Abstract: A computer-based method for automatically evaluating validity and extent of at least one damaged object from image data, comprising: (a) receiving image data comprising one or more images of at least one damaged object; (b) processing said one or more images using an image alteration detection algorithm to detect fraudulent manipulation of said one or more images by classifying at least one attribute of said one or more images; (c) removing any image comprising fraudulent manipulation from said one or more images; (d) processing said one or more images using at least one machine learning algorithm to identify at least one of: a damaged object in any one of said one or more images a damaged area of said damaged object, and an extent of damage of said at least one damaged area; and (e) generating a predictive analysis to repair and/or replace said at least one damaged object.

Abstract: In some examples, an apparatus, such as a mobile phone can include an input module such as a touch screen, a camera, a processor, and computer readable medium. The camera captures one or more images of a person. The processor can use a single machine learning model to estimate the human body features of the person based on the captured images, and use the human body features to generate a disease risk assessment value associated with diabetes and cardiovascular disease risks. The human body features can include at least one or more of: 3D body shape, or body shape indicators; and one or more of: blood flow, blood pressure, heart rate, respiratory rate, heart rate variability, cardiac workload, irregular heartbeats, or stress index. The machine learning model can be trained over a set of training images.

Abstract: In some examples, an apparatus, such as a mobile phone may include a camera, a processor, and computer readable medium. The camera capture one or more images of a person. The processor may use a machine learning model to predict the body volume of the person based on the captured images. The model may be trained based at a training data set comprising at least a plurality of user images. The apparatus may transmit the predicted body volume to a medication and medical treatment management system and receive from the same an adjusted medication or medical treatment plan. The apparatus may further execute the adjusted medication or medical treatment plan.

Abstract: A method for defending against malicious profiles on the web, the method including: i) inspecting a profile to determine its relevance to a brand that it is desired to protect from malicious activity; ii) determining whether said profile is relevant to said brand; iii) if it is determined that said profile is relevant, analyzing it to determine whether it is legitimate or malicious; and iv) if it is determined that the profile is malicious, assembling proof of its malicious activity and submitting same together with a takedown request to the administrator of the website where the profile was located.

Abstract: A saliency detection explicitly models complementary information between appearance, or color, and depth information in images. A mutual-information minimization is used as a regularizer to reduce the redundancy between appearance features from RGB and geometric features from depth in the latent space. Then the latent features of each of the appearance and geometric modalities are fused to achieve multi-modal feature fusion for saliency detection.

Abstract: Videos captured in low light conditions can be processed in order to identify an activity being performed in the video. The processing may use both the video and audio streams for identifying the activity in the low light video. The video portion is processed to generate a darkness-aware feature which may be used to modulate the features generated from the audio and video features. The audio features may be used to generate a video attention feature and the video features may be used to generate an audio attention feature. The audio and video attention features may also be used in modulating the audio video features. The modulated audio and video features may be used to predict an activity occurring in the video.

Abstract: A computer-based method for automatically evaluating validity and extent of at least one damaged object from image data, comprising: (a) receiving image data comprising one or more images of at least one damaged object; (b) processing said one or more images using an image alteration detection algorithm to detect fraudulent manipulation of said one or more images by classifying at least one attribute of said one or more images; (c) removing any image comprising fraudulent manipulation from said one or more images; (d) processing said one or more images using at least one machine learning algorithm to identify at least one of: a damaged object in any one of said one or more images a damaged area of said damaged object, and an extent of damage of said at least one damaged area; and (e) generating a predictive analysis to repair and/or replace said at least one damaged object.

Abstract: A method for defending against malicious profiles on the web comprises the steps of: i) inspecting a profile to determine its relevance to a brand that it is desired to protect from malicious activity; ii) determining whether said profile is relevant to said brand; iii) if it is determined that said profile is relevant, analyzing it to determine whether it is legitimate or malicious; and iv) if it is determined that the profile is malicious, assembling proof of its malicious activity and submitting same together with a takedown request to the administrator of the website where the profile was located.

Abstract: Systems and methods of mitigating leakage of credentials of a user of a computer network, including monitoring at least one data source to scrape data that is compatible with credential data, applying a machine learning algorithm to the scraped data to identify at least one potential leaked credential, wherein the at least one potential leaked credential is identified using at least one neural network, authenticating the identified at least one potential leaked credential by a database of valid credentials of the computer network, and replacing credentials corresponding to the at least one leaked credential.

