In response to the problem of traditional methods ignoring audio modality tampering, this study aims to explore an effective deep forgery video detection technique that improves detection precision and reliability by ...In response to the problem of traditional methods ignoring audio modality tampering, this study aims to explore an effective deep forgery video detection technique that improves detection precision and reliability by fusing lip images and audio signals. The main method used is lip-audio matching detection technology based on the Siamese neural network, combined with MFCC (Mel Frequency Cepstrum Coefficient) feature extraction of band-pass filters, an improved dual-branch Siamese network structure, and a two-stream network structure design. Firstly, the video stream is preprocessed to extract lip images, and the audio stream is preprocessed to extract MFCC features. Then, these features are processed separately through the two branches of the Siamese network. Finally, the model is trained and optimized through fully connected layers and loss functions. The experimental results show that the testing accuracy of the model in this study on the LRW (Lip Reading in the Wild) dataset reaches 92.3%;the recall rate is 94.3%;the F1 score is 93.3%, significantly better than the results of CNN (Convolutional Neural Networks) and LSTM (Long Short-Term Memory) models. In the validation of multi-resolution image streams, the highest accuracy of dual-resolution image streams reaches 94%. Band-pass filters can effectively improve the signal-to-noise ratio of deep forgery video detection when processing different types of audio signals. The real-time processing performance of the model is also excellent, and it achieves an average score of up to 5 in user research. These data demonstrate that the method proposed in this study can effectively fuse visual and audio information in deep forgery video detection, accurately identify inconsistencies between video and audio, and thus verify the effectiveness of lip-audio modality fusion technology in improving detection performance.展开更多
The motivation for this study is that the quality of deep fakes is constantly improving,which leads to the need to develop new methods for their detection.The proposed Customized Convolutional Neural Network method in...The motivation for this study is that the quality of deep fakes is constantly improving,which leads to the need to develop new methods for their detection.The proposed Customized Convolutional Neural Network method involves extracting structured data from video frames using facial landmark detection,which is then used as input to the CNN.The customized Convolutional Neural Network method is the date augmented-based CNN model to generate‘fake data’or‘fake images’.This study was carried out using Python and its libraries.We used 242 films from the dataset gathered by the Deep Fake Detection Challenge,of which 199 were made up and the remaining 53 were real.Ten seconds were allotted for each video.There were 318 videos used in all,199 of which were fake and 119 of which were real.Our proposedmethod achieved a testing accuracy of 91.47%,loss of 0.342,and AUC score of 0.92,outperforming two alternative approaches,CNN and MLP-CNN.Furthermore,our method succeeded in greater accuracy than contemporary models such as XceptionNet,Meso-4,EfficientNet-BO,MesoInception-4,VGG-16,and DST-Net.The novelty of this investigation is the development of a new Convolutional Neural Network(CNN)learning model that can accurately detect deep fake face photos.展开更多
Traditional vehicle detection algorithms use traverse search based vehicle candidate generation and hand crafted based classifier training for vehicle candidate verification.These types of methods generally have high ...Traditional vehicle detection algorithms use traverse search based vehicle candidate generation and hand crafted based classifier training for vehicle candidate verification.These types of methods generally have high processing times and low vehicle detection performance.To address this issue,a visual saliency and deep sparse convolution hierarchical model based vehicle detection algorithm is proposed.A visual saliency calculation is firstly used to generate a small vehicle candidate area.The vehicle candidate sub images are then loaded into a sparse deep convolution hierarchical model with an SVM-based classifier to perform the final detection.The experimental results demonstrate that the proposed method is with 94.81% correct rate and 0.78% false detection rate on the existing datasets and the real road pictures captured by our group,which outperforms the existing state-of-the-art algorithms.More importantly,high discriminative multi-scale features are generated by deep sparse convolution network which has broad application prospects in target recognition in the field of intelligent vehicle.展开更多
