Abstract: A system for generating a cybersecurity investigation case that comprises: an event parser for receiving an event and identifying at least one empty entity from the received event; a case investigator for determining a value to the at least one empty entity to obtain at least one enriched entity; a case correlator for associating at least one existing investigation case to the received event; and a case manager for generating and outputting the cybersecurity investigation case.

Abstract: A method for designing an image filter, the method being executed by a machine learning algorithm (MLA), the method comprising: receiving unfiltered images, a desirable visual task and an undesirable visual task; for each unfiltered image, receiving a first label indicative of whether the desirable visual task is accomplishable and a second label indicative of whether the undesirable visual task is accomplishable; filtering the unfiltered image using a virtual filter model representative of the image filter; using the filtered images, training the MLA to perform the desirable visual task and prevent the undesirable visual task to be performed; using test images, determining a first efficiency for the MLA to perform the desirable task and a second efficiency for the MLA to prevent the undesirable task; adjusting a parameter of the virtual filter model based on the first and second efficiencies; and outputting the adjusted parameter.

Abstract: Systems and methods for automatically detecting and isolating objects in images. An image containing at least one object of interest is segmented by a segmentation module, based on the class of object each pixel of the image depicts. A bounding module then determines coordinates of a predetermined shape that covers at least a portion of the at least one object of interest. An application module then applies a bounding box having those coordinates and having the predetermined shape to the original image. In some embodiments, the coordinates are determined based on a mask layer that is based on the object classes in the image. In other embodiments, the coordinates are determined based on the mask layer and on an edge mask layer. Some embodiments comprise at least one neural network. In some embodiments, the objects of interest are text objects.

Abstract: Systems and methods for managing an asset portfolio. A system generates a detailed trading schedule that converts a current portfolio into a desired portfolio. The schedule is generated using machine learning and is based on a number of inputs including the current portfolio, a desired portfolio, an execution timeline, as well as user supplied constraints. Once generated, the system evaluates the schedule using one or more market models to determine if the schedule will be feasible given market reactions based on the one or more models. The system iterates the generation/evaluation loop until the best possible schedule is arrived at. In addition, the system may provide recommendations for not only brokers to be used when executing the trades but also trading algorithms that the brokers may use when implementing the schedule.

Abstract: A method and a system for joint data augmentation and classification learning, where an augmentation network learns to perform transformations and a classification network is trained. A set of labelled images is received. During an inner loop iteration, an augmentation network applies a transformation on a given labelled image of the set to obtain a transformed image. The classification network classifies the transformed image to obtain a predicted class, and a training loss is determined based on the predicted class and the respective label. The parameters of the classification network are updated based on the classification loss. During an outer loop iteration, the classification network classifies another labelled image of the set to obtain another predicted class, and a validation loss is determined based on the other predicted class and the respective label. The parameters of the augmentation network are updated based on the validation loss.

Abstract: Systems and methods for detecting and predicting text within images. An image is passed to a feature-extraction module. Each image typically contains at least one text object, and each text object contains at least one character. Based on the image, the feature-extraction module generates at least one feature map indicating text object(s) in the image. The feature map(s) is then passed to a decoder module. In son implementations, the decoder module applies a weighted mask to the feature map(s). Based on the feature map(s), the decoder module predicts a sequence of characters in the text object(s). In some embodiments, that prediction is based on previous known data. The decoder module is directed by a query that indicates at least one desired characteristic of the text object(s). An output module then refines the predicted content. At least one neural network may be used.

Abstract: Systems and methods relating to the replacement or removal of sensitive data in images of documents. An initial image of a document with sensitive data is received at an execution module and changes are made based on the execution module's training. The changes include replacing or effectively removing the sensitive data from the image of the document. The resulting sanitized image is then sent to a user for validation of the changes. The feedback from the user is then used in training the execution module to refine its behaviour when applying changes to other initial images of documents. To train the execution module, training data sets of document images with sensitive data manually tagged by users are used. The execution module thus learns to identify sensitive data and its submodules replace that sensitive data with suitable replacement data. The feedback from the user works to improve the resulting sanitized images from the execution module.

Abstract: Systems and methods relating to enhancing capabilities of robotic process automation systems. A system and method includes recognizing and analyzing the components of a user interface on which at least one task is to be executed. The task can be executed regardless of changes to the user interface as the components of the task are based on the presence and function of areas of the user interface and not on the location of the components necessary to execute the task.

Abstract: Systems and methods for routing a document based on the contents of this document. The content of this document is first subjected to a recognition process and then the result is subjected to multiple types of analysis. Based on the results of the analysis (including contextual analysis), a destination is determined along with any timelines detailed in the document. As well, a severity of the document, indicating the severity of consequences if the document is not handled quickly, is determined. Based on these, an urgency tag and/or a severity tag are assigned to the document. A final destination is determined based on the output of the analysis of the severity, the urgency, and of the destination.

Abstract: Systems and method for use in assisting a user in data aggregation tasks. A system determines the type of data needed by the user to complete the data aggregation task and, based on an indication of the data needed, queries multiple data sources. The results from the multiple data sources are then collated and aligned as necessary. Inconsistencies in the data are resolved or flagged to the user for attention. A completed form or a presentation set of data is then presented to the user for validation.

