Abstract: There is provided a method of generating a reduced ML model, comprising: obtaining a sample dataset, extracting global features from the sample dataset, applying a feature selection process for selecting a first subset of the global features, analyzing a classification performance of the ML model fed the first subset, to identify an error in classification by the ML model, identifying a subset of the sample dataset related to the error, extracting second features from the subset of the sample data, applying the feature selection process for selecting a second subset of the second features, and creating a reduced version of the ML model, comprising an ensemble of: a first ML model component trained by applying the first subset of global features to the sample dataset, and a second ML model component trained by applying the second subset of features to the subset of the sample data related to the error.

Abstract: There is provided a computer implemented method of detecting data drift for changing a machine learning (ML) model, comprising: monitoring a target input dataset for being fed into the ML model, extracting a plurality of target candidate features from the target input dataset, applying a feature selection process for selecting a target subset of the plurality of target candidate features, accessing a historical input dataset previously determined as being suitable for being fed into the ML model, extracting a plurality of historical candidate features from the historical input dataset, applying the feature selection process for selecting a historical subset of the plurality of historical candidate features, computing a comparison between the target subset and the historical subset, and in response to the comparison meeting a requirement indicating a significant difference between the target subset and the historical subset, generating an indication for changing the ML model.

Abstract: A system and a method for analyzing conversation text using a flow of query prompts, an ensemble of close ended questions and a neural network based language model. The method may be used as a tutor or examination bot, for mental coherency screening, data mining, clustering groups of trainees or customers according to training needs or interests, and the likes.

Abstract: Disclosed herein are methods and systems for efficiently retrieving data from an at least partially encrypted table based record using secure Multi-Party Computation (MPC). A query received to retrieve data from a table based record comprising data items arranged in rows and columns may include a queried data item (key) which potentially matches one or more encrypted data items contained in one or more of the columns. The computing nodes, each having a respective one of a plurality of shares of a one-hot representation of each of the encrypted data items engage in the MPC session to match between a one-hot representation of the queried data item and the one-hot representation of each encrypted data item and output each matching row. The match is based on multiplying, in each encrypted data item's one-hot representation, only bits identified as hot in the queried data item's one-hot representation.

Abstract: There is provided a medical imaging processing device, comprising: at least one hardware processor executing a code for: iteratively generating instructions for iterative adjustment of presentation parameter(s) of a 2D frame of the 3D anatomical image presented on the display, for creating a sequence of adapted 2D frames of the 3D anatomical image, the instructions transmitted from the medical imaging processing device to a physical input interface of at least one of the client terminal and display, for each respective 2D frame: capturing the respective 2D frame from video signals transmitted from the client terminal to the display, analyzing the respective captured 2D frame for extraction of a 2D anatomical image, analyzing the respective captured 2D frame to identify metadata element(s), converting the metadata element(s) into converted metadata value(s), and formatting the extracted 2D anatomical images and associated converted metadata values for reconstruction of the 3D anatomical image.

Abstract: There is provided a medical imaging processing device, comprising: at least one hardware processor executing a code for: iteratively generating instructions for iterative adjustment of presentation parameter(s) of a 2D frame of the 3D anatomical image presented on the display, for creating a sequence of adapted 2D frames of the 3D anatomical image, the instructions transmitted from the medical imaging processing device to a physical input interface of at least one of the client terminal and display, for each respective 2D frame: capturing the respective 2D frame from video signals transmitted from the client terminal to the display, analyzing the respective captured 2D frame for extraction of a 2D anatomical image, analyzing the respective captured 2D frame to identify metadata element(s), converting the metadata element(s) into converted metadata value(s), and formatting the extracted 2D anatomical images and associated converted metadata values for reconstruction of the 3D anatomical image.

Abstract: There is provided a medical imaging processing device, comprising: at least one hardware processor executing a code for: iteratively generating instructions for iterative adjustment of presentation parameter(s) of a 2D frame of the 3D anatomical image presented on the display, for creating a sequence of adapted 2D frames of the 3D anatomical image, the instructions transmitted from the medical imaging processing device to a physical input interface of at least one of the client terminal and display, for each respective 2D frame: capturing the respective 2D frame from video signals transmitted from the client terminal to the display, analyzing the respective captured 2D frame for extraction of a 2D anatomical image, analyzing the respective captured 2D frame to identify metadata element(s), converting the metadata element(s) into converted metadata value(s), and formatting the extracted 2D anatomical images and associated converted metadata values for reconstruction of the 3D anatomical image.

Abstract: There is provided a medical imaging processing device, comprising: at least one hardware processor executing a code for: iteratively generating instructions for iterative adjustment of presentation parameter(s) of a 2D frame of the 3D anatomical image presented on the display, for creating a sequence of adapted 2D frames of the 3D anatomical image, the instructions transmitted from the medical imaging processing device to a physical input interface of at least one of the client terminal and display, for each respective 2D frame: capturing the respective 2D frame from video signals transmitted from the client terminal to the display, analyzing the respective captured 2D frame for extraction of a 2D anatomical image, analyzing the respective captured 2D frame to identify metadata element(s), converting the metadata element(s) into converted metadata value(s), and formatting the extracted 2D anatomical images and associated converted metadata values for reconstruction of the 3D anatomical image.