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ORBilu is a project developed by
  Towards Generalizable Machine Learning for Chest X-ray Diagnosis with Multi-task learningGhamizi, Salah  ; Garcia Santa Cruz, Beatriz ; et alE-print/Working paper (2022) Clinicians use chest radiography (CXR) to diagnose common pathologies. Automated classification of these diseases can expedite analysis workflow, scale to growing numbers of patients and reduce healthcare ... [more ▼] Clinicians use chest radiography (CXR) to diagnose common pathologies. Automated classification of these diseases can expedite analysis workflow, scale to growing numbers of patients and reduce healthcare costs. While research has produced classification models that perform well on a given dataset, the same models lack generalization on different datasets. This reduces confidence that these models can be reliably deployed across various clinical settings. We propose an approach based on multitask learning to improve model generalization. We demonstrate that learning a (main) pathology together with an auxiliary pathology can significantly impact generalization performance (between -10% and +15% AUC-ROC). A careful choice of auxiliary pathology even yields competitive performance with state-of-the-art models that rely on fine-tuning or ensemble learning, using between 6% and 34% of the training data that these models required. We, further, provide a method to determine what is the best auxiliary task to choose without access to the target dataset. Ultimately, our work makes a big step towards the creation of CXR diagnosis models applicable in the real world, through the evidence that multitask learning can drastically improve generalization. [less ▲] Detailed reference viewed: 134 (17 UL) IntJect: Vulnerability Intent Bug Seeding; Khanfir, Ahmed ; Soremekun, Ezekiel et alin 22nd IEEE International Conference on Software Quality, Reliability, and Security (2022) Studying and exposing software vulnerabilities is important to ensure software security, safety, and reliability. Software engineers often inject vulnerabilities into their programs to test the ... [more ▼] Studying and exposing software vulnerabilities is important to ensure software security, safety, and reliability. Software engineers often inject vulnerabilities into their programs to test the reliability of their test suites, vulnerability detectors, and security measures. However, state-of-the-art vulnerability injection methods only capture code syntax/patterns, they do not learn the intent of the vulnerability and are limited to the syntax of the original dataset. To address this challenge, we propose the first intent-based vulnerability injection method that learns both the program syntax and vulnerability intent. Our approach applies a combination of NLP methods and semantic-preserving program mutations (at the bytecode level) to inject code vulnerabilities. Given a dataset of known vulnerabilities (containing benign and vulnerable code pairs), our approach proceeds by employing semantic-preserving program mutations to transform the existing dataset to semantically similar code. Then, it learns the intent of the vulnerability via neural machine translation (Seq2Seq) models. The key insight is to employ Seq2Seq to learn the intent (context) of the vulnerable code in a manner that is agnostic of the specific program instance. We evaluate the performance of our approach using 1275 vulnerabilities belonging to five (5) CWEs from the Juliet test suite. We examine the effectiveness of our approach in producing compilable and vulnerable code. Our results show that INTJECT is effective, almost all (99%) of the code produced by our approach is vulnerable and compilable. We also demonstrate that the vulnerable programs generated by INTJECT are semantically similar to the withheld original vulnerable code. Finally, we show that our mutation-based data transformation approach outperforms its alternatives, namely data obfuscation and using the original data. [less ▲] Detailed reference viewed: 21 (3 UL)BURST: a benchmarking platform for uniform random sampling techniques; ; Cordy, Maxime in SPLC '21: 25th ACM International Systems and Software Product Line Conference, Leicester, United Kindom, September 6-11, 2021, Volume B (2021) Detailed reference viewed: 45 (0 UL)Proceedings of the 3rd ACM SIGSOFT International Workshop on Machine Learning Techniques for Software Quality Evaluation; ; et al Book published by ACM (2019) Detailed reference viewed: 69 (2 UL) A Decade of Featured Transition SystemsCordy, Maxime ; ; et alin From Software Engineering to Formal Methods and Tools, and Back - Essays Dedicated to Stefania Gnesi on the Occasion of Her 65th Birthday (2019) Detailed reference viewed: 66 (3 UL)Model-based mutant equivalence detection using automata language equivalence and simulations; ; Papadakis, Mike et alin Journal of Systems and Software (2018) Detailed reference viewed: 123 (3 UL)Towards Security-aware Mutation Testing; ; et al in The 12th International Workshop on Mutation Analysis (Mutation 2017) (2017) Detailed reference viewed: 182 (4 UL)Featured model-based mutation analysis; ; Papadakis, Mike et alin 38th International Conference on Software Engineering (ICSE'16) (2016) Detailed reference viewed: 112 (2 UL)A Variability Perspective of Mutation Analysis; ; et al in Proceedings of the 22nd ACM SIGSOFT International Symposium on the Foundations of Software Engineering (FSE 2014) (2014) Detailed reference viewed: 126 (3 UL) |
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