Accurate traffic prediction is crucial for an intelligent traffic system (ITS). However, the excessive non-linearity and complexity of the spatial-temporal correlation in traffic flow severely limit the prediction acc...Accurate traffic prediction is crucial for an intelligent traffic system (ITS). However, the excessive non-linearity and complexity of the spatial-temporal correlation in traffic flow severely limit the prediction accuracy of most existing models, which simply stack temporal and spatial modules and fail to capture spatial-temporal features effectively. To improve the prediction accuracy, a multi-head attention spatial-temporal graph neural network (MSTNet) is proposed in this paper. First, the traffic data is decomposed into unique time spans that conform to positive rules, and valuable traffic node attributes are mined through an adaptive graph structure. Second, time and spatial features are captured using a multi-head attention spatial-temporal module. Finally, a multi-step prediction module is used to achieve future traffic condition prediction. Numerical experiments were conducted on an open-source dataset, and the results demonstrate that MSTNet performs well in spatial-temporal feature extraction and achieves more positive forecasting results than the baseline methods.展开更多
Due to the closed working environment of shield machines,the construction personnel cannot observe the construction geological environment,which seriously restricts the safety and efficiency of the tunneling process.I...Due to the closed working environment of shield machines,the construction personnel cannot observe the construction geological environment,which seriously restricts the safety and efficiency of the tunneling process.In this study,we present an enhanced multi-head self-attention convolution neural network(EMSACNN)with two-stage feature extraction for geological condition prediction of shield machine.Firstly,we select 30 important parameters according to statistical analysis method and the working principle of the shield machine.Then,we delete the non-working sample data,and combine 10 consecutive data as the input of the model.Thereafter,to deeply mine and extract essential and relevant features,we build a novel model combined with the particularity of the geological type recognition task,in which an enhanced multi-head self-attention block is utilized as the first feature extractor to fully extract the correlation of geological information of adjacent working face of tunnel,and two-dimensional CNN(2dCNN)is utilized as the second feature extractor.The performance and superiority of proposed EMSACNN are verified by the actual data collected by the shield machine used in the construction of a double-track tunnel in Guangzhou,China.The results show that EMSACNN achieves at least 96%accuracy on the test sets of the two tunnels,and all the evaluation indicators of EMSACNN are much better than those of classical AI model and the model that use only the second-stage feature extractor.Therefore,the proposed EMSACNN achieves high accuracy and strong generalization for geological information prediction of shield machine,which is of great guiding significance to engineering practice.展开更多
Estimation of crowd count is becoming crucial nowadays,as it can help in security surveillance,crowd monitoring,and management for different events.It is challenging to determine the approximate crowd size from an ima...Estimation of crowd count is becoming crucial nowadays,as it can help in security surveillance,crowd monitoring,and management for different events.It is challenging to determine the approximate crowd size from an image of the crowd’s density.Therefore in this research study,we proposed a multi-headed convolutional neural network architecture-based model for crowd counting,where we divided our proposed model into two main components:(i)the convolutional neural network,which extracts the feature across the whole image that is given to it as an input,and(ii)the multi-headed layers,which make it easier to evaluate density maps to estimate the number of people in the input image and determine their number in the crowd.We employed the available public benchmark crowd-counting datasets UCF CC 50 and ShanghaiTech parts A and B for model training and testing to validate the model’s performance.To analyze the results,we used two metrics Mean Absolute Error(MAE)and Mean Square Error(MSE),and compared the results of the proposed systems with the state-of-art models of crowd counting.The results show the superiority of the proposed system.展开更多
Cardiovascular disease is the leading cause of death globally.This disease causes loss of heart muscles and is also responsible for the death of heart cells,sometimes damaging their functionality.A person’s life may ...Cardiovascular disease is the leading cause of death globally.This disease causes loss of heart muscles and is also responsible for the death of heart cells,sometimes damaging their functionality.A person’s life may depend on receiving timely assistance as soon as possible.Thus,minimizing the death ratio can be achieved by early detection of heart attack(HA)symptoms.In the United States alone,an estimated 610,000 people die fromheart attacks each year,accounting for one in every four fatalities.However,by identifying and reporting heart attack symptoms early on,it is possible to reduce damage and save many lives significantly.Our objective is to devise an algorithm aimed at helping individuals,particularly elderly individuals living independently,to safeguard their lives.To address these challenges,we employ deep learning techniques.We have utilized a vision transformer(ViT)to address this problem.However,it has a significant overhead cost due to its memory consumption and computational complexity because of scaling dot-product attention.Also,since transformer performance typically relies on large-scale or adequate data,adapting ViT for smaller datasets is more challenging.In response,we propose a three-in-one steam model,theMulti-Head Attention Vision Hybrid(MHAVH).Thismodel integrates a real-time posture recognition framework to identify chest pain postures indicative of heart attacks using transfer learning techniques,such as ResNet-50 and VGG-16,renowned for their robust feature extraction capabilities.By incorporatingmultiple heads into the vision transformer to generate additional metrics and enhance heart-detection capabilities,we leverage a 2019 posture-based dataset comprising RGB images,a novel creation by the author that marks the first dataset tailored for posture-based heart attack detection.Given the limited online data availability,we segmented this dataset into gender categories(male and female)and conducted testing on both segmented and original datasets.The training accuracy of our model reach展开更多
