33 results about "Dependence network" patented technology

An apparatus of identifying software components includes a user interface unit for obtaining object dependency and object usages information from a user; a unit of defining dependency weights for calculating weights of inter-object dependency based on the object dependency and the usages information; a unit of generating an object dependency network for representing the degrees of object importance and inter-object dependency by using the dependency weights; and a unit of identifying software components for controlling the component identification process by using the object dependency network and the threshold values inputted by a user.

The invention provides a cross-project defect influence analysis method based on a program dependency relationship and symbolic analysis. The cross-project defect influence analysis method comprises the following steps: 1) acquiring program source codes of different projects and multiple versions in the same software ecosystem; 2) extracting a calling relationship from a program source code of each project basic version, and constructing an ecosystem basic dependence network; 3) obtaining code changes from program source codes of other versions of each project, and constructing a version-sensitive ecosystem dependent network; 4) for a given cross-project defect, under the condition that an upstream error method of the cross-project defect is known, selecting a candidate downstream module;5) for each candidate downstream module, carrying out code preprocessing; 6) performing symbol coding on each preprocessed candidate downstream module; and 7) carrying out constraint solving on each downstream module after symbol coding, and if a solution exists, prompting a developer that the module is possibly influenced by a given cross-project defect. According to the method, the problems thatat present, cross-project defect influence analysis is lacked, and a downstream project affected module cannot be prompted are solved, so that the design of a cross-project defect repair scheme is guided, the quality of defect repair patches is improved, and healthy development of a software ecosystem can be better controlled and managed.

The embodiment of the invention provides an algorithm transplantation method and device, relates to the field of artificial intelligence, the method specifically comprises the following steps: obtaining a first algorithm used for processing the image into a format meeting the input requirement of the deep learning model in a cloud or a server, a first deep learning model used for outputting an identification result by utilizing the processed image; converting the first algorithm into a target algorithm supported by the target embedded device; converting the first deep learning model into a target deep learning model supported by target embedded equipment, and then the target algorithm and the target deep learning model are transplanted to the target embedded device, so that the target embedded device can realize convenient image recognition based on the target algorithm and the target deep learning model without depending on a network and a server.

The present invention discloses a systematic pharmacological method for personalized medicine. In the present invention, biological networks such as gene dependence networks are employed to reflect relationships between genes in pathogenesis. In combination with the gene expression data of a specific patient, a gene rank algorithm, which is capable of utilizing inter-genetic regulation relationships to mine key genes in the pathogenesis of disease in a specific patient, is used to construct a key genes list. Then, personalized medicine is carried out according to whether the drug targets are significantly targeted to the key genes list in the pathogenesis of disease in the specific patient. The systematic pharmacological method for personalized medicine proposed by the present invention is easy to implement, has low cost and high efficiency, and has wide application prospects in precision medicine and drug discovery.

The present disclosure relates to systems and methods of using machine learning analysis to stratify the risk of spontaneous preterm birth (SPTB). In some variations, to select informative markers that differentiate SPTB from term deliveries, a processed quantification data of the markers can be subjected to univariate receiver operating characteristic (ROC) curve analysis. A Differential Dependency Network (DDN) can then applied in order to extract co-expression patterns among the markers. In order to assess the complementary values among selected markers and the range of their relevant performance, multivariate linear models can be derived and evaluated using bootstrap resampling.

The invention discloses a software package importance measurement method based on a weighted a index. The method comprises the steps of abstracting software source codes compiled by the Java language into a feature dependence network in feature granularity; building a class dependence network based on the feature dependence network; building a package dependence network based on the class dependence network; computing the weighed h index of a node based on the package dependence network; computing the weighted cit index of the node based on the package dependence network; and computing the weighted a index of the node based on the weighted h index and the weighted cit index of the node, and using the weighted a index of the node as a measurement index for the importance of the package. According to the software package importance measurement method based on the weighted a index, the shortage that the prior art hardly relates to the importance measurement is covered, and the important significance is provided for understanding the software and improving the code maintenance efficiency.

The invention improves document generation systems in which data is collected to enable automated, customised document generation. initially, a questionnaire which defines a dataset is completed. The system then determines which variables are relevant, and then determines whether all relevant variables are in the dataset. Finally, the system generates a fully customised document if the dataset is complete.

A method, computer program product, and computer system for applying a firewall security layer to software for hardware interface. Sensor data imported by the hardware interface may be secured using the firewall security layer. The sensor data may be provided to an artificial intelligence (AI) expert system. The sensor data provided to the AI expert system may be analyzed. An indication of an insecure condition may be provided via a user interface based upon, at least in part, analysis of the sensor data.

The invention discloses an invading infection region positioning method based on a computer time sequence dependence network. The method includes the steps of constructing the complete object time sequence dependence network according to an access record, collected in a computer system, of all processes to computer objects; pruning the complete time sequence dependence network, and extracting an object time sequence dependence sub-network related to invading; constructing a Bayesian network according to the object time sequence dependence sub-network; carrying out probabilistic reasoning through the Bayesian network, calculating and quantizing the possibility that other unknown objects in the system suffer from invading infection, and obtaining an invading infection region of the computer system. The method has the advantages that the time-information-combined object dependence network is constructed, and the time sequence dependence relation among the objects and the characteristic that the object infection state is changed along with the time change can be more accurately described; a probabilistic reasoning method is provided for positioning the invading infection region.

