dataesr/scientific-paragraphs-categorization huggingface dataset:
The objective of this paragraph simply aims to position the SEEA method (which is the subject of our study) in the process of safety development. For a more detailed study on the methods of analyzing the safety of industrial systems, the reader can refer to the work by A. Villemeur  [2]. All of these methods, whether quantitative or qualitative, inductive or deductive, are now governed by standards and regulations with a view to meeting European rail safety and interoperability directives. For example, regulation n ° 2018/762 of the European Commission of March 8, 2018 establishes Common Safety Methods (MSC) relating to the requirements in terms of Safety Management System (SMS) in accordance with the European directive n ° 2016/798 relating to railway safety. Regarding the certification of the design, realization (manufacturing, installation, testing, acceptance) and implementation of a rail transport system, it is carried out in accordance with CENELEC EN 50126 standards (for railway systems), EN 50128 (for software of railway control and protection systems), EN 50129 (for signaling, telecommunications and processing products and systems) and EN 50657 (for on-board software of railway rolling stock).
The integrity of software is distributed over five SIL levels, ranging from SIL 0 to SIL 4. The software security standard EN 50128 comes from the European Committee for Electro technical Standardization (CENELEC). The published international version of the CENELEC EN 50128 standard is IEC 62279. The content of the two publications is identical. Annex D of European Standard EN 50128 suggests 71 methods and techniques for developing and assessing the security of critical software used in the rail transport sector. The SEEA (Software Error Effect Analysis) method is part of this list of recommended methods. According to standard EN 50128, the objective of the SEEA method consists not only in identifying the software components and their criticality, but also in proposing a means for detecting software errors and consequently reinforcing the robustness of the software.
The study proposed within the framework of this article concerns the SEEA method and endeavors to develop a new approach to analysis and evaluation of the safety of critical software, based on machine learning and more precisely on the Case-Based Reasoning (CBR). It is currently impossible to conclusively demonstrate that software is free of errors. In France and in the railway sector, coded single-processor technology is used to ensure the safety of software execution. However, this technique does not provide protection against software design errors, code conformance errors, not coded safety software errors, and coded processor implementation errors. SEEA can, for its part, support, among other things, the analysis of these errors SEEA is a safety analysis approach whose purpose is to determine the nature and severity of the consequences of software failures SEEA also guides software validation and maintenance activities by identifying the most critical modules for safety.
Indeed, SEEA makes it possible to estimate the level of effort of validation to be carried out on the various elements of the software and in particular, to guide the readings of code and to better target the tests. This analysis is performed by considering software error assumptions and examining the consequences of these errors on the other modules as well as any systemrelated failures SEEA finally proposes measures to detect errors and improve the robustness of the software. This method, which is derived from the FMECA (hardware safety analysis) method, seeks to identify in particular design and programming errors in order to analyze their internal and external effects. A SEEA is carried out according to the following three stages  [3] : -Preliminary analysis: This first step of the process consists in listing the elements of the software for which there will be a SEEA to perform, to define the levels of deepening of the analysis and finally to assign one of these levels to each element.
-Procedure-by-procedure analysis: The purpose of this analysis is to produce the SEEA files. The SEEA file contains, for each module studied, the SEEA sheets produced on this module. The development of a SEEA form consists of filling in a table containing the following columns: the name of the module studied, the error considered, the consequences on the module, the consequences at the system level, the safety criterion not respected, the criticality of the error, the means of detecting the proposed error, the criteria not respected and finally the residual criticality. -Synthesis of the works: This last step of the SEEA approach makes it possible to group, by module, the unsolved scenarios, the criteria not respected, the means of detection and the distribution of the errors according to the criticality of their manifestation.
All of the above findings show that SEEA is considered as an important part of a system's safety record. It is a fundamental document in the process of building and validating the safety of critical software. Nevertheless, the careful analysis of certain SEEA files of already certified or approved rail transport systems reveals some shortcomings. On the one hand, SEEA documents have extremely varied representation formats from one manufacturer to another, and on the other hand, the process of drawing up and evaluating a SEEA dossier proves to be a particularly delicate and tedious exercise which is not supported by any formalized strategy. Indeed, the completeness and coherence of the analyzes remain essentially based on the know-how, the intelligence and the intuition of the experts of the field.
These findings led us to use artificial intelligence and machine learning techniques, and in particular CBR  [4]. The design and implementation of this tool requires the use of four main steps  [4] [5] [6] [7] : 1) Formalization and modeling of the knowledge involved in SEEA files, 2) Acquisition of historical data from the files of manufacturers and know-how of safety experts, 3) Development of a database and 4) Operation of this database to help certification experts to judge the completeness and consistency of SEEA of a new transport system.