The present study examines the impact of short-term public opinion sentiment on the secondary market,with a focus on the potential for such sentiment to cause dramatic stock price fluctuations and increase investment ...The present study examines the impact of short-term public opinion sentiment on the secondary market,with a focus on the potential for such sentiment to cause dramatic stock price fluctuations and increase investment risk.The quantification of investment sentiment indicators and the persistent analysis of their impact has been a complex and significant area of research.In this paper,a structured multi-head attention stock index prediction method based adaptive public opinion sentiment vector is proposed.The proposedmethod utilizes an innovative approach to transform numerous investor comments on social platforms over time into public opinion sentiment vectors expressing complex sentiments.It then analyzes the continuous impact of these vectors on the market through the use of aggregating techniques and public opinion data via a structured multi-head attention mechanism.The experimental results demonstrate that the public opinion sentiment vector can provide more comprehensive feedback on market sentiment than traditional sentiment polarity analysis.Furthermore,the multi-head attention mechanism is shown to improve prediction accuracy through attention convergence on each type of input information separately.Themean absolute percentage error(MAPE)of the proposedmethod is 0.463%,a reduction of 0.294% compared to the benchmark attention algorithm.Additionally,the market backtesting results indicate that the return was 24.560%,an improvement of 8.202% compared to the benchmark algorithm.These results suggest that themarket trading strategy based on thismethod has the potential to improve trading profits.展开更多
Short-term load forecasting(STLF)plays a crucial role in the smart grid.However,it is challenging to capture the long-time dependence and the nonlinear relationship due to the comprehensive fluctuations of the electri...Short-term load forecasting(STLF)plays a crucial role in the smart grid.However,it is challenging to capture the long-time dependence and the nonlinear relationship due to the comprehensive fluctuations of the electrical load.In this paper,an STLF model based on gated recurrent unit and multi-head attention(GRU-MA)is proposed to address the aforementioned problems.The proposed model accommodates the time series and nonlinear relationship of load data through gated recurrent unit(GRU)and exploits multi-head attention(MA)to learn the decisive features and long-term dependencies.Additionally,the proposed model is compared with the support vector regression(SVR)model,the recurrent neural network and multi-head attention(RNN-MA)model,the long short-term memory and multi-head attention(LSTM-MA)model,the GRU model,and the temporal convolutional network(TCN)model using the public dataset of the Global Energy Forecasting Competition 2014(GEFCOM2014).The results demonstrate that the GRU-MA model has the best prediction accuracy.展开更多
Due to the time-varying topology and possible disturbances in a conflict environment,it is still challenging to maintain the mission performance of flying Ad hoc networks(FANET),which limits the application of Unmanne...Due to the time-varying topology and possible disturbances in a conflict environment,it is still challenging to maintain the mission performance of flying Ad hoc networks(FANET),which limits the application of Unmanned Aerial Vehicle(UAV)swarms in harsh environments.This paper proposes an intelligent framework to quickly recover the cooperative coveragemission by aggregating the historical spatio-temporal network with the attention mechanism.The mission resilience metric is introduced in conjunction with connectivity and coverage status information to simplify the optimization model.A spatio-temporal node pooling method is proposed to ensure all node location features can be updated after destruction by capturing the temporal network structure.Combined with the corresponding Laplacian matrix as the hyperparameter,a recovery algorithm based on the multi-head attention graph network is designed to achieve rapid recovery.Simulation results showed that the proposed framework can facilitate rapid recovery of the connectivity and coverage more effectively compared to the existing studies.The results demonstrate that the average connectivity and coverage results is improved by 17.92%and 16.96%,respectively compared with the state-of-the-art model.Furthermore,by the ablation study,the contributions of each different improvement are compared.The proposed model can be used to support resilient network design for real-time mission execution.展开更多
A significant demand rises for energy-efficient deep neural networks to support power-limited embedding devices with successful deep learning applications in IoT and edge computing fields.An accurate energy prediction...A significant demand rises for energy-efficient deep neural networks to support power-limited embedding devices with successful deep learning applications in IoT and edge computing fields.An accurate energy prediction approach is critical to provide measurement and lead optimization direction.However,the current energy prediction approaches lack accuracy and generalization ability due to the lack of research on the neural network structure and the excessive reliance on customized training dataset.This paper presents a novel energy prediction model,NeurstrucEnergy.NeurstrucEnergy treats neural networks as directed graphs and applies a bi-directional graph neural network training on a randomly generated dataset to extract structural features for energy prediction.NeurstrucEnergy has advantages over linear approaches because the bi-directional graph neural network collects structural features from each layer's parents and children.Experimental results show that NeurstrucEnergy establishes state-of-the-art results with mean absolute percentage error of 2.60%.We also evaluate NeurstrucEnergy in a randomly generated dataset,achieving the mean absolute percentage error of 4.83%over 10 typical convolutional neural networks in recent years and 7 efficient convolutional neural networks created by neural architecture search.Our code is available at https://github.com/NEUSoftGreenAI/NeurstrucEnergy.git.展开更多
