11 results about "Peer review" patented technology

The invention relates to the technical field of intelligent retrieval of papers, and discloses a paper review expert intelligent recommendation method and system.According to the method, word paraphrasing is carried out on review expert text information and to-be-reviewed paper text information, semantic connotation supplementation and extension can be carried out on text information with limited information amount, and therefore the content of paper review experts is improved. Richer and more useful information is provided; multi-source information fusion is carried out on the expanded text information, the expanded rich multi-source text information can be summarized, extracted and summarized, and comprehensive text information with sufficient information content and high distinction degree is provided; and performing overall vectorization on the comprehensive text information, performing similarity calculation and sorting on the review expert text vector and the to-be-reviewed paper text vector, and screening out review expert information with relatively high similarity as output. The method can effectively improve the matching precision of the paper'small peer 'review experts, reduces the rejection rate caused by inconsistent research directions, and provides technical support for improving the review quality and shortening the review period.

The invention discloses a peer review method and device and a medium, and the method comprises the steps: obtaining input expert information and reviewer information, storing the expert information to an expert data table, and storing the reviewer information to a reviewer data table; determining an expert screening rule of each reviewer according to the mixed rule configuration information; determining an expert conflict avoidance rule of each reviewer according to the avoidance rule configuration information; when an expert distribution request is received, matching the expert data table with the reviewer data table according to the expert screening rule to obtain a candidate expert list, meeting conditions, of each reviewer; according to the expert conflict avoidance rule, excluding conflicting experts from the candidate expert list to obtain an expert list; and according to the expert list of each reviewer, assigning a review expert to each reviewer. According to the method, the accuracy and efficiency of peer review are improved.

The invention provides a method for predicting formation of a high-entropy alloy phase structure by adopting a machine learning method, which comprises the following steps of: S1, screening data from a plurality of papers subjected to peer review, and constructing a database with a powerful data set; s2, for the alloy in the database, calculating characteristic parameters by adopting a specific calculation formula; s3, screening out a feature set with the best performance by adopting feature screening; and S4, selecting different machine learning classification models for training. Compared with a traditional experimental method and molecular dynamics simulation, the method for predicting the formation of the high-entropy alloy phase structure by adopting the machine learning method provided by the invention has the advantages that the machine learning technology is more efficient and accurate in predicting the performance of the high-entropy alloy, so that a large amount of experimental time and cost can be saved; and the performance data of the alloy can be more accurately obtained, so that the research and development progress of the material is accelerated.

An object of the system according to the embodiment is to efficiently perform generation, peer review, and publication of lecture content.SOLUTION: A system includes a lecture generation part, a peer review part, and a publishing part. The lecture generation unit includes a generation AI. The peer review unit peer-reviews the lecture content generated by the lecture generation unit. The publishing department publishes the lecture content peer-reviewed by the peer-reviewing department as a scientific magazine.SELECTED DRAWING: Figure 1

The application discloses a method and system for generating collusion attack samples of peer review system. The application mainly aims at the problem that the peer review system colludes with the text submitter and the reviewer to construct an antagonistic text to manipulate the results of the peer review distribution system, explores the method of collusion attack in the peer review scene, and the method comprises the following steps: collecting target text and the text of the colluder in the participating reviewer and performing text preprocessing; finding a target attack word; constructing a candidate attack word table; replacing words according to the order of the attack word table to generate an antagonistic sample. The application replaces the synonyms and the near synonyms of the target text and the colluder text, makes the least changes to the sentences of the target text, generates an antagonistic sample that cannot arouse human awareness in the maximum extent, reduces the quality of the peer review distribution, improves the success rate of the antagonistic sample, and reduces the time and complexity of the algorithm.

The invention provides a system and method for quality control in radiology reports. It collects data from reports, medical scan images, and questionnaires within a Safety, Quality, Efficiency, and Productivity (SQEP) framework. Scores for safety, quality, productivity, and efficiency are computed for radiologists and departments using weighted parameters, peer reviews, and audit outcomes. A consolidated SQEP score evaluates overall performance. The system identifies deviations in report quality through trend analysis and correlations, providing corrective feedback and training recommendations to radiologists. By addressing errors proactively, the system enhances diagnostic accuracy, operational efficiency, and compliance with quality standards. This comprehensive approach ensures improved performance and reliability in radiology departments while maintaining high-quality patient care.

