset, the data points cannot be used directly to construct meaningful models, as their feature are categorical variables. The use of graph theory allows GraphSIF to express the links between each client and their suppliers through the bank account that they share, and to transform the list of categorical variables into a set of embedded graphs.
Experimental settings In order to produce these results, GraphSIF has been run on a laptop running Ubuntu 18. 04. 4 with an Intel Core i7-10510U CPU and a 16 GB memory. The data used to train the model is the History dataset previously described in "SiS-id fraud detection platform" section, consisting in a set of unlabeled transactions between client companies and suppliers companies. The data used to test our model is the Audit dataset described in "SiS-id fraud detection platform" section. In order to perform our experimentation, each of the client company found in the Audit dataset is selected. The corresponding subset of transaction from the History dataset is used to train GraphSIF, and then a sample of at most 1000 transactions from the Audit dataset is used to assert the consistency of the results with SiS-id's expert system.
Global results In this section we focus on GraphSIF's behavior with the other clients of the Audit dataset, in terms of consistency and operational efficiency. The experiment presented in this section is conducted with every transaction of the Audit dataset. As opposed with the case study proposed in the previous subsection, this experiment analyses the behavior of each client company of the dataset in a separate manner, in order to assert the behavior of GraphSIF in various settings, with an emphasis on the number of potential frauds discovered by GraphSIF.
Global Consistency Figure  16  shows the True Negative Rate (TNR) for each client of the Audit dataset. We define True Negative Rate as the number of transactions given a low legitimacy label by both GraphSIF and the expert system, divided by the number of transactions given a low legitimacy label by the expert system. It shows how consistent GraphSIF is with the expert system regarding suspicious transactions. The dotted orange line shows the mean TNR over all the clients (0. 53), while the grey dotted line shows the mean TNR of a random classification machine (0. 29). We can see that while there is still room for improvement, GraphSIF shows promising results in terms of consistency. Furthermore, Fig. 16  allows us to pinpoint a set of client on which GraphSIF is not accurate (rightmost ones).
These clients are a promising starting point for further improvements. Figure  17  shows the True Positive Rate (TPR) for each client in the Audit dataset. TPR is akin to TNR, but instead of counting the number of transactions given the low legitimacy label, the number of transactions given the high legitimacy number is computed. The TPR in our setting is fairly low (0. 24), even lower than the one produced by randomly guessing a label for the transactions (0. 27). This highlights the fact that GraphSIF tends to assign a low number of high legitimacy labels overall. Furthermore, the expert system's usage of outside information might also explain this discrepancy. However, in the context of fraud detection, it is more costly to assign a high legitimacy label to a fraudulent transaction than to assign a low legitimacy label to a legitimate transaction.
Discussion While results shows that GraphSIF is able to provide consistent results in terms of consistency with the expert system, the most pressing issue is the lack of ground truths about actual legitimate and fraudulent transaction. While SiS-id's expert system provides an alternative to expert knowledge regarding the labeled transaction, this system is prone to error, most notably because fraud is dynamic and often changes faster than the rules of an expert system. Furthermore, comparing GraphSIF graph-based approach and SiS-id expert system is difficult, as they both rely on different types of knowledge (expert-based vs data-driven). However, in the absence of a dataset containing trusted labels, comparing our systems with SiS-id's expert system is the only way to propose an evaluation of their performance.
Conclusion In this paper, we introduce GraphSIF, a novel feature-engineering process that creates a feature vector based on the relationships between a client company and the accounts it used to pay its supplier companies, providing a new tool to describe the underlying transaction mechanism involved in their interaction. Several recent papers such as  Wachs and Kertész (2019) ;  Dubols et al. (2007)  and  Mongiovì et al. (2013)  propose a human interpretation of the patterns uncovered by their approach and how they might suggest illegal behavior. The focus of our work is to emphasize on the variation of behavior, instead of the behavior itself. However, the relation between the uncovered patterns and fraud attempts is currently under investigation. In conclusion, we used the temporal information contained in the transactions of the History dataset to create a behavior sequence composed of the transactions emitted by a client aggregated in several bounded time windows.
We showed how to use this behavior sequence to create a data model based on Self-Organizing maps representing the behavior of a client company through time. We then used this data model to infer the legitimacy of new transactions using the K-means clustering algorithm, along with an aggregation algorithm allowing us to combine the results obtained for different window sizes in a comprehensive score. We presented the result of our classification system, first on a single company to investigate its performance locally, and then for a large set of companies from a large real-life dataset. We investigated the consistency of GraphSIF's results with the ones from the SiSid expert system, and analyzed its operational efficiency, showing that GraphSIF is a fast alternative to the expert system.
In this model, the steel fibre-reinforced concrete has been treated as a two phase material in which the steel fibres are explicitly modelled with embedded cables within a three-dimensional (3D) mesh representing the matrix. The numerical simulations of tensile and three-point bending tests showed a good agreement with the experimental results. In a third work by Tian et al. [13] , 3D representative volume elements of short carbon fibrereinforced magnesium composites, with relatively high fibre volume fraction, have been generated by the random sequential absorption algorithm and then analysed by the FEA package ABAQUS. To this end, the 4-node tetrahedron element C3D4 has been considered to model the matrix and the short carbon fibres. The presented results showed firstly the increase of Young's modulus and secondly the decrease of Poisson's ratio with the fibre volume fraction.
Numerical validation The four-node quadrilateral composite plane element PFQ4R was implemented into the commercial finite element code ABAQUS via a user element subroutine (UEL). Numerical models (ABAQUS input files) of the following assessment tests were created by a Fortran program developed by authors that automatically handles the intersections between the matrix mesh and the discrete fibres. To assess the accuracy of PFQ4R, three validation tests are considered in the following. The obtained results of PFQ4R are compared with those of the quadrilateral composite element PFQ4 developed by Tiar et al. [17]  (a combination of the classical four-node quadrilateral membrane element and the twonode truss element). In addition to PFQ4, the solution of PFQ4R is compared with PFQ4R which is a combination of the four-node quadrilateral membrane element with rotational DOFs (PFR4) and the two-node truss element.
Materials and methods Second part of glycolysis experimental data. Experimental data of PGAM, ENO and PPDK activities and pathway flux (J obs ) are obtained from plots of a previous study  8. The free online software WebPlotDigitizer (Version 4. 1, https ://autom eris. io/WebPl otDig itize r/) is used to extract data from plots. These data are available in Tables  S1  and S2. Artificial neural networks (ANNs). ANNs functioning mimics that of biological neurons, the networks consist of many layers allowing input reception and processing and output delivery. This technique can be used for solving classification or regression problems  18. To build the second part of glycolysis in ANNs, different types of software are employed: RStudio (Version 1. 1. 456), an open-source integrated development environment for R  19  and two packages: NeuralNet (Version 1.
Complex pathway SImulator (COPASI) metabolic networks. A first metabolic network of the studied pathway was kindly provided by the authors of a previous study  8. This model is developed on GEPASI  22  , an old software for metabolic pathway modeling, replaced by COPASI since 2002. The second part of the glycolysis is also modeled by using the open source software called COPASI (Version 4. 24)  23. This software is used for metabolic network design, analysis and optimization. The resulting metabolic networks are based on the use of enzyme properties (kinetic parameters and mechanism-based rate equations).