Abstract: A post-deployment drift detection monitoring of a predictive model is described. The method includes accessing a predictive performance metric of a machine learning model that is deployed at a server, the machine learning model being trained with an initial set of training data containing historical data, the predictive performance metric being based on the initial set of training data and an additional set of training data, the additional set of training data containing training data collected since training the machine learning model, detecting a drift based on the predictive performance metric exceeding a drift detection threshold, generating a drift warning notification to a client device, the drift warning notification indicating that the predictive performance metric exceeds the drift detection threshold, receiving a user feedback from the client device, and adjusting one of the machine learning model or the drift detection threshold based on the user feedback.

Abstract: A system for private and secure data portal is described. A method includes receiving, from a first client device, a permission request and a data request for a first dataset that is stored at a second client device, providing the permission request and the data request to the second client device, the second client device configured to generate, in response to the permission request and the data request, a data usage approval document and an encrypted synthesized dataset corresponding to the data usage approval document, the encrypted synthesized dataset includes a synthetic second dataset representative of the first dataset, receiving, from the second client device, the data usage approval document and the encrypted synthesized dataset, performing, at a server, a computation on the encrypted synthesized dataset based on the data request, and providing the data usage approval document and results of the computation to the first client device.

Abstract: A continuous meta-learning system operating at a server is described. In one aspect, a computer-implemented method includes accessing, at the server, a deployed machine learning model, metadata, and new prediction data for the deployed machine learning model, detecting a novel trend in the deployed machine learning model based on the new prediction data, generating label suggestions for the novel trend using metadata, querying a plurality of users to verify the label suggestions, detecting a consensus of the plurality of users, the consensus based on a rate of responses from the plurality of users that confirm the label suggestions, and in response to detecting the consensus, training a new machine learning model based on the new prediction data and the consensus of the plurality of users. The computer-implemented method also includes deploying the new machine learning model at the server.

Abstract: A post-deployment drift detection monitoring of a predictive model is described. The method includes accessing a predictive performance metric of a machine learning model that is deployed at a server, the machine learning model being trained with an initial set of training data containing historical data, the predictive performance metric being based on the initial set of training data and an additional set of training data, the additional set of training data containing training data collected since training the machine learning model, detecting a drift based on the predictive performance metric exceeding a drift detection threshold, generating a drift warning notification to a client device, the drift warning notification indicating that the predictive performance metric exceeds the drift detection threshold, receiving a user feedback from the client device, and adjusting one of the machine learning model or the drift detection threshold based on the user feedback.

Abstract: A system for private and secure data portal is described. A method includes receiving, from a first client device, a permission request and a data request for a first dataset that is stored at a second client device, providing the permission request and the data request to the second client device, the second client device configured to generate, in response to the permission request and the data request, a data usage approval document and an encrypted synthesized dataset corresponding to the data usage approval document, the encrypted synthesized dataset includes a synthetic second dataset representative of the first dataset, receiving, from the second client device, the data usage approval document and the encrypted synthesized dataset, performing, at a server, a computation on the encrypted synthesized dataset based on the data request, and providing the data usage approval document and results of the computation to the first client device.

Abstract: A machine learning platform operating at a server is described. The machine learning platform accesses a dataset from a datastore. A task that identifies a target of a machine learning algorithm from the machine learning platform is defined. The machine learning algorithm forms a machine learning model based on the dataset and the task. The machine learning platform deploys the machine learning model and monitors a performance of the machine learning model after deployment. The machine learning platform updates the machine learning model based on the monitoring.

Abstract: A machine learning platform operating at a server is described. The machine learning platform accesses a dataset from a datastore. A task that identifies a target of a machine learning algorithm from the machine learning platform is defined. The machine learning algorithm forms a machine learning model based on the dataset and the task. The machine learning platform deploys the machine learning model and monitors a performance of the machine learning model after deployment. The machine learning platform updates the machine learning model based on the monitoring.

Abstract: A machine learning platform operating at a server is described. The machine learning platform accesses a dataset from a datastore. A task that identifies a target of a machine learning algorithm from the machine learning platform is defined. The machine learning algorithm forms a machine learning model based on the dataset and the task. The machine learning platform deploys the machine learning model and monitors a performance of the machine learning model after deployment. The machine learning platform updates the machine learning model based on the monitoring.

Abstract: A machine learning platform operating at a server is described. The machine learning platform accesses a dataset from a datastore. A task that identifies a target of a machine learning algorithm from the machine learning platform is defined. The machine learning algorithm forms a machine learning model based on the dataset and the task. The machine learning platform deploys the machine learning model and monitors a performance of the machine learning model after deployment. The machine learning platform updates the machine learning model based on the monitoring.

Abstract: A security device for an electrically powered device includes a remote location-tracking circuit for mounting in a power plug or power cable, a control means for mounting in the power plug or power cable with the remote location-tracking circuit, an immobilizing circuit which is a power shut-down circuit for immobilizing a tool positioned within the tool casing, wherein the power shut-down circuit includes a relay; and a power cable connected between the control means and power shut-down circuit, the power cable being adapted to supply power to the tool, and the power shut-down circuit being controlled by the control means, wherein if the remote location-tracking circuit is removed by severing the power cable, thereby disconnecting the control means from the power shut-down circuit, the power shut-down circuit is configured to automatically shut down the power supply to the tool via the relay until the control means is re-connected.