Deep learning is a practical and efficient technique that has been used extensively in many domains. Using deep learning technology, deepfakes create fake images of a person that people cannot distinguish from the rea...Deep learning is a practical and efficient technique that has been used extensively in many domains. Using deep learning technology, deepfakes create fake images of a person that people cannot distinguish from the real one. Recently, many researchers have focused on understanding how deepkakes work and detecting using deep learning approaches. This paper introduces an explainable deepfake framework for images creation and classification. The framework consists of three main parts: the first approach is called Instant ID which is used to create deepfacke images from the original one;the second approach called Xception classifies the real and deepfake images;the third approach called Local Interpretable Model (LIME) provides a method for interpreting the predictions of any machine learning model in a local and interpretable manner. Our study proposes deepfake approach that achieves 100% precision and 100% accuracy for deepfake creation and classification. Furthermore, the results highlight the superior performance of the proposed model in deep fake creation and classification.展开更多
Many types of real-world information systems, including social media and e-commerce platforms, can be modelled by means of attribute-rich, connected networks. The goal of anomaly detection in artificial intelligence i...Many types of real-world information systems, including social media and e-commerce platforms, can be modelled by means of attribute-rich, connected networks. The goal of anomaly detection in artificial intelligence is to identify illustrations that deviate significantly from the main distribution of data or that differ from known cases. Anomalous nodes in node-attributed networks can be identified with greater precision if both graph and node attributes are taken into account. Almost all of the studies in this area focus on supervised techniques for spotting outliers. While supervised algorithms for anomaly detection work well in theory, they cannot be applied to real-world applications owing to a lack of labelled data. Considering the possible data distribution, our model employs a dual variational autoencoder (VAE), while a generative adversarial network (GAN) assures that the model is robust to adversarial training. The dual VAEs are used in another capacity: as a fake-node generator. Adversarial training is used to ensure that our latent codes have a Gaussian or uniform distribution. To provide a fair presentation of the graph, the discriminator instructs the generator to generate latent variables with distributions that are more consistent with the actual distribution of the data. Once the model has been learned, the discriminator is used for anomaly detection via reconstruction loss which has been trained to distinguish between the normal and artificial distributions of data. First, using a dual VAE, our model simultaneously captures cross-modality interactions between topological structure and node characteristics and overcomes the problem of unlabeled anomalies, allowing us to better understand the network sparsity and nonlinearity. Second, the proposed model considers the regularization of the latent codes while solving the issue of unregularized embedding techniques that can quickly lead to unsatisfactory representation. Finally, we use the discriminator reconstruction loss for anomaly detection as the discriminator is well-trained to separate the normal and generated data distributions because reconstruction-based loss does not include the adversarial component. Experiments conducted on attributed networks demonstrate the effectiveness of the proposed model and show that it greatly surpasses the previous methods. The area under the curve scores of our proposed model for the BlogCatalog, Flickr, and Enron datasets are 0.83680, 0.82020, and 0.71180, respectively, proving the effectiveness of the proposed model. The result of the proposed model on the Enron dataset is slightly worse than other models;we attribute this to the dataset’s low dimensionality as the most probable explanation.展开更多
Metal mineral resources are the product of the deep material and energy exchange with its deep power process(Wan,2017).It is the foundation of contemporary national economic development,a priority area for the country...Metal mineral resources are the product of the deep material and energy exchange with its deep power process(Wan,2017).It is the foundation of contemporary national economic development,a priority area for the country’s implementation of strategic development.展开更多
A 4×4 beta-phase gallium oxide(β-Ga_(2)O_(3))deep-ultraviolet(DUV)rectangular 10-fingers interdigital metalsemiconductor-metal(MSM)photodetector array of high photo responsivity is introduced.The Ga2O_(3)thin fi...A 4×4 beta-phase gallium oxide(β-Ga_(2)O_(3))deep-ultraviolet(DUV)rectangular 10-fingers interdigital metalsemiconductor-metal(MSM)photodetector array of high photo responsivity is introduced.The Ga2O_(3)thin film is prepared through the metalorganic chemical vapor deposition technique,then used to construct the photodetector array via photolithography,lift-off,and ion beam sputtering methods.The one photodetector cell shows dark current of 1.94 p A,phototo-dark current ratio of 6×10_(7),photo responsivity of 634.15 A·W^(-1),specific detectivity of 5.93×1011cm·Hz1/2·W^(-1)(Jones),external quantum efficiency of 310000%,and linear dynamic region of 108.94 d B,indicating high performances for DUV photo detection.Furthermore,the 16-cell photodetector array displays uniform performances with decent deviation of 19.6%for photo responsivity.展开更多
The breaking of wind-generated waves is an important phenomenon in the ocean, having close relation to many aspects of the ocean, such as air-sea interaction, ocean wave dynamics, oceanic remote sensing and ocean engi...The breaking of wind-generated waves is an important phenomenon in the ocean, having close relation to many aspects of the ocean, such as air-sea interaction, ocean wave dynamics, oceanic remote sensing and ocean engineering. The first problem encountered in both its theoretical study and practical measurement is how to detect the breaking of waves.展开更多
