In an approach for reasoning with real-valued propositional logic, a processor receives a set of propositional logic formulae, a set of intervals representing upper and lower bounds on truth values of a set of atomic propositions in the set of propositional logic formulae, and a query. A processor generates a logical neural network based on the set of propositional logic formulae and the set of intervals representing upper and lower bounds on truth values. A processor generates a credal network with a same structure of the logical neural network. A processor runs probabilistic inference on the credal network to compute a conditional probability based on the query. A processor outputs the conditional probability as an answer to the query.

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.

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.

A software robot is designed to carry out an activity (e.g., a mouse click, a text input, etc.) on a target element (e.g., a button, an input field, etc.) of a user interface. The robot is configured to automatically identify the target element at runtime according to a set of attributes of the target element specified in the source-code of the user interface. The robot's code specification includes an indicator of a selected fuzzy attribute and a numerical similarity threshold indicative of an acceptable degree of mismatch between design-time and runtime values of the respective fuzzy attribute. The robot is configured to identify the target element from a set of candidates which are sufficiently similar to it according to the specified degree of mismatch.

A system for determining code ancestry. The system includes: a memory; and a processor communicatively coupled to the memory. The processor is configured to perform a method comprising: receiving a source code file; parsing a plurality of functions out of the source code file; generating fuzzy fingerprints from the plurality of functions; and storing the fuzzy fingerprints in a graph database.

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.

An apparatus for fuzzing firmware according to an embodiment includes an emulator that provides a user mode emulation environment for firmware installed in any Internet of Things (IoT) device, a generator that generates one or more test cases in which at least some of a plurality of pre-set mutation operators are applied to at least one of a plurality of seed files, and an executor that executes mutation-based fuzzing on the firmware in the user mode emulation environment based on the one or more test cases.

Differential coverage-guided feedback (CGF) fuzzing system and methods are provided to identify regressions in a software application. A computing device is configured to execute instructions that perform a fuzzing iteration. The fuzzing iteration includes operations that generate input data based on an initial corpus of samples; communicate the input data to a first application such that the first application performs operations utilizing the input data; collect first coverage information from the first application to identify first regressions; communicate the input data to a second application such that the second application performs operations utilizing the input data; and collect second coverage information from the first application to identify second regressions. The instructions additionally include: compare the first coverage information and the second coverage information; and perform another fuzzing iteration, wherein the computing device is configured to execute instructions that generate input data based on the compared first and second coverage information.

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 software robot is designed to carry out an activity (e.g., a mouse click, a text input, etc.) on a target element (e.g., a button, an input field, etc.) of a user interface. The robot is configured to automatically identify the target element at runtime according to a set of attributes of the target element specified in the source-code of the user interface. The robot's code specification includes an indicator of a selected fuzzy attribute and a numerical similarity threshold indicative of an acceptable degree of mismatch between design-time and runtime values of the respective fuzzy attribute. The robot is configured to identify the target element from a set of candidates which are sufficiently similar to it according to the specified degree of mismatch.