Accurate detection of small objects is critically important in high-stakes applications such as military reconnaissance and emergency rescue.However,low resolution,occlusion,and background interference make small obje...Accurate detection of small objects is critically important in high-stakes applications such as military reconnaissance and emergency rescue.However,low resolution,occlusion,and background interference make small object detection a complex and demanding task.One effective approach to overcome these issues is the integration of multimodal image data to enhance detection capabilities.This paper proposes a novel small object detection method that utilizes three types of multimodal image combinations,such as Hyperspectral-Multispectral(HSMS),Hyperspectral-Synthetic Aperture Radar(HS-SAR),and HS-SAR-Digital Surface Model(HS-SAR-DSM).The detection process is done by the proposed Jaccard Deep Q-Net(JDQN),which integrates the Jaccard similarity measure with a Deep Q-Network(DQN)using regression modeling.To produce the final output,a Deep Maxout Network(DMN)is employed to fuse the detection results obtained from each modality.The effectiveness of the proposed JDQN is validated using performance metrics,such as accuracy,Mean Squared Error(MSE),precision,and Root Mean Squared Error(RMSE).Experimental results demonstrate that the proposed JDQN method outperforms existing approaches,achieving the highest accuracy of 0.907,a precision of 0.904,the lowest normalized MSE of 0.279,and a normalized RMSE of 0.528.展开更多
为了解决信号重构性能差的问题,提出了一种基于广义Jaccard系数的广义正交匹配追踪(generalized orthogonal matching pursuit,g OM P)重构算法。该算法利用广义Jaccard系数相似性匹配准则替换g OM P算法中的内积度量准则,优化了通过感...为了解决信号重构性能差的问题,提出了一种基于广义Jaccard系数的广义正交匹配追踪(generalized orthogonal matching pursuit,g OM P)重构算法。该算法利用广义Jaccard系数相似性匹配准则替换g OM P算法中的内积度量准则,优化了通过感知矩阵来选择与残差余量最匹配原子的匹配方式。实验结果表明,该算法的重构成功率不仅高于g OMP算法,同时也高于OMP、St OMP等算法。展开更多
针对复杂网络群落划分的准确性差和时间复杂度高的问题,设计了一种基于修正Jaccard贴近度和群落合并的用于非堆叠群落的划分算法IJCD(Improved Jaccard community detection)。该算法针对Jaccard贴近度的计算结果中存在距离不同但贴近...针对复杂网络群落划分的准确性差和时间复杂度高的问题,设计了一种基于修正Jaccard贴近度和群落合并的用于非堆叠群落的划分算法IJCD(Improved Jaccard community detection)。该算法针对Jaccard贴近度的计算结果中存在距离不同但贴近度可能相同的情况,引入了改进的Jaccard贴近度算法计算节点之间的贴近度,选择多个贴近节点在一个群落而不是最贴近的两个节点,从而得到初始群落,再进行群落合并。计算所得的初始群落的准确率较高且群落个数较少,提高了整个算法的效率。最后,采用了几种经典的算法对网络进行群落划分,在选取的几个真实网络和计算机生成网络上的实验结果表明:IJCD算法能够有效地对群落进行划分,并且有较高的准确度和较低的时间复杂度。展开更多
Machine Learning(ML)systems often involve a re-training process to make better predictions and classifications.This re-training process creates a loophole and poses a security threat for ML systems.Adversaries leverag...Machine Learning(ML)systems often involve a re-training process to make better predictions and classifications.This re-training process creates a loophole and poses a security threat for ML systems.Adversaries leverage this loophole and design data poisoning attacks against ML systems.Data poisoning attacks are a type of attack in which an adversary manipulates the training dataset to degrade the ML system’s performance.Data poisoning attacks are challenging to detect,and even more difficult to respond to,particularly in the Internet of Things(IoT)environment.To address this problem,we proposed DISTINIT,the first proactive data poisoning attack detection framework using distancemeasures.We found that Jaccard Distance(JD)can be used in the DISTINIT(among other distance measures)and we finally improved the JD to attain an Optimized JD(OJD)with lower time and space complexity.Our security analysis shows that the DISTINIT is secure against data poisoning attacks by considering key features of adversarial attacks.We conclude that the proposed OJD-based DISTINIT is effective and efficient against data poisoning attacks where in-time detection is critical for IoT applications with large volumes of streaming data.展开更多
