This disclosure provides systems, methods and apparatuses for identifying fraudulent accounts associated with an electronic payment service. In some implementations, a computing device may retrieve a data set including a number of attributes for each of a multitude of accounts, and may construct a plurality of different graphs each based on a unique set of the attributes. Each graph may include a plurality of nodes linked together by a multitude of edges, where each node identifies a corresponding account and each edge indicates one or more of the corresponding attributes that are common to a pair of accounts. The computing device may determine a likelihood of each graph correctly identifying fraudulent accounts by analyzing groups of nodes connected to each other by corresponding groups of edges using historical account data, and may select the graph having the greatest determined likelihood to predict whether any of the accounts is fraudulent.

A method is for matching a set of first classes assigned to a first data set with a set of second classes assigned to a second data set. The method includes constructing, via a set of pre-processing functions, a plurality of alignment profiles such that at least one alignment profile is assigned to each of the first classes and each of the second classes. The method includes generating a comparison matrix for each group of the alignment profiles, such that each group includes at least one of the first classes and at least one of the second classes. The method includes training a first machine learning model, through supervised training, based on the generated comparison matrices and based on probabilistic labels generated by a second machine learning model.

A software voting or prediction system iteratively solicits participant preferences between members of a set, with a binary tree built used to minimize the number of iterations required. As each member of the set is considered, it is pairwise-compared with select members represented by nodes already in the binary tree, with iterations beginning at a root node of the tree and continuing to a leaf node. The newly considered member is placed as a new leaf node, and the tree is height-rebalanced as appropriate. Red-black tree coloring and tree rotation rules are optionally used for this purpose. Yes/no preference tallies are kept for each member of the set throughout the tree-building process and are ultimately used for scoring. Height-rebalancing of the tree helps minimize the number of iterations needed to precisely score each member of the set relative to its alternatives.

A software voting or prediction system iteratively solicits participant preferences between members of a set, with a binary tree built used to minimize the number of iterations required. As each member of the set is considered, it is pairwise-compared with select members represented by nodes already in the binary tree, with iterations beginning at a root node of the tree and continuing to a leaf node. The newly considered member is placed as a new leaf node, and the tree is height-rebalanced as appropriate. Red-black tree coloring and tree rotation rules are optionally used for this purpose. Yes/no preference tallies are kept for each member of the set throughout the tree-building process and are ultimately used for scoring. Height-rebalancing of the tree helps minimize the number of iterations needed to precisely score each member of the set relative to its alternatives.

A computer-implemented method, computer program product and computing system for receiving a complex task; processing the complex task to define a plurality of discrete tasks each having a discrete goal; executing the plurality of discrete tasks on a plurality of machine-accessible public computing platforms; determining if any of the plurality of discrete tasks failed to achieve its discrete goal; and if a specific discrete task failed to achieve its discrete goal, defining a substitute discrete task having a substitute discrete goal.

Transaction-enabled systems and methods for royalty apportionment and stacking are disclosed. An example system may include a plurality of royalty generating elements (a royalty stack) each related to a corresponding one or more of a plurality of intellectual property (IP) assets (an aggregate stack of IP). The system may further include a royalty apportionment wrapper to interpret IP licensing terms and apportion royalties to a plurality of owning entities corresponding to the aggregate stack of IP in response to the IP licensing terms and a smart contract wrapper. The smart contract wrapper is configured to access a distributed ledger, interpret an IP description value and IP addition request, to add an IP asset to the aggregate stack of IP, and to adjust the royalty stack.

The embodiment of the present disclosure provides a question-and-answer processing method, including: acquiring a to-be-answered question; determining standard questions meeting a preset condition as a plurality of candidate standard questions, from a plurality of preset standard questions, according to a text similarity with the to-be-answered question, based on a text statistical algorithm; determining, a candidate standard question with the highest semantic similarity with the to-be-answered question as a matching standard question, from the plurality of candidate standard questions, based on a deep text matching algorithm; and determining an answer to the to-be-answered question at least according to the matching standard question. The embodiment of the present disclosure also provides an electronic device and a computer readable medium.

This application provides an in-vehicle user positioning method, an in-vehicle interaction method, a vehicle-mounted apparatus, and a vehicle. In an example, the in-vehicle user positioning method includes: obtaining a sound signal collected by an in-vehicle microphone; in response to that a first voice command is recognized from the sound signal, determining a first user who sends the first voice command; and determining an in-vehicle location of the first user based on a mapping relationship between an in-vehicle user and an in-vehicle location.

The present invention is a combined commodity mining method based on knowledge graph rule embedding, comprising: expressing rules, commodities, attributes, and attribute values as embeddings; splicing and inputting the embeddings of the rules and the embeddings of the attributes into a first neural network to obtain a importance scores of the attributes; splicing and inputting the rules and attributes into a second neural network to obtain the embeddings of the attribute values that the rules should take under the attributes; calculating a similarity between the value of two inputted commodities under the attribute and the embedding of the attribute value calculated by a model; after calculating scores of all attribute-attribute value pairs, summing up to obtain scores of these two commodities under this rule; then making the cross entropy loss with the real scores of these two commodities, and iteratively training based on an optimization algorithm having gradient descent; after the model is trained, parsing the embeddings of the rules in a similar way to obtain rules that can be understood by human beings.

The present invention relates generally to the design of quantum optical configurations and more specifically to using graph theory mapping and fidelity optimization to design optimal quantum optical configurations that have maximal fidelity between the designed optimal quantum optical configuration and the target quantum state. The target quantum state may include resource-efficient heralded multi-photonic quantum states, heralded high-dimensional entanglement, resource states for quantum gates, and high-dimensional multi-photonic GHZ states without ancilla photons.