Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to detect deepfake content. An example apparatus to determine whether input media is authentic includes a classifier to generate a first probability based on a first output of a local binary model manager, a second probability based on a second output of a filter model manager, and a third probability based on a third output of an image quality assessor, a score analyzer to obtain the first, second, and third probabilities from the classifier, and in response to obtaining a first result and a second result, generate a score indicative of whether the input media is authentic based on the first result, the second result, the first probability, the second probability, and the third probability.

Abstract: Aspects of the disclosure relate to detecting an emergency vehicle. For instance, a plurality of images may be taken from a perspective of an autonomous vehicle. One or more gates representing a region of interest at a respective distance from the vehicle may be generated for the images. A plurality of lights may be detected within the one or more gates. A first candidate emergency vehicle may be identified from a detected plurality of lights in one or more gates of one of the images, and a second candidate emergency vehicle may be identified from a detected plurality of lights in one or more gates of another of the images. The first and second candidate emergency vehicles are determined to be the same emergency vehicle and to be active. An operational system of the autonomous vehicle is controlled based on the determination that the given emergency vehicle is active.

Abstract: A radar method is described. According to one exemplary embodiment, the method includes generating a first RF oscillator signal in a first chip and supplying the first RF oscillator signal to a transmission (TX) channel of the first chip and transmitting the first RF oscillator signal from the TX channel of the first chip to the second chip via a transmission line.

Abstract: Systems, methods, and software described herein manage descriptive attributes associated with objects in video data. In one example, a video processing service obtains video data and identifies an object of interest in the video data. The video processing service further identifies attributes for the object of interest from the video data based on the attributes satisfying uniqueness requirements to differentiate the object of interest from other objects. Once the attributes are identified, the attributes may be stored in a storage system to be compared against other objects in second video data.

Abstract: Systems, methods, and other embodiments described herein relate to improving the classification of objects depicted in a scene. In one embodiment, a method includes generating, using an ontological detector, a type classification of a detected object according to a detector ontology of known classes. The detected object is represented as segmented data from sensor data about a surrounding environment. The method includes, in response to determining that the type classification specifies an unknown class that is not defined in the detector ontology, annotating the segmented data as unknown. The method includes providing the segmented data to specify that the type classification for the detected object is unknown.

Abstract: Mitigation of satellite interference is contemplated. The mitigation may include processing satellite transmissions to remove interferences based on an amount of signal overlap, such as to facilitate mitigating interferences resulting from satellite spacing and/or ground antenna dish size.

Abstract: A light emitting device includes a wiring substrate, a light emitting element array that includes a first side surface and a second side surface facing each other, and a third side surface and a fourth side surface connecting the first side surface and the second side surface to each other and facing each other, the light emitting element array being provided on the wiring substrate, a driving element that is provided on the wiring substrate on the first side surface side and drives the light emitting element array, a first circuit element and a second circuit element that are provided on the wiring substrate on the second side surface side to be arranged in a direction along the second side surface, and a wiring member that is provided on the third side surface side and the fourth side surface side and extends from a top electrode of the light emitting element array toward an outside of the light emitting element array.

Abstract: Presented are concepts for matching a subject to one or more resources or workflow steps. Once such concept comprises obtaining data associated with a subject, the data comprising, for each of a plurality of parameters, a parameter value relating to the subject. A plurality of data groups for characterising the subject is then generated and a classification process is applied to each data group so as to generate a classification result for each data group. The subject is then matched to one or more resources or workflow steps based on the classification results.

Abstract: The present disclosure relates to systems, devices and methods for identifying objects and scenarios that have not been trained or are unidentifiable to vehicle perception sensors or vehicle assistive driving systems. Embodiments are directed to using a trained vehicle data set to identify target objects in vehicle sensor data. In one embodiment, a process is provided that includes running a scene detection operation on vehicle to derive a vector of target object attributes of the vehicle sensor data and generating a vector representation for the scene detection operation and the attributes of the vehicle sensor data. The vector representation compared to a familiarity vector to represent effectiveness of the scene detection operation. In addition, the vector representation can be scored to identify one or more target objects or significant scenarios, including unidentifiable objects and/or driving scenes, scenarios for reporting.

Abstract: Apparatus and methods for signal booster systems with compensation for cable loss are provided herein. In certain configurations, a signal booster system includes two or more antennas for wirelessly communicating RF signals and a signal booster including booster circuitry for providing amplification to at least a portion of the RF signals. At least one of the antennas is connected to the signal booster via a cable. Additionally, the signal booster includes a cable loss compensation circuit that adjusts a gain of the booster circuitry to compensate for a loss of the cable.

Abstract: A communication apparatus connectable to a communication network notifies the presence of the communication apparatus using a communication channel assigned to the communication apparatus, then sets one communication channel different from the assigned communication channel of those available in the communication network, and searches for a communication partner apparatus which functions as a providing apparatus that provides communication parameters using the set communication channel. The communication apparatus repetitively executes the notification processing and the search processing, and changes a communication channel used in the search processing every time the repetition is executed one or a plurality of number of times.

