In recent years,deep learning has been widely applied in the fields of recommendation systems and click-through rate(CTR)prediction,and thus recommendation models incorporating deep learning have emerged.In addition,t...In recent years,deep learning has been widely applied in the fields of recommendation systems and click-through rate(CTR)prediction,and thus recommendation models incorporating deep learning have emerged.In addition,the design and implementation of recommendation models using information related to user behavior sequences is an important direction of current research in recommendation systems,and models calculate the likelihood of users clicking on target items based on their behavior sequence information.In order to explore the relationship between features,this paper improves and optimizes on the basis of deep interest network(DIN)proposed by Ali’s team.Based on the user behavioral sequences information,the attentional factorization machine(AFM)is integrated to obtain richer and more accurate behavioral sequence information.In addition,this paper designs a new way of calculating attention weights,which uses the relationship between the cosine similarity of any two vectors and the absolute value of their modal length difference to measure their relevance degree.Thus,a novel deep learning CTR prediction mode is proposed,that is,the CTR prediction network based on user behavior sequence and feature interactions deep interest and machines network(DIMN).We conduct extensive comparison experiments on three public datasets and one private music dataset,which are more recognized in the industry,and the results show that the DIMN obtains a better performance compared with the classical CTR prediction model.展开更多
Factorization machine(FM)is a prevalent approach to modelling pairwise(second-order)feature interactions when dealing with high-dimensional sparse data.However,on the one hand,FMs fail to capture higher-order feature ...Factorization machine(FM)is a prevalent approach to modelling pairwise(second-order)feature interactions when dealing with high-dimensional sparse data.However,on the one hand,FMs fail to capture higher-order feature interactions suffering from combinatorial expansion.On the other hand,taking into account interactions between every pair of features may introduce noise and degrade the prediction accuracy.To solve these problems,we propose a novel approach,the graph factorization machine(GraphFM),which naturally represents features in the graph structure.In particular,we design a mechanism to select beneficial feature interactions and formulate them as edges between features.Then the proposed model,which integrates the interaction function of the FM into the feature aggregation strategy of the graph neural network(GNN),can model arbitrary-order feature interactions on graph-structured features by stacking layers.Experimental results on several real-world datasets demonstrate the rationality and effectiveness of our proposed approach.The code and data are available at https://github.com/CRIPAC-DIG/GraphCTR.展开更多
Heart disease includes a multiplicity of medical conditions that affect the structure,blood vessels,and general operation of the heart.Numerous researchers have made progress in correcting and predicting early heart d...Heart disease includes a multiplicity of medical conditions that affect the structure,blood vessels,and general operation of the heart.Numerous researchers have made progress in correcting and predicting early heart disease,but more remains to be accomplished.The diagnostic accuracy of many current studies is inadequate due to the attempt to predict patients with heart disease using traditional approaches.By using data fusion from several regions of the country,we intend to increase the accuracy of heart disease prediction.A statistical approach that promotes insights triggered by feature interactions to reveal the intricate pattern in the data,which cannot be adequately captured by a single feature.We processed the data using techniques including feature scaling,outlier detection and replacement,null and missing value imputation,and more to improve the data quality.Furthermore,the proposed feature engineering method uses the correlation test for numerical features and the chi-square test for categorical features to interact with the feature.To reduce the dimensionality,we subsequently used PCA with 95%variation.To identify patients with heart disease,hyperparameter-based machine learning algorithms like RF,XGBoost,Gradient Boosting,LightGBM,CatBoost,SVM,and MLP are utilized,along with ensemble models.The model’s overall prediction performance ranges from 88%to 92%.In order to attain cutting-edge results,we then used a 1D CNN model,which significantly enhanced the prediction with an accuracy score of 96.36%,precision of 96.45%,recall of 96.36%,specificity score of 99.51%and F1 score of 96.34%.The RF model produces the best results among all the classifiers in the evaluation matrix without feature interaction,with accuracy of 90.21%,precision of 90.40%,recall of 90.86%,specificity of 90.91%,and F1 score of 90.63%.Our proposed 1D CNN model is 7%superior to the one without feature engineering when compared to the suggested approach.This illustrates how interaction-focused feature analysis can produce precise and useful insights for heart disease diagnosis.展开更多
Semantic segmentation of remote sensing images is a critical research area in the field of remote sensing.Despite the success of Convolutional Neural Networks(CNNs),they often fail to capture inter-layer feature relat...Semantic segmentation of remote sensing images is a critical research area in the field of remote sensing.Despite the success of Convolutional Neural Networks(CNNs),they often fail to capture inter-layer feature relationships and fully leverage contextual information,leading to the loss of important details.Additionally,due to significant intraclass variation and small inter-class differences in remote sensing images,CNNs may experience class confusion.To address these issues,we propose a novel Category-Guided Feature Collaborative Learning Network(CG-FCLNet),which enables fine-grained feature extraction and adaptive fusion.Specifically,we design a Feature Collaborative Learning Module(FCLM)to facilitate the tight interaction of multi-scale features.We also introduce a Scale-Aware Fusion Module(SAFM),which iteratively fuses features from different layers using a spatial attention mechanism,enabling deeper feature fusion.Furthermore,we design a Category-Guided Module(CGM)to extract category-aware information that guides feature fusion,ensuring that the fused featuresmore accurately reflect the semantic information of each category,thereby improving detailed segmentation.The experimental results show that CG-FCLNet achieves a Mean Intersection over Union(mIoU)of 83.46%,an mF1 of 90.87%,and an Overall Accuracy(OA)of 91.34% on the Vaihingen dataset.On the Potsdam dataset,it achieves a mIoU of 86.54%,an mF1 of 92.65%,and an OA of 91.29%.These results highlight the superior performance of CG-FCLNet compared to existing state-of-the-art methods.展开更多
