51 results about "Network Computing System" patented technology

An edge network computing system includes: a plurality of terminal devices; a plurality of edge servers connected to the terminal device through an access network; and a plurality of cloud servers connected to the plurality of edge servers through a core network. Each edge server is configured to: receive a plurality of computing tasks originated from one of the plurality of terminal devices; use a deep Q-learning neural network (DQN) with experience replay to select one of the plurality of could servers to offload a portion of the plurality of computing tasks; and send the portion of the plurality of computing tasks to the selected cloud server and forward results of the portion of the plurality of computing tasks received from the selected cloud server to the originating terminal device.

The invention provides an optical fiber communication system nonlinear compensation module based on a photon chip, and belongs to the field of optical fiber communication. Comprising a photon chip, an FPGA module, an ADC / DAC and a light source, and the photon chip, the FPGA module, the ADC / DAC and the light source are utilized to build a full-connection neural network used for calculating nonlinear disturbance signals in the optical fiber communication system and compensating optical signals at a receiving end so as to improve the transmission performance of the optical fiber communication system. According to the invention, the cascaded Mach-Zehnder Interferometer (MZI) array and the FPGA electronic control unit are utilized to construct a photon neural network computing system together, so that neural network operation can be executed at high speed and low power consumption. The non-linear damage compensation module is used for compensating non-linear damage SPM, IXPM and IFWM generated in the single-mode fiber transmission process, and can be used as a non-linear damage compensation module of a digital signal processing technology in an optical fiber communication system so as to improve the transmission performance of the optical fiber communication system.

Malware activity detection for networked computing systems is described. A network session record is provided to a machine learning (ML) model configured to generate an indication of whether the provided network session record evidences malware activity. The network session record indicates network traffic activity in a time period. Responsive to an indication by the ML model, correlation scores are calculated by, for each process session record in a process session record set, calculating a correlation score indicative of a correlation between the provided network session record and the process session record. Each process session record in the process session record set corresponds to a process executed by a computing device in the time period. A determination that a correlation score indicates a corresponding process session record is indicative of the evidenced malware activity is made. Responsive to the determination, a malware activity alert is generated.

There are provided systems and methods for automated adjustment of security alert components in networked computing systems. An entity, such as an electronic transaction processor for digital transactions, may utilize threat detection within a security information and event management system. The threat detection may implement one or more processes to tune security alerts automatically, which can be done prior to deployment. A security alert may be broken into modular components, which may be run progressively, in increasing sampling numbers, against a set of computing logs to identify hits. The hits are compared to an expected proportion for each modular component to determine whether the modular component is providing proper results. Further, threat detection may utilize a system to obtain justifications for potentially malicious behavior to eliminate false positives. This may be done automatically when detecting suspicious activities.

The application discloses a kind of diffractive light neural network computing systems and methods for realizing pure optical nonlinearity, belong to photonic computing and artificial intelligence hardware technical field.Its system includes coherent light source, linear light computing unit, nonlinear light activation unit and optical detection unit arranged in order along optical path, wherein linear light computing unit is programmable phase modulation using spatial light modulator, nonlinear light activation unit is programmable binary amplitude modulation using digital micromirror device, and both are directly coupled to form pure optical computing path without photoelectric conversion on optical path.Its method trains neural network through customized loss function, drives nonlinear activation function output to binary convergence, and maps the parameters obtained by training into phase map and binary mask respectively and loads to hardware.The application realizes true all-optical nonlinear computation, with the advantages of high energy efficiency, extremely low delay, compact structure and strong parallel processing capability.

Wireless signaling is received from a signal repeater device that repeats signaling originating from a network computing system. The wireless signaling comprises information indicating that the wireless signaling is being repeated. A message responsive to the wireless signaling to the network computing system, wherein the message comprises information indicating that signaling received by the UE is being repeated. A message is sent responsive to the wireless signaling to the network computing system that comprises information indicating that signaling received is being repeated. Information indicative of instructions to switch from a first network slice to a second network slice are received. A network slice switching process is performed to switch from the first network slice configured for a first maximum bitrate to the second network slice configured for a second maximum bitrate less than the first maximum bitrate.

Computing systems methods, and non-transitory storage media are provided for obtaining images, extracting layers from each of the images, extracting segments from each of the layers, generating a compressed version of the segments by storing a single copy of each segment and metadata to reconstruct the layers from the segments and the images from the layers, and simulating a reconstruction of the image from the compressed version.

