Abstract: A computer-implemented method, according to one approach, includes obtaining, on a first edge device, a plurality of artificial intelligence (AI) rules. The method further includes applying, on the first edge device, the AI rules to a plurality of evaluated data samples for determining whether to include the data samples in an inspection collected data set. In response to a determination that a first of the data samples satisfies each of the AI rules, the first data sample is caused to be included in the inspection collected data set. The inspection collected data set is caused to be uploaded to a cloud site. A computer program product, according to another approach, includes a computer readable storage medium having program instructions embodied therewith. The program instructions are readable and/or executable by a first edge device to cause the first edge device to perform any combination of features of the foregoing methodology.

Abstract: Selective locating of missing chat messages for an edge device is provided. The process includes sending a locate chat message request from a user's edge device to at least one other edge device of at least one other participant in a chat with the user via an end-to-end encryption messaging system. The locate chat message request identifies at least one search parameter for at least one missing chat message of the chat on the edge device. The process further includes, based on sending the locate chat message request, receiving at the edge device from the at least one other edge device, the at least one missing chat message of the chat identified via the at least one search parameter.

Abstract: Techniques for generating a set of Deep Learning (DL) models are described. An example method includes training an initial set of DL models using the training data, wherein a topology of each of the DL models is determined based on the parameters vector. The method also includes generating a set of estimate performance functions for each of the DL models in the initial set based on the set of edge-related metrics, and generating a plurality of objective functions based on the set of estimated performance functions. The method also includes generating a final DL model set based on the objective functions, receiving a user selection of a selected DL model from the final DL model set, and deploying the selected DL model to an edge device.

Abstract: An example system includes a processor to train an artificial intelligence (AI) model on a training set of images on a client device associated with a particular user. Images in the training set of images are selected using face recognition from a digital camera roll on the client device based on a set of faces chosen by the particular user of the client device. All of images in the training set of images include a detected face from the set of chosen faces. The processor is to filter a group message received from a second device based on the trained AI model.

Abstract: An example system includes a processor to monitor system resources and performance preferences. The processor is to select model fragments based on the system resources and the performance preferences. The processor is to also construct a running artificial intelligence (AI) model from the selected model fragments. The processor is to further automatically modify the running AI model using the model fragments in response to detecting a change in the system resources or a change in the performance preferences.

Abstract: In an approach for a rule-based filtering system for securing password logins, a processor receives a password input on a user device. A processor determines whether the password requires filtering. Responsive to determining the password requires filtering, a processor filters characters from the password based on a set of filtering rules to create a filtered password. A processor determines whether the filtered password matches a stored password.

Abstract: An example system includes a processor to receive target asset information from an asset management system. The processor can generate an inspection mission based on the target asset information. The processor can generate unmanned aerial vehicle (UAV)-specific commands based on the inspection mission. The processor can transmit the UAV-specific commands to an unmanned aerial vehicle (UAV) platform. The processor can receive images and sensor data from the UAV. The processor can also send the images and sensor data to an artificial intelligence (AI) services module. The processor can receive feedback from the AI services module. The processor can further modify the inspection mission based on the feedback.

Abstract: An example system includes a processor to train an artificial intelligence (AI) model on a training set of images on a client device associated with a particular user. Images in the training set of images are selected using face recognition from a digital camera roll on the client device based on a set of faces chosen by the particular user of the client device. All of images in the training set of images include a detected face from the set of chosen faces. The processor is to filter a group message received from a second device based on the trained AI model.

Abstract: An example system includes a processor to receive an artificial intelligence (AI) model trained on a client device associated with a particular user. The processor is to filter a group message based on the AI model. The processor is to send the filtered group message to the client device.

Abstract: In an approach for a rule-based filtering system for securing password logins, a processor receives a password input on a user device. A processor determines whether the password requires filtering. Responsive to determining the password requires filtering, a processor filters characters from the password based on a set of filtering rules to create a filtered password. A processor determines whether the filtered password matches a stored password.