As a popular infrastructure for distributed systems running on the Internet, middleware has to support much more diverse and complex interactions for coping with the drastically increasing demand on information techno...As a popular infrastructure for distributed systems running on the Internet, middleware has to support much more diverse and complex interactions for coping with the drastically increasing demand on information technology and the extremely open and dynamic nature of the Internet. These supporting mechanisms facilitate the development, deployment, and integration of distributed systems, as well as increase the occasions for distributed systems to interact in an undesired way. The undesired interactions may cause serious problems, such as quality violation, function loss, and even system crash. In this paper, the problem is studied from the perspective of the feature interaction problem （FIP） in telecom, and an online approach to the detection and solution on runtime systems is proposed. Based on a classification of middleware enabled interactions, the existence of FIP in middleware based systems is illustrated by four real cases and a conceptual comparison between middleware based systems and telecom systems. After that, runtime software architecture is employed to facilitate the online detection and solution of FIP. The approach is demonstrated on J2EE （Java 2 Platform Enterprise Edition） and applied to detect and resolve all of the four real cases.展开更多
As a platform-independent software system, a Web service is designed to offer interoperability among diverse and heterogeneous applications. With the introduction of service composition in the Web service creation, va...As a platform-independent software system, a Web service is designed to offer interoperability among diverse and heterogeneous applications. With the introduction of service composition in the Web service creation, various message interactions among the atomic services result in a problem resembling the feature interaction problem in the telecommunication area. This article defines the problem as feature interaction in Web services and proposes a model checking-based detection method. In the method, the Web service description is translated to the Promela language - the input language of the model checker simple promela interpreter （SPIN）, and the specific properties, expressed as linear temporal logic（LTL） formulas, are formulated according to our classification of feature interaction. Then, SPIN is used to check these specific properties to detect the feature interaction in Web services.展开更多
This paper proposed a novel multi-view interactive behavior recognition method based on local self-similarity descriptors and graph shared multi-task learning. First, we proposed the composite interactive feature repr...This paper proposed a novel multi-view interactive behavior recognition method based on local self-similarity descriptors and graph shared multi-task learning. First, we proposed the composite interactive feature representation which encodes both the spatial distribution of local motion of interest points and their contexts. Furthermore, local self-similarity descriptor represented by temporal-pyramid bag of words(BOW) was applied to decreasing the influence of observation angle change on recognition and retaining the temporal information. For the purpose of exploring latent correlation between different interactive behaviors from different views and retaining specific information of each behaviors, graph shared multi-task learning was used to learn the corresponding interactive behavior recognition model. Experiment results showed the effectiveness of the proposed method in comparison with other state-of-the-art methods on the public databases CASIA, i3Dpose dataset and self-built database for interactive behavior recognition.展开更多
This paper presents a feature-based method for machining process planning in integrated product designing and manufacturing system for CE(Concurrent Engineering) application. The feature setup generation and machining...This paper presents a feature-based method for machining process planning in integrated product designing and manufacturing system for CE(Concurrent Engineering) application. The feature setup generation and machining sequence can be determined automatically in this system. The set of knowledge-based rules for process planning and manufacturability evaluation is provided and can be shared by all stages of full product life-cycle. An approach for MTAD (Multiple Tool Axis Direction) feature setup generation is presented and the appropriate Tool Axis Direction(TAD) is chosen to minimize the total setup numbers of a part. The classification and process planning of interacting feature are discussed and the knowledge-based rules are used to solve the feature interaction problem.展开更多
This paper presents methodologies and technologies of feature_based integrated product designing and manufacturing system for CE(Concurrent Engineering) application. The product information is represented on the basis...This paper presents methodologies and technologies of feature_based integrated product designing and manufacturing system for CE(Concurrent Engineering) application. The product information is represented on the basis of hierarchical and dynamic structure of feature representation. The Object_Oriented feature modeling method is adopted to represent the feature classification, feature relationship and feature interaction. The set of knowledge_based rule for process planing and manufacturiability evaluation is provided and can be shared by all stages of full product life_cycle. The feature_based machining operation and machining sequence can be determined automatically. The machining process of the machining feature can be determined according to the set of knowledge_based rule.展开更多
Interactivity in online newspapers is the focus of this chapter in eliciting readers’evaluation of Zambian online newspapers.This aspect of the study investigates and characterises the motivations(gratification sough...Interactivity in online newspapers is the focus of this chapter in eliciting readers’evaluation of Zambian online newspapers.This aspect of the study investigates and characterises the motivations(gratification sought)for use of interactivity features(“process motivation”)and how widely they are used.It also attempts to ascertain the gratification obtained from their use among readers.The probable relationships between use of the interactivity features(“audience interactivity”)and gratification obtained from them(“process gratification”)and the impact of the perceived credibility of the online newspapers on gratification are also examined.Past studies present mixed results on use of interactivity and gratification obtained from it.This study finds that use of interactivity in Zambian online newspapers is at a low level,although among the three broad categorisations of features of online newspapers,interactivity attracts greater use than hyper-textuality and multi-mediality.Human interactivity features-“knowing what others think about an issue”,“chat on the Facebook page of the newspaper”,“ability to navigate on the Facebook page of the newspaper”,and“posting own comments on stories”-are the main motivations for use of online newspapers,the most frequently used,and the most gratifying to the readers.While readers express an interest in interacting with other readers via online newspapers,they seem less interested in posting their own stories as“citizen journalists”and linking up with the publishers and editors.This finding challenges the notion that all new media are catalysts of participatory and cyclic communication.展开更多