Nowadays,with the rapid development of industrial Internet technology,on the one hand,advanced industrial control systems(ICS)have improved industrial production efficiency.However,there are more and more cyber-attack...Nowadays,with the rapid development of industrial Internet technology,on the one hand,advanced industrial control systems(ICS)have improved industrial production efficiency.However,there are more and more cyber-attacks targeting industrial control systems.To ensure the security of industrial networks,intrusion detection systems have been widely used in industrial control systems,and deep neural networks have always been an effective method for identifying cyber attacks.Current intrusion detection methods still suffer from low accuracy and a high false alarm rate.Therefore,it is important to build a more efficient intrusion detection model.This paper proposes a hybrid deep learning intrusion detection method based on convolutional neural networks and bidirectional long short-term memory neural networks(CNN-BiLSTM).To address the issue of imbalanced data within the dataset and improve the model’s detection capabilities,the Synthetic Minority Over-sampling Technique-Edited Nearest Neighbors(SMOTE-ENN)algorithm is applied in the preprocessing phase.This algorithm is employed to generate synthetic instances for the minority class,simultaneously mitigating the impact of noise in the majority class.This approach aims to create a more equitable distribution of classes,thereby enhancing the model’s ability to effectively identify patterns in both minority and majority classes.In the experimental phase,the detection performance of the method is verified using two data sets.Experimental results show that the accuracy rate on the CICIDS-2017 data set reaches 97.7%.On the natural gas pipeline dataset collected by Lan Turnipseed from Mississippi State University in the United States,the accuracy rate also reaches 85.5%.展开更多
Long-term urban traffic flow prediction is an important task in the field of intelligent transportation,as it can help optimize traffic management and improve travel efficiency.To improve prediction accuracy,a crucial...Long-term urban traffic flow prediction is an important task in the field of intelligent transportation,as it can help optimize traffic management and improve travel efficiency.To improve prediction accuracy,a crucial issue is how to model spatiotemporal dependency in urban traffic data.In recent years,many studies have adopted spatiotemporal neural networks to extract key information from traffic data.However,most models ignore the semantic spatial similarity between long-distance areas when mining spatial dependency.They also ignore the impact of predicted time steps on the next unpredicted time step for making long-term predictions.Moreover,these models lack a comprehensive data embedding process to represent complex spatiotemporal dependency.This paper proposes a multi-scale persistent spatiotemporal transformer(MSPSTT)model to perform accurate long-term traffic flow prediction in cities.MSPSTT adopts an encoder-decoder structure and incorporates temporal,periodic,and spatial features to fully embed urban traffic data to address these issues.The model consists of a spatiotemporal encoder and a spatiotemporal decoder,which rely on temporal,geospatial,and semantic space multi-head attention modules to dynamically extract temporal,geospatial,and semantic characteristics.The spatiotemporal decoder combines the context information provided by the encoder,integrates the predicted time step information,and is iteratively updated to learn the correlation between different time steps in the broader time range to improve the model’s accuracy for long-term prediction.Experiments on four public transportation datasets demonstrate that MSPSTT outperforms the existing models by up to 9.5%on three common metrics.展开更多
Considering the stealthiness and persistence of Advanced Persistent Threats(APTs),system audit logs are leveraged in recent studies to construct system entity interaction provenance graphs to unveil threats in a host....Considering the stealthiness and persistence of Advanced Persistent Threats(APTs),system audit logs are leveraged in recent studies to construct system entity interaction provenance graphs to unveil threats in a host.Rule-based provenance graph APT detection approaches require elaborate rules and cannot detect unknown attacks,and existing learning-based approaches are limited by the lack of available APT attack samples or generally only perform graph-level anomaly detection,which requires lots of manual efforts to locate attack entities.This paper proposes an APT-exploited process detection approach called ThreatSniffer,which constructs the benign provenance graph from attack-free audit logs,fits normal system entity interactions and then detects APT-exploited processes by predicting the rationality of entity interactions.Firstly,ThreatSniffer understands system entities in terms of their file paths,interaction sequences,and the number distribution of interaction types and uses the multi-head self-attention mechanism to fuse these semantics.Then,based on the insight that APT-exploited processes interact with system entities they should not invoke,ThreatSniffer performs negative sampling on the benign provenance graph to generate non-existent edges,thus characterizing irrational entity interactions without requiring APT attack samples.At last,it employs a heterogeneous graph neural network as the interaction prediction model to aggregate the contextual information of entity interactions,and locate processes exploited by attackers,thereby achieving fine-grained APT detection.Evaluation results demonstrate that anomaly-based detection enables ThreatSniffer to identify all attack activities.Compared to the node-level APT detection method APT-KGL,ThreatSniffer achieves a 6.1%precision improvement because of its comprehensive understanding of entity semantics.展开更多
