Abstract: Examples of the present disclosure describe systems and methods for state-based entity behavior analysis. In an example, entities of a computing environment may be represented using a hierarchical entity web. In some examples, an entity may have a state associated with it, which may be modeled using a place/transition (PT) network. Events within the computing environment may be evaluated by transitions of a PT network to determine whether an entity should change state. If an entity transitions from one state to another, one or more actions may be performed, including, but not limited to, taking a remedial action, generating a recommendation, and updating the state of one or more associated entities. Thus, aspects disclosed herein may provide a high-level overview of the state of entities of a computing environment, but may also be used to view in-depth information of entities at lower levels of the hierarchical entity web.

Abstract: Pursuant to some embodiments, an LED lighting device is provided that includes an elongated lamp tube, two end caps, each of the two end caps coupled to a respective end of the elongated lamp tube, an LED light bar disposed on an inner surface of the elongated lamp tube, the LED light bar having a plurality of LED light sources mounted thereon, a power source disposed on at least a first end of the lamp tube, the power source electrically connected to the plurality of LED light sources, and a reflective strip disposed on the inner surface of the elongated lamp tube in a position diametrically opposite the LED light bar.

Abstract: Certain aspects of the present disclosure may include methods, systems, and non-transitory computer readable media for receiving one or more first images via a first camera associated with a first zone, identifying first features relating to a first object based on the one or more first images, receiving one or more second images via a second camera associated with a second zone, identifying second features relating to a second object based on the one or more second images, comparing the first features and the second features to generate a probability score indicating whether the first object is the same as the second object, determining, based on the probability score being higher than a threshold value, that the first object is the same as the second object, identifying the first object and the second object as the target object, and tracking the target object.

Abstract: Examples of the present disclosure describe systems and methods for identifying anomalous network behavior. In aspects, a network event may be observed network sensors. One or more characteristics may be extracted from the network event and used to construct an evidence vector. The evidence vector may be compared to a mapping of previously-identified events and/or event characteristics. The mapping may be represented as one or more clusters of expected behaviors and anomalous behaviors. The mapping may be modeled using analytic models for direction detection and magnitude detection. One or more centroids may be identified for each of the clusters. A “best fit” may be determined and scored for each of the analytic models. The scores may be fused into single binocular score and used to determine whether the evidence vector is likely to represent an anomaly.

Abstract: An apparatus according to an embodiment includes a bean roaster core, an autoloader configured to be removably coupled to the bean roaster core, and a bulk receiver configured to be removably coupled to the bean roaster core. The bean roaster core includes a roasting drum and a recirculating air subsystem fluidically coupled to the roasting drum, and has a single-batch configuration and a multi-batch configuration. The bean roaster core is in the multi-batch configuration when the autoloader is removably coupled to the bean roaster core. The autoloader is not removably coupled to the bean roaster core when the bean roaster core is in the single-batch configuration. The bulk receiver is configured to receive a product or a byproduct of a roasting process from the bean roaster core when the bean roaster core is in the multi-batch configuration and the bulk receiver is removably coupled to the bean roaster core.

Abstract: Examples of the present disclosure describe systems and methods for identifying anomalous network behavior. In aspects, a network event may be observed network sensors. One or more characteristics may be extracted from the network event and used to construct an evidence vector. The evidence vector may be compared to a mapping of previously-identified events and/or event characteristics. The mapping may be represented as one or more clusters of expected behaviors and anomalous behaviors. The mapping may be modeled using analytic models for direction detection and magnitude detection. One or more centroids may be identified for each of the clusters. A “best fit” may be determined and scored for each of the analytic models. The scores may be fused into single binocular score and used to determine whether the evidence vector is likely to represent an anomaly.

Abstract: Examples of the present disclosure describe systems and methods for state-based entity behavior analysis. In an example, entities of a computing environment may be represented using a hierarchical entity web. In some examples, an entity may have a state associated with it, which may be modeled using a place/transition (PT) network. Events within the computing environment may be evaluated by transitions of a PT network to determine whether an entity should change state. If an entity transitions from one state to another, one or more actions may be performed, including, but not limited to, taking a remedial action, generating a recommendation, and updating the state of one or more associated entities. Thus, aspects disclosed herein may provide a high-level overview of the state of entities of a computing environment, but may also be used to view in-depth information of entities at lower levels of the hierarchical entity web.

Abstract: Examples of the present disclosure describe systems and methods for state-based entity behavior analysis. In an example, entities of a computing environment may be represented using a hierarchical entity web. In some examples, an entity may have a state associated with it, which may be modeled using a place/transition (PT) network. Events within the computing environment may be evaluated by transitions of a PT network to determine whether an entity should change state. If an entity transitions from one state to another, one or more actions may be performed, including, but not limited to, taking a remedial action, generating a recommendation, and updating the state of one or more associated entities. Thus, aspects disclosed herein may provide a high-level overview of the state of entities of a computing environment, but may also be used to view in-depth information of entities at lower levels of the hierarchical entity web.

Abstract: Examples of the present disclosure describe systems and methods for identifying anomalous network behavior. In aspects, a network event may be observed network sensors. One or more characteristics may be extracted from the network event and used to construct an evidence vector. The evidence vector may be compared to a mapping of previously-identified events and/or event characteristics. The mapping may be represented as one or more clusters of expected behaviors and anomalous behaviors. The mapping may be modeled using analytic models for direction detection and magnitude detection. One or more centroids may be identified for each of the clusters. A “best fit” may be determined and scored for each of the analytic models. The scores may be fused into single binocular score and used to determine whether the evidence vector is likely to represent an anomaly.