As part of our study on the safety analysis of critical software, the objective of improving the quality of accident risk analyzes, guided us towards the development of a tool based on the CBR allowing us to suggest potential accidents and / or the most appropriate protective or preventive measures to protect against a particular risk. However, the artificial intelligence approaches cannot provide satisfactory answers to our research objectives. Indeed, despite the undeniable interest of these approaches, to our knowledge, to date there are no applications of artificial intelligence to improve the safety of critical software used in the rail transport sector and in particular tools to improve the SEEA method. Specifically, the bibliographic study carried out on machine learning and in particular on CBR shows the absence of work on the use of CBR in the analysis and evaluation of the safety of critical software used in the rail transport sector.
Method: Approach used for CBR-based critical software safety assessment In order to show the interest of machine learning and more precisely CBR in the field of the safety of railway transport, we have developed a tool called "SAUTREL". This tool helps safety experts in their SEEA document analysis and assessment tasks. The design and implementation of this tool required the following three major phases: -Acquisition of data involved in SEEA. This analysis and abstraction stage led to the development of a conceptual model based on eight descriptive parameters: system, subsystem, module, envisaged error, safety criterion, feared risk, severity of damage and finally the error detection means. -Based on the study of two already certified rail transport systems (MAGGALY and TGV Nord), we have built up a learning example base which brings together, to date, 250 historical cases relating to SEEAs.
-Design and implementation of a tool "SAUTREL" to aid in the evaluation of SEEA  [36] [37]. The CBR process was implemented using Recall software from Isoft. These three major phases of development of the tool to aid the assessment of the safety level of critical software are detailed in Figure  2  in nine steps presented in the following paragraphs. As shown in Figure  2 , in front of each step of the proposed methodology, we presented the result obtained. For example, Step 1 on Knowledge Acquisition and Modeling allowed the development of a generic SEEA representation model. Step 2 on the definition of the description language of the SEEA learning examples led to the elaboration of the descriptive parameters (or characteristics) of the SEEAs. Step 3 on the development of the database the SEEA made it possible to compile all the source cases.
Acquisition and modeling of knowledge This paragraph presents the results of the phase of formalization and acquisition of the knowledge necessary for the development of a historical case base (experience feedback) in order to capitalize and perpetuate the knowledge related to the SEEA. The first step of the study is devoted to the research and identification of descriptors and characteristic parameters to represent and formalize the SEEA. After a second step of data collection necessary to list the possible values taken by each parameter (or descriptor), the third step proposes, a formalism of representation of SEEA documents. Finally, on the base of this formalism, which constitutes the basic language of SEEA representation, the fourth stage of the study focuses on building the case base that currently comprises 224 cases, each of which represents a particular situation that is contrary to safety (Problem) and one or more preventive measures or corrective measures to guard against, avoid, reduce, or permanently eliminate the potential risk envisaged (Solution).
At the end of this review, we finally proposed a first SEEA representation model that relies heavily on the manufacturers' practices and our experience in the field of railway safety. This formalism is based on eight characteristic parameters: Studied system, subsystem studied, module studied, error envisaged (family, class, type), safety criterion not respected by the error, dreaded event, type and gravity of the damage, barrier and means for detecting the error. This model proposes a methodological framework for preparing SEEA files and thus contributes to ensuring the quality of future analyzes. An excerpt from this formalism is presented in Figure  3. On the basis of this representation model of the SEEA forms, we have created a library of 224 typical cases.
It can also give a weight vector to indicate the relative importance of a descriptor over others. In our example, we chose to extract only the 10 most similar cases, and to give a weight equivalent to all the descriptors. -Adaptation strategies for reuse of similar cases: Generally, there are two possibilities: 1) if the case found in the database (source case) is identical to the new problem to be solved (target case), then the solution of the problem is immediate; 2) if, on the other hand, the case found presents a certain similitude (or analogy) with the new case, then an adaptation procedure is necessary whose objective is to adapt the solution found to the need of the new situation (target case). Thus, in the first hypothesis, we apply directly the solution found and in the second hypothesis, we must find a suitable technique to adapt the recovered solution and include it in the new problem.
