Abstract: Systems and methods for an AI agent for agent applications including performing inline monitoring of traffic originating from the user device; receiving a query from a user associated with the user device, the query being associated with an issue affecting connectivity of the user device to one or more resources; analyzing the query via an Artificial Intelligence (AI) agent of the agent application; and any of providing one or more remediation steps for resolving the connectivity issue, generating and submitting an Information Technology (IT) support ticket, and autonomously performing one or more actions to resolve the connectivity issue via the AI agent of the agent application.

Abstract: Systems and methods for detecting and bypassing network throttling in User Datagram Protocol (UDP) connections. Various embodiments include monitoring network traffic to and from a user device, wherein the network traffic is facilitated over a communication mode; collecting telemetry from the network traffic; identifying network throttling based on the telemetry; and responsive to identifying network throttling, utilizing an alternate communication mode, thereby bypassing the network throttling. The network throttling can be identified by calculating a loss value based on the telemetry and determining network throttling based on the loss value.

Abstract: A system described herein may maintain a plurality of support classification models and a plurality of resolution action models; determine respective associations between the plurality of support classification models and the plurality of resolution action models; receive a plurality of support requests; identify a particular support classification model, of the plurality of support classification models, that is associated with a particular support request of the plurality of support requests; identify, based on the determined associations between the plurality of support classification models and the plurality of resolution action models, a particular resolution action model associated with the particular support classification model; identify one or more resolution actions associated with the particular resolution action model; generate an enhanced support request based on the one or more resolution actions; and output the enhanced support request.

Abstract: One or more computing devices, systems, and/or methods for code auto-propagation amongst branches of a software development project are provided. A source branch, of the software development project, is identified for which auto-propagation is enabled for code modifications to the source branch. The software development project is evaluated to identify a destination branch to which the code modifications are to be propagated. In response to identifying a trigger associated with the auto-propagation for the source branch, a code modification made to the source branch is identified. A merge request is automatically generated to merge the code modification from the source branch to the destination branch. The merge request is executed to merge the code modification from the source branch to the destination branch.

Abstract: Systems and methods for detecting malicious mobile applications using machine learning in a cloud-based system utilize a traffic forwarding technique and a cloud-based Machine Learning (ML) model to assess the security of apps installed on a user device. This architecture enables the cloud-based system to have visibility of user devices, train the ML model in real-time with a vast amount of mobile app data from multiple users, and enforce security on the user devices from the cloud-based system. Advantageously, the ML model is trained with a vast amount of mobile traffic, leading to better accuracy of prediction. The cloud-based system can be multi-tenant (enterprise), have a large user base, be spread over a large geographic area, etc. This provides a great opportunity for training data. Feedback from live production data can be fed back into the ML model.

Abstract: Systems and methods for securely handling data traffic on local or private networks, such as by using cloud computing, are provided. A non-transitory computer-readable medium, according to one implementation, may be configured to store executable instructions enabling a processor of a user device to perform the step of discovering an origin of a source application associated with network packets bound for a private address space. The executable instructions may further enable the processor to send a tuple regarding the discovered origin to a cloud server to request an analysis of the tuple. Upon receiving an allow instruction from the cloud server, the instructions enable the processor to allow the network packets to flow normally to a destination associated with the private address space. Upon receiving a deny instruction from the cloud server, the instructions enable the processor to drop the network packets.

Abstract: Systems and methods for detecting and bypassing network throttling in User Datagram Protocol (UDP) connections. Various embodiments include monitoring network traffic to and from a user device, wherein the network traffic is facilitated over a communication mode; collecting telemetry from the network traffic; identifying network throttling based on the telemetry; and responsive to identifying network throttling, utilizing an alternate communication mode, thereby bypassing the network throttling. The network throttling can be identified by calculating a loss value based on the telemetry and determining network throttling based on the loss value.

Abstract: Systems and methods implemented by a mobile device include establishing a plurality of tunnels to a gateway, wherein each of the plurality of tunnels is on one of a plurality of link layer channels at the mobile device; intercepting network traffic on the mobile device; forwarding the network traffic to one of the plurality of tunnels based on a set of traffic forwarding rules; and responsive to a network change for the mobile device, managing the plurality of tunnels and continuing the forwarding based on the managing. The systems and methods can further include determining characteristics including bandwidth of each of the plurality of link layer channels; and utilizing the characteristics with the set of traffic forwarding rules for the forwarding.

Abstract: Disclosed are methods and systems for predicting time varying loudness in a geographic region. Training data, including noise information, weather information, and traffic information is collected from a plurality of sensors located in a plurality of geographic regions. The information is collected during multiple time periods. The noise information includes time varying loudness. Static features of the geographic regions are also defined and included in the training data. The static and time varying dynamic features train a model. The model is used predict time varying loudness within a different region and at a time later than times the training data is collected. The predicted loudness levels are utilized, in some aspects, to determine a route for an aircraft.

Abstract: Systems and methods include intercepting traffic on the user device; forwarding the traffic to a cloud-based system for security processing therein; and, responsive to unavailability of the cloud-based system preventing the forwarding, performing local security processing of the traffic at the user device including determining whether the traffic is allowed based on a cache at the user device, forwarding the traffic separate from the cloud-based system when it is allowed, and blocking the traffic when it is not allowed.

