43 results about "Web tables" patented technology

In order to manage virtual private cloud (VPC) network configuration, a network topology and configuration of each network element are firstly obtained, and then a reachability intention including a target function and an intention attribute is defined. In one embodiment, a network representation is generated that describes how traffic is processed in a network element through an operable filter function, and a minimization problem is defined by converting the network representation into a MaxSAT formula that is used to minimize the number of decision variables to be modified. And according to a target function, defining a decision weight for the decision variable, and obtaining a solution of a minimization problem through a MaxSAT resolution algorithm weighted by the decision weight to generate an optimized network configuration.

Transmissions in a data center communications network are monitored at a sending computer, the transmission of data packets being controlled by control messages sent by a receiving computer. The monitor at the sending computer measures the time that a burst of data takes to be transmitted from the sending computer to the receiving computer. Based on feedback received from the receiving computer, the monitor at the sending computer calculates one of more estimated completion times for the transmission. The estimated completion times will approximately match the measured completion time if the network is not the bottleneck for communication. When there is a mismatch between the estimated and measured completion times, this is logged and the information is used to trigger analysis to detect the reason and possible causes for the network under-performing.

Insight summary and prompt generation techniques are described. In one or more examples, a plurality of insights is generated from data extracted from digital content. A network representation is produced having a plurality of nodes based on the plurality of insights and a plurality of connections between corresponding insights. A selection is received of a subset of nodes from the plurality of nodes. A prompt is formed by grouping respective insights from the subset of nodes. An insight summary of the digital content is generated based on the prompt using generative artificial intelligence as implemented using one or more machine-learning models. The insight summary is then presented for output in a user interface.

The invention provides a video recommendation method and device based on dynamic bipartite network representation, and relates to the technical field of data processing. The method comprises the following steps: intercepting a plurality of bipartite network snapshots according to a set time interval in a set time period, and filling virtual nodes in other bipartite network snapshots according to the bipartite network snapshot with the maximum scale; acquiring a processed bipartite network snapshot and an adjacent matrix formed by splicing user nodes and project nodes, and capturing explicit features of the nodes; wherein the user node is a user account in the social media platform; the project nodes are videos in the social media platform; obtaining the processed bipartite network snapshots and the explicit features of the nodes, and capturing the implicit features of the nodes in the bipartite network snapshots; the explicit features and the implicit features of the nodes are expressed through low-dimensional vectors, and the low-dimensional vectors are partially stored in a cache region; and generating a video recommendation scheme for the user based on the low-dimensional vector representation of the node. The accuracy of the recommendation result can be improved.

The invention discloses an intelligent fault tree generation method based on cooperation of a multi-modal large model and a large language model, and relates to the field of fault tree analysis. In order to solve the defects of low tree building efficiency, insufficient cross-modal information processing and incomplete fault causal chain reasoning in the prior art, the technical scheme provided by the invention comprises the following steps: carrying out topological structure analysis on a system flow chart by adopting a multi-modal large model to obtain an equipment network table; taking the equipment network list as input, and performing fault mode directional retrieval on the equipment information base by adopting a large language model to obtain a fault mode list; taking the equipment network list and the fault mode list as input, performing failure reason reasoning by adopting a large language model, and establishing a fault causal chain from a system layer to an equipment layer; and taking the fault causal chain as input, constructing a fault tree by adopting a large language model, and generating a complete fault tree structure from a system top event to an equipment basic event. The method is suitable for safety and reliability analysis work of a complex industrial system.

Disclosed in the present application are a supply chain planning method, an apparatus, a computer device and a storage medium. Specifically, the disclosed supply chain planning method comprises: receiving information of an stock keeping unit and information of facilities; receiving information of activities, the information of the activities comprising the information of the stock keeping unit and the information of the facilities; and, according to the information of the stock keeping unit, the information of the facilities and the information of the activities, generating nodes and arcs to form a supply chain planning network, the nodes and the arcs respectively containing the number of the activities and decision variables of a traffic. In this way, supply chain planning can be abstracted into three different object layers and then, according to actual service requirements, constraint and object configuration are performed so as to form a network expression of the supply chain planning, such that a subsequent supply chain planning optimization result can be calculated as a reference for actual decision making.

