dataesr/scientific-paragraphs-categorization huggingface dataset:
The generative methods fine-tuned on the novel s2orc-9K dataset, introduced in this paper, demonstrate superior performance compared to the method employing arXiv-nlp. The improvement is particularly remarkable considering that s2orc-9K is about half detected a statistically significant difference among the annotators. Conversely, if the 𝑝-value exceeds this threshold, it indicates that the annotators performed consistently. As expected, the three senior researchers exhibit similar behaviors (𝑝 = 0. 26). Notably, GM-s2orc-H results are also not statistically different from those of the three senior users: the group including GM-s2orc-H and the three researchers yields 𝑝 = 0. 51. To validate this finding, we also conducted individual comparisons between GM-s2orc-H and each of the three researchers.
Conclusions Statements in scientific articles may describe new research methodology, datasets, and findings, or they make reference to prior research and conceptual knowledge that served as the foundation for the current discoveries. For the latter, researchers refer to other papers citing them in their claims. In this paper, we presented two types of methodologies to tackle the task of determining whether a sentence from a scientific paper needs a citation and identifying the most suitable placement. Specifically, we reframed this challenging task as either a Mask-Filling problem or as a Named Entity Recognition problem. In the first case, we proposed an approach leveraging the text-generative capability of the GPT-2 Transformer. In the second case, we developed a methodology based on fine-tuning a BERT Transformer on a relevant NER task.
On top of the results of the proposed methods, we applied a set of heuristics for further adjusting the citation placement. We evaluated the suggested methodologies and heuristics using a gold standard of manually annotated sentences. Notably, the generative approach, fine-tuned on the 2orc-9K dataset, outperformed the other methods. Particularly significant is that this approach yielded results comparable to those achieved by human researchers. To reinforce the validity of these findings, we conducted a thorough statistical analysis to assess the consistency between senior researchers and the computational models. The outcomes of our analysis provide compelling evidence that the proposed solution exhibits a level of performance that is statistically indistinguishable from that of senior researchers.
CRediT authorship contribution statement Davide Buscaldi: Conceived and designed the analysis, Wrote the paper. Danilo Dessí: Collected the data, Contributed data or analysis tools, Wrote the paper. Enrico Motta: Conceived and designed the analysis, Supervised the work. Marco Murgia: Contributed data or analysis tools, Performed the analysis, Wrote the paper. Francesco Osborne: Performed the analysis, Wrote the paper, Supervised the work. Diego Reforgiato Recupero: Collected the data, Contributed data or analysis tools, Wrote the paper, Supervised the work.
Acknowledgments We acknowledge financial support under the  National Recovery and Resilience Plan (NRRP) ,  Mission 4 Component 2 Investment   1. 5 -Call  for tender No. 3277 published on December 30, 2021 by the  Italian Ministry of University and Research (MUR)  funded by the  European Union -NextGenerationEU. Project Code  ECS0000038 - Project Title  eINS Ecosystem of Innovation for Next Generation Sardinia -CUP   F53C22000430001- Grant Assignment Decree No. 1056 adopted on June 23, 2022 by the  Italian Ministry of University and Research (MUR).
VA's outcomes are first collected by the Local Correlation Component of the agent and then forwarded to the client through a Kafka message broker. The VA identifies vulnerabilities in the installed applications based on known CVEs as existing in NIST's National Vulnerability Database (NVD) to produce the results. One of the limitations of this module is that if software running on the platform is not indexed in the National Vulnerability Database (NVD), it can pose a challenge for conducting a comprehensive vulnerability assessment. The NVD is a well-known repository that provides a centralised source of vulnerability information for widely used software and systems. However, it may not cover less popular or custom-built applications that are unique to a specific organisation or industry. However, if the software vulnerability is also based on a misconfiguration from the operating system, the VA module will be able to identify the risk.
