dataesr/scientific-paragraphs-categorization huggingface dataset:
The output is the code line to replace the suspicious line. They used the copy mechanism to deal with the vocabulary problem, instead of renaming identifiers. SequenceR was evaluated on Defects4J  [72]  and was able to fix 14 out of 75 bugs. The biggest difference compared to this previous work is the usage of transfer learning in VRepair, whereas SequenceR is only trained on a single domain, which is the bug fix task. Additionally, VRepair is able to generate multiline patches as opposed to the single line fixes of SequenceR. CoCoNut is an approach that combines multiple seq2seq models to repair bugs in Java programs  [73]. They used two encoders to encode the buggy program; one encoder generates a representation for the buggy line, and another encoder generates a representation for the context.
These two representations are then merged to predict the bug fix. They trained multiple different models and used ensemble learning to combine the predictions from all models. DLFix is an automated program repair tool for fixing Java bugs  [74]. It is different from other approaches in that it uses tree-based RNN and has two layers. The first layer is a tree-based RNN that encodes the AST that surrounds the buggy source code, which is passed to the second layer. The second layer takes the context vector and learns how to transform the buggy sub-tree. It will generate multiple patches for a single bug, and it deploys a character level CNN to rank all the generated patches. Code2Seq  [75]  uses AST paths to represent a code snippet for the task of code summarization. An AST path is a path between two leaf nodes in the AST.
Transfer Learning in Software Engineering To the best of our knowledge, there are only a few works that use transfer learning in the software engineering domain, and none of them use it for generating code fixes. Recently, Ding has done a comprehensive study on applying transfer learning to different software engineering problems  [78] , such as code documentation generation and source code summarization. He found that transfer learning improves performance on all problems, especially when the dataset is tiny and could be easily overfitted. In our work, we deploy transfer learning for vulnerability fixing and show that it also improves accuracy. Also, we show that our model trained with transfer learning has a more stable and superior performance compared to training on the small dataset. Huang, Zhou, and Chin used transfer learning to avoid the problem of having a small dataset for the error type classification task, i.
e. , predict the vulnerability type  [79]. They trained a Transformer model on the small dataset and achieved 7. 1% accuracy. When training first on a bigger source dataset and tuned afterward on the same dataset, Huang et al. [79]  managed to get 69. 1% accuracy. However, their work is not about transfer learning and therefore the transfer learning experiment was relatively simple and short. In our work, we conduct multiple experiments to show the advantages of using transfer learning for vulnerability fixing. Sharma et al. have applied transfer learning on the task of detecting code smell  [80]. They train the models on C# code and use them to detect code smells with Java code examples and vice versa. They found that such models achieved similar performance to models directly trained on the same program language.
CONCLUSION In this paper, we have proposed VRepair, a novel system for automatically fixing C vulnerabilities using neural networks. To tackle the problem of having only small vulnerability fix datasets, our key insight is to use transfer learning: we first train VRepair with a big bug-fix corpus, and then we fine-tune on a curated vulnerability fix dataset. We have performed a series of original and large scale experiments. In our experiments, we found that VRepair's transfer learning outperforms a neural network that is only trained on the small vulnerability fix dataset, attaining 21. 86% accuracy instead of 7. 86% on Big-Vul dataset, and 22. 73% accuracy instead of 11. 29% on CVEfixes dataset. This result shows that transfer learning is a promising way to address the small dataset problem in the domain of machine learning for vulnerability fixing.
Put in another way, our experiments show the knowledge learned from the bug fixing task can be transferred to the vulnerability fixing task, which has never been studied before to the best of our knowledge. In the future, we would like to explore the possibility of using an even larger source domain dataset. We believe that one can achieve results similar to those of GPT-3 -a massive, generic natural language model with 175 billion parameters  [81]. In the context of software engineering, we envision that we could train a single model on all code changes from GitHub, not just bug fixes, and tune it on tasks such as code comment generation, function name prediction, and vulnerability fixing. It is also possible to enlarge the considered target domain datasets, for example with vulnerabilities reported by issue tracking tools and various static analyzers.
