Neural decoding plays a vital role in the interaction between the brain and the outside world. Our task in this paper is to decode the movement track of a finger directly based on the neural data. Existing neural deco...
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Digital twinning and edge computing are attractive solutions to support computing-intensive and servicesensitive Internet of Vehicles *** of the existing Internet of Vehicles service offloading solutions only consider...
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Digital twinning and edge computing are attractive solutions to support computing-intensive and servicesensitive Internet of Vehicles *** of the existing Internet of Vehicles service offloading solutions only consider edge–cloud collaboration,but the collaboration between small cell eNodeB(SCeNB)should not be *** delays far lower than offloading tasks to the cloud can be obtained through reasonable collaborative computing between *** proposed framework realizes and maintains the simulation of collaboration between SCeNB nodes by constructing a digital twin that maintains SCeNB nodes in the central controller,thereby realizing user task offloading positions,sub-channel allocation,and computing resource *** an algorithm named AUC-AC is proposed,based on the dominant actor–critic network and the auction *** order to obtain a better command of global information,the convolutional block attention mechanism(CBAM)is used in the digital twin of each SCeNB node to observe its environment and learn *** results show that our experimental scheme is better than several baseline algorithms in terms of service delay.
This study presents a deep learning model for efficient intracranial hemorrhage(ICH)detection and subtype classification on non-contrast head computed tomography(CT)*** refers to bleeding in the skull,leading to the m...
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This study presents a deep learning model for efficient intracranial hemorrhage(ICH)detection and subtype classification on non-contrast head computed tomography(CT)*** refers to bleeding in the skull,leading to the most critical life-threatening health condition requiring rapid and accurate *** is classified as intra-axial hemorrhage(intraventricular,intraparenchymal)and extra-axial hemorrhage(subdural,epidural,subarachnoid)based on the bleeding location inside the *** computer-aided diagnoses(CAD)-based schemes have been proposed for ICH detection and classification at both slice and scan ***,these approaches performonly binary classification and suffer from a large number of parameters,which increase storage ***,the accuracy of brain hemorrhage detection in existing models is significantly low for medically critical *** overcome these problems,a fast and efficient system for the automatic detection of ICH is *** designed a double-branch model based on xception architecture that extracts spatial and instant features,concatenates them,and creates the 3D spatial context(common feature vectors)fed to a decision tree classifier for final *** data employed for the experimentation was gathered during the 2019 Radiologist Society of North America(RSNA)brain hemorrhage detection *** model outperformed benchmark models and achieved better accuracy in intraventricular(99.49%),subarachnoid(99.49%),intraparenchymal(99.10%),and subdural(98.09%)categories,thereby justifying the performance of the proposed double-branch xception architecture for ICH detection and classification.
Continuously publishing histograms in data streams is crucial to many real-time applications,as it provides not only critical statistical information,but also reduces privacy leaking *** the importance of elements usu...
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Continuously publishing histograms in data streams is crucial to many real-time applications,as it provides not only critical statistical information,but also reduces privacy leaking *** the importance of elements usually decreases over time in data streams,in this paper we model a data stream by a sequence of weighted sliding windows,and then study how to publish histograms over these windows *** existing literature can hardly solve this problem in a real-time way,because they need to buffer all elements in each sliding window,resulting in high computational overhead and prohibitive storage *** this paper,we overcome this drawback by proposing an online algorithm denoted by Efficient Streaming Histogram Publishing(ESHP)to continuously publish histograms over weighted sliding ***,our method first creates a novel sketching structure,called Approximate-Estimate Sketch(AESketch),to maintain the counting information of each histogram interval at every time instance;then,it creates histograms that satisfy the differential privacy requirement by smartly adding appropriate noise values into the sketching *** experimental results and rigorous theoretical analysis demonstrate that the ESHP method can offer equivalent data utility with significantly lower computational overhead and storage costs when compared to other existing methods.
With the advancement of network communication technology,network traffic shows explosive ***,network attacks occur *** intrusion detection systems are still the primary means of detecting ***,two challenges continue t...
