The emergence of adversarial examples has revealed the inadequacies in the robustness of image classification models based on Convolutional Neural Networks (CNNs). Particularly in recent years, the discovery of natura...The emergence of adversarial examples has revealed the inadequacies in the robustness of image classification models based on Convolutional Neural Networks (CNNs). Particularly in recent years, the discovery of natural adversarial examples has posed significant challenges, as traditional defense methods against adversarial attacks have proven to be largely ineffective against these natural adversarial examples. This paper explores defenses against these natural adversarial examples from three perspectives: adversarial examples, model architecture, and dataset. First, it employs Class Activation Mapping (CAM) to visualize how models classify natural adversarial examples, identifying several typical attack patterns. Next, various common CNN models are analyzed to evaluate their susceptibility to these attacks, revealing that different architectures exhibit varying defensive capabilities. The study finds that as the depth of a network increases, its defenses against natural adversarial examples strengthen. Lastly, Finally, the impact of dataset class distribution on the defense capability of models is examined, focusing on two aspects: the number of classes in the training set and the number of predicted classes. This study investigates how these factors influence the model’s ability to defend against natural adversarial examples. Results indicate that reducing the number of training classes enhances the model’s defense against natural adversarial examples. Additionally, under a fixed number of training classes, some CNN models show an optimal range of predicted classes for achieving the best defense performance against these adversarial examples.展开更多
Recent years have witnessed the ever-increasing performance of Deep Neural Networks(DNNs)in computer vision tasks.However,researchers have identified a potential vulnerability:carefully crafted adversarial examples ca...Recent years have witnessed the ever-increasing performance of Deep Neural Networks(DNNs)in computer vision tasks.However,researchers have identified a potential vulnerability:carefully crafted adversarial examples can easily mislead DNNs into incorrect behavior via the injection of imperceptible modification to the input data.In this survey,we focus on(1)adversarial attack algorithms to generate adversarial examples,(2)adversarial defense techniques to secure DNNs against adversarial examples,and(3)important problems in the realm of adversarial examples beyond attack and defense,including the theoretical explanations,trade-off issues and benign attacks in adversarial examples.Additionally,we draw a brief comparison between recently published surveys on adversarial examples,and identify the future directions for the research of adversarial examples,such as the generalization of methods and the understanding of transferability,that might be solutions to the open problems in this field.展开更多
Transfer-based Adversarial Attacks(TAAs)can deceive a victim model even without prior knowledge.This is achieved by leveraging the property of adversarial examples.That is,when generated from a surrogate model,they re...Transfer-based Adversarial Attacks(TAAs)can deceive a victim model even without prior knowledge.This is achieved by leveraging the property of adversarial examples.That is,when generated from a surrogate model,they retain their features if applied to other models due to their good transferability.However,adversarial examples often exhibit overfitting,as they are tailored to exploit the particular architecture and feature representation of source models.Consequently,when attempting black-box transfer attacks on different target models,their effectiveness is decreased.To solve this problem,this study proposes an approach based on a Regularized Constrained Feature Layer(RCFL).The proposed method first uses regularization constraints to attenuate the initial examples of low-frequency components.Perturbations are then added to a pre-specified layer of the source model using the back-propagation technique,in order to modify the original adversarial examples.Afterward,a regularized loss function is used to enhance the black-box transferability between different target models.The proposed method is finally tested on the ImageNet,CIFAR-100,and Stanford Car datasets with various target models,The obtained results demonstrate that it achieves a significantly higher transfer-based adversarial attack success rate compared with baseline techniques.展开更多
Deep neural networks(DNNs)are poten-tially susceptible to adversarial examples that are ma-liciously manipulated by adding imperceptible pertur-bations to legitimate inputs,leading to abnormal be-havior of models.Plen...Deep neural networks(DNNs)are poten-tially susceptible to adversarial examples that are ma-liciously manipulated by adding imperceptible pertur-bations to legitimate inputs,leading to abnormal be-havior of models.Plenty of methods have been pro-posed to defend against adversarial examples.How-ever,the majority of them are suffering the follow-ing weaknesses:1)lack of generalization and prac-ticality.2)fail to deal with unknown attacks.To ad-dress the above issues,we design the adversarial na-ture eraser(ANE)and feature map detector(FMD)to detect fragile and high-intensity adversarial examples,respectively.Then,we apply the ensemble learning method to compose our detector,dealing with adver-sarial examples with diverse magnitudes in a divide-and-conquer manner.Experimental results show that our approach achieves 99.30%and 99.62%Area un-der Curve(AUC)scores on average when tested with various Lp norm-based attacks on CIFAR-10 and Im-ageNet,respectively.Furthermore,our approach also shows its potential in detecting unknown attacks.展开更多
