Website Fingerprinting(WF)attacks can extract side channel information from encrypted traffic to form a fingerprint that identifies the victim’s destination website,even if traffic is sophisticatedly anonymized by **...
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Website Fingerprinting(WF)attacks can extract side channel information from encrypted traffic to form a fingerprint that identifies the victim’s destination website,even if traffic is sophisticatedly anonymized by *** offline defenses have been proposed and claimed to have achieved good ***,such work is more of a theoretical optimization study than a technology that can be applied to real-time traffic in the practical *** defenders generate optimized defense schemes only if the complete traffic traces are *** practicality and effectiveness are *** this paper,we provide an in-depth analysis of the difficulties faced in porting existing offline defenses to the online *** then the online WF defense based on the non-targeted adversarial patch is *** reduce the overhead,we use the Gradient-weighted Class Activation Mapping(Grad-CAM)algorithm to identify critical segments that have high contribution to the *** addition,we optimize the adversarial patch generation process by splitting patches and limiting the values,so that the pre-trained patches can be injected and discarded in real-time *** experiments are carried out to evaluate the effectiveness of our *** bandwidth overhead is set to 20%,the accuracies of the two state-of-the-art attacks,DF and Var-CNN,drop to 10.83%and 15.49%,***,we implement the real-time patch traffic injection based on WFPadTools framework in the online scenario,and achieve a defense accuracy of 95.50%with 12.57%time overhead.
With the arrival of 5G,latency-sensitive applications are becoming increasingly *** Edge Computing(MEC)technology has the characteristics of high bandwidth,low latency and low energy consumption,and has attracted much...
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With the arrival of 5G,latency-sensitive applications are becoming increasingly *** Edge Computing(MEC)technology has the characteristics of high bandwidth,low latency and low energy consumption,and has attracted much attention among *** improve the Quality of Service(QoS),this study focuses on computation offloading in *** consider the QoS from the perspective of computational cost,dimensional disaster,user privacy and catastrophic forgetting of new *** QoS model is established based on the delay and energy consumption and is based on DDQN and a Federated Learning(FL)adaptive task offloading algorithm in *** proposed algorithm combines the QoS model and deep reinforcement learning algorithm to obtain an optimal offloading policy according to the local link and node state information in the channel coherence time to address the problem of time-varying transmission channels and reduce the computing energy consumption and task processing *** solve the problems of privacy and catastrophic forgetting,we use FL to make distributed use of multiple users’data to obtain the decision model,protect data privacy and improve the model *** the process of FL iteration,the communication delay of individual devices is too large,which affects the overall delay ***,we adopt a communication delay optimization algorithm based on the unary outlier detection mechanism to reduce the communication delay of *** simulation results indicate that compared with existing schemes,the proposed method significantly reduces the computation cost on a device and improves the QoS when handling complex tasks.
Traditional autonomous driving usually requires a large number of vehicles to upload data to a central server for training. However, collecting data from vehicles may violate personal privacy as road environmental inf...
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Three-dimensional(3D)surface geometry provides elemental information in various sciences and precision *** Projection Profilometry(FPP)is one of the most powerful non-contact(thus non-destructive)and non-interferometr...
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Three-dimensional(3D)surface geometry provides elemental information in various sciences and precision *** Projection Profilometry(FPP)is one of the most powerful non-contact(thus non-destructive)and non-interferometric(thus less restrictive)3D measurement techniques,featuring at its high ***,the measurement precision of FPP is currently evaluated experimentally,lacking a complete theoretical model for *** propose the first complete FPP precision model chain including four stage models(camera intensity,fringe intensity,phase and 3D geometry)and two transfer models(from fringe intensity to phase and from phase to 3D geometry).The most significant contributions include the adoption of a non-Gaussian camera noise model,which,for the first time,establishes the connection between camera’s electronics parameters(known in advance from the camera manufacturer)and the phase precision,and the formulation of the phase to geometry transfer,which makes the precision of the measured geometry representable in an explicit and concise *** a result,we not only establish the full precision model of the 3D geometry to characterize the performance of an FPP system that has already been set up,but also explore the expression of the highest possible precision limit to guide the error distribution of an FPP system that is yet to *** theoretical models make FPP a more designable technique to meet the challenges from various measurement demands concerning different object sizes from macro to micro and requiring different measurement precisions from a few millimeters to a few micrometers.
Code review is a critical process in software development, contributing to the overall quality of the product by identifying errors early. A key aspect of this process is the selection of appropriate reviewers to scru...