The invention discloses an authentication verification method, system and device. The method comprises the following steps: receiving a target request and a first hash value from a data bearing network, and performing hash operation on a first user identifier and a target key to obtain the first hash value; performing hash operation on the first user identifier and the target key to obtain a second hash value; comparing the first hash value with the second hash value, determining the target request as a legal message under the condition that a comparison result indicates that the first hash value is consistent with the second hash value, and generating a response message of the target request; a response message is returned to the terminal, the response message at least carries an authentication file, and the authentication file is used for marking the identity of the terminal so that the rich media communication suite equipment management system can recognize the terminal. According to the method and the device, the technical problems of complex authentication process and low use efficiency caused by the fact that authentication verification needs to be carried out on a user independently by depending on network side equipment in related technologies are solved.

The invention discloses a mobile edge computing offloading method based on multi-agent reinforcement learning, which belongs to the field of edge computing and wireless networks, and provides an intelligent task offloading method for complex scenarios of "multi-user-multi-edge nodes". This method adopts a multi-agent reinforcement learning algorithm. Each user device establishes an Actor and Critic deep learning network locally, and performs action selection and action scoring according to the state and actions of itself and other devices, and comprehensively considers spectrum resources, computing resources, and energy resources. , formulate offloading and resource allocation strategies with the goal of optimizing task latency. This method does not depend on the specific model of the network, and each device can autonomously and intelligently formulate the optimal strategy through the learning process of "exploration-feedback", and can adapt to the dynamic changes of the network environment.

The invention discloses a terminal network performance detection method and system. The terminal sends a detection data packet with a preset fixed length to a network device to be detected at a preset fixed frequency, and by calculating the round-trip time of each detection data packet for each detection, To obtain the estimated packet loss rate and network bandwidth of the channel between the terminal and the network device to be detected, so as to detect the network performance of the terminal. Compared with the traditional solution, the present invention does not need to build web services at the network device to be detected, and, because the packet sending frequency in the present invention is fixed, it effectively solves the problem of continuously increasing the packet sending frequency until the RTT value in the traditional solution. Or a stress test scheme in which the packet loss rate exceeds a certain threshold, resulting in an increase in the load of the network device on the terminal side and affecting other functions on the terminal that depend on network applications.

The application discloses an application service monitoring method, device, electronic equipment and readable storage medium. Among them, the method includes pre-installing in the cloud host of the cloud platform a communication device that interacts with the host without relying on the network, and deploying a data collection agent in the host, and at the same time installing the data collection program and various applications that match the data collection agent. The data collection template of the service is integrated in the application service installation package. When the target application service is detected, the data collection configuration file is generated according to the target data collection template matching the attribute information of the target application service and its type; when the target application service is successfully deployed on the cloud host, the data collected by the cloud host based on the data collection configuration file is obtained Monitoring performance data, so that various types of application services on the cloud platform can be monitored safely and efficiently.

The invention discloses a method for measuring the importance of a software class based on a weighted q2 index. The method comprises the following steps: abstracting a software source code written by Java language into a feature dependence network with the feature granularity; constructing a class dependence network based on the feature dependence network; calculating the weighted h index of a node based on the class dependence network; calculating the weighted m index of the node based on the class dependence network; and calculating the weighted q2 index of the node based on the weighted h index and the weighted m index of the node, and taking the weighted q2 index of the node as the measurement index of the class importance. According to the invention, the disadvantage that measurement of the class importance is little related in the prior art can be made up; and, to understand software, increasing of the code maintenance efficiency has an important significance.

The invention belongs to the technical field of air traffic network analysis, and specifically relates to an air traffic network vulnerability analysis system based on complex network theory, which includes: a building module for constructing an air traffic interdependence network and obtaining the characteristics of the air traffic interdependence network; and an analysis module , according to the vulnerability analysis of the air traffic interdependent network, unify the associated sub-networks (airports, air routes and control sectors) in the air traffic network, deeply explore its complex network attributes, and then follow the air traffic management rules, With the help of complex network theory, the model of air traffic interdependence network is constructed to analyze the vulnerability of the network and give reasonable optimization suggestions.

The invention discloses a portable artificial intelligence device and a configuration method thereof. The configuration method includes: connecting the portable artificial intelligence device to a mobile device, and installing a client according to the type of the mobile device; starting the mobile device in the mobile device. The above client, thereby creating or logging in an enterprise account in the client; obtain industry information according to the enterprise account, and load corresponding components according to the industry information; obtain images through the camera, and use the loaded components to The images undergo localized quasi-online processing or localized offline processing. The beneficial effects of the invention are: through the offline deep learning function, it does not rely on the intelligence independence of the network, and does not need the support of a huge server; with high scalability.

The invention discloses a safety monitoring method of electric power industrial control terminal based on service logic consistency, the invention aims at the safety problem of the electric power industrial control terminal equipment introduced by the diversified information in the 'Internet +' intelligent grid system, breaks through the existing attack detection method simply relying on the network space information, and puts forward a safety method of the electric power industrial control terminal based on the service logic consistency. By sampling the multi-modal data of physical world information and cyberspace information, normalized processing and temporal correlation, and through training, the safety monitoring set and parameter constraint set are obtained for safety monitoring andanomaly identification. From the business level, the method can effectively early-warn the state abnormality caused by equipment performance difference, equipment failure and hacker intrusion, and realize the 'time-state-service' trinity consistency safety monitoring of the electric power industrial control terminal based on equipment time logic, state logic and business logic.

The invention discloses a resource exchange method. The method comprises steps: when a resource exchange request based on a non network environment is received, an encryption identifier corresponding to the resource exchange request is acquired, wherein the encryption identifier comprises a public key encryption identifier and a private key encryption identifier; through verification information corresponding to the encryption identifier, decryption is completed to acquire resource exchange information corresponding to the resource exchange request; and after an exchange confirmation instruction for the resource exchange information is received, resource exchange for the resource exchange request is completed. The invention also discloses a resource exchange device. Thus, resource exchange does not need to rely on the network, and the resource exchange use adaptability is improved.