In order to address the issues of low recall rate, long clustering time, and low accuracy in the quality assessment of traditional softwareengineering talent cultivation methods, a new quality evaluation method of so...
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In order to address the issues of low recall rate, long clustering time, and low accuracy in the quality assessment of traditional softwareengineering talent cultivation methods, a new quality evaluation method of softwareengineering professional talent training under the background of new engineering is proposed. The intrinsic dependency relationship among the evaluation indicators of softwareengineering talent cultivation quality is analysed in depth using factor analysis, and a talent cultivation quality assessment indicator system is constructed. Indicator data is collected. The ant colony clustering algorithm is used to cluster the collected data, and the processed data is inputted into the talent cultivation quality assessment model based on fuzzy comprehensive evaluation to obtain relevant assessment results. The experimental results showed that the recall rate of this method is between 95% and 99%, the average clustering time of indicators is 7.75 s, and the maximum accuracy rate is 97%.
Considering the testing process of the software system as a stochastic process is a primary approach to the software reliability modeling technique. Besides some popular distributions, the Poisson distribution has bee...
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Considering the testing process of the software system as a stochastic process is a primary approach to the software reliability modeling technique. Besides some popular distributions, the Poisson distribution has been considered the best based on its advantage when modeling the times at which arrivals enter a system. In the non-homogeneous Poisson process group of models, the S-shaped function is a value curve with many good results. This paper proposes a new imperfect debugging software reliability model based on (1) an Imperfect debugging assumption (the testing process could cause new faults);and (2) the Fault detection rate can be controlled more effectively by the appearance of a growth-rate-controller. The real data from industrial projects verify the application of this model based on good popular criteria values.
Geometry problem solving always attracts much attention of research and engineering. Solving geometry problems requires the capacity of parsing multi-modal information and utilizing theorem knowledge for comprehensive...
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engineering AI software systems is starting to evolve from the pure development of machine learning (ML) models to a more structured discipline that treats ML components as part of much larger software systems. As suc...
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engineering AI software systems is starting to evolve from the pure development of machine learning (ML) models to a more structured discipline that treats ML components as part of much larger software systems. As such, more structured principles are required for their development, such as established design principles, established development models, and safeguards for deployed ML models. This column focuses on papers presented at the Third internationalconference on AI engineering-softwareengineering for AI (CAIN 2024). The selected papers reflect the current development of the field of AI systems engineering and AI software development, taking it to the next level of maturity. Feedback or suggestions are welcome. In addition, if you try or adopt any of the practices included in the column, please send us and the authors of the paper(s) a note about your experiences.
The proceedings contain 37 papers. The special focus in this conference is on knowledge Science, engineering and Management. The topics include: A Sparse Matrix Optimization Method for Graph Neural Networks Train...
ISBN:
(纸本)9783031402821
The proceedings contain 37 papers. The special focus in this conference is on knowledge Science, engineering and Management. The topics include: A Sparse Matrix Optimization Method for Graph Neural Networks Training;dual-Dimensional Refinement of knowledge Graph Embedding Representation;contextual Information Augmented Few-Shot Relation Extraction;dynamic and Static Feature-Aware Microservices Decomposition via Graph Neural Networks;an Enhanced Fitness-Distance Balance Slime Mould Algorithm and Its Application in Feature Selection;low Redundancy Learning for Unsupervised Multi-view Feature Selection;Dynamic Feed-Forward LSTM;black-Box Adversarial Attack on Graph Neural Networks Based on Node Domain knowledge;role and Relationship-Aware Representation Learning for Complex Coupled Dynamic Heterogeneous Networks;automatic Gaussian Bandwidth Selection for Kernel Principal Component Analysis;twin Graph Attention Network with Evolution Pattern Learner for Few-Shot Temporal knowledge Graph Completion;subspace Clustering with Feature Grouping for Categorical Data;learning Graph Neural Networks on Feature-Missing Graphs;dealing with Over-Reliance on Background Graph for Few-Shot knowledge Graph Completion;kernel-Based Feature Extraction for Time Series Clustering;cluster Robust Inference for Embedding-Based knowledge Graph Completion;community-Enhanced Contrastive Siamese Networks for Graph Representation Learning;Distant Supervision Relation Extraction with Improved PCNN and Multi-level Attention;enhancing Adversarial Robustness via Anomaly-aware Adversarial Training;an Improved Cross-Validated Adversarial Validation Method;boosting LightWeight Depth Estimation via knowledge Distillation;EACCNet: Enhanced Auto-Cross Correlation Network for Few-Shot Classification;a Flexible Generative Model for Joint Label-Structure Estimation from Multifaceted Graph Data;dual Channel knowledge Graph Embedding with Ontology Guided Data Augmentation;multi-Dimensional Graph Rule Learner;MixUN
The proceedings contain 37 papers. The special focus in this conference is on knowledge Science, engineering and Management. The topics include: A Sparse Matrix Optimization Method for Graph Neural Networks Train...
