The present disclosure generally relates to computer security and malware protection. In particular, the present disclosure is generally directed towards systems and methods for detecting and mitigating a code injection attack. In one embodiment the systems and methods may detect a code injection attack by scanning identified sections of memory for non-operational machine instructions (“no-ops”), detecting a code injection attack based on the scan(s) and mitigating the code injection attack by taking one or more defensive actions.

In a one-shot state transition encoder, L-bits of user data are received and encoded into a codeword of N-bits, wherein N>L. The encoding of the user data involves repeatedly performing: a) encoding a portion of user bits from the user data to a portion of encoded bits of the codeword based on a set of state transition probabilities, thereby reducing a size of a remaining buffer of the codeword and reducing a number of unencoded bits of the user data; and b) based on the number of unencoded bits of the user data being greater than or equal to the remaining buffer size of the codeword, terminating further encoding and storing the unencoded bits of the user data into the remaining buffer of the codeword.

According to an embodiment, a decoder includes a token operating unit, a node adder, and a connection detector. The token operating unit is configured to, every time a signal or a feature is input, propagate each of a plurality of tokens, which is an object assigned with a state of the of a path being searched, according to a digraph until a state or a transition assigned with a non-empty input symbol is reached. The node adder is configured to, in each instance of token propagating, add, in a lattice, a node corresponding to a state assigned to each of the plurality of tokens. The connection detector is configured to refer to the digraph and detect a node that is connected to a node added in an i-th instance in the lattice and that is added in an i+1-th instance in the lattice.

A method for identifying a target, a non-transitory computer-readable storage medium, and an electronic device include: acquiring a first image and a second image, the first image and the second image each including a target to be determined; generating a prediction path based on the first image and the second image, both ends of the prediction path respectively corresponding to the first image and the second image; and performing validity determination on the prediction path and determining, according to a determination result, whether the targets to be determined in the first image and the second image are the same target to be determined.

The present disclosure provides a jump detection system for inertial measurement unit (IMU) integrated with a barometric altimeter in the same device (IMU-baro). The processor is configured to record time-series data of both a vertical component of the measured IMU-baro acceleration and the estimated vertical velocity of the IMU-baro, detect a potential jump by comparing the vertical component of the measured IMU-baro acceleration to one or more acceleration thresholds, and, validate the potential jump by comparing a difference between a maximum velocity and a minimum velocity within a vicinity of the potential jump in the time-series data of the estimated vertical velocity of the IMU-baro to a velocity threshold.

Disclosed embodiments include systems and methods relevant to improvements to natural language processing used to determine an intent and one or more associated parameters from a given input string. In an example, an input string is received and first and second different n-grams are applied to the input string. Recurrent neural network models are then used to generate output data based in part on the first and second different n-grams. Intent detection and semantic labeling are applied to the output of the recurrent neural network models.

Rapid and data-efficient training of an artificial intelligence (AI) algorithm are disclosed. Ground truth data are not available and a policy must be learned based on limited interactions with a system. A policy bank is used to explore different policies on a target system with shallow probing. A target policy is chosen by comparing a good policy from the shallow probing with a base target policy which has evolved over other learning experiences. The target policy then interacts with the target system and a replay buffer is built up. The base target policy is then updated using gradients found with respect to the transition experience stored in the replay buffer. The base target policy is quickly learned and is robust for application to new, unseen, systems.

A detection-based segmentation-free method and system for license plate recognition. An image of a vehicle is initially captured utilizing an image-capturing unit. A license plate region is located in the image of the vehicle. A set of characters can then be detected in the license plate region and a geometry correction performed based on a location of the set of characters detected in the license plate region. An operation for sweeping an OCR across the license plate region can be performed to infer characters with respect to the set of characters and locations of the characters utilizing a hidden Markov model and leveraging anchored digit/character locations.

A code completion tool uses machine learning models to more precisely predict the likelihood of a method invocation completing a code fragment that follows one or more method invocations of different classes in a same document during program development. In one aspect, the machine learning model is a n-order Markov chain model that is trained on features that represent characteristics of the context of method invocations found in commonly-used programs from a sampled population. The machine learning model is implemented as a hash table contained a ranked order of hash values in descending order of probability of completing a partially-formed method invocation.

A digital medium environment includes an image processing application that performs object segmentation on an input image. An improved object segmentation method implemented by the image processing application comprises receiving an input image that includes an object region to be segmented by a segmentation process, processing the input image to provide a first segmentation that defines the object region, and processing the first segmentation to provide a second segmentation that provides pixel-wise label assignments for the object region. In some implementations, the image processing application performs improved sky segmentation on an input image containing a depiction of a sky.