The present invention provides an improved system and method for using cryptography to secure computer-implemented choice mechanisms. In several preferred embodiments, a process is provided for securing participants' submissions while simultaneously providing the capability of validating their submissions. This is referred to as a random permutation. In several other preferred embodiments, a process is provided for securing participants' advance instructions while simultaneously providing the capability of validating their advance instructions. This is referred to as a secure advance instruction. Applications include voting mechanisms, school choice mechanisms, and auction mechanisms.

An apparatus and methods are provided to cross-check the reliability of data. Referring to one of the methods, the cross-checking includes receiving a client request containing data in the form of geographic-related information associated with a location. The method also includes determining one or more knowledge providers to determine one or more confidence levels for the data of the client request based on a type of the geographic-related information at the specific location. The method further includes causing the transmission of at least some of the geographic-related information the client request to the one or more knowledge providers. The method still further includes determining one or more confidence levels of the geographic-related information based on a comparison of the geographic-related information and a known resource associated the specific location. A corresponding apparatus and additional method are also provided.

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.

The present disclosure provides a method of user clustering, and the method includes: acquiring a clustering condition for a predetermined user group, wherein the clustering condition includes a time selecting condition and an event selecting condition; determining at least one target time period for each user behavior data in a user behavior database based on the time selecting condition; determining association data indicating a relationship between the each user behavior data and each target time period based on the each user behavior data and the each target time period; and selecting target association data for a time period to be monitored based on the time period to be monitored and the event selecting condition, so as to determine a target user belonging to the predetermined user group according to the target association data. The present disclosure also provides an apparatus of user clustering, a computer device and a non-transitory medium.

One or more embodiments of the disclosure provide systems and methods for providing media presentations to users of a media presentation system. A media presentation generally includes a plurality of media segments provided by multiple users of the media presentation system. In one or more embodiments, a user of the media presentation system may share a media presentation with a co-user. The media presentation system can provide a number of features to assist a user in sharing, filtering, and accessing media presentations.

Examples described herein may perform various operations based on mood congruency. An example method involves accessing, by a processor, from a database, a score that represents a degree of congruency between a first mood vector that describes first media data and a second mood vector that describes second media data, wherein the score is generated based on (i) a first value that the first mood vector associates with a first mood, (ii) a second value that the second mood vector associates with a second mood, and (iii) a degree of congruency between the first and second moods, based on the score, comparing, by the processor, a first characteristic of the first media data, other than the first mood, with a second characteristic of the second media data, other than the second mood, and based at least in part on an output of the comparing, providing an indicator to a module.

An apparatus for providing similar contents, using a neural network, includes a memory storing instructions, and a processor configured to execute the instructions to obtain a plurality of similarity values between a user query and a plurality of images, using a similarity neural network, obtain a rank of each the obtained plurality of similarity values, and provide, as a most similar image to the user query, at least one among the plurality of images that has a respective one among the plurality of similarity values that corresponds to a highest rank among the obtained rank of each of the plurality of similarity values. The similarity neural network is trained with a divergence neural network for outputting a divergence between a first distribution of first similarity values for positive pairs, among the plurality of similarity values, and a second distribution of second similarity values for negative pairs, among the plurality of similarity values.

Providing access to usage reports on a cloud-based data warehouse including maintaining, by a management module, a metadata table on the cloud-based data warehouse, wherein the metadata table comprises usage reports for a plurality of organizations; receiving, by the management module, a request for the metadata table from an administrator account for a first organization of the plurality of organizations; granting, by the management module, the administrator account permission to access a filtered portion of the metadata table, wherein the filtered portion of the metadata table is generated by filtering the metadata table by an organization identifier of the first organization; and providing, by the management module, the filtered portion of the metadata table to the administrator account.

Providing access to usage reports on a cloud-based data warehouse including maintaining, by a management module, a metadata table on the cloud-based data warehouse, wherein the metadata table comprises usage reports for a plurality of organizations; receiving, by the management module, a request for the metadata table from an administrator account for a first organization of the plurality of organizations; granting, by the management module, the administrator account permission to access a filtered portion of the metadata table, wherein the filtered portion of the metadata table is generated by filtering the metadata table by an organization identifier of the first organization; and providing, by the management module, the filtered portion of the metadata table to the administrator account.

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.