Learning rules from incomplete training examples by rough sets

Machine learning can extract desired knowledge from existing training examples and ease the development bottleneck in building expert systems. Most learning approaches derive rules from complete data sets. If some attribute values are unknown in a data set, it is called incomplete. Learning from incomplete data sets is usually more difficult than learning from complete data sets. In the past, the rough-set theory was widely used in dealing with data classification problems. In this paper, we deal with the problem of producing a set of certain and possible rules from incomplete data sets based on rough sets. A new learning algorithm is proposed, which can simultaneously derive rules from incomplete data sets and estimate the missing values in the learning process. Unknown values are first assumed to be any possible values and are gradually refined according to the incomplete lower and upper approximations derived from the given training examples. The examples and the approximations then interact on each other to derive certain and possible rules and to estimate appropriate unknown values. The rules derived can then serve as knowledge concerning the incomplete data set.     

Learning rules for graph transformations by induction from examples

The input to the described program, in learning mode, consists of examples of starting graph and result graph pairs. The starting graph is transformable into the result graph by adding or deleting certain edges and vertices. The essential common features of the starting graphs are stored together with specifications of the edges and vertices to be deleted or added. This latter information is obtained by mapping each starting graph onto the corresponding result graph. On subsequent input of similar starting graphs without a result graph, the program, in performance mode, recognizes the characterizing set of features in the starting graph and can perform the proper transformation on the starting graph to obtain the corresponding result graph. The program also adds the production to its source code so that after recompilation it is permanently endowed with the new production. If any feature which lacks the property "ordinary" is discovered in the starting graph and only one example has been given, then there is feedback to the user including a request for more examples to ascertain whether the extraordinary property is a necessary part of the situation.     

A new method for discovering rules from examples in expert systems

The experts often cannot explain why they choose this or that decision in terms of formalized “if-then” rules; in these cases we have a set of examples of their real decisions, and it is necessary to reveal the rules from these examples. The existing methods of discovering rules from examples either demand that the set of examples be in some sense complete (and it is often not complete) or they are too complicated. We present a new algorithm which is always applicable. This algorithm is based on the formalization of rough set theory, a formalization which describes the case of incomplete information.     

在多数据库中确定支持度的有效方法

提出了一种用于在多数据库环境下确定模式的支持度的方法.数据挖掘总是在强关联规则模式中进行挖掘,因此计算模式的支持度必不可少.由于多数据库与单数据库的本质不同,单数据库中的支持度只具有本地效应,它的数值和计算方法在多数据库中都是不适用的,因此需要找到一个在多数据库中计算支持度的方法.又由于每个本地数据库的各方面情况不同,因此各自在总部制定决策时的地位和重要性也就各异.于是,在考虑某模式的支持度时,不能简单的将该模式在各本地数据库中的支持度进行平均,而需要引进"权值"的概念.在综合考虑了这些因素之后,提出了一些确定权值的方案,并给出了一个基于权值计算支持度的方法.     

How to obtain full privacy in auctions

Privacy has become a factor of increasing importance in auction design. We propose general techniques for cryptographic first-price and (M+1)st-price auction protocols that only yield the winners' identities and the selling price. Moreover, if desired, losing bidders learn no information at all, except that they lost. Our security model is merely based on computational intractability. In particular, our approach does not rely on trusted third parties, e.g., auctioneers. We present an efficient implementation of the proposed techniques based on El Gamal encryption whose security only relies on the intractability of the decisional Diffie—Hellman problem. The resulting protocols require just three rounds of bidder broadcasting in the random oracle model. Communication complexity is linear in the number of possible bids.     

快速寻优的遗传交叉策略

介绍了遗传算法中一种新的交叉策略, 它利用逼近方法决定遗传算法中子代个体的位置. 采用这种交叉策略可以使子代快速地向更高适应度的区域移动, 实验数据说明使用这种交叉策略的遗传算法可以极大地提高算法获得最优解的速度.     

Combination of deep neural networks and logical rules for record segmentation in historical handwritten registers using few examples

International Journal on Document Analysis and Recognition (IJDAR) - This work focuses on the layout analysis of historical handwritten registers, in which local religious ceremonies were recorded....     