Abstract: Examples of the present disclosure describe systems and methods for identifying anomalous network behavior. In aspects, a network event may be observed network sensors. One or more characteristics may be extracted from the network event and used to construct an evidence vector. The evidence vector may be compared to a mapping of previously-identified events and/or event characteristics. The mapping may be represented as one or more clusters of expected behaviors and anomalous behaviors. The mapping may be modeled using analytic models for direction detection and magnitude detection. One or more centroids may be identified for each of the clusters. A “best fit” may be determined and scored for each of the analytic models. The scores may be fused into single binocular score and used to determine whether the evidence vector is likely to represent an anomaly.

Abstract: A lighted temporary barrier having a body of at least one length of hollow translucent tubing, a string of LEDs with a power source, and connectors on each end for coupling to a structure such as a traffic cone or traffic tube so as to provide a solid barrier between the two structures. The body may be made of at least a first length of translucent tubing forming a body and an optional second length of translucent tubing fitting within the first length of translucent tubing to form an extendible body. Alternatively, the body is made from a plurality of telescoping tubes, in one or both ends. The LED strand may be secured within the body or to the outside of the body.

Abstract: A mobile bathroom assembly with a support frame member assembly operably configured to attach to a trailer hitch and support a lower shell and an upper shell. The lower shell includes a bottom wall and a sidewall enclosing a lower shell cavity and having a lower sidewall door. The bottom wall includes a toilet assembly coupled thereto fluidly coupled to a liquid supply tank and a circulation pump operably configured to induce a flow of liquid from the liquid supply tank to the toilet assembly. The upper shell also includes a sidewall surrounding a ceiling wall and defining an upper shell cavity, wherein the upper shell is operably configured with a motor assembly to extend upwards and contract downwards with respect to the lower shell to increase and decrease the internal assembly height and lower and upper shell cavities within the lower and upper shells.

Abstract: A battery system for an electric motor vehicle comprises first and second battery cartridges that are installed in corresponding first and second battery compartments of the electric motor vehicle. The first battery cartridge has a power connector port with at least one of a shape, size, configuration and orientation that is different than the corresponding power connector port of the second battery cartridge. The first and second battery compartments are physically spaced apart and electrically coupled together by a bridging power cable. The bridging power cable comprises first and second connector ends that extend respectively into the first and second battery compartments, and the first connector end mates more easily with the first power connector port than with the second power connector port, and the second connector end mates more easily with the second power connector port than with the first power connector port.

Abstract: Examples of the present disclosure describe systems and methods for state-based entity behavior analysis. In an example, entities of a computing environment may be represented using a hierarchical entity web. In some examples, an entity may have a state associated with it, which may be modeled using a place/transition (PT) network. Events within the computing environment may be evaluated by transitions of a PT network to determine whether an entity should change state. If an entity transitions from one state to another, one or more actions may be performed, including, but not limited to, taking a remedial action, generating a recommendation, and updating the state of one or more associated entities. Thus, aspects disclosed herein may provide a high-level overview of the state of entities of a computing environment, but may also be used to view in-depth information of entities at lower levels of the hierarchical entity web.

Abstract: Examples of the present disclosure describe systems and methods for state-based entity behavior analysis. In an example, entities of a computing environment may be represented using a hierarchical entity web. In some examples, an entity may have a state associated with it, which may be modeled using a place/transition (PT) network. Events within the computing environment may be evaluated by transitions of a PT network to determine whether an entity should change state. If an entity transitions from one state to another, one or more actions may be performed, including, but not limited to, taking a remedial action, generating a recommendation, and updating the state of one or more associated entities. Thus, aspects disclosed herein may provide a high-level overview of the state of entities of a computing environment, but may also be used to view in-depth information of entities at lower levels of the hierarchical entity web.

Abstract: Examples of the present disclosure describe systems and methods for identifying anomalous network behavior. In aspects, a network event may be observed network sensors. One or more characteristics may be extracted from the network event and used to construct an evidence vector. The evidence vector may be compared to a mapping of previously-identified events and/or event characteristics. The mapping may be represented as one or more clusters of expected behaviors and anomalous behaviors. The mapping may be modeled using analytic models for direction detection and magnitude detection. One or more centroids may be identified for each of the clusters. A “best fit” may be determined and scored for each of the analytic models. The scores may be fused into single binocular score and used to determine whether the evidence vector is likely to represent an anomaly.

Abstract: Examples of the present disclosure describe systems and methods for identifying anomalous network behavior. In aspects, a network event may be observed network sensors. One or more characteristics may be extracted from the network event and used to construct an evidence vector. The evidence vector may be compared to a mapping of previously-identified events and/or event characteristics. The mapping may be represented as one or more clusters of expected behaviors and anomalous behaviors. The mapping may be modeled using analytic models for direction detection and magnitude detection. One or more centroids may be identified for each of the clusters. A “best fit” may be determined and scored for each of the analytic models. The scores may be fused into single binocular score and used to determine whether the evidence vector is likely to represent an anomaly.

Abstract: Examples of the present disclosure describe systems and methods for identifying anomalous network behavior. In aspects, a network event may be observed network sensors. One or more characteristics may be extracted from the network event and used to construct an evidence vector. The evidence vector may be compared to a mapping of previously-identified events and/or event characteristics. The mapping may be represented as one or more clusters of expected behaviors and anomalous behaviors. The mapping may be modeled using analytic models for direction detection and magnitude detection. One or more centroids may be identified for each of the clusters. A “best fit” may be determined and scored for each of the analytic models. The scores may be fused into single binocular score and used to determine whether the evidence vector is likely to represent an anomaly.