Abstract: The present invention discloses a method for breeding cruciferous vegetable materials and varieties with a double haploid induction line of rapeseed, including: 1) collecting resources of cruciferous vegetables, identifying and classifying same, and regulating the sowing time of the double haploid induction line of rapeseed to ensure flower synchronization; 2) pollinating the cruciferous vegetables with the double haploid induction line of rapeseed; 3) performing bagged selfing or bud peeling forced selfing at a bud stage on the induced progeny individual plants; 4) identifying the strain stability of the induced selfing progenies; 5) test-crossing stable strains with sterile lines; 6) investigating the sterile degree of test-cross progenies to breed excellent maintainer lines; 7) pollinating sterile plants of the test-cross progenies with the double haploid induction line of rapeseed; 8) investigating the fertility of the induced progenies, and continuing to pollinate sterile individual plants with the induc

Abstract: The present invention discloses a method for breeding Brassica napus varieties and materials with a double haploid induction line of rapeseed, including: 1) determining target traits of breeding restorer lines, maintainer lines and conventional varieties of Brassica napus; 2) crossing or convergently crossing two or more Brassica napus with the target traits; 3) pollinating the cross or back-cross progenies with the double haploid induction line of rapeseed; 4) identifying the stability of the induced progenies; 5) performing test-cross identification or yield and resistance identification on the stable progenies; and 6) forming stable restorer lines and maintainer lines for cross breeding combination or for forming conventional varieties.

Abstract: Entity group behavior profiling. An entity group is created that includes multiple entities, where each entity represents one of a user, a machine, and a service. A behavior profile is created for each one of the entities of the entity group. The behavior of each of one of the entities of the entity group is monitored to detect behavior change. An indicator of compromise is detected based on multiple ones of the entities experiencing substantially a same behavior change.

Abstract: Entity group behavior profiling. An entity group is created that includes multiple entities, where each entity represents one of a user, a machine, and a service. A behavior profile is created for each one of the entities of the entity group. The behavior of each of one of the entities of the entity group is monitored to detect behavior change. An indicator of compromise is detected based on multiple ones of the entities experiencing substantially a same behavior change.

Abstract: Systems, methods, and other embodiments associated with data stream classification are described. One example method includes identifying packets associated with the data stream. The example method may also include updating a set of characterization data associated with the data stream based on information associated with a packet. The example method may also include assigning a data stream classifier to the data stream by comparing characterization data to identification data upon determining that the set of characterization data indicates that the data stream is able to be classified. The example method may also include providing a signal associated with the data stream classifier.

Abstract: Systems, methods, and other embodiments associated with data stream classification are described. One example method includes identifying packets associated with the data stream. The example method may also include updating a set of characterization data associated with the data stream based on information associated with a packet. The example method may also include assigning a data stream classifier to the data stream by comparing characterization data to identification data upon determining that the set of characterization data indicates that the data stream is able to be classified. The example method may also include providing a signal associated with the data stream classifier.

Abstract: Systems, methods, and other embodiments associated with data stream classification are described. One example method includes identifying packets associated with the data stream. The example method may also include updating a set of characterization data associated with the data stream based on information associated with a packet. The example method may also include assigning a data stream classifier to the data stream by comparing characterization data to identification data upon determining that the set of characterization data indicates that the data stream is able to be classified. The example method may also include providing a signal associated with the data stream classifier.

Abstract: The invention provides a system for calcining active lime, including a precalcining furnace for receiving limestone materials to be calcined and precalcining the limestone materials, and a rotary kiln for receiving the materials containing precalcined product from the precalcining furnace, outputting the formed active lime, and at the same time supplying a first flue gas to the precalcining furnace. The calcining system further includes a precalcining furnace combustion device, which generates a second flue gas and supplies it to the precalcining furnace, such that the limestone materials are precalcined in the precalcining furnace under the action of the first flue gas and the second flue gas. In this way, the precalcining rate of the limestone materials in the precalcining furnace is significantly improved.