The deep learning advancements have greatly improved the performance of speech recognition systems,and most recent systems are based on the Recurrent Neural Network(RNN).Overall,the RNN works fine with the small seque...The deep learning advancements have greatly improved the performance of speech recognition systems,and most recent systems are based on the Recurrent Neural Network(RNN).Overall,the RNN works fine with the small sequence data,but suffers from the gradient vanishing problem in case of large sequence.The transformer networks have neutralized this issue and have shown state-of-the-art results on sequential or speech-related data.Generally,in speech recognition,the input audio is converted into an image using Mel-spectrogram to illustrate frequencies and intensities.The image is classified by the machine learning mechanism to generate a classification transcript.However,the audio frequency in the image has low resolution and causing inaccurate predictions.This paper presents a novel end-to-end binary view transformer-based architecture for speech recognition to cope with the frequency resolution problem.Firstly,the input audio signal is transformed into a 2D image using Mel-spectrogram.Secondly,the modified universal transformers utilize the multi-head attention to derive contextual information and derive different speech-related features.Moreover,a feedforward neural network is also deployed for classification.The proposed system has generated robust results on Google’s speech command dataset with an accuracy of 95.16%and with minimal loss.The binary-view transformer eradicates the eventuality of the over-fitting problem by deploying a multiview mechanism to diversify the input data,and multi-head attention captures multiple contexts from the data’s feature map.展开更多
Fraud cases have been a risk in society and people’s property security has been greatly threatened.In recent studies,many promising algorithms have been developed for social media offensive text recognition as well a...Fraud cases have been a risk in society and people’s property security has been greatly threatened.In recent studies,many promising algorithms have been developed for social media offensive text recognition as well as sentiment analysis.These algorithms are also suitable for fraudulent phone text recognition.Compared to these tasks,the semantics of fraudulent words are more complex and more difficult to distinguish.Recurrent Neural Networks(RNN),the variants ofRNN,ConvolutionalNeuralNetworks(CNN),and hybrid neural networks to extract text features are used by most text classification research.However,a single network or a simple network combination cannot obtain rich characteristic knowledge of fraudulent phone texts relatively.Therefore,a new model is proposed in this paper.In the fraudulent phone text,the knowledge that can be learned by the model includes the sequence structure of sentences,the correlation between words,the correlation of contextual semantics,the feature of keywords in sentences,etc.The new model combines a bidirectional Long-Short Term Memory Neural Network(BiLSTM)or a bidirectional Gate Recurrent United(BiGRU)and a Multi-Head attention mechanism module with convolution.A normalization layer is added after the output of the final hidden layer.BiLSTM or BiGRU is used to build the encoding and decoding layer.Multi-head attention mechanism module with convolution(MHAC)enhances the ability of the model to learn global interaction information and multi-granularity local interaction information in fraudulent sentences.A fraudulent phone text dataset is produced by us in this paper.The THUCNews data sets and fraudulent phone text data sets are used in experiments.Experiment results show that compared with the baseline model,the proposed model(LMHACL)has the best experiment results in terms of Accuracy,Precision,Recall,and F1 score on the two data sets.And the performance indexes on fraudulent phone text data sets are all above 0.94.展开更多
The deep learning methods based on syntactic dependency tree have achieved great success on Aspect-based Sentiment Analysis(ABSA).However,the accuracy of the dependency parser cannot be determined,which may keep aspec...The deep learning methods based on syntactic dependency tree have achieved great success on Aspect-based Sentiment Analysis(ABSA).However,the accuracy of the dependency parser cannot be determined,which may keep aspect words away from its related opinion words in a dependency tree.Moreover,few models incorporate external affective knowledge for ABSA.Based on this,we propose a novel architecture to tackle the above two limitations,while fills up the gap in applying heterogeneous graphs convolution network to ABSA.Specially,we employ affective knowledge as an sentiment node to augment the representation of words.Then,linking sentiment node which have different attributes with word node through a specific edge to form a heterogeneous graph based on dependency tree.Finally,we design a multi-level semantic heterogeneous graph convolution network(Semantic-HGCN)to encode the heterogeneous graph for sentiment prediction.Extensive experiments are conducted on the datasets SemEval 2014 Task 4,SemEval 2015 task 12,SemEval 2016 task 5 and ACL 14 Twitter.The experimental results show that our method achieves the state-of-the-art performance.展开更多
文摘Accurate traffic prediction is crucial for an intelligent traffic system (ITS). However, the excessive non-linearity and complexity of the spatial-temporal correlation in traffic flow severely limit the prediction accuracy of most existing models, which simply stack temporal and spatial modules and fail to capture spatial-temporal features effectively. To improve the prediction accuracy, a multi-head attention spatial-temporal graph neural network (MSTNet) is proposed in this paper. First, the traffic data is decomposed into unique time spans that conform to positive rules, and valuable traffic node attributes are mined through an adaptive graph structure. Second, time and spatial features are captured using a multi-head attention spatial-temporal module. Finally, a multi-step prediction module is used to achieve future traffic condition prediction. Numerical experiments were conducted on an open-source dataset, and the results demonstrate that MSTNet performs well in spatial-temporal feature extraction and achieves more positive forecasting results than the baseline methods.