Abstract: We describe a method for training, inferencing, and a system, for controlling a swarm of unmanned aerial vehicles (UAV). The method comprises introducing a plurality of real time, past and/or simulated records documenting a plurality of sensor readings generated based on measurements taken at a region associated with an emergency event to a system. The system comprises at least one processor adapted to execute code and at least one memory storing a machine learning based model. The system produces code instructions for controlling a plurality of UAVs for presenting at the region a plurality of visual navigation instructions.

Abstract: An example system includes a processor to receive media and an event from a deployed unmanned aerial vehicle (UAV). The processor is to send the media and the event to an artificial intelligence (AI) service and receive smart insights from the AI service. The processor is to dynamically generate an execution block based on the smart insights. The processor is to send the generated execution block to an edge device for generating vehicle specific commands.

Abstract: An example system includes a processor to receive an artificial intelligence (AI) model trained on a client device associated with a particular user. The processor is to filter a group message based on the AI model. The processor is to send the filtered group message to the client device.

Abstract: Techniques for generating a set of Deep Learning (DL) models are described. An example method includes training an initial set of DL models using the training data, wherein a topology of each of the DL models is determined based on the parameters vector. The method also includes generating a set of estimate performance functions for each of the DL models in the initial set based on the set of edge-related metrics, and generating a plurality of objective functions based on the set of estimated performance functions. The method also includes generating a final DL model set based on the objective functions, receiving a user selection of a selected DL model from the final DL model set, and deploying the selected DL model to an edge device.

Abstract: An example system includes a processor to monitor system resources and performance preferences. The processor is to select model fragments based on the system resources and the performance preferences. The processor is to also construct a running artificial intelligence (AI) model from the selected model fragments. The processor is to further automatically modify the running AI model using the model fragments in response to detecting a change in the system resources or a change in the performance preferences.

Abstract: Displaying Web 1.0 application content on a mobile computing device includes one or more processors request website content, based on a URL corresponding to the website. The web page is generated and includes dynamic content and references to static resources. The web page is crawled and the static resources of the web page are identified. The identified static resources of the web page are compared to the static resources stored on the mobile computing device. In response to determining the absence of a static resource of the web page stored on the mobile computing device, the absent static resource is downloaded and stored within the mobile computing device. In response to accessing the web page of the website, the web page is generated by the dynamic content from the web server and the static content stored on the mobile computing device.

Abstract: A method of presenting hyperlink data. The method comprises identifying when a web browser running on a web browser client retrieves, in response to a web document data request submitted to a target server, a first web document data for displaying a first web document containing a hyperlink having a label for display and a target destination defining an address of a second web document, evaluating a risk from content of the second web document, generating by the web browser to a reference evaluation indication of the risk, and processing the web document data and the reference evaluation indication by the web browser for generating a presentation on the client terminal which combines the first web document data and the reference evaluation indication such that when the reference evaluation indication is presented when the label is presented by the web browser.

Abstract: According to some embodiments of the present invention, there is provided a method for revealing at least a portion of a disguised password on a display device. The method comprises receiving a plurality of password characters typed by a user, presenting a plurality of replacement symbols in a password field displayed by a display device of a terminal having a touch surface, each the replacement symbol being indicative of the reception of another of the plurality of password characters, detecting a fingertip which slides contiguously against the touch surface, and temporarily replacing the presentation of at least some of the plurality of replacement symbols with of the presentation of at least some of the plurality of password characters in a sequential manner correspondingly with a movement of the fingertip.

Abstract: Displaying a representative item for a collection of items includes obtaining, from at least one source, a history of interests associated with a user, analyzing the history of interests associated with the user to determine preference criteria for the user, identifying, based on the preference criteria for the user, a representative item for a collection of items, and displaying, to the user, the representative item.

Abstract: Displaying a representative item for a collection of items includes obtaining, from at least one source, a history of interests associated with a user, analyzing the history of interests associated with the user to determine preference criteria for the user, identifying, based on the preference criteria for the user, a representative item for a collection of items, and displaying, to the user, the representative item.