提高软件Agent策略生成的语义互操作能力

软件Agent遇到语义二义性时无法正确地为用户解决矛盾问题.利用本体支持语义互操作的特点,在软件Agent策略生成机制中引入复合元本体,加入Agent智能引导的人机交互方法,实现了具有语义歧义消除能力的可拓策略生成系统.当用户输入的问题信息语义模糊时,Agent根据本体中的知识与用户交互,逐步理解用户的信息语义,直到能为用户生成解决矛盾问题的策略.以旅游与购物问题为例进行了实验,结果表明了软件Agent策略生成的语义互操作能力得到了提高.     

P2P环境下基于WSN和RFID的供应链管理语义监控平台

提出了一种基于语义P2P的无线传感器网络应用架构,在此架构下融合了RFID技术提出了供应链管理的语义监控平台.该平台采用语义P2P技术解决了信息过载通信瓶颈问题,方便了传感器节点部署、使得传感器网络易于扩展,同时采用本体技术实现了供应链管理中的信息语义交互和共享,为供应链中信息的及时、无歧义理解提供了便利.     

基于Vague集信息的多属性群决策专家水平评判方法

针对基于Vague集信息的多属性群决策专家水平评判问题提出了两种评判方法.首先引进了基于Vague集信息的多属性群决策信息体(即决策信息体)的相关概念,通过决策信息体构造了基于Vague集信息的一致性决策矩阵及模糊熵,其次利用Vague集信息的相似度量以及Vague集信息的模糊熵两种信息不确定性度量方法,对基于Vague集信息的多属性群决策专家水平评判问题提出了两种评判方法,即统计分析方法和模糊熵分析方法,对专家的评判水平进行排序.最后,通过一个算例说明两种方法的一致性、有效性和实用性.     

基于相对熵的多粒度语言信息的多属性群决策方法

针对具有多粒度语言评价信息的多属性群决策问题,提出了一种基于二元语义信息处理和相对熵的群决策方法。该方法首先给出了多粒度语言评价信息一致化为由基本语言评价集表示的相同粒度二元语义信息的方法,然后对于属性权重信息不完全的情形,建立了基于相对熵的多目标规划模型获得相应的属性权重,并利用二元语义的集结算子对语言评价信息进行加权集成,从而获得各个决策方案的排序和择优结果;最后给出一个实例分析,说明了该方法的有效性和可行性。     

基于自协调函数的信道容量和最大熵的计算

信道容量和最大熵的计算是信息论中的经典问题.讨论了利用自协调函数理论计算信道容量,尤其是带约束的信道容量的方法.将最大熵的计算作为信道容量计算的特殊情况.作为应用,在证明了单位成本信道容量函数的单峰性的基础上,提出了其相应的多项式时间算法.     

基于犹豫模糊二元语义的多属性决策方法

深入研究了犹豫模糊二元语义多属性决策问题。首先利用幂均算子给出了犹豫模糊二元语义集的均值函数,并基于均匀分布概率准则和二元语义的距离测度提出了犹豫模糊二元语义集两两比较的可能度公式,进一步给出了可能度排序公式的性质。针对属性值为犹豫模糊二元语义集的多属性决策问题,提出了一种基于熵权的多属性决策方法。最后结合实际问题,验证了该方法的有效性和可行性。     

基于最大熵和最小交叉熵方法的上证50ETF期权定价

基于最大熵方法和最小交叉熵方法,给出了根据期权价格推断标的资产价格分布的模型和已知先验信息下推断标的资产价格分布的模型,利用拉格朗日乘子法给出了模型的简化解,通过粒子群算法求出标的资产价格的密度函数,进而对上证50ETF期权进行定价比较.实证结果表明:基于最大熵方法推断的分布可以作为标的资产价格分布的较好估计;基于最小交叉熵方法推断的分布是在先验信息下标的资产价格分布的较好估计,两种方法适用于我国上证50ETF期权定价.     

融合交叉自注意力和预训练模型的文本语义相似性评估方法

评估两个句子的语义相似性是文本检索和文本摘要等自然语言处理任务的重要组成部分.学者利用深度神经网络执行该任务,然而它们依赖于上下文独立词向量,从而导致性能不佳.为了缓解该问题,采用预训练模型BERT替换传统的词向量,并提出交叉自注意力以增强两个句子的语义,然后与BERT结合.在提出的模型中,为了实现交叉自注意力操作,设计了向量的对齐方法.最后,将BERT输出输入一个双向循环神经网络,以稳定性能,克服BERT自身带来的波动性.实验中,采用3个公开数据集DBMI2019、CDD-ref和CDD-ful对提出的混合模型进行评价.实验结果表明,由于使用了BERT生成的语境词向量,提出模型的性能始终优于现存方法;交叉自注意力实现了彼此的语义交互而增强了句对的语义,使得相似句对的语义差异更小,而不相似句对的语义差异更大,提高了相似性评估的性能.最终,提出模型在DBMI2019、CDD-ref和CDD-ful上分别取得了0.846,0.849和0.845的皮尔逊相关系数,超越了直接以[CLS]输出向量作为评估的方法.     