Time to detect an attack The time it takes to detect an attack varies depending on many factors. Some of these include the attack type, the efficiency of the systems in place to detect the attack, and the overall level of security the target employs. More specifically, in our study, the application layer's risks, attack surfaces, and vulnerabilities can be detected as soon as their definition is available in the databases. The evaluation is made periodically as a scanning task, and the periodicity can be configured by the administrator depending on the module and focus. Considering this, the detection time can be computed as the delta time between definition availability and the next task time. For detecting attacks, private information leaks, and malware we provide real-time availability of detection since, for the Network, Threat Detection, and SIEM modules, the traffic is continuously monitored.
Related Work In the subsequent sections, we expound upon the pertinent research in the context of this paper. Our examination of the field is organised into three primary categories: (i) real-time threat monitoring, (ii) threat intelligence, and (iii) risk assessment. Elaboration on these categories is provided as follows. Real-Time Threat Monitoring. The categorisation of vast datasets at high velocities employs three primary distributed processing platforms: Apache Spark  [24] , Apache Storm  [25] , and Apache Flink  [26]. The fundamental distinction between these platforms lies in Spark's execution of batch processing, while Storm and Flink excel in native flow processing. Derived from the Apache Spark Platform, two notable projects are Apache Spot  [27]  and Hogzilla  [28]. Apache Spot leverages telemetry and machine learning techniques to scrutinize data packets for threat detection.
The Global RAMA score is accessible to interested parties through the Observatory. The Observatory is a web-based platform responsible to collect, analyse and present the results of all the deployed clients in order to provide global insights on the level of security in healthcare environments. The collected information is stored in the Observatory database and will be analysed by the Analytics Engine in order to produce statistics, historical analysis and trends. Furthermore, data is collected from the Aggregators deployed in each hospital. The Global RAMA Score Calculator consumes this data, generates the Global RAMA score, and provides relevant metadata. The results are presented in the Observatory GUI, which represents the 2 nd Layer of visualizations. The Global RAMA Score also comes with metadata provided by the Aggregator, initially produced from the Local RAMA calculator and the Global RAMA calculator, such as the top 10, most severe and most frequent vulnerabilities in all the involved healthcare facilities.
This paper uses a dataset covering the universe of French firm-level value added, imports, and exports over the period 1995-2007 and a quantitative multi-country model to study the international transmission of business cycle shocks at both the micro and the macro levels. Because the largest firms are the most likely to trade internationally, foreign shocks are transmitted to the domestic economy primarily through the large firms. We first document a novel stylized fact: larger French firms are significantly more sensitive to foreign GDP growth. We then implement a quantitative framework calibrated to the full extent of the observed heterogeneity in firm size, exporting, and importing. We simulate the propagation of foreign shocks to the French economy and report one micro and one macro finding.
Our analysis combines a dataset covering the universe of French firm-level value added and countryspecific imports and exports over the period 1995-2007 and a quantitative multi-country multisector model with heterogeneous firms. We report a novel reduced-form stylized fact, one micro finding, and one macro finding. In the data, larger French firms are significantly more sensitive to foreign GDP growth. Our quantitative exercises show that at the micro level, foreign shocks are granular fluctuations: GDP changes following a foreign shock are driven primarily by the large firms. At the macro level, observed heterogeneity across firms attenuates the aggregate impact of foreign shocks. All in all, our main conclusion is that the firm-level differential exposures to trade are quantitatively important for understanding the propagation of foreign shocks to the domestic economy.
It is common in international macro and trade, and can be implemented with only sector-level data such as the World Input-Output Database. Following the same foreign shock, the GDP change in the homogeneous firm model is 10 -20% larger than the GDP change in the baseline economy. Surprisingly, the granularity of the economy attenuates the GDP responses to foreign shocks, and thus quantifying the propagation of shocks using models that neglect firm heterogeneity can be misleading. The rest of the paper explores the macro attenuation result and provides the intuition for it. We connect the micro granularity and the macro attenuation results by exploiting the crosssection of partner countries. In the data, firm-level patterns of trade differ across trading partners. This means that the propagation of country-specific shocks to France depends on which firms trade with that partner.