Antivirus vendors and the research community employ Machine Learning(ML)or Deep Learning(DL)-based static analysis techniques for efficient identification of new threats,given the continual emergence of novel malware ...Antivirus vendors and the research community employ Machine Learning(ML)or Deep Learning(DL)-based static analysis techniques for efficient identification of new threats,given the continual emergence of novel malware variants.On the other hand,numerous researchers have reported that Adversarial Examples(AEs),generated by manipulating previously detected malware,can successfully evade ML/DL-based classifiers.Commercial antivirus systems,in particular,have been identified as vulnerable to such AEs.This paper firstly focuses on conducting black-box attacks to circumvent ML/DL-based malware classifiers.Our attack method utilizes seven different perturbations,including Overlay Append,Section Append,and Break Checksum,capitalizing on the ambiguities present in the PE format,as previously employed in evasion attack research.By directly applying the perturbation techniques to PE binaries,our attack method eliminates the need to grapple with the problem-feature space dilemma,a persistent challenge in many evasion attack studies.Being a black-box attack,our method can generate AEs that successfully evade both DL-based and ML-based classifiers.Also,AEs generated by the attack method retain their executability and malicious behavior,eliminating the need for functionality verification.Through thorogh evaluations,we confirmed that the attack method achieves an evasion rate of 65.6%against well-known ML-based malware detectors and can reach a remarkable 99%evasion rate against well-known DL-based malware detectors.Furthermore,our AEs demonstrated the capability to bypass detection by 17%of vendors out of the 64 on VirusTotal(VT).In addition,we propose a defensive approach that utilizes Trend Locality Sensitive Hashing(TLSH)to construct a similarity-based defense model.Through several experiments on the approach,we verified that our defense model can effectively counter AEs generated by the perturbation techniques.In conclusion,our defense model alleviates the limitation of the most promising defense method,adversarial training,which is only effective against the AEs that are included in the training classifiers.展开更多
[Objective] Taking the characteristic of flower diameter of Tagetes L.as an example,this study aimed to select example varieties used in the DUS Test Guideline of Tagetes L.[Method] Two continuous years of measurement...[Objective] Taking the characteristic of flower diameter of Tagetes L.as an example,this study aimed to select example varieties used in the DUS Test Guideline of Tagetes L.[Method] Two continuous years of measurements of flower diameter of 25 varieties were collected and then analyzed by using the box plot to illustrate the uniformity and stability of flower diameter of each variety.[Result] According to the information of variability,distribution symmetry of measurements and outliers of flower diameter of varieties provided by box plots,variety 16,2 and 4 were selected as the example varieties for the three expression states with respective flower diameter of 3.0-4.4,6.0-7.4 and 9.0-10.4 cm.[Conclusion] The box plot is an efficient method for the general analysis of varieties,which provides information covering the actual and possible expression range,median and outliers of measurements of flower diameter of each variety.It also provides references for selecting example varieties for other quantitative characteristics and evaluating the quality of varieties.展开更多
In order to narrow the semantic gap existing in content-based image retrieval （CBIR）,a novel retrieval technology called auto-extended multi query examples （AMQE） is proposed.It expands the single one query image ...In order to narrow the semantic gap existing in content-based image retrieval （CBIR）,a novel retrieval technology called auto-extended multi query examples （AMQE） is proposed.It expands the single one query image used in traditional image retrieval into multi query examples so as to include more image features related with semantics.Retrieving images for each of the multi query examples and integrating the retrieval results,more relevant images can be obtained.The property of the recall-precision curve of a general retrieval algorithm and the K-means clustering method are used to realize the expansion according to the distance of image features of the initially retrieved images.The experimental results demonstrate that the AMQE technology can greatly improve the recall and precision of the original algorithms.展开更多
