Quantum circuit fidelity is a crucial metric for assessing the accuracy of quantum computation results and indicating the precision of quantum algorithm execution. The primary methods for assessing quantum circuit fid...Quantum circuit fidelity is a crucial metric for assessing the accuracy of quantum computation results and indicating the precision of quantum algorithm execution. The primary methods for assessing quantum circuit fidelity include direct fidelity estimation and mirror circuit fidelity estimation. The former is challenging to implement in practice, while the latter requires substantial classical computational resources and numerous experimental runs. In this paper, we propose a fidelity estimation method based on Layer Interleaved Randomized Benchmarking, which decomposes a complex quantum circuit into multiple sublayers. By independently evaluating the fidelity of each layer, one can comprehensively assess the performance of the entire quantum circuit. This layered evaluation strategy not only enhances accuracy but also effectively identifies and analyzes errors in specific quantum gates or qubits through independent layer evaluation. Simulation results demonstrate that the proposed method improves circuit fidelity by an average of 6.8% and 4.1% compared to Layer Randomized Benchmarking and Interleaved Randomized Benchmarking methods in a thermal relaxation noise environment, and by 40% compared to Layer RB in a bit-flip noise environment. Moreover, the method detects preset faulty quantum gates in circuits generated by the Munich Quantum Toolkit Benchmark, verifying the model’s validity and providing a new tool for faulty gate detection in quantum circuits.展开更多
Medical visual question answering(MedVQA)faces unique challenges due to the high precision required for images and the specialized nature of the questions.These challenges include insufficient feature extraction capab...Medical visual question answering(MedVQA)faces unique challenges due to the high precision required for images and the specialized nature of the questions.These challenges include insufficient feature extraction capabilities,a lack of textual priors,and incomplete information fusion and interaction.This paper proposes an enhanced bootstrapping language-image pre-training(BLIP)model for MedVQA based on multimodal feature augmentation and triple-path collaborative attention(FCA-BLIP)to address these issues.First,FCA-BLIP employs a unified bootstrap multimodal model architecture that integrates ResNet and bidirectional encoder representations from Transformer(BERT)models to enhance feature extraction capabilities.It enables a more precise analysis of the details in images and questions.Next,the pre-trained BLIP model is used to extract features from image-text sample pairs.The model can understand the semantic relationships and shared information between images and text.Finally,a novel attention structure is developed to fuse the multimodal feature vectors,thereby improving the alignment accuracy between modalities.Experimental results demonstrate that the proposed method performs well in clinical visual question-answering tasks.For the MedVQA task of staging diabetic macular edema in fundus imaging,the proposed method outperforms the existing major models in several performance metrics.展开更多
Studies to enhance the management of electrical energy have gained considerable momentum in recent years. The question of how much energy will be needed in households is a pressing issue as it allows the management pl...Studies to enhance the management of electrical energy have gained considerable momentum in recent years. The question of how much energy will be needed in households is a pressing issue as it allows the management plan of the available resources at the power grids and consumer levels. A non-intrusive inference process can be adopted to predict the amount of energy required by appliances. In this study, an inference process of appliance consumption based on temporal and environmental factors used as a soft sensor is proposed. First, a study of the correlation between the electrical and environmental variables is presented. Then, a resampling process is applied to the initial data set to generate three other subsets of data. All the subsets were evaluated to deduce the adequate granularity for the prediction of the energy demand. Then, a cloud-assisted deep neural network model is designed to forecast short-term energy consumption in a residential area while preserving user privacy. The solution is applied to the consumption data of four appliances elected from a set of real household power data. The experiment results show that the proposed framework is effective for estimating consumption with convincing accuracy.展开更多
TarGuess-I is a leading model utilizing Personally Identifiable Information for online targeted password guessing.Due to its remarkable guessing performance,the model has drawn considerable attention in password secur...TarGuess-I is a leading model utilizing Personally Identifiable Information for online targeted password guessing.Due to its remarkable guessing performance,the model has drawn considerable attention in password security research.However,through an analysis of the vulnerable behavior of users when constructing passwords by combining popular passwords with their Personally Identifiable Information,we identified that the model fails to consider popular passwords and frequent substrings,and it uses overly broad personal information categories,with extensive duplicate statistics.To address these issues,we propose an improved password guessing model,TGI-FPR,which incorporates three semantic methods:(1)identification of popular passwords by generating top 300 lists from similar websites,(2)use of frequent substrings as new grammatical labels to capture finer-grained password structures,and(3)further subdivision of the six major categories of personal information.To evaluate the performance of the proposed model,we conducted experiments on six large-scale real-world password leak datasets and compared its accuracy within the first 100 guesses to that of TarGuess-I.The results indicate a 2.65%improvement in guessing accuracy.展开更多
Computer-vision and deep-learning techniques are widely applied to detect,monitor,and assess pavement conditions including road crack detection.Traditional methods fail to achieve satisfactory accuracy and generalizat...Computer-vision and deep-learning techniques are widely applied to detect,monitor,and assess pavement conditions including road crack detection.Traditional methods fail to achieve satisfactory accuracy and generalization performance in for crack detection.Complex network model can generate redundant feature maps and computational complexity.Therefore,this paper proposes a novel model compression framework based on deep learning to detect road cracks,which can improve the detection efficiency and accuracy.A distillation loss function is proposed to compress the teacher model,followed by channel pruning.Meanwhile,a multi-dilation model is proposed to improve the accuracy of the model pruned.The proposed method is tested on the public database CrackForest dataset(CFD).The experimental results show that the proposed method is more efficient and accurate than other state-of-art methods.展开更多
