Abstract: Systems, methods, and devices for a cyberphysical (IoT) software application development platform based upon a model driven architecture and derivative IoT SaaS applications are disclosed herein. The system may include concentrators to receive and forward time-series data from sensors or smart devices. The system may include message decoders to receive messages comprising the time-series data and storing the messages on message queues. The system may include a persistence component to store the time-series data in a key-value store and store the relational data in a relational database. The system may include a data services component to implement a type layer over data stores. The system may also include a processing component to access and process data in the data stores via the type layer, the processing component comprising a batch processing component and an iterative processing component.

Abstract: Using a traffic monitoring service to detect traffic pattern shifts can include obtaining netflow data and routing data for core routers. Based on the netflow data and the routing data, an augmented traffic matrix can be generated. A router-level analysis can be performed on the augmented traffic matrix to identify a pair of core routers that are associated with a traffic pattern shift in the network. An entity-level analysis can be performed on the pair of core routers to identify an entity that is responsible for the traffic pattern shift in the network. Traffic shift data that identifies the pair of core routers and the entity that is responsible for the traffic pattern shift in the network can be output.

Abstract: Some embodiments provide a novel method for assessing the suitability of network links for connecting compute nodes located at different geographic sites. The method of some embodiments identifies and analyzes sample packets from a set of flows exchanged between first and second compute sites that are connected through a first network link in order to identify attributes of the sampled packets. The method also computes attributes of predicted packets between the identified samples in order to identify attributes of each flow in the set of flows. The method then uses the identified and computed attributes of each flow in the set of flows to emulate the set of flows passing between the two compute sites through the second network link in order to assess whether a second network link should be used for future flows (e.g., future flows exchanged between the first and second compute sites).

Abstract: A remote artificial intelligence (AI) acceleration system is provided. The system includes a plurality of application servers, wherein each of the plurality of application server is configured to execute AI applications over an AI software framework; at least one artificial intelligence accelerator (AIA) appliance server configured to execute AI processing tasks in response to requests from the AI applications; and at least one switch configured to allow connectivity between the plurality of application servers and the at least one AIA appliance server, wherein the plurality of network attached artificial intelligence accelerator (NA-AIA) engines connected to the AIA switch, wherein each of the plurality of NA-AIA engines connected is configured to execute at least one processing AI task.

Abstract: A system and method for observing and controlling a programmable network via higher layer attributes is disclosed. According to one embodiment, the system includes one or more collectors and a remote network manager. The one or more collectors are configured to receive network traffic data from a plurality of network elements in the network. The remote network manager is configured to connect to the one or more collectors over the Internet via a network interface. The one or more collectors extract metadata from the network traffic data and send the metadata to the network manager.

Abstract: Examples for analyzing the usage of virtualized network instances within a communication network, are described. In an example, a usage monitoring service may receive a data change notification from a unified data repository. The data change notification is generated by the unified data repository in response to a data modification in subscriber data of a virtualized network instance. In an example, the data modification retrieved from the generated data change notification is utilized to obtain usage indication information for the virtualized network instance.

Abstract: A field device of automation technology having a function for checking quality of a network connection includes an operating electronics and at least one communication interface for connecting to a communication network having one or more network participants and for building a communication connection, wherein a communication stack and a PHY are associated with the communication interface, wherein the communication stack and the PHY are embodied continually to gain and to store communication information regarding the communication connection, wherein the operating electronics is embodied to read out communication information from the communication stack and from the PHY, and by means of an algorithm to subject the communication information to computation and based on the result of the computation to classify a communication state.

Abstract: Apparatuses, methods, and systems for generating short messages for electronic messages of electronic marketing messages. One method includes receiving, by a server, information related to the electronic marketing message, receiving, by the server, a plurality of N short messages (text) generated based on the received information from a generative text engine model, reducing, by the server, the plurality of N short messages down to M short messages, displaying messages based on the M short messages to a merchant user, identifying merchant actions in response to the displaying of the messages based on the M short messages, and fine-tuning, by the server, the generative text engine model based on the identified merchant actions.

Abstract: In some implementations, a user device can perform personalized translation of content identifiers. For example, the user device can request a content identifier mapping file from a server device that defines how to translate a human readable content identifier into a content server content identifier. The server device can select a mapping file from among a collection of mapping files based on various criteria associated with the user device and send the selected mapping file to the user device. When the user device receives a human readable content identifier, the user device can compare the human readable content identifier to the mapping file to determine how to translate the human readable content identifier into the content server content identifier. Once the content server content identifier corresponding to the human readable content identifier is determined, the client device can use the content server content identifier to obtain corresponding content.

Abstract: A network-accessible service provides an enterprise with a view of all identity and data activity in the enterprise's cloud accounts. The service enables distinct cloud provider management models to be normalized with centralized analytics and views across large numbers of cloud accounts. The service enables an enterprise to model all activity and relationships across cloud vendors, accounts and third party stores. Display views of this information preferably can pivot on cloud provider, country, cloud accounts, application or data store. Using a domain-specific query language, the system enables rapid interrogation of a complete and centralized data model of all data and identity relationships. User reports may be generated showing all privileges and data to which a particular identity has access. Similarly, data reports shown all entities having access to an asset can be generated.