The last few years have witnessed the widespread use of blockchain technology in several works because of its effectiveness in terms of privacy,security,and trustworthiness.However,the challenges of cyber-attacks repr...The last few years have witnessed the widespread use of blockchain technology in several works because of its effectiveness in terms of privacy,security,and trustworthiness.However,the challenges of cyber-attacks represent a real threat to systems based on this technology.The resort to the systems of anomaly detection focused on deep learning,also called deep anomaly detection,is an appropriate and efficient means to tackle cyber-attacks on the blockchain.This paper provides an overview of the blockchain technology concept,including its characteristics,challenges and limitations,and its system taxonomy.Numerous blockchain cyber-attacks are discussed,such as 51%attacks,selfish mining attacks,double spending attacks,and Sybil attacks.Furthermore,we survey an overview of deep anomaly detection systems with their challenges and unresolved issues.In addition,this article gives a glimpse of various deep learning approaches implemented for anomaly detection in the blockchain environment and presents several methods that enhance the security features of anomaly detection systems.Finally,we discuss the benefits and drawbacks of these recent advanced approaches in light of three categories—discriminative learning,generative learning,and hybrid learning—with other methods based on graphs,and we highlight the ability of the proposed approaches to perform real-time anomaly detection.展开更多
Gallium oxide(Ga_(2)O_(3)),with an ultrawide bandgap corresponding to the deep ultraviolet(DUV)spectra range,provides a potential subversive scheme for the filter-free DUV photodetection.Meanwhile,the various crystal ...Gallium oxide(Ga_(2)O_(3)),with an ultrawide bandgap corresponding to the deep ultraviolet(DUV)spectra range,provides a potential subversive scheme for the filter-free DUV photodetection.Meanwhile,the various crystal phases of Ga_(2)O_(3) provide more substrate options for achieving heteroepitaxy,with the coupling of Ga_(2)O_(3) to SiC substrates conducive to developing integrated Ga_(2)O_(3) DUV photodetectors.Phase engineering ofβ-Ga_(2)O_(3) andε-Ga_(2)O_(3) was achieved on the commercial 4H-SiC substrate via metal-organic chemical vapor deposition.According to the in-depth analysis of different Ga_(2)O_(3) growth stages,it was found thatβ-Ga_(2)O_(3) is easy to form under high-pressure growth conditions,while low-pressure conditions promote the formation ofε-Ga_(2)O_(3) at 500°C.Furthermore,the developedε-phase dominated Ga_(2)O_(3) DUV photodetector exhibits obvious advantages in high responsivity(∼639 A/W),photo-to-dark current ratio(∼2.4×10^(7)),external quantum efficiency(∼3.15×10^(5)%),and specific detectivity(∼9.62×10^(13) Jones)under 254 nm illumination.This work not only reveals the growth mechanism of Ga_(2)O_(3) films under various pressures but also ensures the great potential ofε-Ga_(2)O_(3) for highly sensitive DUV detection on the heterogeneous substrate,which is expected to expand the application of Ga_(2)O_(3) optoelectronic devices.展开更多
Smartphones and mobile tablets are rapidly becoming indispensable in daily life. Android has been the most popular mobile operating system since 2012. However, owing to the open nature of Android, countless malwares a...Smartphones and mobile tablets are rapidly becoming indispensable in daily life. Android has been the most popular mobile operating system since 2012. However, owing to the open nature of Android, countless malwares are hidden in a large number of benign apps in Android markets that seriously threaten Android security. Deep learning is a new area of machine learning research that has gained increasing attention in artificial intelligence. In this study, we propose to associate the features from the static analysis with features from dynamic analysis of Android apps and characterize malware using deep learning techniques. We implement an online deep-learning-based Android malware detection engine（Droid Detector） that can automatically detect whether an app is a malware or not. With thousands of Android apps, we thoroughly test Droid Detector and perform an indepth analysis on the features that deep learning essentially exploits to characterize malware. The results show that deep learning is suitable for characterizing Android malware and especially effective with the availability of more training data. Droid Detector can achieve 96.76% detection accuracy, which outperforms traditional machine learning techniques. An evaluation of ten popular anti-virus softwares demonstrates the urgency of advancing our capabilities in Android malware detection.展开更多