Lithium-rich layered oxides (LLOs) are increasingly recognized as promising cathode materials for nextgeneration high-energy-density lithium-ion batteries (LIBs).However,they suffer from voltage decay and low initial ...Lithium-rich layered oxides (LLOs) are increasingly recognized as promising cathode materials for nextgeneration high-energy-density lithium-ion batteries (LIBs).However,they suffer from voltage decay and low initial Coulombic efficiency (ICE) due to severe structural degradation caused by irreversible O release.Herein,we introduce a three-in-one strategy of increasing Ni and Mn content,along with Li/Ni disordering and TM–O covalency regulation to boost cationic and anionic redox activity simultaneously and thus enhance the electrochemical activity of LLOs.The target material,Li_(1.2)Ni_(0.168)Mn_(0.558)Co_(0.074)O_(2)(L1),exhibits an improved ICE of 87.2%and specific capacity of 293.2 mA h g^(-1)and minimal voltage decay of less than 0.53 m V cycle-1over 300 cycles at 1C,compared to Li_(1.2)Ni_(0.13)Mn_(0.54)Co_(0.13)O_(2)(Ls)(274.4 mA h g^(-1)for initial capacity,73.8%for ICE and voltage decay of 0.84 mV/cycle over 300 cycles at 1C).Theoretical calculations reveal that the density of states (DOS) area near the Fermi energy level for L1 is larger than that of Ls,indicating higher anionic and cationic redox reactivity than Ls.Moreover,L1 exhibits increased O-vacancy formation energy due to higher Li/Ni disordering of 4.76%(quantified by X-ray diffraction Rietveld refinement) and enhanced TM–O covalency,making lattice O release more difficult and thus improving electrochemical stability.The increased Li/Ni disordering also leads to more Ni^(2+)presence in the Li layer,which acts as a pillar during Li+de-embedding,improving structural stability.This research not only presents a viable approach to designing low-Co LLOs with enhanced capacity and ICE but also contributes significantly to the fundamental understanding of structural regulation in high-performance LIB cathodes.展开更多
Automatic return oriented programming （ROP） technology can effectively improve the efficiency of ROP constructed, but the existing research results still have some shortcomings including needing more address space, ...Automatic return oriented programming （ROP） technology can effectively improve the efficiency of ROP constructed, but the existing research results still have some shortcomings including needing more address space, poor generality. In order to solve these problems, this paper presents an improved ROP auto-constructor QExtd. Firstly, we design a Turing-complete language QExtdL and provide the basis of gadgets analysis. Secondly, we represent the MI instruction and realize precise process of side-effect instructions for improving the efficiency of automatic construction. At last, we establish a three-layer language conversion mechanism, making it convenient for users to construct ROP. Theoretical and experimental data show that the QExtd automatic construction method is much better than the ROPgadget based on syntax. In addition, the proposed method succeeds in constructing gadgets of ROP with the probability of 84% for programs whose sizes are more than 20 KB and whose directory is ＂/usr/bin＂ in Ubuntu, which proves that the construction capability improves significantly.展开更多
Automatic pavement crack detection plays an important role in ensuring road safety.In images of cracks,information about the cracks can be conveyed through high-frequency and low-fre-quency signals that focus on fine ...Automatic pavement crack detection plays an important role in ensuring road safety.In images of cracks,information about the cracks can be conveyed through high-frequency and low-fre-quency signals that focus on fine details and global structures,respectively.The output features obtained from different convolutional layers can be combined to represent information about both high-frequency and low-frequency signals.In this paper,we propose an encoder-decoder framework called octave hierarchical network(Octave-H),which is based on the U-Network(U-Net)architec-ture and utilizes an octave convolutional neural network and a hierarchical feature learning module for performing crack detection.The proposed octave convolution is capable of extracting multi-fre-quency feature maps,capturing both fine details and global cracks.We propose a hierarchical feature learning module that merges multi-frequency-scale feature maps with different levels(high and low)of octave convolutional layers.To verify the superiority of the proposed Octave-H,we employed the CrackForest dataset(CFD)and AigleRN databases to evaluate this method.The experimental results demonstrate that Octave-H outperforms other algorithms with satisfactory performance.展开更多
In recent years,the rapid development of computer software has led to numerous security problems,particularly software vulnerabilities.These flaws can cause significant harm to users’privacy and property.Current secu...In recent years,the rapid development of computer software has led to numerous security problems,particularly software vulnerabilities.These flaws can cause significant harm to users’privacy and property.Current security defect detection technology relies on manual or professional reasoning,leading to missed detection and high false detection rates.Artificial intelligence technology has led to the development of neural network models based on machine learning or deep learning to intelligently mine holes,reducing missed alarms and false alarms.So,this project aims to study Java source code defect detection methods for defects like null pointer reference exception,XSS(Transform),and Structured Query Language(SQL)injection.Also,the project uses open-source Javalang to translate the Java source code,conducts a deep search on the AST to obtain the empty syntax feature library,and converts the Java source code into a dependency graph.The feature vector is then used as the learning target for the neural network.Four types of Convolutional Neural Networks(CNN),Long Short-Term Memory(LSTM),Bi-directional Long Short-Term Memory(BiLSTM),and Attention Mechanism+Bidirectional LSTM,are used to investigate various code defects,including blank pointer reference exception,XSS,and SQL injection defects.Experimental results show that the attention mechanism in two-dimensional BLSTM is the most effective for object recognition,verifying the correctness of the method.展开更多
With the increasing proportion of encrypted traffic in cyberspace, the classification of encrypted traffic has becomea core key technology in network supervision. In recent years, many different solutions have emerged...With the increasing proportion of encrypted traffic in cyberspace, the classification of encrypted traffic has becomea core key technology in network supervision. In recent years, many different solutions have emerged in this field.Most methods identify and classify traffic by extracting spatiotemporal characteristics of data flows or byte-levelfeatures of packets. However, due to changes in data transmission mediums, such as fiber optics and satellites,temporal features can exhibit significant variations due to changes in communication links and transmissionquality. Additionally, partial spatial features can change due to reasons like data reordering and retransmission.Faced with these challenges, identifying encrypted traffic solely based on packet byte-level features is significantlydifficult. To address this, we propose a universal packet-level encrypted traffic identification method, ComboPacket. This method utilizes convolutional neural networks to extract deep features of the current packet andits contextual information and employs spatial and channel attention mechanisms to select and locate effectivefeatures. Experimental data shows that Combo Packet can effectively distinguish between encrypted traffic servicecategories (e.g., File Transfer Protocol, FTP, and Peer-to-Peer, P2P) and encrypted traffic application categories (e.g.,BitTorrent and Skype). Validated on the ISCX VPN-non VPN dataset, it achieves classification accuracies of 97.0%and 97.1% for service and application categories, respectively. It also provides shorter training times and higherrecognition speeds. The performance and recognition capabilities of Combo Packet are significantly superior tothe existing classification methods mentioned.展开更多