In our sample application, to date, the tool does not offer a real adaptation method, but allows the user to program his own methods with daemons. Currently, this adaptation can be done either implicitly by the safety domain expert, by comparing cases similar to the target case, or by the voting technique. In this second case, the value of the attribute to be adapted is calculated on all the similar cases by a vote weighted by the percentage of similarity of each case. For example, if a case C has 3 descriptors of which 2 are 100% similar to the target case and the third descriptor has no similarity (0%), then case C will be similar with the target case at 66%. If all the descriptors are of equal weight: (100 × Descriptor weight 1 + 100 × Descriptor weight 2 + 0 × Descriptor weight 3)/3 = 66.
There are some learning tools to try to determine and correct this data. In our case of application, during the phase of acquisition and collection of SEEA data, particular attention was paid to this problem of noisy or inconsistent data. The search for SEEA cases similar to the target case, is broken down into two filtering and selection stages that use static and dynamic indexes. There are different ways to determine the characteristics of indexes: All characteristics, some characteristics, the most discriminating characteristics, etc. In our application we adopted a similarity search based on the set of characteristics. To find similar SEEA cases from the case database archived in memory (source cases or reference cases), several techniques can be used, such as the "Nearest Neighbor" algorithm whose objective is to measure the similarity between the problem (target case) and potential source cases.
Generally, one distinguishes two types of adaptation: Transformational adaptation and derivative adaptation. In the first approach, it is a question of directly reusing the solutions of the past cases. This type of transformational adaptation does not tell us how the solutions of similar cases were generated. It is the role of derived adaptation that allows, for each case stored in the database, to explain the reasoning process leading to the solutions. In this case, the derivative adaptation consists in applying the same reasoning to the new problem by choosing the paths taken by the old solutions selected and thus avoiding any unsuccessful paths. In our application case, the "ReCall" tool used to demonstrate the feasibility of the proposed approach does not yet propose relevant adaptation strategies.
Nevertheless, this knowledge is necessary to stimulate and assist the expert in his task of safety assessment. Indeed, faced with a new problem (scenarios of accident/potential incident) described by a set of characteristic descriptors, it is interesting to know the possible feared event or events (collision, derailment, electrocution, fall). This last step of updating knowledge is to perform the automatic learning by adding the appropriate target case in the SEEA historical case base. In the "ReCall" software, this learning is not incremental since the new case will be integrated into the hierarchy without it being reconstructed. It is up to the user to take the initiative to revive the indexing of the case base. Therefore, during this phase of the CBR cycle, it is wiser that the new case with its new solution is validated by the domain expert before being added to the case base (source cases).
In addition, it is interesting at the end of this learning phase to test the system by relying on the same problem that it has just treated to ensure that the system behaves as expected. Finally, it is essential to determine how to index this new case in the database without questioning the historical knowledge learned in previous phases and thus avoid new problems of inconsistency, redundancy, etc. In particular, the focus must be on this problem of incrementality. Should we adopt a monotonous incremental learning approach (accumulation of knowledge without questioning knowledge previously learned) or non-monotonous (examination of knowledge learned with each addition of new knowledge)? This is a problem that remains crucial in almost all machine learning systems. As part of our prototype of feasibility, this work is not yet completed.
Discussion and Conclusion Please note that the first paragraph of a section or subsection is not indented. The first paragraphs that follows a table, figure, equation etc. does not have an indent, either. In order to rationalize and reinforce conventional approaches to safety analysis and assessment, we have agreed to use artificial intelligence and machine learning techniques and in particular case-based reasoning (CBR). The main objective consists, from a set of data in the form of accident scenarios or incidents experienced on rail transport systems (experience feedback), to exploit by automatic learning this mass of data in to stimulate the imagination of safety experts and assist them in their difficult task of analyzing and evaluating the safety of new critical software. This historical data concerns SEEA.
The domain expert is not only able to control, validate, adapt and complete the knowledge learned by the system, but also to adjust certain learning parameters. To demonstrate the feasibility of the proposed approach, we used a case-based reasoning generator named "ReCall" from ISOFT. Despite the undeniable interest of this ReCall tool, several shortcomings have been noted in particular for methods for calculating similarity, coping strategies and processing missing values (noisy data). However, this contribution made it possible to demonstrate the feasibility of a new approach to modeling, capitalization and evaluation of the SEEA method, based on the use of machine learning techniques. This approach of evaluating critical software, used in rail safety, is also based on the joint and complementary use of machine learning and knowledge acquisition techniques to reinforce and systematize the phase of acquisition and transfer of knowledge in the field of railway safety.