Gaze following aims to interpret human-scene interactions by predicting the person’s focal point of gaze.Prevailing approaches often adopt a two-stage framework,whereby multi-modality information is extracted in the ...Gaze following aims to interpret human-scene interactions by predicting the person’s focal point of gaze.Prevailing approaches often adopt a two-stage framework,whereby multi-modality information is extracted in the initial stage for gaze target prediction.Consequently,the efficacy of these methods highly depends on the precision of the previous modality extraction.Others use a single-modality approach with complex decoders,increasing network computational load.Inspired by the remarkable success of pre-trained plain vision transformers(ViTs),we introduce a novel single-modality gaze following framework called ViTGaze.In contrast to previous methods,it creates a novel gaze following framework based mainly on powerful encoders(relative decoder parameters less than 1%).Our principal insight is that the inter-token interactions within self-attention can be transferred to interactions between humans and scenes.Leveraging this presumption,we formulate a framework consisting of a 4D interaction encoder and a 2D spatial guidance module to extract human-scene interaction information from self-attention maps.Furthermore,our investigation reveals that ViT with self-supervised pre-training has an enhanced ability to extract correlation information.Many experiments have been conducted to demonstrate the performance of the proposed method.Our method achieves state-of-the-art performance among all single-modality methods(3.4%improvement in the area under curve score,5.1% improvement in the average precision)and very comparable performance against multi-modality methods with 59% fewer parameters.展开更多
Occlusion relationship reasoning aims to locate where an object occludes others and estimate the depth order of these objects in three-dimensional(3D)space from a two-dimensional(2D)image.The former sub-task demands b...Occlusion relationship reasoning aims to locate where an object occludes others and estimate the depth order of these objects in three-dimensional(3D)space from a two-dimensional(2D)image.The former sub-task demands both the accurate location and the semantic indication of the objects,while the latter sub-task needs the depth order among the objects.Although several insightful studies have been proposed,a key characteristic of occlusion relationship reasoning,i.e.,the specialty and complementarity between occlusion boundary detection and occlusion orientation estimation,is rarely discussed.To verify this claim,in this paper,we integrate these properties into a unified end-to-end trainable network,namely the feature separation and interaction network(FSINet).It contains a shared encoder-decoder structure to learn the complementary property between the two sub-tasks,and two separated paths to learn specialized properties of the two sub-tasks.Concretely,the occlusion boundary path contains an image-level cue extractor to capture rich location information of the boundary,a detail-perceived semantic feature extractor,and a contextual correlation extractor to acquire refined semantic features of objects.In addition,a dual-flow cross detector has been customized to alleviate false-positive and false-negative boundaries.For the occlusion orientation estimation path,a scene context learner has been designed to capture the depth order cue around the boundary.In addition,two stripe convolutions are built to judge the depth order between objects.The shared decoder supplies the feature interaction,which plays a key role in exploiting the complementarity of the two paths.Extensive experimental results on the PIOD and BSDS ownership datasets reveal the superior performance of FSINet over state-of-the-art alternatives.Additionally,abundant ablation studies are offered to demonstrate the effectiveness of our design.展开更多
With the rapid advancement of mechanical automation and intelligent processing technology,ac-curately measuring the surfaces of complex parts has emerged as a significant research challenge.Robotic measurement technol...With the rapid advancement of mechanical automation and intelligent processing technology,ac-curately measuring the surfaces of complex parts has emerged as a significant research challenge.Robotic measurement technology plays a crucial role in facilitating rapid quality inspections during the manufacturing process due to its inherent flexibility.However,the irregular shapes and viewpoint occlusions of complex parts complicate precise measurement.To address these challenges,this work proposes a point cloud registration network for robotic scanning systems and introduces a DBR-Net(Dual-line Registration Network)to overcome the issues of low overlap rates and perspective occlusion that currently impede the registration of certain workpieces.First,feature extraction is performed using a bilinear encoder and multi-level feature interactions of both point-wise and global features.Subsequently,the features are sampled through unanimous voting and fed into the RANSAC(Random Sample Consensus)algorithm for pose estimation,enabling multi-view point cloud registration.Experimental results demonstrate that this method significantly outperforms many existing techniques in terms of feature extraction and registration accuracy,thereby enhancing the overall performance of point cloud registration.展开更多
CO_(2) injection not only effectively enhances oil recovery(EOR)but also facilitates CO_(2) utilization and storage.Rapid screening and optimization of CO_(2)-EOR operations is urgently needed for unconventional reser...CO_(2) injection not only effectively enhances oil recovery(EOR)but also facilitates CO_(2) utilization and storage.Rapid screening and optimization of CO_(2)-EOR operations is urgently needed for unconventional reservoirs.However,it remains challenging due to a limited understanding of fluid flow in multiscale porous media and the problem complexity invoked by numerous factors.This work developed a new interpretable machine learning(ML)framework to specifically address this issue.Three different methods,namely random forest(RF),support vector regression(SVR),and artificial neural network(ANN),were used to establish proxy models using the data from a specific unconventional reservoir,and the RF model demonstrated a preferable performance.To enhance the interpretability of the established models,the multiway feature importance analysis and Shapley Additive Explanations(SHAP)were proposed to quantify the contribution of individual features to the model output.Based on the results of model interpretability,the genetic algorithm(GA)was coupled with RF(RF-GA model)to optimize the CO_(2)-EOR process.The proposed framework was validated by comparing the GA-RF predictions with simulation results under different reservoir conditions,which yielded a minimum relative error of 0.34%and an average relative error of 5.3%.The developed interpretable ML method was capable of rapidly screening suitable CO_(2)-EOR strategies based on reservoir conditions and provided a practical example for field applications.展开更多
Online advertising click-through rate(CTR) prediction is aimed at predicting the probability of a user clicking an ad,and it has undergone considerable development in recent years.One of the hot topics in this area is...Online advertising click-through rate(CTR) prediction is aimed at predicting the probability of a user clicking an ad,and it has undergone considerable development in recent years.One of the hot topics in this area is the construction of feature interactions to facilitate accurate prediction.Factorization machine provides second-order feature interactions by linearly multiplying hidden feature factors.However,real-world data present a complex and nonlinear structure.Hence,second-order feature interactions are unable to represent cross information adequately.This drawback has been addressed using deep neural networks(DNNs),which enable high-order nonlinear feature interactions.However,DNN-based feature interactions cannot easily optimize deep structures because of the absence of cross information in the original features.In this study,we propose an effective CTR prediction algorithm called CAN,which explicitly exploits the benefits of attention mechanisms and DNN models.The attention mechanism is used to provide rich and expressive low-order feature interactions and facilitate the optimization of DNN-based predictors that implicitly incorporate high-order nonlinear feature interactions.The experiments using two real datasets demonstrate that our proposed CAN model performs better than other cross feature-and DNN-based predictors.展开更多