Examples of the present disclosure describe systems and methods for state-based entity behavior analysis. In an example, entities of a computing environment may be represented using a hierarchical entity web. In some examples, an entity may have a state associated with it, which may be modeled using a place / transition (PT) network. Events within the computing environment may be evaluated by transitions of a PT network to determine whether an entity should change state. If an entity transitions from one state to another, one or more actions may be performed, including, but not limited to, taking a remedial action, generating a recommendation, and updating the state of one or more associated entities. Thus, aspects disclosed herein may provide a high-level overview of the state of entities of a computing environment, but may also be used to view in-depth information of entities at lower levels of the hierarchical entity web.

Embodiments of the present application provide a representation method, a speech recognition method, a device and an electronic device. The representation method comprises: obtaining to-be-processed data, wherein the to-be-processed data is one of the following: to-be-processed speech data, to-be-processed text data, and to-be-processed image data; obtaining a feature vector generated after feature extraction is performed on a data vector corresponding to the to-be-processed data; performing conversion processing on the feature vector by using a filter to obtain a triple used for self-attention calculation, wherein the triple comprises a query vector, a key vector, and a value vector; and performing self-attention calculation according to the query vector, the key vector, and the value vector to obtain a corresponding network representation. Through the technical solution provided in the embodiments of the present application, the number of parameters corresponding to the self-attention mechanism can be directly reduced, and then the number of parameters of the neural network using the self-attention mechanism can be reduced, that is, the memory occupied by the neural network is reduced, which is conducive to the popularization of the neural network.

The application discloses a dense pedestrian target detection method based on an improved YOLOv11, and realizes efficient detection of dense pedestrians by constructing an improved YOLOv11 model. The method first collects a dense pedestrian dataset and performs image preprocessing, and then constructs the improved YOLOv11 model. The specific improvement steps are as follows: the GhostConv module is used to replace the ordinary convolution in the backbone network, the redundant information in the feature map is reduced, and the calculation cost is reduced, so that the model is lightweight; the C3K2 module is replaced by C3K2-WTConv, the convolution receptive field is expanded, the low-frequency information in the image is effectively captured, and the dense target detection effect is significantly improved; the lightweight MLCA attention mechanism is introduced into the C2PSA module to form a new C2PSA-MLCA module, and the local and global information are combined to enhance the network expression ability. After the model construction is completed, training, testing and performance evaluation are performed. The application realizes model lightweight, improves the accuracy of target detection, and is suitable for traffic monitoring and safety monitoring in public places.

The embodiment of the invention discloses a network sub-health state simulation method and device, a distributed storage system, a distributed storage node, a storage medium and a computer program product. Receiving a simulation type for network sub-health state simulation input by a user and a first simulation parameter corresponding to the simulation type; performing simulation processing corresponding to the simulation type on a data transmission link aiming at a second node based on the first simulation parameter through a second module deployed in the second node of the distributed storage cluster; wherein the second node is a node needing network sub-health state simulation; through a third module deployed in the second node, collecting a simulation result generated by the second module in the processing process; wherein the simulation result represents the network performance of the distributed storage cluster.

The invention belongs to the technical field of artificial intelligence, and relates to an artificial intelligence-based risk assessment method and device, computer equipment and a storage medium, and the method comprises the steps: carrying out the preprocessing of collected face image data of a user, and obtaining standardized face data; performing feature extraction on the standardized face data to obtain visual features and semantic features, and fusing the visual features and the semantic features to obtain target face feature representation; processing the target face feature representation based on a contour expert network, an expression expert network and a comprehensive expert network to obtain a first risk assessment result, a second risk assessment result and a third risk assessment result; and based on the gating network and the target face feature representation, performing comprehensive calculation on the first risk assessment result, the second risk assessment result and the third risk assessment result, and generating a Parkinson risk assessment result of the user. The Parkinson risk assessment method can be applied to a Parkinson risk assessment scene in the medical field, and the accuracy of Parkinson risk assessment is effectively improved through the Parkinson risk assessment method.