Abstract: Systems and methods for automatically applying style characteristics to images. The images may comprise text. Additionally, the images may be synthetically generated. A style template containing information about style characteristics is passed to an extraction module, which extracts that information and thus determines the style characteristics. The style characteristics are then passed to an application module, which also receives an input image. The application module applies the style characteristics to the image, thereby producing an output image in the intended style. The extraction module and the application module may comprise machine learning elements. The output image may be used in later processes, including, among others, in training processes for optical character recognition models.

Abstract: Systems and methods for managing a supply chain. A multi-stage system receives data regarding different components and parts of a supply chain. These data points are formatted, streamed, and classified into a multitude of analysis modules that predictively assess potential problems in the supply chain. Identified potential problems are then further classified, ranked, and routed to relevant users who need to be informed of the potential problems. These users can then implement mitigating actions that mitigate if not prevent the consequences of these potential problems in the supply chain.

Abstract: A method and a system for generating an abstractive summary of a document using an abstractive machine learning algorithm (MLA) and a method and a system for training the abstractive MLA. A document including a plurality of text sequences is received. An extractive summary of the document is generated, the extractive summary including a set of summary text sequences which is a subset of the plurality of text sequences. The abstractive MLA generates, based on the set of summary text sequences and at least a portion of the plurality of text sequences, an abstractive summary of the document including a set of abstractive text sequences, at least one abstractive text sequence not being included in the plurality of text sequences. In some aspects, the extractive summary is generated by an extractive MLA having been trained to generate extractive summaries.

Abstract: Systems and methods for privacy and sensitive data protection. An image of a document is received at a pre-processing stage and image pre-processing is applied to the image to ensure that the resulting image is sufficient for further processing. Pre-processing may involve processing relating to image quality and image orientation. The image is then passed to an initial processing stage. At the initial processing stage, the relevant data in the document are located and bounding boxes are placed around the data. The resulting image is then passed to a processing stage. At this stage, the type of data within the bounding boxes is determined and suitable replacement data is generated. The replacement data is then inserted into the image to thereby remove and replace the sensitive data in the image.

Abstract: Systems and methods for document analysis. An image containing at least one document is received at a pre-processing stage and the image is analyzed for image quality. If the image quality is insufficient for further processing, this is adjusted until the image is suitable for further processing. After the image quality adjustment, the image is then passed to an initial processing stage. At the initial processing stage, the boundaries of one or more documents within the image are determined. In addition, the orientation of the image may be adjusted and the type of document(s) within the image is determined. From the initial processing stage, the adjusted image is then passed to a data extraction stage. At this stage, clusters of data within the document are determined and bounding boxes are placed around the clusters. Data regarding each of the clusters of data is then gathered.

Abstract: Methods and systems for generating adversarial examples are disclosed. The method comprises accessing a set of inputs and generating an instance of a variable auto-encoder (VAE), the instance of the VAE encoding the set of inputs into latent representation elements associated with a latent space. The method further comprises applying a manifold learning routine on the instance of the VAE to establish a characterization of a manifold in the latent space and applying a perturbation routine to generate perturbed latent representation elements while constraining the perturbed latent representation elements to remain within the manifold. The method further comprises generating adversarial examples based on the perturbed latent representation elements and outputting the adversarial examples.

Abstract: A method and a system for generating training images for training an instance segmentation machine learning algorithm (MLA). A set of image-level labelled images are received, where a given image is labelled with a label indicative of a presence of an object having an object class in the image. A classification MLA detects the object having the object class in each image. A class activation map (CAM) indicative of discriminative regions used by the classification MLA for detecting the object in each image is generated. A region proposal MLA is used to generate region proposals for each image. A pseudo mask of the respective object is generated based on the region proposals and the CAM, where a pseudo mask is indicative of pixels corresponding to the respective object class. The pseudo masks are used as a label with the image-level labelled images for training the instance segmentation MLA.

Abstract: Systems and methods for selecting at least one unlabeled data object from a set of unlabeled data objects. The present invention receives a set of unlabeled data objects and identifies at least one data object in the set that is considered to differ from the others. The at least one data object is selected for further processing, which may include labeling processes. In some embodiments, the data objects are passed through at least one representation-generating module, and the resulting representations are compared to each other. Differences between the representations are evaluated against at least one criterion. If the differences meet the at least one criterion, corresponding data objects are considered to differ from the others and are then selected for further processing. In some implementations, a sample set of sample data objects may be used. In some implementations, the at least one representation-generating module may comprise a neural network.

Abstract: Systems and methods for an improved bounding box tool. The system can have processors configured to display a user interface on a display of a device, the user interface displaying image data. The processor can activate a virtual tool to define a bounding box. The processor can receive a first input data point at a first location relative to the image data. This can be defined by actuation of the input device. The processor can receive a movement input in a direction relative to the first location. The movement input can be defined by movement commands from the input device. The processor can receive a second input data point at a second location relative to the image data. The second input data point can be triggered by a second actuation of the input device. The processor can display, at the user interface, a graphical object representing the bounding box as an overlay of the image data. The bounding box can have corners defined by the first location and the second location.

Abstract: A method and a system for joint data augmentation and classification learning, where an augmentation network learns to perform transformations and a classification network is trained. A set of labelled images is received. During an inner loop iteration, an augmentation network applies a transformation on a given labelled image of the set to obtain a transformed image. The classification network classifies the transformed image to obtain a predicted class, and a training loss is determined based on the predicted class and the respective label. The parameters of the classification network are updated based on the classification loss. During an outer loop iteration, the classification network classifies another labelled image of the set to obtain another predicted class, and a validation loss is determined based on the other predicted class and the respective label. The parameters of the augmentation network are updated based on the validation loss.