Lower Approximation Reduction in Ordered Information System with Fuzzy Decision

Attribute reduction is one of the most important problems in rough set theory. This paper introduces the concept of lower approximation reduction in ordered information systems with fuzzy decision. Moreover, the judgment theorem and discernable matrix are obtained, in which case an approach to attribute reduction in ordered information system with fuzzy decision is constructed. As an application of lower approximation reduction, some examples are applied to examine the validity of works obtained in our works..

Pedestrian lane formation with following–overtaking model and measurement of system order

Pedestrian self-organizing movement plays a significant role in evacuation studies and architectural design.Lane formation,a typical self-organizing phenomenon,helps pedestrian system to become more orderly,the majority of following behavior model and overtaking behavior model are imprecise and unrealistic compared with pedestrian movement in the real world.In this study,a pedestrian dynamic model considering detailed modelling of the following behavior and overtaking behavior is constructed,and a method of measuring the lane formation and pedestrian system order based on information entropy is proposed.Simulation and analysis demonstrate that the following and avoidance behaviors are important factors of lane formation.A high tendency of following results in good lane formation.Both non-selective following behavior and aggressive overtaking behavior cause the system order to decrease.The most orderly following strategy for a pedestrian is to overtake the former pedestrian whose speed is lower than approximately 70%of his own.The influence of the obstacle layout on pedestrian lane and egress efficiency is also studied with this model.The presence of a small obstacle does not obstruct the walking of pedestrians;in contrast,it may help to improve the egress efficiency by guiding the pedestrian flow and mitigating the reduction of pedestrian system orderliness.

Fusion of multiagent preference orderings with information on agent's importance being incomplete certain

The problem of fusing multiagent preference orderings, with information on agent＇s importance being incomplete certain with respect to a set of possible courses of action, is described. The approach is developed for dealing with the fusion problem described in the following sections and requires that each agent provides a preference ordering over the different alternatives completely independent of the other agents, and the information on agent＇s importance is incomplete certain. In this approach, the ternary comparison matrix of the alternatives is constructed, the eigenvector associated with the maximum eigenvalue of the ternary comparison matrix is attained so as to normalize priority vector of the alternatives. The interval number of the alternatives is then obtained by solving two sorts of linear programming problems. By comparing the interval numbers of the alternatives, the ranking of alternatives can be generated. Finally, some examples are given to show the feasibility and effectiveness of the method.

Quality oriented multimode processes monitoring based on a novel hierarchical common and specific structure with different order information

Due to higher demands on product diversity,flexible shift between productions of different products in one equipment becomes a popular solution,resulting in existence of multiple operation modes in a single process.In order to handle such multi-mode process,a novel double-layer structure is proposed and the original data are decomposed into common and specific characteristics according to the relationship between variables among each mode.In addition,both low and high order information are considered in each layer.The common and specific information within each mode can be captured and separated into several subspaces according to the different order information.The performance of the proposed method is further validated through a numerical example and the Tennessee Eastman(TE)benchmark.Compared with previous methods,superiority of the proposed method is validated by the better monitoring results.

Study of three-dimensional PET and MR image registration based on higher-order mutual information

Mutual information has currently been one of the most intensivelyresearched measures. It has been proven to be accurate and effective registrationmeasure. Despite the general promising results, mutual information sometimes mightlead to misregistration because of neglecting spatial information and treating intensityvariations with undue sensitivity. In this paper, an extension of mutual informationframework was proposed in which higher-order spatial information regarding imagestructures was incorporated into the registration processing of PET and MR. Thesecond-order estimate of mutual information algorithm was applied to the registrationof seven patients. Evaluation from Vanderbilt University and our visual inspectionshowed that sub-voxel accuracy and robust results were achieved in all cases withsecond-order mutual information as the similarity measure and with Powell's multi-dimensional direction set method as optimization strategy.