Abstract: Dynamically optimizing routing within a decentralized network is described. In accordance with the described techniques, a node operator accesses historical state data associated with a decentralized network layered on top of a blockchain network. Using machine learning, the node operator trains one or more models based on the historical state data. For example, the node operator trains the one or more models to optimize routing within the decentralized network. In near-real time and using the one or more models, the node operator monitors state data (e.g., current state data) associated with the decentralized network. Based at least in part on the monitoring, the node operator performs one or more actions to optimize the routing within the decentralized network.

Abstract: This disclosure describes techniques for providing a network diagnostic system with on-premise node processing and cloud node processing to optimize bandwidth usage and decrease memory footprint. The on-premise node may receive streaming telemetry from connected network devices and encode to the telemetry data into filtered data objects. The on-premise node may determine whether the state of a network device has changed to determine to push the filtered data object to a cloud node for further diagnostic analysis. The cloud node may include a gateway and a pool of proxy servers, wherein each proxy server is designated to perform diagnostic analysis on a single product type.

Abstract: An embodiment of the invention may include a method, computer program product, and system for creating a data analysis tool. The method may include a computing device that generates an AI pipeline based on an input dataset, wherein the AI pipeline is generated using an Automated Machine Learning program. The method may include converting the AI pipeline to a non-native format of the Automated Machine Learning program. This may enable the AI pipeline to be used outside of the Automated Machine Learning program, thereby increasing the usefulness of the created program by not tying it to the Automated Machine Learning program. Additionally, this may increase the efficiency of running the AI pipeline by eliminating unnecessary computations performed by the Automated Machine Learning program.

Abstract: A detection device includes: a statistic unit that acquires statistical value data on 5-tuple flows in a certain time period; a degree centrality calculation unit that calculates a degree centrality of a node including an indegree, which is a total sum of a weight of each edge flowing into the node, and an outdegree, which is a total sum of a weight of each edge flowing out of the node, based on the statistical value data on the 5-tuple flows in the certain time period; and a degree centrality impartation unit that imparts the degree centrality, as a feature, to the statistical value data on the 5-tuple flows in the certain time period.

Abstract: Described herein is an issue tracking system (ITS) comprising: one or more processors; one or more non-transitory computer-readable storage media storing sequences of instructions which, when executed by the one or more processors, cause the one or more processors to: render a user interface including a plurality of user interface elements each listing an external service from which the ITS can receive external objects and associate the received external object with one or more issues maintained by the ITS; determine, in response to a user interaction with one of the user interface elements, whether an issue maintained by the ITS is associated with an external object received from the listed external service; and render within the ITS, a user interface for an application marketplace, the application marketplace user interface including a listing of one or more integration applications that, once installed, connect the listed external service to the ITS and associate external objects received from the listed e

Abstract: A network monitoring apparatus includes a memory configured to store a performance management data sample of a network; and at least one processor configured to input the performance management data sample and an abnormality probability value to an auto-encoder of an abnormality detection model that is trained based on performance management data of the network, wherein the abnormality probability value is a default value, obtain, from the auto-encoder of the abnormality detection model, a reconstructed performance management data sample and an abnormality score value, and detect an abnormal sample based on the abnormality score value, wherein the auto-encoder is configured to receive the performance management data sample and the abnormality probability value, and output the reconstructed performance management data sample and the abnormality score value.

Abstract: A computer-implemented method and system are provided to utilize machine learning technology to process user financial transaction data to predict a personalized payment screen architecture. A plurality of feature datasets associated with transaction data of a plurality of electronic invoices are obtained by a computing device. Each feature dataset comprises a plurality of features, a payment screen and a payment method configured to be presented on at least one payment screen. The computing device is configured to train a machine learning model with the feature datasets to produce a probability matrix with probabilities of each payment method used to pay the invoices through each payment screen. The computing device may weigh the probability matrix to generate a recommendation matrix and determine a prediction of a payment screen based on the recommendation matrix.

Abstract: Disclosed are a system comprising a computer-readable storage medium storing at least one program, and a computer-implemented method for event messaging over a network. A subscription interface receives data indicative of a subscription request for sessionized data. An allocation module allocates a sessionizer bank linked to the subscription request. A messaging interface module provisions identifiers linked to the respective processing engines of the sessionizer bank. The messaging interface module registers the allocated sessionizer bank as available to process event messages matching the subscription request by providing the provisioned identifiers. The messaging interface module receives event messages from a producer device linked by a collection server to a selected one of the processing engines of the sessionizer bank. The selected one of the processing engine processes the received event messages in accordance with session rule data linked to the subscription request to generate sessionized data.

Abstract: A method for encoding data streams into a combined file and decoding of same, including accessing a first file having a first plurality of data bytes, accessing a second file having a second plurality of data bytes, combining the first file and the second file, comprising the steps of storing a block of data bytes of a first byte block size in the body of the combined file as a first file byte block, storing a block of data bytes of a second byte block size in the body of the combined file as a second file byte block, repeating the first and second storing steps to sequentially store all of the data bytes in the first file and the second file in the combined file, and decoding the combined file to separate the bytes of the first file and the bytes of the second file from the combined file.

Abstract: Live video streams are produced using a network server system. The network server system initially receives, via a network, one or more captured video streams that are live streams captured from a remotely-located camera, phone or the like. The captured streams are forwarded to a control device via the network to thereby permit a user of the control device to select one of the captured video streams for output to the video production stream. In response to a command received from the control device that indicates the selected capture stream, the selected capture video stream is encoded for output as the video production stream.