Wind power is one of the fastest-growing renewable energy sectors instrumental in the ongoing decarbonizationprocess. However, wind turbines are subjected to a wide range of dynamic loads which can cause more frequent...Wind power is one of the fastest-growing renewable energy sectors instrumental in the ongoing decarbonizationprocess. However, wind turbines are subjected to a wide range of dynamic loads which can cause more frequentfailures and downtime periods, leading to ever-increasing attention to effective Condition Monitoring strategies.In this paper, we propose a novel unsupervised deep anomaly detection framework to detect anomalies in windturbines based on SCADA data. We introduce a promising neural architecture, namely a Graph ConvolutionalAutoencoder for Multivariate Time series, to model the sensor network as a dynamical functional graph. Thisstructure improves the unsupervised learning capabilities of Autoencoders by considering individual sensormeasurements together with the nonlinear correlations existing among signals. On this basis, we developeda deep anomaly detection framework that was validated on 12 failure events occurred during 20 months ofoperation of four wind turbines. The results show that the proposed framework successfully detects anomaliesand anticipates SCADA alarms by outperforming other two recent neural approaches.展开更多
Due to polymorphic nature of malware attack,a signature-based analysis is no longer sufficient to solve polymorphic and stealth nature ofmalware attacks.On the other hand,state-of-the-art methods like deep learning re...Due to polymorphic nature of malware attack,a signature-based analysis is no longer sufficient to solve polymorphic and stealth nature ofmalware attacks.On the other hand,state-of-the-art methods like deep learning require labelled dataset as a target to train a supervised model.This is unlikely to be the case in production network as the dataset is unstructured and has no label.Hence an unsupervised learning is recommended.Behavioral study is one of the techniques to elicit traffic pattern.However,studies have shown that existing behavioral intrusion detection model had a few issues which had been parameterized into its common characteristics,namely lack of prior information(p(θ)),and reduced parameters(θ).Therefore,this study aims to utilize the previously built Feature Selection Model subsequently to design a Predictive Analytics Model based on Bayesian Network used to improve the analysis prediction.Feature Selection Model is used to learn significant label as a target and Bayesian Network is a sophisticated probabilistic approach to predict intrusion.Finally,the results are extended to evaluate detection,accuracy and false alarm rate of the model against the subject matter expert model,Support Vector Machine(SVM),k nearest neighbor(k-NN)using simulated and ground-truth dataset.The ground-truth dataset from the production traffic of one of the largest healthcare provider in Malaysia is used to promote realism on the real use case scenario.Results have shown that the proposed model consistently outperformed other models.展开更多
Earthquakes pose a significant threat to life and property worldwide.Rapid and accurate assessment of earthquake damage is crucial for effective disaster response efforts.This study investigates the feasibility of emp...Earthquakes pose a significant threat to life and property worldwide.Rapid and accurate assessment of earthquake damage is crucial for effective disaster response efforts.This study investigates the feasibility of employing deep learning models for damage detection using drone imagery.We explore the adaptation of models like VGG16 for object detection through transfer learning and compare their performance to established object detection architectures like YOLOv8(You Only Look Once)and Detectron2.Our evaluation,based on various metrics including mAP,mAP50,and recall,demonstrates the superior performance of YOLOv8 in detecting damaged buildings within drone imagery,particularly for cases with moderate bounding box overlap.This finding suggests its potential suitability for real-world applications due to the balance between accuracy and efficiency.Furthermore,to enhance real-world feasibility,we explore two strategies for enabling the simultaneous operation of multiple deep learning models for video processing:frame splitting and threading.In addition,we optimize model size and computational complexity to facilitate real-time processing on resource-constrained platforms,such as drones.This work contributes to the field of earthquake damage detection by(1)demonstrating the effectiveness of deep learning models,including adapted architectures,for damage detection from drone imagery,(2)highlighting the importance of evaluation metrics like mAP50 for tasks with moderate bounding box overlap requirements,and(3)proposing methods for ensemble model processing and model optimization to enhance real-world feasibility.The potential for real-time damage assessment using drone-based deep learning models offers significant advantages for disaster response by enabling rapid information gathering to support resource allocation,rescue efforts,and recovery operations in the aftermath of earthquakes.展开更多
As an important branch of geophysical exploration method,the electromagnetic method with artificial source has advanced rapidly in the past decade.These methods are classified as airborne electromagnetic method,ground...As an important branch of geophysical exploration method,the electromagnetic method with artificial source has advanced rapidly in the past decade.These methods are classified as airborne electromagnetic method,ground-air electromagnetic method,ground electromagnetic method,and marine electromagnetic method.Over the years,researchers in China have made significant improvement to the fundamental theory,forward modeling and inverse for series of electromagnetic detection methods.Conversely,significant progress was made in the development of corresponding equipment.The researched techniques and their developed equipment have been successfully utilized to detect underground targets as deep as 10 km.However,there is increasing need for deep resources exploration,urban subsurface study,and prediction,monitoring and detection of geological hazards.To meet the increasing need and catch up with the advanced international level of exploration technologies and developed equipment,there is urgent necessity and requirement to continue developing geophysical methods and the corresponding equipment.展开更多
