Abstract: LiDAR systems and methods for detecting objects in a region of interest (ROI) comprising radiation source for emitting an output beam; optical fiber communicatively coupled to radiation source for receiving and transmitting output beam, and emitting output beam from output end with first spread; actuator coupled to the optical fiber for imparting movement to output end, the movement comprising positions of the output end defining total first spread of output beam; optical lens positioned by focal distance from output end and configured to transmit output beam towards the ROI and to cause output beam to spread by second spread which is larger than first spread, and a total second spread when output end is moving being larger than total first spread; and a processor for controlling the actuator and the movement to modulate angle of spread of the output beam in the ROI.

Abstract: There is disclosed a method of and a system for training and using a Machine Learning Algorithm (MLA), the MLA using a decision tree model having a decision tree. During training a training object being associated with a categorical feature and is processed at a node of the decision tree. The method comprises calculating a numeric representation of the categorical feature and the value of the splits for the node “in-line” with generating a given iteration of the decision tree.

Abstract: A method and system for providing scan data for decision making in a self-driving vehicle are provided. The self-driving vehicle includes a scanning sensor that scans a space around the self-driving vehicle in consecutive scanning cycles. The method includes receiving a request for scan information associated with a requested segment of space. The method determines whether to wait for a next scan of the requested segment and use scan data from the next scan, or to extract and use scan data from a previous scan of the requested segment. The method also includes processing the scan data to generate the scan information for the requested segment, providing the scan information for the requested segment in response to the request, and operating the self-driving vehicle in accordance with a decision made about a maneuver to be performed by the self-driving vehicle based, at least in part, on the scan information.

Abstract: Methods and electronic devices for executing offline translation of a source word into a target word via a Neural Network an encoder and a decoder. The method includes splitting the source word into input tokens, generating vector representations for input tokens, and generating a first sequence of output tokens representative of a first candidate word. In response to the first candidate word not respecting at least one pre-determined rule, the method includes triggering the decoder to generate a second sequence of output tokens having a different at least one last output token than at least one last output token of the first sequence. The second sequence is representative of a second candidate word. In response to the second candidate word respecting the at least one pre-determined rule, the method includes determining that the second candidate word is the target word.

Abstract: Method and server for training an Attention-based Neural Network (ANN) to generate a textual output sequence being a content summary to be used as a response to a query. The ANN has an encoder and a decoder sub-network. The method includes, (i) inputting a query and input sequence into the encoder sub-network where the input sequence is a sequence of input groups associated with corresponding content snippets, (ii) generating an encoded representation of the input sequence, which includes generating attention-type outputs for respective words from the input sequence by applying an attention-limiting mask configured to attend only to words from the given input group, (iii) generating a decoded representation being a predicted textual output sequence, (iv) generating a penalty and (iii) adjusting the ANN based on the penalty score.

Abstract: Methods and electronic devices for processing a spoken utterance associated with a user of an electronic device are disclosed. The method includes generating a textual representation of the spoken utterance having words, identifying a nonce word and a non-normalized word amongst the words, and generating a plurality of candidate textual representations based on the textual representation. The candidates have at least one of a first set of candidate textual representations and a second set of candidate textual representations, such that candidates from the first set are missing the nonce word from the words of the textual representation, and candidates from the second set have the non-normalized word from the words of the textual representation replaced by a normalized version thereof. The method includes comparing the candidates against grammars, and in response to a match, triggering an action associated with the grammar.

Abstract: A method and a system for augmenting an audio feed for inclusion of data therein suitable for determining an identity of a human assessor are provided. The method comprises: receiving the audio feed; receiving an indication of identity of the human assessor to whom the audio feed is to be transmitted, the indication of identity being representable by a unique sequence of bits; generating, based on the unique sequence of bits, an identity watermark associated with the human assessor to be included in the audio feed to generate an augmented audio feed, by modifying the audio signal to have predetermined energy level at each of at least two different frequency levels to indicate presence of the given bit of the unique sequence of bits associated with the human assessor in the augmented audio feed; and transmit the augmented audio feed to an electronic device associated with the human assessor.

Abstract: A method and a system for training a machine-learning algorithm (MLA) to determine a user class of a user of an electronic device are provided. The method comprises: receiving a training audio signal representative of a training user utterance; soliciting, by the processor, a plurality of assessor-generated labels for the training audio signal, the given one of the plurality of assessor-generated labels being indicative of whether the training user is perceived to be one of a first class and a second class; generating an amalgamated assessor-generated label for the training audio signal, the amalgamated assessor-generated label being indicative of a label distribution of the plurality of assessor-generated labels between the first class and the second class; generating a training set of data including the training audio signal and the amalgamated assessor-generated to train the MLA to determine the user class of the user producing an in-use user utterance.

