Abstract: A system and method of open-world semi-supervised satellite object detection involves a machine learning engine configured with a training component and an inference component. The method of object detection detects objects in satellite imagery represents a solution for difficult challenges of arbitrary orientations, wide variation in object sizes, large number of densely packed objects, and highly complex background. A transformer network detects unknown objects in the satellite image. The transformer network includes a rotation-aware pyramidal pseudo-labeling operation that captures scale-specific pyramidal features at oriented box regions for pseudo-labeling unknown objects in the satellite image. A semi-supervised learning pipeline learns a new set of object classes to be detected. A prediction head outputs the satellite image annotated with a predicted object class for an unknown object.

Abstract: This disclosure describes techniques for an email security system to detect a malicious email and take remedial actions in response to the detected malicious email. The techniques described herein may enable the email security system to detect whether an email is malicious based on whether one or more files attached to the email are malicious. In some cases, the email security system determines whether an email attachment file is malicious based on a set of features that are specific to both a classification of the email (e.g., a semantic classification of the email) and a format of the email attachment file.

Abstract: Provisioning of day-1 configuration templates is automated. Users are provided access to a Configuration Template User Interface (UI) for creating configuration templates. Input is received on the Configuration Template UI to create the configuration templates. A trigger is received by a configuration manager to initiate configuration of network devices or functions. The trigger includes one or more Template Identifiers (IDs) corresponding to the one or more configuration templates. Configuration files are generated based on the trigger and one or more Template IDs corresponding to the one or more configuration templates. Configuration files are sent from the configuration manager to the one or more network devices or functions to configure the one or more network devices or functions.

Abstract: Provided are apparatus, method, and device for managing network neighbor data. According to embodiments, the method may be configured to: receiving, by a configuration manager, updated data of one or more neighboring cells in a telecommunications network; saving, by the configuration manager, the updated data into persistent storage; displaying, by the configuration manager, the updated data in a graphical user interface (GUI); and receiving, by the configuration manager, a first instruction to change a configuration in the one or more neighboring cells.

Abstract: A computerized method is disclosed including obtaining an electronic message, performing a cyberthreat detection process thereon, which includes a first sub-analysis of the body and subject line information of deploying a probabilistic generative model resulting in determination of a likelihood that the electronic message is directed to one of a predefined set of topics, and when the likelihood that the electronic message is directed to one of the predefined set of topics meets or exceeds a first threshold, deploying one or more artificial intelligence models to determine or classify a semantics of the email body or subject, and a second sub-analysis of the header information of performing a heuristic analysis thereof, and performing a determination operation by either a relationship compiler resulting in a determination as to whether the electronic message is malicious or benign, and generating a display that provides the maliciousness determination.

Abstract: A computerized method is disclosed including operations of obtaining and parsing an electronic message into components including body and subject line information and determining a likelihood that the electronic message is directed to one of a predefined set of topics by deploying a probabilistic generative model, wherein the electronic message has a likelihood of being directed to a first topic of at least a first threshold. The operations may also include generating a prompt for a language model based on the first topic, providing the prompt and the body and subject line information of the electronic message to the language model, and generating a semantic result based on a response to the prompt from the language model. The electronic message may be classified by a relationship compiler or a neural network as malicious or benign based on the semantic result.

Abstract: A system and method for performing tactical segmentation including a supply chain network having a tactical segmentation planner, an inventory system, a transportation network, supply chain entities and a computer.

Abstract: This disclosure describes techniques for an email security system to detect a malicious email and take remedial actions in response to the detected malicious email. The techniques described herein may enable the email security system to detect whether an email is malicious based on whether one or more files attached to the email are malicious. In some cases, the email security system determines whether an email attachment file is malicious based on a set of features that are specific to both a classification of the email (e.g., a semantic classification of the email) and a format of the email attachment file.

Abstract: Systems and methods of visualizing data in a cellular network are disclosed. In one embodiments, a method includes presenting a network topology using a graphical user interface (GUI), wherein the network topology is a representation of at least a portion of the cellular network. A network topology selection is received related to the network topology. Additionally, parameter configuration data is generated related to the network topology selection. An audit of parameters is performed related with network elements of the cellular network in accordance with the parameter configuration data. Finally, a report of the audit is transmitted to a user device and visual representations of the report are presented using the GUI.

Abstract: A document processing system processes a document image to identify document image regions including floating images, structured data units, and unstructured floating text. A first masked image is generated by deleting any floating images from the document image and a second masked image is generated by deleting any structured data units from the first masked image. The structured data units and the unstructured floating text are thus identified serially one after another. Textual data is extracted from the structured data units and the unstructured floating text by processing the corresponding document image regions via optical character recognition (OCR). Entities are extracted from the textual data using natural language processing (NLP) techniques.

Abstract: The system obtains a hierarchy representing a construction site, where the hierarchy includes a parent node and a child node, and obtains a first computer model of a first construction site including a first multiplicity of virtual objects. Based on the hierarchy representing the construction site, and the first computer model of the first construction site, the system creates a first map between the hierarchy representing the construction site and the first computer model of the first construction site. The system trains an artificial intelligence based on the first map, and then provides to the artificial intelligence a second computer model of a second construction site. The system receives from the artificial intelligence a second map between the hierarchy representing the construction site and the second computer model.