文摘In response to the problem of traditional methods ignoring audio modality tampering, this study aims to explore an effective deep forgery video detection technique that improves detection precision and reliability by fusing lip images and audio signals. The main method used is lip-audio matching detection technology based on the Siamese neural network, combined with MFCC (Mel Frequency Cepstrum Coefficient) feature extraction of band-pass filters, an improved dual-branch Siamese network structure, and a two-stream network structure design. Firstly, the video stream is preprocessed to extract lip images, and the audio stream is preprocessed to extract MFCC features. Then, these features are processed separately through the two branches of the Siamese network. Finally, the model is trained and optimized through fully connected layers and loss functions. The experimental results show that the testing accuracy of the model in this study on the LRW (Lip Reading in the Wild) dataset reaches 92.3%;the recall rate is 94.3%;the F1 score is 93.3%, significantly better than the results of CNN (Convolutional Neural Networks) and LSTM (Long Short-Term Memory) models. In the validation of multi-resolution image streams, the highest accuracy of dual-resolution image streams reaches 94%. Band-pass filters can effectively improve the signal-to-noise ratio of deep forgery video detection when processing different types of audio signals. The real-time processing performance of the model is also excellent, and it achieves an average score of up to 5 in user research. These data demonstrate that the method proposed in this study can effectively fuse visual and audio information in deep forgery video detection, accurately identify inconsistencies between video and audio, and thus verify the effectiveness of lip-audio modality fusion technology in improving detection performance.
基金Science and Technology Funds from the Liaoning Education Department(Serial Number:LJKZ0104).
文摘The motivation for this study is that the quality of deep fakes is constantly improving,which leads to the need to develop new methods for their detection.The proposed Customized Convolutional Neural Network method involves extracting structured data from video frames using facial landmark detection,which is then used as input to the CNN.The customized Convolutional Neural Network method is the date augmented-based CNN model to generate‘fake data’or‘fake images’.This study was carried out using Python and its libraries.We used 242 films from the dataset gathered by the Deep Fake Detection Challenge,of which 199 were made up and the remaining 53 were real.Ten seconds were allotted for each video.There were 318 videos used in all,199 of which were fake and 119 of which were real.Our proposedmethod achieved a testing accuracy of 91.47%,loss of 0.342,and AUC score of 0.92,outperforming two alternative approaches,CNN and MLP-CNN.Furthermore,our method succeeded in greater accuracy than contemporary models such as XceptionNet,Meso-4,EfficientNet-BO,MesoInception-4,VGG-16,and DST-Net.The novelty of this investigation is the development of a new Convolutional Neural Network(CNN)learning model that can accurately detect deep fake face photos.
基金Supported by National Natural Science Foundation of China(Grant Nos.U1564201,61573171,61403172,51305167)China Postdoctoral Science Foundation(Grant Nos.2015T80511,2014M561592)+3 种基金Jiangsu Provincial Natural Science Foundation of China(Grant No.BK20140555)Six Talent Peaks Project of Jiangsu Province,China(Grant Nos.2015-JXQC-012,2014-DZXX-040)Jiangsu Postdoctoral Science Foundation,China(Grant No.1402097C)Jiangsu University Scientific Research Foundation for Senior Professionals,China(Grant No.14JDG028)
文摘Traditional vehicle detection algorithms use traverse search based vehicle candidate generation and hand crafted based classifier training for vehicle candidate verification.These types of methods generally have high processing times and low vehicle detection performance.To address this issue,a visual saliency and deep sparse convolution hierarchical model based vehicle detection algorithm is proposed.A visual saliency calculation is firstly used to generate a small vehicle candidate area.The vehicle candidate sub images are then loaded into a sparse deep convolution hierarchical model with an SVM-based classifier to perform the final detection.The experimental results demonstrate that the proposed method is with 94.81% correct rate and 0.78% false detection rate on the existing datasets and the real road pictures captured by our group,which outperforms the existing state-of-the-art algorithms.More importantly,high discriminative multi-scale features are generated by deep sparse convolution network which has broad application prospects in target recognition in the field of intelligent vehicle.
