Handwritten Mathematical Expression Recognition (HMER) is an important task in pattern recognition. It is a challenging task due to symbols resembling each other in appearance("z/2", "B/β") and th...
详细信息
Advancements in the vehicular network technology enable real-time interconnection,data sharing,and intelligent cooperative driving among ***,malicious vehicles providing illegal and incorrect information can compromis...
详细信息
Advancements in the vehicular network technology enable real-time interconnection,data sharing,and intelligent cooperative driving among ***,malicious vehicles providing illegal and incorrect information can compromise the interests of vehicle *** mechanisms serve as an effective solution to this *** recent years,many researchers have incorporated blockchain technology to manage and incentivize vehicle nodes,incurring significant overhead and storage requirements due to the frequent ingress and egress of vehicles within the *** this paper,we propose a distributed vehicular network scheme based on trust ***,the designed architecture partitions multiple vehicle regions into ***,cloud supervision systems(CSSs)verify the accuracy of the information transmitted by ***,the trust scores for vehicles are calculated to reward or penalize them based on the trust evaluation *** proposed scheme demonstrates good scalability and effectively addresses the main cause of malicious information distribution among *** theoretical and experimental analysis show that our scheme outperforms the compared schemes.
Automatic crack detection of cement pavement chiefly benefits from the rapid development of deep learning,with convolutional neural networks(CNN)playing an important role in this ***,as the performance of crack detect...
详细信息
Automatic crack detection of cement pavement chiefly benefits from the rapid development of deep learning,with convolutional neural networks(CNN)playing an important role in this ***,as the performance of crack detection in cement pavement improves,the depth and width of the network structure are significantly increased,which necessitates more computing power and storage *** limitation hampers the practical implementation of crack detection models on various platforms,particularly portable devices like small mobile *** solve these problems,we propose a dual-encoder-based network architecture that focuses on extracting more comprehensive fracture feature information and combines cross-fusion modules and coordinated attention mechanisms formore efficient feature ***,we use small channel convolution to construct shallow feature extractionmodule(SFEM)to extract low-level feature information of cracks in cement pavement images,in order to obtainmore information about cracks in the shallowfeatures of *** addition,we construct large kernel atrous convolution(LKAC)to enhance crack information,which incorporates coordination attention mechanism for non-crack information filtering,and large kernel atrous convolution with different cores,using different receptive fields to extract more detailed edge and context ***,the three-stage feature map outputs from the shallow feature extraction module is cross-fused with the two-stage feature map outputs from the large kernel atrous convolution module,and the shallow feature and detailed edge feature are fully fused to obtain the final crack prediction *** evaluate our method on three public crack datasets:DeepCrack,CFD,and *** results on theDeepCrack dataset demonstrate the effectiveness of our proposed method compared to state-of-the-art crack detection methods,which achieves Precision(P)87.2%,Recall(R)87.7%,and F-score(F1)87.4%.Thanks to our lightweight cr
There is a growing concern about adversarial attacks against automatic speech recognition (ASR) systems. Although research into targeted universal adversarial examples (AEs) has progressed, current methods are constra...
详细信息
Brain stroke is the world's leading cause of death, impacting numerous lives annually. The chances of having a stroke have increased by 50% over one's lifetime, impacting one in four people worldwide. Machine ...
详细信息
Knowledge distillation is a highly effective method for transferring knowledge from a cum-bersome teacher network to a lightweight student network. However, teacher networks are not always available. An alternative me...
详细信息
Knowledge distillation is a highly effective method for transferring knowledge from a cum-bersome teacher network to a lightweight student network. However, teacher networks are not always available. An alternative method called online knowledge distillation, which applies a group of peer networks to learn collaboratively with each other, has been pro -posed previously. In this study, we revisit online knowledge distillation and find that the existing training strategy limits the diversity among peer networks. Thus, online knowledge distillation cannot achieve its full potential. To address this, a novel online knowledge dis-tillation with elastic peer (KDEP) strategy is introduced here. The entire training process is divided into two phases by KDEP. In each phase, a specific training strategy is applied to adjust the diversity to an appropriate degree. Extensive experiments have been conducted on individual benchmarks, including CIFAR-100, CINIC-10, Tiny ImageNet, and Caltech-UCSD Birds. The results demonstrate the superiority of KDEP. For example, when the peer networks are ShuffleNetV2-1.0 and ShuffleNetV2-0.5, the target peer network ShuffleNetV2-0.5 achieves 57:00% top-1 accuracy on Tiny ImageNet via KDEP. (c) 2021 Elsevier Inc. All rights reserved.