This invention discloses a method for aggregating peer ratings in online learning based on hypergraph neural networks, comprising: constructing a hypergraph of social relationships among students and a corresponding first matrix, a hypergraph of peer ratings among students and a corresponding second matrix, and a hypergraph of student self-ratings and a corresponding third matrix; inputting the first, second, and third matrices into a preset deep hypergraph convolutional model to obtain first, second, and third output features; the deep hypergraph convolutional model includes a convolutional network, a graph attention network, and a residual network; performing feature interaction on the first, second, and third output features to obtain modal interaction features; aggregating the modal interaction features and calculating the predicted score for each student based on the aggregated modal interaction features. This invention can achieve accurate prediction of peer ratings in online learning and can be widely applied in the field of peer rating prediction.

This application discloses a multimodal AI collaborative processing system and method for translation peer review, relating to the field of data processing technology. The system includes: a data acquisition module for acquiring data to be translated; a preprocessing module for acquiring structured data to be translated; a cross-modal association labeling module for establishing a cross-modal association graph; a cross-modal knowledge enhancement module for acquiring a knowledge-enhanced cross-modal association graph; a context-aware feature extraction module for acquiring text semantic vectors, image feature vectors, speech feature vectors, and text features enhanced by cross-modal association; a dynamic modal weight fusion module for acquiring modal weight configurations; a contradiction pre-screening module for generating a list of potential contradictions; and a cross-modal contradiction detection and classification module for generating misclassification results. This application can solve the information silo problem of traditional single-modal systems and improve the comprehensiveness of peer review.

Merge requests (MRs) are vital for maintaining code quality, fostering collaboration, and catching potential issues through peer review before merging changes into the code base. However, in practice, this activity is often neglected due to its time consumption, team members lacking shared understanding of the code, or underestimating its long-term benefits in favor of quick fixes and immediate results. Code Guardian (CG), a GenAI- based context-aware MR review assistant is designed to support development teams to identify and resolve potential problems in software systems during the development phase. CG integrates with DevOps platforms, analyzing code changes within MRs. The system's core objectives include enhancing code quality, speeding up development processes, and reducing the cognitive load of reviewers.

A system, method, and platform for converting academic research publications into quality-assured, practitioner-accessible content. The invention employs a network of specialized, autonomous AI agents working collaboratively with original authors to transform complex academic papers into industry-relevant, multimedia-enriched articles. The system performs contextual analysis of academic papers, identifies key patterns and structural elements, and correlates these with industry concepts. It dynamically integrates multimedia elements, establishes collaborative feedback loops with original authors, and incorporates simulated peer review mechanisms. The invention features continuous learning capabilities that leverage author interactions to improve future performance. This approach bridges the gap between academic research and industry application by making complex research publications more accessible, understandable, and relevant to practitioners while maintaining the accuracy and integrity of the original research.

The invention discloses a civil aircraft requirement-oriented numerical control system control software evidence chain development method. The method comprises the steps of developing and identifying software requirements, establishing bidirectional traceability of the requirements, developing requirement attributes of the software, deriving feedback evaluation of the requirements, reviewing the requirements, and forming review instructions and review conclusions. According to the method, engineering implementation steps of software demand evidence chain development are clarified, the requirements of DO-178C for software development process activities are implemented, and guidance is provided for civil aircraft numerical control system software evidence chain development; a'verification method 'and'verification consideration' are established, the understanding and implementation effect of a demand developer on the demand are transmitted to verification personnel, and the verification personnel are helped to improve the accuracy and sufficiency of case verification; and carrying out peer-to-peer review of demand-by-demand and check-by-check items and forming a review description and a review conclusion, so that the deviation condition of the demand to the DO-178C target can be fully detected, and the achievement of the DO-178C target is ensured.