文摘Deep learning is a practical and efficient technique that has been used extensively in many domains. Using deep learning technology, deepfakes create fake images of a person that people cannot distinguish from the real one. Recently, many researchers have focused on understanding how deepkakes work and detecting using deep learning approaches. This paper introduces an explainable deepfake framework for images creation and classification. The framework consists of three main parts: the first approach is called Instant ID which is used to create deepfacke images from the original one;the second approach called Xception classifies the real and deepfake images;the third approach called Local Interpretable Model (LIME) provides a method for interpreting the predictions of any machine learning model in a local and interpretable manner. Our study proposes deepfake approach that achieves 100% precision and 100% accuracy for deepfake creation and classification. Furthermore, the results highlight the superior performance of the proposed model in deep fake creation and classification.
文摘Many types of real-world information systems, including social media and e-commerce platforms, can be modelled by means of attribute-rich, connected networks. The goal of anomaly detection in artificial intelligence is to identify illustrations that deviate significantly from the main distribution of data or that differ from known cases. Anomalous nodes in node-attributed networks can be identified with greater precision if both graph and node attributes are taken into account. Almost all of the studies in this area focus on supervised techniques for spotting outliers. While supervised algorithms for anomaly detection work well in theory, they cannot be applied to real-world applications owing to a lack of labelled data. Considering the possible data distribution, our model employs a dual variational autoencoder (VAE), while a generative adversarial network (GAN) assures that the model is robust to adversarial training. The dual VAEs are used in another capacity: as a fake-node generator. Adversarial training is used to ensure that our latent codes have a Gaussian or uniform distribution. To provide a fair presentation of the graph, the discriminator instructs the generator to generate latent variables with distributions that are more consistent with the actual distribution of the data. Once the model has been learned, the discriminator is used for anomaly detection via reconstruction loss which has been trained to distinguish between the normal and artificial distributions of data. First, using a dual VAE, our model simultaneously captures cross-modality interactions between topological structure and node characteristics and overcomes the problem of unlabeled anomalies, allowing us to better understand the network sparsity and nonlinearity. Second, the proposed model considers the regularization of the latent codes while solving the issue of unregularized embedding techniques that can quickly lead to unsatisfactory representation. Finally, we use the discriminator reconstruction loss for anomaly detection as the discriminator is well-trained to separate the normal and generated data distributions because reconstruction-based loss does not include the adversarial component. Experiments conducted on attributed networks demonstrate the effectiveness of the proposed model and show that it greatly surpasses the previous methods. The area under the curve scores of our proposed model for the BlogCatalog, Flickr, and Enron datasets are 0.83680, 0.82020, and 0.71180, respectively, proving the effectiveness of the proposed model. The result of the proposed model on the Enron dataset is slightly worse than other models;we attribute this to the dataset’s low dimensionality as the most probable explanation.
基金supported by Science and Technology Innovation Fund(Grant No.KDY2019001)Integrated Geophysical Simulation Lab of Chang’an University(Key Laboratory of Chinese Geophysical Society)
文摘Metal mineral resources are the product of the deep material and energy exchange with its deep power process(Wan,2017).It is the foundation of contemporary national economic development,a priority area for the country’s implementation of strategic development.
基金Project supported by the National Natural Science Foundation of China(Grant No.61774019)Natural Science Research Start-up Foundation of Recruiting Talents of Nanjing University of Posts and Telecommunications(Grant Nos.XK1060921115 and XK1060921002)。
文摘A 4×4 beta-phase gallium oxide(β-Ga_(2)O_(3))deep-ultraviolet(DUV)rectangular 10-fingers interdigital metalsemiconductor-metal(MSM)photodetector array of high photo responsivity is introduced.The Ga2O_(3)thin film is prepared through the metalorganic chemical vapor deposition technique,then used to construct the photodetector array via photolithography,lift-off,and ion beam sputtering methods.The one photodetector cell shows dark current of 1.94 p A,phototo-dark current ratio of 6×10_(7),photo responsivity of 634.15 A·W^(-1),specific detectivity of 5.93×1011cm·Hz1/2·W^(-1)(Jones),external quantum efficiency of 310000%,and linear dynamic region of 108.94 d B,indicating high performances for DUV photo detection.Furthermore,the 16-cell photodetector array displays uniform performances with decent deviation of 19.6%for photo responsivity.
文摘The breaking of wind-generated waves is an important phenomenon in the ocean, having close relation to many aspects of the ocean, such as air-sea interaction, ocean wave dynamics, oceanic remote sensing and ocean engineering. The first problem encountered in both its theoretical study and practical measurement is how to detect the breaking of waves.
