Abstract: In some examples, generative network-based floor plan generation may include receiving, for a floor plan that is to be classified, a layout graph for which user constraints are encoded as a plurality of room types. The user constraints may include spatial connections therebetween. Based on the layout graph, embedding vectors for each room type of the plurality of room types may be generated. Bounding boxes and segmentation masks may be determined for each room embedding from the layout graph, and based on an analysis of the embedding vectors. A space layout may be generated by combining the bounding boxes and the segmentation masks. The floor plan may be generated based on an analysis of the space layout, and synthesized based on the space layout, noise, and a contextual graph embedding to generate a synthesized floor plan. The synthesized floor plan may be classified as authentic or not-authentic.

Abstract: A code remediation system accesses a programming code including vulnerabilities such as potential secrets and remediates at least a subset of the potential secrets to generate modified programming code wherein the subset of potential secrets which are determined to be actual secrets are replaced with access mechanisms to storage locations on a vault wherein the actual secrets are secured. To identify the subset of potential secrets forming the actual secrets to be remediated, the code remediation system is configured to filter out false positives among the potential secrets and identify true positives. When an application executing the modified code encounters an access mechanism, it accesses the vault to retrieve the actual secrets.

Abstract: A device may receive historical operational technology data and historical information technology data associated with historical systems and may train a machine learning model with the historical operational technology data and the historical information technology data to generate a trained machine learning model. The device may receive real-time operational technology data and real-time information technology data associated with a system and may process the real-time operational technology data and the real-time information technology data, with the trained machine learning model, to determine a trust score and a risk score for an event or set of events associated with the real-time operational technology data and the real-time information technology data. The device may perform one or more actions based on the trust score and the risk score.

Abstract: In some implementations, a target object identification system may train a machine learning model to identify a target object in a volume of solid waste. The target object identification system may receive, from at least one imaging device positioned in a waste processing facility, imaging data associated with a portion of a volume of solid waste. The target object identification system may identify, using the trained machine learning model and based at least in part on the imaging data, the target object, wherein the target object is disposed within the volume of solid waste. The target object identification system may provide, to a separator control assembly that controls a separator, an output associated with the target object, wherein the output is to facilitate removal of the target object from the volume of solid waste.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a shipping platform are disclosed. In one aspect, a method includes the actions of receiving data identifying goods for shipping, data identifying a shipper of the goods, and a cryptographic key. The actions further include processing the data identifying the goods and the data identifying the shipper using the cryptographic key. The actions further include storing, in a blockchain, the processed data identifying the goods and the processed data identifying the shipper. The actions further include receiving a query requesting data associated with the goods. The actions further include accessing the data associated with the goods from the blockchain. The actions further include providing, for output, the data associated with the goods.

Abstract: A transport stack may control identity information that may be owned by a user. An information record of the identity information may be stored on a distributed ledger. Transactors may request a viewing-share for the identity information to support transactions with the user. The transport stack may generate a grant record when a transactor is provided with a viewing-share of the identity information. The grant record may be stored on the distributed ledger. The distributed ledger may provide a verifiable record of the identity information content and history of viewing-share grants.

Abstract: Methods, systems, and apparatus for transmitting qubits encoding quantum information with reduced risk of interception from an eavesdropper. In one aspect, a method includes encoding quantum information into an information qubit; encrypting the information qubit, comprising performing i) a parity operation on the information qubit and a parity control qubit and ii) a phase operation on the information qubit and a phase control qubit; performing, by a sender party, a sequence of one or more quantum logic gates on the phase control qubit; sending the information qubit, parity control qubit, and phase control qubit to a recipient party; and sending data identifying the sequence of one or more quantum logic gates to the recipient party, wherein the recipient party obtains the quantum information encoded into the information qubit using the information qubit, parity control qubit, phase control qubit, and data identifying the sequence of one or more quantum logic gates.

Abstract: Systems and methods are provided for the classification of identified security vulnerabilities in software applications, and their triage based on automated decision-tree triage and/or machine learning. The disclosed system may generate a report listing detected potential vulnerability issues, and automatically determine whether the potential vulnerability issues are exploitable using automated triage policies containing decision trees or by extracting vulnerability features from the report and processing the extracted vulnerability features using machine learning models.

Abstract: Systems and methods for data storage and data streaming in decentralized, self-organized networks are provided. A plurality of computing devices are disposed in a unidirectional communication ring having a plurality of serially-connected spikes. Each spike includes n computing devices, and n×p connections directly connecting each of the n computing devices to p downstream computing devices. Each computing device is configured to request and receive an inventory of the plurality of computing devices; select a computing device from the plurality of computing devices; transmit a join request comprising the inventory to the selected computing device; and request reorganizing the unidirectional communication ring in response to the receipt of the transmitted join request after propagation through each of the plurality of spikes of the unidirectional communication ring.

Abstract: A device may receive software data identifying current logs and events associated with software products utilized by an entity and may process the software data, with a machine learning model, to generate error severity scores for the software products. The machine learning model may be trained based on historical software data identifying events and logs associated with software products utilized by the entity and based on a combination of historical health scores, historical sentiment scores, and historical dissimilarity scores for the software products. The device may process the error severity scores, with a prioritization model, to generate prioritized error scores and may process the error severity scores and the prioritized error scores, with a root cause analysis model, to generate root cause data identifying root causes associated with the error severity scores. The device may perform one or more actions based on the root cause data.

