A multi-class method is proposed based on Error Correcting Output Codes algorithm in order to get better performance of attack recognition in Wireless Sensor Networks. Aiming to enhance the accuracy of attack detectio...A multi-class method is proposed based on Error Correcting Output Codes algorithm in order to get better performance of attack recognition in Wireless Sensor Networks. Aiming to enhance the accuracy of attack detection, the multi-class method is constructed with Hadamard matrix and two-class Support Vector Machines. In order to minimize the complexity of the algorithm, sparse coding method is applied in this paper. The comprehensive experimental results show that this modified multi-class method has better attack detection rate compared with other three coding algorithms, and its time efficiency is higher than Hadamard coding algorithm.展开更多
针对小样本学习过程中样本数量不足导致的性能下降问题,基于原型网络(Prototype network,ProtoNet)的小样本学习方法通过实现查询样本与支持样本原型特征间的距离度量,从而达到很好的分类性能.然而,这种方法直接将支持集样本均值视为类...针对小样本学习过程中样本数量不足导致的性能下降问题,基于原型网络(Prototype network,ProtoNet)的小样本学习方法通过实现查询样本与支持样本原型特征间的距离度量,从而达到很好的分类性能.然而,这种方法直接将支持集样本均值视为类原型,在一定程度上加剧了对样本数量稀少情况下的敏感性.针对此问题,提出了基于自适应原型特征类矫正的小样本学习方法(Few-shot learning based on class rectification via adaptive prototype features,CRAPF),通过自适应生成原型特征来缓解方法对数据细微变化的过度响应,并同步实现类边界的精细化调整.首先,使用卷积神经网络构建自适应原型特征生成模块,该模块采用非线性映射获取更为稳健的原型特征,有助于减弱异常值对原型构建的影响;然后,通过对原型生成过程的优化,提升不同类间原型表示的区分度,进而强化原型特征对类别表征的整体效能;最后,在3个广泛使用的基准数据集上的实验结果显示,该方法提升了小样本学习任务的表现.展开更多
A new method to evaluate the fitness of the Bayesian networks according to the observed data is provided. The main advantage of this criterion is that it is suitable for both the complete and incomplete cases while th...A new method to evaluate the fitness of the Bayesian networks according to the observed data is provided. The main advantage of this criterion is that it is suitable for both the complete and incomplete cases while the others not. Moreover it facilitates the computation greatly. In order to reduce the search space, the notation of equivalent class proposed by David Chickering is adopted. Instead of using the method directly, the novel criterion, variable ordering, and equivalent class are combined,moreover the proposed mthod avoids some problems caused by the previous one. Later, the genetic algorithm which allows global convergence, lack in the most of the methods searching for Bayesian network is applied to search for a good model in thisspace. To speed up the convergence, the genetic algorithm is combined with the greedy algorithm. Finally, the simulation shows the validity of the proposed approach.展开更多
Goal of this research is to detect possible relations between animal-related attitudes and verbal aggressiveness as well as types combining such parameters. The sample collected in 2015 contains two adult education cl...Goal of this research is to detect possible relations between animal-related attitudes and verbal aggressiveness as well as types combining such parameters. The sample collected in 2015 contains two adult education classes equivalent to secondary school level (class A = 23 inmates and B = 12 inmates, all male) at a correctional facility. Questionnaires were used. Network analysis software (Visone) and conventional statistics (SPSS) are used for calculating network variables (indegree, outdegree, katz, pageranketc) and implementing Spearman test and Principal Component Analysis. Inmates who have adopted an animal-friendly value system and are too coward to react against torture of animals, maintain a repressed emotion. If they do not intervene and provoke, then they are also not targeted by others. No reaction against torture is also connected with a deep-rooted aggressiveness. Concerning superficial aggressiveness, a profile, whose characterize is multiple verbal aggressiveness, can be attributed to repressed emotions. A type is torturing and indifferently restricts his aggressiveness, as he can satisfy his need of dominance by being aggressive towards animals. A type of inmate who loves animals and reacts against their torture, presents the most restricted and relatively smooth aggressiveness, as he discharges his repressed emotions to this reaction. Under condition of indifference, keeping pets is not evidence of loving but of a need of companionship. As for the deep-rooted aggressiveness (over-extroversion), it does not seem to be triggered by any repression.展开更多
文摘A multi-class method is proposed based on Error Correcting Output Codes algorithm in order to get better performance of attack recognition in Wireless Sensor Networks. Aiming to enhance the accuracy of attack detection, the multi-class method is constructed with Hadamard matrix and two-class Support Vector Machines. In order to minimize the complexity of the algorithm, sparse coding method is applied in this paper. The comprehensive experimental results show that this modified multi-class method has better attack detection rate compared with other three coding algorithms, and its time efficiency is higher than Hadamard coding algorithm.
