Abstract: Systems and methods for use in scheduling processes for execution on one or more data processors. A centralized governor module manages scheduling processes requesting access to one or more data processors. Each process is associated with a project and each project is allocated a computing budget. Once a process has been scheduled, a cost for that scheduling is subtracted from the associated projects computing budget. Each process is also associated with a specific process agent that, when requested by the governor module, provides the necessary data and parameters for the process. The governor module can thus implement multiple scheduling algorithms based on changing conditions and on optimizing changing loss functions. A log module logs all data relating to the scheduling as well as the costs, execution time, and utilization of the various data processors. The data in the logs can thus be used for analyzing the effectiveness of various scheduling algorithms.

Abstract: There is provided a method and server for estimating an uncertainty parameter of a sequence of computer-implemented models comprising at least one machine learning algorithm (MLA). A set of labelled digital documents is received, which is to be processed by the sequence of models. For a given model of the sequence of models, at least one of a respective set of input features, a respective set of model-specific features and a respective set of output features are received. The set of predictions output by the sequence of models is received. A second MLA is trained to estimate uncertainty of the sequence of models based on the set of labelled digital documents, and the at least one of the respective set of input features, the respective set of model-specific features, the respective set of output features, and the set of predictions.

Abstract: Methods of and systems for operating an embedded system of a vehicle during a coupling operation of the vehicle with a trailer having a coupler. The method comprises accessing a video stream generated by a video camera; operating an object detection module on the video stream so as to detect the trailer and/or the coupler and establish one or more regions of interest. The method also comprises generating a vehicle referential point estimated position of a vehicle referential point and generating a coupler position with respect to the vehicle referential point estimated position based on the one or more regions of interests and the vehicle referential point estimated position.

Abstract: Systems and methods for automated generation of documents. In one system, different databases, each having a different type of data, are used in conjunction with a database of document templates. Each template has a number of empty data fields, each data field being associated with a specific type of data present in at least one of the different databases. A document generation module retrieves a document template from the template database and determines which data fields need data. Databases containing the type of data needed by the data fields in the retrieved template are then accessed and suitable data is then retrieved/used and inserted into the retrieved template. Once the template is suitably complete, a document is then output from system and the image of this generated document can then be used with machine learning systems.

Abstract: Systems and methods for determining similarities between an input data set and a target data set with the data sets being vector representations of the features of a candidate potential copy and a target original. A feature extraction module receives an image of the potential copy and extracts the features of that candidate. The features of the target original may already be extracted or may be separately extracted. The resulting data sets for the candidate and the original are then passed through a decision module. The decision module determines a level of similarity between the features of the candidate and the features of the original. The output of the decision module provides an indication of this level of similarity and, depending on this level of similarity, an alert may be generated. A report module may be included to provide an explanation regarding the level of similarity.

Abstract: Systems and methods for automatic labeling of data with user validation and/or correction of the labels. In one implementation, unlabeled images are received at an execution module and changes are made to the unlabeled images based on the execution module's training. The resulting labeled images are then sent to a user for validation of the changes. The feedback from the user is then used in further training the execution module to further refine its behaviour when applying changes to unlabeled images. To train the execution module, training data sets of images with changes manually applied by users are used. The execution module thus learns to apply the changes to unlabeled images. The feedback from the user works to improve the resulting labeled images from the execution module.

Abstract: A system for anomaly estimation for a telephonic call is described, receiving a call object including an identifier field associating the call object to a purported user; retrieving a user data object associated with the purported user, and processing the user data object to retrieve one or more vectorized user features associated with the purported user. A neural network processes the one or more vectorized call features and the one or more vectorized user features through a machine learning model.

Abstract: Systems and methods for counting objects in images based on each object's approximate location in the images. An image is passed to a segmentation module. The segmentation module segments the image into at least one object blob. Each object blob is an indication of a single object. The object blobs are counted by a counting module. In some embodiments, the segmentation module segments the image by classifying each image pixel and grouping nearby pixels of the same class together. In some embodiments, the segmentation module comprises a neural network that is trained to group pixels based on a set of training images. A plurality of the training images contain at least one point marker corresponding to a single training object. The segmentation module learns to group pixels into training object blobs that each contain a single point marker. Each training object blob is thus an indication of a single object.

Abstract: Systems and methods for use in training a convolutional neural network (CNN) for image and video transformations. The CNN is trained by adding noise to training data set images, transforming both the noisy image and the source image, and then determining the difference between the transformed noisy image and the transformed source image. The CNN is further trained by using an object classifier network and noting the node activation levels within that classifier network when transformed images (from the CNN) are classified. By iteratively adjusting the CNN to minimize a combined loss function that includes the differences between the node activation levels for the transformed references images and when transformed source are classified and the differences between the transformed noisy image and the transformed source image, the artistic style being transferred is maintained in the transformed images.

Abstract: There is described a computer-implemented method for proactively increasing a satisfaction of a customer, comprising: receiving information about a given customer; identifying patterns of disruptive events associated with a risk factor; using the information about the given customer, associating the given customer to a given one of the identified patterns; determining a given action configured for increasing the satisfaction of the given customer, the given action being determined based on the given one of the identified patterns, the given action being one of an action to be performed and a proposed action of which a performance is to be inhibited; and outputting the action.