Abstract: A system and method provide a trained model that uses vectorized word embeddings that are averaged or summed to form representations for sentences and phrases. The representations are processed in a Siamese neural network including multiple LSTM stages to find semantically related matches in catalogs for non-catalog queries. The model is trained using catalog data and randomized data using a contrastive loss function to generate similarity metrics for catalog-non-catalog pairs.

Abstract: A device may receive text data, audio data, and video data associated with a user, and may process the received data, with a first model, to determine a stress level of the user. The device may process the received data, with second models, to determine depression levels of the user, and may combine the depression levels to identify an overall depression level. The device may process the received data, with a third model, to determine a continuous affect prediction, and may process the received data, with a fourth model, to determine an emotion of the user. The device may process the received data, with a fifth model, to determine a response to the user, and may utilize a sixth model to determine a context for the response. The device may utilize seventh models to generate contextual conversation data, and may perform actions based on the contextual conversational data.

Abstract: A device may obtain identification information concerning a clinical trial, and may obtain, based on the identification information, selection information concerning the clinical trial and input information concerning the clinical trial. The device may select at least one machine learning model, of a plurality of machine learning models, based on the selection information, and may process, using the at least one machine learning model, the input information to determine predicted rate of recruitment (RoR) information concerning the clinical trial.

Abstract: This document describes a generative design platform that uses style to generate product designs. In one aspect, a method includes receiving, from a client computing device of a user, data identifying a set of design parameters including a product template for a product and one or more style grammars for the product. Each style grammar includes a set of stylistic parameters that define aesthetic characteristics of a group of related products. One or more physical constraints on a design of the product are obtained. A set of candidate product designs for the product are generated based on the product template, each style grammar, and the one or more physical constraints. A set of scores are generated for each candidate product design based on an evaluation of the candidate product designs. A subset of the candidate product designs are selected based on the scores.

Abstract: A system for electronic lead generation including a semantic graph database including a knowledge graph and a dynamic profiling module comprising automated computing machinery configured to identify a near-term surge in product interest for a number of companies of a particular size in a particular industry in a particular region of the world in dependence upon the knowledge graph and create a company profile in dependence upon the size of the identified companies, the industry of the identified companies, and the region of the world of the identified companies associated with the near-term surge. Embodiments also include a lead purchase module comprising automated computing machinery configured to generate an electronic lead purchase order in dependence upon the company profile; transmit the lead purchase order to a lead generation engine; and receive, from the lead generation engine, a plurality of leads in dependence upon the lead purchase order.

Abstract: A lifecycle platform for creation, ingestion, version control, and contextual query of knowledge graph is disclosed. Such a platform may be used to create and deploy a knowledge graph by reusing and merging knowledge defined in existing and validated data models. The platform tracks changes made to the knowledge graph after being deployed and provides version tracking of the knowledge graph and its underlying namespaces. The platform further provides a subscribable service for contextual viewing and query of portions and/or subset versions of the knowledge graph. Such a platform may be provided as an agnostic plugin to a specific vendor knowledge graph solution space.

Abstract: A system provides feedback driven end-to-end state control of a data model. A data model may be used to model the behavior of a petrochemical refinery to predict future events. The system may be used to ensure proper operation of the data model. Contingency data models may be executed when a failure is detected. Further, when the system detects accuracy that is out of tolerance, the system may initiate retraining of the data model being currently used.

Abstract: This application relates generally to intelligent and explainable link prediction in knowledge graph systems that automatically incorporate user feedback. In one aspect, this application discloses an iterative process for predicting a link set as a group of links in a knowledge graph in an embedding space by expanding the knowledge graph with predicted and validated single links in each iteration such that a final set of links are predicted with each one being added to the set depending on previously added predicted links. In another aspect, this application also discloses automatically extracting rules from user feedback of link predictions and generating a user feedback knowledge graph from the extracted rules, which in combination with an original knowledge graph are used for the generation of the link predictions.

Abstract: This application relates generally to intelligent and explainable link prediction in knowledge graph systems that automatically incorporate user feedback. In one aspect, this application discloses an iterative process for predicting a link set as a group of links in a knowledge graph in an embedding space by expanding the knowledge graph with predicted and validated single links in each iteration such that a final set of links are predicted with each one being added to the set depending on previously added predicted links. In another aspect, this application also discloses automatically extracting rules from user feedback of link predictions and generating a user feedback knowledge graph from the extracted rules, which in combination with an original knowledge graph are used for the generation of the link predictions.

Abstract: Implementations are directed to a pressure-sensing device including a pressure-sensitive sheet, one or more pressure-sensitive input regions disposed along the pressure-sensitive sheet including a first conductive thread including a first length in contact with the pressure-sensitive sheet, and a second conductive thread including a second length in contact with the pressure-sensitive sheet. At least a first portion of the first length of the first conductive thread passes through the pressure-sensitive sheet through a first hole in the pressure-sensitive sheet at a first location and a second portion of the second length of the second conductive thread passes through the pressure-sensitive sheet through a second hole in the pressure-sensitive sheet at a second location.