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Code review is a critical process in software development, contributing to the overall quality of the product by identifying errors early. A key aspect of this process is the selection of appropriate reviewers to scrutinize changes made to source code. However, in large-scale open-source projects, selecting the most suitable reviewers for a specific change can be a challenging task. To address this, we introduce the Code Context Based Reviewer Recommendation (CCB-RR), a model that leverages information from changesets to recommend the most suitable reviewers. The model takes into consideration the paths of modified files and the context derived from the changesets, including their titles and descriptions. Additionally, CCB-RR employs KeyBERT to extract the most relevant keywords and compare the semantic similarity across changesets. The model integrates the paths of modified files, keyword information, and the context of code changes to form a comprehensive picture of the changeset. We conducted extensive experiments on four open-source projects, demonstrating the effectiveness of CCB-RR. The model achieved a Top-1 accuracy of 60%, 55%, 51%, and 45% on the Android, OpenStack, QT, and LibreOffice projects respectively. For Mean Reciprocal Rank (MRR), CCB achieved 71%, 62%, 52%, and 68% on the same projects respectively, thereby highlighting its potential for practical application in code reviewer recommendation.
The overgeneralisation may happen because most studies on data publishing for multiple sensitive attributes(SAs)have not considered the personalised privacy ***,sensitive information disclosure may also be caused by t...
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The overgeneralisation may happen because most studies on data publishing for multiple sensitive attributes(SAs)have not considered the personalised privacy ***,sensitive information disclosure may also be caused by these personalised *** address the matter,this article develops a personalised data publishing method for multiple *** to the requirements of individuals,the new method partitions SAs values into two categories:private values and public values,and breaks the association between them for privacy *** the private values,this paper takes the process of anonymisation,while the public values are released without this *** algorithm is designed to achieve the privacy mode,where the selectivity is determined by the sensitive value frequency and undesirable *** experimental results show that the proposed method can provide more information utility when compared with previous *** theoretic analyses and experiments also indicate that the privacy can be guaranteed even though the public values are known to an *** overgeneralisation and privacy breach caused by the personalised requirement can be avoided by the new method.
Despite the effectiveness of vision-language supervised fine-tuning in enhancing the performance of vision large language models(VLLMs), existing visual instruction tuning datasets include the following limitations.(1...
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Despite the effectiveness of vision-language supervised fine-tuning in enhancing the performance of vision large language models(VLLMs), existing visual instruction tuning datasets include the following limitations.(1) Instruction annotation quality: despite existing VLLMs exhibiting strong performance,instructions generated by those advanced VLLMs may still suffer from inaccuracies, such as hallucinations.(2) Instructions and image diversity: the limited range of instruction types and the lack of diversity in image data may impact the model's ability to generate diversified and closer to real-world scenarios outputs. To address these challenges, we construct a high-quality, diverse visual instruction tuning dataset MMInstruct,which consists of 973k instructions from 24 domains. There are four instruction types: judgment, multiplechoice, long visual question answering, and short visual question answering. To construct MMInstruct, we propose an instruction generation data engine that leverages GPT-4V, GPT-3.5, and manual correction. Our instruction generation engine enables semi-automatic, low-cost, and multi-domain instruction generation at 1/6 the cost of manual construction. Through extensive experiment validation and ablation experiments,we demonstrate that MMInstruct could significantly improve the performance of VLLMs, e.g., the model fine-tuning on MMInstruct achieves new state-of-the-art performance on 10 out of 12 benchmarks. The code and data shall be available at https://***/yuecao0119/MMInstruct.
In the era of network communication,digital image encryption(DIE)technology is critical to ensure the security of image ***,there has been limited research on combining deep learning neural networks with chaotic mappi...
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In the era of network communication,digital image encryption(DIE)technology is critical to ensure the security of image ***,there has been limited research on combining deep learning neural networks with chaotic mapping for the encryption of digital ***,this paper addresses this gap by studying the generation of pseudo-random sequences(PRS)chaotic signals using dual logistic chaotic *** signals are then predicted using long and short-term memory(LSTM)networks,resulting in the reconstruction of a new chaotic *** the research process,it was discovered that there are numerous training parameters associated with the LSTM network,which can hinder training *** overcome this challenge and improve training efficiency,the paper proposes an improved particle swarm optimization(IPSO)algorithm to optimize the LSTM ***,the obtained chaotic signal from the optimized model training is further scrambled,obfuscated,and diffused to achieve the final encrypted *** research presents a digital image encryption(DIE)algorithm based on a double chaotic map(DCM)and *** algorithm demonstrates a high average NPCR(Number of Pixel Change Rate)of 99.56%and a UACI(Unified Average Changing Intensity)value of 33.46%,indicating a strong ability to resist differential ***,the proposed algorithm realizes secure and sensitive digital image encryption,ensuring the protection of personal information in the Internet environment.
The rapid acceleration of urbanization and industrialization has led to a significant increase in PM2.5 pollution, making it a critical global concern. The accurate prediction of PM2.5 concentrations is of utmost impo...
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All the software products developed will need testing to ensure the quality and accuracy of the product. It makes the life of testers much easier when they can optimize on the effort spent and predict defects for the ...
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