In recent work,adversarial stickers are widely used to attack face recognition(FR)systems in the physical world.However,it is difficult to evaluate the performance of physical attacks because of the lack of volunteers...In recent work,adversarial stickers are widely used to attack face recognition(FR)systems in the physical world.However,it is difficult to evaluate the performance of physical attacks because of the lack of volunteers in the experiment.In this paper,a simple attack method called incomplete physical adversarial attack(IPAA)is proposed to simulate physical attacks.Different from the process of physical attacks,when an IPAA is conducted,a photo of the adversarial sticker is embedded into a facial image as the input to attack FR systems,which can obtain results similar to those of physical attacks without inviting any volunteers.The results show that IPAA has a higher similarity with physical attacks than digital attacks,indicating that IPAA is able to evaluate the performance of physical attacks.IPAA is effective in quantitatively measuring the impact of the sticker location on the results of attacks.展开更多
Practical activities are an indispensable piece of science education.As such,assessment of these practical activities is vital for continually enhancing the quality of science instruction.This article presents a tenta...Practical activities are an indispensable piece of science education.As such,assessment of these practical activities is vital for continually enhancing the quality of science instruction.This article presents a tentative framework structured for assessing practical activities in science education.The proposed framework is built upon seven components:engagement and participation,relational thinking,stepwise mastery,contextualization,multidimensional integration,empowerment,and scientific articulacy.The framework might provide science educators with an alternative lens through which to enhance the effectiveness of practical activities,promote student engagement,and better prepare learners for the future.This framework is designed to be additive and complementary to existing assessment approaches and can be adapted and customized to suit specific local contexts and varying grade levels by science educators in the pursuit of excellence in an ever-evolving science education landscape.展开更多
In the new era,the impact of emerging productive forces has permeated every sector of industry.As the core production factor of these forces,data plays a pivotal role in industrial transformation and social developmen...In the new era,the impact of emerging productive forces has permeated every sector of industry.As the core production factor of these forces,data plays a pivotal role in industrial transformation and social development.Consequently,many domestic universities have introduced majors or courses related to big data.Among these,the Big Data Management and Applications major stands out for its interdisciplinary approach and emphasis on practical skills.However,as an emerging field,it has not yet accumulated a robust foundation in teaching theory and practice.Current instructional practices face issues such as unclear training objectives,inconsistent teaching methods and course content,insufficient integration of practical components,and a shortage of qualified faculty-factors that hinder both the development of the major and the overall quality of education.Taking the statistics course within the Big Data Management and Applications major as an example,this paper examines the challenges faced by statistics education in the context of emerging productive forces and proposes corresponding improvement measures.By introducing innovative teaching concepts and strategies,the teaching system for professional courses is optimized,and authentic classroom scenarios are recreated through illustrative examples.Questionnaire surveys and statistical analyses of data collected before and after the teaching reforms indicate that the curriculum changes effectively enhance instructional outcomes,promote the development of the major,and improve the quality of talent cultivation.展开更多
Deep neural networks remain susceptible to adversarial examples,where the goal of an adversarial attack is to introduce small perturbations to the original examples in order to confuse the model without being easily d...Deep neural networks remain susceptible to adversarial examples,where the goal of an adversarial attack is to introduce small perturbations to the original examples in order to confuse the model without being easily detected.Although many adversarial attack methods produce adversarial examples that have achieved great results in the whitebox setting,they exhibit low transferability in the black-box setting.In order to improve the transferability along the baseline of the gradient-based attack technique,we present a novel Stochastic Gradient Accumulation Momentum Iterative Attack(SAMI-FGSM)in this study.In particular,during each iteration,the gradient information is calculated using a normal sampling approach that randomly samples around the sample points,with the highest probability of capturing adversarial features.Meanwhile,the accumulated information of the sampled gradient from the previous iteration is further considered to modify the current updated gradient,and the original gradient attack direction is changed to ensure that the updated gradient direction is more stable.Comprehensive experiments conducted on the ImageNet dataset show that our method outperforms existing state-of-the-art gradient-based attack techniques,achieving an average improvement of 10.2%in transferability.展开更多
