Abstract: A computer implemented method to determine a security configuration for a target virtual machine (VM) in a virtualized computing environment, the method including training a machine learning algorithm to determine a vector of security vulnerabilities for the target VM based on a vector of configuration characteristics for the target VM, the machine learning algorithm being trained using training examples each including a configuration for a training VM and an associated vulnerability vector based on an observed security occurrence at the training VM, wherein each training example further includes an identification of one of set of security configurations for the training VM; selecting at least a subset of the set of security configurations and, for each security configuration in the subset, executing the machine learning algorithm with the vector of configuration characteristics for the target VM and an identification of the security configuration, so as to generate a set of vulnerability vectors including a

Abstract: A computer implemented method to generate training data for a machine learning algorithm for determining security vulnerabilities of a virtual machine (VM) in a virtualized computing environment is disclosed. The machine learning algorithm determines the vulnerabilities based on a vector of configuration characteristics for the VM.

Abstract: A computer implemented method of sharing a data message containing multiple data fields between a provider computer system and a consumer computer system, wherein the provider and consumer computer systems have mutual mistrust, is disclosed.

Abstract: A computer implemented method of sharing a data message containing multiple data fields between a provider computer system and a consumer computer system, wherein the provider and consumer computer systems have mutual mistrust, is disclosed.

Abstract: A computer implemented method to generate training data for a machine learning algorithm for determining security vulnerabilities of a virtual machine (VM) in a virtualized computing environment is disclosed. The machine learning algorithm determines the vulnerabilities based on a vector of configuration characteristics for the VM.

Abstract: A computer implemented method to determine a security configuration for a target virtual machine (VM) in a virtualized computing environment, the method including training a machine learning algorithm to determine a vector of security vulnerabilities for the target VM based on a vector of configuration characteristics for the target VM, the machine learning algorithm being trained using training examples each including a configuration for a training VM and an associated vulnerability vector based on an observed security occurrence at the training VM, wherein each training example further includes an identification of one of set of security configurations for the training VM; selecting at least a subset of the set of security configurations and, for each security configuration in the subset, executing the machine learning algorithm with the vector of configuration characteristics for the target VM and an identification of the security configuration, so as to generate a set of vulnerability vectors including a

Abstract: In a wireless network formed of short range femtocells, each femtocell provides wireless connectivity to user equipment devices and the user equipment can move around the topographical range covered by the network by handing over to a neighboring femtocell. Due to the limited range of a femtocell, there will be coverage gaps. If the device moves to a location not covered by a femtocell, it will try to connect to a macrocell of a different wide area cellular network until it is within range of another femtocell network. To minimize handovers from the femtocell network to the macrocell network, each femtocell is arranged to analyze historic log data to detect coverage gaps experienced by the user equipment as it moves along a user equipment route and try to close the gaps by increasing the coverage range of femtocells on either side of the coverage gap to close the gap.

Abstract: This disclosure provides a method of allocating a resource in a network of small cells, and a device for implementing the method, the method comprising: a first small cell detecting that its resource demand exceeds its resource allocation; the first small cell selecting a new resource that is being used by a second small cell; and the first small cell allocating the new resource to either the first or second small cell, wherein the probability the new resource is allocated to the first small cell is based on the first small cell's resource demand.

Abstract: In a wireless network formed of short range femtocells, each femtocell provides wireless connectivity to user equipment devices and the user equipment can move around the topographical range covered by the network by handing over to a neighboring femtocell. Due to the limited range of a femtocell, there will be coverage gaps. If the device moves to a location not covered by a femtocell, it will try to connect to a macrocell of a different wide area cellular network until it is within range of another femtocell network. To minimize handovers from the femtocell network to the macrocell network, each femtocell is arranged to analyze historic log data to detect coverage gaps experienced by the user equipment as it moves along a user equipment route and try to close the gaps by increasing the coverage range of femtocells on either side of the coverage gap to close the gap.

Abstract: This invention relates to methods and systems for limiting consumption, particularly power consumption, more particularly by appliances in a building, and is generally suitable for integration with building management systems. Embodiments of the invention provide arrangements in which the aggregated power load of a plurality of appliances is capped to a selected value (which may be arbitrary, or may be dictated by conditions) while seeking to minimize the deviation from target environmental conditions within the building through a combination of distributed decision making by the appliances themselves and centralized orchestration, which may be informed by real-time sensor readings and/or known properties of the building. The distributed decision-making by individual devices may be based on projected deviation from the target conditions after a period of activity or inactivity but with a central controller which determines which devices should be switched on.