文摘针对小样本学习过程中样本数量不足导致的性能下降问题,基于原型网络(Prototype network,ProtoNet)的小样本学习方法通过实现查询样本与支持样本原型特征间的距离度量,从而达到很好的分类性能.然而,这种方法直接将支持集样本均值视为类原型,在一定程度上加剧了对样本数量稀少情况下的敏感性.针对此问题,提出了基于自适应原型特征类矫正的小样本学习方法(Few-shot learning based on class rectification via adaptive prototype features,CRAPF),通过自适应生成原型特征来缓解方法对数据细微变化的过度响应,并同步实现类边界的精细化调整.首先,使用卷积神经网络构建自适应原型特征生成模块,该模块采用非线性映射获取更为稳健的原型特征,有助于减弱异常值对原型构建的影响;然后,通过对原型生成过程的优化,提升不同类间原型表示的区分度,进而强化原型特征对类别表征的整体效能;最后,在3个广泛使用的基准数据集上的实验结果显示,该方法提升了小样本学习任务的表现.
基金This project was supported by the National Natural Science Foundation of China (70572045).
文摘A new method to evaluate the fitness of the Bayesian networks according to the observed data is provided. The main advantage of this criterion is that it is suitable for both the complete and incomplete cases while the others not. Moreover it facilitates the computation greatly. In order to reduce the search space, the notation of equivalent class proposed by David Chickering is adopted. Instead of using the method directly, the novel criterion, variable ordering, and equivalent class are combined,moreover the proposed mthod avoids some problems caused by the previous one. Later, the genetic algorithm which allows global convergence, lack in the most of the methods searching for Bayesian network is applied to search for a good model in thisspace. To speed up the convergence, the genetic algorithm is combined with the greedy algorithm. Finally, the simulation shows the validity of the proposed approach.
文摘Goal of this research is to detect possible relations between animal-related attitudes and verbal aggressiveness as well as types combining such parameters. The sample collected in 2015 contains two adult education classes equivalent to secondary school level (class A = 23 inmates and B = 12 inmates, all male) at a correctional facility. Questionnaires were used. Network analysis software (Visone) and conventional statistics (SPSS) are used for calculating network variables (indegree, outdegree, katz, pageranketc) and implementing Spearman test and Principal Component Analysis. Inmates who have adopted an animal-friendly value system and are too coward to react against torture of animals, maintain a repressed emotion. If they do not intervene and provoke, then they are also not targeted by others. No reaction against torture is also connected with a deep-rooted aggressiveness. Concerning superficial aggressiveness, a profile, whose characterize is multiple verbal aggressiveness, can be attributed to repressed emotions. A type is torturing and indifferently restricts his aggressiveness, as he can satisfy his need of dominance by being aggressive towards animals. A type of inmate who loves animals and reacts against their torture, presents the most restricted and relatively smooth aggressiveness, as he discharges his repressed emotions to this reaction. Under condition of indifference, keeping pets is not evidence of loving but of a need of companionship. As for the deep-rooted aggressiveness (over-extroversion), it does not seem to be triggered by any repression.
