Abstract: An example operation may include one or more of monitoring, by a blockchain node, a delivery of a service to a first node from a second node based on a service contract and an order retrieved from a blockchain, determining, by the blockchain node, an incremental charge for a partial delivery of the service based on the monitoring, and executing, by the blockchain node, a smart contract to issue the incremental charge for the partial delivery of the service, and responsive to a resolution of a dispute raised for the incremental charge, add the incremental charge to an incremental invoice.

Abstract: An example operation may include one or more of detecting, by a blockchain (BC) node, a contract update on a blockchain, determining, by the BC node, at least one charge affected by the contract update, executing, by the BC node, a smart contract to re-generate an invoice based on the at least one charge, and recording the invoice on a ledger of the blockchain.

Abstract: An example operation may include one or more of determining, by a blockchain node, a first node and a second node involved in a transaction based on a service contract and order data retrieved from a blockchain, and executing, by the a blockchain node, a smart contract to generate an invoice based on the service contract and the order data, responsive to a consensus on the invoice from the first node and the second node, generate a final invoice, and record the final invoice on the blockchain.

Abstract: A technique is described where the switch and/or tunable control circuit for use with an active multi-mode antenna is positioned remote from the antenna structure itself for integration into host communication systems. Electrical delay and impedance characteristics are compensated for in the design and configuration of transmission lines or parasitic elements as the active multi-mode antenna structure is positioned in optimal locations such that significant electrical delay is introduced between the RF front-end circuit and multi-mode antenna. This technique can be implemented in designs where it is convenient to locate switches in a front-end module (FEM) and the FEM is located in vicinity to the transceiver.

Abstract: An example method includes a sensor detecting multiple packets of a flow during a specified total time period (e.g., a reporting time period). The total time period can be subdivided into multiple time periods. The sensor can analyze the detected packets to determine an amount of network utilization for each of the time periods. The sensor can then generate a flow summary based on the network utilization and the flow and send the flow summary to an analytics engine. Multiple other sensors can do similarly for their respective packets and flows. The analytics engine can receive the flow summaries from the various sensors and determine a correspondence between flow with high network utilization at a specific time period and a node or nodes. These nodes that experienced multiple flows with high network utilization for a certain period of time can be identified as experiencing a microburst.

Abstract: A method for controlling operation of a multi-mode antenna is provided. The multi-mode antenna is configurable in a plurality of modes, and each mode associated with a different radiation pattern or polarization. The method includes receiving, at the multi-mode antenna a first RF signal. The method further obtaining, by one or more control devices, data indicative of a receive signal channel quality indicator for the first RF signal. The method includes configuring, by the one or more control device, the multi-mode antenna for transmitting a second RF signal in a selected mode of the plurality of modes based, at least in part, on the data indicative of the receive signal CQI. The method further includes transmitting, by the multi-mode antenna the second RF signal while the multi-mode antenna is configured in the selected mode.

Abstract: Determining priority of conflicting resource requests using dynamic logic includes analyzing current computer resource availability using cognitive capabilities and determining priority for the computer resources. The method may further include continuously assessing impact of the priority determinations, dynamically configuring the defined logic based on the assessment and modifying the defined logic using machine learning of effectiveness of the priority determinations. The method may also include predicting an urgency of the received computer resources requests and determining a pattern computer resource requests by users.

Abstract: Provided are techniques for domain specific correction of output from automatic speech recognition. An output of an automatic speech recognition engine is received. An alphanumeric sequence is extracted from the output, where the alphanumeric sequence represents an erroneous translation by the automatic speech recognition engine. Candidates for the alphanumeric sequence are generated. The candidates are ranked based on scores associated with the candidates. A candidate of the candidates having a highest score of the scores is selected. The output is corrected by replacing the alphanumeric sequence with the selected candidate. The corrected output is returned.

Abstract: According to one embodiment, a computerized method for acquiring updated predictive model is described. The updated predictive model is achieved through machine learning analyses of information by a training engine, which issues a control message in response to a discrepancy in a determination of the suspect object as malicious or non-malicious by a detection engine and a classification engine. The detection engine analyzes a content of a suspect object to determine whether the suspect object is malicious or non-malicious. Similarly, the classification engine analyses the suspect object based on the predictive model to determine whether the suspect object is malicious or non-malicious. The control message causes the training engine to update the predictive model based on machine learning analyses of information provided via the control message and to return an updated predictive model to the classification engine.

Abstract: In one embodiment, a device forms a neural network envelope cell that comprises a plurality of convolution-based filters in series or parallel. The device constructs a convolutional neural network by stacking copies of the envelope cell in series. The device trains, using a training dataset of images, the convolutional neural network to perform image classification by iteratively collecting variance metrics for each filter in each envelope cell, pruning filters with low variance metrics from the convolutional neural network, and appending a new copy of the envelope cell into the convolutional neural network.