基金supported by the National Key R&D Program of China(Grant No.2019YFB1705203)Shanghai Municipal Science and Technology Major Project(2021SHZDZX0102).
文摘Due to the closed working environment of shield machines,the construction personnel cannot observe the construction geological environment,which seriously restricts the safety and efficiency of the tunneling process.In this study,we present an enhanced multi-head self-attention convolution neural network(EMSACNN)with two-stage feature extraction for geological condition prediction of shield machine.Firstly,we select 30 important parameters according to statistical analysis method and the working principle of the shield machine.Then,we delete the non-working sample data,and combine 10 consecutive data as the input of the model.Thereafter,to deeply mine and extract essential and relevant features,we build a novel model combined with the particularity of the geological type recognition task,in which an enhanced multi-head self-attention block is utilized as the first feature extractor to fully extract the correlation of geological information of adjacent working face of tunnel,and two-dimensional CNN(2dCNN)is utilized as the second feature extractor.The performance and superiority of proposed EMSACNN are verified by the actual data collected by the shield machine used in the construction of a double-track tunnel in Guangzhou,China.The results show that EMSACNN achieves at least 96%accuracy on the test sets of the two tunnels,and all the evaluation indicators of EMSACNN are much better than those of classical AI model and the model that use only the second-stage feature extractor.Therefore,the proposed EMSACNN achieves high accuracy and strong generalization for geological information prediction of shield machine,which is of great guiding significance to engineering practice.
基金funded by Naif Arab University for Security Sciences under grant No.NAUSS-23-R10.
文摘Estimation of crowd count is becoming crucial nowadays,as it can help in security surveillance,crowd monitoring,and management for different events.It is challenging to determine the approximate crowd size from an image of the crowd’s density.Therefore in this research study,we proposed a multi-headed convolutional neural network architecture-based model for crowd counting,where we divided our proposed model into two main components:(i)the convolutional neural network,which extracts the feature across the whole image that is given to it as an input,and(ii)the multi-headed layers,which make it easier to evaluate density maps to estimate the number of people in the input image and determine their number in the crowd.We employed the available public benchmark crowd-counting datasets UCF CC 50 and ShanghaiTech parts A and B for model training and testing to validate the model’s performance.To analyze the results,we used two metrics Mean Absolute Error(MAE)and Mean Square Error(MSE),and compared the results of the proposed systems with the state-of-art models of crowd counting.The results show the superiority of the proposed system.
基金Researchers Supporting Project Number(RSPD2024R576),King Saud University,Riyadh,Saudi Arabia。
文摘Cardiovascular disease is the leading cause of death globally.This disease causes loss of heart muscles and is also responsible for the death of heart cells,sometimes damaging their functionality.A person’s life may depend on receiving timely assistance as soon as possible.Thus,minimizing the death ratio can be achieved by early detection of heart attack(HA)symptoms.In the United States alone,an estimated 610,000 people die fromheart attacks each year,accounting for one in every four fatalities.However,by identifying and reporting heart attack symptoms early on,it is possible to reduce damage and save many lives significantly.Our objective is to devise an algorithm aimed at helping individuals,particularly elderly individuals living independently,to safeguard their lives.To address these challenges,we employ deep learning techniques.We have utilized a vision transformer(ViT)to address this problem.However,it has a significant overhead cost due to its memory consumption and computational complexity because of scaling dot-product attention.Also,since transformer performance typically relies on large-scale or adequate data,adapting ViT for smaller datasets is more challenging.In response,we propose a three-in-one steam model,theMulti-Head Attention Vision Hybrid(MHAVH).Thismodel integrates a real-time posture recognition framework to identify chest pain postures indicative of heart attacks using transfer learning techniques,such as ResNet-50 and VGG-16,renowned for their robust feature extraction capabilities.By incorporatingmultiple heads into the vision transformer to generate additional metrics and enhance heart-detection capabilities,we leverage a 2019 posture-based dataset comprising RGB images,a novel creation by the author that marks the first dataset tailored for posture-based heart attack detection.Given the limited online data availability,we segmented this dataset into gender categories(male and female)and conducted testing on both segmented and original datasets.The training accuracy of our model reach
基金funded by the Major Humanities and Social Sciences Research Projects in Zhejiang higher education institutions,grant number 2023QN082,awarded to Cheng ZhaoThe National Natural Science Foundation of China also provided funding,grant number 61902349,awarded to Cheng Zhao.