The originality of the tool developed lies not only in its ability to model, capitalize, sustain and disseminate SEEA expertise, but to the best of our knowledge, it represents the first research on the application of CBR to SEEA. In fact, in the field of rail transport, there are currently no software tools for assisting SEEAs based on machine learning techniques and in particular based on CBR. Currently, project is at the mock-up stage. Initial validation has demonstrated the interest of the suggested approaches, but improvements and extensions are required before they could be used in an industrial environment or adapted to other areas where the problem of investigating safety arises. These improvements include the improvement of the adaptation strategies of the solutions proposed by the system, the enrichment of the SEEA case base to cover the whole problem and finally, it is necessary to construct an integrated version of a prototype in order to finalize the results of demonstration model.
4 ), in each case showing positive modulations (CPP: mean ± SEM = 0. 202 ± 0. 053, t 31 = 3. 78, p < 0. 001; RU: 0. 392 ± 0. 077, t 31 = 5. 11, p < 0. 001; residual updating: 0. 177 ± 0. 079, t 31 = 2. 23, p = 0. 033; Fig. 4c ). We also evaluated the influence of head motion by including motion, as indexed as the relative root-mean-square of the six motion parameters, in the regression model. We only kept 247 ROIs that had usable data from all subjects. Each ROI can be assigned to one of 13 putative functional systems. The brain figure was visualized by the BrainNet Viewer 42 under the Creative Commons Attribution (CC BY) license (https://creativecommons. org/licenses/by/4. 0/). b An example of Pearson correlation coefficients calculated between regional BOLD time series over the course of the experiment.
Each BOLD time series was divided into 10-TR (25 s) time windows, and consecutive time windows were placed every 2 TRs leading to 80% overlap between consecutive time windows. Pairwise Pearson correlation coefficients were calculated between ROI time series in each time window. c An example of edge strength over time. In each time window, there were 247*(247 -1)/2 edges. d Nonnegative matrix factorization (NMF). In each time window, the matrix of edge strengths was unfolded into one column. Then, edges from all time windows in all participants were concatenated into a single matrix. Each row in the full data matrix contained an edge (pairwise correlation coefficients between BOLD time series from two ROIs) and each column contained a time window (across all scans and participants). Correlation values in this matrix were strictly non-negative; the full data matrix was divided into two halves, with one half containing the positive pairwise correlation coefficients (zero if the correlation coefficient was negative) and one half containing the absolute values of negative pairwise correlation coefficients (zero if the correlation coefficient was positive).
We then related this metric to activation from our previous study 2 , as measured by the z-statistic of the modulation effect of CPP or RU. This z-statistic indicated the effect size of change of univariate activity in response to CPP or RU across participants. Across all ROIs, there was a positive correlation between edge strength in subgraph 4 and activation for CPP (r = 0. 403, p < 0. 0001; Fig. 6a ) and activation for RU (r = 0. 704, p < 0. 0001; Fig. 6b ). The Surf Ice software (https:// www. nitrc. org/projects/surfice) was used to show the map of normalized mean edge strengths for subgraph 4 alongside the thresholded activation maps for CPP and RU (Fig. 6c ). Regions with stronger edge strength in subgraph 4, such as the insula, dorsomedial frontal cortex, dorsolateral prefrontal cortex, posterior parietal cortex, and occipital cortex, also tended to show stronger increases in activation with increases in CPP and RU.
The subgraph modulated by surprise and uncertainty included interactions between multiple functional systems, most prominently the fronto-parietal task control, memory retrieval, salience, and dorsal attention systems (Figs. 3b  and 4a ). These systems, include multiple regions in the anterior insula, dorsolateral and dorsomedial frontal cortex, and lateral and medial parietal cortex (Figs. 4b  and 6c ). These regions showed a large degree of overlap with areas that we have previously shown to have increased univariate activation in response to both surprise and uncertainty (in this same dataset; Fig. 6 )  2. A smaller subset of these regions, including parts of the dorsomedial frontal cortex, anterior insula, inferior frontal cortex, posterior cingulate cortex, and posterior parietal cortex, was modulated not only by both normative (surprise and uncertainty) factors, but also by a non-normative one (reward).
Methods Participants. The dataset has been described in our previous reports  2. Thirty-two individuals participated in the fMRI experiment: 17 females, mean age = 22. 4 years (SD = 3. 0; range: 18-30). Human subject protocols were approved by the Internal Review Board in University of Pennsylvania. All participants provided informed consent before the experiment. Task. Each participant completed four 120-trial runs during functional magnetic resonance imaging. In each run, participants performed a predictive-inference task (Fig. 1a ). On each trial, participants made a prediction about where the next bag would be dropped from an occluded helicopter by positioning a bucket along the horizontal axis (0-300) of the screen. The location of the bag was sampled from a Gaussian distribution with a mean (the location of the helicopter) and a standard deviation (noise).