In conventional networked computing systems, each system may have many atomic values shared across multiple application servers. Each atomic value may require different levels of computational resources to store, maintain, and access, and may provide a different level of usefulness within the computing system. As such, a need exists for a system of improving the efficacy of each data element with a neural network or networked computing system. The system provided herein solves with problem by identifying each data element within a computing system; crawling multiple application servers to collect usage values for each data element; calculating a computational weightage of each data elements, identifying critical values; and creating hash values for each critical value.

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for improved network computing for network function testing. In some implementations, a method includes providing an application programming interface (API) to a user device of a network computing system; receiving input data from the user device indicating one or more application requirements for testing one or more network functions; generating one or more virtual processing machines using the one or more application requirements; obtaining testing data from one or more communication networks; providing the testing data to the one or more virtual processing machines configured to generate results based on processing the testing data using the one or more network functions; and providing the results to the user device.

A neural network computing system includes a processor including heterogeneous computing devices, a memory, a memory controller that controls the memory, and a system bus that communicates between the processor and the memory controller. The processor generates a frequency level combination table representing frequency level combinations of hardware devices that include the heterogeneous computing devices, the memory controller, and the system bus, each of the frequency level combinations maximizing a decreasing amount of execution time relative to an increasing amount of energy consumption when a performance level of the neural network model is increased by one level, determines, a target performance level, selects a frequency level combination among the frequency level combinations according to the target performance level, and controls, based on a selected frequency level combination, the hardware devices while the neural network model is executed.

A network computer system generates a personalized program for a population of users, which includes predicting a portion of an amount owed to individual creditors for settlement of user accounts. When an opportunity for settlement is detected, the network computer system determines one or more users that can meet a deposit requirement of the given creditor with a modification to their existing deposit schedule that meets a set of criteria.

A big data analysis system may include a big data repository communicatively coupled to a data accumulation server and a predictive graph processing system. The data accumulation server may be configured to receive information from a plurality of data sources, the information corresponding to user interaction with one or more computing devices associated with an organization via a networked computing system, store the information received from the plurality of sources in the big data repository; and monitor the plurality of data sources to update the data stored in the big data repository. The predictive graph processing system is configured to receive information stored in the big data repository, transform the information received from the big data repository into a predictive graph data set based on a predictive model, and store the predictive graph data set to a visualization data repository.

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for improved network computing for network function testing. In some implementations, a method includes providing an application programming interface (API) to a user device of a network computing system; receiving input data from the user device indicating one or more application requirements for testing one or more network functions; generating one or more virtual processing machines using the one or more application requirements; obtaining testing data from one or more communication networks; providing the testing data to the one or more virtual processing machines configured to generate results based on processing the testing data using the one or more network functions; and providing the results to the user device.

Embodiments disclose herein implement a computing system in a computing network with hardware and software components of clinical system resources. The computing network may be a closed, private network. The clinical system resources may be situated on-premises or otherwise geographically proximate to the clinical installation (e.g., hospital, healthcare clinic). The clinical system resources may be heterogenous resources. The clinical system resources may access or execute interface programs (e.g., APIs) for performing interoperability operations on input data or instructions from the clinic database or upstream clinical system resource of the clinical networked computing system. For instance, the interface program may, for example, translate the data format of instructions or application data, or authenticate an upstream clinical system resource that sent the application data or instructions, among others. In an onboarding process, the integration program may perform operations for authentication, license validation, and testing and verifying interoperability of the clinical system resource.

A firing neural network computing system and method for brain-like intelligence and cognitive computing. The system includes a model description module, a parameter database, a configuration description module, a configuration manager, a rule manager, a data manager, a network builder, a network manager, an operation manager, a scheduler, a log manager, an operation monitoring module and a graphical display module. The system provides the function of automatically executing synapse and neuron pruning and genesis according to certain conditions and rules, and provides a variety of flexible trigger conditions for starting up pruning and genesis processes as well as rules for executing the processes, which eliminates the burden of neural network developers needing to write synapse and neuron pruning and genesis programs by themselves, thereby effectively solving several problems in existing brain-like firing neural network computing frameworks.

