The present disclosure relates to an approach for generating a compiled version of a program source code. At least two different executable files may be generated applying at least two different compiler optimization settings for compiling a first part and applying at least two different compiler optimization settings for compiling a second part of the source code. A main performance part of the source code may be determined dependent on values of a target quantity of the at least two different executable files. The main performance part is the part of the first part and the second part that has a greater influence on the target quantity. The compiled version of the program source code may be generated by compiling the source code applying a higher optimization level of the compiler for compiling the main performance part than for compiling the remaining part of the source code.

According to one example, a method includes receiving a query from a client device, the query comprising a specified build identifier and a specified source code file name, determining, by a server device, a source code file from a plurality of archives using the specified build identifier and the specified source code file name, wherein determining the source code file comprises matching a longest shared prefix of the archive name associated with the specified build identifier and an archive name from a set of archive names having archived file names corresponding to the specified source code file name, and after the determining, responding to the query with the source code file.

Systems and methods for censoring text-based data are provided. In some embodiments a censoring system may include at least one processor and at least one non-transitory memory storing application programming interface instructions. The censoring system may be configured to perform operations comprising storing a target pattern type and a computer-based model for identifying a target data pattern corresponding to a target pattern type within text based data. The censoring system may also be configured to receive text-based data by a server, and to retrieve the stored target pattern type to be censored in the text-based data. The censoring system may be configured to identify within the received text-based data, a target data pattern corresponding to the retrieved target pattern type. The censoring system may be configured to censor target characters within the identified target data pattern, and transmit the censored text-based data to a receiving party.

Systems and methods for managing Application Programming Interfaces (APIs) are disclosed. Systems may involve automatically generating a honeypot. For example, the system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving, from a client device, a call to an API node and classifying the call as unauthorized. The operation may include sending the call to a node-imitating model associated with the API node and receiving, from the node-imitating model, synthetic node output data. The operations may include sending a notification based on the synthetic node output data to the client device.

Systems and methods for managing Application Programming Interfaces (APIs) are disclosed. For example, the system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving a call to an API node. The operations may include determining that the call is associated with the first version of the API. The operations may include determining that the API node is associated with a second version of the API. The operations may include translating the call into a translated call using a translation model, the translated call being associated with the second version of the API.

A method, system, or apparatus to debug software that is reorganized in memory is presented. An interactive debugging session is established with an executable code component corresponding to a packed binary file includes machine code that corresponds to blocks of original source code. A randomly reorganized layout of the machine code is generated in memory based on a transformation defined in a function randomization library. An in-memory object file is created by using a debug data component corresponding to the packed binary file. The debug data component includes symbol table information to debug the blocks of the original source code generated prior to the randomly reorganized layout. The symbol table information is updated based on the randomly reorganized layout of the machine code, and the debugger program is instructed to load the in-memory object file with the updated symbol information to debug the blocks of the original source code.

Mines and construction sites are pnme applications for automation. The invention is composed of a protocol that allows multiple machines to be coordinated from a single application. The invention provides what in classical control is called a coordination layer between the machines (4D-RCS). This coordination layer is currently provided by humans as machines only interact with each other in the physical world, but there is no infrastructure to have them coordinated from an autonomous control standpoint. The system described in the present invention is a coordinating mining or construction machinery that is comprised of two or more mining (or construction) equipment with sensors and actuators, a database of stored behavior and sensing capabilities for each machine, a scripting editor that can concatenate sensing and behavior blocks, a controller (centralized or at each machine) that can interpret the scripts and command the machines according to the script, and a communication infrastructure that allows the machines to communicate.

Systems and methods for generating synthetic video are disclosed. For example, a system may include a memory unit and a processor configured to execute the instructions to perform operations. The operations may include receiving video data, normalizing image frames, generating difference images, and generating an image sequence generator model. The operations may include training an autoencoder model using difference images, the autoencoder comprising an encoder model and a decoder model. The operations may include identifying a seed image frame and generating a seed difference image from the seed image frame. The operations may include generating, by the image sequence generator model, synthetic difference images based on the seed difference image. In some aspects, the operations may include using the decoder model to synthetic normalized image frames from the synthetic difference images. The operations may include generating synthetic video by adding background to the synthetic normalized image frames.

Methods for generating data models using a generative adversarial network can begin by receiving a data model generation request by a model optimizer from an interface. The model optimizer can provision computing resources with a data model. As a further step, a synthetic dataset for training the data model can be generated using a generative network of a generative adversarial network, the generative network trained to generate output data differing at least a predetermined amount from a reference dataset according to a similarity metric. The computing resources can train the data model using the synthetic dataset. The model optimizer can evaluate performance criteria of the data model and, based on the evaluation of the performance criteria of the data model, store the data model and metadata of the data model in a model storage. The data model can then be used to process production data.

Systems and methods for generating synthetic video are disclosed. For example, the system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include generating a static background image and determining the location of a reference edge. The operations may include determining a perspective of an observation point. The operations may include generating synthetic difference images that include respective synthetic object movement edges. The operations may include determining a location of the respective synthetic object movement edge and generating adjusted difference images corresponding to the individual synthetic difference images. Adjusted difference images may be based on synthetic difference images, locations of the respective synthetic object movement edges, the perspective of the observation point, and the location of the reference edge. The operations may include generating texturized images based on the adjusted difference images.