Contribution in Information Signal Processing for Solving State Space Nonlinear Estimation Problems

In this paper, comprehensive methods to apply several formulations of nonlinear estimators to integrated navigation problems are considered and developed. The problem of linear and nonlinear filters such as Kalman Filter (KF) and Extended Kalman Filter (EKF) is stated. Analog solution which is based on fisher information matrix propagation for linear and nonlinear filtering is also developed. Additionally, the idea of iterations is included through the update step both for Kalman filters and Information filters in order to improve accuracy. Through this development, two new formulations of High order Kalman filters and High order Information filters are presented. Finally, in order to compare these different nonlinear filters, special applications are analyzed by using the proposed techniques to estimate two well-known mathematical state space models, which are based on nonlinear time series used to apply these estimation algorithms. A criterion used for comparison is the root mean square error RMSE and several simulations under specific conditions are illustrated.

A theoretical extraction scheme of transport information based on exclusion models

In order to explore how to extract more transport information from current fluctuation, a theoretical extraction scheme is presented in a single barrier structure based on exclusion models, which include counter-flows model and tunnel model. The first four cumulants of these two exclusion models are computed in a single barrier structure, and their characteristics are obtained. A scheme with the help of the first three cumulants is devised to check a transport process to follow the counter-flows model, the tunnel model or neither of them. Time series generated by Monte Carlo techniques is adopted to validate the abstraction procedure, and the result is reasonable.

移动设备用户的数字素养对信息安全保护意愿和行为的影响研究——基于 Ordered Probit 模型的估计

[研究目的]数字素养作为数字经济时代社会公众的核心素养,为移动设备用户的信息安全保护意愿和行为的研究提供新的思路和动力。[研究方法]从理论层面探讨移动设备用户数字素养对信息安全保护意愿和行为的影响效应及其传导路径,并基于实际调查数据,运用Ordered Probit模型,对影响效应和传导路径进行实证检验。[研究结论]研究发现:移动设备用户的数字素养显著促进了信息安全保护意愿和行为,在考虑内生性问题之后,研究结论依然成立,且促进效应更为明显;反应效能和自我效能在数字素养影响信息安全保护意愿和行为间起传导作用,感知威胁的传导作用则不显著;提升移动设备用户的数字素养水平能够缩小信息安全保护意愿与行为之间的差异,有助于用户将信息安全保护意愿转化为保护行为;通过更换自变量测度方式及替换估计方法进行稳健性检验,结论仍然成立。

基于多阶近邻约束的深度不完整多视图聚类方法

多视图聚类是重要的无监督学习方法之一,然而在实际应用中很难获取完整的多视图数据,导致不完整多视图聚类问题.大多数已有的不完整多视图聚类方法只考虑了视图的属性信息,而忽视了数据结构信息对聚类的影响,使提取的特征不能充分表示原始数据的潜在结构.针对以上问题,提出一种基于多阶近邻约束的深度不完整多视图聚类方法.首先,利用具有自注意力机制的深度自编码器获取带有视图间信息交互的深层次隐含特征,并采用加权融合的方式获取视图的公共语义信息;然后,对于不完整多视图中的缺失数据,利用多视图的公共表示进行补全;最后,提出一种多阶近邻约束机制,该机制考虑不完整多视图数据的深层结构信息,利用多视图的互补性构建近似完整的近邻图,引导编码器学习更紧致、更有判别性的高级语义特征.在公共数据集上的实验结果证明了所提方法的有效性.

基于语序知识的双通道图卷积网络方面级情感分析

当前大多数基于图卷积网络的方面级情感分析方法利用文本的句法知识、语义知识、情感知识构建文本依赖,但少有研究利用文本语序知识构建文本依赖,导致图卷积网络不能有效地利用文本语序知识引导方面项学习上下文情感信息,从而限制了其性能。针对上述问题,提出基于语序知识的双通道图卷积网络(dual-channel graph convolutional network with word-order knowledge,WKDGCN)模型,该模型由语序图卷积网络(word-order graph convolutional network,WoGCN)和情感知识结合语义知识增强的句法图卷积网络(sentiment and attention-enhanced graph convolutional network,SAGCN)组成。具体地,WoGCN基于文本的语序知识构建图卷积网络,由文本的语序依赖引导方面项特征学习上下文情感信息;SAGCN利用SenticNet中的情感知识结合注意力机制增强句法依赖,利用增强后的句法依赖构建图卷积网络,以此引导方面项特征学习上下文情感信息;最后融合两个图卷积网络的特征用于情感分类。此外,设计了一种权重分配策略,在放大方面项权重的同时保持上下文权重一致,避免错误的计算方面项特征与重要特征的语义相关性。在多个公开数据集上的实验结果表明,所提方法优于对比模型。