Essential proteins are vital to the survival of a cell. There are various features related to the essentiality of proteins, such as biological and topological features. Many computational methods have been developed t...Essential proteins are vital to the survival of a cell. There are various features related to the essentiality of proteins, such as biological and topological features. Many computational methods have been developed to identify essential proteins by using these features. However, it is still a big challenge to design an effective method that is able to select suitable features and integrate them to predict essential proteins. In this work, we first collect 26 features, and use SVM-RFE to select some of them to create a feature space for predicting essential proteins, and then remove the features that share the biological meaning with other features in the feature space according to their Pearson Correlation Coefficients（PCC）. The experiments are carried out on S. cerevisiae data. Six features are determined as the best subset of features. To assess the prediction performance of our method, we further compare it with some machine learning methods, such as SVM, Naive Bayes, Bayes Network, and NBTree when inputting the different number of features. The results show that those methods using the 6 features outperform that using other features, which confirms the effectiveness of our feature selection method for essential protein prediction.展开更多
Acoustic quality detection is vital in the manufactured products quality control field since it represents the conditions of machines or products.Recent work employed machine learning models in manufactured audio data...Acoustic quality detection is vital in the manufactured products quality control field since it represents the conditions of machines or products.Recent work employed machine learning models in manufactured audio data to detect anomalous patterns.A major challenge is how to select applicable audio features to meliorate model’s accuracy and precision.To relax this challenge,we extract and analyze three audio feature types including Time Domain Feature,Frequency Domain Feature,and Cepstrum Feature to help identify the potential linear and non-linear relationships.In addition,we design a visual analysis system,namely AFExplorer,to assist data scientists in extracting audio features and selecting potential feature combinations.AFExplorer integrates four main views to present detailed distribution and relevance of the audio features,which helps users observe the impact of features visually in the feature selection.We perform the case study with AFExplore according to the ToyADMOS and MIMII Dataset to demonstrate the usability and effectiveness of the proposed system.展开更多
Multimodal Sentiment analysis refers to analyzing emotions in infor-mation carriers containing multiple modalities.To better analyze the features within and between modalities and solve the problem of incomplete multi...Multimodal Sentiment analysis refers to analyzing emotions in infor-mation carriers containing multiple modalities.To better analyze the features within and between modalities and solve the problem of incomplete multimodal feature fusion,this paper proposes a multimodal sentiment analysis model MIF(Modal Interactive Feature Encoder For Multimodal Sentiment Analysis).First,the global features of three modalities are obtained through unimodal feature extraction networks.Second,the inter-modal interactive feature encoder and the intra-modal interactive feature encoder extract similarity features between modal-ities and intra-modal special features separately.Finally,unimodal special features and the interaction information between modalities are decoded to get the fusion features and predict sentimental polarity results.We conduct extensive experi-ments on three public multimodal datasets,including one in Chinese and two in English.The results show that the performance of our approach is significantly improved compared with benchmark models.展开更多
Factorization machine (FM) is an effective model for feature-based recommendation that utilizes inner products to capture second-order feature interactions. However, one of the major drawbacks of FM is that it cannot ...Factorization machine (FM) is an effective model for feature-based recommendation that utilizes inner products to capture second-order feature interactions. However, one of the major drawbacks of FM is that it cannot capture complex high-order interaction signals. A common solution is to change the interaction function, such as stacking deep neural networks on the top level of FM. In this work, we propose an alternative approach to model high-order interaction signals at the embedding level, namely generalized embedding machine (GEM). The embedding used in GEM encodes not only the information from the feature itself but also the information from other correlated features. Under such a situation, the embedding becomes high-order. Then we can incorporate GEM with FM and even its advanced variants to perform feature interactions. More specifically, in this paper, we utilize graph convolution networks (GCN) to generate high-order embeddings. We integrate GEM with several FM-based models and conduct extensive experiments on two real-world datasets. The results demonstrate significant improvement of GEM over the corresponding baselines.展开更多
文摘In recent years,deep learning has been widely applied in the fields of recommendation systems and click-through rate(CTR)prediction,and thus recommendation models incorporating deep learning have emerged.In addition,the design and implementation of recommendation models using information related to user behavior sequences is an important direction of current research in recommendation systems,and models calculate the likelihood of users clicking on target items based on their behavior sequence information.In order to explore the relationship between features,this paper improves and optimizes on the basis of deep interest network(DIN)proposed by Ali’s team.Based on the user behavioral sequences information,the attentional factorization machine(AFM)is integrated to obtain richer and more accurate behavioral sequence information.In addition,this paper designs a new way of calculating attention weights,which uses the relationship between the cosine similarity of any two vectors and the absolute value of their modal length difference to measure their relevance degree.Thus,a novel deep learning CTR prediction mode is proposed,that is,the CTR prediction network based on user behavior sequence and feature interactions deep interest and machines network(DIMN).We conduct extensive comparison experiments on three public datasets and one private music dataset,which are more recognized in the industry,and the results show that the DIMN obtains a better performance compared with the classical CTR prediction model.