Since the largest firms are more likely to be internationally connected, we would expect them to have a larger increase in value added following a positive foreign shock, and thus the covariance in (5) to be positive. To observe a quantitatively important Γ F requires heterogeneity both in size and in responsiveness to foreign shocks (driven by differences in importing and exporting behavior), and a correlation between the two. Simply put, if firms were heterogeneous in im/exporting, but firm sizes were either homogeneous across firms or uncorrelated with trade participation, Γ F would be zero. From here, we proceed as follows. After introducing the dataset, Section 3 provides reducedform regression evidence that the covariance (5) is positive, by estimating the differential sensitivity of larger firms to foreign GDP growth.
Data and Basic Facts We combine administrative data on the universe of French firms' value added, imports, and exports with standard multi-country sector-level databases of production and trade. The use of micro data for one country allows us to capture the heterogeneous exposure of individual firms to foreign shocks. While such heterogeneity obviously exists in all countries, firm-level information at this level of detail and coverage is not available for multiple countries at once. As a consequence, we will study the impact of firm heterogeneity using the French firm-level data, suppressing heterogeneity within sectors in the rest of the country sample.
Firm-Level Variables We make use of an administrative dataset that contains balance sheet information collected from individual firms' tax forms, and includes sales, value added, total exports, the cost structure, as well as the sector of activity for the universe of French firms over 1995-2007. 4 This source is complemented with customs data on bilateral export and import flows at the firm level. The resulting dataset is described in greater detail in di  Giovanni et al. (2014 Giovanni et al. ( , 2018)). Table  A1  reports the distribution of firms across sectors in 2005. Sectors with the largest contribution to aggregate value added are Wholesale, Retail Trade, and Post and Telecommunications. More generally, nontraded sectors are a large share of the French economy, accounting for more than 80% of firms and 69% of the value added in our sample.
Aggregate and Sectoral Variables The main source of data at the country-sector level is the World Input Output Database (WIOD)  (Timmer et al. , 2015). This dataset combines national input-output tables and data on bilateral trade flows to build the matrix of all intra-and international flows of goods and services between sectors and final consumers. We use the 2013 release of the dataset which covers 40 countries plus a rest of the world aggregate and 35 sectors classified according to the ISIC Revision 3 nomenclature. These data are available over 1995 to 2011 and the benchmark year for the calibration of the quantitative model is 2005. The WIOD dataset is used to recover: i) final consumption spending (P n C n ); ii) the value of bilateral sales by sector (X mn,j ); and iii) the sectoral production function parameters, which are used whenever more disaggregated data are not available.
We use these data to measure the share of labor in country n sector j's total costs (π l n,j ) as well as the components of the input-output matrix, as measured by the share of inputs sourced from country m sector j by firms operating in country n sector i (π M mn,ji ). The IO coefficients are readily available from the WIOD. Labor shares are measured by the ratio of value added over output, to be consistent with the interpretation of L n as "equipped labor. " The French administrative data and the WIOD data must be made consistent with each other, as the final dataset must feature firm-level trade flows that aggregate up to the sector-level bilateral trade flows reported in WIOD. In addition, shares of value added in total output implied by the French data must match those implied by WIOD for France.
