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
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
With oily wastewater treatment emerging as a critical global issue,porous media and shear forces have received significant attention as environmentally friendly methods for oil–water separation.This study systematica...With oily wastewater treatment emerging as a critical global issue,porous media and shear forces have received significant attention as environmentally friendly methods for oil–water separation.This study systematically simulates the dynamics of oil-in-water emulsion demulsification under porous media and shear forces using a color-gradient Lattice Boltzmann model.The morphological evolution and demulsification efficiency of emulsions are governed by porous media and shear forces.The effects of porosity and shear velocity on demulsification are quantitatively analyzed.(1)The presence of porous media enhances the ability of the flow field to trap oil droplets,with lower porosity corresponding to improved demulsification performance.Moreover,a more orderly arrangement of porous media promotes oil droplet coalescence.(2)Higher shear velocity in the flow field facilitates the aggregation of oil droplets.However,oscillatory shear conditions reduce the demulsification efficiency of emulsions.(3)Among the combined effects of shear velocity and porosity,porosity emerges as the dominant factor influencing emulsion demulsification.(4)Higher initial oil concentrations enhance demulsification efficiency.These simulation results provide valuable insights for further research on emulsion demulsification.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
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
The scientists are dedicated to studying the detection of Alzheimer’s disease onset to find a cure, or at the very least, medication that can slow the progression of the disease. This article explores the effectivene...The scientists are dedicated to studying the detection of Alzheimer’s disease onset to find a cure, or at the very least, medication that can slow the progression of the disease. This article explores the effectiveness of longitudinal data analysis, artificial intelligence, and machine learning approaches based on magnetic resonance imaging and positron emission tomography neuroimaging modalities for progression estimation and the detection of Alzheimer’s disease onset. The significance of feature extraction in highly complex neuroimaging data, identification of vulnerable brain regions, and the determination of the threshold values for plaques, tangles, and neurodegeneration of these regions will extensively be evaluated. Developing automated methods to improve the aforementioned research areas would enable specialists to determine the progression of the disease and find the link between the biomarkers and more accurate detection of Alzheimer’s disease onset.展开更多
In order to provide a practicable solution to data confidentiality in cloud storage service,a data assured deletion scheme,which achieves the fine grained access control,hopping and sniffing attacks resistance,data dy...In order to provide a practicable solution to data confidentiality in cloud storage service,a data assured deletion scheme,which achieves the fine grained access control,hopping and sniffing attacks resistance,data dynamics and deduplication,is proposed.In our scheme,data blocks are encrypted by a two-level encryption approach,in which the control keys are generated from a key derivation tree,encrypted by an All-OrNothing algorithm and then distributed into DHT network after being partitioned by secret sharing.This guarantees that only authorized users can recover the control keys and then decrypt the outsourced data in an ownerspecified data lifetime.Besides confidentiality,data dynamics and deduplication are also achieved separately by adjustment of key derivation tree and convergent encryption.The analysis and experimental results show that our scheme can satisfy its security goal and perform the assured deletion with low cost.展开更多
In this paper,vibration analysis of functionally graded porous beams is carried out using the third-order shear deformation theory.The beams have uniform and non-uniform porosity distributions across their thickness a...In this paper,vibration analysis of functionally graded porous beams is carried out using the third-order shear deformation theory.The beams have uniform and non-uniform porosity distributions across their thickness and both ends are supported by rotational and translational springs.The material properties of the beams such as elastic moduli and mass density can be related to the porosity and mass coefficient utilizing the typical mechanical features of open-cell metal foams.The Chebyshev collocation method is applied to solve the governing equations derived from Hamilton's principle,which is used in order to obtain the accurate natural frequencies for the vibration problem of beams with various general and elastic boundary conditions.Based on the numerical experiments,it is revealed that the natural frequencies of the beams with asymmetric and non-uniform porosity distributions are higher than those of other beams with uniform and symmetric porosity distributions.展开更多