Machine learning systems and methods for computer modeling and optimization of telecommunications networks are provided. The system processes information relating to a telecommunications network, such as a cellular telephone network, and generates a directed network graph that represents the network as a series of connected edges and nodes with associated attributes such as transmitter power, antenna tilt, radio resource control information, handover attempts, collisions, and other parameters. The network graph is stored in a graph database, and is processed by one or more network graph model inference modules which apply one or more specially-trained artificial intelligence (AI) models to the network graph to generate an optimized version of the network graph that can be utilized to improve the telecommunications network. A modeling visualization interface is also provided, and allows users of the system to visualize modeling and optimization performed by the system.

Examples of the present disclosure describe systems and methods for state-based entity behavior analysis. In an example, entities of a computing environment may be represented using a hierarchical entity web. In some examples, an entity may have a state associated with it, which may be modeled using a place / transition (PT) network. Events within the computing environment may be evaluated by transitions of a PT network to determine whether an entity should change state. If an entity transitions from one state to another, one or more actions may be performed, including, but not limited to, taking a remedial action, generating a recommendation, and updating the state of one or more associated entities. Thus, aspects disclosed herein may provide a high-level overview of the state of entities of a computing environment, but may also be used to view in-depth information of entities at lower levels of the hierarchical entity web.

The application discloses a Web table abnormal data discovery method based on a text semantic mapping relationship. The application aims at discovering abnormal data with fuzzy or even wrong semantic information in a Web table. The method mainly comprises three parts: a semantic representation module, a column type inference module and an error discovery module. First, the semantic representation module represents the meaning of cell text. For a cell in a table, the string text in the cell is represented as a semantic vector according to context information. Then, the column type inference module infers the type of the column where the cell is located, and obtains the mode information of the column. Finally, based on the mapping relationship between the column type and the semantic vector of the cell text of the main column cell and the target cell, abnormal data in the table is discovered and labeled.

The invention provides an information detection method and device, electronic equipment, a computer readable storage medium and a computer program product. The information detection method and device are applied to various mold penetrating detection scenes such as cloud technology, artificial intelligence, intelligent transportation and games. The information detection method comprises the steps that attention processing is carried out on initial image features of a to-be-detected image, to-be-detected image features are obtained, and the to-be-detected image is an image of a virtual object model; performing through-mold detection on the to-be-detected image features to obtain a first through-mold detection result; based on the image semantic information of the to-be-detected image, an element graph network is constructed, and the element graph network represents a semantic relationship between image elements in the to-be-detected image; performing mold penetrating detection on the element graph network to obtain a second mold penetrating detection result; and combining the first mold penetrating detection result and the second mold penetrating detection result to obtain a target mold penetrating detection result of the to-be-detected image. According to the invention, the accuracy and efficiency of mold penetration detection can be improved.

The present application belongs to the field of computer and online social network, and particularly relates to a propagation network reconstruction and prediction method combined with multiple types of dynamic social relationship. A social relationship representation method is provided in the present application, which represents social relationship by using a multi-layer social network, and the multi-layer social network G includes a relationship topology network corresponding to multiple relationship types one by one; the relationship topology network includes network nodes corresponding to users one by one, and edges of the relationship topology network are used to represent relationship strength between users. The social relationship representation method provided in the present application uses a multi-layer graph neural network to describe various different types of social interaction features. The present application comprehensively considers multi-level, multi-type social relationship and information propagation characteristics, and is suitable for various different complex social networks.

Systems and methods for diversifying k-shortest path computation results for multiple vertices as destination or as inclusion constraint include receiving a request for k paths, in a network from a source port at a source node to a node, wherein the node includes a plurality of ports; representing the network as a graph with vertices representing ports, edges representing connections between the ports, and with weights assigned to each of the edges; assigning a dummy destination vertex to connect to each of the plurality of ports and associated edges between each of the plurality of ports and the dummy destination vertex having a same weight; determining up to the k paths between the source port and the dummy destination vertex, and, subsequent to the determining each of the k paths, assigning a corresponding edge of the associated edges in each of the k paths a predetermined value.

