Abstract: An apparatus for detecting fraudulent activity includes a device property data processing circuit, a security analysis circuit, an alert circuit, and an alert provisioning circuit. The device property data processing circuit is structured to interpret device property data corresponding to a device registered with an Internet of Things (IoT) device registry. The security analysis circuit is structured to determine, based at least in part on the device property data, that the device is subject to a fraud event. The alert circuit is structured to generate, responsive to the determined fraud event, a message that identifies the device. The alert provisioning circuit is structured to transmit the message.

Abstract: Methods and systems for assessing a software bill of materials (SBOM) include building a knowledge graph from repositories, using function fingerprints of software packages in the repositories. Dependencies of an application are identified using function fingerprints of the application and comparing to function fingerprints of the software packages. A quality score for an SBOM of the application is generated based on a comparison of the identified dependencies to claimed dependencies of the SBOM. An action is performed responsive to the quality score.

Abstract: A processor-implemented method generates adversarial example objects. One or more processors represent an adversarial input generation process as a graph. The processor(s) explore the graph, such that a sequence of edges on the graph are explored. The processor(s) create, based on the exploring, an adversarial example object, and utilize the created adversarial example object to harden an existing process model against vulnerabilities.

Abstract: A data storage system is operable to generate an operation energy utilization estimation function based on historic energy utilization data and historic operation execution data. An operation for execution is determined and energy utilization estimation input data is determined for the operation. The operation energy utilization estimation function is performed upon the energy utilization estimation input data to generate energy utilization estimate data for the operation. An energy efficiency strategy for the data storage system is applied based on the energy utilization estimate data generated for the operation.

Abstract: A data storage system is operable to generate an energy utilization-based operation scheduling function based on power usage policy data for a data storage system. An operation for execution is determined and energy utilization-based operation scheduling input data is determined for the operation. The operation energy utilization-based operation scheduling function is performed upon the energy utilization-based operation scheduling input data to generate energy utilization-based operation scheduling data for the operation. The operation is executed in accordance with the energy utilization-based operation scheduling data.

Abstract: A method including receiving a first set of one or more street level images of houses into a Machine Learned Model trained with a second set of street level images with one or more exterior features of the houses labeled, identifying the one or more exterior features in the first set of street level images by way of the Machine Learned Model, and quantifying and outputting the counts and/or two-dimensional areas for each of the identified exterior features in the first set of one or more street level images is described. Non-transitory, computer-readable storage media having instructions for executing the method steps by one or more processors as well as computer or computer systems capable of performing the method steps are also described.

Abstract: A data storage system is operable to perform a power supply strategy selection function based on first energy utilization-based operation optimizer input data to generate first energy utilization-based power supply strategy data that includes first power supply resource type proportion data denoting, for each power supply type of a plurality of power supply types, a corresponding first proportion of power supply resources that be implemented as the each power supply type. Based on the first energy utilization-based power supply strategy data, a first set of power supply resources is selected to execute a first operation is selected. The power supply strategy selection function is performed based on second energy utilization-based operation optimizer input data to generate second energy utilization-based power supply strategy data. Based on the second energy utilization-based power supply strategy data, a second set of power supply resources is selected to execute a second operation.

Abstract: A data storage system is operable to generate operation requirement data based on power usage policy data. An energy utilization-based operation limitation enforcement function is performed upon energy utilization-based operation restriction enforcement input data to generate energy utilization-based operation restriction enforcement data for an operation based on applying the operation requirement data. The operation is executed when the energy utilization-based operation restriction enforcement data indicates execution of the operation is allowed, and execution of the operation is foregone when the energy utilization-based operation restriction enforcement data indicates execution of the operation is disallowed.

Abstract: An apparatus for detecting fraudulent activity includes a device property data processing circuit, a security analysis circuit, an alert circuit, and an alert provisioning circuit. The device property data processing circuit is structured to interpret device property data corresponding to a device registered with an Internet of Things (IoT) device registry. The security analysis circuit is structured to determine, based at least in part on the device property data, that the device is subject to a fraud event. The alert circuit is structured to generate, responsive to the determined fraud event, a message that identifies the device. The alert provisioning circuit is structured to transmit the message.

Abstract: A data storage system is operable to storing a first plurality of files and a second plurality of files in memory resources of an object storage system of a data storage system. The first plurality of files store a plurality of records of at least one table, and the second plurality of files store a set of index structures indexing the plurality of records. Table metadata is generated for storage, mapping the first plurality of files and the second plurality of files to the at least one table, via a metadata processing system of the data storage system. A filtered row set identifying a proper subset of the plurality of records meeting filtering parameter data is generated based on accessing the table metadata, and based on further accessing at least one file of the second plurality of files in the object storage system storing at least one index structure.

Abstract: A data storage system is operable to store a plurality of records of a plurality of tables via a plurality of files in memory resources of an object storage system of a data storage system and to store configuration data indicating table metadata, mapping storage of the plurality of records of the plurality of tables via the plurality of files, via a metadata processing system of the data storage system. A record identification pipeline is generated for execution based on filtering parameter data and the configuration data. A filtered row set identifying a proper subset of the plurality of records meeting the filtering parameter data is generated based on executing the record identification by accessing at least one file of the plurality of files.