The multitrip pickup and delivery problem with time windows and manpower planning(MTPDPTW-MP)determines a set of ambulance routes and finds staff assignment for a hospital. It involves different stakeholders with dive...The multitrip pickup and delivery problem with time windows and manpower planning(MTPDPTW-MP)determines a set of ambulance routes and finds staff assignment for a hospital. It involves different stakeholders with diverse interests and objectives. This study firstly introduces a multiobjective MTPDPTW-MP(MO-MTPDPTWMP) with three objectives to better describe the real-world scenario. A multiobjective iterated local search algorithm with adaptive neighborhood selection(MOILS-ANS) is proposed to solve the problem. MOILS-ANS can generate a diverse set of alternative solutions for decision makers to meet their requirements. To better explore the search space, problem-specific neighborhood structures and an adaptive neighborhood selection strategy are carefully designed in MOILS-ANS. Experimental results show that the proposed MOILS-ANS significantly outperforms the other two multiobjective algorithms. Besides, the nature of objective functions and the properties of the problem are analyzed. Finally, the proposed MOILS-ANS is compared with the previous single-objective algorithm and the benefits of multiobjective optimization are discussed.展开更多
Precise localization techniques for indoor Wi-Fi access points(APs)have important application in the security inspection.However,due to the interference of environment factors such as multipath propagation and NLOS(No...Precise localization techniques for indoor Wi-Fi access points(APs)have important application in the security inspection.However,due to the interference of environment factors such as multipath propagation and NLOS(Non-Line-of-Sight),the existing methods for localization indoor Wi-Fi access points based on RSS ranging tend to have lower accuracy as the RSS(Received Signal Strength)is difficult to accurately measure.Therefore,the localization algorithm of indoor Wi-Fi access points based on the signal strength relative relationship and region division is proposed in this paper.The algorithm hierarchically divide the room where the target Wi-Fi AP is located,on the region division line,a modified signal collection device is used to measure RSS in two directions of each reference point.All RSS values are compared and the region where the RSS value has the relative largest signal strength is located as next candidate region.The location coordinate of the target Wi-Fi AP is obtained when the localization region of the target Wi-Fi AP is successively approximated until the candidate region is smaller than the accuracy threshold.There are 360 experiments carried out in this paper with 8 types of Wi-Fi APs including fixed APs and portable APs.The experimental results show that the average localization error of the proposed localization algorithm is 0.30 meters,and the minimum localization error is 0.16 meters,which is significantly higher than the localization accuracy of the existing typical indoor Wi-Fi access point localization methods.展开更多
With serious cybersecurity situations and frequent network attacks,the demands for automated pentests continue to increase,and the key issue lies in attack planning.Considering the limited viewpoint of the attacker,at...With serious cybersecurity situations and frequent network attacks,the demands for automated pentests continue to increase,and the key issue lies in attack planning.Considering the limited viewpoint of the attacker,attack planning under uncertainty is more suitable and practical for pentesting than is the traditional planning approach,but it also poses some challenges.To address the efficiency problem in uncertainty planning,we propose the APU-D*Lite algorithm in this paper.First,the pentest framework is mapped to the planning problem with the Planning Domain Definition Language(PDDL).Next,we develop the pentest information graph to organize network information and assess relevant exploitation actions,which helps to simplify the problem scale.Then,the APU-D*Lite algorithm is introduced based on the idea of incremental heuristic searching.This method plans for both hosts and actions,which meets the requirements of pentesting.With the pentest information graph as the input,the output is an alternating host and action sequence.In experiments,we use the attack success rate to represent the uncertainty level of the environment.The result shows that APU-D*Lite displays better reliability and efficiency than classical planning algorithms at different attack success rates.展开更多