Final demand for goods from m is: P mn,j C mn,j = µ mn,j P 1-σ mn,j P 1-σ n,j P n,j C n,j = µ mn,j P 1-σ mn,j P 1-σ n,j ϑ n,j P n C n. Denote by Π n the aggregate profits of firms owned by households in n, and by D n any trade imbalance. Then the final expenditure in n on goods coming from country m sector j is: P mn,j C mn,j = µ mn,j P 1-σ j mn,j P 1-σ j n,j ϑ n,j w n 1 ψ 0 w n P n 1 ψ-1 L n + Π n + D n. Note that we use the French customs data for imports at the firm level, and thus every import transaction is associated with a French firm (which may be a wholesaler or a retailer). Thus, French final consumers are never observed to import final consumption goods directly, and as a result French final consumption is composed only of domestically-supplied final goods. 9 For all the other countries, we do not have firm-level data on imports, but instead have final consumption data by source country from WIOD.
g. the Handbook chapter by  Costinot and Rodríguez-Clare, 2014). For the other countries we use WIOD to obtain sector-level counterparts of these shares. For French firms, π M f,mn,ji 's are available for imported inputs, but not domestic ones. The domestic input-output linkages are inferred using firm-level data on overall input usage and sector-level information on domestic IO linkages. See Section 3 and Appendix A for details on the construction of all firm-and sector-specific shares. In addition to the initial-period values taken from the data, solving the model requires a small number of structural parameters. Table  2  summarizes the calibration. We set the elasticity of substitution between firms in the same sector selling to the same destination to ρ = 3, a common value according to standard estimates (see e.
This implies that the imported input coefficients in this implementation are lowered for the firms that in the data actually import inputs, but raised for firms that in the data do not. Similarly, firms that export nothing in the data export to all countries in this counterfactual scenario. This model can be implemented using only the WIOD sectoral production and trade data, and does not require any firm-level information. Table  3  reports the results in the row labeled "Homogeneous firms. " The main macro finding is that the aggregate GDP change following the world productivity shock is 22% larger in the homogeneous firm model than in the baseline. The attenuation effect also appears for the foreign demand shock, though here the disparity is smaller at 9%. In all cases, the average-granular decomposition shows that the entirety of the GDP change is now accounted for by the unweighted average value added change E F , with zero contribution of the granular residual.
20 19 The rescaling procedure implies that some rescaled firm-level coefficients end up lying outside of the range of possible values ([0, 1]). The corresponding coefficients are winsorized at the maximum and minimum values. This affects less than 2% of the firms in the total sample. The rescaling procedure is applied to all sectors but three, namely Wholesale; Retail, including Motor Vehicles; and Fuel. For these three sectors, the average labor share is low in the French data compared to the WIOD. This comes from the treatment of merchandise, which we categorize as intermediates while WIOD does not. Our approach is consistent with the model in the case of France, where we assume that consumers never interact directly with foreign firms. From that point of view, all merchandise imported from abroad is used as inputs by a French firm which ultimately sells to the final consumer.
The solid (red) lines correspond to the unweighted distribution and the (blue) circles to the weighted distribution, where firms' weights are defined according to their share in aggregate value added. Calculated from French balance-sheet data together with the WIOD information on sectoral labor shares, for 2005. sectors and source countries using the information on imports, by product. This allows us to recover the π M f,mn,ij coefficients for n = F rance. While in principle straightforward, calibrating these parameters entails two key difficulties: i) it requires the use of two sources of firm-level data, which raises concerns regarding comparability; and ii) not all of these coefficients can be recovered from the firm-level data. In particular, we do not have detailed information on inputs purchased domestically and thus need to infer their sectoral breakdown using (more aggregated) information from WIOD.
We proceed as follows. For each sector i among the subset of tradable sectors and each source country m ̸ = n, we first compute the share of bilateral imports of goods produced by country m, sector i in the firm's total input expenses. 21  Since this ratio uses data collected from two databases, the overall import share obtained from the summation of these π M f,mn,ij coefficients over all tradable sectors and foreign countries is larger than one in some cases (for less than 1. 5% of firms). Whenever this happens, the import share is winsorized to one and the bilateral sectoral coefficients rescaled accordingly. Beyond comparability issues between the two firm-level sources, the introduction of these firmlevel import shares into the broader multi-country model also means we must ensure consistency with the sectoral coefficients in the global data.