Machine learning systems and methods for computer modeling and optimization of telecommunications networks are provided. The system processes information relating to a telecommunications network, such as a cellular telephone network, and generates a directed network graph that represents the network as a series of connected edges and nodes with associated attributes such as transmitter power, antenna tilt, radio resource control information, handover attempts, collisions, and other parameters. The network graph is stored in a graph database, and is processed by one or more network graph model inference modules which apply one or more specially-trained artificial intelligence (AI) models to the network graph to generate an optimized version of the network graph that can be utilized to improve the telecommunications network. A modeling visualization interface is also provided, and allows users of the system to visualize modeling and optimization performed by the system.

This invention discloses a microblog topic mining method based on text enhancement and high-order conversational relationship guidance, including: (1) constructing a user-level dialogue network containing high-order conversational relationships; (2) user embedding based on text enhancement: using a text enhancement method based on a large language model to interpret colloquial words to utilize their semantic information, and using a pooling function based on a multilayer perceptron to learn the mutual influence between different words, generating user representations based on text sequences; (3) self-fusion network representation: capturing the nonlinear relationship between text content and high-order network structure, and introducing an attention mechanism to model the influence of different users on the topic in the sequence, obtaining user sequence representations; (4) topic generation based on neural variational reasoning: using the user sequence representation with structural information supplementation as input to neural variational reasoning, learning potential generation factors through network reconstruction, and finally generating topics with better consistency.

The invention relates to the technical field of geological exploration data processing, and discloses a geological multi-modal data AI fusion identification analysis method and system. According to the method, multi-modal data of a physical field, chemical elements, a geological structure, a drilling core and the like are subjected to convergence and structured processing; constructing and iteratively optimizing a knowledge topology network based on geological semantics, and expressing semantic association and spatial constraints among data entities; actively detecting a high-value information link and a contradiction point in the knowledge network; generating a plurality of sets of parallel fusion decision plans, and defining a data coordination rule and a feature enhancement direction; introducing an external intervention instruction to screen the reconstruction plan, and forming a target fusion operation sequence; executing the sequence and synchronizing the evolved knowledge network. According to the method, the transformation from static data association to dynamic knowledge guidance and from a fixed algorithm to man-machine collaborative active decision is realized, and the reliability and intelligent analysis level of geological target recognition are improved.

In particular embodiments, a Data Access Webform Crawling System is configured to: (1) identify a webform used to collect one or more pieces of personal data; (2) robotically complete the identified webform; (3) analyze the completed webform to determine one or more processing activities that utilize the one or more pieces of personal data collected by the webform; (4) identify a first data asset in the data model that is associated with the one or more processing activities; (5) modify a data inventory for the first data asset in the data model to include data associated with the webform; and (6) modify the data model to include the modified data inventory for the first data asset.

This invention discloses a method for reverse dynamic synthesis of schematic diagrams, an electronic device, and a storage medium. The method includes: extracting the underlying physical network topology from the point map file of the device to be analyzed, generating a standardized physical network table; performing reverse semantic deduction on the standardized physical network table based on a pre-fabricated device pin semantic model library, outputting a semantic network table; and using a front-end rendering engine to dynamically render the semantic network table to generate a core circuit topology reference schematic diagram. This invention completely breaks the original manufacturer's monopoly on system-level schematic diagrams, achieving engineering-level reverse synthesis from physical point coordinates to system-level logical semantic topology. It provides high-value core topology reference schematic diagrams for devices without schematics, reduces industry manpower and time costs, decreases misjudgment rates, and provides high-value reference data for electronic equipment repair and reverse engineering.

The invention discloses a high-order network propagation dynamics classification method based on node weighted clique degree and deep learning, and belongs to the technical field of computers, and the method specifically comprises the steps: data collection and preprocessing: constructing a hypergraph and network representation thereof from real data; establishment of propagation dynamic models: respectively constructing the propagation dynamic models on the graph network, the simple complex and the hypergraph, and describing dynamic evolution laws under different network structures; feature processing, including three sub-tasks of structural feature calculation, dynamic feature processing and feature merging and sample division; and inputting the features into a model for model training, and performing prediction and effect evaluation on the trained model. According to the method, a real system structure is described by adopting a hypergraph, a pure complex and other high-order networks, so that the defect of insufficient consideration of a node multi-element interaction mode in an existing network propagation dynamic process is overcome.