Abstract: The present invention provides a computer-based method for automatically evaluating validity and extent of at least one damaged object from image data, comprising the steps of: (a) receive image data comprising one or more images of at least one damaged object; (b) inspect any one of said one or more images for existing image alteration utilising an image alteration detection algorithm, and remove any image comprising image alterations from said one or more images; (c) identify and classify said at least one damaged object in any one of said one or more images, utilising at least one first machine learning algorithm; (d) detect at least one damaged area of said classified damaged object, utilising at least one second machine learning algorithm; (e) classify, quantitatively and/or qualitatively, an extent of damage of said at least one damaged area, utilising at least one third machine learning algorithm, and characteristic information of said damaged object and/or an undamaged object that is at least equivale

Abstract: Systems and methods of mitigating leakage of credentials of a user of a computer network, including monitoring at least one data source to scrape data that is compatible with credential data, applying a machine learning algorithm to the scraped data to identify at least one potential leaked credential, wherein the at least one potential leaked credential is identified using at least one neural network, authenticating the identified at least one potential leaked credential by a database of valid credentials of the computer network, and replacing credentials corresponding to the at least one leaked credential.

Abstract: System and method of detecting malicious interactions in a computer network, the method including generating, by a processor, at least one decoy segment, broadcasting, by the processor, the generated at least one decoy segment in a public database, monitoring, by the processor, communication within the computer network to identify interactions associated with the generated at least one decoy segment, determining, by the processor, at least one indicator of compromise (IOC) for the identified interactions, and blocking communication between the computer network and any computer associated with the determined at least one IOC.

Abstract: A system and a method of obtaining a location of a document on a computer network based on a document property. The method may include: receiving at least one basic marker and an encoding function associated with the document property; generating a search term according to the encoding function, based on the at least one basic marker; providing the search term to at least one search engine and obtaining therefrom one or more search results corresponding, where each search result may include one or more references to locations of documents on the computer network; discovering at least one document having the document property from the one or more search results and obtaining a discovered location of the document on the computer network; and performing at least one rule-based action, according to at least one document property of the discovered document.

Abstract: A system and a method of obtaining a location of a document on a computer network based on a document property. The method may include: receiving at least one basic marker and an encoding function associated with the document property; generating a search term according to the encoding function, based on the at least one basic marker; providing the search term to at least one search engine and obtaining therefrom one or more search results corresponding, where each search result may include one or more references to locations of documents on the computer network; discovering at least one document having the document property from the one or more search results and obtaining a discovered location of the document on the computer network; and performing at least one rule-based action, according to at least one document property of the discovered document.

Abstract: Systems and methods of blocking phishing attempts in a computer network, including receiving a list of assets of the computer network, wherein each asset is associated with at least one computer network address, generating at least one address permutation on the at least one computer network address of each asset of the computer network, wherein the generated at least one address permutation is different from the address associated with each asset of the computer network, receiving a communication request at a gateway server of the computer network, determining a destination address of the communication request, comparing the determined destination address with the at least one address permutation, and when the determined destination address is the same as at least one address permutation, blocking the communication request at the gateway server.

Abstract: Repetitive activities can be captured in audio video content. The AV content can be processed in order to predict the number of repetitive activities present in the AV content. The accuracy of the predicted number may be improved, especially for AV content with challenging conditions, by basing the predictions on both the audio and video portions of the AV content.

Abstract: A system and a method of obtaining a location of a document on a computer network based on a document property. The method may include: receiving at least one basic marker and an encoding function associated with the document property; generating a search term according to the encoding function, based on the at least one basic marker; providing the search term to at least one search engine and obtaining therefrom one or more search results corresponding, where each search result may include one or more references to locations of documents on the computer network; discovering at least one document having the document property from the one or more search results and obtaining a discovered location of the document on the computer network; and performing at least one rule-based action, according to at least one document property of the discovered document.

Abstract: A transformer based vision-linguistic (VL) model and training technique uses a number of different image patches covering the same portion of an image, along with a text description of the image to train the model. The model and pre-training techniques may be used in domain specific training of the model. The model can be used for fine-grained image-text tasks in the fashion domain.

Abstract: System and method of detecting malicious interactions in a computer network, the method including generating, by a processor, at least one decoy segment, broadcasting, by the processor, the generated at least one decoy segment in a public database, monitoring, by the processor, communication within the computer network to identify interactions associated with the generated at least one decoy segment, determining, by the processor, at least one indicator of compromise (IOC) for the identified interactions, and blocking communication between the computer network and any computer associated with the determined at least one IOC.

Abstract: Systems and methods for maintaining a project schedule in a dataset management system are provided. For example, a progress update comprising an indication of a status of training of a first machine learning algorithm may be received using at least one communication device from an external device involved in the training the first machine learning algorithm. A project schedule record may be accessed. An expected running time of the training of the first machine learning algorithm may be updated in the project schedule record based on the status of the training of the first machine learning algorithm. Information related to training of a second machine learning algorithm may be updated in the project schedule record based on the status of the training of the first machine learning algorithm.