Edge computing, which achieves quick data processing by sinking data computing and storage to the network edge, has grown rapidly along with the Internet of things. The new network architecture of edge computing bring...
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Edge computing, which achieves quick data processing by sinking data computing and storage to the network edge, has grown rapidly along with the Internet of things. The new network architecture of edge computing brings new security challenges. Based on this, this paper investigates the edge computing security literature published in recent years and summarizes and analyzes research work on edge computing security from different attack surfaces. We start with the definition and architecture of edge computing. From the attack surface between device and edge server, as well as on edge servers, the research describes the security threats anddefense methods of edge computing. In addition, the cause of the attack and the pros and cons of defense methods is introduced. The challenges and future research directions of edge computing are given.
With the development of knowledge graphs, a series of applications based on knowledge graphs have emerged. The incompleteness of knowledge graphs makes the effect of the downstream applications affected by the quality...
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With the development of knowledge graphs, a series of applications based on knowledge graphs have emerged. The incompleteness of knowledge graphs makes the effect of the downstream applications affected by the quality of the knowledge graphs. To improve the quality of knowledge graphs, translation-based graph embeddings such as TransE, learn structural information by representing triples as low-dimensional dense vectors. However, it is difficult to generalize to the unseen entities that are not observedduring training but appearduring testing. Other methods use the powerful representational ability of pre-trained language models to learn entity descriptions and contextual representation of triples. Although they are robust to incompleteness, they need to calculate the score of all candidate entities for each triple during inference. We consider combining two models to enhance the robustness of unseen entities by semantic information, and prevent combined explosion by reducing inference overhead through structured information. We use a pre-training language model to code triples and learn the semantic information within them, and use a hyperbolic space-baseddistance model to learn structural information, then integrate the two types of information together. We evaluate our model by performing link prediction experiments on standarddatasets. The experimental results show that our model achieves better performances than state-of-the-art methods on two standarddatasets.
Multi-modal Named Entity recognition(MNEr)aims to better identify meaningful textual entities by integrating information from *** work has focused on extracting visual semantics at a fine-grained level,or obtaining en...
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Multi-modal Named Entity recognition(MNEr)aims to better identify meaningful textual entities by integrating information from *** work has focused on extracting visual semantics at a fine-grained level,or obtaining entity related external knowledge from knowledge bases or Large Language Models(LLMs).However,these approaches ignore the poor semantic correlation between visual and textual modalities in MNErdatasets anddo not explore different multi-modal fusion *** this paper,we present MMAVK,a multi-modal named entity recognition model with auxiliary visual knowledge and word-level fusion,which aims to leverage the Multi-modal Large Language Model(MLLM)as an implicit knowledge *** also extracts vision-based auxiliary knowledge from the image formore accurate and effective ***,we propose vision-based auxiliary knowledge generation,which guides the MLLM to extract external knowledge exclusively derived from images to aid entity recognition by designing target-specific prompts,thus avoiding redundant recognition and cognitive confusion caused by the simultaneous processing of image-text ***,we employ a word-level multi-modal fusion mechanism to fuse the extracted external knowledge with each word-embedding embedded from the transformerbased *** experimental results demonstrate that MMAVK outperforms or equals the state-of-the-art methods on the two classical MNErdatasets,even when the largemodels employed have significantly fewer parameters than other baselines.
Multi-modal knowledge graph completion(MMKGC)aims to complete missing entities orrelations in multi-modal knowledge graphs,thereby discovering more previously unknown *** to the continuous growth of data and knowledg...
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Multi-modal knowledge graph completion(MMKGC)aims to complete missing entities orrelations in multi-modal knowledge graphs,thereby discovering more previously unknown *** to the continuous growth of data and knowledge and the limitations of data sources,the visual knowledge within the knowledge graphs is generally of low quality,and some entities suffer from the issue of missing visual ***,previous studies of MMKGC have primarily focused on how to facilitate modality interaction and fusion while neglecting the problems of low modality quality and modality *** this case,mainstream MMKGC models only use pre-trained visual encoders to extract features and transfer the semantic information to the joint embeddings through modal fusion,which inevitably suffers from problems such as error propagation and increased *** address these problems,we propose a Multi-modal knowledge graph Completion model based on Super-resolution anddetaileddescription Generation(MMCSd).Specifically,we leverage a pre-trainedresidual network to enhance the resolution and improve the quality of the visual ***,we design multi-level visual semantic extraction and entity description generation,thereby further extracting entity semantics from structural triples and visual ***,we train a variational multi-modal auto-encoder and utilize a pre-trained multi-modal language model to complement the missing visual *** conducted experiments on FB15K-237 anddB13K,and the results showed that MMCSd can effectively perform MMKGC and achieve state-of-the-art performance.
