A system includes a memory storing instructions; and a processor in communication with the memory. When the processor executes the instructions, the instructions are configured to cause the processor to: obtain text data (410), conduct pre-processing on the text data to obtain pre-processed text data (420), extract an entity list and a keyword list based on the pre-processed text data (430), generate an entity embedding list based on the entity list (440), clusterize the entity list based on the entity embedding list to obtain a plurality of entity clusters, each entity cluster comprising at least one entity (450), retrieve a co-occurring keyword list based on the plurality of entity clusters, the entity list, and the keyword list (460), and obtain a topic for each entity cluster of the plurality of entity clusters based on the co-occurring keyword list (470).
In some examples, scalable source code vulnerability remediation may include receiving source code that includes at least one vulnerability, and receiving remediated code that remediates the at least one vulnerability associated with the source code. At least one machine learning model may be trained to analyze a vulnerable code snippet of the source code. The vulnerable code snippet may correspond to the at least one vulnerability associated with the source code. The machine learning model may be trained to generate, for the vulnerable code snippet, a remediated code snippet to remediate the at least one vulnerability associated with the source code. The remediated code snippet may be validated based on an analysis of whether the remediated code snippet remediates the at least one vulnerability associated with the source code.
G06F 21/54 - Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems during program execution, e.g. stack integrity, buffer overflow or preventing unwanted data erasure by adding security routines or objects to programs
G06F 21/56 - Computer malware detection or handling, e.g. anti-virus arrangements
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
3.
SYSTEMS AND METHODS FOR TRIAGING SOFTWARE VULNERABILITIES
Systems and methods are provided for the classification of identified security vulnerabilities in software applications, and their triage based on automated decision-tree triage and/or machine learning. The disclosed system may generate a report listing detected potential vulnerability issues, and automatically determine whether the potential vulnerability issues are exploitable using automated triage policies containing decision trees or by extracting vulnerability features from the report and processing the extracted vulnerability features using machine learning models.
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating customized recommendations for environmentally-conscious cloud computing frameworks for replacing computing resources of existing datacenters. One of the methods involves receiving, through a user interface presented on a display of a computing device, data regarding a user's existing datacenter deployment and the user's preferences for the new cloud computing framework, generating one or more recommendations for environmentally-conscious cloud computing frameworks based on the received data, and presenting such recommendations through the user interface for the user's review and consideration.
A contract platform may receive, from data structures, historical contract data; read, from the historical contract data, numerical categorical data; process the numerical categorical data, with one or more artificial intelligence models, to convert the numerical categorical data into vector format; generate a taxonomy in vector format based on the converted numerical categorical data; read, from the historical contract data, nonnumerical noncategorical data; process the nonnumerical noncategorical data, with the models, to convert the nonnumerical noncategorical data into vector format; generate a knowledge graph in vector format based on the converted nonnumerical noncategorical data. The contract platform may receive a request that includes data identifying a new contract to be created; identify, in the taxonomy and in the knowledge graph, similar contracts; determine similarity scores between the data identifying the new contract and data identifying the similar contracts; and provide the similarity scores and the similar contracts.
A device may receive a first set of historical financial data, a second set of historical financial data, and a plurality of machine learning models. The device may determine a respective forecasted financial score associated with each of the plurality of machine learning models based on the first set of historical financial data and the second set of historical financial data. The device may select two or more machine learning models from the plurality of machine learning models based on the respective forecasted financial score and may determine a set of evaluation data. The device may select an optimum machine learning model from the two or more machine learning models based on a comparison of the respective forecasted financial score of each of the two or more machine learning models relative to the set of evaluation data.
This document described digital decoupling architectures that enable existing computing systems to run in parallel with new computing technologies. In some aspects, a method includes receiving, by a digital decoupling system and from a source computing system, one or more updated data sets that each include data that has been updated at the source computing system. A source data entry of a source table of a database of the digital decoupling system is updated based on each updated data set. In response to detecting the change to the source table, a target data entry that includes data of the updated source data entry is added to a target table of the database. An adapter module obtains the data of the target data entry and generates an event that specifies the data of the target data entry. The event is sent to one or more destination computing elements.
