Abstract: A system may be configured to provide natural language search over security videos. In some aspects, the system may generate a first representation of sampled video information in a multidimensional format via a first machine learning model, and receive a request including a natural language input. Further, the system may generate a second representation of the natural language input in the multidimensional format via a second machine learning model that is a different from the first machine learning model, and determine that the first representation has a predefined relationship with the second representation. In addition, the system may present the second representation as a response to the request based on the first representation having the predefined relationship with the second representation.

Abstract: Example implementations include a method, apparatus and computer-readable medium for detecting an object in an image, by applying, on a first input image, a first object detector configured to generate a bounding box around the object in the first input image. The implementations further include identifying a first bounding box having a first bounding box size around the object in the first input image and determining that the first bounding box size is less than a threshold size. Additionally, the implementations further include generating a second input image by cropping an area of the original image corresponding to the first bounding box and identifying, by applying a second object detector on the second input image, a second bounding box around the object in the second input image. Additionally, the implementations further include performing an image analysis function on the object using information from the second bounding box.

Abstract: Example implementations include a method, apparatus and computer-readable medium of object/action recognition using a text-based classification model, comprising generating an image vector configured to represent one or more features of the first image. Additionally, the implementations further include computing a vector distance between the image vector and each of a first text vector and a second text vector, wherein the first text vector is configured to represent a first text, and wherein the second text vector is configured to represent a second text. Additionally, the implementations further include classifying the first image according to the first text or the second text based on which computed vector distance indicates a highest similarity between the image vector and either the first text vector or the second text vector relative to the other of the first text vector or the second text vector.

Abstract: A system may be configured to provide natural language search over security videos. In some aspects, the system may generate a first representation of sampled video information in a multidimensional format via a first machine learning model, and receive a request including a natural language input. Further, the system may generate a second representation of the natural language input in the multidimensional format via a second machine learning model that is a different from the first machine learning model, and determine that the first representation has a predefined relationship with the second representation. In addition, the system may present the second representation as a response to the request based on the first representation having the predefined relationship with the second representation.

Abstract: Example implementations include a method, apparatus and computer-readable medium for detecting an object in an image, by applying, on a first input image, a first object detector configured to generate a bounding box around the object in the first input image. The implementations further include identifying a first bounding box having a first bounding box size around the object in the first input image and determining that the first bounding box size is less than a threshold size. Additionally, the implementations further include generating a second input image by cropping an area of the original image corresponding to the first bounding box and identifying, by applying a second object detector on the second input image, a second bounding box around the object in the second input image. Additionally, the implementations further include performing an image analysis function on the object using information from the second bounding box.

Abstract: Systems and methods are described herein for generating an adaptive response to a user request. Input indicative of a user request may be received and utilized to identify an item in an electronic catalog. Title segments may be identified from the item's title. Significant segments of the user request may be determined. In response to the user request, a shortened title may be generated from the identified title segments and provided as output at the user device (e.g., via audible output provided at a speaker of the user device, via textual output, or the like). At least one of the title segments provided in the shortened title may correlate to the significant segment identified from the user request. In some embodiments, the length and content of the shortened title may vary based at least in part on the contextual intent of the user's request.

Abstract: A method comprising using at least one hardware processor for: receiving a topic under consideration (TUC) and content relevant to the TUC; detecting one or more claims relevant to the TUC in the content, based on detection of boundaries of the claims in the content; and outputting a list of said detected one or more claims.

Abstract: Systems and methods are described herein for generating adaptive feedback in response to a user request. Input indicative of a user request may be received and utilized to identify an item in an electronic catalog. A title for the item may be retrieved and provided, as input, to a machine-learning model. The machine-learning model may be trained to identify one or more segments of an input title. A shortened title may be generated from these identified segments and provided as output at the user device (e.g., via audible output provided at a speaker of the user device). In some embodiments, the length and content of the shortened title may vary based at least in part on the contextual intent of the user's request.

Abstract: A computer-implemented method, computerized apparatus and computer program product for claim generation, the method comprising: selecting at least one subject according to a given topic; selecting at least one verb from a first data source; selecting at least one object from a second data source; generating one or more candidate claim sentences, each of which composed of a subject selected from the at least one subject, a verb selected from the at least one verb and an object selected from the at least on object; and determining validity of the candidate claim sentences using a machine learning process.