ISBN:
(纸本)9783031402883
The proceedings contain 37 papers. The special focus in this conference is on knowledge Science, engineering and Management. The topics include: A Sparse Matrix Optimization Method for Graph Neural Networks Training;dual-Dimensional Refinement of knowledge Graph Embedding Representation;contextual Information Augmented Few-Shot Relation Extraction;dynamic and Static Feature-Aware Microservices Decomposition via Graph Neural Networks;an Enhanced Fitness-Distance Balance Slime Mould Algorithm and Its Application in Feature Selection;low Redundancy Learning for Unsupervised Multi-view Feature Selection;Dynamic Feed-Forward LSTM;black-Box Adversarial Attack on Graph Neural Networks Based on Node Domain knowledge;role and Relationship-Aware Representation Learning for Complex Coupled Dynamic Heterogeneous Networks;automatic Gaussian Bandwidth Selection for Kernel Principal Component Analysis;twin Graph Attention Network with Evolution Pattern Learner for Few-Shot Temporal knowledge Graph Completion;subspace Clustering with Feature Grouping for Categorical Data;learning Graph Neural Networks on Feature-Missing Graphs;dealing with Over-Reliance on Background Graph for Few-Shot knowledge Graph Completion;kernel-Based Feature Extraction for Time Series Clustering;cluster Robust Inference for Embedding-Based knowledge Graph Completion;community-Enhanced Contrastive Siamese Networks for Graph Representation Learning;Distant Supervision Relation Extraction with Improved PCNN and Multi-level Attention;enhancing Adversarial Robustness via Anomaly-aware Adversarial Training;an Improved Cross-Validated Adversarial Validation Method;boosting LightWeight Depth Estimation via knowledge Distillation;EACCNet: Enhanced Auto-Cross Correlation Network for Few-Shot Classification;a Flexible Generative Model for Joint Label-Structure Estimation from Multifaceted Graph Data;dual Channel knowledge Graph Embedding with Ontology Guided Data Augmentation;multi-Dimensional Graph Rule Learner;MixUN
The proceedings contain 37 papers. The special focus in this conference is on knowledge Science, engineering and Management. The topics include: A Sparse Matrix Optimization Method for Graph Neural Networks Train...
ISBN:
(纸本)9783031402913
The proceedings contain 37 papers. The special focus in this conference is on knowledge Science, engineering and Management. The topics include: A Sparse Matrix Optimization Method for Graph Neural Networks Training;dual-Dimensional Refinement of knowledge Graph Embedding Representation;contextual Information Augmented Few-Shot Relation Extraction;dynamic and Static Feature-Aware Microservices Decomposition via Graph Neural Networks;an Enhanced Fitness-Distance Balance Slime Mould Algorithm and Its Application in Feature Selection;low Redundancy Learning for Unsupervised Multi-view Feature Selection;Dynamic Feed-Forward LSTM;black-Box Adversarial Attack on Graph Neural Networks Based on Node Domain knowledge;role and Relationship-Aware Representation Learning for Complex Coupled Dynamic Heterogeneous Networks;automatic Gaussian Bandwidth Selection for Kernel Principal Component Analysis;twin Graph Attention Network with Evolution Pattern Learner for Few-Shot Temporal knowledge Graph Completion;subspace Clustering with Feature Grouping for Categorical Data;learning Graph Neural Networks on Feature-Missing Graphs;dealing with Over-Reliance on Background Graph for Few-Shot knowledge Graph Completion;kernel-Based Feature Extraction for Time Series Clustering;cluster Robust Inference for Embedding-Based knowledge Graph Completion;community-Enhanced Contrastive Siamese Networks for Graph Representation Learning;Distant Supervision Relation Extraction with Improved PCNN and Multi-level Attention;enhancing Adversarial Robustness via Anomaly-aware Adversarial Training;an Improved Cross-Validated Adversarial Validation Method;boosting LightWeight Depth Estimation via knowledge Distillation;EACCNet: Enhanced Auto-Cross Correlation Network for Few-Shot Classification;a Flexible Generative Model for Joint Label-Structure Estimation from Multifaceted Graph Data;dual Channel knowledge Graph Embedding with Ontology Guided Data Augmentation;multi-Dimensional Graph Rule Learner;MixUN
The proceedings contain 37 papers. The special focus in this conference is on knowledge Science, engineering and Management. The topics include: A Sparse Matrix Optimization Method for Graph Neural Networks Train...
ISBN:
(纸本)9783031402852
The proceedings contain 37 papers. The special focus in this conference is on knowledge Science, engineering and Management. The topics include: A Sparse Matrix Optimization Method for Graph Neural Networks Training;dual-Dimensional Refinement of knowledge Graph Embedding Representation;contextual Information Augmented Few-Shot Relation Extraction;dynamic and Static Feature-Aware Microservices Decomposition via Graph Neural Networks;an Enhanced Fitness-Distance Balance Slime Mould Algorithm and Its Application in Feature Selection;low Redundancy Learning for Unsupervised Multi-view Feature Selection;Dynamic Feed-Forward LSTM;black-Box Adversarial Attack on Graph Neural Networks Based on Node Domain knowledge;role and Relationship-Aware Representation Learning for Complex Coupled Dynamic Heterogeneous Networks;automatic Gaussian Bandwidth Selection for Kernel Principal Component Analysis;twin Graph Attention Network with Evolution Pattern Learner for Few-Shot Temporal knowledge Graph Completion;subspace Clustering with Feature Grouping for Categorical Data;learning Graph Neural Networks on Feature-Missing Graphs;dealing with Over-Reliance on Background Graph for Few-Shot knowledge Graph Completion;kernel-Based Feature Extraction for Time Series Clustering;cluster Robust Inference for Embedding-Based knowledge Graph Completion;community-Enhanced Contrastive Siamese Networks for Graph Representation Learning;Distant Supervision Relation Extraction with Improved PCNN and Multi-level Attention;enhancing Adversarial Robustness via Anomaly-aware Adversarial Training;an Improved Cross-Validated Adversarial Validation Method;boosting LightWeight Depth Estimation via knowledge Distillation;EACCNet: Enhanced Auto-Cross Correlation Network for Few-Shot Classification;a Flexible Generative Model for Joint Label-Structure Estimation from Multifaceted Graph Data;dual Channel knowledge Graph Embedding with Ontology Guided Data Augmentation;multi-Dimensional Graph Rule Learner;MixUN
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