文摘The present study examines the impact of short-term public opinion sentiment on the secondary market,with a focus on the potential for such sentiment to cause dramatic stock price fluctuations and increase investment risk.The quantification of investment sentiment indicators and the persistent analysis of their impact has been a complex and significant area of research.In this paper,a structured multi-head attention stock index prediction method based adaptive public opinion sentiment vector is proposed.The proposedmethod utilizes an innovative approach to transform numerous investor comments on social platforms over time into public opinion sentiment vectors expressing complex sentiments.It then analyzes the continuous impact of these vectors on the market through the use of aggregating techniques and public opinion data via a structured multi-head attention mechanism.The experimental results demonstrate that the public opinion sentiment vector can provide more comprehensive feedback on market sentiment than traditional sentiment polarity analysis.Furthermore,the multi-head attention mechanism is shown to improve prediction accuracy through attention convergence on each type of input information separately.Themean absolute percentage error(MAPE)of the proposedmethod is 0.463%,a reduction of 0.294% compared to the benchmark attention algorithm.Additionally,the market backtesting results indicate that the return was 24.560%,an improvement of 8.202% compared to the benchmark algorithm.These results suggest that themarket trading strategy based on thismethod has the potential to improve trading profits.
基金supported by the National Natural Science Foundation of China(61771258)。
文摘Short-term load forecasting(STLF)plays a crucial role in the smart grid.However,it is challenging to capture the long-time dependence and the nonlinear relationship due to the comprehensive fluctuations of the electrical load.In this paper,an STLF model based on gated recurrent unit and multi-head attention(GRU-MA)is proposed to address the aforementioned problems.The proposed model accommodates the time series and nonlinear relationship of load data through gated recurrent unit(GRU)and exploits multi-head attention(MA)to learn the decisive features and long-term dependencies.Additionally,the proposed model is compared with the support vector regression(SVR)model,the recurrent neural network and multi-head attention(RNN-MA)model,the long short-term memory and multi-head attention(LSTM-MA)model,the GRU model,and the temporal convolutional network(TCN)model using the public dataset of the Global Energy Forecasting Competition 2014(GEFCOM2014).The results demonstrate that the GRU-MA model has the best prediction accuracy.
基金the National Natural Science Foundation of China(NNSFC)(Grant Nos.72001213 and 72301292)the National Social Science Fund of China(Grant No.19BGL297)the Basic Research Program of Natural Science in Shaanxi Province(Grant No.2021JQ-369).
文摘Due to the time-varying topology and possible disturbances in a conflict environment,it is still challenging to maintain the mission performance of flying Ad hoc networks(FANET),which limits the application of Unmanned Aerial Vehicle(UAV)swarms in harsh environments.This paper proposes an intelligent framework to quickly recover the cooperative coveragemission by aggregating the historical spatio-temporal network with the attention mechanism.The mission resilience metric is introduced in conjunction with connectivity and coverage status information to simplify the optimization model.A spatio-temporal node pooling method is proposed to ensure all node location features can be updated after destruction by capturing the temporal network structure.Combined with the corresponding Laplacian matrix as the hyperparameter,a recovery algorithm based on the multi-head attention graph network is designed to achieve rapid recovery.Simulation results showed that the proposed framework can facilitate rapid recovery of the connectivity and coverage more effectively compared to the existing studies.The results demonstrate that the average connectivity and coverage results is improved by 17.92%and 16.96%,respectively compared with the state-of-the-art model.Furthermore,by the ablation study,the contributions of each different improvement are compared.The proposed model can be used to support resilient network design for real-time mission execution.
基金supported by the Natural Science Foundation of Liaoning Province(2020-BS-054)the Fundamental Research Funds for the Central Universities(N2017005)the National Natural Science Foundation of China(62162050).
文摘A significant demand rises for energy-efficient deep neural networks to support power-limited embedding devices with successful deep learning applications in IoT and edge computing fields.An accurate energy prediction approach is critical to provide measurement and lead optimization direction.However,the current energy prediction approaches lack accuracy and generalization ability due to the lack of research on the neural network structure and the excessive reliance on customized training dataset.This paper presents a novel energy prediction model,NeurstrucEnergy.NeurstrucEnergy treats neural networks as directed graphs and applies a bi-directional graph neural network training on a randomly generated dataset to extract structural features for energy prediction.NeurstrucEnergy has advantages over linear approaches because the bi-directional graph neural network collects structural features from each layer's parents and children.Experimental results show that NeurstrucEnergy establishes state-of-the-art results with mean absolute percentage error of 2.60%.We also evaluate NeurstrucEnergy in a randomly generated dataset,achieving the mean absolute percentage error of 4.83%over 10 typical convolutional neural networks in recent years and 7 efficient convolutional neural networks created by neural architecture search.Our code is available at https://github.com/NEUSoftGreenAI/NeurstrucEnergy.git.