The large scale and distribution of cloud computing storage have become the major challenges in cloud forensics for file extraction. Current disk forensic methods do not adapt to cloud computing well and the forensic ...The large scale and distribution of cloud computing storage have become the major challenges in cloud forensics for file extraction. Current disk forensic methods do not adapt to cloud computing well and the forensic research on distributed file system is inadequate. To address the forensic problems, this paper uses the Hadoop distributed file system （HDFS） as a case study and proposes a forensic method for efficient file extraction based on three-level （3L） mapping. First, HDFS is analyzed from overall architecture to local file system. Second, the 3L mapping of an HDFS file from HDFS namespace to data blocks on local file system is established and a recovery method for deleted files based on 3L mapping is presented. Third, a multi-node Hadoop framework via Xen virtualization platform is set up to test the performance of the method. The results indicate that the proposed method could succeed in efficient location of large files stored across data nodes, make selective image of disk data and get high recovery rate of deleted files.展开更多
Although many classical IP geolocation algorithms are suitable to rich-connected networks, their performances are seriously affected in poor-connected networks with weak delay-distance correlation. This paper tries to...Although many classical IP geolocation algorithms are suitable to rich-connected networks, their performances are seriously affected in poor-connected networks with weak delay-distance correlation. This paper tries to improve the performances of classical IP geolocation algorithms by finding rich-connected sub-networks inside poor-connected networks. First, a new delay-distance correlation model （RTD-Corr model） is proposed. It builds the relationship between delay-distance correlation and actual network factors such as the tortuosity of the network path and the ratio of propagation delay. Second, based on the RTD-Corr model and actual network characteristics, this paper discusses about how to find rich-connected networks inside China Intemet which is a typical actual poor-connected network. Then we find rich-connected sub-networks of China Intemet through a large-scale network measurement which covers three major ISPs and thirty provinces. At last, based on the founded rich-connected sub-networks, we modify two classical IP geolocation algorithms and the experiments in China Intemet show that their accuracy is significantly increased.展开更多
Privacy protection is the key to maintaining the Internet of Things(IoT)communication strategy.Steganography is an important way to achieve covert communication that protects user data privacy.Steganalysis technology ...Privacy protection is the key to maintaining the Internet of Things(IoT)communication strategy.Steganography is an important way to achieve covert communication that protects user data privacy.Steganalysis technology is the key to checking steganography security,and its ultimate goal is to extract embedded messages.Existing methods cannot extract under known cover images.To this end,this paper proposes a method of extracting embedded messages under known cover images.First,the syndrome-trellis encoding process is analyzed.Second,a decoding path in the syndrome trellis is obtained by using the stego sequence and a certain parity-check matrix,while the embedding process is simulated using the cover sequence and parity-check matrix.Since the decoding path obtained by the stego sequence and the correct parity-check matrix is optimal and has the least distortion,comparing the path consistency can quickly filter the coding parameters to determine the correct matrices,and embedded messages can be extracted correctly.The proposed method does not need to embed all possible messages for the second time,improving coding parameter recognition significantly.The experimental results show that the proposed method can identify syndrome-trellis coding parameters in stego images embedded by adaptive steganography quickly to realize embedded message extraction.展开更多
Mining penetration testing semantic knowledge hidden in vast amounts of raw penetration testing data is of vital importance for automated penetration testing.Associative rule mining,a data mining technique,has been st...Mining penetration testing semantic knowledge hidden in vast amounts of raw penetration testing data is of vital importance for automated penetration testing.Associative rule mining,a data mining technique,has been studied and explored for a long time.However,few studies have focused on knowledge discovery in the penetration testing area.The experimental result reveals that the long-tail distribution of penetration testing data nullifies the effectiveness of associative rule mining algorithms that are based on frequent pattern.To address this problem,a Bayesian inference based penetration semantic knowledge mining algorithm is proposed.First,a directed bipartite graph model,a kind of Bayesian network,is constructed to formalize penetration testing data.Then,we adopt the maximum likelihood estimate method to optimize the model parameters and decompose a large Bayesian network into smaller networks based on conditional independence of variables for improved solution efficiency.Finally,irrelevant variable elimination is adopted to extract penetration semantic knowledge from the conditional probability distribution of the model.The experimental results show that the proposed method can discover penetration semantic knowledge from raw penetration testing data effectively and efficiently.展开更多
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.