Network intrusion detection systems(IDS)are a prevalent method for safeguarding network traffic against attacks.However,existing IDS primarily depend on machine learning(ML)models,which are vulnerable to evasion throu...Network intrusion detection systems(IDS)are a prevalent method for safeguarding network traffic against attacks.However,existing IDS primarily depend on machine learning(ML)models,which are vulnerable to evasion through adversarial examples.In recent years,the Wasserstein Generative Adversarial Network(WGAN),based on Wasserstein distance,has been extensively utilized to generate adversarial examples.Nevertheless,several challenges persist:(1)WGAN experiences the mode collapse problem when generating multi-category network traffic data,leading to subpar quality and insufficient diversity in the generated data;(2)Due to unstable training processes,the authenticity of the data produced by WGAN is often low.This study improves WGAN to address these issues and proposes a new adversarial sample generation algorithm called Distortion Enhanced Multi-Generator Generative Adversarial Network(DEMGAN).DEMGAN effectively evades ML-based IDS by proficiently obfuscating network traffic data samples.We assess the efficacy of our attack method against five ML-based IDS using two public datasets.The results demonstrate that our method can successfully bypass IDS,achieving average evasion rates of 97.42%and 87.51%,respectively.Furthermore,empirical findings indicate that retraining the IDS with the generated adversarial samples significantly bolsters the system’s capability to detect adversarial samples,resulting in an average recognition rate increase of 86.78%.This approach not only enhances the performance of the IDS but also strengthens the network’s resilience against potential threats,thereby optimizing network security measures.展开更多
In this paper, we conduct research on the category management for chain retail enterprises while taking Jiajiayue as the example. Category management theory research has more than ten years history, its core has been ...In this paper, we conduct research on the category management for chain retail enterprises while taking Jiajiayue as the example. Category management theory research has more than ten years history, its core has been basically mature theory. The future study of category management focuses more on how to make the category management theory and be combined closely with the enterprise actual and pays more attention to the effect of the implementation and effect. Implementing category management investment return period is how to curve and how to evaluate the effect of the category management. The supermarket of the category management innovation is a huge project without a reasonable and predictable return that will be conducive to category management decision-making is unfavorable to the supermarket profits change after the implementation of category management right evaluation. Under this background, we propose our novel perspective on the corresponding issues to form the better theoretical analysis on the issues that is meaningful.展开更多
Objective The dissolution and precipitation of carbonate during burial diagenetic process controls the reservoir property in deep buried strata. The geological process related with it has become a research focus durin...Objective The dissolution and precipitation of carbonate during burial diagenetic process controls the reservoir property in deep buried strata. The geological process related with it has become a research focus during recent years. The most important dissolution fluids to carbonates are probably H2S and CO2 as byproducts of sulfate reduction in deep-buried setting with sulfate minerals, but carbonates are more soluble in relatively low temperature, which is the so-called retrograde solubility. Several geological processes can result in the decrease of temperature, including the upward migration of thermal fluids and tectonic uplift.展开更多
A simple and effective image inpainting method is proposed in this paper, which is proved to be suitable for different kinds of target regions with shapes from little scraps to large unseemly objects in a wide range o...A simple and effective image inpainting method is proposed in this paper, which is proved to be suitable for different kinds of target regions with shapes from little scraps to large unseemly objects in a wide range of images. It is an important improvement upon the traditional image inpainting techniques. By introducing a new bijeetive-mapping term into the matching cost function, the artificial repetition problem in the final inpainting image is practically solved. In addition, by adopting an inpainting error map, not only the target pixels are refined gradually during the inpainting process but also the overlapped target patches are combined more seamlessly than previous method. Finally, the inpainting time is dramatically decreased by using a new acceleration method in the matching process.展开更多