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With the advancement of network communication technology,network traffic shows explosive ***,network attacks occur *** intrusion detection systems are still the primary means of detecting ***,two challenges continue to stymie the development of a viable network intrusion detection system:imbalanced training data and new undiscovered ***,this study proposes a unique deep learning-based intrusion detection *** use two independent in-memory autoencoders trained on regular network traffic and attacks to capture the dynamic relationship between traffic features in the presence of unbalanced training *** the original data is fed into the triplet network by forming a triplet with the data reconstructed from the two encoders to ***,the distance relationship between the triples determines whether the traffic is an *** addition,to improve the accuracy of detecting unknown attacks,this research proposes an improved triplet loss function that is used to pull the distances of the same class closer while pushing the distances belonging to different classes farther in the learned feature *** proposed approach’s effectiveness,stability,and significance are evaluated against advanced models on the Android Adware and General Malware Dataset(AAGM17),Knowledge Discovery and Data Mining Cup 1999(KDDCUP99),Canadian Institute for Cybersecurity Group’s Intrusion Detection Evaluation Dataset(CICIDS2017),UNSW-NB15,Network Security Lab-Knowledge Discovery and Data Mining(NSL-KDD)*** achieved results confirmed the superiority of the proposed method for the task of network intrusion detection.
Wearing a safety helmet at the work site of a chemical plant can effectively prevent safety accidents caused by head injuries, so it is very important to detect whether employees wear safety helmets. In order to solve...
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Human life is the most important asset of human beings. Every year millions of people lose their lives to cardiovascular diseases. It is a group of diseases related to blood vessels and the heart. The chances of devel...
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K-nearest neighbor(KNN)is one of the most fundamental methods for unsupervised outlier detection because of its various advantages,e.g.,ease of use and relatively high ***,most data analytic tasks need to deal with hi...
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K-nearest neighbor(KNN)is one of the most fundamental methods for unsupervised outlier detection because of its various advantages,e.g.,ease of use and relatively high ***,most data analytic tasks need to deal with high-dimensional data,and the KNN-based methods often fail due to“the curse of dimensionality”.AutoEncoder-based methods have recently been introduced to use reconstruction errors for outlier detection on high-dimensional data,but the direct use of AutoEncoder typically does not preserve the data proximity relationships well for outlier *** this study,we propose to combine KNN with AutoEncoder for outlier ***,we propose the Nearest Neighbor AutoEncoder(NNAE)by persevering the original data proximity in a much lower dimension that is more suitable for performing ***,we propose the K-nearest reconstruction neighbors(K NRNs)by incorporating the reconstruction errors of NNAE with the K-distances of KNN to detect ***,we develop a method to automatically choose better parameters for optimizing the structure of ***,using five real-world datasets,we experimentally show that our proposed approach NNAE+K NRN is much better than existing methods,i.e.,KNN,Isolation Forest,a traditional AutoEncoder using reconstruction errors(AutoEncoder-RE),and Robust AutoEncoder.
With the benefits of reducing time and workforce,automated testing has been widely used for the quality assurance of mobile applications(APPs).Compared with automated testing,manual testing can achieve higher coverage...
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With the benefits of reducing time and workforce,automated testing has been widely used for the quality assurance of mobile applications(APPs).Compared with automated testing,manual testing can achieve higher coverage in complex interactive *** the effectiveness of manual testing is highly dependent on the user operation process(UOP)of experienced *** on the UOP,we propose an iterative Android automated testing(IAAT)method that automatically records,extracts,and integrates UOPs to guide the test logic of the tool across the complex Activity *** feedback test results can train the UOPs to achieve higher coverage in each *** extracted 50 UOPs and conducted experiments on 10 popular mobile APPs to demonstrate IAAT’s effectiveness compared with Monkey and the initial automated *** experimental results show a noticeable improvement in the IAAT compared with the test logic without human *** the 60 minutes test time,the average code coverage is improved by 13.98%to 37.83%,higher than the 27.48%of Monkey under the same conditions.
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