A digital content management system for account management and predictive analytics may be provided. The system may include an analytics system that communicates with one or more servers and one or more data stores to provide digital content management in a network. The analytics system may include a data access interface to receive data associated with a customer, as well as a processor to: standardize the received data using a standardization technique; process the standardized data using a dark data processing technique; generate a customer fit score and a digital density score based on the dark data processing of the standardized data; match received data associated with a customer against at least one variable using at least one matching technique; create a lead analytical record (LAR); prioritize leads in the LAR using a predictive modeling technique; and establish optimized channel assignment based on at least one of the customer fit score, the digital intensity score, the LAR, or the matching and prioritization actions.
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a logistics planner are disclosed. In one aspect, a method includes the actions of receiving data indicating orders for materials to be delivered to first locations and data indicating amounts of the materials stored at second locations. The actions further include providing the data indicating the orders for the materials to be delivered to the first locations and the data indicating the amounts of the materials stored at the second locations as inputs to models. The actions further include generating a graph that includes first nodes and second nodes. The actions further include generating a sub-graph. The actions further include determining an amount of each material to be delivered. The actions further include determining a route to travel and an amount of each material to transport.
G06Q 10/08 - Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 50/28 - Logistics, e.g. warehousing, loading, distribution or shipping
Systems, apparatuses, and methods directed to receiving detected data related to at least one of a driver, a vehicle of the driver, and an external environment of the vehicle; determining one or more near-accident indicators based on a plurality of different detected data and historical data related to the detected data, the near-accident indicators relating to a probability of an occurrence of an accident; calculating at least one of the probability that the accident will occur, a risk score, and information related to a reduction of risk, based on the determined one or more near-accident indicators and machine-learning data; continually updating the machine-learning data based on new detected data; and outputting the at least one of the probability that the event will occur, the risk score, and the information related to the reduction of risk.
A device may receive first user input indicating creation of a first software component for inclusion in a first software application, the first software component being selected for creation in a first software framework associated with the first software application. The device may generate, based on the first software component, a second software component for inclusion in a second software application, the second software component being generated in a second software framework associated with the second software application. In addition, the device may cause display of the second software component in the second software application. The device may also provide component data to a second device, the component data defining at least one property associated with the first software component.
DIGITAL SECURITIES EXCHANGE LIMITED (United Kingdom)
Inventor
Ivanov, Aleksandr Nikolaevich
Kazennov, Aleksei Vladimirovich
Mavchun, Georgii Valerievich
Rymanov, Mikhail
De Rooij, Peter
Velissarios, John
Abstract
A system may facilitate distributed ledger technology (DLT) record based (for example, blockchain-based) voting. A node configured to support a voter role may access a DLT record to determine answers and/or parameters for a ballot. An organizer of the ballot may provide the voter with a voter token that confers vote- value to the voter. The voter may distribute the vote-value to the answers using committed tokens that bind the voter to a particular vote-value without divulging the particular vote value while in a cryptographic form. The voter may distribute committed tokens to multiple answers. In some cases, the distribution of the committed tokens to multiple answers may frustrate attempts to determine the one or more targets to which the voter delivers a non-null vote-value.
H04L 9/32 - Arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system
13.
BLOCKCHAIN OPERATION STACK FOR REWRITABLE BLOCKCHAIN
A system includes a blockchain operation stack which may control access to reading and writing operations for a blockchain. The blockchain operation stack may include a credential authority layer that may control permissions for profiles maintained by the credential authority layer. When the permissions are granted by the credential authority layer, a presentation layer may generate a display that may include information on the blockchain structure. The presentation layer may provide tools to facilitate write layer operations to rewrite and append to the blockchain.
H04L 9/32 - Arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system
14.
INDUSTRIAL DATA SERVICE, DATA MODELING, AND DATA APPLICATION PLATFORM
Industrial data services, data modeling and applications for controlling an industrial operation. A platform is disclosed for allocating a data modeling request to a collaborative group of experts based on a two-dimensional data modeling flow data structure and a multilayer resource allocation graph to obtain a data model for controlling the industrial operation. The two-dimensional data modeling flow data structure and the multilayer resource allocation graph are established from an industrial graph knowledgebase using various data analytics and machine learning techniques.
