Abstract: A system and method for securing input signals when using input interfaces such as touch-screens and other input interfaces that are suitable for handheld, mobile, computing and other devices, by generating fake signals. In accordance with an embodiment, the technique can be used to protect input signals from, e.g. a touch-screen keyboard, and make it more difficult for malicious software applications to intercept and understand such input signals. The technique can also be implemented without requiring significant changes to the underlying operating system (OS) or graphical user interface (GUI). In accordance with an embodiment, a security enhancer or security enhancing application injects fake signals into the OS/GUI input signals queue. The security enhancer or security enhancing application is also associated with a communication channel that allows it to establish a cryptographic signature or key with authorized or sensitive application receivers that are authorized to receive the input signals.

Abstract: A system and method for securing input signals when using input interfaces such as touch-screens and other input interfaces that are suitable for handheld, mobile, computing and other devices, by generating fake signals. In accordance with an embodiment, the technique can be used to protect input signals from, e.g. a touch-screen keyboard, and make it more difficult for malicious software applications to intercept and understand such input signals. The technique can also be implemented without requiring significant changes to the underlying operating system (OS) or graphical user interface (GUI). In accordance with an embodiment, a security enhancer or security enhancing application injects fake signals into the OS/GUI input signals queue. The security enhancer or security enhancing application is also associated with a communication channel that allows it to establish a cryptographic signature or key with authorized or sensitive application receivers that are authorized to receive the input signals.

Abstract: A system and method for adaptive or dynamic garbage collection and memory management in computer systems and virtual machine environments, including Java virtual machines. Adaptive techniques attempt to constrain the system to consistently move towards the desired goal. In accordance with an embodiment, the system successively calculates a value of error from the “perfect state”, and adjusts parameters so that this value will be reduced on the next round of calculations. In accordance with an embodiment, certain parameters, such as nursery size, can be defined; and the variation in that parameter used to calculate its effect on a particular goal, for example, the pause-time minimization. An error value is then evaluated as the difference between the desired or “goal” pause-time, and the current pause-time. By varying the nursery size in an adaptive manner, the pause-time can then be minimized.

Abstract: A system and method for use with a virtual machine, including an adaptive, automated memory management process that takes decisions regarding which garbage collector technique should be used, based on information extracted from the currently active applications. Reinforcement learning is used to decide under which circumstances to invoke the garbage collecting processing. The learning task is specified by rewards and penalties that indirectly tell the RLS agent what it is supposed to do instead of telling it how to accomplish the task. The decision is based on information about the memory allocation behavior of currently running applications. Embodiments of the system can be applied to the task of intelligent memory management in virtual machines, such as the Java Virtual Machine (JVM).

Abstract: A system and method for adaptive or dynamic garbage collection and memory management in computer systems and virtual machine environments, including Java virtual machines. Adaptive techniques attempt to constrain the system to consistently move towards the desired goal. In accordance with an embodiment, the system successively calculates a value of error from the “perfect state”, and adjusts parameters so that this value will be reduced on the next round of calculations. In accordance with an embodiment, certain parameters, such as nursery size, can be defined; and the variation in that parameter used to calculate its effect on a particular goal, for example, the pause-time minimization. An error value is then evaluated as the difference between the desired or “goal” pause-time, and the current pause-time. By varying the nursery size in an adaptive manner, the pause-time can then be minimized.

Abstract: A system and method for use with a virtual machine, including an adaptive, automated memory management process that takes decisions regarding which garbage collector technique should be used, based on information extracted from the currently active applications. Reinforcement learning is used to decide under which circumstances to invoke the garbage collecting processing. The learning task is specified by rewards and penalties that indirectly tell the RLS agent what it is supposed to do instead of telling it how to accomplish the task. The decision is based on information about the memory allocation behavior of currently running applications. Embodiments of the system can be applied to the task of intelligent memory management in virtual machines, such as the Java Virtual Machine (JVM).