According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an extrapolation component that extrapolates a system parameter of a parameter set to determine a starting parameter value of a variational circuit. The computer executable components can further comprise a variational component that determines a system parameter value of the parameter set based on the starting parameter value.

Techniques for a network-accessible cognitive automation platform for a self-driving application are provided. The platform includes a cognitive operating system, which includes a data crawler configured to discover and extract data stored in internal systems and external third-party systems, a data processing engine configured to cleanse, correlate, link, etc., transactional and unstructured data, and a cognitive data layer (CDL) configured to process the transformed crawled data with various models and algorithmic libraries of metrics, trends, and metadata to identify a root cause of an issue and prescribe actions to mitigate or overcome risks related to the issue. A cognitive development kit is configured to build a specific skill application on top of the cognitive operating system, the specific skill application generating recommendations, actions, and predictions. A search processor and GUI are configured to perform a search request of the data in the CDL and display the results in the GUI.

A method is provided, including: storing comments generated in response to a content item served over a network; analyzing the comments to determine features associated with each of the comments; using a scoring model to score each comment based on the comment's corresponding features; receiving a request to serve a subset of the comments; responsive to the request, selecting a ranking of the comments that is one permutation from possible rankings of the comments, wherein selecting the ranking is in accordance with a probability distribution of the possible rankings that is based on the scores of the comments; serving comments identified by the selected ranking over the network to a client device; determining a dwell time on the served comments; applying the dwell time to update the scoring model.

A system and method of responding to fuzzing including receiving a fuzzing input while in fuzzed mode, performing a perceptual hashing function using the fuzzing input to generate a perceptual hash value, selecting an action from a list of actions using the perceptual hash value, and performing the selected action in response to the fuzzing input. Parameters may be generated using the perceptual hash value for actions that use parameters. Instead of normal hashing, perceptual hashing generates the same hash value for substantially similar fuzzing inputs so that corresponding fuzzing response actions appear to be random but instead are intentional. Hardware or software version numbers may be combined with a shared secret key and hashed using a non-perceptual hashing function to further impede comparison analysis by a fuzzer. Some embodiments combine perceptual hashing with non-perceptual hashing, such as cryptographic hashing or the like.

Techniques for a network-accessible cognitive automation platform for a self-driving application are provided. The platform includes a cognitive operating system, which includes a data crawler configured to discover and extract data stored in internal systems and external third-party systems, a data processing engine configured to cleanse, correlate, link, etc., transactional and unstructured data, and a cognitive data layer (CDL) configured to process the transformed crawled data with various models and algorithmic libraries of metrics, trends, and metadata to identify a root cause of an issue and prescribe actions to mitigate or overcome risks related to the issue. A cognitive development kit is configured to build a specific skill application on top of the cognitive operating system, the specific skill application generating recommendations, actions, and predictions. A search processor and GUI are configured to perform a search request of the data in the CDL and display the results in the GUI.

Efficient hardware design of the fuzzy-inference engine has become necessary for high-performance applications. The disclosed technology applies unary processing to the platform of fuzzy-logic. To mitigate the latency, the proposed design processes right-aligned bit-streams. A one-hot decoder is used for fast detection of the bit-stream with maximum value. Implementing a fuzzy-inference engine with 81 fuzzy-inference rules, the disclosed architecture provides 82%, 46%, and 67% saving in the hardware area, power and energy consumption, respectively, and 94% reduction in the number of used LUTs compared to conventional binary implementation.