Geolocating social media users aims to discover the real geographical locations of users from their publicly available data,which can support online location-based applications such as disaster alerts and local conten...Geolocating social media users aims to discover the real geographical locations of users from their publicly available data,which can support online location-based applications such as disaster alerts and local content recommen-dations.Social relationship-based methods represent a classical approach for geolocating social media.However,geographically proximate relationships are sparse and challenging to discern within social networks,thereby affecting the accuracy of user geolocation.To address this challenge,we propose user geolocation methods that integrate neighborhood geographical distribution and social structure influence(NGSI)to improve geolocation accuracy.Firstly,we propose a method for evaluating the homophily of locations based on the k-order neighbor-hood geographic distribution(k-NGD)similarity among users.There are notable differences in the distribution of k-NGD similarity between location-proximate and non-location-proximate users.Exploiting this distinction,we filter out non-location-proximate social relationships to enhance location homophily in the social network.To better utilize the location-proximate relationships in social networks,we propose a graph neural network algorithm based on the social structure influence.The algorithm enables us to perform a weighted aggregation of the information of users’multi-hop neighborhood,thereby mitigating the over-smoothing problem of user features and improving user geolocation performance.Experimental results on real social media dataset demonstrate that the neighborhood geographical distribution similarity metric can effectively filter out non-location-proximate social relationships.Moreover,compared with 7 existing social relationship-based user positioning methods,our proposed method can achieve multi-granularity user geolocation and improve the accuracy by 4.84%to 13.28%.展开更多
With the rapid advancement in exploring perceptual interactions and digital twins,metaverse technology has emerged to transcend the constraints of space-time and reality,facilitating remote AI-based collaboration.In t...With the rapid advancement in exploring perceptual interactions and digital twins,metaverse technology has emerged to transcend the constraints of space-time and reality,facilitating remote AI-based collaboration.In this dynamic metasystem environment,frequent information exchanges necessitate robust security measures,with Authentication and Key Agreement(AKA)serving as the primary line of defense to ensure communication security.However,traditional AKA protocols fall short in meeting the low-latency requirements essential for synchronous interactions within the metaverse.To address this challenge and enable nearly latency-free interactions,a novel low-latency AKA protocol based on chaotic maps is proposed.This protocol not only ensures mutual authentication of entities within the metasystem but also generates secure session keys.The security of these session keys is rigorously validated through formal proofs,formal verification,and informal proofs.When confronted with the Dolev-Yao(DY)threat model,the session keys are formally demonstrated to be secure under the Real-or-Random(ROR)model.The proposed protocol is further validated through simulations conducted using VMware workstation compiled in HLPSL language and C language.The simulation results affirm the protocol’s effectiveness in resisting well-known attacks while achieving the desired low latency for optimal metaverse interactions.展开更多
In order to improve the models capability in expressing features during few-shot learning,a multi-scale features prototypical network(MS-PN)algorithm is proposed.The metric learning algo-rithm is employed to extract i...In order to improve the models capability in expressing features during few-shot learning,a multi-scale features prototypical network(MS-PN)algorithm is proposed.The metric learning algo-rithm is employed to extract image features and project them into a feature space,thus evaluating the similarity between samples based on their relative distances within the metric space.To sufficiently extract feature information from limited sample data and mitigate the impact of constrained data vol-ume,a multi-scale feature extraction network is presented to capture data features at various scales during the process of image feature extraction.Additionally,the position of the prototype is fine-tuned by assigning weights to data points to mitigate the influence of outliers on the experiment.The loss function integrates contrastive loss and label-smoothing to bring similar data points closer and separate dissimilar data points within the metric space.Experimental evaluations are conducted on small-sample datasets mini-ImageNet and CUB200-2011.The method in this paper can achieve higher classification accuracy.Specifically,in the 5-way 1-shot experiment,classification accuracy reaches 50.13%and 66.79%respectively on these two datasets.Moreover,in the 5-way 5-shot ex-periment,accuracy of 66.79%and 85.91%are observed,respectively.展开更多
In this study,the separation and coalescence of oil-in-water emulsions are explored in an ultrasonic field using the lattice Boltzmann method.By simulating the propagation of ultrasonic waves,this study focuses on exa...In this study,the separation and coalescence of oil-in-water emulsions are explored in an ultrasonic field using the lattice Boltzmann method.By simulating the propagation of ultrasonic waves,this study focuses on examining the effects of acoustic wave frequency,the ratio of oil to water components,and the aspect ratio of the boundary on the emulsification and separation processes of oil-water mixtures.The following conclusions are drawn.①Frequency affects the speed of oil droplet separation,leading to an increase in droplet size over time.Larger droplets are found near the source,while smaller droplets are distributed throughout the wave web.②As the boundary aspect ratio increases,the emulsification efficiency of the droplets weakens,and the system takes longer to stabilize.③Emulsions with a higher component of oil can better resist acoustic waves.④At the same acoustic frequency,longer wavelength ultrasonic fields promote the formation of uniformly distributed,smaller oil droplets,which is beneficial to the storage of emulsions.These numerical simulation results offer insights for optimizing conditions for oil-in-water separation and serve as a numerical reference for the study of oil-in-water emulsion separation in ultrasonic environments.展开更多
To solve the shortcomings of Particle Swarm Optimization(PSO)algorithm,local optimization and slow convergence,an Opposition-based Learning Adaptive Chaotic PSO(LCPSO)algorithm was presented.The chaotic elite oppositi...To solve the shortcomings of Particle Swarm Optimization(PSO)algorithm,local optimization and slow convergence,an Opposition-based Learning Adaptive Chaotic PSO(LCPSO)algorithm was presented.The chaotic elite opposition-based learning process was applied to initialize the entire population,which enhanced the quality of the initial individuals and the population diversity,made the initial individuals distribute in the better quality areas,and accelerated the search efficiency of the algorithm.The inertia weights were adaptively customized during evolution in the light of the degree of premature convergence to balance the local and global search abilities of the algorithm,and the reverse search strategy was introduced to increase the chances of the algorithm escaping the local optimum.The LCPSO algorithm is contrasted to other intelligent algorithms on 10 benchmark test functions with different characteristics,and the simulation experiments display that the proposed algorithm is superior to other intelligence algorithms in the global search ability,search accuracy and convergence speed.In addition,the robustness and effectiveness of the proposed algorithm are also verified by the simulation results of engineering design problems.展开更多