Abstract: A security device 10 for an electrically powered device includes a remote tracking circuit 35 for mounting in a power plug or supply cable, a control means 38 for mounting in the power plug 20 or supply cable 14, a power shut-down circuit 16 for immobilising a tool 12 and positioning within the tool casing; and a power cable 14 connected between the control means 38 and power shut down circuit 16, the power cable 14 being adapted to supply power to the tool 12, and the power shut down circuit 14 being controlled by the control means 38, the power shut down circuit 14 being configured to automatically shut down the power supply to the tool until the control means 38 is re-connected, if the cable 14 is severed.

Abstract: A steel including, in mass %: 0.005 to 0.015% carbon; 0.05 to 0.35% silicon, 7.45 to 8.4% nickel; 1.00%> or less manganese; 0.025%> or less sulphur; 0.030%> or less phosphorous; 24.0 to 26.0% chromium; 0.50 to 1.00% copper; 3.0 to 4.0% C molybdenum; 0.002 to 0.010% niobium; 0.75% or less cobalt; 0.015% or less aluminium; 0.20 to 0.30% nitrogen; 0.50 to 0.85%) tungsten; the balance being iron and incidental impurities.

Abstract: An electromechanical generator for converting mechanical vibrational energy into electrical energy, the electromechanical generator comprising a housing, an electrically conductive coil assembly fixedly mounted in the housing, a magnetic core assembly movably mounted in the housing for linear vibrational motion along an axis, a first biasing device, mounted between the housing and the magnetic core assembly, adapted to apply a centering force acting to oppose movement of the magnetic core assembly away from a central position on the linear axis and a second magnetic biasing device adapted to provide a compensating force to compensate for variations in the centering force of the first biasing device due to temperature.

Abstract: A tactical capsule charge system includes a plurality of plastic containers connected serially, mechanically, and explosively by detonating cord. Each plastic container contains composition C-4 that is detonated by the detonating cord that passes through the interior of the container. The quantity of PETN inside the container is enhanced by tying a knot in the detonating cord in the interior of the container or by wrapping the detonating cord in the interior of the container with PETN sheet. Any number of containers may be used and they may be spaced along the detonating cord at any desired interval.

Abstract: An electromechanical generator for converting mechanical vibrational energy into electrical energy. The electromechanical generator has a housing and an electrically conductive coil assembly movably mounted in the housing. The coil assembly has radially inner and outer sides, and upper and lower edges. A mount for the coil assembly extends inwardly of the radially inner side for mounting the coil assembly for linear vibrational motion along an axis. A first biasing device is mounted between the housing and the mount to bias the electrically conductive coil assembly in opposed directions along the axis towards a central coil position, a magnetic core assembly is movably mounted in the housing for linear vibrational motion along the axis, and a second biasing device is mounted between the housing and the magnetic core assembly to bias the magnetic core assembly in opposed directions along the axis towards a central magnet position.

Abstract: A vibration energy harvester for converting mechanical vibrational energy into electrical energy, the vibration energy harvester comprising a device for generating electrical energy when subjected to mechanical vibration, and a current control circuit electrically connected to the device for providing a substantially constant output current at an electrical output of the vibration energy harvester.

Abstract: An electromechanical generator for converting mechanical vibrational energy into electrical energy, the electromechanical generator comprising an electrically conductive coil assembly and a magnet assembly, the magnet assembly comprising at least one magnet and a two-part magnetic core, the two parts of the core being mounted by a biasing device so as to be relatively vibratable at a resonant frequency along an axis about a central position thereby to cause a change of magnetic flux linked with the coil assembly to generate an electrical potential in the coil assembly.

Abstract: An electromechanical generator for converting mechanical vibrational energy into electrical energy. The electromechanical generator has a housing and an electrically conductive coil assembly movably mounted in the housing. The coil assembly has radially inner and outer sides, and upper and lower edges. A mount for the coil assembly extends inwardly of the radially inner side for mounting the coil assembly for linear vibrational motion along an axis. A first biasing device is mounted between the housing and the mount to bias the electrically conductive coil assembly in opposed directions along the axis towards a central coil position, a magnetic core assembly is movably mounted in the housing for linear vibrational motion along the axis, and a second biasing device is mounted between the housing and the magnetic core assembly to bias the magnetic core assembly in opposed directions along the axis towards a central magnet position.

Abstract: An electromechanical generator for converting mechanical vibrational energy into electrical energy, the electromechanical generator comprising a housing, an electrically conductive coil assembly movably mounted in the housing, the coil assembly having radially inner and outer sides, and upper and lower edges, thereof, a mount for the coil assembly extending inwardly of the radially inner side for mounting the coil assembly for linear vibrational motion along an axis about, a first biasing device mounted between the housing and the mount to bias the electrically conductive coil assembly in opposed directions along the axis towards a central coil position, a magnetic core assembly movably mounted in the housing for linear vibrational motion along the axis, and a second biasing device mounted between the housing and the magnetic core assembly to bias the magnetic core assembly in opposed directions along the axis towards a central magnet position, wherein the magnetic core assembly encloses the electrically cond

Abstract: An electromechanical generator comprising an electromechanical device for converting mechanical vibrational energy into electrical energy, the electromechanical device being a velocity damped resonator having a damping coefficient and a resonant frequency, a power detector for detecting the output electrical power from the electromechanical device, a controller, and a damping coefficient adjuster for adjusting the damping coefficient of the electromechanical device, the controller being arranged to control the damping coefficient adjuster in response to the output electrical power detected by the power detector.