The role of authigenic clay growth in clay gouge is increasingly recognized as a key to understanding the mechanics of berittle faulting and fault zone processes,including creep and seismogenesis,and providing new ins...The role of authigenic clay growth in clay gouge is increasingly recognized as a key to understanding the mechanics of berittle faulting and fault zone processes,including creep and seismogenesis,and providing new insights into the ongoing debate about the frictional strength of brittle fault(Haines and van der Pluijm,2012).However,neither the conditions nor the processes which展开更多
文摘The emergence of adversarial examples has revealed the inadequacies in the robustness of image classification models based on Convolutional Neural Networks (CNNs). Particularly in recent years, the discovery of natural adversarial examples has posed significant challenges, as traditional defense methods against adversarial attacks have proven to be largely ineffective against these natural adversarial examples. This paper explores defenses against these natural adversarial examples from three perspectives: adversarial examples, model architecture, and dataset. First, it employs Class Activation Mapping (CAM) to visualize how models classify natural adversarial examples, identifying several typical attack patterns. Next, various common CNN models are analyzed to evaluate their susceptibility to these attacks, revealing that different architectures exhibit varying defensive capabilities. The study finds that as the depth of a network increases, its defenses against natural adversarial examples strengthen. Lastly, Finally, the impact of dataset class distribution on the defense capability of models is examined, focusing on two aspects: the number of classes in the training set and the number of predicted classes. This study investigates how these factors influence the model’s ability to defend against natural adversarial examples. Results indicate that reducing the number of training classes enhances the model’s defense against natural adversarial examples. Additionally, under a fixed number of training classes, some CNN models show an optimal range of predicted classes for achieving the best defense performance against these adversarial examples.
基金Supported by the National Natural Science Foundation of China(U1903214,62372339,62371350,61876135)the Ministry of Education Industry University Cooperative Education Project(202102246004,220800006041043,202002142012)the Fundamental Research Funds for the Central Universities(2042023kf1033)。
文摘Recent years have witnessed the ever-increasing performance of Deep Neural Networks(DNNs)in computer vision tasks.However,researchers have identified a potential vulnerability:carefully crafted adversarial examples can easily mislead DNNs into incorrect behavior via the injection of imperceptible modification to the input data.In this survey,we focus on(1)adversarial attack algorithms to generate adversarial examples,(2)adversarial defense techniques to secure DNNs against adversarial examples,and(3)important problems in the realm of adversarial examples beyond attack and defense,including the theoretical explanations,trade-off issues and benign attacks in adversarial examples.Additionally,we draw a brief comparison between recently published surveys on adversarial examples,and identify the future directions for the research of adversarial examples,such as the generalization of methods and the understanding of transferability,that might be solutions to the open problems in this field.
基金supported by the Intelligent Policing Key Laboratory of Sichuan Province(No.ZNJW2022KFZD002)This work was supported by the Scientific and Technological Research Program of Chongqing Municipal Education Commission(Grant Nos.KJQN202302403,KJQN202303111).
文摘Transfer-based Adversarial Attacks(TAAs)can deceive a victim model even without prior knowledge.This is achieved by leveraging the property of adversarial examples.That is,when generated from a surrogate model,they retain their features if applied to other models due to their good transferability.However,adversarial examples often exhibit overfitting,as they are tailored to exploit the particular architecture and feature representation of source models.Consequently,when attempting black-box transfer attacks on different target models,their effectiveness is decreased.To solve this problem,this study proposes an approach based on a Regularized Constrained Feature Layer(RCFL).The proposed method first uses regularization constraints to attenuate the initial examples of low-frequency components.Perturbations are then added to a pre-specified layer of the source model using the back-propagation technique,in order to modify the original adversarial examples.Afterward,a regularized loss function is used to enhance the black-box transferability between different target models.The proposed method is finally tested on the ImageNet,CIFAR-100,and Stanford Car datasets with various target models,The obtained results demonstrate that it achieves a significantly higher transfer-based adversarial attack success rate compared with baseline techniques.
基金This work was partly supported by the National Natural Science Foundation of China under No.62372334,61876134,and U1836112.
