International Business Machines Corporation

Julian Timothy Dolby

Kavitha Srinivas

Frank Tip

Mihaela Ancuta Bornea

Anastasios Kementsietsidis

Marco Pistoia

Achille Belly Fokoue-Nkoutche

Shay Artzi

Omer Tripp

Martin J Hirzel

Manu Sridharan

Ibrahim Abdelaziz

Evelyn Duesterwald

Pietro Ferrara

John Erik Wittern

Avraham Ever Shinnar

Bjorn De Sutter

Guillaume A Baudart

Takaaki Tateishi

Jim Alain Laredo

Salvatore Angelo Guarnieri

Richard Thomas Goodwin

Yunhui Zheng

Mandana Vaziri

Louis Mandel

David John Piorkowski

Kiran A Kate

Anca Andreea Ivan

Manas R Kumar Singh

Annie T T Ying

Emmanuel Geay

Barbara G Ryder

Max Schaefer

Mandana Vaziri-Farahani

Bishwaranjan Bhattacharjee

Cheng Chi

Aditya Anand Kalyanpur

Li Li Ma

Salman Abdul Baset

Stephen Fink

Octavian Udrea

Patrick Dantressangle

Aleksander A Slominski

Christopher Charles Young

Matthew R Arnold

Horst Cornelius Samulowitz

Satish Chandra

Edith Gail Schonberg

Philippe Suter

Igor Anatolyevich Naumov

Jason Tsay

Emina Torlak

Aaron Stephen Jay Kershenbaum

Annie T Ying

Jinqiu Yang

Simon H Jensen

Christian J Hammer

Wenting Zhao

Marianna Rapoport

A computer-implemented method is provided for creating a photolithographic mask. The method includes, in a model building stage, obtaining lithography polygon coordinates from an input lithography target layout. The method further includes, in the model building stage, obtaining mask polygon coordinates from an input mask layout from a test mask. The method also includes, in the model building stage, obtaining correlated mask to lithography features from the lithography polygon coordinates and the mask polygon coordinates. The method additionally includes, in the model building stage, performing linear regression on the correlated mask to lithography features to obtain a machine learning model for predicting an output mask from an input lithography target design. The method further includes, in an inference stage, predicting a given output mask from a given input lithography target design using the machine learning model.

Machine learning for computational patterning

A corpus of source code from a code database is accessed and a language prediction model is trained based on the corpus of source code. A given program is accessed and a completion of a given line of the given program is predicted by performing inferencing using the language prediction model and at least a portion of the given program. The given line is completed based upon the prediction.

Performing code completion using data flow

Techniques for computer software code analysis are disclosed. One or more data flows are generated, based on analyzing software code using static analysis. A data object is identified in the software code using the one or more data flows, the data object relating to a structured dataset. A correspondence between a code expression in the software code and a characteristic of the structured dataset is identified, based on analyzing one or more reads from and one or more writes to the data object using the one or more data flows. The code expression for the structured dataset is analyzed, based on the correspondence, including at least one of: (i) generating a software code recommendation engine based on the code expression and the structured dataset, or (ii) generating one or more lambda expressions for application to the structured dataset, based on the code expression.

Mining code expressions for data analysis

In an approach to improve detecting and correcting errors in one or more machine learning pipelines. Embodiments comprise generating a plurality of test machine learning pipeline instances based upon a target machine learning pipeline and evaluating the plurality of test machine learning pipeline instances for failure in a task. Further, embodiments identify one or more root causes of error based upon the evaluated plurality of test machine learning pipeline instances and failure in the task, and create a remediated target machine learning pipeline based upon the identified one or more root causes of error. Additionally, embodiments output the remediated machine learning pipelines.

Repairing machine learning pipelines

To improve the technological process of programming a computer using a dynamic programming language, generate a first portion of training data which maps types in the dynamic programming language to corresponding functions and methods by performing information retrieval on documentation libraries in the dynamic programming language and/or generate a second portion of training data which maps program variables to the corresponding functions and methods by performing data flow analysis on a plurality of pre-existing programs written in the dynamic programming language. Train a neural network on the first and/or second portions of training data to infer unknown types in the dynamic programming language. Carry out inference with the trained neural network to infer the unknown types. Facilitate programming in the dynamic programming language based on the inferred unknown types. Optionally, execute a resulting program.