Abstract: A database system is operable to store a plurality of segment row data via a first storage mechanism corresponding to a first durability level and facilitate storage of the plurality of segment row data via at least one file stored in a second storage mechanism corresponding to a second durability level, where table metadata corresponding to the at least one file is further stored via the second storage mechanism. A failure of storage of one of the plurality of segment row data via the first storage mechanism is detected. The one of the plurality of segment row data is recovered for storage via the first storage mechanism based on accessing of the at least one file based on accessing the table metadata corresponding to the at least one file based on communications between at least one storage system interface and a metadata processing system of the second storage mechanism.

Abstract: Adaptive verifiable training enables the creation of machine learning models robust with respect to multiple robustness criteria. In general, such training exploits inherent inter-class similarities within input data and enforces multiple robustness criteria based on this information. In particular, the approach exploits pairwise class similarity and improves the performance of a robust model by relaxing robustness constraints for similar classes and increasing robustness constraints for dissimilar classes. Between similar classes, looser robustness criteria (i.e., smaller ?) are enforced so as to minimize possible overlap when estimating the robustness region during verification. Between dissimilar classes, stricter robustness regions (i.e., larger ?) are enforced. If pairwise class relationships are not available initially, preferably they are generated by receiving a pre-trained classifier and then applying a clustering algorithm (e.g., agglomerative clustering) to generate them.

Abstract: A froth flotation unit including a flotation tank, a froth collecting launder having a froth overflow lip and a near coarse launder having a second lip. The second lip is arranged closer to the bottom of the flotation tank than the froth overflow lip. A froth flotation method for treating coarse particles suspended in slurry in the froth flotation unit is also provided.

Abstract: Compositions and methods of improving radiation therapy are provided. The composition typically includes an effective amount of 7-DHC or a derivative or analog thereof. The methods typically include administering the composition to a subject in need thereof in combination with one or more doses of radiation therapy, preferably non-ultraviolet radiation therapy. A preferred radiation is ionizing radiation. The compositions and methods can be used to treat tumors, e.g., benign and malignant tumors. Thus, methods of treating cancer are provided. In preferred embodiments, the 7-DHC or a derivative or analog thereof is encapsulated or incorporated into nanoparticles. The particles can be, for example, polymeric nanoparticles, lipoprotein or lipoprotein-like particles, liposomes, inorganic nanoparticles, or a combination thereof. In some embodiments, the 7-DHC or analog thereof is derivatized with a polymer or saccharide, which is some embodiments, can self-assemble into particles.

Abstract: Computer-implemented methods for telemetry data processing and representations for foundation models are provided. Aspects include generating a graph data structure based on processing telemetry data associated with at least one computer system, where the graph data structure is representative of a set of events and a set of entities associated with the at least one computer system. Aspects include generating a textual representation of behavioral information associated with at least one entity of the set of entities and at least one event of the set of events, based on one or more subgraphs included in the graph data structure. Aspects include obtaining an analysis result associated with the at least one computer system based on processing the textual representation by one or more models.

Abstract: A method for determining a trust indicator is provided. The method may include interpreting, via an Internet of Things Universal Identifier (IoT UID) processing circuit, an IoT UID corresponding to a device and identifying, via a record management circuit and based at least in part on the IoT UID, a record in a database corresponding to the device. The method may further include determining, via a trust analysis circuit and based at least in part on the record, a risk indicator of the device and transmitting, via an indicator provisioning circuit, the risk indicator.

Abstract: A method including receiving a first set of one or more street level images of houses into a Machine Learned Model trained with a second set of street level images with one or more exterior features of the houses labeled, identifying the one or more exterior features in the first set of street level images by way of the Machine Learned Model, and quantifying and outputting the counts and/or two-dimensional areas for each of the identified exterior features in the first set of one or more street level images is described. Non-transitory, computer-readable storage media having instructions for executing the method steps by one or more processors as well as computer or computer systems capable of performing the method steps are also described.

Abstract: A method including receiving a first set of one or more street level images of houses into a Machine Learned Model trained with a second set of street level images with one or more exterior features of the houses labeled, identifying the one or more exterior features in the first set of street level images by way of the Machine Learned Model, and quantifying and outputting the counts and/or two-dimensional areas for each of the identified exterior features in the first set of one or more street level images is described. Non-transitory, computer-readable storage media having instructions for executing the method steps by one or more processors as well as computer or computer systems capable of performing the method steps are also described.

Abstract: A method including receiving a first set of one or more street level images of houses into a Machine Learned Model trained with a second set of street level images with one or more exterior features of the houses labeled, identifying the one or more exterior features in the first set of street level images by way of the Machine Learned Model, and quantifying and outputting the counts and/or two-dimensional areas for each of the identified exterior features in the first set of one or more street level images is described. Non-transitory, computer-readable storage media having instructions for executing the method steps by one or more processors as well as computer or computer systems capable of performing the method steps are also described.