2. 1. Next, we randomly divide the vulnerable and fixed function pairs from Big-Vul into training Big-Vul rand train , validation Big-Vul rand val and testing Big-Vul rand test sets, with 2226 (70%), 318 (10%) and 636 (20%) examples in each respective set. Similarity, we also randomly divide the vulnerable and fixed function pairs from CVEfixes into training CVEfixes rand train , validation CVEfixes rand val and testing CVEfixes rand test sets, with 2383 (70%), 340 (10%) and 681 (20%) examples in each respective set. Big-Vul rand test and CVEfixes rand test are used to evaluate the models trained with source and target domain training. For all of the data splits in each dataset, we make sure that all of the examples are mutually exclusive, as recommended by Allamanis [48]. The number of examples for the top-10 CWE values from Big-Vul rand test and CVEfixes rand test are given in Table 2 and Table 3.
study the test sequence accuracy of models trained only with source or only with target domain training. Given our datasets, source domain training means training the model only with bug fix examples from B train , while target domain training uses only Big-Vul rand train (or CVEfixes rand train ). Both models are evaluated on the vulnerability fixing examples in Big-Vul rand test (or CVEfixes rand test ). Table 6 gives the results on Big-Vul rand test. In the first column we list the top-10 most common CWE IDs in Big-Vul rand test. The second column shows the performance of the model only trained with source domain training. The third column shows the performance of the model only trained with target domain training. The last row of the table presents the test sequence accuracy on the wholeBig-Vul rand test.
The result for CVEfixes rand test is in Table 7. Even when the model is trained on the different domain of bug fixing, the model still achieves a 18. 24% accuracy on Big-Vul rand test and 15. 98% accuracy on CVEfixes rand test. This is better than the models that are trained with only target domain training, i. e. , training on a small vulnerability fix dataset, that has a performance of 7. 86% on Big-Vul rand test and 11. 29% on CVEfixes rand test. The result shows that training on a small dataset indeed is ineffective and even training on a bug fix corpus, which is a different domain but has a bigger dataset size, will increase the performance. RQ1: Test sequence accuracy on Big-Vul rand test with source/target domain training, we also present the accuracy on the top-10 most common CWE IDs in Big-Vul rand test.
The results are given in Table8and Table9. The first column lists the top-10 most common CWE IDs. The second column presents the performance of the model trained with transfer learning, i. e. , taking the source domain trained model from Table 6 (or Table 7) and tuning it with target domain training. The third and fourth column gives the performance increase compared to the model with only source or target domain training. The last row of the table presents the test sequence accuracy on the whole Big-Vul rand test (or CVEfixes rand test ). The main takeaway is that the transfer learning model achieves the highest test sequence accuracy on both Big-Vul rand test and CVEfixes rand test with an accuracy of 21. 86% and 22. 73% respectively. Notably, transfer learning is superior to just target domain training on the small vulnerability fix dataset.
and Table 11, we see that the transfer learning model dominates both test datasets, with 21. 86% versus 13. 36% on Big-Vul rand test and 22. 73% versus 13. 34% on CVEfixes rand test. This means that first training on the bug fixing task is better than first training on a denoising task. But when comparing the same result against only training on the vulnerability fix dataset (target domain training) in Table 6 and Table 7, we can see that denoising pre-training RQ3: Comparison between transfer learning and pre-training + target domain training on CVEfixes rand test.
[69] vulnerability fix dataset based on CVE records from National Vulnerability Database (NVD). The vulnerability fixes are automatically gathered from the associated open-source repositories. It contains CVEs up to 9 June 2021, with 5365 CVE records in 1754 projects. Reis & Abreu collected a dataset of security patches by mining the entire CVE details database[67]. This dataset contains 5942 security patches gathered from 1339 projects with 146 different vulnerability types in 20 languages. They also collected 110k non-security related commits which are useful in training a system for identifying security relevant commits. A vulnerability detection system, VulDeePecker[31], created the Code Gadget Database. The dataset contains 61,638 C/C++ code gadgets, in which 17,725 of them are labeled as vulnerable and the remaining 43,913 code gadgets are labeled as not vulnerable.