Type inference in dynamic languages

Embodiments are disclosed for a method. The method includes identifying a prefix updated by a searcher of a machine learning model. The machine learning model is configured to generate source code in a programming language. The method also includes determining whether the prefix violates a semantic correctness property of the programming language. Additionally, the method includes instructing the searcher, in response to the determination, to prune the prefix from a set of prefixes under consideration by the searcher.

Reducing semantic errors in code generated by machine learning models

A debugging tool and method for statically verifying programs that invoke web-based services through API calls is provided. The tool receives source code that comprises one or more invocation of web APIs for requesting web-based services. The tool also receives a set of web API specifications. The tool extracts a set of request information for each web API invocation in the source code, the set of request information including a usage string of an URL endpoint. The tool verifies whether the set of request information complies with the received web API specifications and reports a result of the verification.

Analysis to check web API code usage and specification

Techniques for code analysis are provided. User code is received, and an import statement is identified in the user code. A first empty object is generated based on the import statement, and the first empty object is named based on the name of an import reference included in the import statement. A knowledge graph is generated based at least in part on the first empty object.

Generation of knowledge graphs based on repositories of code

Systems and methods for indexing and accessing code snippets in repositories. A program graph index is maintained for code snippets within a repository with documents that have at least one code snippet. The program graph index includes a program graph indicating a relationship between program elements within each source code snippet within the documents. A user provided code snippet is received and a target program graph indicating a relationship between program elements within the user provided code snippet is determined and compared to each respective program graph. Based on the comparison, an identified set of documents within the repository of documents is determined that have code snippets with respective program graphs that are also at least a sub-tree of the target program graph. At least one document in the identified set of documents is presented to a user.

Indexing and accessing source code snippets contained in documents

A processor may analyze one or more sections of code within a program. The processor may compute a context for each of the one or more sections of code. The processor may generate one or more search terms that are respectively associated with each of the one or more sections of code. The processor may automatically display reference data to a user.

Automatic identification of reference data

A method and system of protecting user sensitive information from an application program of a user device are provided. The application program to be installed is received on the user device. Permissions to resources of the user device for the application program are identified. For each permission, mapping the permission to one or more sections of a code of the application program. For each mapped section of the code, a recipient of user sensitive information facilitated by the permission is determined. For each recipient, it is determined whether the recipient should be restricted. Upon determining that the recipient should not be restricted, the user sensitive information facilitated by the permission is provided to the recipient. However, upon determining that the recipient should be restricted, alternate information to the recipient.

User device privacy protection

Aspects of the present invention disclose a method, computer program product, and system for detecting and mitigating adversarial virtual interactions. The method includes one or more processors initiating a mitigation protocol on interactions between the user and the virtual agent, wherein the mitigation protocol is based on the actions performed by the user while interacting with the virtual agent. The method further includes one or more processors, in response to initiating the mitigation protocol on interactions between the user and the virtual agent, generating a lower fidelity response from the virtual agent to the user, wherein the lower fidelity response is a progressive dilution of the precision of language of an original response from the virtual agent to the user prior to the user exceeding the risk level threshold.

Cognitive virtual detector

Methods and systems are provided for configurable and non-invasive protection of private information in a user input to a software application that handles real-time information. A method includes detecting, by a filter in real-time, private information in the user input. The method further includes forming, by the filter, a filtered user input from the user input, by maintaining non-private information from the user input in the filtered user input, extracting and encrypting the private information in the user input and attaching the encrypted private information to the filtered user input, and replacing the private information in the user input with unique identifiers in the filtered user input. The unique identifiers are configured to be exploitable by the software application to achieve an intended function of the software application for the user. The method also includes transmitting, by a communications redirector, the filtered user input over a communication channel.

Configurable and non-invasive protection of private information for conversational agents

A system for identifying a schema for storing graph data includes a database containing a graph dataset of data and relationships between data pairs and a list of storage methods that each are a distinct structural arrangement of the data and relationships from the graph data set. An analyzer module collects statistics for the graph dataset, and a data classification module uses the collected statistics to calculate metrics describing the data and relationships in the graph dataset, uses the calculated metrics to group the data and relationships into a plurality of graph dataset subsets and associates each graph dataset subset with one of the plurality of storage methods. The resulting group of storage methods associated with the plurality of graph dataset subsets includes a unique storage method for each graph dataset subset. The data and relationships in each graph dataset subset are arranged in accordance with associated storage methods.

Method and apparatus for identifying the optimal schema to store graph data in a relational store

Growing community of inventors

Bronx, NY, United States of America

Julian Timothy Dolby