Abstract: Aspects of the disclosure relate to detecting an emergency vehicle. For instance, a plurality of images may be taken from a perspective of an autonomous vehicle. One or more gates representing a region of interest at a respective distance from the vehicle may be generated for the images. A plurality of lights may be detected within the one or more gates. A first candidate emergency vehicle may be identified from a detected plurality of lights in one or more gates of one of the images, and a second candidate emergency vehicle may be identified from a detected plurality of lights in one or more gates of another of the images. The first and second candidate emergency vehicles are determined to be the same emergency vehicle and to be active. An operational system of the autonomous vehicle is controlled based on the determination that the given emergency vehicle is active.

Abstract: Implementations disclosed herein provide a charging station configured to attach to or be positioned adjacent to a wall in a confinement institution and to simultaneously charge multiple tablets, mobile phones, laptops, or other portable electronic devices. In some implementations, the charging station is configured with protective sides that may help protect inserted electronic devices from damage. The charging and use of the electronic devices within the confinement institution may be automatically managed based connections at the charging stations, communications with the electronic devices, audio, images, or video of the electronic devices or users captured cameras on the charging station or the electronic devices, and/or information provided by the users.

Abstract: A device and method of verifying protective case usage, is described. The device includes one or more processors to drive an electromechanical transducer with an input signal. The electromechanical transducer generates an input force based on the input signal, and a sensor of the device generates a test output signal in response to the input force. The one or more processors determine, based on the test output signal, a level of verification that a protective case is mounted on the device. The determination can include determining whether the test output signal matches a predetermined impulse response signal indicative of an unprotected device or a protected device. A digital identification tag of the protective case can be read by a radio-frequency transceiver of the device to provide an additional level of verification that the protective case is mounted on the device. Other aspects are described and claimed.

Abstract: Implementations disclosed herein provide a charging station configured to attach to or be positioned adjacent to a wall in a confinement institution and to simultaneously charge multiple tablets, mobile phones, laptops, or other portable electronic devices. In some implementations, the charging station is configured with protective sides that may help protect inserted electronic devices from damage. The charging and use of the electronic devices within the confinement institution may be automatically managed based connections at the charging stations, communications with the electronic devices, audio, images, or video of the electronic devices or users captured cameras on the charging station or the electronic devices, and/or information provided by the users.

Abstract: An aircraft includes a vessel checker, an image generator, an appearance determining unit, and an information transmitter. The vessel checker identifies a suspicious vessel candidate by comparing a marine vessel detected by a marine search radar with a marine vessel transmitting data with an automatic identification system. The image generator generates an image by photographing the suspicious vessel candidate after the aircraft approaches the suspicious vessel candidate in accordance with a route for approaching the suspicious vessel candidate. The appearance determining unit determines whether the suspicious vessel candidate in the image has an appearance characteristic of a suspicious vessel. The information transmitter transmits, to an external apparatus, information indicating that the suspicious vessel candidate has the appearance characteristic of the suspicious vessel if the suspicious vessel candidate has the appearance characteristic of the suspicious vessel.

Abstract: A device may obtain first data relating to a machine learning model. The device may pre-process the first data to alter the first data to generate second data. The device may process the second data to select a set of features from the second data. The device may analyze the set of features to evaluate a plurality of types of machine learning models with respect to the set of features. The device may select a particular type of machine learning model for the set of features based on analyzing the set of features to evaluate the plurality of types of machine learning models. The device may tune a set of parameters of the particular type of machine learning model to train the machine learning model. The device may receive third data for prediction. The device may provide a prediction using the particular type of machine learning model.

Abstract: A dynamic gesture recognition method includes: performing detection on each frame of image of a video stream using a preset static gesture detection model to obtain a static gesture in each frame of image of the video stream; in response to detection of a change of the static gesture from a preset first gesture to a second gesture, suspending the static gesture detection model and activating a preset dynamic gesture detection model; and performing detection on multiple frames of images that are pre-stored in a storage medium using the dynamic gesture detection model to obtain a dynamic gesture recognition result.

Abstract: A method, computer program product, and a system where a processor(s) ingests content from a source(s) with an attribute(s) comprising a verified temporal context(s) of the source(s). The processor(s) cognitively analyzes the content, by applying an entity recognition algorithm(s) to identify and extract entities in the source(s). The processor(s) classifies each extracted entity into a given grouping from a plurality of groupings based on at least one attribute comprising the verified temporal context of the source from which the extracted entity was extracted. The processor(s) generates a corpus comprising the groupings; each grouping comprises extracted entities with verified temporal contexts consistent with a defined time period.

Abstract: Systems and methods to configure sidelink resources in a dual connectivity operation are provided. In some aspect, a method comprising: transmitting, from a user equipment (UE), a first message to a first evolved Node B (eNB), wherein the first message indicates a request to establish a sidelink communication; receiving, at the UE, a second message from the first eNB, wherein the second message indicates a frequency that is used by a secondary evolved Node B (SeNB); transmitting, from the UE, a measurement result to the first eNB; and receiving, at the UE, sidelink configuration information from the first eNB, wherein the sidelink configuration information indicates sidelink resources managed by the secondary eNB.