Co-free Li-rich layered oxides(LLOs)are emerging as promising cathode materials for Li-ion batteries due to their low cost and high capacity.However,they commonly face severe structural instability and poor electroche...Co-free Li-rich layered oxides(LLOs)are emerging as promising cathode materials for Li-ion batteries due to their low cost and high capacity.However,they commonly face severe structural instability and poor electrochemical activity,leading to diminished capacity and voltage performance.Herein,we introduce a Co-free LLO,Li_(1.167)Ni_(0.222)Mn_(0.611)O_(2)(Cf-L1),which features a cooperative structure of Li/Ni mixing and stacking faults.This structure regulates the crystal and electronic structures,resulting in a higher discharge capacity of 300.6 mA h g^(-1)and enhanced rate capability compared to the typical Co-free LLO,Li_(1.2)Ni_(0.2)Mn_(0.6)O_(2)(Cf-Ls).Density functional theory(DFT)indicates that Li/Ni mixing in LLOs leads to increased Li-O-Li configurations and higher anionic redox activities,while stacking faults further optimize the electronic interactions of transition metal(TM)3d and non-bonding O 2p orbitals.Moreover,stacking faults accommodate lattice strain,improving electrochemical reversibility during charge/discharge cycles,as demonstrated by the in situ XRD of Cf-L1 showing less lattice evolution than Cf-Ls.This study offers a structured approach to developing Co-free LLOs with enhanced capacity,voltage,rate capability,and cyclability,significantly impacting the advancement of the next-generation Li-ion batteries.展开更多
This paper presents the design,calibration,and survey strategy of the Fast Radio Burst(FRB)digital backend and its real-time data processing pipeline employed in the Tianlai Cylinder Pathfinder Array.The array,consist...This paper presents the design,calibration,and survey strategy of the Fast Radio Burst(FRB)digital backend and its real-time data processing pipeline employed in the Tianlai Cylinder Pathfinder Array.The array,consisting of three parallel cylindrical reflectors and equipped with 96 dual-polarization feeds,is a radio interferometer array designed for conducting drift scans of the northern celestial semi-sphere.The FRB digital backend enables the formation of 96 digital beams,effectively covering an area of approximately 40 square degrees with the 3 dB beam.Our pipeline demonstrates the capability to conduct an automatic search of FRBs,detecting at quasi-realtime and classifying FRB candidates automatically.The current FRB searching pipeline has an overall recall rate of88%.During the commissioning phase,we successfully detected signals emitted by four well-known pulsars:PSR B0329+54,B2021+51,B0823+26,and B2020+28.We report the first discovery of an FRB by our array,designated as FRB 20220414A.We also investigate the optimal arrangement for the digitally formed beams to achieve maximum detection rate by numerical simulation.展开更多
Adopting digital twin technology in the chemical industry is reshaping process optimisation,operational efficiency,and safety management.By leveraging data from sensors and control systems,the digital twins provide ac...Adopting digital twin technology in the chemical industry is reshaping process optimisation,operational efficiency,and safety management.By leveraging data from sensors and control systems,the digital twins provide actionable insights,enabling more precise control over chemical reactions,improved quality assurance,and reduced environmental impact.Additionally,the ability to simulate“what-if”scenarios accelerates the innovation cycle and supports compliance with stringent regulatory standards.This research article explores the implementation and impact of digital twins in chemical manufacturing environments.It examines how digital twins enable continuous monitoring and control by mirroring chemical processes,predicting equipment failures,and simulating complex reactions under various conditions.The study highlights the benefits of digital twins,including improved process efficiency,enhanced product quality,and reduced environmental and operational risks.The research also addresses challenges and limitations,such as data integration complexities and the need for high-fidelity models.By providing a comprehensive analysis of current applications and future prospects,this paper aims to advance the understanding of digital twins'role in driving innovation and sustainability within the chemical industry.展开更多
High-entropy carbide ceramics (HECs) have drawn increasing attention as their excellent mechanical and thermal properties. In this work, the crystal stability,mechanical behavior, electronic and thermodynamic properti...High-entropy carbide ceramics (HECs) have drawn increasing attention as their excellent mechanical and thermal properties. In this work, the crystal stability,mechanical behavior, electronic and thermodynamic properties of (TiZrNbTa)C HEC are investigated by the first-principles calculations. Obtained results reveal that the disordered transition-metal (TM) atoms result in serious local lattice distortion within the crystal. The lattice distortion plays a key role for the structural stabilization,mechanical anisotropy and thermodynamic behaviors of(TiZrNbTa)C. Increasing pressure leads to decrease the lattice parameter, volume and brittleness, meanwhile increase the elastic constants, elastic moduli, mechanical anisotropy, sound velocity, and Debye temperature. It is also discovered that charge delocalization occurs with the increase in pressure. The mechanical stability and anisotropy of (TiZrNbTa)C are attributed primarily to TM-C bonding.展开更多
A novel numerical method is explored and named as mesh-free poly-cell Galerkin method. An improved moving least-square （MLS） scheme is presented, which can avoid the matrix inversion in standard MLS and can be used ...A novel numerical method is explored and named as mesh-free poly-cell Galerkin method. An improved moving least-square （MLS） scheme is presented, which can avoid the matrix inversion in standard MLS and can be used to construct shape functions possessing delta Kronecher property. A new type of local support is introduced to ensure the alignment of integral domains with the cells of the back-ground mesh, which will reduce the difficult in integration. An intensive numerical study is conducted to test the accuracy of the present method. It is observed that solutions with good accuracy can be obtained with the present method.展开更多
文摘Quantum circuit fidelity is a crucial metric for assessing the accuracy of quantum computation results and indicating the precision of quantum algorithm execution. The primary methods for assessing quantum circuit fidelity include direct fidelity estimation and mirror circuit fidelity estimation. The former is challenging to implement in practice, while the latter requires substantial classical computational resources and numerous experimental runs. In this paper, we propose a fidelity estimation method based on Layer Interleaved Randomized Benchmarking, which decomposes a complex quantum circuit into multiple sublayers. By independently evaluating the fidelity of each layer, one can comprehensively assess the performance of the entire quantum circuit. This layered evaluation strategy not only enhances accuracy but also effectively identifies and analyzes errors in specific quantum gates or qubits through independent layer evaluation. Simulation results demonstrate that the proposed method improves circuit fidelity by an average of 6.8% and 4.1% compared to Layer Randomized Benchmarking and Interleaved Randomized Benchmarking methods in a thermal relaxation noise environment, and by 40% compared to Layer RB in a bit-flip noise environment. Moreover, the method detects preset faulty quantum gates in circuits generated by the Munich Quantum Toolkit Benchmark, verifying the model’s validity and providing a new tool for faulty gate detection in quantum circuits.