文摘Deep neural networks(DNNs)are poten-tially susceptible to adversarial examples that are ma-liciously manipulated by adding imperceptible pertur-bations to legitimate inputs,leading to abnormal be-havior of models.Plenty of methods have been pro-posed to defend against adversarial examples.How-ever,the majority of them are suffering the follow-ing weaknesses:1)lack of generalization and prac-ticality.2)fail to deal with unknown attacks.To ad-dress the above issues,we design the adversarial na-ture eraser(ANE)and feature map detector(FMD)to detect fragile and high-intensity adversarial examples,respectively.Then,we apply the ensemble learning method to compose our detector,dealing with adver-sarial examples with diverse magnitudes in a divide-and-conquer manner.Experimental results show that our approach achieves 99.30%and 99.62%Area un-der Curve(AUC)scores on average when tested with various Lp norm-based attacks on CIFAR-10 and Im-ageNet,respectively.Furthermore,our approach also shows its potential in detecting unknown attacks.
基金supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)Grant funded by the Korea government,Ministry of Science and ICT(MSIT)(No.2017-0-00168,Automatic Deep Malware Analysis Technology for Cyber Threat Intelligence).
文摘Antivirus vendors and the research community employ Machine Learning(ML)or Deep Learning(DL)-based static analysis techniques for efficient identification of new threats,given the continual emergence of novel malware variants.On the other hand,numerous researchers have reported that Adversarial Examples(AEs),generated by manipulating previously detected malware,can successfully evade ML/DL-based classifiers.Commercial antivirus systems,in particular,have been identified as vulnerable to such AEs.This paper firstly focuses on conducting black-box attacks to circumvent ML/DL-based malware classifiers.Our attack method utilizes seven different perturbations,including Overlay Append,Section Append,and Break Checksum,capitalizing on the ambiguities present in the PE format,as previously employed in evasion attack research.By directly applying the perturbation techniques to PE binaries,our attack method eliminates the need to grapple with the problem-feature space dilemma,a persistent challenge in many evasion attack studies.Being a black-box attack,our method can generate AEs that successfully evade both DL-based and ML-based classifiers.Also,AEs generated by the attack method retain their executability and malicious behavior,eliminating the need for functionality verification.Through thorogh evaluations,we confirmed that the attack method achieves an evasion rate of 65.6%against well-known ML-based malware detectors and can reach a remarkable 99%evasion rate against well-known DL-based malware detectors.Furthermore,our AEs demonstrated the capability to bypass detection by 17%of vendors out of the 64 on VirusTotal(VT).In addition,we propose a defensive approach that utilizes Trend Locality Sensitive Hashing(TLSH)to construct a similarity-based defense model.Through several experiments on the approach,we verified that our defense model can effectively counter AEs generated by the perturbation techniques.In conclusion,our defense model alleviates the limitation of the most promising defense method,adversarial training,which is only effective against the AEs that are included in the training classifiers.
基金Supported by the Special Fund for Agro-scientific Research in the Public Interest(200903008-14)the National "948" Project(2009-Z11)~~
文摘[Objective] Taking the characteristic of flower diameter of Tagetes L.as an example,this study aimed to select example varieties used in the DUS Test Guideline of Tagetes L.[Method] Two continuous years of measurements of flower diameter of 25 varieties were collected and then analyzed by using the box plot to illustrate the uniformity and stability of flower diameter of each variety.[Result] According to the information of variability,distribution symmetry of measurements and outliers of flower diameter of varieties provided by box plots,variety 16,2 and 4 were selected as the example varieties for the three expression states with respective flower diameter of 3.0-4.4,6.0-7.4 and 9.0-10.4 cm.[Conclusion] The box plot is an efficient method for the general analysis of varieties,which provides information covering the actual and possible expression range,median and outliers of measurements of flower diameter of each variety.It also provides references for selecting example varieties for other quantitative characteristics and evaluating the quality of varieties.
基金The National High Technology Research and Develop-ment Program of China (863 Program) (No.2002AA413420).