文摘Accurate detection of small objects is critically important in high-stakes applications such as military reconnaissance and emergency rescue.However,low resolution,occlusion,and background interference make small object detection a complex and demanding task.One effective approach to overcome these issues is the integration of multimodal image data to enhance detection capabilities.This paper proposes a novel small object detection method that utilizes three types of multimodal image combinations,such as Hyperspectral-Multispectral(HSMS),Hyperspectral-Synthetic Aperture Radar(HS-SAR),and HS-SAR-Digital Surface Model(HS-SAR-DSM).The detection process is done by the proposed Jaccard Deep Q-Net(JDQN),which integrates the Jaccard similarity measure with a Deep Q-Network(DQN)using regression modeling.To produce the final output,a Deep Maxout Network(DMN)is employed to fuse the detection results obtained from each modality.The effectiveness of the proposed JDQN is validated using performance metrics,such as accuracy,Mean Squared Error(MSE),precision,and Root Mean Squared Error(RMSE).Experimental results demonstrate that the proposed JDQN method outperforms existing approaches,achieving the highest accuracy of 0.907,a precision of 0.904,the lowest normalized MSE of 0.279,and a normalized RMSE of 0.528.
文摘为了解决信号重构性能差的问题,提出了一种基于广义Jaccard系数的广义正交匹配追踪(generalized orthogonal matching pursuit,g OM P)重构算法。该算法利用广义Jaccard系数相似性匹配准则替换g OM P算法中的内积度量准则,优化了通过感知矩阵来选择与残差余量最匹配原子的匹配方式。实验结果表明,该算法的重构成功率不仅高于g OMP算法,同时也高于OMP、St OMP等算法。
文摘针对复杂网络群落划分的准确性差和时间复杂度高的问题,设计了一种基于修正Jaccard贴近度和群落合并的用于非堆叠群落的划分算法IJCD(Improved Jaccard community detection)。该算法针对Jaccard贴近度的计算结果中存在距离不同但贴近度可能相同的情况,引入了改进的Jaccard贴近度算法计算节点之间的贴近度,选择多个贴近节点在一个群落而不是最贴近的两个节点,从而得到初始群落,再进行群落合并。计算所得的初始群落的准确率较高且群落个数较少,提高了整个算法的效率。最后,采用了几种经典的算法对网络进行群落划分,在选取的几个真实网络和计算机生成网络上的实验结果表明:IJCD算法能够有效地对群落进行划分,并且有较高的准确度和较低的时间复杂度。
基金This work was supported by a National Research Foundation of Korea(NRF)grant funded by the Korea Government(MSIT)under Grant 2020R1A2B5B01002145.
文摘Machine Learning(ML)systems often involve a re-training process to make better predictions and classifications.This re-training process creates a loophole and poses a security threat for ML systems.Adversaries leverage this loophole and design data poisoning attacks against ML systems.Data poisoning attacks are a type of attack in which an adversary manipulates the training dataset to degrade the ML system’s performance.Data poisoning attacks are challenging to detect,and even more difficult to respond to,particularly in the Internet of Things(IoT)environment.To address this problem,we proposed DISTINIT,the first proactive data poisoning attack detection framework using distancemeasures.We found that Jaccard Distance(JD)can be used in the DISTINIT(among other distance measures)and we finally improved the JD to attain an Optimized JD(OJD)with lower time and space complexity.Our security analysis shows that the DISTINIT is secure against data poisoning attacks by considering key features of adversarial attacks.We conclude that the proposed OJD-based DISTINIT is effective and efficient against data poisoning attacks where in-time detection is critical for IoT applications with large volumes of streaming data.