基金Supported by the Program for Liaoning Excellent Talents in University(No.LR15045)the Liaoning Provincial Science and Technology Department Applied Basic Research Plan(No.101300243).
文摘Medical visual question answering(MedVQA)faces unique challenges due to the high precision required for images and the specialized nature of the questions.These challenges include insufficient feature extraction capabilities,a lack of textual priors,and incomplete information fusion and interaction.This paper proposes an enhanced bootstrapping language-image pre-training(BLIP)model for MedVQA based on multimodal feature augmentation and triple-path collaborative attention(FCA-BLIP)to address these issues.First,FCA-BLIP employs a unified bootstrap multimodal model architecture that integrates ResNet and bidirectional encoder representations from Transformer(BERT)models to enhance feature extraction capabilities.It enables a more precise analysis of the details in images and questions.Next,the pre-trained BLIP model is used to extract features from image-text sample pairs.The model can understand the semantic relationships and shared information between images and text.Finally,a novel attention structure is developed to fuse the multimodal feature vectors,thereby improving the alignment accuracy between modalities.Experimental results demonstrate that the proposed method performs well in clinical visual question-answering tasks.For the MedVQA task of staging diabetic macular edema in fundus imaging,the proposed method outperforms the existing major models in several performance metrics.
基金funded by NARI Group’s Independent Project of China(Grant No.524609230125)the Foundation of NARI-TECH Nanjing Control System Ltd.of China(Grant No.0914202403120020).
文摘Studies to enhance the management of electrical energy have gained considerable momentum in recent years. The question of how much energy will be needed in households is a pressing issue as it allows the management plan of the available resources at the power grids and consumer levels. A non-intrusive inference process can be adopted to predict the amount of energy required by appliances. In this study, an inference process of appliance consumption based on temporal and environmental factors used as a soft sensor is proposed. First, a study of the correlation between the electrical and environmental variables is presented. Then, a resampling process is applied to the initial data set to generate three other subsets of data. All the subsets were evaluated to deduce the adequate granularity for the prediction of the energy demand. Then, a cloud-assisted deep neural network model is designed to forecast short-term energy consumption in a residential area while preserving user privacy. The solution is applied to the consumption data of four appliances elected from a set of real household power data. The experiment results show that the proposed framework is effective for estimating consumption with convincing accuracy.
基金supported by the Joint Funds of National Natural Science Foundation of China(Grant No.U23A20304)the Fund of Laboratory for Advanced Computing and Intelligence Engineering(No.2023-LYJJ-01-033)+1 种基金the Special Funds of Jiangsu Province Science and Technology Plan(Key R&D ProgramIndustryOutlook and Core Technologies)(No.BE2023005-4)the Science Project of Hainan University(KYQD(ZR)-21075).
文摘TarGuess-I is a leading model utilizing Personally Identifiable Information for online targeted password guessing.Due to its remarkable guessing performance,the model has drawn considerable attention in password security research.However,through an analysis of the vulnerable behavior of users when constructing passwords by combining popular passwords with their Personally Identifiable Information,we identified that the model fails to consider popular passwords and frequent substrings,and it uses overly broad personal information categories,with extensive duplicate statistics.To address these issues,we propose an improved password guessing model,TGI-FPR,which incorporates three semantic methods:(1)identification of popular passwords by generating top 300 lists from similar websites,(2)use of frequent substrings as new grammatical labels to capture finer-grained password structures,and(3)further subdivision of the six major categories of personal information.To evaluate the performance of the proposed model,we conducted experiments on six large-scale real-world password leak datasets and compared its accuracy within the first 100 guesses to that of TarGuess-I.The results indicate a 2.65%improvement in guessing accuracy.
基金supported in part by the Jiangsu Province Construction System Science and Technology Project(No.2024ZD056)the Research Development Fund of Xi’an Jiaotong-Liverpool University(No.RDF-24-01-097).
文摘Computer-vision and deep-learning techniques are widely applied to detect,monitor,and assess pavement conditions including road crack detection.Traditional methods fail to achieve satisfactory accuracy and generalization performance in for crack detection.Complex network model can generate redundant feature maps and computational complexity.Therefore,this paper proposes a novel model compression framework based on deep learning to detect road cracks,which can improve the detection efficiency and accuracy.A distillation loss function is proposed to compress the teacher model,followed by channel pruning.Meanwhile,a multi-dilation model is proposed to improve the accuracy of the model pruned.The proposed method is tested on the public database CrackForest dataset(CFD).The experimental results show that the proposed method is more efficient and accurate than other state-of-art methods.