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funded by the National Natural Science Foundation of China,grant number:12161058Heping Wang is the recipient of this funding.This research was funded by the National Natural Science Foundation of China,grant number:12361096+1 种基金Heping Wang is the recipient of this funding.This research was also funded by the Science and Technology Plan Project of Qinghai Province-Applied Basic Research Plan,grant number:2023-ZJ-736Yanggui Li is the recipient of this funding.
文摘With oily wastewater treatment emerging as a critical global issue,porous media and shear forces have received significant attention as environmentally friendly methods for oil–water separation.This study systematically simulates the dynamics of oil-in-water emulsion demulsification under porous media and shear forces using a color-gradient Lattice Boltzmann model.The morphological evolution and demulsification efficiency of emulsions are governed by porous media and shear forces.The effects of porosity and shear velocity on demulsification are quantitatively analyzed.(1)The presence of porous media enhances the ability of the flow field to trap oil droplets,with lower porosity corresponding to improved demulsification performance.Moreover,a more orderly arrangement of porous media promotes oil droplet coalescence.(2)Higher shear velocity in the flow field facilitates the aggregation of oil droplets.However,oscillatory shear conditions reduce the demulsification efficiency of emulsions.(3)Among the combined effects of shear velocity and porosity,porosity emerges as the dominant factor influencing emulsion demulsification.(4)Higher initial oil concentrations enhance demulsification efficiency.These simulation results provide valuable insights for further research on emulsion demulsification.
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.
文摘The scientists are dedicated to studying the detection of Alzheimer’s disease onset to find a cure, or at the very least, medication that can slow the progression of the disease. This article explores the effectiveness of longitudinal data analysis, artificial intelligence, and machine learning approaches based on magnetic resonance imaging and positron emission tomography neuroimaging modalities for progression estimation and the detection of Alzheimer’s disease onset. The significance of feature extraction in highly complex neuroimaging data, identification of vulnerable brain regions, and the determination of the threshold values for plaques, tangles, and neurodegeneration of these regions will extensively be evaluated. Developing automated methods to improve the aforementioned research areas would enable specialists to determine the progression of the disease and find the link between the biomarkers and more accurate detection of Alzheimer’s disease onset.
基金supported by the National Key Basic Research Program of China(973 program) under Grant No.2012CB315901
文摘In order to provide a practicable solution to data confidentiality in cloud storage service,a data assured deletion scheme,which achieves the fine grained access control,hopping and sniffing attacks resistance,data dynamics and deduplication,is proposed.In our scheme,data blocks are encrypted by a two-level encryption approach,in which the control keys are generated from a key derivation tree,encrypted by an All-OrNothing algorithm and then distributed into DHT network after being partitioned by secret sharing.This guarantees that only authorized users can recover the control keys and then decrypt the outsourced data in an ownerspecified data lifetime.Besides confidentiality,data dynamics and deduplication are also achieved separately by adjustment of key derivation tree and convergent encryption.The analysis and experimental results show that our scheme can satisfy its security goal and perform the assured deletion with low cost.
文摘In this paper,vibration analysis of functionally graded porous beams is carried out using the third-order shear deformation theory.The beams have uniform and non-uniform porosity distributions across their thickness and both ends are supported by rotational and translational springs.The material properties of the beams such as elastic moduli and mass density can be related to the porosity and mass coefficient utilizing the typical mechanical features of open-cell metal foams.The Chebyshev collocation method is applied to solve the governing equations derived from Hamilton's principle,which is used in order to obtain the accurate natural frequencies for the vibration problem of beams with various general and elastic boundary conditions.Based on the numerical experiments,it is revealed that the natural frequencies of the beams with asymmetric and non-uniform porosity distributions are higher than those of other beams with uniform and symmetric porosity distributions.