文摘In order to narrow the semantic gap existing in content-based image retrieval （CBIR）,a novel retrieval technology called auto-extended multi query examples （AMQE） is proposed.It expands the single one query image used in traditional image retrieval into multi query examples so as to include more image features related with semantics.Retrieving images for each of the multi query examples and integrating the retrieval results,more relevant images can be obtained.The property of the recall-precision curve of a general retrieval algorithm and the K-means clustering method are used to realize the expansion according to the distance of image features of the initially retrieved images.The experimental results demonstrate that the AMQE technology can greatly improve the recall and precision of the original algorithms.
文摘In recent work,adversarial stickers are widely used to attack face recognition(FR)systems in the physical world.However,it is difficult to evaluate the performance of physical attacks because of the lack of volunteers in the experiment.In this paper,a simple attack method called incomplete physical adversarial attack(IPAA)is proposed to simulate physical attacks.Different from the process of physical attacks,when an IPAA is conducted,a photo of the adversarial sticker is embedded into a facial image as the input to attack FR systems,which can obtain results similar to those of physical attacks without inviting any volunteers.The results show that IPAA has a higher similarity with physical attacks than digital attacks,indicating that IPAA is able to evaluate the performance of physical attacks.IPAA is effective in quantitatively measuring the impact of the sticker location on the results of attacks.
基金supported by the Scientific Research Program of Hefei Normal University(2024KY71)the Partnership Program of Hefei Normal University(HXXM2022094).
文摘Practical activities are an indispensable piece of science education.As such,assessment of these practical activities is vital for continually enhancing the quality of science instruction.This article presents a tentative framework structured for assessing practical activities in science education.The proposed framework is built upon seven components:engagement and participation,relational thinking,stepwise mastery,contextualization,multidimensional integration,empowerment,and scientific articulacy.The framework might provide science educators with an alternative lens through which to enhance the effectiveness of practical activities,promote student engagement,and better prepare learners for the future.This framework is designed to be additive and complementary to existing assessment approaches and can be adapted and customized to suit specific local contexts and varying grade levels by science educators in the pursuit of excellence in an ever-evolving science education landscape.
文摘In the new era,the impact of emerging productive forces has permeated every sector of industry.As the core production factor of these forces,data plays a pivotal role in industrial transformation and social development.Consequently,many domestic universities have introduced majors or courses related to big data.Among these,the Big Data Management and Applications major stands out for its interdisciplinary approach and emphasis on practical skills.However,as an emerging field,it has not yet accumulated a robust foundation in teaching theory and practice.Current instructional practices face issues such as unclear training objectives,inconsistent teaching methods and course content,insufficient integration of practical components,and a shortage of qualified faculty-factors that hinder both the development of the major and the overall quality of education.Taking the statistics course within the Big Data Management and Applications major as an example,this paper examines the challenges faced by statistics education in the context of emerging productive forces and proposes corresponding improvement measures.By introducing innovative teaching concepts and strategies,the teaching system for professional courses is optimized,and authentic classroom scenarios are recreated through illustrative examples.Questionnaire surveys and statistical analyses of data collected before and after the teaching reforms indicate that the curriculum changes effectively enhance instructional outcomes,promote the development of the major,and improve the quality of talent cultivation.
基金supported in part by the National Natural Science Foundation(62202118,U24A20241)in part by Major Scientific and Technological Special Project of Guizhou Province([2024]014,[2024]003)+1 种基金in part by Scientific and Technological Research Projects from Guizhou Education Department(Qian jiao ji[2023]003)in part by Guizhou Science and Technology Department Hundred Level Innovative Talents Project(GCC[2023]018).
文摘Deep neural networks remain susceptible to adversarial examples,where the goal of an adversarial attack is to introduce small perturbations to the original examples in order to confuse the model without being easily detected.Although many adversarial attack methods produce adversarial examples that have achieved great results in the whitebox setting,they exhibit low transferability in the black-box setting.In order to improve the transferability along the baseline of the gradient-based attack technique,we present a novel Stochastic Gradient Accumulation Momentum Iterative Attack(SAMI-FGSM)in this study.In particular,during each iteration,the gradient information is calculated using a normal sampling approach that randomly samples around the sample points,with the highest probability of capturing adversarial features.Meanwhile,the accumulated information of the sampled gradient from the previous iteration is further considered to modify the current updated gradient,and the original gradient attack direction is changed to ensure that the updated gradient direction is more stable.Comprehensive experiments conducted on the ImageNet dataset show that our method outperforms existing state-of-the-art gradient-based attack techniques,achieving an average improvement of 10.2%in transferability.