When, in 2008, Satoshi Nakamoto envisioned the first distributed database management system that relied on cryptographically secured chain of blocks to store data in an immutable and tamper-resistant manner, his prima...
详细信息
When, in 2008, Satoshi Nakamoto envisioned the first distributed database management system that relied on cryptographically secured chain of blocks to store data in an immutable and tamper-resistant manner, his primary use case was the introduction of a digital currency. Owing to this use case, the blockchain system was geared towards efficient storage of data, whereas the processing of complex queries, such as provenance analyses of data history, is out of focus. The increasing use of Internet of Things technologies and the resulting digitization in many domains, however, have led to a plethora of novel use cases for a secure digital ledger. For instance, in the healthcare sector, blockchain systems are used for the secure storage and sharing of electronic health records, while the food industry applies such systems to enable a reliable food-chain traceability, e.g., to prove compliance with cold chains. In these application domains, however, querying the current state is not sufficient-comprehensive history queries are required instead. Due to these altered usage modes involving more complex query types, it is questionable whether today's blockchain systems are prepared for this type of usage and whether such queries can be processed efficiently by them. In our paper, we therefore investigate novel use cases for blockchain systems and elicit their requirements towards a data store in terms of query capabilities. We reflect the state of the art in terms of query support in blockchain systems and assess whether it is capable of meeting the requirements of such more sophisticated use cases. As a result, we identify future research challenges with regard to query processing in blockchain systems.
Aiming to meet the growing demand for observation and analysis in power systems that based on Internet of Things(IoT),machine learning technology has been adopted to deal with the data-intensive power electronics appl...
详细信息
Aiming to meet the growing demand for observation and analysis in power systems that based on Internet of Things(IoT),machine learning technology has been adopted to deal with the data-intensive power electronics applications in *** feeding previous power electronic data into the learning model,accurate information is drawn,and the quality of IoT-based power services is ***,the data-intensive electronic applications with machine learning are split into numerous data/control constrained tasks by workflow *** efficient execution of this data-intensive Power Workflow(PW)needs massive computing resources,which are available in the cloud ***,the execution efficiency of PW decreases due to inappropriate sub-task and data *** addition,the power consumption explodes due to massive data *** address these challenges,a PW placement method named PWP is ***,the Non-dominated Sorting Differential Evolution(NSDE)is used to generate placement *** simulation experiments show that PWP achieves the best trade-off among data acquisition time,power consumption,load distribution and privacy preservation,confirming that PWP is effective for the placement problem.
Online bipartite matching has attracted wide interest since it can successfully model the popular online carhailing problem and sharing economy. Existing works consider this problem under either adversary setting or i...
详细信息
Online bipartite matching has attracted wide interest since it can successfully model the popular online carhailing problem and sharing economy. Existing works consider this problem under either adversary setting or i.i.d. setting. The former is too pessimistic to improve the performance in the general case;the latter is too optimistic to deal with the varying distribution of vertices. In this article, we initiate the study of the non-stationary online bipartite matching problem, which allows the distribution of vertices to vary with time and is more practical. We divide the non-stationary online bipartite matching problem into two subproblems, the matching problem and the selecting problem, and solve them individually. Combining Batch algorithms and deep Q-learning networks, we first construct a candidate algorithm set to solve the matching problem. For the selecting problem, we use a classical online learning algorithm, Exp3, as a selector algorithm and derive a theoretical bound. We further propose CDUCB as a selector algorithm by integrating distribution change detection into UCB. Rigorous theoretical analysis demonstrates that the performance of our proposed algorithms is no worse than that of any candidate algorithms in terms of competitive ratio. Finally, extensive experiments showthat our proposed algorithms havemuch higher performance for the non-stationary online bipartite matching problem comparing to the state-of-the-art.
Machine learning[1]studies focus mostly on prediction,where a model is built from a set of observational data formaking correct predictions on unseen *** has beenaddressed very well by modem techniques such as deeplea...
详细信息
Machine learning[1]studies focus mostly on prediction,where a model is built from a set of observational data formaking correct predictions on unseen *** has beenaddressed very well by modem techniques such as deeplearming[2],though some issues,c.g,open environmentmachine learning[3],remain to be developed.
暂无评论