A computer-implemented method, computer system, and computer program product for introspection of classes within source code. A computing device receives access to object-oriented source code. The computing device parses the source code to determine one or more objects contained in the source code. The computing device determines one or more classes associated with the one or more objects. The computing device generates a class table including one or more class data points associated with the one or more determined classes. The computing device generates one or more source code methods to perform a class-level introspection based upon the generated class table.

A method for automatically enhancing security and fixing security vulnerabilities in the source code of a computer program in an object oriented run time environment includes evaluating the source code file of a monitored computer program. The source code file includes a plurality of class files. Each session includes two or more session segments. A security assessment on each of the plurality of class files is performed to identify one or more potential security issues associated with the plurality of class files. One or more security controls configured to address the identified potential security issues are automatically injected into a source code of one or more class files identified as having potential security issues. The automatically modified source code file of the monitored computer program is deployed to the run-time environment.

A robotics process automation (RPA) automatic enhancement system captures a video demonstration of task performance and converts the activity into nodes by a task specification encoder and a task interpreter and then process the nodes by a reformer transformer to automatically generate an enhanced RPA script. Once created, the RPA automatic enhancement system use a rules-based validation process to perform a quality review on the generated RPA script. The RPA automatic enhancement system may include a hierarchical reinforcement learner configured to use a recurrent neural network (e.g., a long short-term memory (LSTM) tensor flow network) along with one or more deep learning application programming interfaces within an interpreted language framework (e.g., Java).

Methods and systems that allow for supply chain logic to be instrumented in such a way that a supply planner can see the major factors driving KPIs, as well as drill down to the lower level to see the impact of each item at the smallest possible level.

Apparatuses, systems, and techniques to operate a network switching device using predicated instructions that implement conditional algorithms of data packet processing are disclosed. The disclosed techniques relate to compiling source codes into objects codes for execution on target network switching devices as well as the actual execution of such compiled object codes. Compilation of a source code may include identifying conditional instructions (CIs) in the source code, which specify contingent actions to be performed by the NSD on a data packet, and compiling the identified CIs to generate corresponding sets of predicated instructions (PIs) of the object code executable by the NSD.

The subject technology transforms a machine learning model into a transformed machine learning model in accordance with a particular model specification when the machine learning model does not conform to the particular model specification, the particular model specification being compatible with an integrated development environment (IDE). The subject technology generates a code interface and code for the transformed machine learning model, the code interface including code statements in the object oriented programming language, the code statements corresponding to an object representing the transformed machine learning model. Further, the subject technology provides the generated code interface and the code for display in the IDE, the IDE enabling modifying of the generated code interface and the code.

The present invention provides a general description language data system for directed acyclic graph automatic task flow, including: Step definition layer, Workflow definition layer and Template definition layer; The Step definition layer is the description of a single task, for the input and output declarations of each docker image or other executor, comprises name, type, file and parameters. The Workflow definition layer is a workflow composed of one or more Steps, the dependency topology of these Steps needs to be defined, and shared parameters can also be defined. The Template definition layer is based on a Workflow definition layer. The Template definition layer pre-sets the parameters, and supplies the descriptions, checkers or data source definitions of the parameters. The data center of the present invention is used with the task execution tool, and a programming language needs to be used to implement the corresponding tool.

This disclosure generally relates to high-level synthesis (HLS) platforms, and, more particularly, enable print functionality in high-level synthesis (HLS) platforms. The recent availability FPGA-HLS is a great success due to availability of compilers for FPGAs as opposed to hardware description languages (HDLs) that requires special skills. However, the compilers within the HLS design platform includes limited support for all the standard libraries, wherein features like print functionality is not supported. The invention discloses techniques to enable print functionality in HLS design platforms based on source-to-source transformations and stream combining scheme. In addition to enabling print functionality, the invention also discloses a formatter technique to receive-format FPGA data into human interpretable data.

Disclosed herein is a system architecture that structures commodity heterogeneous interconnected computing platforms around universal object abstractions, which are a fundamental system abstraction and building block that provides practical and provable end-to-end guarantees of security, correctness, and timeliness for the platform.

Adaptable internet-of-things (IoT) computing systems and methods are disclosed for improved and flexible declarative control of streaming data, such as Big Data, in compute intense environments. A declarative scripting engine determines an input data stream based on a first declarative statement defining input data stream variable(s) of a declarative scripting language in declarative scripting module(s). The input data stream is bound to a stream controller and is ingested into computer memory. The declarative scripting engine generates a snapshot data stream based on a second declarative statement in the declarative scripting module(s), and is derived from the input data stream. A stream model is defined, where a listener entity comprising an event is triggered based on the input data stream or the snapshot data stream as ingested into the stream model.