Abstract: A method comprising using at least one hardware processor for receiving a topic under consideration (TUC); providing the TUC as input to a claim function, wherein the claim function is configured to mine at least one content resource, and applying the claim function to the at least one content resource, to extract claims with respect to the TUC; and providing the TUC as input to a classification function, and applying the classification function to one or more claims of the extracted claims, to output corresponding one or more classification tags, wherein each classification tag is associated with its corresponding claim.

Abstract: A method comprising using at least one hardware processor for: receiving a topic under consideration (TUC) and content relevant to the TUC; detecting one or more claims relevant to the TUC in the content, based on detection of boundaries of the claims in the content; and outputting a list of said detected one or more claims.

Abstract: There is provided a computer-implemented method for outputting one or more cross-layer patterns to identify a target semantic phenomenon in text, the method comprising: extracting, for each word of at least some words of each training text fragment of training text fragments designated as representing a target semantic phenomenon, feature-values defined by respective layers; statistically analyzing the feature-values identified for the training text fragments to identify one or more cross-layer patterns comprising layers representing a common pattern for the training text fragments, the common cross-layer pattern defining one or more feature-values of a respective layer of one or more words and at least another feature-value of another respective layer of another word; and outputting the identified cross-layer pattern(s) for identifying a text fragment representing the target semantic phenomenon.

Abstract: A method comprising using at least one hardware processor for: receiving a topic under consideration (TUC) and content relevant to the TUC; detecting one or more claims relevant to the TUC in the content, based on detection of boundaries of the claims in the content; and outputting a list of said detected one or more claims.

Abstract: A method comprising using at least one hardware processor for: receiving a context; identifying evidence with respect to the context in at least one content resource, wherein the identifying comprises: identifying context-free features that generally characterize evidence in the at least one content resource, and identifying context features indicative of the relevance of text segments in the at least one content resource to the context; and outputting a list of said identified evidence.

Abstract: A computer-implemented method, computerized apparatus and computer program product for claim generation, the method comprising: selecting at least one subject according to a given topic; selecting at least one verb from a first data source; selecting at least one object from a second data source; generating one or more candidate claim sentences, each of which composed of a subject selected from the at least one subject, a verb selected from the at least one verb and an object selected from the at least on object; and determining validity of the candidate claim sentences using a machine learning process.

Abstract: There is provided a computer-implemented method for outputting one or more cross-layer patterns to identify a target semantic phenomenon in text, the method comprising: extracting, for each word of at least some words of each training text fragment of training text fragments designated as representing a target semantic phenomenon, feature-values defined by respective layers; statistically analyzing the feature-values identified for the training text fragments to identify one or more cross-layer patterns comprising layers representing a common pattern for the training text fragments, the common cross-layer pattern defining one or more feature-values of a respective layer of one or more words and at least another feature-value of another respective layer of another word; and outputting the identified cross-layer pattern(s) for identifying a text fragment representing the target semantic phenomenon.

Abstract: A method comprising using at least one hardware processor for: receiving a context; identifying evidence with respect to the context in at least one content resource, wherein the identifying comprises: identifying context-free features that generally characterize evidence in the at least one content resource, and identifying context features indicative of the relevance of text segments in the at least one content resource to the context; and outputting a list of said identified evidence.

Abstract: A method comprising using at least one hardware processor for: receiving a topic under consideration (TUC) and content relevant to the TUC; detecting one or more claims relevant to the TUC in the content, based on detection of boundaries of the claims in the content; and outputting a list of said detected one or more claims.

Abstract: There is provided a method for navigation of a drone through a geographical air space, comprising: identifying a drone within or in proximity to a geographical air space; receiving flight data representing a certain flight path through the geographical air space; evaluating the flight data based on a flight risk map to determine the flight risk through the geographical air space, wherein the flight risk map includes zones, each zone being associated with a certain flight safety score; and one or more of: approving the certain flight path when the flight risk of the drone is within an acceptable risk threshold, blocking the certain flight path when the flight risk of the drone is outside the acceptable risk threshold, and obtaining external control of navigation of the drone to navigate the drone through at least one zone having the acceptable risk threshold.

Abstract: A method comprising using at least one hardware processor for receiving a topic under consideration (TUC); providing the TUC as input to a claim function, wherein the claim function is configured to mine at least one content resource, and applying the claim function to the at least one content resource, to extract claims with respect to the TUC; and providing the TUC as input to a classification function, and applying the classification function to one or more claims of the extracted claims, to output corresponding one or more classification tags, wherein each classification tag is associated with its corresponding claim.