Abstract: This disclosure describes a card-scan system that can update a card-scan machine learning model to improve card-character predictions for character-bearing cards by using an active-learning technique that learns from card-scan representations indicating corrections by users to predicted card characters. In particular, the disclosed systems can use a client device to capture and analyze a set of card images of a character-bearing card to predict card characters using a card-scan machine learning model. The disclosed systems can further receive card-scan gradients representing one or more corrections to incorrectly predicted card characters. Based on the card-scan gradients, the disclosed systems can generate active-learning metrics and retrain or update the card-scan machine learning model based on such active-learning metrics.

Abstract: Disclosed are aspects of network function placement in virtual graphics processing unit (vGPU)-enabled environments. In one example a network function request is associated with a network function. A scheduler selects a vGPU-enabled GPU to handle the network function request. The vGPU-enabled GPU is selected in consideration of a network function memory requirement or a network function IO requirement. The network function request is processed using an instance of the network function within a virtual machine that is executed using the selected vGPU-enabled GPU.

Abstract: Embodiments of a device and method are disclosed. In an embodiment, a method for network security involves determining whether a device connected to a network port of a switch of a network is a native device or a non-native device for the network and in response to determining whether the device is the native device or the non-native device for the network, performing native device authentication or non-native device authentication.

Abstract: Techniques related to calibration of wide-baseline outside-in multi camera systems are discussed. Such techniques include receiving video sequences and intrinsic parameters corresponding to each of the cameras in the system, generating first extrinsic parameters for the cameras using tracked features and the intrinsic parameters and second extrinsic parameters using location parameters corresponding to motion control systems coupled to the cameras, and combining the first and second extrinsic parameters to provide real-time calibration of extrinsic parameters for the cameras.

Abstract: Embodiments of a device and method are disclosed. In an embodiment, a method for network device authentication involves using Remote Procedure Call (RPC) proxies of network devices of a network at a customer site, accessing identity certificates from Trusted Platform Modules (TPMs) of the network devices, and using the RPC proxies, sequentially establishing Transport Layer Security (TLS) connections between the network devices to perform mutual authentication between the network devices based on the identity certificates.

Abstract: A bearing assembly includes an inner race to receive a rotating component having an axis of rotation, and at least one bearing element coupled to the inner race. The at least one bearing element is to rotate with the inner race along the axis of rotation. The bearing assembly includes an outer race coupled to the at least one bearing element, and a bearing housing coupled to the outer race and to be coupled to a static structure. The bearing housing includes a first frangible portion to release upon receipt of a moment load greater than a moment threshold and a second frangible portion to release upon receipt of a radial load greater than a radial threshold.

Abstract: Examples disclosed herein involve a computing system configured to (i) obtain (a) a first set of sensor data captured by a first sensor system of a first vehicle that indicates the first vehicle's movement and location with a first degree of accuracy and (b) a second set of sensor data captured by a second sensor system of a second vehicle that indicates the second vehicle's movement and location with a second degree of accuracy that differs from the first degree of accuracy, (ii) based on the first set of sensor data, derive a first trajectory for the first vehicle that is defined in terms of a source-agnostic coordinate frame, (iii) based on the second set of sensor data, derive a second trajectory for the second vehicle that is defined in terms of the source-agnostic coordinate frame, and (iv) store the first and second trajectories in a database of source-agnostic trajectories.

Abstract: A method of virtually inspecting a quality of a product in a production environment includes receiving production information associated with the product, wherein the information is indicative of an operation performed on the product in relation to a first process of the production environment. The method further includes: determining a label for the product using a first classifier model based on the received information associated with the product; comparing the determined label against inspection data associated with an inspection of the product; and storing the production information and the inspection data associated with the product in an extension buffer, based on the comparison of the determined label and the inspection data, for retraining the first classifier model. The above method allows for storing new fault category information that is then dynamically used to retrain the model. Accordingly, this allows for retraining of classifier models without human intervention.

Abstract: This disclosure describes a card-scan system that can update a card-scan machine learning model to improve card-character predictions for payment cards, driver licenses, or other character-bearing cards by using an active-learning technique that learns from card-scan representations indicating corrections by users to predicted card characters. In particular, the disclosed systems can use a client device to capture and analyze a set of card images of a character-bearing card to predict card characters using a card-scan machine learning model. The disclosed systems can further receive card-scan gradients representing one or more corrections to incorrectly predicted card characters. Based on the card-scan gradients, the disclosed systems can generate active-learning metrics and retrain or update the card-scan machine learning model based on such active-learning metrics.

Abstract: Disclosed herein are methods, systems, and programming for evaluating a medical workflow. For example, one or more images depicting a medical environment and a medical workflow performed in the medical environment may be received. Contextual information associated with the medical workflow may be determined, and a stage of the medical workflow may be identified, based on the one or more images. A subsequent stage of the medical workflow may be determined based on the identified stage, and first medical content associated with the contextual information and second medical content associated with the subsequent stage may be presented to one or more medical staff located within the medical environment.

