Massachusetts Institute of Technology

Bae Systems Information and Electronic Systems Integration Inc.

Go Daddy Operating Company, LLC

Locu, Inc.

Stylianos Sidiroglou

Marek Olszewski

Rene Reinsberg

Jason Ansel

Marc Piette

Martin Conway Rinard

Timothy Alan Anderson

Jeff Herbert Perkins

Basil C Krikeles

Greg Timothy Sullivan

Eric Patrick Lahtinen

Paolo Mario Piselli

Illustrative embodiments improve upon prior machine learning techniques by introducing an additional classification layer that mimics human visual pattern recognition. Building upon classification passes that extract contextual information, illustrative embodiments look for hints of high-level semantic categorization that manifest as visual artifacts in the document, such as font family, font weight, text color, text justification, white space, or CSS class name. An improved lightweight markup language enables display of machine-categorized tokens on a screen for human correction, thereby providing ground truths for further machine classification.

Method and apparatus for forming a structured document from unstructured information

An approach to detection and repair of application level semantic errors in deployed software includes inferring aspects of correct operation of a program. For instance, a suite of examples of operations that are known or assumed to be correct are used to infer correct operation. Further operation of the program can be compared to results found during correct operation and the logic of the program can be augmented to ensure that aspects of further examples of operation of the program are sufficiently similar to the examples in the correct suite. In some examples, the similarity is based on identifying invariants that are satisfied at certain points in the program execution, and augmenting (e.g., 'patching') the logic includes adding tests to confirm that the invariants are satisfied in the new examples. In some examples, the logic invokes an automatic or semi-automatic error handling procedure if the test is not satisfied. Augmenting the logic in this way may prevent malicious parties from exploiting the semantic errors, and may prevent failures in execution of the programs that may have been avoided.

Automatic correction of program logic

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Cambridge, MA, United States of America

Stylianos Sidiroglou