A system for transmitting information may include a server that generates pseudo-random superpositions, each superposition including multiple packet fragments encoded using a Galois field. The system may transmit the superpositions across a plurality of communication links, which form a single logical path, to a client device. Communication links may include a combination of diverse communication channels, and more preferably one or more low latency (but low bandwidth) communication links and one or more high bandwidth (but high latency) communication links. Advantageously, the use of a plurality of communication links may facilitate transmitting information quickly and reliably.

Systems and methods can automatically and dynamically determine an optimum frequency for data being input into a drilling optimization tool in order to provide predictive modeling for well drilling operations. The methods and systems selectively input sets of data having different frequencies into the drilling optimization tool to build different predictive models at different frequencies. An optimization algorithm such as Bayesian optimization is then applied to the models to identify in real time an optimum frequency for the data sets being input into the drilling optimization tool based on current operational and environmental parameters.

A content explanation method and apparatus applied to content explanation includes identifying, by a content explanation apparatus, an emotion of the user, when identifying a negative emotion showing that the user is confused about delivered multimedia information, obtaining, by the content explanation apparatus, a target representation manner of target content in a target intelligence type, where the target content is content about which the user is confused in the multimedia information delivered to the user by an information delivery apparatus associated with the content explanation apparatus, and presenting, by the content explanation apparatus, the target content to the user in the target representation manner.

A system configured to perform decision tasks carried out by a machine learning engine operates with a machine learning model, and includes a training component for improving the machine learning model, a device for carrying out decisions based on a set of input data, and an interaction interface for switching the machine learning model between training component and a device that includes a model attestation checker. The device performs acquiring input data, and ascertaining at least one machine learning model over the interaction interface. The model attestation checker performs checking if said machine learning model is trusted by a model attestation, and considering, for decision making, only those machine learning models that are trusted. The machine learning engine performs carrying out the decision task for input data by using a trusted machine learning model, and providing a result attestation for the decision output.

An output-decision-based negative feedback control method and system. The method includes: receiving, by an output decider, output responses of at least two heterogeneous functional equivalents, and dividing numbers corresponding to the at least two heterogeneous functional equivalents into at least one set according to the output responses; determining, by the output decider, credibility of each set according to the output responses, and sending the at least one set and the credibility corresponding to each set to a feedback controller via decision information; generating, by the feedback controller, a first scheduling policy and/or a second scheduling policy according to the decision information; and sending, by the feedback controller, the first scheduling policy to an input proxy, and/or sending a change instruction to the heterogeneous functional equivalent indicated by the second scheduling policy. The method can prevent in advance and process a heterogeneous functional equivalent that may be faulty.

The recognition system for security check and control method thereof. The recognition system for security check is integrated with a reinforcement learning algorithm and an attention region proposal network. The recognition system for security check comprises the following modules: an object feature extraction module (1); a dangerous item region segmentation module (2); a preliminary classification module (3); a preliminary classification result determination module (4); and a fine-grained recognition module (5). In the invention, optimization of a dangerous item region segmentation module and provision of a fine-grained recognition module greatly improve accuracy and efficiency of security check, shorten the duration of security check, alleviate congestion, save labor, and reduce pressure on security check personnel.

Systems and methods for censoring text characters in text-based data are provided. In some embodiments, an artificial intelligence system may be configured to receive text-based data and store the text-based data in a database. The artificial intelligence system may be configured to receive a list of target pattern types identifying sensitive data and receive censorship rules for the target pattern types determining target pattern types requiring censorship. The artificial intelligence system may be configured to assemble a computer-based model related to a received target pattern type in the list of target pattern types. The artificial intelligence system may be configured to use a computer-based model to identify a target data pattern corresponding to the received target pattern type within the text-based data, identify target characters within the target data pattern, and to assign an identification token to the target characters.

Systems and methods for censoring text characters in text-based data are provided. In some embodiments, an artificial intelligence system may be configured to receive text-based data and store the text-based data in a database. The artificial intelligence system may be configured to receive a list of target pattern types identifying sensitive data and receive censorship rules for the target pattern types determining target pattern types requiring censorship. The artificial intelligence system may be configured to assemble a computer-based model related to a received target pattern type in the list of target pattern types. The artificial intelligence system may be configured to use a computer-based model to identify a target data pattern corresponding to the received target pattern type within the text-based data, identify target characters within the target data pattern, and to assign an identification token to the target characters.

A system and method for adapting an errant automated decisioning workflow includes reconfiguring digital abuse or digital fraud logic parameters associated with automated decisioning routes of an automated decisioning workflow in response to identifying an anomalous drift or an anomalous shift in efficacy metrics of the automated decisioning workflow, wherein the automated decisioning workflow includes a plurality of distinct automated decisioning routes that, when applied in a digital threat evaluation of data associated with a target digital event, automatically compute a decision for disposing the target digital event based on a probability digital fraud; simulating, by computers, a performance of the automated decisioning routes in a reconfigured state based on inputs of historical digital event data; calculating simulation metrics based on simulation output data of the simulation; and promoting to an in-production state the automated decisioning workflow having the automated decisioning routes in the reconfigured state.

An endpoint system receives a target file for evaluation for malicious scripts. The original content of the target file is normalized and stored in a normalized buffer. Tokens in the normalized buffer are translated to symbols, which are stored in a tokenized buffer. Strings in the normalized buffer are stored in a string buffer. Tokens that are indicative of syntactical structure of the normalized content are extracted from the normalized buffer and stored in a structure buffer. The content of the tokenized buffer and counts of tokens represented as symbols in the tokenized buffer are compared against heuristic rules indicative of malicious scripts. The contents of the tokenized buffer and string buffer are compared against signatures of malicious scripts. The contents of the tokenized buffer, string buffer, and structure buffer are input to a machine learning model that has been trained to detect malicious scripts.