文摘The last few years have witnessed the widespread use of blockchain technology in several works because of its effectiveness in terms of privacy,security,and trustworthiness.However,the challenges of cyber-attacks represent a real threat to systems based on this technology.The resort to the systems of anomaly detection focused on deep learning,also called deep anomaly detection,is an appropriate and efficient means to tackle cyber-attacks on the blockchain.This paper provides an overview of the blockchain technology concept,including its characteristics,challenges and limitations,and its system taxonomy.Numerous blockchain cyber-attacks are discussed,such as 51%attacks,selfish mining attacks,double spending attacks,and Sybil attacks.Furthermore,we survey an overview of deep anomaly detection systems with their challenges and unresolved issues.In addition,this article gives a glimpse of various deep learning approaches implemented for anomaly detection in the blockchain environment and presents several methods that enhance the security features of anomaly detection systems.Finally,we discuss the benefits and drawbacks of these recent advanced approaches in light of three categories—discriminative learning,generative learning,and hybrid learning—with other methods based on graphs,and we highlight the ability of the proposed approaches to perform real-time anomaly detection.
基金supported by the National Key Research and Development Program of China(2023YFB3610200 and 2024YFA1208800)the National Natural Science Foundation of China(61925110,U20A20207,62304215,and 62171426)+2 种基金the University of Science and Technology of China(WK2100000025,YD2100002009,YD2100002010,and YD2100002007)the China Postdoctoral Science Foundation(2023M733367)the CAS Project for Young Scientists in Basic Research(YSBR-029)。
文摘Gallium oxide(Ga_(2)O_(3)),with an ultrawide bandgap corresponding to the deep ultraviolet(DUV)spectra range,provides a potential subversive scheme for the filter-free DUV photodetection.Meanwhile,the various crystal phases of Ga_(2)O_(3) provide more substrate options for achieving heteroepitaxy,with the coupling of Ga_(2)O_(3) to SiC substrates conducive to developing integrated Ga_(2)O_(3) DUV photodetectors.Phase engineering ofβ-Ga_(2)O_(3) andε-Ga_(2)O_(3) was achieved on the commercial 4H-SiC substrate via metal-organic chemical vapor deposition.According to the in-depth analysis of different Ga_(2)O_(3) growth stages,it was found thatβ-Ga_(2)O_(3) is easy to form under high-pressure growth conditions,while low-pressure conditions promote the formation ofε-Ga_(2)O_(3) at 500°C.Furthermore,the developedε-phase dominated Ga_(2)O_(3) DUV photodetector exhibits obvious advantages in high responsivity(∼639 A/W),photo-to-dark current ratio(∼2.4×10^(7)),external quantum efficiency(∼3.15×10^(5)%),and specific detectivity(∼9.62×10^(13) Jones)under 254 nm illumination.This work not only reveals the growth mechanism of Ga_(2)O_(3) films under various pressures but also ensures the great potential ofε-Ga_(2)O_(3) for highly sensitive DUV detection on the heterogeneous substrate,which is expected to expand the application of Ga_(2)O_(3) optoelectronic devices.
文摘Smartphones and mobile tablets are rapidly becoming indispensable in daily life. Android has been the most popular mobile operating system since 2012. However, owing to the open nature of Android, countless malwares are hidden in a large number of benign apps in Android markets that seriously threaten Android security. Deep learning is a new area of machine learning research that has gained increasing attention in artificial intelligence. In this study, we propose to associate the features from the static analysis with features from dynamic analysis of Android apps and characterize malware using deep learning techniques. We implement an online deep-learning-based Android malware detection engine（Droid Detector） that can automatically detect whether an app is a malware or not. With thousands of Android apps, we thoroughly test Droid Detector and perform an indepth analysis on the features that deep learning essentially exploits to characterize malware. The results show that deep learning is suitable for characterizing Android malware and especially effective with the availability of more training data. Droid Detector can achieve 96.76% detection accuracy, which outperforms traditional machine learning techniques. An evaluation of ten popular anti-virus softwares demonstrates the urgency of advancing our capabilities in Android malware detection.