基金supported by the National Key R&D Program of China(2018AAA0101203)the National Natural Science Foundation of China(61673403,71601191)the JSPS KAKENHI(JP17K12751)。
文摘The multitrip pickup and delivery problem with time windows and manpower planning(MTPDPTW-MP)determines a set of ambulance routes and finds staff assignment for a hospital. It involves different stakeholders with diverse interests and objectives. This study firstly introduces a multiobjective MTPDPTW-MP(MO-MTPDPTWMP) with three objectives to better describe the real-world scenario. A multiobjective iterated local search algorithm with adaptive neighborhood selection(MOILS-ANS) is proposed to solve the problem. MOILS-ANS can generate a diverse set of alternative solutions for decision makers to meet their requirements. To better explore the search space, problem-specific neighborhood structures and an adaptive neighborhood selection strategy are carefully designed in MOILS-ANS. Experimental results show that the proposed MOILS-ANS significantly outperforms the other two multiobjective algorithms. Besides, the nature of objective functions and the properties of the problem are analyzed. Finally, the proposed MOILS-ANS is compared with the previous single-objective algorithm and the benefits of multiobjective optimization are discussed.
基金The work presented in this paper is supported by the National Key R&D Program of China(No.2016YFB0801303,2016QY01W0105)the National Natural Science Foundation of China(No.U1636219,61602508,61772549,U1736214,61572052)+1 种基金Plan for Scientific Innovation Talent of Henan Province(No.2018JR0018)the Key Technologies R&D Program of Henan Province(No.162102210032).
文摘Precise localization techniques for indoor Wi-Fi access points(APs)have important application in the security inspection.However,due to the interference of environment factors such as multipath propagation and NLOS(Non-Line-of-Sight),the existing methods for localization indoor Wi-Fi access points based on RSS ranging tend to have lower accuracy as the RSS(Received Signal Strength)is difficult to accurately measure.Therefore,the localization algorithm of indoor Wi-Fi access points based on the signal strength relative relationship and region division is proposed in this paper.The algorithm hierarchically divide the room where the target Wi-Fi AP is located,on the region division line,a modified signal collection device is used to measure RSS in two directions of each reference point.All RSS values are compared and the region where the RSS value has the relative largest signal strength is located as next candidate region.The location coordinate of the target Wi-Fi AP is obtained when the localization region of the target Wi-Fi AP is successively approximated until the candidate region is smaller than the accuracy threshold.There are 360 experiments carried out in this paper with 8 types of Wi-Fi APs including fixed APs and portable APs.The experimental results show that the average localization error of the proposed localization algorithm is 0.30 meters,and the minimum localization error is 0.16 meters,which is significantly higher than the localization accuracy of the existing typical indoor Wi-Fi access point localization methods.
文摘With serious cybersecurity situations and frequent network attacks,the demands for automated pentests continue to increase,and the key issue lies in attack planning.Considering the limited viewpoint of the attacker,attack planning under uncertainty is more suitable and practical for pentesting than is the traditional planning approach,but it also poses some challenges.To address the efficiency problem in uncertainty planning,we propose the APU-D*Lite algorithm in this paper.First,the pentest framework is mapped to the planning problem with the Planning Domain Definition Language(PDDL).Next,we develop the pentest information graph to organize network information and assess relevant exploitation actions,which helps to simplify the problem scale.Then,the APU-D*Lite algorithm is introduced based on the idea of incremental heuristic searching.This method plans for both hosts and actions,which meets the requirements of pentesting.With the pentest information graph as the input,the output is an alternating host and action sequence.In experiments,we use the attack success rate to represent the uncertainty level of the environment.The result shows that APU-D*Lite displays better reliability and efficiency than classical planning algorithms at different attack success rates.
