Abstract: Described herein are methods and a system for analyzing the impact of multiple components with one another that support a cloud service. Events are collected in time series from the components and aggregated in a relationship tree that groups the components. Propositions as to the events are created from which a conjunctive normal form (CNF) statement is derived. The CNF statement is converted to one or more directed acyclic graphs (DAG). The DAGs are traversed to determine TRUE values used to provide remediations solutions.

Abstract: A board damage classification system includes a Convolutional Neural Network (CNN) sub-engine and a Graph Convolutional Network (GCN) sub-engine that were trained based on digital images of structures that have experienced natural disasters. The CNN sub-engine receives a board digital image of a board, analyzes the board digital image to identify board features, and determines a board feature damage classification for the board features. The CGN sub-engine receives a board feature graph that was generated using the board digital image and that includes nodes that correspond to the board features in the board digital image, and defines relationships between the nodes included in the board feature graph. The board feature damage classification determined by the CNN sub-engine and the relationships defined by the GCN sub-engine are then used to generate a board damage classification that includes a damage probability for board features in the board digital image.

Abstract: A design of an infrastructure deployment is used for new deployments or as part of an addition to an existing deployment. Because of the complexity of modern infrastructure deployment designs, these designs are subject to a number of problems that are too complex and extensive for manual detection of potential issues. Furthermore, these infrastructure deployments are often critical infrastructure, which means their timely deployment and proper functioning are necessary. While improper design can cause functional problems, selection of an infrastructure element that suffers a supply chain delay so as to delay deployment can be as negatively impactful as a poor design. Accordingly, embodiments herein help automate the analysis of an infrastructure deployment design. In one or more embodiments, a trained neural network receives as input a design and analyzes it to classify a particular issue or issues of the design.

Abstract: Methods, systems, and devices for providing computer implemented services using managed systems are disclosed. To provide the computer implemented services, the managed systems may be deployed to a location and operate in a predetermined manner conducive to, for example, execution of applications that provide the computer implemented services. When deployed to a location, the managed systems may be housed in a managed system frame. The managed system frames may include systems to guide placement of managed system in preferred frame units, remotely identify occupancy of the frame units, and/or the frame units against unexpected removals of or insertion of devices in the frame units.

Abstract: Methods, systems, and devices for providing computer implemented services using managed systems are disclosed. To provide the computer implemented services, the managed systems may be deployed to a location and operate in a predetermined manner conducive to, for example, execution of applications that provide the computer implemented services. When deployed to a location, the managed systems may be housed in a managed system frame. The managed system frames may include systems to guide placement of managed system in preferred frame units, remotely identify occupancy of the frame units, and/or the frame units against unexpected removals of or insertion of devices in the frame units.

Abstract: Methods, systems, and devices for providing computer implemented services using managed systems are disclosed. To provide the computer implemented services, the managed systems may be deployed to a location and operate in a predetermined manner conducive to, for example, execution of applications that provide the computer implemented services. When deployed to a location, the managed systems may be housed in a managed system frame. The managed system frames may include systems to guide placement of managed system in preferred frame units, remotely identify occupancy of the frame units, and/or the frame units against unexpected removals of or insertion of devices in the frame units.

Abstract: Methods, systems, and devices for providing computer implemented services using managed systems are disclosed. To provide the computer implemented services, the managed systems may be deployed to a location and operate in a predetermined manner conducive to, for example, execution of applications that provide the computer implemented services. When deployed to a location, the managed systems may be housed in a managed system frame. The managed system frames may include systems to guide placement of managed system in preferred frame units, remotely identify occupancy of the frame units, and/or the frame units against unexpected removals of or insertion of devices in the frame units.

Abstract: Presented herein are embodiments that use a language model to embed or encode configuration elements (e.g., commands, prompts, etc.) into dense, latent representations that incorporate semantic and contextual information. Using a trained language model, a configuration for a network device may be converted into a set of configuration path sentences. Given a first set of encoded configuration path sentences for a first configuration and a second set of encoded configuration path sentences for a second configuration, these two sets may be compared to gauge a degree of difference between the two sets. In one or more embodiments, an Optimal Transport method with Wasserstein distance metric may be used to obtain a comparison value that gauges difference between the two configurations. In one or more embodiments, the comparison valuation may be labeled or classified by comparing the comparison value to one or more pre-defined thresholds.

Abstract: A board damage classification system includes a Convolutional Neural Network (CNN) sub-engine and a Graph Convolutional Network (GCN) sub-engine that were trained based on digital images of structures that have experienced natural disasters. The CNN sub-engine receives a board digital image of a board, analyzes the board digital image to identify board features, and determines a board feature damage classification for the board features. The CGN sub-engine receives a board feature graph that was generated using the board digital image and that includes nodes that correspond to the board features in the board digital image, and defines relationships between the nodes included in the board feature graph. The board feature damage classification determined by the CNN sub-engine and the relationships defined by the GCN sub-engine are then used to generate a board damage classification that includes a damage probability for board features in the board digital image.

Abstract: Hybrid deep learning systems and methods allow for detecting anomalies in objects, such as electrical printed circuit board (PCB) components, based on image data. In one or more embodiments, a hybrid deep learning model comprises a Graph Attention Network (GAT) that uses spatial properties of the PCB components to extract latent semantic information and generate an output set of hidden representations. The GAT treats each of the electrical components as a node and each connection between them as edges in a graph. The hybrid system further comprises a Convolutional Neural Network (CNN) that uses pixel data to obtain its own output set of hidden representations. The hybrid deep learning model concatenates both sets to detect anomalies that may be present on the PCB.