Novel DDoS Feature Representation Model Combining Deep Belief Network and Canonical Correlation Analysis

Distributed denial of service (DDoS) attacks launch more and more frequently and are more destructive. Feature representation as an important part of DDoS defense technology directly affects the efficiency of defense. Most DDoS feature extraction methods cannot fully utilize the information of the original data, resulting in the extracted features losing useful features. In this paper, a DDoS feature representation method based on deep belief network (DBN) is proposed. We quantify the original data by the size of the network flows, the distribution of IP addresses and ports, and the diversity of packet sizes of different protocols and train the DBN in an unsupervised manner by these quantified values. Two feedforward neural networks (FFNN) are initialized by the trained deep belief network, and one of the feedforward neural networks continues to be trained in a supervised manner. The canonical correlation analysis (CCA) method is used to fuse the features extracted by two feedforward neural networks per layer. Experiments show that compared with other methods, the proposed method can extract better features.     

基于深度信念网络的列车故障音频诊断方法

铁路在交通运输行业有着举足轻重的地位，一旦列车发生故障将会导致严重的生命财产损失。由于列车发生故障的概率相对较低，因此难以捕获列车的故障样本。针对上述问题，提出了一种无监督学习的列车故障识别方法，通过检测列车音频信号来识别列车故障。该方法基于深度信念网络(DBN)，利用小波包分解提取检测信号的特征向量并将其作为DBN的输入，待网络充分训练后，由训练好的DBN识别当前列车的运行状况。现场监测实验结果表明，该方法能够在无监督的条件下有效识别列车故障，保障了列车的运行安全。     

Mythical systems: mathematic and logical theory

The process of elaboration of the symbolic universe leads to exciting insights regarding the search for human emotional security. The symbols end up as explanatory axes of universal reality and on them are constructed myths that form a superstructure for belief systems. Human society is a multi-level system with a material structure (society), an ideological superstructure (belief systems, values, etc.) and a super superstructure with two parts: mythical (origin and justification) and utopic (final goal). All mythical belief systems have a numinous-religious nature.     

Proposed network structures and combined adaptive algorithms for electrocardiogram signal denoising

An electrocardiogram (ECG) signal is a record of the electrical activities of heart muscle and is used clinically to diagnose heart diseases. An ECG signal should be presented as clear as possible to support accurate decisions made by doctors. This article proposes different combinations of combined adaptive algorithms to derive different noise-cancelling structures to remove (denoise) different kinds of noise from ECG signals. The algorithms are applied to the following types of noise: power line interference, baseline wander, electrode motion artifact, and muscle artifacts. Moreover, the results of the suggested models and algorithms are compared with those of conventional denoising tools such as the discrete wavelet transform, an adaptive filter, and a multilayer neural network (NN) to ensure the superiority of the proposed combined structures and algorithms. Furthermore, the hybrid concept is based on dual, triple, and quadruple combinations of well-known algorithms that derive adaptive filters, such as the least mean squares, normalized least mean squares and recursive least squares algorithms. The combinations are formulated based on partial update, variable step-size (VSS), and second iterative VSS algorithms, which are considered in different combinations. In addition, biased NN and unbiased linear neural network (ULNN) structures are considered. The performance of the different structures and related algorithms are evaluated by measuring the post-signal-to-noise ratio, mean square error, and percentage root mean square difference.     

Integrating social and cognitive aspects of belief dynamics: towards a unifying framework

Belief change and spread have been studied in many disciplines—from psychology, sociology, economics and philosophy, to biology, computer science and statistical physics—but we still do not have a firm grasp on why some beliefs change more easily and spread faster than others. To fully capture the complex social-cognitive system that gives rise to belief dynamics, we first review insights about structural components and processes of belief dynamics studied within different disciplines. We then outline a unifying quantitative framework that enables theoretical and empirical comparisons of different belief dynamic models. This framework uses a statistical physics formalism, grounded in cognitive and social theory, as well as empirical observations. We show how this framework can be used to integrate extant knowledge and develop a more comprehensive understanding of belief dynamics.     

[设备健康管理]深度置信网络在齿轮故障诊断中的应用

针对齿轮传动系统中齿轮等零部件易出现故障或失效等问题，提出了一种基于深度学习理论的齿轮传动系统故障诊断方法。首先利用深度置信网络强大的特征自提取能力，对齿轮传动系统的振动信号进行特征提取，然后通过DBNs的复杂映射表征能力对故障信号进行故障判别。诊断实例表明，若不对齿轮振动的原始时域信号进行特征提取，直接利用DBNs对其进行诊断时，故障识别正确率只能达到 60%左右；如果对时域信号进行简单的傅里叶变换后，再利用 DBNs 对处理后的振动信号频谱进行诊断分析，正确率能达到 99.7%，从而证明了所提故障诊断方法的简易性和有效性。     

Validating the moderating role of age in multi-perspective acceptance model of wearable healthcare technology

The recent trend of aging population, not to mention the unprecedented pandemic, draws great attention from the general public about health concerns. Since healthcare information technology is different from non-healthcare information technology, additional contexts should be properly incorporated into technology acceptance research to accurately identify influential factors affecting the acceptance of wearable healthcare technology. Thus, we selectively reconfigured factors from health, privacy, and socio-demographic contexts to formulate a health-aware acceptance model. Then, it was empirically analyzed using structural equation modeling. Based on the results, whereas privacy concerns were directly associated with intention to use wearable healthcare technology, health concerns were not. Moreover, age had a moderating effect on social influence and facilitating conditions. These findings suggest valuable insights that the adoption rate of healthcare technology is increased by 1) keeping personal information securely, 2) facilitating social interaction among users, and 3) offering intuitive user experience for elderly people.     

基于Agent的信任的情感劝说决策模型

基于智能体(Agent)的信任的情感劝说能更好地发挥Agent的各项人工智能优势。针对其中决策模型建立不够的现状,首先基于Agent的自身性格、情感衰减及外界刺激,运用大五性格、OCC情感模型及情感强度第一定律等,构建了相应的情感强度算法;其次考虑交易质量对信任的反馈,综合运用语言评价法、三角模糊数及将模糊数转换为具体分数(CFCS)算法,对Agent的交易质量进行模糊化和去模糊化处理,构建了相应的交易质量算法;再次对已有的信任更新因子进行改进后,构建了Agent的信任更新算法;最后在构建以上算法的基础上,采用交互式多准则决策(TODIM)法建立了基于Agent的信任的情感劝说决策模型,并采用熵权法对模型中的属性权重进行设定。为验证以上研究的合理性和有效性,设计了相应算例进行阐述,分析了不同参数取不同值得到的决策结果,并与相关研究进行了比较分析,得出了相应结论和管理启示,进一步归纳了不足之处和未来研究方向。     

容迟网络中基于演化博弈的合作行为

针对容迟网络中节点由于资源有限而表现出来的自私特性,为改善网络中节点的合作行为,进而提高网络的整体性能,提出一种基于演化博弈(EGT)的节点合作行为促进机制。首先,采用囚徒困境模型建立节点与其邻居博弈的收益矩阵;其次,基于度中心性定义节点的社会权威性;进一步地,在节点策略更新规则时考虑社会权威的影响,选择当前邻居中社会权威较高的节点进行模仿学习;最后,在机会网络环境仿真器上基于真实的动态网络拓扑数据进行仿真实验。仿真结果表明,与随机选择邻居的费米(Fermi)更新规则相比,考虑社会权威的更新规则能够更好地促进节点合作行为的涌现,进而提升网络的整体性能。