The Code Gadget Database only focuses on two types of vulnerability categories: buffer error (CWE-119) and resource management error (CWE-399). By contrast, the Big-Vul that is used in this paper contains vulnerabilities with 91 different CWE IDs. Ponta et al. created a manually curated dataset of Java vulnerability fixes[68]which has been used to train Se-qTrans, a vulnerability fixing system[6]presented above. The dataset has been collected through a vulnerability assessment tool called "project KB", which is open sourced. In total, Ponta's dataset contains 624 vulnerabilities collected from 205 open sourced Java projects. SATE IV is a vulnerability fix dataset originally used to evaluate static analysis tools on the task of finding security relevant defects[57]. SATE IV consists of 117,738 synthetic C/C++ functions with vulnerabilities spanning across 116 CWE IDs, where 41,171 functions contain a vulnerability and 76,567 do not.
VulinOSS is a vulnerability dataset gathered from open source projects[69]. The dataset is created from the vulnerability reports of the National Vulnerability Database. They manually assessed the dataset to remove any projects that do not have a publicly available repository. In total, the dataset contains 17,738 vulnerabilities from 153 projects across 219 programming languages. Cao et al. collected a C/C++ vulnerability dataset from GitHub and the National Vulnerability Database, consisting of 2149 vulnerabilities.
09%/+2 +40. 90%/+9 All 22. 73% (155/682) +6. 75%/+46 +11. 44%/+78 } INST_HANDLER (lds) { // LDS Rd, k + if (len < 4) { + return; + } int d = ((buf[0] >> 4) & 0xf) | ((buf[1] & 0x1) << 4); int k = (buf[3] << 8) | buf[2]; op->ptr = k; Listing 1: CVE-2018-11375 is correctly predicted by the model trained with target domain only. Answer to RQ1: Training a VRepair Transformer on a small vulnerability fix dataset achieves accuracies of 7. 86% on Big-Vul and 11. 29% on CVEfixes. Surpris- ingly, by training only on bug fixes, the same neural network achieves better accuracies of 18. 24% on Big- Vul and 15. 98% on CVEfixes, which shows the inef- fectiveness of just training on a small vulnerability dataset.
Introduction The major interface between humans and the ocean occurs in the coastal seas. Marine industries thrive in this area while European citizens make daily use of the coastal ocean for tourism, leisure and recreation. Operational oceanography assists both industry and the general public to make decisions about their use of and access to the coastal ocean. The EuroGOOS community has developed, based on in situ and satellite observations, data and modelling capacities, a wide range of products and services for such use cases. EuroGOOS (www. eurogoos. eu) is the European component of the Global Ocean Observing System of the Intergovernmental Oceanographic Commission of UNESCO (IOC GOOS). The EuroGOOS Coastal working group examines the entire value chain from coastal observations, satellite data, ocean forecasts and analysis, to products and services for coastal users.
The working group reviews sustainability and fitness for purpose of the existing system and identifies gaps and future steps needed to secure and improve all elements of the coastal value chain. The EuroGOOS Coastal Working Group builds upon significant initiatives already completed or underway that have focused on coastal observatories. These include, but are not limited to, the work of EMODnet, SeaDataNet, CLMS (Copernicus Land Monitoring Service), and Copernicus Marine Service In Situ Thematic Centres, which play significant role in making available key datasets for coastal areas, the JERICO-NEXT, JERICO-S3 and JERICO-DS EC projects, which work towards sustaining the JERICO-RI (Research Infrastructure) on long term, as well as activities within EuroGOOS working groups, platforms task teams, and the five regional operational oceanographic systems (ROOS).
