The Linux kernel adopts a large number of security checks to prevent security-sensitive operations from being executed under unsafe *** a security-sensitive operation is unchecked,a missing-check issue *** check is a ...
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The Linux kernel adopts a large number of security checks to prevent security-sensitive operations from being executed under unsafe *** a security-sensitive operation is unchecked,a missing-check issue *** check is a class of severe bugs in software programs especially in operating system kernels,which may cause a variety of security issues,such as out-of-bound accesses,permission bypasses,and privilege *** to the lack of security specifications,how to automatically identify security-sensitive operations and their required security checks in the Linux kernel becomes a challenge for missing-check *** this paper,we present an accurate missing-check analysis method for Linux kernel,which can automatically infer possible security-sensitive ***,we first automatically identify all possible security check functions of *** according to their callsites,a two-direction analysis method is leveraged to identify possible security-sensitive operations.A missing-check bug is reported when the security-sensitive operation is not protected by its corresponding security *** have implemented our method as a tool,named AMCheX,on top of the LLVM(Low Level Virtual Machine)framework and evaluated it on the Linux *** reported 12 new missing-check bugs which can cause security *** of them have been confirmed by Linux maintainers.
The increase in obesity, a sedentary lifestyle, and population aging are considered the main factors for the increase in Type 2 Diabetes Mellitus (T2DM) worldwide. Global estimates indicate that around 400 million peo...
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Large Language Models (LLM) have demonstrated their strong ability in the field of machine translation (MT), yet they suffer from high computational cost and latency. Therefore, transferring translation knowledge from...
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作者：
Zhou, ZhengyuLiu, WeiweiSchool of Computer Science
National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China
Goodness-of-fit testing, a classical statistical tool, has been extensively explored in the batch setting, where the sample size is ***, practitioners often prefer methods that adapt to the complexity of a problem rat...
Goodness-of-fit testing, a classical statistical tool, has been extensively explored in the batch setting, where the sample size is ***, practitioners often prefer methods that adapt to the complexity of a problem rather than fixing the sample size *** batch tests are generally unsuitable for streaming data, as valid inference after data peeking requires multiple testing corrections, resulting in reduced statistical *** address this issue, we delve into the design of consistent sequential goodness-of-fit *** the principle of testing by betting, we reframe this task as selecting a sequence of payoff functions that maximize the wealth of a fictitious bettor, betting against the null in a repeated *** conduct experiments to demonstrate the adaptability of our sequential test across varying difficulty levels of problems while maintaining control over type-I errors. Copyright 2024 by the author(s)
Autonomous spacecraft rendezvous poses significant challenges in increasingly complex space missions. Recently, Reinforcement Learning (RL) has proven effective in the domain of spacecraft rendezvous, owing to its hig...
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作者：
Li, BoqiLiu, WeiweiSchool of Computer Science
National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China
The rising threat of backdoor poisoning attacks (BPAs) on Deep Neural Networks (DNNs) has become a significant concern in recent years. In such attacks, the adversaries strategically target a specific class and genera...
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The rising threat of backdoor poisoning attacks (BPAs) on Deep Neural Networks (DNNs) has become a significant concern in recent years. In such attacks, the adversaries strategically target a specific class and generate a poisoned training set. The neural network (NN), well-trained on the poisoned training set, is able to predict any input with the trigger pattern as the targeted label, while maintaining accurate outputs for clean inputs. However, why the BPAs work remains less explored. To fill this gap, we employ a dirty-label attack and conduct a detailed analysis of BPAs in a two-layer convolutional neural network. We provide theoretical insights and results on the effectiveness of BPAs. Our experimental results on two real-world datasets validate our theoretical findings. Copyright 2024 by the author(s)
Conventional machine learning methods for software effort estimation (SEE) have seen an increase in research interest. Conversely, there are few research that try to evaluate how well deep learning techniques work in ...
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Exploring the correlations between medical features is essential for extracting patient health patterns from electronic health records (EHR) data, and strengthening medical predictions and decision-making. To constrai...
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Lake level,area and volume are sensitive indicators of climate *** present,many studies have focused on the interannual water balance of lakes,but lake level and area can change remarkably with seasons,especially for ...
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Lake level,area and volume are sensitive indicators of climate *** present,many studies have focused on the interannual water balance of lakes,but lake level and area can change remarkably with seasons,especially for lakes with seasonal ice *** Namco,a seasonal frozen lake,was selected as an example to investigate its seasonal water ***-source altimetry and Landsat data were used to obtain the seasonal lake level and area from 1992 to 2019,and seasonal lake volume variations were also *** results indicated the average lake level,area and volume in autumn were the *** lake level,area,and volume experienced three turning points approximately in 2000,2010,and 2016,and showed an overall increasing trend from 1992 to 2019,with slopes of 0.15 m/year,2.17 km^(2)/year,and 0.14 km^(3)/year,*** lake area expanded significantly in autumn,which was related to the abundant *** time of land surface runoff,increased temperature,and evaporation may be the reason for the low lake level and volume in *** precipitation was the dominant factor of water balance,which explained 62.09%,62.43%,and 62.10%of the variations in lake level,area,and volume,respectively.
Human verification and activity analysis(HVAA)are primarily employed to observe,track,and monitor human motion patterns using redgreen-blue(RGB)images and *** human interaction using RGB images is one of the most comp...
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Human verification and activity analysis(HVAA)are primarily employed to observe,track,and monitor human motion patterns using redgreen-blue(RGB)images and *** human interaction using RGB images is one of the most complex machine learning tasks in recent *** models rely on various parameters,such as the detection rate,position,and direction of human body components in RGB *** paper presents robust human activity analysis for event recognition via the extraction of contextual intelligence-based *** use human interaction image sequences as input data,we first perform a few denoising ***,human-to-human analyses are employed to deliver more precise *** phase follows feature engineering techniques,including diverse feature ***,we used the graph mining method for feature optimization and AdaBoost for *** tested our proposed HVAA model on two benchmark *** testing of the proposed HVAA system exhibited a mean accuracy of 92.15%for the Sport Videos in theWild(SVW)*** second benchmark dataset,UT-interaction,had a mean accuracy of 92.83%.Therefore,these results demonstrated a better recognition rate and outperformed other novel techniques in body part tracking and event *** proposed HVAA system can be utilized in numerous real-world applications including,healthcare,surveillance,task monitoring,atomic actions,gesture and posture analysis.
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