Abstract: Methods, apparatuses, and systems are presented for generating natural language models using a novel system architecture for feature extraction. A method for extracting features for natural language processing comprises: accessing one or more tokens generated from a document to be processed; receiving one or more feature types defined by user; receiving selection of one or more feature types from a plurality of system-defined and user-defined feature types, wherein each feature type comprises one or more rules for generating features; receiving one or more parameters for the selected feature types, wherein the one or more rules for generating features are defined at least in part by the parameters; generating features associated with the document to be processed based on the selected feature types and the received parameters; and outputting the generated features in a format common among all feature types.

Abstract: One embodiment of this invention describes a method and apparatus for the use of Machine Learning to efficiently detect, identify, prevent, and predict cyber-attacks on Low Power and Low Complexity Sensor 100 (FIG. 1) networks that have low data transmission requirements, something that all current Machine Learning techniques are unable to accomplish due to numerous restrictions when applied to Low Power and Low Complexity Sensors. Low Power and Low Complexity Sensors are frequently found in various Internet of Things (IOT) network architectures. The IOT is a network of physical devices, vehicles, home appliances, and other items embedded with electronics, software, sensors, actuators, and connectivity which enables them to connect and exchange data, providing more direct integration of the physical world into computer-based systems. However, this should not restrict the applicability of any potential embodiment of this invention as described in this patent application.

Abstract: One embodiment of this invention describes a method and apparatus for the use of Machine Learning to efficiently detect, identify, prevent, and predict cyber-attacks on Low Power and Low Complexity Sensor 100 (FIG. 1) networks that have low data transmission requirements, something that all current Machine Learning techniques are unable to accomplish due to numerous restrictions when applied to Low Power and Low Complexity Sensors. Low Power and Low Complexity Sensors are frequently found in various Internet of Things (IOT) network architectures. The IOT is a network of physical devices, vehicles, home appliances, and other items embedded with electronics, software, sensors, actuators, and connectivity which enables them to connect and exchange data, providing more direct integration of the physical world into computer-based systems. However, this should not restrict the applicability of any potential embodiment of this invention as described in this patent application.

Abstract: One embodiment of this invention describes a method and apparatus for the secure identification and validation of low complexity sensors or devices on a network. In addition any data transmitted between the sensors/devices to/from the network is secured by means of encryption techniques. The method as described herein is intended to protect and secure sensors/devices that might transmit limited amounts of data and have reduced levels of onboard processing power. However it could easily be adapted to other types of networks to provide comparable levels of security and protection. A further understanding of the nature and the advantages of the particular embodiments disclosed herein may be realized by referencing the remaining portions to the specification and the attached drawings.

Abstract: One embodiment of this invention describes a method and apparatus for the secure identification and validation of devices on a network using a low complexity method. In addition, any data transmitted between the devices to/from the network is secured by means of encryption techniques. The method as described herein is intended to protect and secure devices that might need to access a secured sensor or device type of network, for example an Internet of Things (IOT) network. However it could easily be adapted to other types of networks to provide comparable levels of security and protection. A further understanding of the nature and the advantages of particular embodiments disclosed herein may be realized by referencing the remaining portions of the specification and the attached drawings.

Abstract: One embodiment of this invention describes a method and apparatus for the secure identification and validation of low complexity sensors or devices on a network. In addition any data transmitted between the sensors/devices to/from the network is secured by means of encryption techniques. The method as described herein is intended to protect and secure sensors/devices that might transmit limited amounts of data and have reduced levels of onboard processing power. However it could easily be adapted to other types of networks to provide comparable levels of security and protection. A further understanding of the nature and the advantages of the particular embodiments disclosed herein may be realized by referencing the remaining portions to the specification and the attached drawings.

Abstract: One embodiment of this invention describes a method and apparatus for identifying, predicting, and preventing malicious attacks against low complexity sensors or devices (sensors/devices) on a network through the use of an Artificial Neural Network (ANN) which is defined as a connectionist system (i.e. interconnected networks) functioning as a computing system inspired by living neural networks (e.g. the human brain). An ANN employs Machine Learning computational models that, without being programmed with any task-specific rules, can “learn” capabilities such as image recognition by simply considering relevant examples. This approach is similar to how a person might learn a new task. There are many forms and types of malicious attacks that are sometimes more commonly referred to as cyber attacks. In one instance of this invention the malicious attacks are identified by organizing and classifying encryption keys and/or messages sent to or received from a sensor/device.

Abstract: Hydraulic shock absorber and pneumatic leveling unit for vehicles which incorporates an auxiliary damping device within the pneumatic height adjuster chamber to provide for supplemental damping of vehicle suspension springs by compressing gas in the chamber and restricting gas flow in subdivisions thereof on compression or rebound stroke of the unit.

Abstract: Electrical circuits responsive to a logic signal pair produced when the sprung mass of a motor vehicle is above a selected trim band and to another logic signal pair produced when the sprung mass of the vehicle is below the trim band produce respective sprung mass high and sprung mass low signals for a leveling system of the type operative to level the sprung mass of the vehicle within a predetermined trim band in response thereto. A delay circuit effective to inhibit the production of both the sprung mass high and sprung mass low signals for a predetermined time duration is provided to prevent unnecessary operation of the leveling system and respective accumulator circuits inhibit the production of each the sprung mass high and sprung mass low signals at the conclusion of respective predetermined accumulated periods of time.