单片机在电缆设备故障检测中的应用

文章介绍了单片机在电缆充气设备检测系统的功能、设计原理、应用及其发展前景。通过单片机技术及时发现电缆充气设备的故障情况，减少了误差，使维修工作取得主动，提高了工作效率，保证了通信畅通，使故障检测达到信息化。     

Reducing examples to accelerate support vector regression

With increasing of the number of training examples, training time for support vector regression machine augments greatly. In this paper we develop a method to cut the training time by reducing the number of training examples based on the observation that support vector’s target value is usually a local extremum or near extremum. The proposed method first extracts extremal examples from the full training set, and then the extracted examples are used to train a support vector regression machine. Numerical results show that the proposed method can reduce training time of support regression machine considerably and the obtained model has comparable generalization capability with that trained on the full training set.     

Logical methods to obtain deterministic reliability estimates for systems

We consider the construction of deterministic integral estimates of sucessive failure and recovery times of a system. The mathematical model of system reliability is described by a dynamic automaton using the mathematical apparatus of many-valued and infinite-valued logic.Translated from Kibernetika, No. 5, pp. 104–111, September–October, 1990.     

基于神经网络生成图像语义的算法研究

提出一种利用神经网络获取图像语义的算法。通过构建一个RBF神经网络,在图像的颜色、纹理、形状等低层视觉特征和高层语义特征之间建立映射关系。利用遗传算法训练RBF网络,获得RBF网络的隐节点个数、中心、宽度和连接权值等参数值,训练成功后的神经网络能够自动获取图像的语义。实验结果表明,该算法具有较好的基于语义的检索效果,体现了人对图像内容的理解,符合人的思维习惯。     

利用HyperChem获取反应过渡态的方法

利用HyperChem的动态数据交换DDE(Dynamic Data Exchange)功能,通过Excel中的Visual BasicA编程来控制HyperChem的运行,从而控制反应过程中的反应坐标和反应步长,计算得到反应系统沿反应坐标的能量变化曲线,从而可获取反应的过渡态和活化能△E。利用该计算方法搜寻了喹啉分子在S-Mo-Ni催化剂晶体表面催化加氢反应的过渡态,取得较好的效果。利用该方法可以确定复杂反应过渡态,从而为实验研究起理论指导作用。     

A novel approach to generating high-resolution adversarial examples

Deep neural networks (DNNs) have improved expressive performance in many artificial intelligence (AI) fields in recent years. However, they can easily induce incorrect behavior due to adversarial examples. The state-of-the-art strategies for generating adversarial examples were established as generative adversarial nets (GAN). Due to a large amount of data and the high computational resources required, previous GAN-based work has only generated adversarial examples for small datasets, resulting in a less favorable visualization of the generated images. To address this problem, we propose a feasible approach, which improves on the AdvGAN framework through data augmentation, combined with PCA and KPCA to map the input instance’s main features onto the latent variables. Experimental results indicate that our approach can generate more natural perturbations on high-resolution images while maintaining 96%?+?of the features of the original input instance. Moreover, we measured 90.30% attack success rates on CIFAR-10 against the target model ResNet152, a small improvement compared to 88.69% for AdvGAN. We applied the same idea to ImageNet and LSUN, and the results showed that it not only achieves a high attack success rate,but can generate strongly semantically adversarial examples with better transferability on prevailing DNNs classification models. We also show that our approach yields competitive results compared to sensitivity analysis-based or optimization-based attacks notable in the literature.

     

A Knowledge-Based Approach to Program Synthesis from Examples

This paper proposes an approach to synthesize functional programs of Backus‘ FP system^[1,2] from input/output instances.Based on a theory of orthogonal expansion of programs^3[,4],the task of program synthesis is expressed in program equations,and fulfilled by solving them according to the knowledge about the equivalence between programs.Some general knowledge of solving program equations with a number of examples are given in the paper.     

利用计算机视觉获取无人机飞行姿态角

计算机视觉辅助无人机自主着陆技术中,通过计算机视觉获得无人机相对于跑道的位置是很重要的。在利用图像处理技术获得跑道两条边缘线在摄像机像素坐标系下的直线方程后,提出了一种在给定无人机俯仰角的情况下获取无人机机体与跑道边缘线夹角的方法,介绍了一种利用N矢量计算摄像机外参数旋转参数的方法,和传统的方法相比,该方法在求解非线性方程组时非线性方程的数量大大减少,运算速度提高。最后提出了一种能够精确验证该算法的实验手段。实验表明:该方法算法简单、计算结果精确,完全符合无人机着陆系统对于参数的要求。