Abstract: Techniques are disclosed relating to configuring a neural network based on information received via a dashboard user interface. In some embodiments, a computing system displays a dashboard that includes a set of plots for displaying data and user interface elements that may be used to configure the number and type of the plots. The plots may display information of various kinds, including raw or processed data, relationships between data, processes applied to data, etc. and may be different types, including, e.g., spark lines, scatter, or time series, etc. The dashboard module is operable to communicate the user input to a module operable to generate a neural network topology. User input to the dashboard may provide information regarding sources of data to be used for generating plots, or training or running the neural network. Results based on processing data using the trained neural network may be displayed on the dashboard.

Abstract: A method of operating an authentication node is disclosed. Transaction information for a transaction is received from a merchant node. The transaction information includes an identification for a credit/debit account, a name on the credit/debit account, and a transaction amount for the transaction. Responsive to receiving the transaction information, a complete account number for the credit/debit account is determined using the identification for the credit/debit account and the name on the credit/debit account. The identification for the credit/debit account is different than the complete account number for the credit/debit account. In addition, account information may be transmitted to an acquirer node, and the account information may include the complete account number for the credit/debit account, the name on the credit/debit account, and the transaction amount. Related methods of operating merchant nodes, related computer program products, and related computer systems are also disclosed.

Abstract: In an embodiment, a password risk evaluator may receive a request including a user identifier (ID) and a password. The password risk evaluator may retrieve a password preference model associated with the user ID, and may determine a risk score indicating a likelihood that the password is associated with the user ID. For example, the password preference model may be based on previous passwords used by the user, and may identify one or more characteristics, formulas, rules, or other indicia typically employed by the user in creating passwords. If the password supplied in the request matches or is similar to one or more elements of the password preference model, it may be more likely that the password in the request is a password supplied by the user. That is, the risk score may be an authentication of the user, or part of the authentication of the user, in some embodiments.

Abstract: Determining the physical location of wirelessly connected devices within a network can provide a number of security benefits. However, manually determining and configuring the physical location of each device within a system can be burdensome. To ease this burden, devices within a network are equipped with a location detection sensor that is capable of automatically determining a device's location in relation to other devices within the network. A location detection sensor (“sensor”) may include a light source, a light direction sensor, a rangefinder, and a radio or wireless network interface. Two location detection sensors can perform a location detection process to determine their relative locations to each other, such as the distance between them. As more sensors are added to a network, a sensor management system uses the relative locations determined by the sensors to map the sensors to a physical space layout.

Abstract: Determining the physical location of wirelessly connected devices within a network can provide a number of security benefits. However, manually determining and configuring the physical location of each device within a system can be burdensome. To ease this burden, devices within a network are equipped with a location detection sensor that is capable of automatically determining a device's location in relation to other devices within the network. A location detection sensor (“sensor”) may include a light source, a light direction sensor, a rangefinder, and a radio or wireless network interface. Two location detection sensors can perform a location detection process to determine their relative locations to each other, such as the distance between them. As more sensors are added to a network, a sensor management system uses the relative locations determined by the sensors to map the sensors to a physical space layout.

Abstract: According to an example computer-implemented method, a first client on a proxy server joins a collaborative meeting on behalf of a user. The collaborative meeting is hosted on a first meeting platform, and the first client utilizes the first meeting platform. The first client receives media content from the collaborative meeting on behalf of the user. The proxy server transmits the received media content to a second client on a computing device of the remote user that utilizes a different, second meeting platform. A computing device and computer program product implementing similar features are also disclosed.

Abstract: Systems and methods may include receiving data of a physical system. The systems and methods may include determining whether an anomalous event has occurred based on the data. The systems and methods may include, in response to determining that the anomalous event has occurred, capturing data of the physical system and of an environment. The systems and methods may include generating a virtual representation of the physical system and the environment based on the captured data. The systems and methods may include repeatedly simulating data of the virtual representation by varying parameters of the captured data. The systems and methods may include determining which parameters are a root cause of the anomalous event based on the simulated performance data. The systems and methods may include determining particular values of the parameters that are likely the root cause of the anomalous event to use as a trigger for a proactive process.

Abstract: Provided is a computer system that includes a processor and a memory coupled to the processor, the memory including computer readable program code embodied therein that, when executed by the processor, causes the processor to generate a catalog that identifies a plurality of tasks that a plurality of network resources are available to perform, the network resources including Internet-of-things devices and human network resources and to generate, in response to receiving a request to perform a complex project, a solution path that includes an ordered list corresponding to selected ones of the plurality of tasks that are capable of aggregately performing the complex project, wherein the selected ones of the plurality of tasks define the solution path in an edge graph that include the plurality of tasks represented as edges therein.

Abstract: According to an example computer-implemented method, a first client on a proxy server joins a collaborative meeting on behalf of a user. The collaborative meeting is hosted on a first meeting platform, and the first client utilizes the first meeting platform. The first client receives media content from the collaborative meeting on behalf of the user. The proxy server transmits the received media content to a second client on a computing device of the remote user that utilizes a different, second meeting platform. A computing device and computer program product implementing similar features are also disclosed.

Abstract: Data privacy is provided in a computer network using a security inference control processor of a network device in the computer network which receives a query from a user device through a network interface circuit. In response to the query, a query result data set is generated based on information in a database stored in a non-volatile data storage device. Personally Identifiable Information (PII) exposure risk associated with the query result data set is determined based on an evaluation of combining the query result data set with an exposure storage log that includes result data sets from past queries associated with the user. Based on the PII exposure risk, the query result data set is provided to the user, so as to refrain from providing the query result data set if the PII exposure risk is greater than a risk threshold. Related methods, devices, and computer program products are provided.