基金supported by the National Science Foundation of China(No.62141608).
文摘Factorization machine(FM)is a prevalent approach to modelling pairwise(second-order)feature interactions when dealing with high-dimensional sparse data.However,on the one hand,FMs fail to capture higher-order feature interactions suffering from combinatorial expansion.On the other hand,taking into account interactions between every pair of features may introduce noise and degrade the prediction accuracy.To solve these problems,we propose a novel approach,the graph factorization machine(GraphFM),which naturally represents features in the graph structure.In particular,we design a mechanism to select beneficial feature interactions and formulate them as edges between features.Then the proposed model,which integrates the interaction function of the FM into the feature aggregation strategy of the graph neural network(GNN),can model arbitrary-order feature interactions on graph-structured features by stacking layers.Experimental results on several real-world datasets demonstrate the rationality and effectiveness of our proposed approach.The code and data are available at https://github.com/CRIPAC-DIG/GraphCTR.
基金supported by the Competitive Research Fund of the University of Aizu,Japan(Grant No.P-13).
文摘Heart disease includes a multiplicity of medical conditions that affect the structure,blood vessels,and general operation of the heart.Numerous researchers have made progress in correcting and predicting early heart disease,but more remains to be accomplished.The diagnostic accuracy of many current studies is inadequate due to the attempt to predict patients with heart disease using traditional approaches.By using data fusion from several regions of the country,we intend to increase the accuracy of heart disease prediction.A statistical approach that promotes insights triggered by feature interactions to reveal the intricate pattern in the data,which cannot be adequately captured by a single feature.We processed the data using techniques including feature scaling,outlier detection and replacement,null and missing value imputation,and more to improve the data quality.Furthermore,the proposed feature engineering method uses the correlation test for numerical features and the chi-square test for categorical features to interact with the feature.To reduce the dimensionality,we subsequently used PCA with 95%variation.To identify patients with heart disease,hyperparameter-based machine learning algorithms like RF,XGBoost,Gradient Boosting,LightGBM,CatBoost,SVM,and MLP are utilized,along with ensemble models.The model’s overall prediction performance ranges from 88%to 92%.In order to attain cutting-edge results,we then used a 1D CNN model,which significantly enhanced the prediction with an accuracy score of 96.36%,precision of 96.45%,recall of 96.36%,specificity score of 99.51%and F1 score of 96.34%.The RF model produces the best results among all the classifiers in the evaluation matrix without feature interaction,with accuracy of 90.21%,precision of 90.40%,recall of 90.86%,specificity of 90.91%,and F1 score of 90.63%.Our proposed 1D CNN model is 7%superior to the one without feature engineering when compared to the suggested approach.This illustrates how interaction-focused feature analysis can produce precise and useful insights for heart disease diagnosis.
基金funded by National Natural Science Foundation of China(61603245).
文摘Semantic segmentation of remote sensing images is a critical research area in the field of remote sensing.Despite the success of Convolutional Neural Networks(CNNs),they often fail to capture inter-layer feature relationships and fully leverage contextual information,leading to the loss of important details.Additionally,due to significant intraclass variation and small inter-class differences in remote sensing images,CNNs may experience class confusion.To address these issues,we propose a novel Category-Guided Feature Collaborative Learning Network(CG-FCLNet),which enables fine-grained feature extraction and adaptive fusion.Specifically,we design a Feature Collaborative Learning Module(FCLM)to facilitate the tight interaction of multi-scale features.We also introduce a Scale-Aware Fusion Module(SAFM),which iteratively fuses features from different layers using a spatial attention mechanism,enabling deeper feature fusion.Furthermore,we design a Category-Guided Module(CGM)to extract category-aware information that guides feature fusion,ensuring that the fused featuresmore accurately reflect the semantic information of each category,thereby improving detailed segmentation.The experimental results show that CG-FCLNet achieves a Mean Intersection over Union(mIoU)of 83.46%,an mF1 of 90.87%,and an Overall Accuracy(OA)of 91.34% on the Vaihingen dataset.On the Potsdam dataset,it achieves a mIoU of 86.54%,an mF1 of 92.65%,and an OA of 91.29%.These results highlight the superior performance of CG-FCLNet compared to existing state-of-the-art methods.