基金supported by the National Defense Basic Scientific Research Programof China under grant No.JCKY2023602C026.
文摘Network intrusion detection systems(IDS)are a prevalent method for safeguarding network traffic against attacks.However,existing IDS primarily depend on machine learning(ML)models,which are vulnerable to evasion through adversarial examples.In recent years,the Wasserstein Generative Adversarial Network(WGAN),based on Wasserstein distance,has been extensively utilized to generate adversarial examples.Nevertheless,several challenges persist:(1)WGAN experiences the mode collapse problem when generating multi-category network traffic data,leading to subpar quality and insufficient diversity in the generated data;(2)Due to unstable training processes,the authenticity of the data produced by WGAN is often low.This study improves WGAN to address these issues and proposes a new adversarial sample generation algorithm called Distortion Enhanced Multi-Generator Generative Adversarial Network(DEMGAN).DEMGAN effectively evades ML-based IDS by proficiently obfuscating network traffic data samples.We assess the efficacy of our attack method against five ML-based IDS using two public datasets.The results demonstrate that our method can successfully bypass IDS,achieving average evasion rates of 97.42%and 87.51%,respectively.Furthermore,empirical findings indicate that retraining the IDS with the generated adversarial samples significantly bolsters the system’s capability to detect adversarial samples,resulting in an average recognition rate increase of 86.78%.This approach not only enhances the performance of the IDS but also strengthens the network’s resilience against potential threats,thereby optimizing network security measures.
文摘In this paper, we conduct research on the category management for chain retail enterprises while taking Jiajiayue as the example. Category management theory research has more than ten years history, its core has been basically mature theory. The future study of category management focuses more on how to make the category management theory and be combined closely with the enterprise actual and pays more attention to the effect of the implementation and effect. Implementing category management investment return period is how to curve and how to evaluate the effect of the category management. The supermarket of the category management innovation is a huge project without a reasonable and predictable return that will be conducive to category management decision-making is unfavorable to the supermarket profits change after the implementation of category management right evaluation. Under this background, we propose our novel perspective on the corresponding issues to form the better theoretical analysis on the issues that is meaningful.
基金financially supported by the NationalScience Foundation of China(grants No.41402293 and 41502089)the China Geological Survey Program (grant No.121201021000150009)
文摘Objective The dissolution and precipitation of carbonate during burial diagenetic process controls the reservoir property in deep buried strata. The geological process related with it has become a research focus during recent years. The most important dissolution fluids to carbonates are probably H2S and CO2 as byproducts of sulfate reduction in deep-buried setting with sulfate minerals, but carbonates are more soluble in relatively low temperature, which is the so-called retrograde solubility. Several geological processes can result in the decrease of temperature, including the upward migration of thermal fluids and tectonic uplift.
基金Supported by the National Natural Science Foundation of China （No. 60403044, No. 60373070） and partly funded by Microsoft Research Asia： Project 2004-Image-01.
文摘A simple and effective image inpainting method is proposed in this paper, which is proved to be suitable for different kinds of target regions with shapes from little scraps to large unseemly objects in a wide range of images. It is an important improvement upon the traditional image inpainting techniques. By introducing a new bijeetive-mapping term into the matching cost function, the artificial repetition problem in the final inpainting image is practically solved. In addition, by adopting an inpainting error map, not only the target pixels are refined gradually during the inpainting process but also the overlapped target patches are combined more seamlessly than previous method. Finally, the inpainting time is dramatically decreased by using a new acceleration method in the matching process.
基金financed by the National Youth Sciences Foundation of China (No. 41502044)
文摘The role of authigenic clay growth in clay gouge is increasingly recognized as a key to understanding the mechanics of berittle faulting and fault zone processes,including creep and seismogenesis,and providing new insights into the ongoing debate about the frictional strength of brittle fault(Haines and van der Pluijm,2012).However,neither the conditions nor the processes which