Abstract: Disclosed are methods and systems for predicting time varying loudness in a geographic region. Training data, including noise information, weather information, and traffic information is collected from a plurality of sensors located in a plurality of geographic regions. The information is collected during multiple time periods. The noise information includes time varying loudness. Static features of the geographic regions are also defined and included in the training data. The static and time varying dynamic features train a model. The model is used predict time varying loudness within a different region and at a time later than times the training data is collected. The predicted loudness levels are utilized, in some aspects, to determine a route for an aircraft.

Abstract: Systems and methods include providing functionality for the user device while operating in background on the user device including providing secure connectivity with a cloud-based system over a network; continuously collecting packets intercepted by the enterprise application over a time interval, wherein the collected packets are collected over the time interval; and responsive to an issue with functionality of the enterprise application, transmitting the collected packets to a back end server for troubleshooting of the issue. The time interval is a set amount of time, and each collected packet is deleted at the expiration of the time interval.

Abstract: Systems and methods include intercepting traffic on a mobile device based on a set of rules; determining whether a connection associated with the traffic is allowed based on a local map associated with an application; responsive to the connection being allowed or blocked based on the local map, one of forwarding the traffic associated with the connection when allowed and generating a block of the connection at the mobile device when blocked; and, responsive to the connection not having an entry in the local map, forwarding a request for the connection to a cloud-based system for processing therein. The cloud-based system is configured to allow or block the connection based on the connection not having an entry in the local map.

Abstract: Systems and methods include providing functionality for the user device while operating in background on the user device including providing secure connectivity with a cloud-based system over a network; continuously collecting packets intercepted by the enterprise application over a time interval, wherein the collected packets are collected over the time interval; and responsive to an issue with functionality of the enterprise application, transmitting the collected packets to a back end server for troubleshooting of the issue. The time interval is a set amount of time, and each collected packet is deleted at the expiration of the time interval.

Abstract: Systems and methods include providing functionality for the user device while operating in background on the user device including providing secure connectivity with a cloud-based system over a network; continuously collecting packets intercepted by the enterprise application over a time interval, wherein the collected packets are collected over the time interval; and responsive to an issue with functionality of the enterprise application, transmitting the collected packets to a back end server for troubleshooting of the issue. The time interval is a set amount of time, and each collected packet is deleted at the expiration of the time interval.

Abstract: Systems and methods for detecting malicious mobile applications using machine learning in a cloud-based system utilize a traffic forwarding technique and a cloud-based Machine Learning (ML) model to assess the security of apps installed on a user device. This architecture enables the cloud-based system to have visibility of user devices, train the ML model in real-time with a vast amount of mobile app data from multiple users, and enforce security on the user devices from the cloud-based system. Advantageously, the ML model is trained with a vast amount of mobile traffic, leading to better accuracy of prediction. The cloud-based system can be multi-tenant (enterprise), have a large user base, be spread over a large geographic area, etc. This provides a great opportunity for training data. Feedback from live production data can be fed back into the ML model.

Abstract: A method and apparatus for monitoring tension and detecting slip of industrial belts while in running condition on belt drive system by using ultrasonic transducers. The sensing part involves two ultrasonic transducers which are installed on an assembly which is placed within a constrained space between the tension side and slack side of belt. Sinusoidal waves having predefined amplitude are sent from transmitting transducer on the belt and amplitude of the signal reflected from the belt is measured and compared with a threshold value to detect reduced tension. An acoustic sleeve is mounted on driving and driven pulley locknuts to blank the reflected signal periodically to measure the speeds of the pulleys compared to detect the slip of the belt. The apparatus generates an alert to user when the belt tension is reduced and/or when slip is detected. The processing circuit involves microcontroller with Wi-Fi module to send alert messages.

Abstract: Systems and methods include providing functionality for the user device while operating in background on the user device including providing secure connectivity with a cloud-based system over a network; continuously collecting packets intercepted by the enterprise application over a time interval, wherein the collected packets are collected over the time interval; and responsive to an issue with functionality of the enterprise application, transmitting the collected packets to a back end server for troubleshooting of the issue. The time interval is a set amount of time, and each collected packet is deleted at the expiration of the time interval.

Abstract: Systems and methods for securely handling data traffic on local or private networks, such as by using cloud computing, are provided. A non-transitory computer-readable medium, according to one implementation, may be configured to store executable instructions enabling a processor of a user device to perform the step of discovering an origin of a source application associated with network packets bound for a private address space. The executable instructions may further enable the processor to send a tuple regarding the discovered origin to a cloud server to request an analysis of the tuple. Upon receiving an allow instruction from the cloud server, the instructions enable the processor to allow the network packets to flow normally to a destination associated with the private address space. Upon receiving a deny instruction from the cloud server, the instructions enable the processor to drop the network packets.

Abstract: An enterprise application executed on a user device is configured to perform steps of providing functionality for a user device while operating in background on the user device; determining an issue with the functionality of the enterprise application; presenting a user of the user device a list of a plurality of issue types for selection thereof; receiving a selection from the user of an issue type for the issue; and collecting data from the user device based on the selected issue type. The steps can further include transmitting the collected data to a back end server for troubleshooting of the issue.