基金Supported by the National High Technology Research and Development Program of China(863 Program)(2015AA016006)the National Natural Science Foundation of China(60903220)
文摘The large scale and distribution of cloud computing storage have become the major challenges in cloud forensics for file extraction. Current disk forensic methods do not adapt to cloud computing well and the forensic research on distributed file system is inadequate. To address the forensic problems, this paper uses the Hadoop distributed file system （HDFS） as a case study and proposes a forensic method for efficient file extraction based on three-level （3L） mapping. First, HDFS is analyzed from overall architecture to local file system. Second, the 3L mapping of an HDFS file from HDFS namespace to data blocks on local file system is established and a recovery method for deleted files based on 3L mapping is presented. Third, a multi-node Hadoop framework via Xen virtualization platform is set up to test the performance of the method. The results indicate that the proposed method could succeed in efficient location of large files stored across data nodes, make selective image of disk data and get high recovery rate of deleted files.
基金Supported by the National Natural Science Foundation of China(61379151,61274189,61302159 and 61401512)the Excellent Youth Foundation of Henan Province of China(144100510001)Foundation of Science and Technology on Information Assurance Laboratory(KJ-14-108)
文摘Although many classical IP geolocation algorithms are suitable to rich-connected networks, their performances are seriously affected in poor-connected networks with weak delay-distance correlation. This paper tries to improve the performances of classical IP geolocation algorithms by finding rich-connected sub-networks inside poor-connected networks. First, a new delay-distance correlation model （RTD-Corr model） is proposed. It builds the relationship between delay-distance correlation and actual network factors such as the tortuosity of the network path and the ratio of propagation delay. Second, based on the RTD-Corr model and actual network characteristics, this paper discusses about how to find rich-connected networks inside China Intemet which is a typical actual poor-connected network. Then we find rich-connected sub-networks of China Intemet through a large-scale network measurement which covers three major ISPs and thirty provinces. At last, based on the founded rich-connected sub-networks, we modify two classical IP geolocation algorithms and the experiments in China Intemet show that their accuracy is significantly increased.
基金upported by the National Natural Science Foundation of China(No.U1804263,61772549,62172435)the Zhongyuan Science and Technology Innovation Leading Talent Project(No.214200510019)Thanks to the recommendation of SPDE2020,which gives us the opportunity to publish an expanded and full version of this paper.
文摘Privacy protection is the key to maintaining the Internet of Things(IoT)communication strategy.Steganography is an important way to achieve covert communication that protects user data privacy.Steganalysis technology is the key to checking steganography security,and its ultimate goal is to extract embedded messages.Existing methods cannot extract under known cover images.To this end,this paper proposes a method of extracting embedded messages under known cover images.First,the syndrome-trellis encoding process is analyzed.Second,a decoding path in the syndrome trellis is obtained by using the stego sequence and a certain parity-check matrix,while the embedding process is simulated using the cover sequence and parity-check matrix.Since the decoding path obtained by the stego sequence and the correct parity-check matrix is optimal and has the least distortion,comparing the path consistency can quickly filter the coding parameters to determine the correct matrices,and embedded messages can be extracted correctly.The proposed method does not need to embed all possible messages for the second time,improving coding parameter recognition significantly.The experimental results show that the proposed method can identify syndrome-trellis coding parameters in stego images embedded by adaptive steganography quickly to realize embedded message extraction.
基金the National Natural Science Foundation of China No.61502528.
文摘Mining penetration testing semantic knowledge hidden in vast amounts of raw penetration testing data is of vital importance for automated penetration testing.Associative rule mining,a data mining technique,has been studied and explored for a long time.However,few studies have focused on knowledge discovery in the penetration testing area.The experimental result reveals that the long-tail distribution of penetration testing data nullifies the effectiveness of associative rule mining algorithms that are based on frequent pattern.To address this problem,a Bayesian inference based penetration semantic knowledge mining algorithm is proposed.First,a directed bipartite graph model,a kind of Bayesian network,is constructed to formalize penetration testing data.Then,we adopt the maximum likelihood estimate method to optimize the model parameters and decompose a large Bayesian network into smaller networks based on conditional independence of variables for improved solution efficiency.Finally,irrelevant variable elimination is adopted to extract penetration semantic knowledge from the conditional probability distribution of the model.The experimental results show that the proposed method can discover penetration semantic knowledge from raw penetration testing data effectively and efficiently.