In this study,a triple spark ignition scheme was first designed on a three-cylinder 1.5-L dedicated hybrid engine(dHE).On this basis,the effect of different ignition modes on engine combustion and emission characteris...
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In this study,a triple spark ignition scheme was first designed on a three-cylinder 1.5-L dedicated hybrid engine(dHE).On this basis,the effect of different ignition modes on engine combustion and emission characteristics was studied,especially under high dilution *** results tested at 2000 r/min and 0.8 MPa BMEP(brake mean effective pressure)show that with highly increased in-cylinder flow intensity,using only passive prechamber(PPC)has a lower lean limit than that with single central spark plug(CSP),thereby leading to slightly higher minimum fuel consumption and nitrogen oxides(NO_(x))*** side spark plugs(SSP)based on PPC can result in improved capability of lean limit extension and engine performance than ***,the improvement level is lower than that with triple spark plugs(TSP).As the excess airratio λ increases,the advantage of PPC and PPC with SSP in improving the combustion phasing compared with CSP gradually ***,the increasing tendency of their ignition delay and combustion duration is more *** added SSP based on PPC can effectively shorten the ignition delay of leaner mixture,but the combustion duration can be only slightly *** a result,under extremely lean condition,the advantage of PPC and PPC with SSP in terms of combustion characteristics over CSP becomes much *** contrast,the TSP ignition can achieve much shorter ignition delay and combustion duration simultaneously under this *** to the highest available dilution level,the TSP ignition achieves the lowest raw NO_(x)***,it can also reduce the raw carbon monoxide(CO)and hydrocarbons(HC)emissions compared to CSP due to a more thorough combustion of the end gas *** on the excellent performance of TSP,the highest engine brake thermal efficiency(BTE)was further *** results show that with normal rON 92 fuel,the engine finally achieved 43.69% and 45.02% BTE under stochiometric mode with exhaus
Adversarial distillation(Ad)has emerged as a potential solution to tackle the challenging optimization problem of loss with hard labels in adversarial ***,fixed sample-agnostic and student-egocentric attack strategies...
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Adversarial distillation(Ad)has emerged as a potential solution to tackle the challenging optimization problem of loss with hard labels in adversarial ***,fixed sample-agnostic and student-egocentric attack strategies are unsuitable for ***,the reliability of guidance from static teachers diminishes as target models become more *** paper proposes an Ad method called Learnable distillation Attack Strategies and Evolvable Teachers Adversarial distillation(LdAS&ET-Ad).Firstly,a learnable distillation attack strategies generating mechanism is developed to automatically generate sample-dependent attack strategies tailored fordistillation.A strategy model is introduced to produce attack strategies that enable adversarial examples(AEs)to be created in areas where the target model significantly diverges from the teachers by competing with the target model in minimizing or maximizing the Ad ***,a teacher evolution strategy is introduced to enhance the reliability and effectiveness of knowledge in improving the generalization performance of the target *** calculating the experimentally updated target model’s validation performance on both clean samples and AEs,the impact of distillation from each training sample and AE on the target model’s generalization androbustness abilities is assessed to serve as feedback to fine-tune standard androbust teachers *** evaluate the performance of LdAS&ET-Ad against different adversarial attacks on the CIFAr-10 and CIFAr-100 *** experimental results demonstrate that the proposed method achieves a robust precision of 45.39%and 42.63%against AutoAttack(AA)on the CIFAr-10 dataset forresNet-18 and MobileNet-V2,respectively,marking an improvement of 2.31%and 3.49%over the baseline *** comparison to state-of-the-art adversarial defense techniques,our method surpasses Introspective Adversarial distillation,the top-performing method in terms of robustne
The high-speed maglev vehicle/guideway coupled model is an essential simulation tool for investigating vehicle dynamics and mitigating coupled *** improve its accuracy efficiently,this study investigated a hierarchica...
