毒理学关注阈值(threshold of toxicological concern,TTC)是一种基于化合物结构及其结构相关毒性数据库的风险评估工具。文章对TTC数据库的发展、应用步骤及其在化妆品原料中的应用进行总结,并对TTC的前瞻性应用,包括在植物来源原料的...毒理学关注阈值(threshold of toxicological concern,TTC)是一种基于化合物结构及其结构相关毒性数据库的风险评估工具。文章对TTC数据库的发展、应用步骤及其在化妆品原料中的应用进行总结,并对TTC的前瞻性应用,包括在植物来源原料的应用、吸入途径毒理学关注阈值、内部毒理学关注阈值、皮肤致敏阈值等进行了介绍,旨在为化妆品原料安全性评价中规范使用TTC方法提供参考。展开更多
针对新型时空众包平台出现的3类对象在线任务匹配问题,现有工作往往假设工人拥有最大可匹配任务数量,将多个任务一次性分配给一个工人,忽略了工人的工作时间,可能会导致后匹配到的任务等待时间过长。因此,本文考虑了工作时长的在线3类...针对新型时空众包平台出现的3类对象在线任务匹配问题,现有工作往往假设工人拥有最大可匹配任务数量,将多个任务一次性分配给一个工人,忽略了工人的工作时间,可能会导致后匹配到的任务等待时间过长。因此,本文考虑了工作时长的在线3类对象动态匹配(online dynamic assginment for three types of objects,ODAT)问题,结合遗传算法(genetic algorithm,GA)提出一种延迟匹配算法来解决该问题。通过构造任务森林结构,借鉴蒙特卡罗树搜索思想随机模拟生成初始解,采用双重变异算子、局部最优算子融合贪心算法实现定向最优进化,使用随机部分重启机制跳出局部最优解;同时还提出一种延迟阈值策略来进一步提升效用。最终在真实数据集和合成数据集上进行大量实验,验证了算法的有效性和可行性。展开更多
Intrusion detection is critical to guaranteeing the safety of the data in the network.Even though,since Internet commerce has grown at a breakneck pace,network traffic kinds are rising daily,and network behavior chara...Intrusion detection is critical to guaranteeing the safety of the data in the network.Even though,since Internet commerce has grown at a breakneck pace,network traffic kinds are rising daily,and network behavior characteristics are becoming increasingly complicated,posing significant hurdles to intrusion detection.The challenges in terms of false positives,false negatives,low detection accuracy,high running time,adversarial attacks,uncertain attacks,etc.lead to insecure Intrusion Detection System(IDS).To offset the existing challenge,the work has developed a secure Data Mining Intrusion detection system(DataMIDS)framework using Functional Perturbation(FP)feature selection and Bengio Nesterov Momentum-based Tuned Generative Adversarial Network(BNM-tGAN)attack detection technique.The data mining-based framework provides shallow learning of features and emphasizes feature engineering as well as selection.Initially,the IDS data are analyzed for missing values based on the Marginal Likelihood Fisher Information Matrix technique(MLFIMT)that identifies the relationship among the missing values and attack classes.Based on the analysis,the missing values are classified as Missing Completely at Random(MCAR),Missing at random(MAR),Missing Not at Random(MNAR),and handled according to the types.Thereafter,categorical features are handled followed by feature scaling using Absolute Median Division based Robust Scalar(AMDRS)and the Handling of the imbalanced dataset.The selection of relevant features is initiated using FP that uses‘3’Feature Selection(FS)techniques i.e.,Inverse Chi Square based Flamingo Search(ICS-FSO)wrapper method,Hyperparameter Tuned Threshold based Decision Tree(HpTT-DT)embedded method,and Xavier Normal Distribution based Relief(XavND-Relief)filter method.Finally,the selected features are trained and tested for detecting attacks using BNM-tGAN.The Experimental analysis demonstrates that the introduced DataMIDS framework produces an accurate diagnosis about the attack with low computation time.The work avoids false alarm rate of attacks and remains to be relatively robust against malicious attacks as compared to existing methods.展开更多
文摘毒理学关注阈值(threshold of toxicological concern,TTC)是一种基于化合物结构及其结构相关毒性数据库的风险评估工具。文章对TTC数据库的发展、应用步骤及其在化妆品原料中的应用进行总结,并对TTC的前瞻性应用,包括在植物来源原料的应用、吸入途径毒理学关注阈值、内部毒理学关注阈值、皮肤致敏阈值等进行了介绍,旨在为化妆品原料安全性评价中规范使用TTC方法提供参考。
文摘针对新型时空众包平台出现的3类对象在线任务匹配问题,现有工作往往假设工人拥有最大可匹配任务数量,将多个任务一次性分配给一个工人,忽略了工人的工作时间,可能会导致后匹配到的任务等待时间过长。因此,本文考虑了工作时长的在线3类对象动态匹配(online dynamic assginment for three types of objects,ODAT)问题,结合遗传算法(genetic algorithm,GA)提出一种延迟匹配算法来解决该问题。通过构造任务森林结构,借鉴蒙特卡罗树搜索思想随机模拟生成初始解,采用双重变异算子、局部最优算子融合贪心算法实现定向最优进化,使用随机部分重启机制跳出局部最优解;同时还提出一种延迟阈值策略来进一步提升效用。最终在真实数据集和合成数据集上进行大量实验,验证了算法的有效性和可行性。
文摘Intrusion detection is critical to guaranteeing the safety of the data in the network.Even though,since Internet commerce has grown at a breakneck pace,network traffic kinds are rising daily,and network behavior characteristics are becoming increasingly complicated,posing significant hurdles to intrusion detection.The challenges in terms of false positives,false negatives,low detection accuracy,high running time,adversarial attacks,uncertain attacks,etc.lead to insecure Intrusion Detection System(IDS).To offset the existing challenge,the work has developed a secure Data Mining Intrusion detection system(DataMIDS)framework using Functional Perturbation(FP)feature selection and Bengio Nesterov Momentum-based Tuned Generative Adversarial Network(BNM-tGAN)attack detection technique.The data mining-based framework provides shallow learning of features and emphasizes feature engineering as well as selection.Initially,the IDS data are analyzed for missing values based on the Marginal Likelihood Fisher Information Matrix technique(MLFIMT)that identifies the relationship among the missing values and attack classes.Based on the analysis,the missing values are classified as Missing Completely at Random(MCAR),Missing at random(MAR),Missing Not at Random(MNAR),and handled according to the types.Thereafter,categorical features are handled followed by feature scaling using Absolute Median Division based Robust Scalar(AMDRS)and the Handling of the imbalanced dataset.The selection of relevant features is initiated using FP that uses‘3’Feature Selection(FS)techniques i.e.,Inverse Chi Square based Flamingo Search(ICS-FSO)wrapper method,Hyperparameter Tuned Threshold based Decision Tree(HpTT-DT)embedded method,and Xavier Normal Distribution based Relief(XavND-Relief)filter method.Finally,the selected features are trained and tested for detecting attacks using BNM-tGAN.The Experimental analysis demonstrates that the introduced DataMIDS framework produces an accurate diagnosis about the attack with low computation time.The work avoids false alarm rate of attacks and remains to be relatively robust against malicious attacks as compared to existing methods.
