Abstract: Computer implemented method, systems, and computer program products include program code executing on a processor(s) that determines that a user has purchased a product. The program code classifies, with at least one trained algorithm, the product into a product type classification. The program code implements one or more trigger events; based on each trigger event occurring, the processor(s) generates and transmits an inquiry to the user. The program code determines that a trigger event has occurred. The program code generates the inquiry, obtains responsive feedback, and generates a product review.

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: Approaches, techniques, and mechanisms are disclosed for efficiently buffering data units within a network device. A traffic manager or other network device component receives Transport Data Units (“TDUs”), which are sub-portions of Protocol Data Units (“PDUs”). Rather than buffer an entire TDU together, the component divides the TDU into multiple Storage Data Units (“SDUs”) that can fit in SDU buffer entries within physical memory banks. A TDU-to-SDU Mapping (“TSM”) memory stores TSM lists that indicate which SDU entries store SDUs for a given TDU. Physical memory banks in which the SDUs are stored may be grouped together into logical SDU banks that are accessed together as if a single bank. The TSM memory may include a number of distinct TSM banks, with each logical SDU bank having a corresponding TSM bank. Techniques for maintaining inter-packet and intra-packet linking data compatible with such buffers are also disclosed.

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: The disclosure describes, in part, techniques for attempting to diagnose the cause of a wireless connection problem in a client computing device and, in response, performing one or more actions for remedying the problem. In some instances, the techniques describe the client device communicating with one or more other client devices in the environment to determine whether they are connected to the WAP using the same password or a different password as the client device. If the former, then the client device may determine that the network stack is likely the cause of the connection error and may attempt to restart the network stack to current problem. If the other devices indicate that they are using the same password, however, then the client device may prompt a user to re-enter the network password.

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.

Abstract: Methods, apparatuses, and computer-readable storage medium for resolving state mismatch between a dual subscriber identity module (SIM) dual standby (DSDS)UE and a network are provided. An example method may include receiving, from at least one base station, a connected mode discontinuous reception (DRX) (CDRX) configuration and an idle DRX (IDRX) configuration, the CDRX configuration being associated with a first subscriber and the IDRX configuration being associated with a second subscriber. The example method may further include transmitting, to an upper layer, a radio resource control (RRC) release request based on the CDRX configuration and the IDRX configuration. The example method may further include transmitting, to an associated base station of the at least one base station, a synchronization message to get in synchronization with the associated base station.

Abstract: Aspects described herein relate to transmitting a scheduling request (SR) for an uplink grant based at least in part on presence of data in a buffer, wherein the SR is scheduled for transmission according to a SR periodicity, and aborting, based at least in part on expiration of a discard timer corresponding to the buffer and/or the buffer being empty, at least a next SR that is scheduled for transmission according to the SR periodicity.

Abstract: In an example aspect, a first object (e.g., an electronic component) is inserted by a robot into a second object (e.g., a PCB). An autonomous system can capture a first image of the first object within a physical environment. The first object can define a mounting interface configured to insert into the second object. Based on the first image, a robot can grasp the first object within the physical environment. While the robot grasps the first object, the system can capture a second image of the first object. The second image can include the mounting interface of the first object. Based on the second image of the first object, the system can determine a grasp offset associated with the first object. The grasp offset can indicate movement associated with the robot grasping the first object within the physical environment. The system can also capture an image of the second object. Based on the grasp offset and the image of the second object, the robot can insert the first object into the second object.

Abstract: Various embodiments of the teachings herein include methods and/or systems for enhancing performance of a machine learning (ML) classification task. An example method includes: obtaining a first prediction generated by a first ML classification model provided with production data as input; obtaining a second prediction generated by a second ML classification model provided with the production data as input; and determining a prediction result for the production data by calculating a weighted sum of the first prediction and the second prediction based on weights for the first ML classification model and the second ML classification model. The first ML classification model comprises a few-shot learning model having a first feature extractor followed by a metric-based classifier. The second ML classification model has a second feature extractor followed by a fully-connected classifier.

Abstract: In some embodiments, a method includes receiving, at a processor of a server, a first application programming interface (API) call from a client device and providing an indication associated with the first API call as an input to a machine learning model such that the machine learning model identifies a set of parameters associated with a set of likely subsequent API calls. The method can further include receiving a second API call from the client device, identifying the second API call as an anomalous API call based on the second API call not meeting the set of parameters associated with the set of likely subsequent API calls, and sending a signal to perform a remedial action based on the identifying.

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: 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.