Abstract: A method for processing medical information includes identifying a first patient in a first state, identifying a second patient in a second state, calculating a first risk score for the first patient, calculating a first risk score for the second patient, and determining a risk prone area in a medical facility based on the first risk score for the first patient and the first risk score for the second patient. The first state is an infected state and the second state is different from the first state. The first risk score of the first patient provides an indication of a severity of the infected state of the first patient, and the first risk score of the second patient provides an indication of the second patient being infected by the first patient.

Abstract: In one example, a system is disclosed, which may include a network device, a new server connected to the network device, and a management server communicatively connected to a cloud-based service and the network device. The management server may include a server deployment engine to discover the new server in the system using the network device; obtain an encrypted data blob associated with the new server from the cloud-based service; establish a trust, via a secure protocol, with the new server using the encrypted data blob; and deploy the new server in the system upon establishing the trust with the new server.

Abstract: Techniques are provided for automatically resizing applications. In one technique, policy data that indicates an order of multiple policies is stored. The policies include (1) a first policy that corresponds to a first computer resource and a first resizing action and (2) a second policy that is lower in priority than the first policy and that corresponds to a second resizing action and a second computer resource. Resource utilization data is received from at least one application executing in a cloud environment. Based on the order, the first policy is identified. Based on the resource utilization data, it is determined whether criteria associated with the first policy are satisfied with respect to the application. If satisfied, then the first resizing action is performed with respect to the application; otherwise, based on the computer resource utilization data, it is determined whether criteria associated with the second policy are satisfied.

Abstract: Systems and methods are provided for implementing an adaptive machine learning platform for security penetration and risk assessment. For example, the system can receive publicly-available information associated with a client computer system, process the information to identify an input feature, and implement a machine learning model to identify the corresponding risk associated with the input feature . The system can recommend a penetration test for discovered weaknesses associated with the input feature and help make changes to the client computer system to improve security and reduce risk overall.

Abstract: A modular gripper has a body with a trident section and cylindrical section. A powering assembly is positioned in the cylindrical section. A pair of oppositing jaws is pivotally secured between a center wall and a pair of side wall, respectively, of the trident section. The piston rod moves a cam assembly on the center wall. The cam assembly passes through a body having a trident section and cylindrical section. The trident section comprises a trident structure that includes, at one end, a pair of side walls along with a center wall. The cylindrical section includes a fluid driven powering assembly. A pair of opposing jaw member are pivotally secured to the trident portion about separate pivot pins. A cam assembly is operatively connected to a piston rod of the fluid driven powering assembly. The cam assembly extends laterally outward with respect to a cylinder bore axis to receive a cam bush that engages with a through slot on each of the pair of opposing jaw members.

Abstract: In one embodiment, a computing device scans a plurality of available data sources associated with a profiled identity for an individual, and categorizes instances of the data sources according to recognized terms within the data sources. Once determining whether the profiled identity contributed positively to each categorized instance, categorized instances that have a positive contribution by the profiled identity may be clustered into clusters. The computing device may then rank the clusters based on size of the clusters and frequency of recognized terms within the clusters, and can then infer an expertise of the profiled identity based on one or more best-ranked clusters. The inferred expertise of the profiled identity may then be stored.

Abstract: In some examples, a system receives input information relating to a security level for an information technology (IT) stack comprising a plurality of layers including a hardware layer and a software layer, where the input information is technology and product agnostic. The system discovers components of the plurality of layers of the IT stack, accesses a knowledge base that maps the security level and the discovered components to configuration instructions relating to security controls, and configures the IT stack with the security controls using the configuration instructions.

Abstract: A power supply system for a lighting unit, comprises a driver, a local energy storage device and a converter. The converter implements a first, charging mode, mode by connecting to the output of said driver for diverting at least a part of the driving current from the lighting unit to charge the energy storage device, a second, battery driving, mode by connecting to the lighting unit for converting the local energy storage device power supply to drive the lighting unit, and a third, grid driving, mode to neither charge the energy storage device nor convert the secondary power supply. The converter and the driver are controlled actively and synchronously to maintain the current through the lighting unit when switching the converter between modes.

Abstract: Example implementations relate to application-specific policies for failing over from an edge site to a cloud. When an application becomes operational within an edge site, a discovery phase is performed by a local disaster recovery (DR) agent. I/O associated with a workload of the application is monitored. An I/O rate for data replication that satisfies latency characteristics of the application is predicted based on the incoming I/O. Based on results of tests against multiple clouds indicative of their respective RTO/RPO values, information regarding a selected cloud to serve as a secondary system is stored in an application-specific policy. The application-specific policy is transferred to a remote DR agent running in the selected cloud. Responsive to a failover event, infrastructure within a virtualized environment of the selected cloud is enabled to support a failover workload for the application based on the application-specific policy.

Abstract: Some examples relate to detecting a security intrusion in a computer system. In an example, the detection of the security intrusion may be performed by analyzing a plurality of log records generated corresponding to a plurality of milestone actions performed during invocation of an operation on the computing system, based on a rule-set. Upon detection of a deviation in the plurality of log record from the rule-set, a security action is performed.