The present application relates to a kind of computing task scheduling method and system considering the uncertainty risk of data center;The present application is based on Markov decision process to the task scheduling process of data center is modeled, obtains task scheduling model, and the information and electricity price of task are as uncertain parameter, using policy network to represent the decision process of task scheduling, based on conditional risk value measurement risk of running cost being too high, and conditional risk value is embedded as constraint under reinforcement learning framework, optimize data center running cost under the condition of guaranteeing risk controllable, solve task scheduling strategy by fusing the improved policy gradient algorithm of Monte Carlo sampling, train the task scheduling model, and output the task scheduling strategy of data center by the task scheduling model trained, the present application can carry out task scheduling according to time-varying electricity price, reduce data center running cost, and can perceive the risk caused by uncertainty, avoid potential loss.

A system and method for generating unified representation models for network entities is presented. The method includes collecting network entity data feature for a network entity of a plurality of network entities, the network entity data feature including a network entity property; genericizing the network entity based on the collected network entity data feature to generate a generic network entity; identifying a functionality executing in a cloud computing environment by another entity; generating an imputed entity, wherein the imputed entity corresponds to the identified functionality; generating a network representation based on the generic network entity and the imputed entity, wherein the generated network representation is a multi-dimensional data structure representing at least the plurality of network entities and relations between the network entities of the plurality of network entities; and storing the generated network representation.

This invention relates to a dynamic adaptive security penetration testing method based on reinforcement learning, belonging to the fields of cybersecurity and artificial intelligence. It includes: constructing and dynamically maintaining a dynamic attack graph that represents the target network as belief states; a high-level strategic planner containing a large language model receiving the overall penetration testing task objective and decomposing the objective into a sequence of sub-objectives based on the global state of the dynamic attack graph; for each sub-objective, a low-level tactical planner based on a reinforcement learning agent searching for the optimal action path to achieve the sub-objective on the dynamic attack graph; and an execution module executing this path, with the results fed back in real time to update the dynamic attack graph, enabling the high-level strategic planner to dynamically adjust the plan according to environmental changes. This invention solves the problem that existing automated tools, using linear processes, cannot plan and execute complex attack chains through a hierarchical architecture that combines a large model and reinforcement learning, achieving dynamic adaptation and automatic optimization of attack paths.

The invention provides a method and system for supporting collaborative configuration check data based on a WEB table, and belongs to the technical field of data processing. The method comprises the following steps: defining a specification of configuration data; a configuration data table is created or edited by accessing the WEB application, and multiple editors perform collaborative editing based on the same configuration data table; verifying whether the edited data meets the specification or not; if not, marking the edited cells, and sending an error prompt; and if yes, exporting the special data after data editing is completed. According to the method, the accuracy of the configuration data can be improved, a tedious configuration data management process can be simplified, manual operation steps are reduced, the error probability is reduced, and the overall working efficiency is improved.

The application belongs to the field of guided topic network analysis, and particularly relates to a social network group discovery method based on user preferences, comprising: obtaining relevant information from a social network platform; calculating the interaction degree and reputation degree of users; defining a random walk strategy of IBT2vec algorithm, and representing the user topic network as a feature vector matrix; using RoBERTa to obtain the long-term and short-term preferences of users, fusing the same with the initial feature vector to form an enhanced user node representation; compensating for the network space features through meta-transfer learning to alleviate the data sparsity problem; classifying the user features through a spectral clustering method, and obtaining key influential user groups with similar interests and behaviors after full connection layer and normalization processing. The application not only considers the similarity between users, but also comprehensively considers the influence of users, so that the group discovery is more accurate and practical, and reliable support is provided for applications such as public opinion monitoring, information dissemination and marketing promotion.

Systems and methods are disclosed for identifying a set of internal edges on a representation of a network that satisfy a set of demands on the network. The disclosed systems and methods perform a multi-step process of selecting the internal edges. In a first step, an initial set of internal edges can be selected using a clique graph (or in another suitable manner). In a second step, a second set of internal edges can be selected using stream graph(s) (or in another suitable manner). The second set of internal edges can be used when determining network paths that satisfy the demands. When the representation of the network has a cut of two, the disclosed systems and methods can identify a set of internal edges providing a degree of protection against link failure.