Various methods, systems, and use cases for a stable and automated transformation of a networked computing system are provided, to enable a transformation to the configuration of the computing system (e.g., software or firmware upgrade, hardware change, etc.). In an example, automated operations include: identifying a transformation to apply to a configuration of the computing system, for a transformation that affects a network service provided by the computing system; identifying operational conditions used to evaluate results of the transformation; attempting to apply the transformation, using a series of stages that have rollback positions when the identified operational conditions are not satisfied; and determining a successful or unsuccessful result of the attempt to apply the transformation. For an unsuccessful result, remediation may be performed to the configuration, with use of one or more rollback positions; for a successful result, a new restore state is established from the completion state.

A system includes a network computing system and a user computing device. The network computing system includes a plurality of software services associated with aircraft operations. The user computing device is operable to connect to the network computing system, over a network, to access data from one or more software services of the plurality of software services. The user computing device is operable to automatically perform one or more pre-flight computing functions for an aircraft based on the data from the one or more software services of the network computing system. At least one of the pre-flight computing functions includes an automatic weight and balance computation for a payload of the aircraft based on the data accessed from the one or more software services of the network computing system.

In conventional networked computing systems, each system may have many atomic values shared across multiple application servers. Each atomic value may require different levels of computational resources to store, maintain, and access, and may provide a different level of usefulness within the computing system. As such, a need exists for a system of improving the efficacy of each data element with a neural network or networked computing system. The system provided herein solves with problem by identifying each data element within a computing system; crawling multiple application servers to collect usage values for each data element; calculating a computational weightage of each data elements, identifying critical values; and creating hash values for each critical value.

A network computing system receives, over one or more networks, a service request from a requester device. The network computer system predicts a processing time interval, where the processing time extends from a time when the service request is received until a time when a service provider that is selected by the network computer system accepts an invitation to fulfill the service request. The network computer system transmits content data to the requester device, where the content data causes the requester device to display content that is based on the predicted processing time interval.

A computing system is disclosed in which a tensor-network structure mutates autonomously during execution. A structural mutation unit modifies tensor-network topology concur- rently with tensor contraction execution without halting computation or requiring host processor intervention.

Malware activity detection for networked computing systems is described. A network session record is provided to a machine learning (ML) model configured to generate an indication of whether the provided network session record evidences malware activity. The network session record indicates network traffic activity in a time period. Responsive to an indication by the ML model, correlation scores are calculated by, for each process session record in a process session record set, calculating a correlation score indicative of a correlation between the provided network session record and the process session record. Each process session record in the process session record set corresponds to a process executed by a computing device in the time period. A determination that a correlation score indicates a corresponding process session record is indicative of the evidenced malware activity is made. Responsive to the determination, a malware activity alert is generated.

A network computing system can coordinate on-demand transport serviced by transport providers operating throughout a transport service region. The transport providers can comprise a set of internal autonomous vehicles (AVs) and a set of third-party AVs. The system can receive a transport request from a requesting user of the transport service region, where the transport request indicates a pick-up location and a destination. The system can determine a subset of the transport providers to service the respective transport request, and executing a selection process among the subset of the transport providers to select a transport provider to service the transport request. The system may then transmit a transport assignment to the selected transport provider to cause the selected transport provider to service the transport request.

The application discloses a resource high-efficiency utilization in-network computing system, which comprises a task analyzer, a network in computing optimizer and a task placer. The task analyzer is used for automatically summarizing the attributes of each task, including the components and their dependency relationship, so as to obtain the analysis result of the task analyzer output. The network in computing optimizer is used for accepting the network topology and the in-network computing intention and the analysis result of the task analyzer output as the input thereof, taking the number of task copies between switches as the optimization target, and optimizing the task by using the mixed integer programming, so as to obtain the optimal unloading scheme. The task placer is used for enforcing the optimal unloading scheme and ensuring the execution correctness of each task. The in-network computing problem is converted into an optimization problem, so that the task copy copies generated in the existing in-network computing can be reduced, and the resource utilization rate of the in-network computing is increased. Compared with the prior art, the application has faster computing speed and lower overhead. The application is simple and convenient to operate and has strong practicability.