基金support from the Liaoning Province Nature Fund Project(No.2022-MS-291)the Scientific Research Project of Liaoning Province Education Department(LJKMZ20220781,LJKMZ20220783,LJKQZ20222457,JYTMS20231488).
文摘Nowadays,with the rapid development of industrial Internet technology,on the one hand,advanced industrial control systems(ICS)have improved industrial production efficiency.However,there are more and more cyber-attacks targeting industrial control systems.To ensure the security of industrial networks,intrusion detection systems have been widely used in industrial control systems,and deep neural networks have always been an effective method for identifying cyber attacks.Current intrusion detection methods still suffer from low accuracy and a high false alarm rate.Therefore,it is important to build a more efficient intrusion detection model.This paper proposes a hybrid deep learning intrusion detection method based on convolutional neural networks and bidirectional long short-term memory neural networks(CNN-BiLSTM).To address the issue of imbalanced data within the dataset and improve the model’s detection capabilities,the Synthetic Minority Over-sampling Technique-Edited Nearest Neighbors(SMOTE-ENN)algorithm is applied in the preprocessing phase.This algorithm is employed to generate synthetic instances for the minority class,simultaneously mitigating the impact of noise in the majority class.This approach aims to create a more equitable distribution of classes,thereby enhancing the model’s ability to effectively identify patterns in both minority and majority classes.In the experimental phase,the detection performance of the method is verified using two data sets.Experimental results show that the accuracy rate on the CICIDS-2017 data set reaches 97.7%.On the natural gas pipeline dataset collected by Lan Turnipseed from Mississippi State University in the United States,the accuracy rate also reaches 85.5%.
基金the National Natural Science Foundation of China under Grant No.62272087Science and Technology Planning Project of Sichuan Province under Grant No.2023YFG0161.
文摘Long-term urban traffic flow prediction is an important task in the field of intelligent transportation,as it can help optimize traffic management and improve travel efficiency.To improve prediction accuracy,a crucial issue is how to model spatiotemporal dependency in urban traffic data.In recent years,many studies have adopted spatiotemporal neural networks to extract key information from traffic data.However,most models ignore the semantic spatial similarity between long-distance areas when mining spatial dependency.They also ignore the impact of predicted time steps on the next unpredicted time step for making long-term predictions.Moreover,these models lack a comprehensive data embedding process to represent complex spatiotemporal dependency.This paper proposes a multi-scale persistent spatiotemporal transformer(MSPSTT)model to perform accurate long-term traffic flow prediction in cities.MSPSTT adopts an encoder-decoder structure and incorporates temporal,periodic,and spatial features to fully embed urban traffic data to address these issues.The model consists of a spatiotemporal encoder and a spatiotemporal decoder,which rely on temporal,geospatial,and semantic space multi-head attention modules to dynamically extract temporal,geospatial,and semantic characteristics.The spatiotemporal decoder combines the context information provided by the encoder,integrates the predicted time step information,and is iteratively updated to learn the correlation between different time steps in the broader time range to improve the model’s accuracy for long-term prediction.Experiments on four public transportation datasets demonstrate that MSPSTT outperforms the existing models by up to 9.5%on three common metrics.
基金This work was supported by the National Natural Science Foundation of China(Nos.U19A2081,62202320)the Fundamental Research Funds for the Central Universities(Nos.2022SCU12116,2023SCU12129,2023SCU12126)+1 种基金the Science and Engineering Connotation Development Project of Sichuan University(No.2020SCUNG129)the Key Laboratory of Data Protection and Intelligent Management(Sichuan University),Ministry of Education.
文摘Considering the stealthiness and persistence of Advanced Persistent Threats(APTs),system audit logs are leveraged in recent studies to construct system entity interaction provenance graphs to unveil threats in a host.Rule-based provenance graph APT detection approaches require elaborate rules and cannot detect unknown attacks,and existing learning-based approaches are limited by the lack of available APT attack samples or generally only perform graph-level anomaly detection,which requires lots of manual efforts to locate attack entities.This paper proposes an APT-exploited process detection approach called ThreatSniffer,which constructs the benign provenance graph from attack-free audit logs,fits normal system entity interactions and then detects APT-exploited processes by predicting the rationality of entity interactions.Firstly,ThreatSniffer understands system entities in terms of their file paths,interaction sequences,and the number distribution of interaction types and uses the multi-head self-attention mechanism to fuse these semantics.Then,based on the insight that APT-exploited processes interact with system entities they should not invoke,ThreatSniffer performs negative sampling on the benign provenance graph to generate non-existent edges,thus characterizing irrational entity interactions without requiring APT attack samples.At last,it employs a heterogeneous graph neural network as the interaction prediction model to aggregate the contextual information of entity interactions,and locate processes exploited by attackers,thereby achieving fine-grained APT detection.Evaluation results demonstrate that anomaly-based detection enables ThreatSniffer to identify all attack activities.Compared to the node-level APT detection method APT-KGL,ThreatSniffer achieves a 6.1%precision improvement because of its comprehensive understanding of entity semantics.