A customized industrial graph knowledge base for an industrial operation includes a graph database storing nodes of multiple dimensions predefined according to the nature and characteristics of the industrial operation. The nodes are extracted from baseline， domain-specific， and implementation specific industrial knowledge data sources using various analytics for structured and unstructured data. The customized industrial graph knowledge base further includes edges representing relationships between nodes that are either inter-dimensional or intra-dimensional. The importance of each node to the industrial operation is further quantified using a graph model and is included in the graph database as a composite filtering parameter.
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for helping service providers. The methods, systems, and apparatus include actions of obtaining a mobile computing device identifier, identifying a customer corresponding to the mobile computing device identifier, obtaining information regarding the customer that is identified, determining a likely intent of the customer from the information regarding the customer, and providing, using the likely intent of the customer, information that helps a provider.
An iterative classifier for unsegmented electronic documents is based on machine learning algorithms. The textual strings in the electronic document are segmented using a composite dictionary that combines a conventional dictionary and an adaptive dictionary developed based on the context and nature of the electronic document. The classifier is built using a corpus of training and testing samples automatically extracted from the electronic document by detecting signatures for a set of pre-established classes for the textual strings. The classifier is further iteratively improved by automatically expanding the corpus of training and testing samples in real-time when textual strings in new electronic documents are processed and classified.
A secure data system facilitates efficient searching of extensive data storage layers, e.g., a blockchain. The system may create an index for data elements of a predefined type recorded in the blockchain. The system may generate the index by invoking executable instructions stored in an executable data element (e.g., a smart contract) recorded in the blockchain and associated with the predefined type of data elements. The index facilitates fast query into the blockchain for the data elements of the predefined type.
H04L 9/06 - Arrangements for secret or secure communications; Network security protocols the encryption apparatus using shift registers or memories for blockwise coding, e.g. D.E.S. systems
19.
DYNAMIC RESOURCE ALLOCATION FOR APPLICATION CONTAINERS
Cloud service based on container technology provides a lightweight, portable, but robust architecture for deploying service applications. Service volume and resource requirement for a typical modern-day application may fluctuate dramatically in time. A dynamic resource allocation scheme based on machine learning algorithms and executed by the cloud for applications deployed as independent containers allows for efficient resource usage and improvement of quality of service for the applications.
Direct measurement and simulation of real-time production rates of chemical products in complex chemical plants is complex. A predictive model developed based on machine learning algorithms using historical sensor data and production data provides accurate real-time prediction of production rates of chemical products in chemical plants. An optimization model based on machine learning algorithms using clustered historical sensor data and production data provides optimal values for controllable parameters for production maximization.
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
Modern day-to-day life depends on reliable operation of network devices in a wide range of service network such as an electric grid. An analysis and control system executes a complex technical analysis to determine maintenance optimizations for the service network. The system arrives at the optimizations after taking into consideration the maintenance tasks to be performed across a time-dependent network topology and service sensitivity of the network devices to service outage.
H02J 3/00 - Circuit arrangements for ac mains or ac distribution networks
G06F 19/00 - Digital computing or data processing equipment or methods, specially adapted for specific applications (specially adapted for specific functions G06F 17/00;data processing systems or methods specially adapted for administrative, commercial, financial, managerial, supervisory or forecasting purposes G06Q;healthcare informatics G16H)
22.
IDENTIFYING ATTRIBUTES ASSOCIATED WITH AN ENTITY USING NATURAL LANGUAGE PROCESSING
A device may receive information associated with an entity. The information may include a first resource and a second resource. The first resource may be associated with a first file type, and the second resource may be associated with a second file type that is different than the first file type. The first resource may be associated with a first source, and the second resource may be associated with a second source that is different than the first source. The device may extract a plurality of attributes associated with the entity based on the information. The device may implement a natural language processing technique to extract the plurality of attributes. The device may associate the plurality of attributes with a plurality of elements based on extracting the plurality of attributes. The device may provide information that identifies the plurality of elements and the plurality of attributes to permit and/or cause an action to be performed.