基金National Natural Science Foundation of China (No.52202046)Natural Science Foundation of Shaanxi Province (No.2021JQ-034)。
文摘Lithium-rich layered oxides (LLOs) are increasingly recognized as promising cathode materials for nextgeneration high-energy-density lithium-ion batteries (LIBs).However,they suffer from voltage decay and low initial Coulombic efficiency (ICE) due to severe structural degradation caused by irreversible O release.Herein,we introduce a three-in-one strategy of increasing Ni and Mn content,along with Li/Ni disordering and TM–O covalency regulation to boost cationic and anionic redox activity simultaneously and thus enhance the electrochemical activity of LLOs.The target material,Li_(1.2)Ni_(0.168)Mn_(0.558)Co_(0.074)O_(2)(L1),exhibits an improved ICE of 87.2%and specific capacity of 293.2 mA h g^(-1)and minimal voltage decay of less than 0.53 m V cycle-1over 300 cycles at 1C,compared to Li_(1.2)Ni_(0.13)Mn_(0.54)Co_(0.13)O_(2)(Ls)(274.4 mA h g^(-1)for initial capacity,73.8%for ICE and voltage decay of 0.84 mV/cycle over 300 cycles at 1C).Theoretical calculations reveal that the density of states (DOS) area near the Fermi energy level for L1 is larger than that of Ls,indicating higher anionic and cationic redox reactivity than Ls.Moreover,L1 exhibits increased O-vacancy formation energy due to higher Li/Ni disordering of 4.76%(quantified by X-ray diffraction Rietveld refinement) and enhanced TM–O covalency,making lattice O release more difficult and thus improving electrochemical stability.The increased Li/Ni disordering also leads to more Ni^(2+)presence in the Li layer,which acts as a pillar during Li+de-embedding,improving structural stability.This research not only presents a viable approach to designing low-Co LLOs with enhanced capacity and ICE but also contributes significantly to the fundamental understanding of structural regulation in high-performance LIB cathodes.
基金Supported by the National High Technology Research and Development Program of China(863 Program)(2012AA012902)
文摘Automatic return oriented programming （ROP） technology can effectively improve the efficiency of ROP constructed, but the existing research results still have some shortcomings including needing more address space, poor generality. In order to solve these problems, this paper presents an improved ROP auto-constructor QExtd. Firstly, we design a Turing-complete language QExtdL and provide the basis of gadgets analysis. Secondly, we represent the MI instruction and realize precise process of side-effect instructions for improving the efficiency of automatic construction. At last, we establish a three-layer language conversion mechanism, making it convenient for users to construct ROP. Theoretical and experimental data show that the QExtd automatic construction method is much better than the ROPgadget based on syntax. In addition, the proposed method succeeds in constructing gadgets of ROP with the probability of 84% for programs whose sizes are more than 20 KB and whose directory is ＂/usr/bin＂ in Ubuntu, which proves that the construction capability improves significantly.
基金supported in part by the National Natural Foundation of China(No.62176147)。
文摘Automatic pavement crack detection plays an important role in ensuring road safety.In images of cracks,information about the cracks can be conveyed through high-frequency and low-fre-quency signals that focus on fine details and global structures,respectively.The output features obtained from different convolutional layers can be combined to represent information about both high-frequency and low-frequency signals.In this paper,we propose an encoder-decoder framework called octave hierarchical network(Octave-H),which is based on the U-Network(U-Net)architec-ture and utilizes an octave convolutional neural network and a hierarchical feature learning module for performing crack detection.The proposed octave convolution is capable of extracting multi-fre-quency feature maps,capturing both fine details and global cracks.We propose a hierarchical feature learning module that merges multi-frequency-scale feature maps with different levels(high and low)of octave convolutional layers.To verify the superiority of the proposed Octave-H,we employed the CrackForest dataset(CFD)and AigleRN databases to evaluate this method.The experimental results demonstrate that Octave-H outperforms other algorithms with satisfactory performance.
基金This work is supported by the Provincial Key Science and Technology Special Project of Henan(No.221100240100)。
文摘In recent years,the rapid development of computer software has led to numerous security problems,particularly software vulnerabilities.These flaws can cause significant harm to users’privacy and property.Current security defect detection technology relies on manual or professional reasoning,leading to missed detection and high false detection rates.Artificial intelligence technology has led to the development of neural network models based on machine learning or deep learning to intelligently mine holes,reducing missed alarms and false alarms.So,this project aims to study Java source code defect detection methods for defects like null pointer reference exception,XSS(Transform),and Structured Query Language(SQL)injection.Also,the project uses open-source Javalang to translate the Java source code,conducts a deep search on the AST to obtain the empty syntax feature library,and converts the Java source code into a dependency graph.The feature vector is then used as the learning target for the neural network.Four types of Convolutional Neural Networks(CNN),Long Short-Term Memory(LSTM),Bi-directional Long Short-Term Memory(BiLSTM),and Attention Mechanism+Bidirectional LSTM,are used to investigate various code defects,including blank pointer reference exception,XSS,and SQL injection defects.Experimental results show that the attention mechanism in two-dimensional BLSTM is the most effective for object recognition,verifying the correctness of the method.
基金the National Natural Science Foundation of China Youth Project(62302520).
文摘With the increasing proportion of encrypted traffic in cyberspace, the classification of encrypted traffic has becomea core key technology in network supervision. In recent years, many different solutions have emerged in this field.Most methods identify and classify traffic by extracting spatiotemporal characteristics of data flows or byte-levelfeatures of packets. However, due to changes in data transmission mediums, such as fiber optics and satellites,temporal features can exhibit significant variations due to changes in communication links and transmissionquality. Additionally, partial spatial features can change due to reasons like data reordering and retransmission.Faced with these challenges, identifying encrypted traffic solely based on packet byte-level features is significantlydifficult. To address this, we propose a universal packet-level encrypted traffic identification method, ComboPacket. This method utilizes convolutional neural networks to extract deep features of the current packet andits contextual information and employs spatial and channel attention mechanisms to select and locate effectivefeatures. Experimental data shows that Combo Packet can effectively distinguish between encrypted traffic servicecategories (e.g., File Transfer Protocol, FTP, and Peer-to-Peer, P2P) and encrypted traffic application categories (e.g.,BitTorrent and Skype). Validated on the ISCX VPN-non VPN dataset, it achieves classification accuracies of 97.0%and 97.1% for service and application categories, respectively. It also provides shorter training times and higherrecognition speeds. The performance and recognition capabilities of Combo Packet are significantly superior tothe existing classification methods mentioned.