基金Supported in part by the National Basic Research Program (973) of China (Grant No. 2002CB312003)the National Natural Science Foundation of China (Grant Nos. 60233010, 90612011, 90412011, 60403030)the IBM University Joint Study Program
文摘As a popular infrastructure for distributed systems running on the Internet, middleware has to support much more diverse and complex interactions for coping with the drastically increasing demand on information technology and the extremely open and dynamic nature of the Internet. These supporting mechanisms facilitate the development, deployment, and integration of distributed systems, as well as increase the occasions for distributed systems to interact in an undesired way. The undesired interactions may cause serious problems, such as quality violation, function loss, and even system crash. In this paper, the problem is studied from the perspective of the feature interaction problem （FIP） in telecom, and an online approach to the detection and solution on runtime systems is proposed. Based on a classification of middleware enabled interactions, the existence of FIP in middleware based systems is illustrated by four real cases and a conceptual comparison between middleware based systems and telecom systems. After that, runtime software architecture is employed to facilitate the online detection and solution of FIP. The approach is demonstrated on J2EE （Java 2 Platform Enterprise Edition） and applied to detect and resolve all of the four real cases.
文摘As a platform-independent software system, a Web service is designed to offer interoperability among diverse and heterogeneous applications. With the introduction of service composition in the Web service creation, various message interactions among the atomic services result in a problem resembling the feature interaction problem in the telecommunication area. This article defines the problem as feature interaction in Web services and proposes a model checking-based detection method. In the method, the Web service description is translated to the Promela language - the input language of the model checker simple promela interpreter （SPIN）, and the specific properties, expressed as linear temporal logic（LTL） formulas, are formulated according to our classification of feature interaction. Then, SPIN is used to check these specific properties to detect the feature interaction in Web services.
基金Project(51678075)supported by the National Natural Science Foundation of ChinaProject(2017GK2271)supported by Hunan Provincial Science and Technology Department,China
文摘This paper proposed a novel multi-view interactive behavior recognition method based on local self-similarity descriptors and graph shared multi-task learning. First, we proposed the composite interactive feature representation which encodes both the spatial distribution of local motion of interest points and their contexts. Furthermore, local self-similarity descriptor represented by temporal-pyramid bag of words(BOW) was applied to decreasing the influence of observation angle change on recognition and retaining the temporal information. For the purpose of exploring latent correlation between different interactive behaviors from different views and retaining specific information of each behaviors, graph shared multi-task learning was used to learn the corresponding interactive behavior recognition model. Experiment results showed the effectiveness of the proposed method in comparison with other state-of-the-art methods on the public databases CASIA, i3Dpose dataset and self-built database for interactive behavior recognition.
文摘This paper presents a feature-based method for machining process planning in integrated product designing and manufacturing system for CE(Concurrent Engineering) application. The feature setup generation and machining sequence can be determined automatically in this system. The set of knowledge-based rules for process planning and manufacturability evaluation is provided and can be shared by all stages of full product life-cycle. An approach for MTAD (Multiple Tool Axis Direction) feature setup generation is presented and the appropriate Tool Axis Direction(TAD) is chosen to minimize the total setup numbers of a part. The classification and process planning of interacting feature are discussed and the knowledge-based rules are used to solve the feature interaction problem.
文摘This paper presents methodologies and technologies of feature_based integrated product designing and manufacturing system for CE(Concurrent Engineering) application. The product information is represented on the basis of hierarchical and dynamic structure of feature representation. The Object_Oriented feature modeling method is adopted to represent the feature classification, feature relationship and feature interaction. The set of knowledge_based rule for process planing and manufacturiability evaluation is provided and can be shared by all stages of full product life_cycle. The feature_based machining operation and machining sequence can be determined automatically. The machining process of the machining feature can be determined according to the set of knowledge_based rule.
文摘Interactivity in online newspapers is the focus of this chapter in eliciting readers’evaluation of Zambian online newspapers.This aspect of the study investigates and characterises the motivations(gratification sought)for use of interactivity features(“process motivation”)and how widely they are used.It also attempts to ascertain the gratification obtained from their use among readers.The probable relationships between use of the interactivity features(“audience interactivity”)and gratification obtained from them(“process gratification”)and the impact of the perceived credibility of the online newspapers on gratification are also examined.Past studies present mixed results on use of interactivity and gratification obtained from it.This study finds that use of interactivity in Zambian online newspapers is at a low level,although among the three broad categorisations of features of online newspapers,interactivity attracts greater use than hyper-textuality and multi-mediality.Human interactivity features-“knowing what others think about an issue”,“chat on the Facebook page of the newspaper”,“ability to navigate on the Facebook page of the newspaper”,and“posting own comments on stories”-are the main motivations for use of online newspapers,the most frequently used,and the most gratifying to the readers.While readers express an interest in interacting with other readers via online newspapers,they seem less interested in posting their own stories as“citizen journalists”and linking up with the publishers and editors.This finding challenges the notion that all new media are catalysts of participatory and cyclic communication.
基金supported by the National Science and Technology Major Project(No.2022YFB4500602).
