Abstract: An apparatus includes a memory and a processor. The memory stores a dictionary and a machine learning algorithm trained to classify text. The processor receives an image of a page, converts the image into a set of text, and identifies a plurality of tokens within the text. Each token includes one or more contiguous characters that are both preceded and followed by whitespace within the text. The processor identifies invalid tokens by removing tokens of the plurality of tokens that correspond to words of the dictionary. The processor calculates, based on a ratio of a total number of valid tokens to a total number of tokens, a score. In response to determining that the score is greater than a threshold, the processor applies the machine learning algorithm to classify the text into a category and stores the image and/or text in a database according to the category.

Abstract: An apparatus includes a memory and processor. The memory stores document categories, text generated from an image a physical document page, and a machine learning algorithm. The machine learning algorithm is configured to extract features associated with natural language processing and features associated with the text. The machine learning algorithm is also configured to generate a feature vector that includes the first and second pluralities of features, and to generate, based on the feature vector, a set of probabilities, each of which is associated with a document category and indicates a probability that the physical document from which the text was generated belongs to that document category. The processor applies the machine learning algorithm to the text, to generate the set of probabilities, identifies a largest probability, and assigns the image to the associated document category.

Abstract: An apparatus includes a memory and processor. The memory stores document categories, text generated from an image a physical document page, and a machine learning algorithm. The text includes errors associated with noise in the image. The machine learning algorithm is configured to extract features associated with natural language processing and features associated with the errors from the text. The machine learning algorithm is also configured to generate a feature vector that includes the first and second pluralities of features, and to generate, based on the feature vector, a set of probabilities, each of which is associated with a document category and indicates a probability that the physical document from which the text was generated belongs to that document category. The processor applies the machine learning algorithm to the text, to generate the set of probabilities, identifies a largest probability, and assigns the image to the associated document category.

Abstract: According to an embodiment there is provided a method for managing wireless communication between a sending wireless device and a receiving wireless device in a wireless network, the method comprising the sending wireless device: transmitting to the receiving wireless device one or more uplink communications using a first transmission setting; determining whether a link quality of the communication link falls outside of a predefined range; and in response to determining that the link quality falls outside of the predefined range: determining at least first and second probing transmission settings that differ from each other and from the first transmission setting; transmitting a first probing transmission using the first probing transmission setting and a second probing transmission using the second probing transmission setting; receiving from the receiving wireless device a selection of one of the first and second probing transmission settings; and adopting the selected probing transmission setting for use w

Abstract: A neural network system for inferring a location of a target from a plurality of localization parameters derived from a wireless signal and a method of training thereof. The neural network system comprises first and second neural networks. The plurality of localization parameters comprise one or more parameters relating to a velocity of a target and one or more other parameters. The first neural network is trained to infer a set of candidate locations of a target from values of the one or more other parameters. The second neural network is trained to infer a location of the target from values of the one or more parameters relating to a velocity of the target and a set of candidate locations of the target.

Abstract: An apparatus includes a memory and processor. The memory stores OCR and NLP algorithms. The processor receives an image of a physical document page and executes the OCR algorithm to convert the image into text. The processor identifies errors in the text, which are associated with noise in the image. The processor generates a feature vector that includes features obtained by executing the NLP algorithm on the text, and features associated with the identified errors in the text. The processor uses the feature vector to assign the image to a document category. Documents assigned to the document category share one or more characteristics, and the feature vector is associated with a probability greater than a threshold that the physical document associated with the image includes those characteristics. The processor then stores the image in a database as a page of an electronic document belonging to the assigned document category.

Abstract: A method for communicating a plurality of numerical parameter updates of a machine learning model from a first node to a second node. The method includes dividing each of the parameter updates into a respective primary segment and one or more respective additional segments, wherein the primary segment of each parameter update is the segment that has the greatest influence on the value of that parameter update. The method further includes constructing primary packet containing the primary segments of each of the plurality of parameter updates, and one or more additional packets including the one or more additional segments of the plurality of parameter updates. The method further includes transmitting the plurality of packets from the first node, wherein the primary packet is transmitted with a higher priority than any of the one or more additional packets.

Abstract: An apparatus includes a memory and a processor. The memory stores a dictionary and a machine learning algorithm trained to classify text. The processor receives an image of a page, converts the image into a set of text, and identifies a plurality of tokens within the text. Each token includes one or more contiguous characters that are both preceded and followed by whitespace within the text. The processor identifies invalid tokens by removing tokens of the plurality of tokens that correspond to words of the dictionary. The processor calculates, based on a ratio of a total number of valid tokens to a total number of tokens, a score. In response to determining that the score is greater than a threshold, the processor applies the machine learning algorithm to classify the text into a category and stores the image and/or text in a database according to the category.

Abstract: A method for managing a deployment of a machine learning model in a system comprising a training node and an inference node, the method comprising: training, by the training node, the machine learning model; generating, by the training node, a first set of confidence scores; transmitting, by the training node to the inference node, the first set of confidence scores and a representation of the machine learning model; generating, by the inference node: inferences by inputting data obtained by the inference node into the machine learning model; and a second set of confidence scores comprising confidence scores associated with the inferences; determining, by the inference node, whether the first set of confidence scores and the second set of confidence scores are similar; and if not, transmitting, by the inference node, at least part of the data for training an updated machine learning model at the training node.

Abstract: The present disclosure provides an error detector for determining an error vector between a radio trajectory and an image trajectory. The error detector includes: an input for monitoring a radio trajectory of an object from a radio signal and an image trajectory of an object from an image over an observation area; a correlation module arranged to correlate the radio trajectory with the image trajectory; an error module arranged to determine an error vector between the radio trajectory and the image trajectory; and an output arranged to transmit the error vector for use in determining an estimated trajectory of a target based on a target trajectory from a radio signal.