基金This research was supported by Suranaree University of Technology,Thailand,Grant Number:BRO7-709-62-12-03.
文摘The deep learning advancements have greatly improved the performance of speech recognition systems,and most recent systems are based on the Recurrent Neural Network(RNN).Overall,the RNN works fine with the small sequence data,but suffers from the gradient vanishing problem in case of large sequence.The transformer networks have neutralized this issue and have shown state-of-the-art results on sequential or speech-related data.Generally,in speech recognition,the input audio is converted into an image using Mel-spectrogram to illustrate frequencies and intensities.The image is classified by the machine learning mechanism to generate a classification transcript.However,the audio frequency in the image has low resolution and causing inaccurate predictions.This paper presents a novel end-to-end binary view transformer-based architecture for speech recognition to cope with the frequency resolution problem.Firstly,the input audio signal is transformed into a 2D image using Mel-spectrogram.Secondly,the modified universal transformers utilize the multi-head attention to derive contextual information and derive different speech-related features.Moreover,a feedforward neural network is also deployed for classification.The proposed system has generated robust results on Google’s speech command dataset with an accuracy of 95.16%and with minimal loss.The binary-view transformer eradicates the eventuality of the over-fitting problem by deploying a multiview mechanism to diversify the input data,and multi-head attention captures multiple contexts from the data’s feature map.
基金This researchwas funded by the Major Science and Technology Innovation Project of Shandong Province in China(2019JZZY010120).
文摘Fraud cases have been a risk in society and people’s property security has been greatly threatened.In recent studies,many promising algorithms have been developed for social media offensive text recognition as well as sentiment analysis.These algorithms are also suitable for fraudulent phone text recognition.Compared to these tasks,the semantics of fraudulent words are more complex and more difficult to distinguish.Recurrent Neural Networks(RNN),the variants ofRNN,ConvolutionalNeuralNetworks(CNN),and hybrid neural networks to extract text features are used by most text classification research.However,a single network or a simple network combination cannot obtain rich characteristic knowledge of fraudulent phone texts relatively.Therefore,a new model is proposed in this paper.In the fraudulent phone text,the knowledge that can be learned by the model includes the sequence structure of sentences,the correlation between words,the correlation of contextual semantics,the feature of keywords in sentences,etc.The new model combines a bidirectional Long-Short Term Memory Neural Network(BiLSTM)or a bidirectional Gate Recurrent United(BiGRU)and a Multi-Head attention mechanism module with convolution.A normalization layer is added after the output of the final hidden layer.BiLSTM or BiGRU is used to build the encoding and decoding layer.Multi-head attention mechanism module with convolution(MHAC)enhances the ability of the model to learn global interaction information and multi-granularity local interaction information in fraudulent sentences.A fraudulent phone text dataset is produced by us in this paper.The THUCNews data sets and fraudulent phone text data sets are used in experiments.Experiment results show that compared with the baseline model,the proposed model(LMHACL)has the best experiment results in terms of Accuracy,Precision,Recall,and F1 score on the two data sets.And the performance indexes on fraudulent phone text data sets are all above 0.94.
基金supported by the National Natural Science Foundation of China(Grant Nos.62276073,61966004)Guangxi Natural Science Foundation(No.2019GXNSFDA245018)+2 种基金Innovation Project of Guangxi Graduate Education(No.YCSW2022155)Guangxi“Bagui Scholar”Teams for Innovation and Research ProjectGuangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing.
文摘The deep learning methods based on syntactic dependency tree have achieved great success on Aspect-based Sentiment Analysis(ABSA).However,the accuracy of the dependency parser cannot be determined,which may keep aspect words away from its related opinion words in a dependency tree.Moreover,few models incorporate external affective knowledge for ABSA.Based on this,we propose a novel architecture to tackle the above two limitations,while fills up the gap in applying heterogeneous graphs convolution network to ABSA.Specially,we employ affective knowledge as an sentiment node to augment the representation of words.Then,linking sentiment node which have different attributes with word node through a specific edge to form a heterogeneous graph based on dependency tree.Finally,we design a multi-level semantic heterogeneous graph convolution network(Semantic-HGCN)to encode the heterogeneous graph for sentiment prediction.Extensive experiments are conducted on the datasets SemEval 2014 Task 4,SemEval 2015 task 12,SemEval 2016 task 5 and ACL 14 Twitter.The experimental results show that our method achieves the state-of-the-art performance.