文摘Gaze following aims to interpret human-scene interactions by predicting the person’s focal point of gaze.Prevailing approaches often adopt a two-stage framework,whereby multi-modality information is extracted in the initial stage for gaze target prediction.Consequently,the efficacy of these methods highly depends on the precision of the previous modality extraction.Others use a single-modality approach with complex decoders,increasing network computational load.Inspired by the remarkable success of pre-trained plain vision transformers(ViTs),we introduce a novel single-modality gaze following framework called ViTGaze.In contrast to previous methods,it creates a novel gaze following framework based mainly on powerful encoders(relative decoder parameters less than 1%).Our principal insight is that the inter-token interactions within self-attention can be transferred to interactions between humans and scenes.Leveraging this presumption,we formulate a framework consisting of a 4D interaction encoder and a 2D spatial guidance module to extract human-scene interaction information from self-attention maps.Furthermore,our investigation reveals that ViT with self-supervised pre-training has an enhanced ability to extract correlation information.Many experiments have been conducted to demonstrate the performance of the proposed method.Our method achieves state-of-the-art performance among all single-modality methods(3.4%improvement in the area under curve score,5.1% improvement in the average precision)and very comparable performance against multi-modality methods with 59% fewer parameters.
基金supported by the National Natural Science Foundation of China(Nos.62176098 and 61703049)the Natural Science Foundation of Hubei Province of China(No.2019CFA022).
文摘Occlusion relationship reasoning aims to locate where an object occludes others and estimate the depth order of these objects in three-dimensional(3D)space from a two-dimensional(2D)image.The former sub-task demands both the accurate location and the semantic indication of the objects,while the latter sub-task needs the depth order among the objects.Although several insightful studies have been proposed,a key characteristic of occlusion relationship reasoning,i.e.,the specialty and complementarity between occlusion boundary detection and occlusion orientation estimation,is rarely discussed.To verify this claim,in this paper,we integrate these properties into a unified end-to-end trainable network,namely the feature separation and interaction network(FSINet).It contains a shared encoder-decoder structure to learn the complementary property between the two sub-tasks,and two separated paths to learn specialized properties of the two sub-tasks.Concretely,the occlusion boundary path contains an image-level cue extractor to capture rich location information of the boundary,a detail-perceived semantic feature extractor,and a contextual correlation extractor to acquire refined semantic features of objects.In addition,a dual-flow cross detector has been customized to alleviate false-positive and false-negative boundaries.For the occlusion orientation estimation path,a scene context learner has been designed to capture the depth order cue around the boundary.In addition,two stripe convolutions are built to judge the depth order between objects.The shared decoder supplies the feature interaction,which plays a key role in exploiting the complementarity of the two paths.Extensive experimental results on the PIOD and BSDS ownership datasets reveal the superior performance of FSINet over state-of-the-art alternatives.Additionally,abundant ablation studies are offered to demonstrate the effectiveness of our design.
基金co-supported by the National Natural Science Foundation of China(U22A20176)Guangdong Basic and Applied Basic Research Foundation,China(2022B1515120078)+2 种基金the Guangdong Basic and Applied Basic Research Foundation,China(2021A1515110898)GDAS’Project of Science and Technology Development,China(2022GDASZH-2022010108)the Key Areas R&D Program of Dongguan City,China(20201200300062).
文摘With the rapid advancement of mechanical automation and intelligent processing technology,ac-curately measuring the surfaces of complex parts has emerged as a significant research challenge.Robotic measurement technology plays a crucial role in facilitating rapid quality inspections during the manufacturing process due to its inherent flexibility.However,the irregular shapes and viewpoint occlusions of complex parts complicate precise measurement.To address these challenges,this work proposes a point cloud registration network for robotic scanning systems and introduces a DBR-Net(Dual-line Registration Network)to overcome the issues of low overlap rates and perspective occlusion that currently impede the registration of certain workpieces.First,feature extraction is performed using a bilinear encoder and multi-level feature interactions of both point-wise and global features.Subsequently,the features are sampled through unanimous voting and fed into the RANSAC(Random Sample Consensus)algorithm for pose estimation,enabling multi-view point cloud registration.Experimental results demonstrate that this method significantly outperforms many existing techniques in terms of feature extraction and registration accuracy,thereby enhancing the overall performance of point cloud registration.
基金support of National Key Research and Development Program of China(2023YFE0120700)National Natural Science Foundation of China(52274041 and 52304023)+2 种基金Distinguished Young Sichuan Science Scholars(2023NSFSC1954)Natural Science Foundation of Chongqing(CSTB2022NSCQ-MSX0403)Chongqing Municipal Support Program for Overseas Students Returning for Entrepreneurship and Innovation(2205012980950154).
文摘CO_(2) injection not only effectively enhances oil recovery(EOR)but also facilitates CO_(2) utilization and storage.Rapid screening and optimization of CO_(2)-EOR operations is urgently needed for unconventional reservoirs.However,it remains challenging due to a limited understanding of fluid flow in multiscale porous media and the problem complexity invoked by numerous factors.This work developed a new interpretable machine learning(ML)framework to specifically address this issue.Three different methods,namely random forest(RF),support vector regression(SVR),and artificial neural network(ANN),were used to establish proxy models using the data from a specific unconventional reservoir,and the RF model demonstrated a preferable performance.To enhance the interpretability of the established models,the multiway feature importance analysis and Shapley Additive Explanations(SHAP)were proposed to quantify the contribution of individual features to the model output.Based on the results of model interpretability,the genetic algorithm(GA)was coupled with RF(RF-GA model)to optimize the CO_(2)-EOR process.The proposed framework was validated by comparing the GA-RF predictions with simulation results under different reservoir conditions,which yielded a minimum relative error of 0.34%and an average relative error of 5.3%.The developed interpretable ML method was capable of rapidly screening suitable CO_(2)-EOR strategies based on reservoir conditions and provided a practical example for field applications.