基金This work was supported by the National Key R&D Program of China(No.2022YFB3102904)the National Natural Science Foundation of China(No.62172435,U23A20305)Key Research and Development Project of Henan Province(No.221111321200).
文摘Geolocating social media users aims to discover the real geographical locations of users from their publicly available data,which can support online location-based applications such as disaster alerts and local content recommen-dations.Social relationship-based methods represent a classical approach for geolocating social media.However,geographically proximate relationships are sparse and challenging to discern within social networks,thereby affecting the accuracy of user geolocation.To address this challenge,we propose user geolocation methods that integrate neighborhood geographical distribution and social structure influence(NGSI)to improve geolocation accuracy.Firstly,we propose a method for evaluating the homophily of locations based on the k-order neighbor-hood geographic distribution(k-NGD)similarity among users.There are notable differences in the distribution of k-NGD similarity between location-proximate and non-location-proximate users.Exploiting this distinction,we filter out non-location-proximate social relationships to enhance location homophily in the social network.To better utilize the location-proximate relationships in social networks,we propose a graph neural network algorithm based on the social structure influence.The algorithm enables us to perform a weighted aggregation of the information of users’multi-hop neighborhood,thereby mitigating the over-smoothing problem of user features and improving user geolocation performance.Experimental results on real social media dataset demonstrate that the neighborhood geographical distribution similarity metric can effectively filter out non-location-proximate social relationships.Moreover,compared with 7 existing social relationship-based user positioning methods,our proposed method can achieve multi-granularity user geolocation and improve the accuracy by 4.84%to 13.28%.
基金This work has received funding from National Natural Science Foundation of China(No.42275157).
文摘With the rapid advancement in exploring perceptual interactions and digital twins,metaverse technology has emerged to transcend the constraints of space-time and reality,facilitating remote AI-based collaboration.In this dynamic metasystem environment,frequent information exchanges necessitate robust security measures,with Authentication and Key Agreement(AKA)serving as the primary line of defense to ensure communication security.However,traditional AKA protocols fall short in meeting the low-latency requirements essential for synchronous interactions within the metaverse.To address this challenge and enable nearly latency-free interactions,a novel low-latency AKA protocol based on chaotic maps is proposed.This protocol not only ensures mutual authentication of entities within the metasystem but also generates secure session keys.The security of these session keys is rigorously validated through formal proofs,formal verification,and informal proofs.When confronted with the Dolev-Yao(DY)threat model,the session keys are formally demonstrated to be secure under the Real-or-Random(ROR)model.The proposed protocol is further validated through simulations conducted using VMware workstation compiled in HLPSL language and C language.The simulation results affirm the protocol’s effectiveness in resisting well-known attacks while achieving the desired low latency for optimal metaverse interactions.
基金the Scientific Research Foundation of Liaoning Provincial Department of Education(No.LJKZ0139)the Program for Liaoning Excellent Talents in University(No.LR15045).
文摘In order to improve the models capability in expressing features during few-shot learning,a multi-scale features prototypical network(MS-PN)algorithm is proposed.The metric learning algo-rithm is employed to extract image features and project them into a feature space,thus evaluating the similarity between samples based on their relative distances within the metric space.To sufficiently extract feature information from limited sample data and mitigate the impact of constrained data vol-ume,a multi-scale feature extraction network is presented to capture data features at various scales during the process of image feature extraction.Additionally,the position of the prototype is fine-tuned by assigning weights to data points to mitigate the influence of outliers on the experiment.The loss function integrates contrastive loss and label-smoothing to bring similar data points closer and separate dissimilar data points within the metric space.Experimental evaluations are conducted on small-sample datasets mini-ImageNet and CUB200-2011.The method in this paper can achieve higher classification accuracy.Specifically,in the 5-way 1-shot experiment,classification accuracy reaches 50.13%and 66.79%respectively on these two datasets.Moreover,in the 5-way 5-shot ex-periment,accuracy of 66.79%and 85.91%are observed,respectively.
基金the National Natural Science Foundation of China(Program Nos.12161058,61962051,and 12361096)the Science and Technology Plan Project of Qinghai Province-Applied Basic Research Plan(No.2023-ZJ-736)the Open Project of State Key Laboratory of Plateau Ecology and Agriculture,Qinghai University(No.2021-ZZ-02).
文摘In this study,the separation and coalescence of oil-in-water emulsions are explored in an ultrasonic field using the lattice Boltzmann method.By simulating the propagation of ultrasonic waves,this study focuses on examining the effects of acoustic wave frequency,the ratio of oil to water components,and the aspect ratio of the boundary on the emulsification and separation processes of oil-water mixtures.The following conclusions are drawn.①Frequency affects the speed of oil droplet separation,leading to an increase in droplet size over time.Larger droplets are found near the source,while smaller droplets are distributed throughout the wave web.②As the boundary aspect ratio increases,the emulsification efficiency of the droplets weakens,and the system takes longer to stabilize.③Emulsions with a higher component of oil can better resist acoustic waves.④At the same acoustic frequency,longer wavelength ultrasonic fields promote the formation of uniformly distributed,smaller oil droplets,which is beneficial to the storage of emulsions.These numerical simulation results offer insights for optimizing conditions for oil-in-water separation and serve as a numerical reference for the study of oil-in-water emulsion separation in ultrasonic environments.
基金supported by the National Natural Science Foundation of China(61572444,62176238)Natural Science Foundation of Henan Province,China(222300420088)+3 种基金Training Program of Young Backbone teachers in Colleges and universities in Henan Province,China(2020GGJS006)Program for Science&Technology Innovation Talents in Universities of Henan Province,China(23HASTIT023)Program for Science&Technology Innovation Teams in Universities of Henan Province,China(23IRTSTHN010)National Key Research and Development Program of China(2022YFD2001205).
