Abstract: Disclosed herein is a system and method that can be used with any underlying classification technique. The method receives a test dataset and determines the features in that test dataset that are present. From these features the training dataset is modified to only have those features that are present in the test dataset. This modified test dataset is then used to calibrate the classifier for the particular incoming data set. The process repeats itself for each different incoming dataset providing a just in time calibration of the classifier.

Abstract: Methods, systems, apparatuses, and computer program products are provided for increasing an efficiency of queries in program code. A plurality of queries is detected in program code. A laziness is extended by which the queries are evaluated in the program code. The queries are decomposed into a plurality of query components. A ruleset that includes a plurality of rules is applied to the query components to generate a functionally equivalent query set to the plurality of queries that evaluates more efficiently relative to the plurality of queries.

Abstract: Disclosed herein is a system and method that can be used with any underlying classification technique. The method takes into account both the value of the current feature vector. It is based on evaluating the effect of perturbing each feature by bootstrapping it with the negative samples and measuring the change in the classifier output. To assess the importance of a given feature value in the classified feature vector, a random negatively labeled instance is taken out of the training set and replaces the feature at question with a corresponding feature from this set. Then, by classifying the modified feature vector and comparing its predicted label and classifier output a user is able measure and observe the effect of changing each feature.

Abstract: The misuse of public key, private key, and public/private key certificates poses significant security challenges to computer networks that are addressed by certificate monitoring. Certificate monitoring allows network administrators to detect and remedy poor security practices related to public key certificates and to detect and combat the malicious use of public key certificates in a centralized environment. Best practices and detection methods and systems are developed over time via machine learning to improve network security, and any detected misuse may be brought to a network administrator's attention or automatically remedied.

Abstract: Methods, systems, apparatuses, and computer program products are provided for transforming asynchronous code into more efficient, logically equivalent asynchronous code; Program code is converted into a first syntax tree. A dependency graph is generated from the first syntax tree with each node of the dependency graph corresponding to a code statement and having an assigned weight. Weighted topological sorting of the dependency graph is performed to generate a sorted dependency graph. A second syntax tree is generated from the sorted dependency graph. In another implementation, the program code is transformed into await-relaxed and/or loop-relaxed program code prior to being transformed into the first syntax tree.

Abstract: The misuse of public key, private key, and public/private key certificates poses significant security challenges to computer networks that are addressed by certificate monitoring. Certificate monitoring allows network administrators to detect and remedy poor security practices related to public key certificates and to detect and combat the malicious use of public key certificates in a centralized environment. Best practices and detection methods and systems are developed over time via machine learning to improve network security, and any detected misuse may be brought to a network administrator's attention or automatically remedied.

Abstract: Methods, systems, apparatuses, and computer program products are provided for transforming asynchronous code into more efficient, logically equivalent asynchronous code; Program code is converted into a first syntax tree. A dependency graph is generated from the first syntax tree with each node of the dependency graph corresponding to a code statement and having an assigned weight. Weighted topological sorting of the dependency graph is performed to generate a sorted dependency graph. A second syntax tree is generated from the sorted dependency graph. In another implementation, the program code is transformed into await-relaxed and/or loop-relaxed program code prior to being transformed into the first syntax tree.

Abstract: Methods, systems, apparatuses, and computer program products are provided for increasing an efficiency of queries in program code. A plurality of queries is detected in program code. A laziness is extended by which the queries are evaluated in the program code. The queries are decomposed into a plurality of query components. A ruleset that includes a plurality of rules is applied to the query components to generate a functionally equivalent query set to the plurality of queries that evaluates more efficiently relative to the plurality of queries.

Abstract: Disclosed herein is a system and method that can be used with any underlying classification technique. The method receives a test dataset and determines the features in that test dataset that are present. From these features the training dataset is modified to only have those features that are present in the test dataset. This modified test dataset is then used to calibrate the classifier for the particular incoming data set. The process repeats itself for each different incoming dataset providing a just in time calibration of the classifier.

Abstract: Disclosed herein is a system and method that can be used with any underlying classification technique. The method takes into account both the value of the current feature vector. It is based on evaluating the effect of perturbing each feature by bootstrapping it with the negative samples and measuring the change in the classifier output. To assess the importance of a given feature value in the classified feature vector, a random negatively labeled instance is taken out of the training set and replaces the feature at question with a corresponding feature from this set. Then, by classifying the modified feature vector and comparing its predicted label and classifier output a user is able measure and observe the effect of changing each feature.

Abstract: A partitioned multi-die wafer-sort probe card includes an arcuate unit pattern. The arcuate unit pattern is repeated, either in complete or truncated form across the footprint of the multi-die wafer-sort probe card. Wafer testing is carried out by first testing at a first touchdown (TD), stepping the multi-die wafer-sort probe card footprint at least one die-site dimension, and second testing at a second TD.