Abstract: In an embodiment, a password risk evaluator may receive a request including a user identifier (ID) and a password. The password risk evaluator may retrieve a password preference model associated with the user ID, and may determine a risk score indicating a likelihood that the password is associated with the user ID. For example, the password preference model may be based on previous passwords used by the user, and may identify one or more characteristics, formulas, rules, or other indicia typically employed by the user in creating passwords. If the password supplied in the request matches or is similar to one or more elements of the password preference model, it may be more likely that the password in the request is a password supplied by the user. That is, the risk score may be an authentication of the user, or part of the authentication of the user, in some embodiments.

Abstract: Determining the physical location of wirelessly connected devices within a network can provide a number of security benefits. However, manually determining and configuring the physical location of each device within a system can be burdensome. To ease this burden, devices within a network are equipped with a location detection sensor that is capable of automatically determining a device's location in relation to other devices within the network. A location detection sensor (“sensor”) may include a light source, a light direction sensor, a rangefinder, and a radio or wireless network interface. Two location detection sensors can perform a location detection process to determine their relative locations to each other, such as the distance between them. As more sensors are added to a network, a sensor management system uses the relative locations determined by the sensors to map the sensors to a physical space layout.

Abstract: Determining the physical location of wirelessly connected devices within a network can provide a number of security benefits. However, manually determining and configuring the physical location of each device within a system can be burdensome. To ease this burden, devices within a network are equipped with a location detection sensor that is capable of automatically determining a device's location in relation to other devices within the network. A location detection sensor (“sensor”) may include a light source, a light direction sensor, a rangefinder, and a radio or wireless network interface. Two location detection sensors can perform a location detection process to determine their relative locations to each other, such as the distance between them. As more sensors are added to a network, a sensor management system uses the relative locations determined by the sensors to map the sensors to a physical space layout.

Abstract: A thing-sourcing project request including requirements for a thing-sourcing task that requires data input by a thing-sourcing device is received from a requestor device. A determination is made if real-time data is needed in order to complete the thing-sourcing task. In response to determining that real-time data is not needed, a determination is made if a similar thing-sourcing task has been previously completed. If not, the method determines if the thing-sourcing task can be completed using pre-existing data. If so, a data archive is searched for relevant pre-existing data that can be used to complete the thing-sourcing task. The thing-sourcing task is completed using the relevant pre-existing data, and a response to the thing-sourcing project request is transmitted to the requestor device.

Abstract: Techniques for managing distributed state for stateless transactions are disclosed herein. In some embodiments a distributed state manager detects a state-changing event that corresponds to a stateless transaction between a node and an application server. The stateless transaction is generated from a first instance of an application that is executing on the node and hosted by the application server. The distributed state manager records the event in a blockchain comprising blocks that each record a batch of one or more events associated with execution of the application. The distributed state manager detects an update to the blockchain associated with the recorded event and modifies a state of a second instance of the application executing on at least one other node based, at least in part, on the update to the blockchain.

Abstract: According to an example computer-implemented method, shared media content is presented to a plurality of users during a collaborative session, with the session including a plurality of events. A breadcrumb trail of events occurring during the collaborative session is created. For each of the events, a descriptor is determined for the event, and the event is tagged in the breadcrumb trail with the descriptor. Responsive to receipt of a descriptor selection, a filtered breadcrumb trail including only events tagged with the selected descriptor is presented.

Abstract: A method includes applying a correlation rule defining a correlation relationship between a first and second object and determining, using a processor, whether a first motion vector of the first object is correlated at a threshold level of correlation with a second motion vector of the second object, the correlation relationship between the first and second objects identifying the threshold level of correlation between the first and second motion vectors. The method also includes, in response to determining that the first motion vector is not correlated at the threshold level with the second motion vector, determining a convergence point for the first and second objects in accordance with a policy. The method further includes transmitting instructions for arriving at the convergence point to the first and second objects.

Abstract: Systems and methods may include receiving first data of components, which may represent performance characteristics of the components at a first time. The systems and methods may include performing a first cluster analysis of the first data to identify clusters of the components with similar characteristics. The systems and methods may include receiving second data of the components, which may represent performance characteristics of the components at a second time. The systems and methods may include performing a second cluster analysis of the second data to identify clusters of the components with similar characteristics. The systems and methods may include determining whether a component transitioned from a cluster identified in the first cluster analysis to a different cluster identified in the second cluster analysis. The systems and methods may include determining that an anomaly occurred in response to determining that the component transitioned from the cluster to the different cluster.

Abstract: A method includes applying a correlation rule defining a correlation relationship between a first and second object and determining, using a processor, whether a first motion vector of the first object is correlated at a threshold level of correlation with a second motion vector of the second object, the correlation relationship between the first and second objects identifying the threshold level of correlation between the first and second motion vectors. The method also includes, in response to determining that the first motion vector is not correlated at the threshold level with the second motion vector, determining a convergence point for the first and second objects in accordance with a policy. The method further includes transmitting instructions for arriving at the convergence point to the first and second objects.

Abstract: A method includes determining a convergence point for a first object and a second object from position data, capability data, and environment data that are all associated with the first and second object, wherein the convergence point is determined in accordance with a policy. The method also includes determining a first amount of time for the first object to reach the convergence point based on the position data, the capability data, and the environment data. The method still further includes determining a second amount of time for the second object to reach the convergence point based on the position data, the capability data, and the environment data. The method additionally includes determining an estimated amount of time to convergence between the first object and the second object, and transmitting the estimated amount of time to convergence to one or more of the first and second object.