Abstract: An apparatus and method for determining dynamic data. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to receive a plurality of input data associated with a user, wherein the input data comprises sequestered data and non-sequestered data, generate, using a classifier, categorized data as a function of the plurality of input data, wherein the categorized data comprises inflow data and outflow data, the classifier further configured to determine an inflow pattern of the inflow data and an outflow pattern of the outflow data, identify, using an evaluation model, at least a status indicator as a function of the inflow data and the outflow data, determine dynamic data as a function of the categorized data and the at least a status indicator, and display, using a downstream device, the dynamic data.

Abstract: Techniques are described for performing automated operations related to determining and providing information about buildings in multiple geographical regions in response to a query with partial descriptions of the regions and optionally partial descriptions of one or more additional locations in the regions, such as to automatically respond to a free-form natural language request having partial descriptions of names of multiple proximate geographical regions and of one or more point-of-interest (POI) locations by determining the intended geographical regions and POI locations and using them in generating corresponding response information. The described techniques may do inter-position distance filtering for at least some combinations of geographical region candidates and POI location candidates, and using a top-ranked combination that has a candidate for each geographical region and POI location designation in the query to determine responsive information for the received query.

Abstract: A system for interactive searches based on user queries data and a plurality of Large Language Models (LLMs) including a processor of a Human-Machine Interface (HMI) server node configured to host a network of LLMs and at least one machine learning module (ML) and connected to at least one user-entity node over a network and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: receive a search request input data from the at least one user-entity node; evaluate, by a first dedicated LLM, relevance of the search request input data by discerning between primary and secondary information; responsive to evaluation by the first dedicated LLM, derive classifying features from the primary and secondary information and generate a feature vector based on the classifying features; ingest the feature vector into the ML module configured to extract additional search parameters from a predictive search model based on historical search data associated with t

Abstract: The system obtains, from a database, multiple videos and multiple metadata corresponding to the multiple videos. The metadata includes tags generated by a large language model to describe a video. The system converts the metadata into a first vector in a multidimensional space. The first vector encodes information included in the tags as a numerical representation in the multidimensional space. A distance between the first vector and a second vector indicates similarity between the metadata represented by the first vector and a second metadata represented by the second vector. The system obtains a natural language query (NLQ) associated with the database, and converts the NLQ into a third vector in the multidimensional space. The system determines whether the first vector satisfies a distance threshold to the third vector. Upon determining that the first vector satisfies the distance threshold, the system presents the video as a result to the NLQ.

Abstract: The apparatus of an edge computing node, a system, a method and a machine-readable medium. The apparatus includes a processor to perform rounds of federated machine learning training including: processing client reports from a plurality of clients of the edge computing network; selecting a candidate set of clients from the plurality of clients for an epoch of the federated machine learning training; causing a global model to be sent to the candidate set of clients; and performing the federated machine learning training on the candidate set of clients. The processor may perform rounds of federated machine learning training including: obtaining coded training data from each of the selected clients; and performing machine learning training on the coded training data.

Abstract: Methods, systems, and devices for data management are described. A data management system may receive a request to generate backup data for a set of data files from the one or more databases. The data management system may then generate, in response to the request, a file including a set of partitions including respective groups of shard files that correspond to respective groups of data files from among the set of data files. In some examples, a respective group of shard files within a partition of the set of partitions may include a first shard file representative of metadata for the partition and one or more additional shard files representative of the respective group of data files for the partition. The data management system may then distribute the respective groups of shard files to a set of nodes within the distributed backup system.

Abstract: Example systems and methods for AI capability assessment include query-based assessment of sequential decision making agents (SDMAs) in stochastic settings with minimal assumptions on SDMA internals. In these examples, a new approach is presented for modeling the capabilities of black-box AI systems by using an active learning approach that can effectively interact with the black-box AI systems and learn an interpretable probabilistic model describing the capabilities of the black-box AI systems.

Abstract: Various example embodiments described herein provide for systems, methods, devices, instructions, and the like for optimizing a query plan to execute a query by using early row flush and pushing down top-k information in a query plan that includes an inner join, which can be used within a data platform environment. In particular, various example embodiments use early flush operations by one or more aggregation operators to eventually enable information from a top-k operator of a query plan to be pushed down the query plan and through one or more inner join operators of the query plan to one or more select operators (e.g., aggregation operators and table scan operators) positioned below the one or more inner join operators.

Abstract: A method for calibrating parameters of a hydrology forecasting model based on a deep reinforcement learning includes selecting according to basin characteristics, and the parameters and parameter value ranges to be calibrated by the model are determined. The method includes a calibrating the parameters of the hydrology forecasting model is established, and three elements of the reinforcement learning, that is, a state space, an action space and a reward function are determined. The method includes a deep reinforcement learning method DQN is applied to optimize the parameters to be calibrated by the hydrology forecasting model. In the present disclosure, by setting a stride length of the action value for the deep reinforcement learning model, an accuracy finally optimized by the calibration parameters can be freely controlled, and a DQN algorithm is adopted to search the entire space for the calibration parameters to ensure the optimality for optimizing the calibrated parameters.

