Unisys Corporation

In various examples, a new type, <RAW>, is allocated to supply a sequential range of addresses in a Secure Migratable Architecture (SMART). The compiler may perform address calculations that correctly refer to the lowest byte of an item and allow for items of any length to be read or written within the boundaries of the RAW buffer. Each buffer in SMART is bounds checked, which prevents a buffer overrun attack vector at the architectural level. In this way, the SMART processor itself prevents memory allocations from being accessed beyond their bounds.

In various examples, opaque address are utilized by a Secure Migratable Architecture (SMART) processor. The SMART processor is aware of the fundamental type system and, more importantly, only the SMART processor is aware of the addresses assigned to each memory allocation. Software running on the processor is unable to acquire the actual address where data is stored, while remaining capable of reading (if allowed) and writing (if allowed) bytes of data visible to the software. The physical address is opaque in that it may be referenced, but its location is not obtainable.

To determine the optimal orientation of packages within a container, packages are ranked according to a score defined by an algorithm. A first package position is determined from a first set of package dimensions and is oriented within a container. A second package position is determined from a second set of package dimensions and is oriented within the container. The packages are oriented such that the orientation of the first package position is different from the orientation of the second package position with no overlap, and a threshold portion of the lower face of the packages abuts another package or the container base. Packages are continuously oriented into the container volume until the total package volume reaches a threshold container volume, while minimizing the total package volume. The final orientation of the packages is output as a three-dimensional arrangement of the packages within the container volume.

Executable prompts for computers can be generated from a natural language input. An aspect uses two machine learning models. A first model trained using a diverse dataset is used to generate an initial script from a natural language prompt. The initial script is then provided to a second machine learning model that is trained on system specific scripts. The second machine learning model modifies the initial script to generate the executable script. In some cases, executable scripts may be further modified to conform to a specific data serialization format usable by the end computing system. The data serialization format may be determined from language provided in the prompt to the first machine learning model. The executable script is then provided to the end computing system that performs the computer-executable action in response.

A method includes receiving, at a processor, an identification of a storage tape. The method further includes determining, at the processor, a start-point for extracting information from the storage tape. The method further includes extracting the information, by the processor, from the storage tape starting at the start-point. The method further includes determining, by the processor, whether an end-point of the information has been reached. The method further includes reproducing, by the processor, the information to a computer-readable medium. The method further includes formatting, by the processor, the information to conform with format requirements of an activity log analyzing tool.

In various examples, cryptographic operations in a first computing environment are encoded in a packet and executed within a second computing environment. For example, a cryptographic library generates a packet including an indication of the cryptographic operation, a pointer to a memory location storing data, and a status field. The packet causes a second computing environment to perform the cryptographic operation and return the packet including an update to the status field.

In various examples, an executable object including parallel code fragments that allow compromised code within the executable object to be remediated. For example, when a selection mask is enabled, instructions encoded in the executable object are translated for execution by the one or more processors. Continuing this example, the executable object includes the parallel code fragment that allow vulnerable operations to be bypassed. Furthermore, once enabled and the compromised code is bypassed, a remediation is applied to the executable code.

A method of parsing implicit tables within a document includes parsing, by a processor, a document to determine a pattern for a set of text that sets apart the information contained therein from the rest of the text; determining, by the processor, from the pattern of spacing, a first descriptive phrase and a second descriptive phrase; and assigning, by the processor, the first descriptive phrase as an action and the second descriptive phrase as a trigger.

Embodiments of the present disclosure include systems and methods for optimizing functions of a database system. A combination load and dump function is recited that transforms a first set of backup data into a set of transformed data, where the first set of backup data is accessed in response to receiving a request to load the first set of backup data to the database. A set of operations is automatically executed and includes loading the database with the set of transformed data and storing the set of transformed data in a second set of backup data. This combination load and dump function removes the need for a subsequent read function to read data previously loaded to a database file in order to perform the dump function.

Methods, systems, and computer storage media for solving optimization problems are disclosed. Problem parameters of an optimization problem are received where the optimization problem has a solution space larger than a solution space threshold. Clustering is performed to reduce the solution space below a solution space threshold, and routing is performed to determine the routing options using the clusters. Solutions are received using simulated annealing on the clusters with the reduced solution space, the one or more solutions are recombined to generate a solution, and the solution is provided via a user interface.