This is also reflected in the evolution of Copernicus Marine Service Thematic Assembly Centres. For instance, the Copernicus Marine Service Ocean Colour Thematic Assembly Center (OC TAC) has provided highresolution ocean colour datasets in coastal zones since May 2021 that are fully addressing coastal user requirements. Depending on their applications, these users require spatial resolutions of 300m over the shelf, down to 10's of metres in coastal waters. As an example of evolving from opportunistic data acquisition to established sustained monitoring networks, in 2018-2021, the OC data streams shifted from relying on science mission to the operational OC data stream from two OLCI and two VIIRS sensors. From 2020, OCTAC relies also on the operational highresolution products (e. g. , chlorophyll-a concentration, total suspended matter, water turbidity): Landsat 8 OLI (Operational Land Imager) and the two Sentinel-2 Multi Spectral Instruments (MSI), this provide high resolution products for the coastal buffer of all European waters.
g. , using beach images from different sources (i. e. , video monitoring and satellite), which is still an underestimated source of beach morphodynamics data  (Vos et al. , 2019). Remote sensing can also contribute to the better description of land-ocean continuum. Apart from direct measurements of salinity and water constituents in estuaries, which are related to river runoffs, there is indirect information about the river plume structure that can be obtained from satellite data. The interpretation of these observations usually requires a combination with in situ or model data  (Brando et al. , 2015). Recently the Copernicus Land Monitoring Service has also delivered their Coastal Zones (https://land. copernicus. eu/local/ coastal-zones) dataset that includes monitor landscape dynamics in coastal zones (land use/land cover changes).
Coastal modelling Coastal numerical models allow producing tailored products for diverse end-users by integrating ocean and land processes. An overview of European capacity in terms of operational modelling has been provided by a systematic survey conducted by the EuroGOOS Coastal Working Group  (Capet et al. , 2020). This survey reveals the wide diversity of model engines deployed in European waters to address hydrodynamics, waves and biogeochemistry dynamics, and highlight the current standards  (2018) (2019)  in terms of i) technical aspects (resolution, grid typology, forecasting times, model assumptions, etc. ), ii) observational data dependency (for validation and assimilation purposes), iii) preference for upstream forcing datasets and data providers (atmosphere, ocean, and land boundary conditions), iv) accessibility and dissemination of simulation data.
Providing near real-time and forecast information for the aquaculture industry along Europe's Atlantic coast is also of vital importance in mitigating the effects of Harmful Algal Blooms (HABs). Recent projects that aimed at the co-development with end users and demonstration of HAB forecasting services as one of the societal needs from the coastal observing and forecasting systems include the FP7 Asimuth  (Cusack et al. , 2016) , H2020 AtlantOS  (Cusack et al. , 2018)  and Interreg Atlantic Area PRIMROSE (https://www. shellfish-safety. eu/). Further operational services developed in recent years in response to the aquaculture sector needs to include the Weather Window Tool  (Dale et al. , 2018)  designed and developed within the framework of the AtlantOS project and currently rolled out across the European Atlantic coasts in the framework of the Interreg Atlantic Area MyCOAST project (http://mycoast-project.
Examples from the coastal tourism sector Following enquiries from the coastal tourism sector, various coastal services have been developed to this community. A good example is in Ireland where based on the request of the sailing community, Irish Marine Institute (IMI) developed a tailored product based on the North East Atlantic operational forecasting model. Surface currents are subset over five geographical areas around the Irish waters and the English Channel and published in a GRIB format via an ftp site https://sftp. marine. ie/. The service was developed in collaboration with some of the sailors that contacted the IMI. The GRIB format requirement and the subset were determined by the route planning and navigation software OpenCPN (https://opencpn. org/) and constraints on the download size while at sea, respectively.
The system adopts a downscaling approach of circulation numerical models based on the core products of the Copernicus Marine Service: very high resolution hydrodynamic and water quality forecasts, up to 50 meters in certain coastal areas, are provided operationally on a daily basis. Such tools represent a fundamental support for coastal planning, response to emergencies and more broadly for the institutional activities of ARPAL. This scheme is particularly relevant since it proposes a virtuous synergy between the public and the private sectors where downstreaming services of public benefit are developed, implemented and updated by private entities whereas they are managed and delivered by public local institutions, which should be ultimately the responsible subjects of a public service. Finally, the Interreg Atlantic Area MyCOAST project demonstrates the benefits of standardization of datasets used as inputs to cross-sectoral services, which enables their transfer between different coastal observatories.
