Abstract: A machine learning model is scanned to detect actual or potential threats. The threats can be detected before execution of the machine learning model or during an isolated execution environment. The threat detection may include performing a machine learning file format check, vulnerability check, tamper check, and stenography check. The machine learning model may also be monitored in an isolated environment during an execution or runtime session. After performing a scan, the system can generate a signature based on actual, potential, or absence of detected threats.

Abstract: A machine learning model is scanned to detect actual or potential threats. The threats can be detected before execution of the machine learning model or during an isolated execution environment. The threat detection may include performing a machine learning file format check, vulnerability check, tamper check, and stenography check. The machine learning model may also be monitored in an isolated environment during an execution or runtime session. After performing a scan, the system can generate a signature based on actual, potential, or absence of detected threats.

Abstract: A working head for a hydraulic power tool. The working head includes a head frame and a first moveable die head configured to move along the working head frame. The first moveable die head is configured to receive a first moveable die comprising a first body length and a second die head is adapted to receive a second die comprising a second body length. At least one of the moveable die or the stationary die comprises an elongated die.

Abstract: A working head for a hydraulic power tool. The working head includes a head frame and a first moveable die head configured to move along the working head frame. The first moveable die head is configured to receive a first moveable die comprising a first body length and a second die head is adapted to receive a second die comprising a second body length. At least one of the moveable die or the stationary die comprises an elongated die.

Abstract: Adversarial attacks on a machine learning model are detected by receiving vectorized data input into the machine learning model along with outputs of the machine learning model responsive to the vectorized data. The vectorized data corresponds to a plurality of queries of the machine learning model by a requesting user. A confidence level is determined which characterizes a likelihood of the vectorized data being part of a malicious act directed to the machine learning model by the requesting user. Data providing the determined confidence levels can be provided to a consuming application or process. Multi-tenant architectures are also provided in which multiple machine learning models associated with different customers can be centrally monitored.

Abstract: A machine learning model is scanned to detect actual or potential threats. The threats can be detected before execution of the machine learning model or during an isolated execution environment. The threat detection may include performing a machine learning file format check, vulnerability check, tamper check, and stenography check. The machine learning model may also be monitored in an isolated environment during an execution or runtime session. After performing a scan, the system can generate a signature based on actual, potential, or absence of detected threats.

Abstract: Adversarial attacks on a machine learning model are detected by receiving vectorized data input into the machine learning model along with outputs of the machine learning model responsive to the vectorized data. The vectorized data corresponds to a plurality of queries of the machine learning model by a requesting user. A confidence level is determined which characterizes a likelihood of the vectorized data being part of a malicious act directed to the machine learning model by the requesting user. Data providing the determined confidence levels can be provided to a consuming application or process. Multi-tenant architectures are also provided in which multiple machine learning models associated with different customers can be centrally monitored.

Abstract: A machine learning model is scanned to detect actual or potential threats. The threats can be detected before execution of the machine learning model or during an isolated execution environment. The threat detection may include performing a machine learning file format check, vulnerability check, tamper check, and stenography check. The machine learning model may also be monitored in an isolated environment during an execution or runtime session. After performing a scan, the system can generate a signature based on actual, potential, or absence of detected threats.

Abstract: A system detects and responds to malicious acts directed towards machine learning models. Data fed into and output by a machine learning model is collected by a sensor. The data fed into the model includes vectorization data, which is generated from raw data provided from a requester, such as for example a stream of timeseries data. The output data may include a prediction or other output generated by the machine learning model in response to receiving the vectorization data. The vectorization data and machine learning model output data are processed to determine whether the machine learning model is being subject to a malicious act (e.g., attack). The output of the processing may indicate an attack score. A response for handling the request by a requester may be selected based on the output that includes the attack score, and the response may be applied to the requestor.