基于模糊熵的直觉模糊多属性群决策方法

针对专家权重未知、专家判断信息以直觉模糊集给出的多属性群决策问题,提出了一种新的决策方法.通过定义直觉模糊集的模糊熵计算专家判断信息的模糊程度,进而确定每位专家的权重.然后定义直觉模糊集的模糊交叉熵确定备选方案距理想方案和负理想方案的距离,再根据加权算术算子集结专家的判断信息,得到方案的排序.最后,通过一个实例分析验证了方法的有效性.     

基于云计算的双极二元语义决策模型及其应用

针对评价为语言型且准则权重未知情况下风险型决策信息有效集结的问题,提出一种基于云计算与前景理论的双极二元语义决策模型.首先,将语言型决策信息转换成双极二元语义形式并使用G1—离差最大化法计算各准则的组合权重;其次,利用双极二元语义加权平均算子将各状态、各方案多准则下的决策信息集结为综合双极二元语义决策阵;然后,利用云模型的数字特征公式将综合双极二元语义决策矩阵转化为各状态下各方案的综合云决策矩阵,并结合前景理论分析以确定所有方案的综合云前景值,将其排序并择优.最后,对案例的研究验证了新算法的科学性与适用性.     

Typical ambiguity and elementary equivalence

A sentence of the usual language of set theory is said to be stratified if it is obtained by “erasing” type indices in a sentence of the language of Russell's Simple Theory of Types. In this paper we give an alternative presentation of a proof the ambiguity theorem stating that any provable stratified sentence has a stratified proof. To this end, we introduce a new set of ambiguity axioms, inspired by Fraïssé's characterization of elementary equivalence; these axioms can be naturally used to give different proofs of the ambiguity theorem (semantic or syntactic, classical or intuitionistic). MSC: 03B15, 03F50, 03F55.     

Robust parameter estimation with a small bias against heavy contamination

In this paper we consider robust parameter estimation based on a certain cross entropy and divergence. The robust estimate is defined as the minimizer of the empirically estimated cross entropy. It is shown that the robust estimate can be regarded as a kind of projection from the viewpoint of a Pythagorean relation based on the divergence. This property implies that the bias caused by outliers can become sufficiently small even in the case of heavy contamination. It is seen that the asymptotic variance of the robust estimator is naturally overweighted in proportion to the ratio of contamination. One may surmise that another form of cross entropy can present the same behavior as that discussed above. It can be proved under some conditions that no cross entropy can present the same behavior except for the cross entropy considered here and its monotone transformation.     

Single valued neutrosophic cross-entropy for multicriteria decision making problems

A single valued neutrosophic set (SVNS) is an instance of a neutrosophic set, which give us an additional possibility to represent uncertainty, imprecise, incomplete, and inconsistent information which exist in real world. It would be more suitable to apply indeterminate information and inconsistent information measures. In this paper, the cross entropy of SVNSs, called single valued neutrosophic cross entropy, is proposed as an extension of the cross entropy of fuzzy sets. Then, a multicriteria decision-making method based on the proposed single valued neutrosophic cross entropy is established in which criteria values for alternatives are SVNSs. In decision making process, we utilize the single-valued neutrosophic weighted cross entropy between the ideal alternative and an alternative to rank the alternatives corresponding to the cross entropy values and to select the most desirable one(s). Finally, a practical example of the choosing problem of suppliers is provided to illustrate the application of the developed approach.     

计算机代数系统应用研究

计算机代数是近三十年发展起来的关于数学、计算机及人工智能方面的交叉学科,是数学发展的前沿学科,应用广泛.应用计算机代数系统的强大的符合运算功能以及该系统提供的控制语句,对一类弱非线性系统的有效渐近展开式解进行了研究,不但能使其自动求解,而且自动实现在平衡点附近降低微分方程阶数和实行中心流形方法,并试图解决了G r bner基在图论中关于连通图的最短路径问题.     

Physical entropy, information entropy and their evolution equations

Inspired by the evolution equation of nonequilibrium statistical physics entropy and the concise statistical formula of the entropy production rate, we develop a theory of the dynamic information entropy and build a nonlinear evolution equation of the information entropy density changing in time and state variable space. Its mathematical form and physical meaning are similar to the evolution equation of the physical entropy: The time rate of change of information entropy density originates together from drift, diffusion and production. The concise statistical formula of information entropy production rate is similar to that of physical entropy also. Furthermore, we study the similarity and difference between physical entropy and information entropy and the possible unification of the two statistical entropies, and discuss the relationship among the principle of entropy increase, the principle of equilibrium maximum entropy and the principle of maximum information entropy as well as the connection between them and the entropy evolution equation.     

Heisenberg's Inequality and Logarithmic Heisenberg's Inequality for Ambiguity Function

In this article we discuss the relation between Heisenberg's inequality and logarithmic Heisenberg's (entropy) inequality for ambiguity function. After building up a Heisenberg's inequality, we obtain a connection of variance with entropy by variational method. Using classical Taylor's expansion, we prove that the equality in Heisenberg's inequality holds if and only if the entropy of 2k - 1 order is equal to (2k - 1}!.