文摘Online advertising click-through rate(CTR) prediction is aimed at predicting the probability of a user clicking an ad,and it has undergone considerable development in recent years.One of the hot topics in this area is the construction of feature interactions to facilitate accurate prediction.Factorization machine provides second-order feature interactions by linearly multiplying hidden feature factors.However,real-world data present a complex and nonlinear structure.Hence,second-order feature interactions are unable to represent cross information adequately.This drawback has been addressed using deep neural networks(DNNs),which enable high-order nonlinear feature interactions.However,DNN-based feature interactions cannot easily optimize deep structures because of the absence of cross information in the original features.In this study,we propose an effective CTR prediction algorithm called CAN,which explicitly exploits the benefits of attention mechanisms and DNN models.The attention mechanism is used to provide rich and expressive low-order feature interactions and facilitate the optimization of DNN-based predictors that implicitly incorporate high-order nonlinear feature interactions.The experiments using two real datasets demonstrate that our proposed CAN model performs better than other cross feature-and DNN-based predictors.
基金supported by the National Natural Science Foundation of China(Nos.61232001,61502166,61502214,61379108,and 61370024)Scientific Research Fund of Hunan Provincial Education Department(Nos.15CY007 and 10A076)
文摘Essential proteins are vital to the survival of a cell. There are various features related to the essentiality of proteins, such as biological and topological features. Many computational methods have been developed to identify essential proteins by using these features. However, it is still a big challenge to design an effective method that is able to select suitable features and integrate them to predict essential proteins. In this work, we first collect 26 features, and use SVM-RFE to select some of them to create a feature space for predicting essential proteins, and then remove the features that share the biological meaning with other features in the feature space according to their Pearson Correlation Coefficients（PCC）. The experiments are carried out on S. cerevisiae data. Six features are determined as the best subset of features. To assess the prediction performance of our method, we further compare it with some machine learning methods, such as SVM, Naive Bayes, Bayes Network, and NBTree when inputting the different number of features. The results show that those methods using the 6 features outperform that using other features, which confirms the effectiveness of our feature selection method for essential protein prediction.
基金National Key Research and Development Program of China(2020YFB1707700)National Natural Science Foundation of China(61972356,62036009)Fundamental Research Funds for the Provincial Universities of Zhejiang,China(RF-A2020001).
文摘Acoustic quality detection is vital in the manufactured products quality control field since it represents the conditions of machines or products.Recent work employed machine learning models in manufactured audio data to detect anomalous patterns.A major challenge is how to select applicable audio features to meliorate model’s accuracy and precision.To relax this challenge,we extract and analyze three audio feature types including Time Domain Feature,Frequency Domain Feature,and Cepstrum Feature to help identify the potential linear and non-linear relationships.In addition,we design a visual analysis system,namely AFExplorer,to assist data scientists in extracting audio features and selecting potential feature combinations.AFExplorer integrates four main views to present detailed distribution and relevance of the audio features,which helps users observe the impact of features visually in the feature selection.We perform the case study with AFExplore according to the ToyADMOS and MIMII Dataset to demonstrate the usability and effectiveness of the proposed system.
文摘Multimodal Sentiment analysis refers to analyzing emotions in infor-mation carriers containing multiple modalities.To better analyze the features within and between modalities and solve the problem of incomplete multimodal feature fusion,this paper proposes a multimodal sentiment analysis model MIF(Modal Interactive Feature Encoder For Multimodal Sentiment Analysis).First,the global features of three modalities are obtained through unimodal feature extraction networks.Second,the inter-modal interactive feature encoder and the intra-modal interactive feature encoder extract similarity features between modal-ities and intra-modal special features separately.Finally,unimodal special features and the interaction information between modalities are decoded to get the fusion features and predict sentimental polarity results.We conduct extensive experi-ments on three public multimodal datasets,including one in Chinese and two in English.The results show that the performance of our approach is significantly improved compared with benchmark models.
基金supported by National Natural Science Foundation of China(Nos.62032013 and 61972078)the Fundamental Research Funds for the Central Universities,China(No.N2217004).
文摘Factorization machine (FM) is an effective model for feature-based recommendation that utilizes inner products to capture second-order feature interactions. However, one of the major drawbacks of FM is that it cannot capture complex high-order interaction signals. A common solution is to change the interaction function, such as stacking deep neural networks on the top level of FM. In this work, we propose an alternative approach to model high-order interaction signals at the embedding level, namely generalized embedding machine (GEM). The embedding used in GEM encodes not only the information from the feature itself but also the information from other correlated features. Under such a situation, the embedding becomes high-order. Then we can incorporate GEM with FM and even its advanced variants to perform feature interactions. More specifically, in this paper, we utilize graph convolution networks (GCN) to generate high-order embeddings. We integrate GEM with several FM-based models and conduct extensive experiments on two real-world datasets. The results demonstrate significant improvement of GEM over the corresponding baselines.