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The high-speed maglev vehicle/guideway coupled model is an essential simulation tool for investigating vehicle dynamics and mitigating coupled *** improve its accuracy efficiently,this study investigated a hierarchical model updating method integrated with field ***,a high-speed maglev vehicle/guideway coupled model,taking into account the real effect of guideway material properties and elastic restraint of bearings,was developed by integrating the finite element method,multi-body dynamics,and electromagnetic levitation ***,simultaneous in-site measurements of the vehicle/guideway were conducted on a high-speed maglev test line to analyze the system response and structural modal *** the hierarchical updating,an Elman neural network with the optimal Latin hypercube sampling method was used to substitute the FE guideway model,thus improving the computational *** multi-objective particle swarm optimization algorithm with the gray relational projection method was applied to hierarchically update the parameters of the guideway layer and magnetic force layer based on the measured modal parameters and the electromagnet vibration,***,the updated coupled model was compared with the field measurements,and the results demonstrated the model’s accuracy in simulating the actual dynamic response,validating the effectiveness of the updating method.
The moisture generated by the ageing of the oil-paper insulating system can easily lead to the deterioration of the performance of the insulating *** order to alleviate the impact of moisture on the performance of the...
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The moisture generated by the ageing of the oil-paper insulating system can easily lead to the deterioration of the performance of the insulating *** order to alleviate the impact of moisture on the performance of the insulating paper,we developed a kind of lipophilic hydrophobic self-repairing cellulose insulating paper(new insulating paper)based on the ternary system of polytetrafluoroethylene,fluorocarbon surfactant and fluorinated alkyl silane,and accelerated thermal ageing test for 45 days at 130°C was carried *** experimental results show that the new insulating paper with excellent lipophilic property can realise hydrophobic self-repairing through heat treatment after physical *** tensile strength and elongation at break of the new insulating paper increased by 5%–8%and 6.25%–27%respectively compared with the original cellulose insulating *** the end of ageing,the relative dielectric constant anddielectric loss factor of new insulating paper are 10.4%and 9.9%lower than that of the original cel-lulose insulating paper,while the breakdown voltage is 6.8%*** acid and moisture content producedduring the ageing process of the insulating oil impregnated with new insulating paper are also less,indicating that the coating can delay the ageing of the insulating paper.
Chemical looping oxidative dehydrogenation (CL-OdH) is an economically promising method for convertingethane into higher value-added ethylene utilizing lattice oxygen in redox catalysts, also known as oxygen carriers....
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Chemical looping oxidative dehydrogenation (CL-OdH) is an economically promising method for convertingethane into higher value-added ethylene utilizing lattice oxygen in redox catalysts, also known as oxygen carriers. Inthis study, perovskite-type oxide SrCoO_(3-δ) and B-site Mn ion-doped oxygen carriers (SrCo_(1-x)MnxO_(3-δ), x=0.1, 0.2, 0.3)were prepared and tested for the CL-OdH of ethane. The oxygen-deficient perovskite SrCoO_(3-δ) exhibited high ethyleneselectivity of up to 96.7% due to its unique oxygen vacancies and lattice oxygen migration rates. However, its low ethyleneyield limits its application in the CL-OdH of ethane. Mn doping promoted the reducibility of SrCoO_(3-δ) oxygen carriers,thereby improving ethane conversion and ethylene yield, as demonstrated by characterization and evaluation experiments.X-ray diffraction results confirmed the doping of Mn into the lattice of SrCoO_(3-δ), while X-ray photoelectron spectroscopy(XPS) indicated an increase in lattice oxygen ratio upon incorporation of Mn into the SrCoO_(3-δ) lattice. Additionally, H2temperature-programmedreduction (H2-TPr) tests revealed more peaks at lower temperature reduction zones and a declinein peak positions at higher temperatures. Among the four tested oxygen carriers, SrCo0.8Mn0.2O_(3-δ) exhibited satisfactoryperformance with an ethylene yield of 50% at 710 °C and good stability over 20 redox cycles. The synergistic effect of Mnplays a key role in increasing ethylene yields of SrCoO_(3-δ) oxygen carriers. Accordingly, SrCo0.8Mn0.2O_(3-δ) shows promisingpotential for the efficient production of ethylene from ethane via CL-OdH.
As one of the efforts to reduce CO2 emissions, concrete containing high-volume mineral admixtures is being studied, and the authors are also developing high-strength concrete without Portland cement. This paperreport...
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