Abstract: Systems, methods, and other embodiments associated with data stream classification are described. One example method includes identifying packets associated with the data stream. The example method may also include updating a set of characterization data associated with the data stream based on information associated with a packet. The example method may also include assigning a data stream classifier to the data stream by comparing characterization data to identification data upon determining that the set of characterization data indicates that the data stream is able to be classified. The example method may also include providing a signal associated with the data stream classifier.

Abstract: Systems, methods, and other embodiments associated with data stream classification are described. One example method includes identifying packets associated with the data stream. The example method may also include updating a set of characterization data associated with the data stream based on information associated with a packet. The example method may also include assigning a data stream classifier to the data stream by comparing characterization data to identification data upon determining that the set of characterization data indicates that the data stream is able to be classified. The example method may also include providing a signal associated with the data stream classifier.

Abstract: Systems and methods are provided that enable probabilistic application of data traffic scanning in an effort to catch malicious software or code being carried by the data traffic. The methodology and systems operate by monitoring data traffic in an data network via an interface with the data network, calculating a first conditional probability that content in first given data traffic being monitored is malicious, calculating a second conditional probability that content in second given data traffic being monitored is malicious, ranking the first and second conditional probabilities resulting in ranked conditional probabilities, and performing at least one of anti-virus (AV) or anti-malware (AM) scanning of the content of the first or second given data traffic depending on whose conditional probability is ranked higher in the ranked conditional probabilities.

Abstract: Collaborative and adaptive threat intelligence. Data collected on a first customer network is received. One or more local models are trained with at least the received data, where the one or more local models are related to security. An amount of data to transmit to a centralized controller is determined based at least on a result of the training one or more local models and the determined amount of data is transmitted to the centralized controller. Result data is received from the centralized controller that is a result of one or more global models trained on the centralized controller using data collected on multiple customer networks including the first customer network. The one or more local models are adjusted using the received result data and the one or more adjusted local models are trained.

Abstract: Entity group behavior profiling. An entity group is created that includes multiple entities, where each entity represents one of a user, a machine, and a service. A behavior profile is created for each one of the entities of the entity group. The behavior of each of one of the entities of the entity group is monitored to detect behavior change. An indicator of compromise is detected based on multiple ones of the entities experiencing substantially a same behavior change.

Abstract: Systems, methods, and other embodiments associated with data stream classification are described. One example method includes identifying packets associated with the data stream. The example method may also include updating a set of characterization data associated with the data stream based on information associated with a packet. The example method may also include assigning a data stream classifier to the data stream by comparing characterization data to identification data upon determining that the set of characterization data indicates that the data stream is able to be classified. The example method may also include providing a signal associated with the data stream classifier.

Abstract: Systems, methods, and other embodiments associated with data stream classification are described. One example method includes identifying packets associated with the data stream. The example method may also include updating a set of characterization data associated with the data stream based on information associated with a packet. The example method may also include assigning a data stream classifier to the data stream by comparing characterization data to identification data upon determining that the set of characterization data indicates that the data stream is able to be classified. The example method may also include providing a signal associated with the data stream classifier.

Abstract: Systems, methods, and other embodiments associated with data stream classification are described. One example method includes identifying packets associated with the data stream. The example method may also include updating a set of characterization data associated with the data stream based on information associated with a packet. The example method may also include assigning a data stream classifier to the data stream by comparing characterization data to identification data upon determining that the set of characterization data indicates that the data stream is able to be classified. The example method may also include providing a signal associated with the data stream classifier.

Abstract: Systems, methods, and other embodiments associated with data stream classification are described. One example method includes identifying packets associated with the data stream. The example method may also include updating a set of characterization data associated with the data stream based on information associated with a packet. The example method may also include assigning a data stream classifier to the data stream by comparing characterization data to identification data upon determining that the set of characterization data indicates that the data stream is able to be classified. The example method may also include providing a signal associated with the data stream classifier.

Abstract: Systems, methods, and other embodiments associated with data stream classification are described. One example method includes identifying packets associated with the data stream. The example method may also include updating a set of characterization data associated with the data stream based on information associated with a packet. The example method may also include assigning a data stream classifier to the data stream by comparing characterization data to identification data upon determining that the set of characterization data indicates that the data stream is able to be classified. The example method may also include providing a signal associated with the data stream classifier.