Increases in global population have led to unprecedented challenges in meeting every day needs. Vehicle parking is one such challenge given the limited parking spaces available at any destination, whether at work, at a shopping center, or any other location. A parking resource analysis system includes a stepwise simulator system and a role based access system. The stepwise simulator system tests, simulates, and tunes specific models for issuing parking control actions with respect to a specific lot. The role based access system may be physically located at the parking lot. The role based access system implements dynamic loading or dynamic access of role specific models for issuing parking control actions tailored to the role of the particular individual searching for a parking space.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
A system includes circuitry for cryptologically rewriting blockchains in a non-tamper-evident or tamper-evident operation using a key secret held by a trusted party. The blockchains may include a series of blocks secured integrity codes that may prevent non-tamper-evident rewrites by non-trusted parties that are not in possession of the key secret. In some cases, the key may allow valid but tamper-evident rewrites of the blockchain by trusted entities. Integrity outputs may be generated from the integrity codes based on the content of the previous blocks in the series such that attempts by untrusted parties to replace a block may be detected through coding-inconsistencies with other blocks.
H04L 9/32 - Arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system
25.
DISTRIBUTED KEY SECRET FOR CRYPTOLOGIC REWRITABLE BLOCKCHAIN
A cryptologic system includes circuitry for rewriting blockchains in a non-tamper-evident or tamper-evident operation using a key secret held in portions by multiple individually untrusted parties. The blockchains may include a series of blocks secured by integrity codes that may prevent non-tamper-evident rewrites by non-trusted parties that are not in possession of the key secret or individually-untrusted parties in possession of only a portion of the key secret. In some cases, multiple individually-untrusted parties may combine their portions into the key secret. As a group, the multiple individually-untrusted parties may perform non-tamper-evident operation with respect to at least one integrity code within the blockchain.
H04L 9/32 - Arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system
A cryptologic system includes circuitry for rewriting blockchains in a non-tamper-evident or tamper-evident operation using a key secret held by a trusted party. The blockchains may include a series of blocks secured by multiple integrity codes that may prevent non-tamper-evident rewrites by non-trusted parties that are not in possession of the key secret. In some cases, the key may allow valid but tamper-evident rewrites of the blockchain by trusted entities. Further, in some implementations, tamper-evident rewrites may be converted to non-tamper-evident rewrites through multiple-party ratification.
H04L 9/32 - Arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system
A cryptologic system includes circuitry for rewriting blockchains in a non-tamper-evident or tamper-evident operation using a key secret held by a trusted party. The blockchains may include a series of blocks secured by a chameleon hash that may prevent non-tamper-evident rewrites by non-trusted parties that are not in possession of the key secret. Rewrite circuitry of the system may determine randomness data from the chameleon hash and altered data from a rewrite. The randomness data may be written to the randomness field of a block overwritten with the altered data such that the block remains coding-consistent with the chameleon hash and other blocks in the blockchain.
H04L 9/32 - Arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system
28.
TELECOMMUNICATION NETWORK SIGNAL ANALYSIS FOR MATCHING A MOBILE DEVICE CELLULAR IDENTIFIER WITH A MOBILE DEVICE NETWORK IDENTIFIER
A signal analysis system matches a mobile device cellular identifier to a mobile device network identifier. The system obtains multiple cellular positioning records (CPRs) and multiple network positioning records (NPRs). These records include different types of information because they are generated by different types of networks. The system performs spatial matching and temporal matching of the CPRs and the NPRs to identify a specific mobile device cellular identifier among the CPRs that belongs to a mobile device identified by a specific mobile device network interface identifier among the NPRs.
A signal analysis system matches a mobile device cellular identifier to a mobile device network identifier. The system obtains multiple cellular positioning records (CPRs) and multiple network positioning records (NPRs). These records include different types of information because they are generated by different types of networks. The system performs spatial matching and temporal matching of the CPRs and the NPRs to identify a specific mobile device cellular identifier among the CPRs that belongs to a mobile device identified by a specific mobile device network interface identifier among the NPRs.
patents
