Abstract: The present disclosure provides a compound selected from Formula Ia or Formula Ib, its stereoisomers, pharmaceutically acceptable salts, complexes, hydrates, solvates, tautomers, polymorphs, or pharmaceutically active derivatives thereof. The compounds of the present disclosure are antibiotic compounds which are effective in killing and inhibiting growth of microorganisms. The present disclosure also provides a process for preparation of the compounds and methods thereof.

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: The technology described herein is directed towards workload prioritization (ranking) in a cloud (e.g., private/hybrid) environment based on multi-criteria decision analysis using Mahalanobis distance-based variance-covariance matrix that takes into account the pair-wise correlation between criteria attributes from a dataset. The technology described herein calculates relative closeness values for alternatives (e.g., virtual machines), and can perform a final ranking of a group of virtual machines based on their associated relative closeness values. The workload prioritization process can be incorporated into a decision engine. The decision engine can be used in various use cases, including backup prioritization, shutdown prioritization during power failures, workflow scheduling prioritization, and the like.

Abstract: Techniques for service instance management in an information processing system environment are disclosed. For example, a method comprises determining associations for first components of a virtual infrastructure platform and second components of a physical infrastructure platform. The method further comprises generating a graphical representation based on the associations for the first components and the second components. The method further comprises causing instantiation of one or more processing elements configured to perform at least one management function in accordance with at least one of the virtual infrastructure platform and the physical infrastructure platform, based on the graphical representation.

Abstract: Health data relating to health of field replaceable units (FRUs) can be collected, determined, and managed. FRU can comprise a data health management component (DHMC) that can periodically or dynamically analyze sensor data relating to operational characteristics of the FRU received from sensors associated with components of or associated with the FRU, historical sensor data, or historical FRU health data, wherein the sensor data can indicate usage or accelerated usage of the components of the FRU. Based on such analysis, DHMC can determine or update an expected amount of time of remaining usable life of the FRU, which can take into account any accelerated usage of the FRU. DHMC can store, in a data store of the FRU, or present, via an interface of the FRU, information relating to the expected amount of time of remaining usable life of the FRU or other FRU health data.

Abstract: In network security systems, graph-based techniques can be used to analyze data collected for a particular security incident, e.g., a command-and-control incident. In example embodiments, data extracted from data records of network activity and/or security alerts is used to generate a multipartite graph in which different entities (e.g., machines, processes, and domains or IP addresses) are represented as different types of nodes and relationships between the entities are represented as edges. The multipartite graph may be clustered, and the clusters be ranked according to some indicator of maliciousness (e.g., the number of associated security alerts or indicators of compromise (IoCs)). An output generated from the highest-ranking cluster(s) may serve, e.g., to identify new IoCs, or flow into mitigating actions taken in response to the incident.

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.

Abstract: In network security systems, graph-based techniques can be used to analyze data collected for a particular security incident, e.g., a command-and-control incident. In example embodiments, data extracted from data records of network activity and/or security alerts is used to generate a multipartite graph in which different entities (e.g., machines, processes, and domains or IP addresses) are represented as different types of nodes and relationships between the entities are represented as edges. The multipartite graph may be clustered, and the clusters be ranked according to some indicator of maliciousness (e.g., the number of associated security alerts or indicators of compromise (IoCs)). An output generated from the highest-ranking cluster(s) may serve, e.g., to identify new IoCs, or flow into mitigating actions taken in response to the incident.

Abstract: In some implementations, a device may receive historical telematics data associated with a vehicle. The device may determine a set of base line driving parameters based on the historical telematics data. The device may receive current telematics data associated with the vehicle. The device may determine a set of current driving parameters based on the current telematics data. The device may determine, based on processing the set of base line driving parameters and the set of current driving parameters with a machine learning model, a type of driving behavior of a driver of the vehicle. The device may determine, based on processing the set of base line driving parameters and the set of current driving parameters with another machine learning model, a severity score associated with the type of driving behavior. The device may perform an action based on the type of driving behavior and the severity score.

Abstract: Described herein are methods and a system for deployment recommendation of a hardware infrastructure configurations at a customer site. A fabric diagram that represents the hardware infrastructure is converted to a multigraph. Augmented and feature matrices are created the multigraph; deriving feature matrix, and processed by a multi-layer graph convolution network (GCN) to determine a predicted score for the hardware infrastructure.

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: The current invention pertains to a computer-implemented method, a method, a use thereof, a computer system and a computer program product for determining a potential for anomalous activity by a suspect user, particularly for detecting fraud and most particularly for detecting money laundering.

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 vehicle device may receive audio data and other vehicle data associated with a vehicle and may transform the audio data to transformed audio data in a frequency domain. The vehicle device may segment the transformed audio data into a plurality of audio segments and may process the plurality of audio segments, with different feature extraction techniques, to extract a plurality of feature vectors. The vehicle device may merge the plurality of feature vectors into a merged feature vector and may create an audio signature for the audio data based on the merged feature vector. The vehicle device may process the audio signature and the other vehicle data, with a model, to determine a classification of the audio signature and may perform one or more actions based on the classification of the audio signature.

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: The current invention pertains to a computer-implemented method, a method, a use thereof, a computer system and a computer program product for determining a potential for anomalous activity by a suspect user, particularly for detecting fraud and most particularly for detecting money laundering.

Abstract: In network security systems, graph-based techniques can be used to analyze data collected for a particular security incident, e.g., a command-and-control incident. In example embodiments, data extracted from data records of network activity and/or security alerts is used to generate a multipartite graph in which different entities (e.g., machines, processes, and domains or IP addresses) are represented as different types of nodes and relationships between the entities are represented as edges. The multipartite graph may be clustered, and the clusters be ranked according to some indicator of maliciousness (e.g., the number of associated security alerts or indicators of compromise (IoCs)). An output generated from the highest-ranking cluster(s) may serve, e.g., to identify new IoCs, or flow into mitigating actions taken in response to the incident.

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: A vehicle device may receive audio data and other vehicle data associated with a vehicle and may transform the audio data to transformed audio data in a frequency domain. The vehicle device may segment the transformed audio data into a plurality of audio segments and may process the plurality of audio segments, with different feature extraction techniques, to extract a plurality of feature vectors. The vehicle device may merge the plurality of feature vectors into a merged feature vector and may create an audio signature for the audio data based on the merged feature vector. The vehicle device may process the audio signature and the other vehicle data, with a model, to determine a classification of the audio signature and may perform one or more actions based on the classification of the audio signature.