Specific Emitter Identification (SEI) is a critical component of the Industrial Internet of Things (IIoT), enabling effective identification and validation of unauthorized communication devices, thereby preventing mal...
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A Bayesian network security situational awareness technique based on Bayesian networks is proposed to address the current problem of the inability to analyze and predict the security situation of established networks ...
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In this paper,in order to reduce the energy leakage caused by the discretized representation in sparse channel estimation for Orthogonal Frequency Division Multiplexing(OFDM)systems,we systematically have analyzed the...
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In this paper,in order to reduce the energy leakage caused by the discretized representation in sparse channel estimation for Orthogonal Frequency Division Multiplexing(OFDM)systems,we systematically have analyzed the optimal locations of atoms with discrete delays for each path reconstruction from the perspective of linear fitting ***,we have investigated the adverse effects of the non-ideal inner product function on the iteration in one of the most widely used channel estimation method,Orthogonal Matching Pursuit(OMP).The study shows that the distance between the selected atoms for each path in OMP can be larger than the sampling interval,which prevents OMP-based methods from achieving better *** overcome this drawback,the image deblurring-based channel estimation method,in which the channel estimation problem is analogized to one-dimensional image deblurring,was proposed to improve the large compensation distance of traditional *** advantage of the proposed method was validated by the results of numerical simulation and sea trial data decoding.
Images captured in low-light or underwater environments are often accompanied by significant degradation, which can negatively impact the quality and performance of downstream tasks. While convolutional neural network...
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Metapaths with specific complex semantics are critical to learning diverse semantic and structural information of heterogeneous networks(HNs)for most of the existing representation learning ***,any metapaths consistin...
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Metapaths with specific complex semantics are critical to learning diverse semantic and structural information of heterogeneous networks(HNs)for most of the existing representation learning ***,any metapaths consisting of multiple,simple metarelations must be driven by domain *** sensitive,expensive,and limited metapaths severely reduce the flexibility and scalability of the existing models.A metapath-free,scalable representation learning model,called Metarelation2vec,is proposed for HNs with biased joint learning of all metarelations in a bid to address this ***,a metarelation-aware,biased walk strategy is first designed to obtain better training samples by using autogenerating cooperation probabilities for all metarelations rather than using expert-given ***,grouped nodes by the type,a common and shallow skip-gram model is used to separately learn structural proximity for each node ***,grouped links by the type,a novel and shallow model is used to separately learn the semantic proximity for each link ***,supervised by the cooperation probabilities of all meta-words,the biased training samples are thrown into the shallow models to jointly learn the structural and semantic information in the HNs,ensuring the accuracy and scalability of the *** experimental results on three tasks and four open datasets demonstrate the advantages of our proposed model.
With the development of information technology and cloud computing,data sharing has become an important part of scientific *** traditional data sharing,data is stored on a third-party storage platform,which causes the...
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With the development of information technology and cloud computing,data sharing has become an important part of scientific *** traditional data sharing,data is stored on a third-party storage platform,which causes the owner to lose control of the *** a result,there are issues of intentional data leakage and tampering by third parties,and the private information contained in the data may lead to more significant ***,data is frequently maintained on multiple storage platforms,posing significant hurdles in terms of enlisting multiple parties to engage in data sharing while maintaining *** this work,we propose a new architecture for applying blockchains to data sharing and achieve efficient and reliable data sharing among heterogeneous *** design a new data sharing transaction mechanism based on the system architecture to protect the security of the raw data and the processing *** also design and implement a hybrid concurrency control protocol to overcome issues caused by the large differences in blockchain performance in our system and to improve the success rate of data sharing *** took Ethereum and Hyperledger Fabric as examples to conduct crossblockchain data sharing *** results show that our system achieves data sharing across heterogeneous blockchains with reasonable performance and has high scalability.
The precipitation behavior of magnesium-rare earth(Mg-RE)alloys plays a crucial role for their ***,the precipitation happens at small-length and long-time scales,making it challenging to be analyzed by state-of-the-ar...
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The precipitation behavior of magnesium-rare earth(Mg-RE)alloys plays a crucial role for their ***,the precipitation happens at small-length and long-time scales,making it challenging to be analyzed by state-of-the-art experimental ***,we combine the advantages of both molecular dynamic forcefields on describing atom interactions and Monte Carlo method on describing diffusive events to develop an embedded atom method(EAM)potential based kinetic Monte Carlo(KMC)*** the proposed model,we simulated the formation and evolution of Y clusters in Mg-Y alloy formed by the vacancy mechanism,and rationalize the simulation results using aberration-corrected scanning transmission electron microscopy *** conducted a systematic analysis of the atomic structure,the evolution kinetics and path of the Y cluster by tracing Y atoms and comparing with density function theory(DFT)*** work reveals that,all solute columns in a same cluster trend to grow along the[0001]Mg direction *** method presented is not only used for the Mg-Y alloy but also other Mg-RE alloys such as Mg-Gd as illustrated in the last part of the paper.
Background: Lubrication failure has always been a concern in the research of heavy hydrostatic bearings. A preliminary study found that under a certain working condition, the heavy hydrostatic bearing will appear in t...
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Video question answering(VideoQA) is a challenging yet important task that requires a joint understanding of low-level video content and high-level textual semantics. Despite the promising progress of existing efforts...
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Video question answering(VideoQA) is a challenging yet important task that requires a joint understanding of low-level video content and high-level textual semantics. Despite the promising progress of existing efforts, recent studies revealed that current VideoQA models mostly tend to over-rely on the superficial correlations rooted in the dataset bias while overlooking the key video content, thus leading to unreliable results. Effectively understanding and modeling the temporal and semantic characteristics of a given video for robust VideoQA is crucial but, to our knowledge, has not been well investigated. To fill the research gap, we propose a robust VideoQA framework that can effectively model the cross-modality fusion and enforce the model to focus on the temporal and global content of videos when making a QA decision instead of exploiting the shortcuts in datasets. Specifically, we design a self-supervised contrastive learning objective to contrast the positive and negative pairs of multimodal input, where the fused representation of the original multimodal input is enforced to be closer to that of the intervened input based on video perturbation. We expect the fused representation to focus more on the global context of videos rather than some static keyframes. Moreover, we introduce an effective temporal order regularization to enforce the inherent sequential structure of videos for video representation. We also design a Kullback-Leibler divergence-based perturbation invariance regularization of the predicted answer distribution to improve the robustness of the model against temporal content perturbation of videos. Our method is model-agnostic and can be easily compatible with various VideoQA backbones. Extensive experimental results and analyses on several public datasets show the advantage of our method over the state-of-the-art methods in terms of both accuracy and robustness.
Accurate prediction of the state-of-charge(SOC)of battery energy storage system(BESS)is critical for its safety and lifespan in electric *** overcome the imbalance of existing methods between multi-scale feature fusio...
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Accurate prediction of the state-of-charge(SOC)of battery energy storage system(BESS)is critical for its safety and lifespan in electric *** overcome the imbalance of existing methods between multi-scale feature fusion and global feature extraction,this paper introduces a novel multi-scale fusion(MSF)model based on gated recurrent unit(GRU),which is specifically designed for complex multi-step SOC prediction in practical *** correlation analysis is first employed to identify SOC-related *** parameters are then input into a multi-layer GRU for point-wise feature ***,the parameters undergo patching before entering a dual-stage multi-layer GRU,thus enabling the model to capture nuanced information across varying time ***,by means of adaptive weight fusion and a fully connected network,multi-step SOC predictions are *** extensive validation over multiple days,it is illustrated that the proposed model achieves an absolute error of less than 1.5%in real-time SOC prediction.
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