Abstract: A method for operating a real-time control system comprising a first system configured to generate an information signal and a second system configured to use the information signal, wherein the second system comprises a first buffer for storing a previously received information signal. The method comprising: transmitting, by the first system, a first communication packet comprising the first information signal and generating, by the second system, a predicted first information signal for use in the first time slot. The predicted first information signal being generated by: retrieving the previously received information signal from the first buffer; generating a first prediction using a short-term predictor; and concurrently generating a second prediction using a long-term predictor; and setting the predicted first information signal equal to the first prediction unless the second prediction is available.

Abstract: An apparatus includes a memory and processor. The memory stores document categories, text generated from an image a physical document page, and a machine learning algorithm. The text includes errors associated with noise in the image. The machine learning algorithm is configured to extract features associated with natural language processing and features associated with the errors from the text. The machine learning algorithm is also configured to generate a feature vector that includes the first and second pluralities of features, and to generate, based on the feature vector, a set of probabilities, each of which is associated with a document category and indicates a probability that the physical document from which the text was generated belongs to that document category. The processor applies the machine learning algorithm to the text, to generate the set of probabilities, identifies a largest probability, and assigns the image to the associated document category.

Abstract: An apparatus includes a memory and processor. The memory stores OCR and NLP algorithms. The processor receives an image of a physical document page and executes the OCR algorithm to convert the image into text. The processor identifies errors in the text, which are associated with noise in the image. The processor generates a feature vector that includes features obtained by executing the NLP algorithm on the text, and features associated with the identified errors in the text. The processor uses the feature vector to assign the image to a document category. Documents assigned to the document category share one or more characteristics, and the feature vector is associated with a probability greater than a threshold that the physical document associated with the image includes those characteristics. The processor then stores the image in a database as a page of an electronic document belonging to the assigned document category.

Abstract: A method of operating a network comprising an edge node and a server. The method comprises obtaining, by the edge node, a plurality of data samples, determining, by the edge node, a plurality of output labels by applying a first machine learning model using an input memory having a first input memory size to the plurality of data samples, calculating, by the edge node, an error term based on the confidence score of a first output label from the plurality of output labels, determining, by the edge node, based on the error term, whether to modify the first input memory size of the machine learning model and, if so, generating a second machine learning model based on the first machine learning model and a second input memory size.

Abstract: Embodiments of the present invention provide a system for electronic identification of attributes for performing maintenance, monitoring, and distribution of designated resource assets. In particular, the system may be configured to extract one or more legacy resources from a data repository of an entity system associated with an entity, wherein the legacy resources are in a first format, convert the one or more legacy resources from the first format to a second format, process the one or more legacy resources, via one or more machine learning models, identify one or more attributes based on processing the one or more legacy resources via the one or machine learning models, and implement one or more actions based on the one or more attributes.

Abstract: A method for detecting an attack on a distributed artificial intelligence deployment comprising a plurality of worker devices. Each of the plurality of worker devices comprises a local machine learning model. Each local machine learning model comprises a plurality of layers. The method comprises calculating a first inference from first input data using a first machine learning model comprising layers of the plurality of layers of one or more of the local machine learning models and calculating additional inferences from the first input data using one or more additional machine learning models. Each of the additional machine learning models comprises at least one of the layers used in the first machine learning model and at least one layer from the pluralities of layers of the one or more local machine learning models that is not used by the first machine learning model. The method further comprises calculating differences between the first inference and each of the one or more additional inferences.

Abstract: A computer-implemented method for training a machine learning model, the method comprising performing, by a computing device, a plurality of training iterations, wherein each training iteration comprises inputting a set of training data to the machine learning model, determining an output of the model from processing the set of training data, and updating one or more parameters of the model based on the output of the model, the method further comprising, for one or more of the training iterations, determining, based on the output of the model for the training iteration, a measure of the stability of the model; and determining, based on the stability of the model, whether to send the updated model parameters via a communication channel to a remote computing device.

Abstract: A method for operating a real-time control system comprising a first system configured to generate an information signal and a second system configured to use the information signal, wherein the second system comprises a first buffer for storing a previously received information signal. The method comprising: transmitting, by the first system, a first communication packet comprising the first information signal and generating, by the second system, a predicted first information signal for use in the first time slot. The predicted first information signal being generated by: retrieving the previously received information signal from the first buffer; generating a first prediction using a short-term predictor; and concurrently generating a second prediction using a long-term predictor; and setting the predicted first information signal equal to the first prediction unless the second prediction is available.

Abstract: A computer-implemented method for training a machine learning model in a distributed system, the distributed system comprising a plurality of nodes that exchange updates to communally train the machine learning model. The method comprises a node: receiving an update to a local model from one or more other nodes in the distributed system, the local model being a locally maintained version of the machine learning model and the update specifying a change to one or more parameters of the local model; updating the local model based on the received update to determine an updated local model; determining for each parameter in the local model a change in the parameter relative to a previous version of the local model; and sending an update to the one or more other nodes in the distributed system, wherein the update includes an update to each parameter that has a change greater than a threshold.

Abstract: A computer-implemented method for identifying and rectifying a machine learning drift in a federated learning deployment comprising a parameter server and a plurality of worker nodes, wherein a first worker node comprises: a first machine learning model trained using a first data source; and a second machine learning model trained using a second data source; wherein the first data source is generated by the first worker node and the second data source is generated by a second worker node; the method comprising calculating, by the first worker node, using a trusted data set, a first performance metric associated with the first machine learning model and a second performance metric associated with the second machine learning model and determining, by the first worker node, whether a potential drift has occurred in at least one of the first and the second machine learning models.