环境信息呈现顺序与机构持股——来自社会责任报告的证据

随着绿色环保理念的不断深化,上市公司企业社会责任(CSR)报告逐渐成为评估企业未来价值的重要指标。该研究基于心理学顺序效应理论,聚焦CSR报告环境信息披露前置现象,以2011—2021年沪深两市A股上市公司CSR报告为研究对象构建环境信息章节位置衡量变量,研究环境信息章节呈现顺序对机构投资者的影响。研究发现:①CSR报告环境信息章节前置导致机构投资者持股比例显著增加,该结果在一系列稳健性和内生性检验后依然成立。②环境信息章节前置对机构投资者的影响在上市公司环保意识和机构投资者特征角度具有异质性。具体而言,上市公司环境绩效水平、环境信息披露质量及行业环境敏感性均影响环境信息章节前置对机构投资者持股作用的发挥;相比非投资型机构投资者,投资型机构投资者更易受到环境信息章节前置的影响,其中合格境外投资者、保险资金受环境章节顺序的影响更大。③CSR报告环境信息章节前置的经济后果主要体现在对企业未来价值的提升,该结果在不同价值衡量变量中均得以检验。在低托宾Q上市公司中,环境信息章节前置对企业未来价值的增量作用更大。进一步研究表明,机构投资者能够敏锐察觉环境信息章节前置的经济后果,更青睐低托宾Q且前置环境信息章节的上市公司。该结论侧面验证了机构投资者的“聪明钱”效应。该研究剖析了环境信息章节呈现顺序对机构投资者投资决策的影响,为CSR报告环境信息章节存在顺序效应提供了实证证据,进一步拓展了心理学顺序效应在资本市场的研究,对CSR报告披露制度的完善和披露监督力度的加强以及机构投资者决策有用性的增强具有启示意义。

论个人信息保护民事公益诉讼起诉主体的范围与顺位

自个人信息保护民事公益诉讼在我国开展以来,理论界与实务界就对该类诉讼的起诉主体存在较大争议,分歧主要集中在范围界定与诉权顺位两个方面。《个人信息保护法》第70条虽然对该类诉讼的起诉主体作了规定,但主体范围不明以及诉权顺位不清的问题仍未得到彻底解决。要正确实施该制度,应否定“履行个人信息保护职责的部门”的起诉资格,扩张“法律规定的消费者组织”的范围,明确“由国家网信部门确定的组织”的条件,并以《民事诉讼法》第58条及不同类型民事公益诉讼之间的关系为依据,厘清不同起诉主体的诉权顺位。当受侵害对象为普通公民时,应尊重检察机关民事公益诉权的补充性,将“国家网信部门确定的组织”的起诉顺位置于检察机关之前;当受侵害对象为众多消费者时,应考虑到受侵害对象的特殊身份,依次由“法律规定的消费者组织”“由网信部门确定的组织”和检察机关行使起诉权。

双层区分制对网络犯罪帮助行为认定的纠偏与重构

帮助信息网络犯罪活动罪在规范属性的界定问题上存在悖论,且章节设置不尽合理,在“口袋罪”倾向日趋严重的现实情况下,理应限缩适用甚至非必要不适用《刑法》第287条之二。通过双层区分制将网络犯罪帮助行为认定为共同犯罪,符合我国刑法的现行体例,在维护共犯从属性对构成要件定型意义的同时,也妥当处理了网络犯罪帮助行为法益侵害严重程度升格的现象。作为对《刑法》第287条之二的纠偏,双层区分制在犯罪事实无法查明等定罪问题、主犯与从犯难以确定等量刑问题上均能有效应对,是回应认定网络犯罪帮助行为实践难题的合理路径。

基于TOPSIS和GRA的信息安全风险评估

信息安全风险评估是一项非常重要的信息安全保障活动.依据信息安全相关标准,可从资产、威胁和脆弱性3方面识别出重要的风险因素,并确定相应的信息安全风险评估指标.参考等保2.0确定风险评估指标是一种可行的方法.在进行信息安全风险评估时,采用熵权法进行客观的指标赋权,并结合优劣解距离法(technique for order preference by similarity to ideal solution,TOPSIS)和灰色关联分析(grey relational analysis,GRA)进行综合评估.实例分析表明,依据信息熵进行客观赋权相对减少了主观因素的影响;基于TOPSIS和GRA进行信息安全风险评估,综合被评价对象整体因素和内部因素,较有效地将多项信息安全风险评估指标综合成单一评分,便于对多个被评对象进行信息安全风险的择优与排序.

