A computer-implemented system for dynamic aggregation of data and minimization of data loss is disclosed. The system may be configured to perform instructions for: aggregating information from a plurality of networked systems by collecting a set of data from the networked systems, the set of data comprising data associated with a predetermined period of time and comprising one or more central variables that are included in data associated with more than one networked systems of the plurality of networked systems and one or more associated variables that describe one or more aspects of the central variables; retrieving one or more data transformation rules based on a relational map among the central variables and the associated variables; and aggregating the first set of data into one or more master data structures corresponding to the central variables based on the data transformation rules.

A method and/or system for automated detection and automated remediation of anomalies in Robotic Process Automation (RPA) environment is disclosed. The method comprises auto discovering resources (RPA components and its dependencies) in an RPA platform. The discovered resources are monitored though observation metrics whose values are obtained by executing pre-defined scripts. The obtained values are validated against threshold values to determine if there are any anomalies, wherein the threshold values may either be static values or dynamic values. If there is a breach of threshold, a remediation plan is automatically executed causing the remediation of anomalies. The system is trained to determine the dynamic threshold values through machine learning models which are developed and trained through metrics data and by determining error patterns from the historic unstructured log data.

A method for exchange of equipment performance data includes the steps of: obtaining performance data of a communicatively-insulated device; converting the performance data into a scannable code; capturing an image of the scannable code; decoding the scannable code using a communicatively-enabled device to extract an address string encoded in the scannable code, the address string comprising an address of a remote server and the performance data; initiating, by the communicatively-enabled device, a communications link with the remote server using the address string thereby to provide the performance data to the remote server; performing, by the remote server, analytics on the performance data; and sending historic device performance data and/or analytical results to a remote computing device and/or sending a link to the historic device performance data and/or analytical results to the remote computing device; wherein the communicatively-insulated device is packaging equipment and wherein obtaining the performance data comprises: running a calibration phantom through the packaging equipment; scanning the calibration phantom with a calibration unit; and using the calibration unit to generate a system status report identifying one or more operational parameters of the packaging equipment.

An encoder receives an application log file including component values and encodes the component values into lists of preliminary encoded values. The lists of preliminary encoded values are combined into a combined list of preliminary encoded values. An encoder-decoder neural network is trained to encode the combined list of preliminary encoded values into a list of collectively encoded values, to decode the list of collectively encoded values into a list of decoded values, and to optimize a metric measuring the encoder-decoder neural network's functioning, in response to receiving the combined list of preliminary encoded values. The trained encoder-decoder neural network receives combined lists of preliminary encoded values for application log files and encodes the combined lists of preliminary encoded values into lists of collectively encoded values. The lists of collectively encoded values are sent to a detector, thereby enabling the detector to detect an application event associated with the application log files.

Techniques for creating a custom metric type to be added to a set of metrics generated by a data monitoring platform at run-time are disclosed. A system receives values defining properties of a custom metric type based on a custom metric template and a custom schema template. The system generates an instruction set, based on the values associated with the custom metric template, for generating the custom metric type on an executing data monitoring system. The system validates the instruction set and the custom schema to verify that the definitions for the custom metric type and the custom schema may be executed by the data monitoring system. The system adds the custom metric type, at run-time, to a set of metrics generated by the data monitoring system.

Aspects of the disclosure provide for a circuit, in some examples, including a storage element, a co-processor, and a telemetry sequencer coupled to the storage element and the co-processor. The telemetry sequencer is configured to implement a digital state machine to receive configuration information indicating a type of telemetry data for generation, retrieve operations and operands, where the operations and the operands define a sequential series of actions for execution to generate the telemetry data, drive the co-processor with the operations and the operands by passing some of the operations and some of the operands to the co-processor for processing by the co-processor, receive, from the co-processor, and store an intermediate output of the series of actions as the telemetry data in a first format, and receive, from the co-processor, and store a final output of the series of actions as the telemetry data in a second format.

In a processing apparatus having semiconductor integrated circuits, a first status monitoring circuit included in a first semiconductor integrated circuit is configured to instruct a plurality of second semiconductor integrated circuits to transmit status information indicating statuses of the plurality of second semiconductor integrated circuits. When a second status monitoring circuit included in each of the plurality of second semiconductor integrated circuits receives the instruction for transmission of the corresponding status information, the second status monitoring circuit transmits encrypted information in which the status information is encrypted to the first semiconductor integrated circuit.

Embodiments of the present disclosure provide a wireless debugger and a wireless debugging system. The wireless debugger includes: a processor, a wireless communication module, and a first peripheral interface; the processor is electrically connected to the wireless communication module and the first peripheral interface, respectively; the processor, is configured to receive debugging instructions through the wireless communication module, and the debugging instructions are used to instruct debugging/stop debugging a target board; the processor, is further configured to parse the debugging instructions and convert the parsed debugging instructions so that the debugging instructions are adapted to a protocol of the first peripheral interface; and the processor, is further configured to transmit the converted debugging instructions to the to-be-debugged target board through the first peripheral interface. Debugging control is convenient and reliable.

Emulation has become a critical method for the initial phase of verification and validation processes. However, achieving interoperability between emulation systems and ensuring credibility of results currently require significant efforts. This disclosure relates to a system and method for providing an emulation as a service (EaaS) framework for communication networks. The EaaS framework provides discoverable services that are readily available on-demand and deliver a choice of applications in a flexible and adaptive manner. The EaaS framework is used for discovery, composition, execution, and management of emulation services. The EaaS framework defines user-facing capabilities (front-end) and underlying core functional infrastructure (back-end). The front end provides access to a large variety of emulation capabilities from which the user is able to select the services and track the experiences.

Systems, apparatuses, and methods related log compression are described. In an example, a system log that identifies targeted data may be compiled in a memory resource during an execution of an operation using that memory resource. The system log may be analyzed utilizing a portion of the memory resource that would otherwise be available to be utilized in the execution of the operation. The system log may be compressed during the execution of the operation, the level or timing of such compression may be based on the analysis that occurs contemporaneous to or as a result of executing the operation. In some examples, compressing the system log may include discarding a portion of the system log. Compressing the system log may also include extracting the targeted data from the system log as the system log is being compiled and converting the extracted targeted data to structured data.