Abstract: A user equipment (UE) is configured to receive, from a base station of a wireless network, a deactivation indication indicating that a secondary cell group (SCG) of a dual connectivity (DC) wireless communications configuration has been deactivated, receive, from the base station, a remapping indication indicating that SCG data originally mapped to an SCG bearer should be remapped to an alternate bearer, remap the SCG data to the alternate bearer based on the remapping indication and transmit data over the alternate bearer.

Abstract: A user equipment (UE) is configured to deactivate/activate a point-to-multipoint (PTM) link of a multicast radio bearer (MRB). The UE receives, from a base station of a wireless network, a multicast radio bearer (MRB) configuration, wherein the MRB includes configuration of a split MRB including a point to multipoint (PTM) link and a point to point (PTP) link, deactivates or activates the PTM link based on the MRB configuration, reestablishes a radio link control (RLC) associated with the PTM link and recovers a packet data convergence protocol (PDCP).

Abstract: In various embodiments, a remote access controller supporting remote management of an Information Handling System (IHS). The remote access controller receives a first firmware image comprising first firmware for a first hardware component of the IHS. A personality of the first firmware is determined and at least a portion of the first firmware is stored to a first region of a persistent storage of the first hardware component, where the first region is designated as storing firmware for adapting the first hardware component to the first personality. In response to a detected reconfiguration of the IHS, the remote access controller generates a notification of the first firmware that is stored by the first hardware component and that is usable to adapt operation of the first hardware component to the first personality.

Abstract: In embodiments, an Online Software Platform (OSP) is configured to receive datasets having dataset values that represent relationship instance data of relationship instances between a client primary entity and other secondary entities, select digital rules responsive to these dataset values, produce resources for the datasets by applying the selected digital rules to the dataset values, derive a total value of the produced resources, generate a report document that mentions at least derived the total value, and prepare an instantiation that enables viewing a data record that substantiates the derived total value. The OSP may further make this instantiation available for viewing to a domain entity that has access to the report document and wants to confirm the derived total value. For instance, the OSP may provide an access instruction for viewing the instantiation, or even augment the report document itself with such an access instruction.

Abstract: A system and method for continuously improving the cybersecurity rating of a firm includes a feed processor configured to parse at least one cybersecurity report and to feed for building an instance graph. The system may identify, measure, and monitor portfolio dimensions of public-facing digital artifacts. In addition, the system and method may each include the instance graph being compared by a correlation engine with a reference graph to generate automated or semi-automated remediation recommendations that may lead to continuous improvement of a cybersecurity rating for an organization or firm.

Abstract: The present application relates to devices and components including apparatuses, systems, and methods for technologies for packet framing for application data unit transmission in wireless networks.

Abstract: A system with an integrated internal battery, a removable battery, and a power management integrated circuit. The power management integrated circuit is configured to provide power to the image capture device from the removable battery; provide power to the image capture device from the integrated internal battery when the removable battery is disconnected from the image capture device or the removable battery does not have a sufficient charge to drive the image capture device; and recharge the integrated internal battery before the removable battery until the integrated internal battery has a sufficient charge to power the image capture device. The power is continuously supplied to the image capture device from the integrated internal battery while the removable battery is disconnected from the image capture device or does not have sufficient power to drive the image capture device.

Abstract: A system and method for generating security and compliance-aware software delivery pipelines includes a dashboard for configuring and inputting an application profile and an application context. In addition, the system and method each include an application profiler captures, discovers, and stores one or more attributes of an application, along with a pipeline generator that receives information from the dashboard to ultimately generate at least one software delivery pipeline outfitted with security policies and compliance-aware guidelines.

Abstract: A system includes a computing platform having a hardware processor and a memory storing software code and a machine learning (ML) model trained to predict the next element of a sequence. The software code is executed to receive input data identifying an element of the sequence, determine, using the input data, at least one mood driver(s) of the sequence, and predict, based on input data and the mood driver(s), one or more candidate next element(s) of the sequence using the ML model. The software code further obtains expertise data relating to the sequence, evaluates the candidate next element(s), using the expertise data, the input data, and the mood driver(s), to provide aptness score(s) each corresponding to a respective one candidate next element, and determines, using the aptness score(s) and a respective probability assigned to each of the candidate next element(s) by the ML model, the next element of the sequence.

Abstract: A system includes a computing platform having a hardware processor and a memory storing software code, a memory data structure storing memory features for an artificial intelligence interactive character (AIIC), and a trained machine learning (ML) model. The software code is executed to elicit, using the AIIC, a reminiscence from a user, predict, using the trained ML model and the reminiscence, one or more user memory feature(s) of the reminiscence, identify, using the memory data structure, one or more of the memory features for the AIIC as corresponding to the user memory feature(s), and determine, using the user memory feature(s), a mood modifier for a creative composition. The software code is further executed to produce, based on the mood modifier and the corresponding one or more of the plurality of memory features for the AIIC, the creative composition, and provide the creative composition to the AIIC.

Abstract: Systems and methods capture image dynamics and use those captured image dynamics for image feature recognition and classification. Other methods and systems train a neural network to capture image dynamics. An image vector representing image dynamics is extracted from an image of an image stream using a first neural network. A second neural network, predicts a previous and/or subsequent image in the image stream from the image vector. The predicted previous and/or subsequent image is compared with an actual previous and/or subsequent image from the image stream. The first and second neural networks are trained using the result of the comparison.