Abstract: Techniques are disclosed for performing a garbage collection process that processes reference objects in multiple generations of a memory area while a program instance is concurrently manipulating runtime objects in the memory area. During one phase of concurrent operations, the system applies one rule set for processing reference objects in one generation of the memory area. During another phase of concurrent operations, the system applies another rule set for processing reference objects residing in another generation of the memory area. To enable the performance of the one phase of concurrent operations and the other phase of concurrent operations at the same time, the system tracks the accessibility of a given runtime object with respect to both of the one generation and the other generation. The system tracks the given runtime object's accessibility with respect to both generations by embedding metadata into a pointer that refers to the given runtime object.

Abstract: A system for scaling parallelism in sweeping stale data associated with multi-version data management is disclosed. The system is programmed to manage a targeted sweep of stale data stored on distributed storage associated with a set of computing nodes, where the stale data includes metadata for database transactions. The system is programmed to dynamically determine, on a computing node, how many threads to simultaneously perform deletion for the targeted sweep based on historical performance of the computing nodes, historical performance of the distributed storage associated with the computing nodes, or other factors. The system is programmed to then cause a certain number of threads to run on the computing node, where each thread identifies and deletes specific metadata for data transactions within the scope of the targeted sweep.

Abstract: A method for grouping of multiple tabs in a web browser includes displaying a plurality of tabs by the web browser. Each tab has separate webpage content associated therewith. Two or more webpages are grouped into at least one category based on semantics of corresponding webpage content. Two or more tabs associated with the two or more grouped webpages are merged into a single tab. The single tab represents the grouped webpages. An aggregated view of contents associated with the grouped webpages is generated and displayed in a single browser window associated with the single tab. The aggregated view has two or more portions. Each portion of the aggregated view concurrently displays content of a corresponding grouped webpage.

Abstract: Embodiments of this specification provide a graph data loading method and apparatus. In the graph data loading method, in response to obtaining to-be-loaded graph data from the outside, a lock is obtained for the to-be-loaded graph data. A graph data loading identifier of the to-be-loaded graph data is determined based on a graph data loading identifier of previous loaded graph data stored in a graph data loading apparatus. Based on the graph data loading identifier of the to-be-loaded graph data, a corresponding storage location of the to-be-loaded graph data in a graph data storage space created in a local memory of a graph data processing device is determined. After the lock obtained for the to-be-loaded graph data is released, the to-be-loaded graph data is stored at the corresponding storage location in the graph data storage space.

Abstract: Representative embodiments disclose mechanisms to assign search results associated with a user query to locations based in part on features associated with the user's Internet of Things (IoT) devices. This results in a search page that presents results in an order and prominence that improves user understanding and information accessibility. Features from IoT devices are used to train a machine learning model that receives as an input information from an IoT entity index, the query, and results from the search engine along with their static rankings. The resultant IoT positioning scores are used to assign an initial location on the search results page. A second machine learning model trained using IoT features is then used to assign a final location on the page. The system causes display of the page to the user.

Abstract: Digital image hash search techniques are described. These techniques leverage clusters formed from digital image hashes that overcome limitations and computational resource consumption of conventional clustering techniques used to implement a digital image search. In an example, search techniques employ two search stages. In a first stage, clusters are identified based on the cluster centers using a distance measure. The second stage involves a comparison of cluster hashes within the identified cluster with the search query hash until the distance measure is reached.

Abstract: Cryptographically-verifiable immutable database systems and methods for permanently deleting transaction value data from the immutable database system, preserving transaction verification data. Database schema data may be preserved when transaction value data is deleted.

Abstract: In an example embodiment, the execution of a command (such as JSON_TABLE) to convert language-independent data format data to a structured table is performed by parallel processing the data in parallel and without buffering all of the input and output. More specifically, a pipelined approach is used that is parallelizable and reduces memory requirements. Input rows are parallel processed within chunks, while produced rows are output directly.

Abstract: A computer implemented method of clustering computer systems in a plurality of systems to identify computer systems being subject to a common security occurrence, each computer system generating data records corresponding to security events in respect the systems, the method comprising: receiving a set of one or more data records associated with each computer system, each record including a sequence of data fields; generate a vector embedding for each data field in each record; evaluate a distance of each vector embedding from a reference vector as an indicator of semantic distance; identifying similar data records based on a measure of a degree of similarity of the distances of vector embeddings for each record; defining a cluster of computer systems including computer systems associated with the similar data records for applying protective measures to the computer systems in the cluster.

Abstract: A system for real-time data synchronization within a database platform may be provided. The system includes a LRM computing device including a processor in communication with one or more data sources and an eCRM platform. The processor may be configured to (i) cause an input page to be displayed on a user computing device; (ii) create in real-time a query including an identifier received using the input page; (iii) initiate in real-time an API call including the query; (iv) cause, in real-time and using the API call, the eCRM platform and the one or more data sources to compare the identifier to lead referral information stored on the one or more data sources and the eCRM platform; and (v) in response to no match being found in the comparison, automatically create and store a lead referral data entry on the one or more data sources and the eCRM platform.

Abstract: The present teaching relates to a method, system, and programming for content personalization. A request is received from a user to obtain a content item. Information indicative of a literacy-level of the user is obtained and the content item to be provided to the user is retrieved. The content item is modified by updating information included in the content item based on the literacy-level of the user to generate an updated content item. The updated content item is provided to the user in response to the request.