基于深度强化学习的图书馆架序智能识别方法

为了解决现有图书馆架序识别方法存在的不足,设计基于深度强化学习的图书馆架序智能识别方法。利用深度强化学习架构,确定架序识别规则,计算图书馆架序参数,识别图书馆架序中书号字符数据,确定书号字符识别系数的取值范围,实现基于深度强化学习的图书馆架序智能识别。实验结果表明,应用文中提出的识别方法,可使图书资源排列架序与规定架序保持一致,识别准确度较高,有效解决了图书馆管理中的图书乱架问题,具有较好的应用性能。

多通道运动矢量排序的可逆视频信息隐藏

针对现有基于运动矢量排序的视频可逆信息隐藏算法无法根据视频帧视觉特性自适应调节嵌入容量且容量有限的问题,提出一种多通道矢量排序的可逆视频信息隐藏算法.该算法通过计算参考帧的纹理复杂度和运动复杂度情况决定后续帧是否嵌入信息,从而实现后续帧的自适应信息嵌入,算法同时对多通道像素值排序(multi-pass pixel value ordering, multi-pass PVO)技术进行改进,将其应用于视频信息隐藏,有效提升可逆隐藏算法嵌入容量.实验结果表明,相较于同类算法,PSNR和SSIM的变化值分别降低14.5%和8.5%,嵌入容量提升7.4%,在视觉质量和嵌入容量方面有显著提高.

多样性约束和高阶信息挖掘的多视图聚类

在现有的多视图聚类研究中,大多数方法没有考虑多视图的多样性,也没有关注数据的高阶邻域信息,导致聚类结果不够准确,难以挖掘数据集的底层信息。为了解决这些问题,提出了基于多样性约束和高阶信息挖掘的多视图聚类算法(MVCDCHO)。首先设计了视图间多样性测量的方法,利用多样性的约束保留数据的交集特征,同时去除多视图的差异特征;然后提出了一种挖掘视图高阶信息的方法,要求多视图的交集特征接近混合相似图,以挖掘数据间相关性所没有关注到的高阶信息;最后将多视图的交集特征融合成共识图,通过谱聚类来获取聚类目标图;另外,设计了一种交替迭代的方法来迭代学习优化目标函数。实验结果表明,MVCDCHO在归一化互信息(NMI)、调整后的兰德指数(ARI)、聚类精度(ACC)多个聚类评价指标上表现出优异的性能。理论分析和实验研究验证了MVCDCHO中多视图多样性和高阶信息的关键作用,证明了MVCDCHO的优越性。

信息获取渠道对农机社会化服务的影响研究——基于非农就业视角

基于江汉平原372户水稻种植户调查数据,运用有序Probit模型实证考察信息获取渠道对农户农机社会化服务采纳程度的影响,并从非农就业视角探讨信息获取渠道对农户农机社会化服务采纳程度的影响机制。研究结果表明:媒介渠道、组织渠道和人际渠道对农户农机社会化服务采纳程度具有促进作用,经过稳健性检验结果依旧显著;媒介渠道、组织渠道和人际渠道对农户农机社会化服务采纳程度的促进作用通过非农就业实现。进一步异质性分析发现,信息获取渠道对农业收入占比低的农户农机社会化服务采纳程度具有正向影响,而对农业收入占比高的农户农机社会化服务采纳程度具有负向影响;信息获取渠道对受教育程度低的农户农机社会化服务采纳程度影响较小,而对受教育程度高的农户农机社会化服务采纳程度影响显著。因此,建议拓宽农户信息获取渠道,鼓励农户多渠道就业,并针对不同农户实行差异化扶持政策。