Abstract: Systems and methods for distributing over the air (OTA) content are provided. In one example, an antenna system can include a network access point. The antenna system can include an OTA antenna system configured to receive an OTA signal associated with media content. The OTA signal can be a VHF signal or a UHF signal. The OTA antenna system can be coupled to the network access point via a universal serial bus (USB) connection. The access point can be configured to perform operations, including receiving a signal associated with the media content from the OTA antenna system via the universal serial bus connection; and communicating media content via a network communication link to one or more client devices. The OTA antenna system can include a modal antenna configurable in a plurality of antenna modes. Each antenna mode can be associated with a distinct radiation pattern or polarization.

Abstract: The disclosure concerns an antenna subsystem that can be used in various repeater systems to optimize gain of the repeater by increasing isolation between donor and server antennas, wherein at least one of the donor and server antennas is an active multi-mode antenna.

Abstract: Systems and methods for detecting injection exploits in a networked computing environment are disclosed. In one embodiment, a method for detection includes monitoring web applications that are executing and detecting when an execution function is received over a network and invoked, where an execution function is a function that accepts external free-form data values, detecting malicious code by generating a model of legitimate behavior subsequent to invocation of the execution function, comparing actual behavior to the model of legitimate behavior, and generating an alert when the actual behavior deviates from the model of legitimate behavior and validating whether the deviation of the actual behavior is due to one or more functions that accept external input.

Abstract: A system and method for generating an alert regarding a potential attack is described. The method involves receiving data associated with previously analyzed or known malware attacks by a first network device. Additionally, the first network device receives an attack alert associated with an object analyzed and identified as suspicious by a second network device. The attack alert includes information associated with the suspicious object. For alert generation, at least a portion of the information of the attack alert is provided to a system configured to at least (i) extract feature(s) from the attack alert, (ii) determine similarities between the extracted features and features associated with the previously analyzed or known malware attacks to determine a result, (iv) compute an attack value based on the result and at least a portion of the extracted features including time-dependent and/or independent features, and (v) generate an alert based on the attack value.

Abstract: The disclosure pertains to techniques for image processing. One such technique comprises a method for image selection, comprising: obtaining a sequence of images, detecting a first face in one or more images of the sequence of images, determining a first location for the detected first face in each of the images having the detected first face, generating a heat map based on the first location of the detected first face in each of the images of the sequences of images, determining a face quality score for the detected first face for each of the one or more images having the detected first face, determining a peak face quality score for the detected first face based in part on the face quality score and the generated heat map, and selecting a first image of the sequence of images, corresponding with the peak face quality score for the detected first face.

Abstract: A distributed deep learning network may prevent an attacker from reconstructing raw data from activation outputs of an intermediate layer of the network. To achieve this, the loss function of the network may tend to reduce distance correlation between raw data and the activation outputs. For instance, the loss function may be the sum of two terms, where the first term is weighted distance correlation between raw data and activation outputs of a split layer of the network, and the second term is weighted categorical cross entropy of actual labels and label predictions. Distance correlation with the entire raw data may be minimized. Alternatively, distance correlation with only with certain features of the raw data may be minimized, in order to ensure attribute-level privacy. In some cases, a client computer calculates decorrelated representations of raw data before sharing information about the data with external computers.

Abstract: The disclosure describes a tool that decreases the size of a software installation file by segregating and/or allowing customization of the original software installation file. The resulting install file permits multiple configurations to be created using the same install file by using custom configuration settings. The optimized software installation file permits the automated (or semi-automated) installation of software and/or software updates/upgrades on a remotely located electronic device. Moreover, aspects of the tool may also regulate compliance with possible reboot/restart requirements of the software/upgrade installation that may be useful for electronic devices which are remotely located.

Abstract: Techniques for implementing zone redundant computing services in distributed computing systems are described herein. In one embodiment, a computing device is configured to query a database in a first availability zone for a list of computing services corresponding to a user account and provided by servers in a second availability zone different than the first availability zone. The computing device can then selects one or more computing services provided by the servers in the second availability zone from the queried list of computing services and transmit an instruction to the servers in the second availability zone for performing the requested data operation.

Abstract: A multi-mode antenna system include at least a first modal antenna and a second modal antenna. The first modal antenna is disposed on a ground plane of a circuit board and configurable in a plurality of different modes. The first modal antenna can include a driven element, at least one parasitic element and an active element configured to adjust a reactance of the at least one parasitic element. The multi-mode antenna system further includes a second modal antenna disposed on the ground plane and configurable in a plurality of different modes. The second modal antenna can include a driven element, at least one parasitic element, and an active element configured to adjust a reactance of the at least one parasitic element. The parasitic element of the second modal antenna is positioned such that adjusting the reactance of the parasitic element affects the radiation pattern associated with the first modal antenna.

Abstract: A neural network architecture search may be conducted by a controller to generate a neural network. The controller may perform the search by generating a directed acyclic graph across nodes in a search space, the nodes representing compute operations for a neural network. As the search is performed, the controller may retrieve resource availability information to modify the likelihood of a generated neural network architecture including previously unused nodes.