The invention relates to the technical field of network computing, and discloses a cross-regional CSPF network computing system for expanding IGP, and the system comprises a dynamic path computing module which is used for monitoring the network topology change and the traffic demand in real time, and carrying out the path computing based on the latest information; a path calculation algorithm is adopted, including an improved CSPF or a prediction algorithm based on machine learning, so as to reduce the calculation overhead and improve the calculation speed. According to the automatic configuration, deployment and monitoring system, the network equipment management and operation and maintenance process is greatly simplified, the requirement of manual operation is reduced, so that the working efficiency and response speed of an operation and maintenance team are improved, the operation and maintenance cost is effectively reduced by reducing manual intervention and optimizing resource utilization through the automatic operation and maintenance and monitoring module, and the operation and maintenance efficiency is improved. Comprise labor cost and equipment maintenance cost.

The invention discloses a neural network computing system and device, and relates to the technical field of neural networks, the neural network computing system comprises a digital time conversion module, a digital frequency conversion module and a phase accumulator, and the output end of the digital time conversion module and the output end of the digital frequency conversion module are connected with the input end of the phase accumulator; the digital time conversion module is used for compressing and converting sparse data in the received input data to obtain a corresponding time pulse signal and outputting the time pulse signal to the phase accumulator; the digital frequency conversion module is used for converting the received weight data into corresponding frequency signals and outputting the frequency signals to the phase accumulator; and the phase accumulator is used for carrying out multiply-accumulate calculation on the time pulse signal and the frequency signal based on the phase and outputting a calculation result. According to the method, the technical problems of high resource overhead and high energy consumption of a neural network calculation strategy aiming at sparse data at present are solved.

In conventional networked computing systems, stream-processing software is utilized to handle real-time data feeds. For example, a stream-processing software may sort incoming data feeds into data logs, which are then consumed by various application servers within the computing system. However, this consumption process is susceptible to failure as application servers often fail to consume a full payload of data comprising a particular message. As such, a need exists for a streaming architecture with improved fault tolerance. The system provided herein solves the problem via a reliable streaming process, which regularly generates partitions within a series of data lakes. As streaming data is received, the payloads are synced into the different data lakes based on metadata associated with the streaming data. The system can then perform individual quality checks on each partition of data, enabling the system to continuously monitor data quality over time.

A computer implemented method manages a topology for a network computing system. A number of processor units determines tasks for a workload to be performed by computing devices in the network computing system. The number of processor units creates the topology for performing the tasks in the workload. The topology comprises nodes representing the tasks assigned to the computing devices. The nodes are connected to each other based on an execution order for the tasks, and the topology has alternative paths for performing the tasks for the workload. The number of processor units determines energy consumption for the alternative paths in the topology. The number of processor units selects a particular path from the alternative paths for performing the tasks for the workload based on the energy consumption for the alternative paths.

In conventional networked computing systems, stream-processing software is utilized to handle real-time data feeds. For example, a stream-processing software may sort incoming data feeds into data logs, which are then consumed by various application servers within the computing system. However, this consumption process is susceptible to failure as application servers often fail to consume a full payload of data comprising a particular message. As such, a need exists for a streaming architecture with improved fault tolerance. The system provided herein solves the problem via a reliable streaming process, which regularly generates partitions within a series of data lakes. As streaming data is received, the payloads are synced into the different data lakes based on metadata associated with the streaming data. The system can then perform individual quality checks on each partition of data, enabling the system to continuously monitor data quality over time.

A network computing system can coordinate on-demand transport serviced by transport providers operating throughout a transport service region. The transport providers can comprise a set of internal autonomous vehicles (AVs) and a set of third-party AVs. The system can receive a transport request from a requesting user of the transport service region, where the transport request indicates a pick-up location and a destination. The system can determine a subset of the transport providers to service the respective transport request, and executing a selection process among the subset of the transport providers to select a transport provider to service the transport request. The system may then transmit a transport assignment to the selected transport provider to cause the selected transport provider to service the transport request.

A method of a computing system of a communication network for providing video to a portable display device includes establishing a connection with a receiving system comprising the portable display device, obtaining compressed video data and decoding the compressed video data, receiving direction information from the receiving system, the direction information related to a direction of the portable display device, identifying a respective location of each macroblock of a plurality of macroblocks of the decoded video data, determining a respective distance between a location determined based on a field of view identified utilizing the direction information, and the respective location of each macroblock of the decoded video data, encoding the decoded video data utilizing a respective quantization parameter identified based on the respective distance determined for each macroblock to provide re-encoded video data, and sending the re-encoded video data to the receiving system for display on the portable display device.