文摘Wind power is one of the fastest-growing renewable energy sectors instrumental in the ongoing decarbonizationprocess. However, wind turbines are subjected to a wide range of dynamic loads which can cause more frequentfailures and downtime periods, leading to ever-increasing attention to effective Condition Monitoring strategies.In this paper, we propose a novel unsupervised deep anomaly detection framework to detect anomalies in windturbines based on SCADA data. We introduce a promising neural architecture, namely a Graph ConvolutionalAutoencoder for Multivariate Time series, to model the sensor network as a dynamical functional graph. Thisstructure improves the unsupervised learning capabilities of Autoencoders by considering individual sensormeasurements together with the nonlinear correlations existing among signals. On this basis, we developeda deep anomaly detection framework that was validated on 12 failure events occurred during 20 months ofoperation of four wind turbines. The results show that the proposed framework successfully detects anomaliesand anticipates SCADA alarms by outperforming other two recent neural approaches.
基金The work is fully sponsored by the research project grant FRGS/1/2021/ICT07/UITM/02/3。
文摘Due to polymorphic nature of malware attack,a signature-based analysis is no longer sufficient to solve polymorphic and stealth nature ofmalware attacks.On the other hand,state-of-the-art methods like deep learning require labelled dataset as a target to train a supervised model.This is unlikely to be the case in production network as the dataset is unstructured and has no label.Hence an unsupervised learning is recommended.Behavioral study is one of the techniques to elicit traffic pattern.However,studies have shown that existing behavioral intrusion detection model had a few issues which had been parameterized into its common characteristics,namely lack of prior information(p(θ)),and reduced parameters(θ).Therefore,this study aims to utilize the previously built Feature Selection Model subsequently to design a Predictive Analytics Model based on Bayesian Network used to improve the analysis prediction.Feature Selection Model is used to learn significant label as a target and Bayesian Network is a sophisticated probabilistic approach to predict intrusion.Finally,the results are extended to evaluate detection,accuracy and false alarm rate of the model against the subject matter expert model,Support Vector Machine(SVM),k nearest neighbor(k-NN)using simulated and ground-truth dataset.The ground-truth dataset from the production traffic of one of the largest healthcare provider in Malaysia is used to promote realism on the real use case scenario.Results have shown that the proposed model consistently outperformed other models.
文摘Earthquakes pose a significant threat to life and property worldwide.Rapid and accurate assessment of earthquake damage is crucial for effective disaster response efforts.This study investigates the feasibility of employing deep learning models for damage detection using drone imagery.We explore the adaptation of models like VGG16 for object detection through transfer learning and compare their performance to established object detection architectures like YOLOv8(You Only Look Once)and Detectron2.Our evaluation,based on various metrics including mAP,mAP50,and recall,demonstrates the superior performance of YOLOv8 in detecting damaged buildings within drone imagery,particularly for cases with moderate bounding box overlap.This finding suggests its potential suitability for real-world applications due to the balance between accuracy and efficiency.Furthermore,to enhance real-world feasibility,we explore two strategies for enabling the simultaneous operation of multiple deep learning models for video processing:frame splitting and threading.In addition,we optimize model size and computational complexity to facilitate real-time processing on resource-constrained platforms,such as drones.This work contributes to the field of earthquake damage detection by(1)demonstrating the effectiveness of deep learning models,including adapted architectures,for damage detection from drone imagery,(2)highlighting the importance of evaluation metrics like mAP50 for tasks with moderate bounding box overlap requirements,and(3)proposing methods for ensemble model processing and model optimization to enhance real-world feasibility.The potential for real-time damage assessment using drone-based deep learning models offers significant advantages for disaster response by enabling rapid information gathering to support resource allocation,rescue efforts,and recovery operations in the aftermath of earthquakes.
基金supported by the Beijing Science and Technology Plan(Grant No.Z181100005718001)the National Key R&D Program(Grant No.2017YFC0601204)the National Natural Science Foundation of China(Grant Nos.41874088&41830101)。
文摘As an important branch of geophysical exploration method,the electromagnetic method with artificial source has advanced rapidly in the past decade.These methods are classified as airborne electromagnetic method,ground-air electromagnetic method,ground electromagnetic method,and marine electromagnetic method.Over the years,researchers in China have made significant improvement to the fundamental theory,forward modeling and inverse for series of electromagnetic detection methods.Conversely,significant progress was made in the development of corresponding equipment.The researched techniques and their developed equipment have been successfully utilized to detect underground targets as deep as 10 km.However,there is increasing need for deep resources exploration,urban subsurface study,and prediction,monitoring and detection of geological hazards.To meet the increasing need and catch up with the advanced international level of exploration technologies and developed equipment,there is urgent necessity and requirement to continue developing geophysical methods and the corresponding equipment.