Abstract: A system detects and responds to malicious acts directed towards machine learning models. Data fed into and output by a machine learning model is collected by a sensor. The data fed into the model includes vectorization data, which is generated from raw data provided from a requester, such as for example a stream of timeseries data. The output data may include a prediction or other output generated by the machine learning model in response to receiving the vectorization data. The vectorization data and machine learning model output data are processed to determine whether the machine learning model is being subject to a malicious act (e.g., attack). The output of the processing may indicate an attack score. A response for handling the request by a requester may be selected based on the output that includes the attack score, and the response may be applied to the requestor.

Abstract: A system detects and responds to malicious acts directed towards machine learning models. Data fed into and output by a machine learning model is collected by a sensor. The data fed into the model includes vectorization data, which is generated from raw data provided from a requester, such as for example a stream of timeseries data. The output data may include a prediction or other output generated by the machine learning model in response to receiving the vectorization data. The vectorization data and machine learning model output data are processed to determine whether the machine learning model is being subject to a malicious act (e.g., attack). The output of the processing may indicate an attack score. A response for handling the request by a requester may be selected based on the output that includes the attack score, and the response may be applied to the requestor.

Abstract: Adaptable and customizable truss-like robots are provided. The robotic truss has robotic roller modules configured to translate along one or more pliable member and therewith control the shape or design of the robot.

Abstract: A power tool including a moveable piston, a motor capable of driving the moveable piston to perform work on a work piece, and a distance sensor configured to sense a movement of the moveable piston. The distance sensor operable to provide sensor information indicative of the movement of the piston. A controller receives the sensor information from the distance sensor. The controller operates the motor to perform work on the work piece based in part on the sensor information that the controller receives from the distance sensor.

Abstract: A working head for a hydraulic power tool including a head frame and a first moveable die head configured to move along the working head frame. The first moveable die head configured to receive a first moveable die comprising a first body length. A second die head is adapted to receive a second die comprising a second body length. The first body length of the moveable die is different from the second body length of the stationary die.

Abstract: A power tool including a moveable piston, a motor capable of driving the moveable piston to perform work on a work piece, and a distance sensor configured to sense a movement of the moveable piston. The distance sensor operable to provide sensor information indicative of the movement of the piston. A controller receives the sensor information from the distance sensor. The controller operates the motor to perform work on the work piece based in part on the sensor information that the controller receives from the distance sensor.

Abstract: A working head for a hydraulic power tool. The working head includes a head frame and a first moveable die head configured to move along the working head frame. The first moveable die head is configured to receive a first moveable die comprising a first body length and a second die head is adapted to receive a second die comprising a second body length. At least one of the moveable die or the stationary die comprises an elongated die.

Abstract: A working head for a hydraulic power tool. The working head includes a head frame and a first moveable die head configured to move along the working head frame. The first moveable die head is configured to receive a first moveable die comprising a first body length and a second die head is adapted to receive a second die comprising a second body length. At least one of the moveable die or the stationary die comprises an elongated die.

Abstract: A power tool including a moveable piston, a motor capable of driving the moveable piston to perform work on a work piece, and a distance sensor configured to sense a movement of the moveable piston. The distance sensor operable to provide sensor information indicative of the movement of the piston. A controller receives the sensor information from the distance sensor. The controller operates the motor to perform work on the work piece based in part on the sensor information that the controller receives from the distance sensor.

Abstract: Adaptable and customizable truss-like robots are provided. The robotic truss has robotic roller modules configured to translate along one or more pliable member and therewith control the shape or design of the robot.

Abstract: An example tool includes a hydraulic actuator cylinder; a piston slidably accommodated within the hydraulic actuator cylinder, where the piston includes a piston head and a piston rod extending from the piston head along a central axis direction of the hydraulic actuator cylinder, the piston head divides an inside of the hydraulic actuator cylinder into a first chamber and a second chamber, and the piston rod is disposed in the first chamber and configured to move one or more jaws of the tool; a pump configured to provide pressurized fluid; and a sequence valve configured to block the pressurized fluid from flowing into the second chamber of the hydraulic actuator cylinder until pressure of the pressurized fluid exceeds a threshold pressure value.