Abstract: The system obtains a hierarchy representing a construction site, and multiple queries associated with a node in the hierarchy, where a query among the multiple queries requests an indication of a stage of construction associated with an object at the construction site represented by the node, and where the query among the multiple queries requests an answer from a binary set. The system obtains a recording of the object at the construction site and presents the recording and the multiple queries to a user. The system receives the answer from the binary set from the user, and creates an artificial intelligence training dataset based on the answer associated with the binary set and the recording of the object.

Abstract: The system obtains a floor plan associated with a construction site, and a three-dimensional computer model of the construction site, a trajectory through the construction site, and a recording of the construction site. The system establishes a correspondence between the trajectory and the floor plan. Based on the correspondence between the trajectory and the floor plan, the system creates a virtual camera associated with the three-dimensional computer model and a virtual trajectory, where the virtual trajectory influences virtual objects visible to the virtual camera, and the virtual objects visible to the virtual camera match objects visible to a camera making the recording. The system determines a transformation of the camera between an initial image and a subsequent image in the recording, and applies the transformation to the virtual camera to obtain a virtual trajectory of the virtual camera associated with the three-dimensional computer model.

Abstract: The system obtains a first recording of a construction site captured at a first time, and a first virtual trajectory of a first virtual camera, and creates a first data structure representing the first recording. The system obtains a second recording of the construction site captured at a second time, and creates a second data structure representing the second recording. The system establishes a correspondence between the first and second data structures by creating a map between a second image in the second recording and a first image in the first recording by efficiently searching the first data structure for the first image similar to the second image. The system obtains a first location of the first virtual camera corresponding to the first image and, based on the map between the first and second image, determines a second location of a second virtual camera corresponding to the second image.

Abstract: Technologies for fiber array unit (FAU) lid designs are disclosed. In one embodiment, channels in the lid allow for suction to be applied to fibers that the lid covers, pulling the fibers into place in a V-groove. The suction can hold the fibers in place as the fiber array unit is mated with a photonic integrated circuit (PIC) die. Additionally or alternatively, channels can be on pitch, allowing for pulling the FAU towards a PIC die as well as sensing the position and alignment of the FAU to the PIC die. In another embodiment, a warpage amount of a PIC die is characterized, and a FAU lid with a similar warpage is fabricated, allowing for the FAU to position fibers correctly relative to waveguides in the PIC die. In another embodiment, a FAU has an extended lid, which can provide fiber protection as well as position and parallelism tolerance control.

Abstract: Techniques are described herein for detecting an invalid (e.g., spoof) email before it is received by an intended recipient. In some embodiments, the techniques may involve, upon receiving an electronic communication directed to an intended recipient, determining, based on information included in the electronic communication, a claimed source entity, and determining a domain associated with the email communication. The techniques may further involve determining an owner entity associated with the domain and then determining that the electronic communication is valid based on a comparison between the owner entity and the claimed source entity. Upon determining that the electronic communication is not valid, the techniques may further comprise performing one or more mitigation techniques.

Abstract: A method and system are disclosed for collecting and maintaining road event information, where point-based road event location data from transportation authorities, construction companies, vehicle sensors, or combinations thereof, is susceptible to location sensor errors, digital map errors, and/or map mismatching errors. Errors less than a selected threshold are filtered by categorizing or grouping reports of point-based locations into segments along links in a representation of a road, providing improved accuracy of reporting of road events.

Abstract: An improved apparatus 100 for cutting and squeezing sachets/pouches is disclosed, having a cutting and sliding assembly 200 configured to allow linear movement of one or more sachets/pouches and cut the one or more sachets/pouches as the one or more sachets/pouches undergo linear movement: and a roller assembly 300 configured to pull the one or more sachets/pouches through the cutting and sliding assembly. and squeeze the one or more sachets/pouches as the one or more sachets are pulled. the squeezing resulting in extracting contents of the one or more sachets/pouches. The cutting and sliding assembly 200 and the roller assembly 300 are accommodated within a container assembly 400 such that the squeezed out contents are collected within a container of the container assembly 400.

Abstract: This disclosure describes techniques and mechanisms to retroactively identifying, classifying, categorizing, and/or remediating campaigns by an email threat defense system. The described techniques may perform a time-series analysis on record data associated with emails and identify campaigns that have bypassed threat detection mechanisms. The described techniques may extract and correlate features of the record data in order to label and determine whether a campaign is malicious. Where the email campaign is malicious, remedial action(s) can occur. Accordingly, the described techniques may remediate false negatives in a network and improve network security.

Abstract: Techniques for an email-security system to screen emails, extract information from the emails, analyze the information, assign probability scores to the emails, and classify the emails as likely fraudulent or not. The system may analyze emails for users and identify fraudulent emails by analyzing the contents of the emails. The system may evaluate the contents of the emails to determine probability score(s) which may further determine an overall probability score. The system may then classify the email as fraudulent, or not, and may perform actions including blocking the email, allowing the email, flagging the email, etc. In some instances, the screened emails may include legitimate brand domain addresses, names, images, URL(s), and the like. However, the screened emails may contain a reply-to domain address that matches a free email service provider domain. In such instances, the email-security system may assign a probability score indicative that the screened email is fraudulent.