Abstract: There are provided systems and methods for an automatically managed common asset validation framework for platform-based microservices. A service provider, such as an electronic transaction processor for digital transactions, may utilize different decision services that implement rules and/or artificial intelligence models for decision-making from input data including data in a production computing environment. A decision service may normally be used for data processing and decision-making through an execution flow configuration and/or graph identifying a flow of task executions and other computing operations. In this regard, the decision services may share common data assets, such as data tables, shared code for execution of operations and the like. The service provider may utilize an intelligent service to automatically manage and validate these assets between services so that execution errors do not occur when there are changes to these common assets.

Abstract: There are provided systems and methods for increasing availability of a micro-computation decision service by utilizing execution flow configurations. A service provider, such as an electronic transaction processor for digital transactions, may utilize different decision services that implement rules and/or artificial intelligence models for decision-making of data including data in production computing environment. A decision service may normally be used for data processing and decision-making through an execution flow configuration and/or graph identifying a flow of task executions and other computing operations.

Abstract: Some examples relate to classifying IoT malware at a network device. An example includes receiving, by a network device, network traffic from an Internet of Things (IoT) device. Network device may analyze network parameters from the network traffic with a machine learning model. In response to analyzing, network device may classify the network traffic into a category of malware activity. Network device may determine an effectiveness of network traffic classification by measuring a deviation of the network parameters from previously trained network parameters that were used for training the machine learning model. In response to a determination that the deviation of the network parameters from the trained network parameters is more than a pre-defined threshold, network device may generate an alert highlighting the deviation, which allows a user to perform a remedial action pertaining to the IoT device.

Abstract: Techniques are disclosed relating to a computer system identifying usage of a plurality of individual instances of a common computation task by a plurality of users of a networked service. These individual instances of the common computation task may generate a respective data set. Techniques also include creating, by the computer system, a global process to perform the common computation task. Execution of the global process may include generation of a global data set that includes at least portions of the respective data sets. Additionally, techniques include modifying, by the computer system, respective accounts of a subset of the plurality of users to use the global process in place of using a respective instance of the common computation task, as well as providing, by the computer system, the global data set generated by the global process to the respective accounts of the subset of users.

Abstract: There are provided systems and methods for an automatically managed common asset validation framework for platform-based microservices. A service provider, such as an electronic transaction processor for digital transactions, may utilize different decision services that implement rules and/or artificial intelligence models for decision-making from input data including data in a production computing environment. A decision service may normally be used for data processing and decision-making through an execution flow configuration and/or graph identifying a flow of task executions and other computing operations. In this regard, the decision services may share common data assets, such as data tables, shared code for execution of operations and the like. The service provider may utilize an intelligent service to automatically manage and validate these assets between services so that execution errors do not occur when there are changes to these common assets.

Abstract: There are provided systems and methods for increasing availability of a micro-computation decision service by utilizing execution flow configurations. A service provider, such as an electronic transaction processor for digital transactions, may utilize different decision services that implement rules and/or artificial intelligence models for decision-making of data including data in production computing environment. A decision service may normally be used for data processing and decision-making through an execution flow configuration and/or graph identifying a flow of task executions and other computing operations.

Abstract: Techniques for determining range of electric vehicles are described. A sensor provides a signal indicative of the current being supplied by a battery of an electric vehicle to a motor of the electric vehicle. A controller receives the signal and determines the current supplied by the battery. The controller also receives a voltage supplied by the battery and determines a notional distance travelled by the electric vehicle. Based on the voltage, the current, and the notional distance, the controller determines the range of the electric vehicle.

Abstract: There are provided systems and methods for a fallback artificial intelligence (AI) system for redundancy during system failover. A service provider may provide AI systems for automated decision-making, such as for risk analysis, marketing, and the like. An AI system may operate in a production computing environment in order to provide AI decision-making based on input data, for example, by providing an output decision. In order to provide redundancy to the production AI system, the service provider may train a fallback AI system using the input/output data pairs from the production AI system. This may utilize a deep neural network and a continual learning trainer. Thereafter, when a failover condition is detected for the production AI system, the service provider may switch from the production AI system to the fallback AI system, which may provide decision-making operations during failure of within the production computing environment.

Abstract: A diagnostic system for a vehicle includes: a portable wireless handheld diagnostic device which is capable of sending data through wireless mode and receiving data through wireless mode; a vehicle communication interface (VCI) which interfaces between the portable wireless handheld diagnostic device and the vehicle for communicating parameters of the vehicle to the portable wireless handheld diagnostic device; and a coupler including a first port which connects to the vehicle through an in-vehicle network and a second port which connects to the vehicle communication interface (VCI). The VCI includes a vehicle data-collection and transmission component that supports communication software which collects diagnostic data from a vehicle electronic control unit (ECU) and transmits data to the vehicle ECU. The vehicle communication interface (VCI) device includes a separate internal storage device preloaded with a micro kernel which is capable of executing specific subroutines in a standalone manner.

Abstract: There are provided systems and methods for a fallback artificial intelligence (AI) system for redundancy during system failover. A service provider may provide AI systems for automated decision-making, such as for risk analysis, marketing, and the like. An AI system may operate in a production computing environment in order to provide AI decision-making based on input data, for example, by providing an output decision. In order to provide redundancy to the production AI system, the service provider may train a fallback AI system using the input/output data pairs from the production AI system. This may utilize a deep neural network and a continual learning trainer. Thereafter, when a failover condition is detected for the production AI system, the service provider may switch from the production AI system to the fallback AI system, which may provide decision-making operations during failure of within the production computing environment.

Abstract: Methods and systems are presented for dynamically configuring a software application to log different data variables without requiring re-deploying the software application. When a software application is executed, the software application may obtain and/or generate data variables. During runtime of the software application while processing a request, the software application may access a log script external to the software application. The software application may select a subset of data variables for logging, and may record only the subset of data variables in a log file. The log script may be modified without requiring the software application to be modified or re-deployed such that the software application may log different subset of data variables while processing different processes based on different versions of the log script.

Abstract: Provided herein are compositions, methods, systems and/or kits for measuring microbial viability in a sample. Certain embodiments of the present disclosure are related to detection tests comprising compositions, methods, systems and/or kits for measuring an antimicrobial minimum inhibitory concentration and for measuring microbial susceptibility to the antimicrobial. Certain embodiments of the present disclosure are related to detection tests comprising compositions, methods, systems and/or kits for assessing microbial proliferation in a sample.

Abstract: This disclosure involves training classification algorithms to predict end-user behavior based on historical conversation data. For example, a computing system accesses training data with conversational and non-conversational data. The system derives decision points from a textual analysis of the conversational training data. The computing system fits a hidden Markov model having multiple hidden states to the non-conversational data. The computing system groups observations from the non-conversational data and the derived decision points into data segments. Each data segment includes a subset of the observations and the decision points associated with a hidden state. The computing system generates, from each data segment, a predictive model for the hidden state. Subsequently, input non-conversational data is matched to one of the hidden states.