Abstract: Conventional Question and Answer (QA) datasets are created for generating factoid questions only and the present disclosure generates longform technical QA dataset from textbooks. Initially, the system receives a technical textbook document and extracts a plurality of contexts. Further, a first plurality of questions are generated based on the plurality of contexts. A plurality of answerable questions are generated further based on the plurality of contexts using an unsupervised template-based matching technique. Further, a combined plurality of questions are generated by combining the first plurality of questions and the plurality of answerable questions. Further, an answer for the combined plurality of questions are generated using an autoregressive language model and a mapping score is computed. Further, a plurality of optimal answers are selected based on the corresponding mapping score.

Abstract: A method for training a machine learning model. The method includes: determining a plurality of training sequences of training-input data elements, wherein for each training sequence each training-input data element contains sensor data for a time point from a time period assigned to the training sequence in which a prespecified event takes place at least once at one or more respective event time points; determining, for each training-input data element, the temporal distance between the time point for which the training-input data element contains sensor data and one of the one or more respective event time points; and training the machine learning model depending on the determined temporal distances.

Abstract: Multi-camera dynamic calibration can be performed using three or more images, each from a separate camera viewing the same 3D scene. Multi-camera translation magnitude can be determined by incorporating information from an additional image. A relative translation scale is determined for a configuration of three cameras using a ratio of translation magnitudes. The translation scale can be expanded to configurations having more than three cameras using the relative scale of the pair-wise camera translations to determine translation scales for a multi-camera set-up. If the ground-truth translation is known for a pair of cameras, then the translation magnitude can be determined for all pairs of cameras to ground-truth accuracy. Multi-camera scale estimation is divided into smaller overlapping triplet-camera scale estimation, and the translation scale determination corresponding to each image pair is applied iteratively to overlapping sets of three images.

Abstract: The present invention relates to the field of networking and API/application security. In particular, the invention is directed towards methods, systems and computer program products for Application Programming Interface (API) based flow control and API based security at the application layer of the networking protocol stack. The invention additionally provides an API deception environment to protect a server backend from threats, attacks and unauthorized access.

Abstract: Embodiments of a device and method are disclosed. In an embodiment, a method of communications involves from a wireless sensor deployed at a customer site, connecting to a wireless access point (AP) deployed at the customer site and based on a private key stored in the wireless sensor, performing mutual authentication between the wireless sensor and an authentication server connected to the wireless AP.

Abstract: Embodiments provide methods and systems for facilitating scheduled payment transactions to users via an application provided by the server system, the application available on the user device. The method performed by the server system includes receiving a transaction request message from the user device, the transaction request message includes scheduled transaction instruction provided by the user in natural language format. The method includes authenticating the user based on a plurality of user authentication factors received from the user at pre-defined time intervals. In an embodiment, the server system is configured to train a data model using ML algorithms by learning the plurality of user authentication factors based on which the user is automatically authenticated by the server system. Upon successful authentication, the method includes parsing the transaction request message to determine a scheduled transaction instruction.

Abstract: Embodiments provide methods and systems for facilitating scheduled payment transactions to users via an application provided by the server system, the application available on the user device. The method performed by the server system includes receiving a transaction request message from the user device, the transaction request message includes scheduled transaction instruction provided by the user in natural language format. The method includes authenticating the user based on a plurality of user authentication factors received from the user at pre-defined time intervals. In an embodiment, the server system is configured to train a data model using ML algorithms by learning the plurality of user authentication factors based on which the user is automatically authenticated by the server system. Upon successful authentication, the method includes parsing the transaction request message to determine a scheduled transaction instruction.

Abstract: Disclosed are various embodiments for rate proportional scheduling to reduce packet loss in virtualized network function chains. A congestion monitor executed by a first virtual machine executed by a host computing device can detect congestion in a receive queue associated with a first virtualized network function implemented by a first virtual machine. The congestion monitor can send a pause signal to a rate controller executed by a second virtual machine executed by the host computing device. The rate controller can receive the pause signal. In response, the rate controller can pause the processing of packets by a second virtualized network function implemented by the second virtual machine to reduce congestion in the receive queue of the first virtualized network function.

Abstract: The present invention relates to the field of networking and API/application security. In particular, the invention is directed towards methods, systems and computer program products for deep learning based API traffic analysis and network security. The invention provides an automated approach to threat and/or attack detection by machine learning based accumulation and/or interpretation of various API/application traffic patterns, identifying and mapping characteristics of normal traffic for each API, and thereafter identifying any deviations from the normal traffic parameter baselines, which deviations may be classified as anomalies or attacks.

Abstract: A system and method for monitoring operations and detecting failures of a plurality of connected devices installed on a premises determines the correct vendor for failure resolution and provides the vendor with a secure and limited mechanism to access assigned devices remotely for servicing. The system limits vendor access to only devices configured on a respective virtual network provisioned in a container provided on a network monitoring device.