Abstract: Systems, methods, and devices are provided for performing cell selection or reselection in networks that include earth moving beams. In one example, a method includes receiving, via a satellite, control information transmitted by a base station indicating that a beam associated with a cell ID has been redirected; in response, measuring a beam strength of the redirected beam; and performing cell re-selection based on the measured beam strength.

Abstract: Disclosed embodiments relate to instructions for fused multiply-add (FMA) operations with variable-precision inputs. In one example, a processor to execute an asymmetric FMA instruction includes fetch circuitry to fetch an FMA instruction having fields to specify an opcode, a destination, and first and second source vectors having first and second widths, respectively, decode circuitry to decode the fetched FMA instruction, and a single instruction multiple data (SIMD) execution circuit to process as many elements of the second source vector as fit into an SIMD lane width by multiplying each element by a corresponding element of the first source vector, and accumulating a resulting product with previous contents of the destination, wherein the SIMD lane width is one of 16 bits, 32 bits, and 64 bits, the first width is one of 4 bits and 8 bits, and the second width is one of 1 bit, 2 bits, and 4 bits.

Abstract: A method of monitoring building compliance with healthy building guidelines includes obtaining current parameter values for a plurality of different parameters from a plurality of sensors disposed within a plurality of different zones of a building. For each of the parameters, the current parameter value received from each of the plurality of different zones is compared with a corresponding healthy building range for that parameter as specified by the healthy building guidelines. A healthy building dashboard is displayed that includes a summary that shows, for each of the plurality of different parameters, how many zones of the plurality of different zones of the building are within the corresponding healthy building range for that parameter and/or how many zones of the plurality of different zones are not within the corresponding healthy building range for that parameter.

Abstract: Techniques for orchestrating a workflow for configuring a computer networking environment or other complex workflows are described. A Directed Acyclic Graph (DAG) that defines a plurality of tasks to be executed to complete the workflow and a plurality of orders between the tasks is received. Embodiments generate a State-Machine Neural Network (SNN) based on the received DAG, by generating a plurality of SNN neurons for the SNN, based on the plurality of tasks within the received DAG and generating a plurality of SNN connections for the SNN, connecting pairs of SNN neurons within the plurality of SNN neurons, based on the plurality of connections within the received DAG. The SNN is executed to orchestrate the workflow by sending and receiving signals to and from the SNN neurons.

Abstract: Energy consumption modelling requires to consider various factors affecting the energy consumption in buildings, to be able to effectively forecast future consumption. Even though some of the state of the art deep learning based approaches are able to address these requirements to some extent, they are computationally heavy. The disclosure herein generally relates to energy forecasting, and, more particularly, to a method and system for graph signal processing (GSP) based energy modelling and forecasting. The system monitors and collects information on energy consumption in a building and values of associated energy consumption parameters. This input data is further processed using GSP to generate a building energy consumption model, from which a smooth signal is obtained by applying total variation minimization. The system further performs forecasting using the smooth signal.

Abstract: Self-consumable portions generation techniques from a digital document are described. The self-consumable portions are generated based on a determination of an amount of resources available at a receiver device that is to receive the digital document. Examples of the resources include an amount of memory resources, processing resources, and/or network resources associated with the receiver device. The self-consumable portions, once generated, are separately renderable at the receiver device.

Abstract: Various embodiments include an automobile network device that includes a descriptor sorting engine (DSE). The DSE may include a direct memory access (DMA) controller, a memory organized by channel clusters that each include a plurality of first-in first-out (FIFO) memories, a timer, and a time stamp (TS) sorting logic component. The DMA controller may be configured to pull timestamp-pointer pairs from packet descriptors stored in an unsorted descriptor ring memory, store the timestamp-pointer pairs in the FIFO memories, trigger the TS sorting logic component to reorder the timestamp-pointer pairs in the FIFO memories so that they are sorted in ascending order, use the sorted timestamp-pointer pairs in the FIFO memories to read the packet descriptors stored in an unsorted descriptor ring memory, and store the packet descriptors in a sorted descriptor ring memory.

Abstract: Aspects of the preset document relate to social or group-based presentation of cryptographic objects. Cryptographic objects linked to members of a group are visually represented to each group member, such as in a “binder”-like presentation. Further, group activity related to the cryptographic objects is also presented to each group member. Specific object events of the group activity are detected based on searching an immutable cryptographic ledger. The object events are categorized according to value and volume of the cryptographic objects involved in the object events. With this event data (and event data for other related object events), event signifiers are assigned to object events and presented to group members. Example event signifiers include audio, persistent visuals, and/or dynamic visuals that are connected to visual representations of the cryptographic objects involved in the object events.

Abstract: Techniques are described for determining a likelihood that a customer communication is fraudulent using one or more machine learning models. For example, a computing system includes a memory and one or more processors in communication with the memory. The one or more processors are configured to: receive a set of emotion factor values for communication data of a current communication associated with a customer, wherein each emotion factor value indicates a measure of a particular emotion factor in the current communication; classify, using an emotion variance model running on the one or more processors, the current communication into an emotional fraud category based on the set of emotion factor values for the current communication associated with the customer; and determine a risk score for the current communication indicative of a probability that the current communication is fraudulent based on at least the emotional fraud category for the current communication.