Abstract: This disclosure provides a method of allocating a resource in a network of small cells, and a device for implementing the method, the method comprising: a first small cell detecting that its resource demand exceeds its resource allocation; the first small cell selecting a new resource that is being used by a second small cell; and the first small cell allocating the new resource to either the first or second small cell, wherein the probability the new resource is allocated to the first small cell is based on the first small cell's resource demand

Abstract: This invention relates to methods and systems for controlling consumption, particularly power consumption, more particularly by appliances in a building, and is generally suitable for integration with building management systems. Embodiments of the invention provide methods and systems which probabilistically limit the aggregated power load of a plurality of climate control appliances in a building to a selected value, while seeking to minimize the deviation from target environmental conditions within the building. The embodiments of the invention propose distributed decision-making by individual devices based on projected deviation from the target conditions after a period of activity or inactivity.

Abstract: This invention relates to methods and systems for limiting consumption, particularly power consumption, more particularly by appliances in a building, and is generally suitable for integration with building management systems. Embodiments of the invention provide arrangements in which the aggregated power load of a plurality of appliances is capped to a selected value (which may be arbitrary, or may be dictated by conditions) whilst seeking to minimize the deviation from target environmental conditions within the building through a combination of distributed decision making by the appliances themselves and centralized orchestration, which may be informed by real-time sensor readings and/or known properties of the building. The distributed decision-making by individual devices may be based on projected deviation from the target conditions after a period of activity or inactivity but with a central controller which determines which devices should be switched on.

Abstract: This invention relates to methods and systems for controlling consumption, particularly power consumption, more particularly by appliances in a building, and is generally suitable for integration with building management systems. Embodiments of the invention provide methods and systems which probabilistically limit the aggregated power load of a plurality of climate control appliances in a building to a selected value, whilst seeking to minimize the deviation from target environmental conditions within the building. The embodiments of the invention propose distributed decision-making by individual devices based on projected deviation from the target conditions after a period of activity or inactivity.

Abstract: The present invention relates generally to a method for the generation of a substantially homogenous population of undifferentiated cells. More particularly, the present invention relates to a method for isolating a substantially homogenous population of stem cells, and in particular, mammary stem cells (MaSCs). The MaSCs of the present invention are isolated on the basis of differential levels of proteins present on their cell surface. The MaSCs of the present invention are particularly useful as targets for identifying agents which modulate MaSC survival, self-renewal, proliferation and/or differentiation in both normal and diseased tissue such as, but not limited to, tumor tissue, and, also as source of tissue for the regeneration, replacement and/or augmentation of tissue damaged and/or lost after disease or injury.

Abstract: The present invention relates generally to a method for the generation of a substantially homogenous population of undifferentiated cells. More particularly, the present invention relates to a method for isolating a substantially homogenous population of stem cells, and in particular, mammary stem cells (MaSCs). The MaSCs of the present invention are isolated on the basis of differential levels of proteins present on their cell surface. The MaSCs of the present invention are particularly useful as targets for identifying agents which modulate MaSC survival, self-renewal, proliferation and/or differentiation in both normal and diseased tissue such as, but not limited to, tumor tissue, and, also as source of tissue for the regeneration, replacement and/or augmentation of tissue damaged and/or lost after disease or injury.

Abstract: The present invention relates to the location or identification of a waveguide, in particular where a disturbance is applied to the optical waveguide. This is achieved by: (a) monitoring the waveguide at a first location to sense a disturbance along the waveguide; (b) causing a disturbance to the waveguide at a second location; (c) transmitting a sensor signal to the second location; and, (d) inferring, from the sensor signal, the presence or identity of the waveguide at the second location.

Abstract: Embodiments of the invention are concerned with a method of, and apparatus for, identifying types of network behaviour for use in identifying aberrant network behaviour. In particular, embodiments are concerned with identifying email viruses. The method comprises the steps of: collecting data representative of network traffic that has travelled over a network; training a classification means to recognise a plurality of network behaviour types from the collected data; and for unseen data travelling over the network, classifying the unseen data into one of the defined network behaviour types.

Abstract: A browsing process is driven by user interaction. The user is presented with a selection of items 511, 512, . . . etc from a range. The user can give inputs representing interest in one or other of the items displayed. The inputs represent rewards, which are distributed to attribute data items or “keywords” associated with the display items. These keywords are transparent to the user, but represent characteristics of the display items with which they are associated. The browsing system selects items for display according to a probabilistic function weighted to favour those having the keywords which have accrued the highest number of rewards, these being the display items having the most keywords in common with display items previously rewarded by the user during the browsing session. Partial or negative weightings may also be applied to the keyword associations, which are also taken into account in the selection process.