Abstract: A method of and a system for synchronizing data for operating a Self-Driving Vehicle (SDV) are provided. The method comprises: causing, by an electronic device, a camera system and a LIDAR system to provide the image data and the map data to the electronic device in a common time referential, determining, by the electronic device, a first timestamp for the map data, determining, by the electronic device, a second timestamp for the image data, determining, by the electronic device, a temporal offset between the first timestamp and the second timestamp, using, by the electronic device, the temporal offset to trigger a delay between subsequent snapshots generated by the camera system.

Abstract: There is disclosed a method and system for selecting one or more targeted messages to be included within a web resource. The method comprises: receiving a first request for a targeted message, the request including a target context parameter and a target floor price; executing an MLA configured to generate a first confidence parameter vis-a-vis a first server and a second confidence parameter vis-a-vis a second server; transmitting a second request to a selected one of the first server and the second server based on the first confidence parameter and the second confidence parameter; receiving the targeted message from the selected on of the first server and the second server; transmitting the targeted message to the a web server for inclusion within the web resource.

Abstract: A method and server for ranking digital documents for a query are disclosed. T server hosts a search engine associated with an inverted index. The method includes accessing inverted index for retrieving query-independent data for a first document-term pair and a second document-term pair, where the query-independent data is indicative of (i) a term-specific occurrence of the first term in content of the document and (ii) a term-specific occurrence of the second term in the content of the document. The method includes generating a query-dependent feature using the query-independent data and which is indicative of a group occurrence of the first term with the second term in the content of the document. The method includes generating a ranking feature for the given document based on at least the first term, the second term, and the query-dependent feature, and ranking the given document based on at least the ranking feature.

Abstract: Methods and servers for preparing a sequence for a machine processing task. The method includes acquiring: (i) a vocabulary storing tokens, (ii) a merge table indicating possible mergers between pairs of tokens, and (iii) a text sequence. For a given word from the sequence, the method includes using the vocabulary for splitting the word into an initial sequence, and iteratively merging tokens of the initial sequence to generate a final sequence for the given word. The iterative merging includes, at a given merging iteration using the merge table for identifying merges between pairs of adjacent tokens in a current sequence of the given merging iteration, excluding at least one of merge based on a pre-determined probability, and using the reduced set merges for generating a new sequence by performing at least one merge. The new sequence is to be used as a current sequence during a next merging iteration.

Abstract: Method and device for processing LIDAR sensor data are disclosed. The method includes: receiving a first dataset and a second dataset having pluralities of data points; matching at least some of the plurality of first points with at least some of the plurality of second points, thereby determining a plurality of pairs; for the given one of the plurality of pairs, determining a pair-specific filtering parameter by calculating neighbour beam distances between the given first data point and respective ones the set of neighboring points, a given neighbour beam distance being representative of a linear distance between the given first data point and a respective one of the set of neighbouring points; in response to the pair-specific parameter being positive, excluding the given one of the plurality of pairs from further processing; and processing the reduced plurality of pairs for merging the first dataset and the second dataset.

Abstract: Method and device for processing LIDAR sensor data are disclosed. The method includes (i) receiving from the LIDAR sensor a first dataset having a plurality of first data points representative of respective coordinates and associated with respective normal vectors, (ii) determining an uncertainty parameter for a given first data point based on a normal covariance of the normal vector of the given first data point where the normal covariance takes into account a measurement error of the LIDAR sensor when determining the respective coordinates of the given first data point, (iii) in response to the uncertainty parameter being above a pre-determined threshold, excluding the given first data point from the plurality of first data points, (iv) using the filtered plurality of first data points, instead of the plurality of first data points, for merging the first dataset of the LIDAR sensor with a second dataset of the LIDAR sensor.

Abstract: Methods and servers for storing data associated with users and digital items of a recommendation system having access to non-distributed and distributed storages. The server trains a model based for generating first user and item embeddings. The server stores (i) the first user embeddings in the non-distributed storage, and (ii) the first item embeddings in the distributed storage. The server re-trains the model for generating second user and item embeddings. The server stores (i) the second user embeddings in the non-distributed storage in addition to the first user embeddings, and (ii) second item embeddings in the distributed storage instead of the respective first item embeddings by replacing the respective first item embeddings. When the second item embeddings are stored on each node of the distributed storage, the server removes the first user embeddings associated with the first value from the non-distributed storage.