For price indices, in best practice of the statistical agencies, P n,j is just the PPI change. There is some heterogeneity across countries in whether the PPI includes export prices or not. For us, PPI will include exports, and will be computed as P n,j = m f ∈Ω nm,j ω j f,nm,j,-1 p f,nm,j , (B. 2) where ω j f,nm,j,-1 ≡ X f,nm,j,-1 m f ∈Ω nm,j X f,nm,j,-1 is the gross output weight of the firm's sales to m in sector j sales. Note that this is more comprehensive than what is actually done in practice, as the PPI is a survey that catches the minority of firms, and thus implementing (B. 2) amounts to using more data than the statistical agencies do. To construct the input price deflator P M n,j , the statistical agencies use the PPI and the IO tables. We mimic this procedure by computing the input-share weighted change in input prices, where we use the PPI for the domestic inputs, and the foreign sectoral price changes for foreign inputs.
Di Giovanni  gratefully acknowledges the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No. 726168 ), and the  Spanish Ministry of Economy and Competitiveness , through the  Severo Ochoa Programme for Centres of Excellence in R&D  ( SEV-2015-0563 ) for financial support. Mejean gratefully acknowledges support from a public grant overseen by the  French National Research Agency (ANR)  as part of the " Investissements d'Avenir" program  ( Idex Grant  Agreement No. ANR-11-IDEX-0003-02/Labex ECODEC  No. ANR-11-LABEX-0047  and  Equipex  reference:  ANR-10-EQPX-17 - Centre d' accès sécurisé aux données -CASD ) as well as the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No.
For these cases, in order to be able to compute statistics, we take for the execution time of YoungDaly a lower bound, namely the time at which the execution was stopped: ℎ -𝑇 plat. We checked that using this lower-bound or computing the statistics while just discarding these configurations leads to almost identical results (differences below 1%). The simulation code for all experiments is publicly available at http://perso. ens-lyon. fr/frederic. vivien/resilience/nonmemoryless-checkpoint.
Relational data Knowledge graph Instance-based learning Formal concept analysis Graph-FCA Concepts of neighbors Knowledge graphs and other forms of relational data have become a widespread kind of data, and powerful methods to analyze and learn from them are needed. Formal Concept Analysis (FCA) is a mathematical framework for the analysis of symbolic datasets, which has been extended to graphs and relational data, like Graph-FCA. It encompasses various tasks such as pattern mining or machine learning, but its application generally relies on the computation of a concept lattice whose size can be exponential with the number of instances. We propose to follow an instance-based approach where the learning effort is delayed until a new instance comes in, and an inference task is set. This is the approach adopted in k-Nearest Neighbors, and this relies on a distance between instances.
Introduction Nowadays, a lot of information is available in the form of relational data, such as relational databases  [1] , graph databases  [2] , or knowledge graphs  [3]. In a relational dataset, the information is represented in terms of entities and relationships between those entities. Graphs are a common representation of relational data, using nodes for entities and edges for relationships. The different existing formalisms differ in the kind of nodes and edges they support or not: e. g. , labeled edges, n-ary edges, literal values as nodes. The major advantage of relational data is their versatility and representation power. All kinds of structured data can be represented accurately as entity-relation graphs, including tabular data, XML trees, JSON objects, biological sequences or linguistic parse trees.
The growing amount of relational data calls for powerful methods to analyze them and learn from them. Formal Concept Analysis (FCA)  [4, 5]  is a mathematical framework for the analysis of symbolic datasets. The strength of FCA is to encompass in one framework various tasks such as pattern mining, rule mining, machine learning, hierarchical clustering, and information retrieval  [6]. In its simplest setting, an FCA dataset is a binary relation between objects and attributes. However, a number of extensions have been proposed to cope with more complex data and settings. Fuzzy Concept Analysis  [7]  adds truth degrees to the relations between objects and attributes, Temporal Concept Analysis (TCA)  [8]  adds a temporal dimension to the description of objects, and Pattern Structures  [9]  allow for custom descriptions instead of sets of attributes.