These users were only interested in simplified information services, not data services. For this reason, data (measured or modelled) is not delivered to them, only instant messages via Telegram containing a simplified bulletin. For instance, the FORCOAST Marine Conditions and Scheduler for oyster farms forecasts conditions at a user-defined location and sends a bulletin that displays the water level in relation to working environments like wind, precipitation, and daylight. A working limit can be set, for which the bulletin will provide some additional visual support, enabling for faster information extraction. In a more general framework, the UN Ocean Decade identifies among their challenges (the 9 th ) to "ensure comprehensive capacity development and equitable access to data, information, knowledge and technology across all aspects of ocean science and for all stakeholders.
Data fusion and aggregation The pan-European data infrastructures (like Copernicus Marine Service, Copernicus Marine Service in situ TAC, DIAS, EMODnet, SeaDatanet, EIONET) provide a rich background for the development of integrated coastal services, through increasing the open-access availability to homogenised operational and historical datasets and data products. Global multi-observations products are also included in the service catalogue of Copernicus Marine Service. They provide through different data fusion/ blending techniques, products derived from a combination of two or more sensors, satellite and in situ, including 3D temperature, oxygen, salinity, geopotential height and geostrophic current fields, 2D sea surface salinity and sea surface density fields, total surface and near-surface currents, vertical velocity fields, surface carbon data sets of FCO2, pCO2 and pH, nutrient vertical distribution and particulate organic carbon and chlorophyll-a fields (some of them in near real time).
Research institutes perform research field campaigns with high quality and resolution observing. Enhanced public-public partnership is recommended to improve data sharing and accessibility, so that integrated datasets can fit for multi-purposes in operational, environmental and ecosystem applications. Public-private partnership for data integration: in recent years observations made by citizens, NGOs and companies become more and more active in coastal sea monitoring. Ferrybox lines have been widely operated in European regional seas, as a result of successful public-private partnership in ocean monitoring. In an efficient public-private partnership, the public partners can facilitate operations of private monitoring (e. g. as national contact point, providing recommendations on optimizing sampling strategies), demonstrate the quality and usefulness of private observations, facilitate collection, dissemination and usage of private data, share and spread best practices on public-private cooperation for ocean observing; while the private partners can provide more observations either free of charge or in a cost-effective way.
Funding This work was supported by the  FORCOAST  project which received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  870465 ,  INTERREG Atlantic Area Projects MyCOAST  ( EAPA 285/ 2016 ) and  iFADO  ( EAPA 165/2016 ) co-funded by the  ERDF (EU).
| Clinical and Laboratory data Data collected from medical records included age at diagnosis, presence of other autoimmune disorders, AIH treatment before LT, immunoglobulin G (IgG) levels and presence of circulating antibodies. Type I AIH was defined by the presence of antinuclear antibodies (ANA) and/or smooth muscle antibodies (SMA), and type II by the presence of anti-liver/kidney microsomal antibody type 1 (LKM1) and/or anti-liver cytosol type 1. Presence of pre-transplant anti-HLA antibodies was recorded. Indication for LT, Child-Pugh and MELD scores were assessed. Donor characteristics and technical aspects of LT were reported. Donor/recipient HLA mismatches were determined for the main loci A, B and DR.
| Statistical analysis Data were described in their totality and according to recurrence status using median with interquartile range (IQR) or mean with standard deviation (SD) for continuous variables and number (percentage) for categorical variables. Different relevant cut-offs were tested to assess the impact of age. LT centres were dichotomized at the median according to the number of patients included in the study, to define high-volume versus low-volume centres. Categorical variables were compared with the Chi-square or Fischer's exact tests and quantitative variables were compared using the Student's t-test or non-parametric tests (Mann-Whitney or Kruskal-Wallis tests) when appropriate. To analyse the risk factors for CDR, only a univariate Cox regression analysis was performed as a result of the low number of events.