Abstract: In some examples, a system receives information traffic communicated over a network by or with a system under test (SUT), and analyzes the information traffic to identify a potential attack point in the SUT and a technology used by the SUT. The system determines a collection of penetration tests for testing a stack comprising a plurality of layers associated with the SUT based on the identified potential attack point and the identified technology, and further based on a dynamic knowledge base that includes information relating to vulnerabilities and threats.

Abstract: Techniques are provided for incremental snapshot recovery from a remote storage system. One method comprises, in response to a request to restore a snapshot from a remote storage system: obtaining, from a local storage system, a first bitmap for the first snapshot indicating differences with respect to a previous snapshot, comparing the first bitmap to an allocation bitmap of an earliest maintained snapshot to obtain a baseline bitmap representing a baseline version of the first snapshot; recovering the baseline version of the first snapshot, using the baseline bitmap, from the remote storage system. The snapshots may be stored in the remote storage system in a time order, and following the recovery of the first snapshot, a user may move forward or backward in time along a lineage of snapshots by copying only the changed blocks of bitmaps between two desired points in time.

Abstract: A modular gripper has a body with a trident section and cylindrical section. A powering assembly is positioned in the cylindrical section. A pair of oppositing jaws is pivotally secured between a center wall and a pair of side wall, respectively, of the trident section. The clevis cam driver with piston rod moves a cam assembly on the centre wall slot. The cam assembly passes through a body having a trident section and cylindrical section. The trident section comprises a trident structure that includes, at one end, a pair of side walls along with a center wall. The cylindrical section includes a fluid driven powering assembly. A pair of opposing jaw member are pivotally secured to the trident portion about separate pivot pins. A cam assembly is operatively connected to a piston rod of the fluid driven powering assembly. The cam assembly extends laterally outward with respect to a cylinder bore axis to receive a cam bush that engages with a through slot on each of the pair of opposing jaw members.

Abstract: A system includes a light emitter attached to a destination chamber, the light emitter to emit a collimated light beam across an entrance to the destination chamber. The system includes an end effector attached to a distal end of an arm of a robot. The system includes a two-dimensional (2D) area sensor disposed on the end effector at a location that coincides with the collimated light beam while the end effector reaches within the destination chamber. The 2D area sensor is to detect a location of the collimated light beam incident on a surface of the 2D area sensor and transmit, to a controller coupled to the robot, sensing data including the location.

Abstract: A lighting network for altering a power factor of an alternating current, AC, supply (5). The lighting network comprises luminaires (2, 3) having reactive components of a first reactive type which are controllably coupled, by a controller, to the AC supply so as to adjust the power factor of the AC supply. The controller controls the coupling of the luminaires based on a power factor signal indicative of a power factor of the AC supply, which is influenced by an appliance (6) with a reactive load of a second reactive type, which appliance is different to any one of the luminaires. Preferably, the first reactive type is capacitive, and the second reactive type is inductive.

Abstract: A connected car and an onboard user equipment (UE) may establish independent cellular connections and may also establish a connection between each other. The UE establishes a first cellular link between the UE and a first network, establishes a connection with a connected car, wherein the connected car has a second cellular link between the connected car and a second network, evaluates one or more conditions and declares one of the first cellular link or the connection with the connected car a primary network interface for the UE based on, at least, the one or more conditions.

Abstract: Examples disclosed herein relate to determining malware using firmware of a computing device. Firmware can be used to determine that an indication is present that malware is present on the computing device. The firmware can be executed to perform a security action in response to the indication that malware is present on the computing device.

Abstract: A method disclosed herein relates to determining an inventory of a plurality of servers based on unique fingerprints and common fingerprints; creating a common set of installed firmware and a plurality of unique sets of installed firmware based on the inventory of the plurality of servers; creating subcategory sets of installed firmware based on the common set of installed firmware and categories of components included in the plurality of servers; obtaining updated versions of firmware corresponding to the subcategory sets of installed firmware and the unique sets of installed firmware; in response to an update request, creating containers for the updated versions of firmware corresponding to the subcategory sets of the installed firmware and the unique sets of installed firmware; and sending update messages to the plurality of servers including links to the respective containers.

Abstract: The present disclosure is directed to systems and methods for developing an individual-specific patient baseline for a target patient. An exemplary method involves: determining one or more acuity scores for the target patient; identifying patient health data corresponding to one or more low acuity time periods; storing retrospective clinical data from a group of patients in a second database; comparing the patient health data corresponding to the one or more low acuity time periods with retrospective clinical data from a group of patients by identifying one or more patient subgroups; determining the individual-specific patient baseline using an adaptive baseline selection algorithm, wherein the adaptive baseline selection algorithm is used to determine whether to determine the individual-specific patient baseline using patient health data or using retrospective clinical data from one or more patient subgroups; and displaying, using a user interface, the individual-specific patient baseline.

Abstract: A network monitor may receive network log events and identify: a first set of network devices that have reported a target network log event, a second set of network devices that have not reported the target network log event, a first set of network log events reported by the first set of network devices, and a second set of network log events reported by the second set of network devices. The network monitor may determine which network log events are legitimate, and filter the legitimate network log events from the first set of network log events or the second set of network log events to produce a group of suspicious network log events that may be correlated with the target network log event. The network monitor may predict future suspicious network log events that may be correlated with the target network log event in order to predict equipment failures.