A ubiquitous task in processing electronic medical data is the assignment of standardized codes representing diagnoses and or procedures to free-text documents such as medical reports. This is a difficult natural language processing task that requires parsing long, heterogeneous documents and selecting a set of appropriate codes from tens of thousands of possibilities---many of which have very few positive training samples. We present a deep learning system that advances the state of the art for the MIMIC-III dataset, achieving a new best micro F1-measure of 55.85 , significantly outperforming the previous best result ( 2018). We achieve this through a number of enhancements, including two major novel contributions: multi-view convolutional channels, which effectively learn to adjust kernel sizes throughout the input; and attention regularization, mediated by natural-language code descriptions, which helps overcome sparsity for thousands of uncommon codes. These and other modifications are selected to address difficulties inherent to both automated coding specifically and deep learning generally. Finally, we investigate our accuracy results in detail to individually measure the impact of these contributions and point the way towards future algorithmic improvements.
There has been significant work towards the automated coding problem @cite_3 @cite_10 @cite_13 @cite_6 @cite_9 @cite_4 @cite_12 . We review some of the recent relevant work.
@cite_4 @cite_10 @cite_9 @cite_6 @cite_3 @cite_13 @cite_12
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Source code authorship attribution is a significant privacy threat to anonymous code contributors. However, it may also enable attribution of successful attacks from code left behind on an infected system, or aid in resolving copyright, copyleft, and plagiarism issues in the programming fields. In this work, we investigate machine learning methods to de-anonymize source code authors of C C++ using coding style. Our Code Stylometry Feature Set is a novel representation of coding style found in source code that reflects coding style from properties derived from abstract syntax trees. Our random forest and abstract syntax tree-based approach attributes more authors (1,600 and 250) with significantly higher accuracy (94 and 98 ) on a larger data set (Google Code Jam) than has been previously achieved. Furthermore, these novel features are robust, difficult to obfuscate, and can be used in other programming languages, such as Python. We also find that (i) the code resulting from difficult programming tasks is easier to attribute than easier tasks and (ii) skilled programmers (who can complete the more difficult tasks) are easier to attribute than less skilled programmers. We describe a new class of list decodable codes based on Galois extensions of function fields and present a list decoding algorithm. These codes are obtained as a result of folding the set of rational places of a function field using certain elements (automorphisms) from the Galois group of the extension. This work is an extension of Folded Reed Solomon codes to the setting of Algebraic Geometric codes. We describe two constructions based on this framework depending on if the order of the automorphism used to fold the code is large or small compared to the block length. When the automorphism is of large order, the codes have polynomially bounded list size in the worst case. This construction gives codes of rate @math over an alphabet of size independent of block length that can correct a fraction of @math errors subject to the existence of asymptotically good towers of function fields with large automorphisms. The second construction addresses the case when the order of the element used to fold is small compared to the block length. In this case a heuristic analysis shows that for a random received word, the expected list size and the running time of the decoding algorithm are bounded by a polynomial in the block length. When applied to the Garcia-Stichtenoth tower, this yields codes of rate @math over an alphabet of size @math , that can correct a fraction of @math errors. The single most serious issue in the development of a parallel implementation of non-deterministic programming languages and systems (e.g., logic programming, constraint programming, search-based artificial intelligence systems) is the dynamic management of the binding environments-i.e., the ability to associate with each parallel computation the correct set of bindings values representing the solution generated by that particular branch of the non-deterministic computation. The problem has been abstracted and formally studied previously (ACM Trans. Program. Lang. Syst. 15(4) (1993) 659; New Generation Comput. 17(3) (1999) 285), but to date only relatively inefficient data structures (ACM Trans. Program. Lang. Syst. (2002); New Generation Comput. 17(3) (1999) 285; J. Funct. Logic Program. Special issue #1 (1999)) have been developed to solve it. We provide a very efficient solution to the problem (O(lgn) per operation). This is a significant improvement over previously best known @W(n3) solution. Our solution is provably optimal for the pointer machine model. We also show how the solution can be extended to handle the abstraction of search problems in object-oriented systems, with the same time complexity. The ability to identify authors of computer programs based on their coding style is a direct threat to the privacy and anonymity of programmers. While recent work found that source code can be attributed to authors with high accuracy, attribution of executable binaries appears to be much more difficult. Many distinguishing features present in source code, e.g. variable names, are removed in the compilation process, and compiler optimization may alter the structure of a program, further obscuring features that are known to be useful in determining authorship. We examine programmer de-anonymization from the standpoint of machine learning, using a novel set of features that include ones obtained by decompiling the executable binary to source code. We adapt a powerful set of techniques from the domain of source code authorship attribution along with stylistic representations embedded in assembly, resulting in successful de-anonymization of a large set of programmers. We evaluate our approach on data from the Google Code Jam, obtaining attribution accuracy of up to 96 with 100 and 83 with 600 candidate programmers. We present an executable binary authorship attribution approach, for the first time, that is robust to basic obfuscations, a range of compiler optimization settings, and binaries that have been stripped of their symbol tables. We perform programmer de-anonymization using both obfuscated binaries, and real-world code found "in the wild" in single-author GitHub repositories and the recently leaked Nulled.IO hacker forum. We show that programmers who would like to remain anonymous need to take extreme countermeasures to protect their privacy.