文摘To solve the shortcomings of Particle Swarm Optimization(PSO)algorithm,local optimization and slow convergence,an Opposition-based Learning Adaptive Chaotic PSO(LCPSO)algorithm was presented.The chaotic elite opposition-based learning process was applied to initialize the entire population,which enhanced the quality of the initial individuals and the population diversity,made the initial individuals distribute in the better quality areas,and accelerated the search efficiency of the algorithm.The inertia weights were adaptively customized during evolution in the light of the degree of premature convergence to balance the local and global search abilities of the algorithm,and the reverse search strategy was introduced to increase the chances of the algorithm escaping the local optimum.The LCPSO algorithm is contrasted to other intelligent algorithms on 10 benchmark test functions with different characteristics,and the simulation experiments display that the proposed algorithm is superior to other intelligence algorithms in the global search ability,search accuracy and convergence speed.In addition,the robustness and effectiveness of the proposed algorithm are also verified by the simulation results of engineering design problems.
基金financially supported by the National Natural Science Foundation of China(52202046,51602246,and 51801144)the Natural Science Foundation of Shanxi Provincial(2021JQ-034)。
文摘Co-free Li-rich layered oxides(LLOs)are emerging as promising cathode materials for Li-ion batteries due to their low cost and high capacity.However,they commonly face severe structural instability and poor electrochemical activity,leading to diminished capacity and voltage performance.Herein,we introduce a Co-free LLO,Li_(1.167)Ni_(0.222)Mn_(0.611)O_(2)(Cf-L1),which features a cooperative structure of Li/Ni mixing and stacking faults.This structure regulates the crystal and electronic structures,resulting in a higher discharge capacity of 300.6 mA h g^(-1)and enhanced rate capability compared to the typical Co-free LLO,Li_(1.2)Ni_(0.2)Mn_(0.6)O_(2)(Cf-Ls).Density functional theory(DFT)indicates that Li/Ni mixing in LLOs leads to increased Li-O-Li configurations and higher anionic redox activities,while stacking faults further optimize the electronic interactions of transition metal(TM)3d and non-bonding O 2p orbitals.Moreover,stacking faults accommodate lattice strain,improving electrochemical reversibility during charge/discharge cycles,as demonstrated by the in situ XRD of Cf-L1 showing less lattice evolution than Cf-Ls.This study offers a structured approach to developing Co-free LLOs with enhanced capacity,voltage,rate capability,and cyclability,significantly impacting the advancement of the next-generation Li-ion batteries.
基金support of the National SKA program of China(Nos.2022SKA0110100 and 2022SKA0110101)the National Natural Science Foundation of China(NSFC,Grant Nos.1236114814,12203061,12273070,and 12303004)。
文摘This paper presents the design,calibration,and survey strategy of the Fast Radio Burst(FRB)digital backend and its real-time data processing pipeline employed in the Tianlai Cylinder Pathfinder Array.The array,consisting of three parallel cylindrical reflectors and equipped with 96 dual-polarization feeds,is a radio interferometer array designed for conducting drift scans of the northern celestial semi-sphere.The FRB digital backend enables the formation of 96 digital beams,effectively covering an area of approximately 40 square degrees with the 3 dB beam.Our pipeline demonstrates the capability to conduct an automatic search of FRBs,detecting at quasi-realtime and classifying FRB candidates automatically.The current FRB searching pipeline has an overall recall rate of88%.During the commissioning phase,we successfully detected signals emitted by four well-known pulsars:PSR B0329+54,B2021+51,B0823+26,and B2020+28.We report the first discovery of an FRB by our array,designated as FRB 20220414A.We also investigate the optimal arrangement for the digitally formed beams to achieve maximum detection rate by numerical simulation.
文摘Adopting digital twin technology in the chemical industry is reshaping process optimisation,operational efficiency,and safety management.By leveraging data from sensors and control systems,the digital twins provide actionable insights,enabling more precise control over chemical reactions,improved quality assurance,and reduced environmental impact.Additionally,the ability to simulate“what-if”scenarios accelerates the innovation cycle and supports compliance with stringent regulatory standards.This research article explores the implementation and impact of digital twins in chemical manufacturing environments.It examines how digital twins enable continuous monitoring and control by mirroring chemical processes,predicting equipment failures,and simulating complex reactions under various conditions.The study highlights the benefits of digital twins,including improved process efficiency,enhanced product quality,and reduced environmental and operational risks.The research also addresses challenges and limitations,such as data integration complexities and the need for high-fidelity models.By providing a comprehensive analysis of current applications and future prospects,this paper aims to advance the understanding of digital twins'role in driving innovation and sustainability within the chemical industry.
基金financially supported by the National Natural Science Foundation of China (No. 51801179)Yunnan Science and Technology Projects (Nos. 2018ZE001, 2019ZE001-1, 202002AB080001-6, 2018IC058, 2018FB083 and 2018FD011)the support from the Yunnan Provincial High-level Talents Introduction Projects。
文摘High-entropy carbide ceramics (HECs) have drawn increasing attention as their excellent mechanical and thermal properties. In this work, the crystal stability,mechanical behavior, electronic and thermodynamic properties of (TiZrNbTa)C HEC are investigated by the first-principles calculations. Obtained results reveal that the disordered transition-metal (TM) atoms result in serious local lattice distortion within the crystal. The lattice distortion plays a key role for the structural stabilization,mechanical anisotropy and thermodynamic behaviors of(TiZrNbTa)C. Increasing pressure leads to decrease the lattice parameter, volume and brittleness, meanwhile increase the elastic constants, elastic moduli, mechanical anisotropy, sound velocity, and Debye temperature. It is also discovered that charge delocalization occurs with the increase in pressure. The mechanical stability and anisotropy of (TiZrNbTa)C are attributed primarily to TM-C bonding.
文摘A novel numerical method is explored and named as mesh-free poly-cell Galerkin method. An improved moving least-square （MLS） scheme is presented, which can avoid the matrix inversion in standard MLS and can be used to construct shape functions possessing delta Kronecher property. A new type of local support is introduced to ensure the alignment of integral domains with the cells of the back-ground mesh, which will reduce the difficult in integration. An intensive numerical study is conducted to test the accuracy of the present method. It is observed that solutions with good accuracy can be obtained with the present method.
