Branching Strategy Selection Approach Based on Vivification Ratio

The two most effective branching strategies LRB and VSIDS perform differently on different types of instances. Generally, LRB is more effective on crafted instances, while VSIDS is more effective on application ones. However, distinguishing the types of instances is difficult. To overcome this drawback, we propose a branching strategy selection approach based on the vivification ratio. This approach uses the LRB branching strategy more to solve the instances with a very low vivification ratio. We tested the instances from the main track of SAT competitions in recent years. The results show that the proposed approach is robust and it significantly increases the number of solved instances. It is worth mentioning that, with the help of our approach, the solver Maple_CM can solve additional 16 instances for the benchmark from the 2020 SAT competition.     

JDO实例的状态管理研究

Java Data Objects(JDO)是在Sun公司倡导下制定的新的Java对象级别的透明持久化标准。JDO关注于那些代表着应用领域数据模型的Java类，它们代表的数据要被持久存储到各种数据源，这些类被称为可持久类，它们的实例被称为可持久实例或JDO实例。JDO实例分为瞬时的和持久的，代表着数据源中数据的JDO实例为持久的，它们已经与数据源中的数据发生关联；否则就为瞬时的。JDO必须使持久JDO实例的值与相应数据源中的数据保持同步，因此有了JDO实例的状态管理，包括JDO实例的生命周期状态管理和JDO实例的字段管理。该文的重点是对JDO实例的状态管理进行研究。对相关规范进行了深入分析，并讨论了具体的实现方法。     

基于正则化的半监督弱标签分类方法

针对多标签学习中实例标签的缺失补全和预测问题,本文提出一种基于正则化的半监督弱标签分类方法(简称SWCMR),方法同时兼顾实例相似性和标签相关性.SWCMR首先根据标签相关性对弱标签实例的缺失标签进行初步预估,然后利用弱标签实例和无标签实例构造邻域图,从实例相似性和标签相关性角度构建基于平滑性假设的正则化项,接下来利用预估后的弱标签实例结合无标签实例训练半监督弱标签分类模型.在多种公共多标签数据集上的实验结果表明,SWCMR提高了分类性能,尤其是标签信息较少时,分类效果提升更显著.     

基于新型间谍技术的半监督自训练正例无标记学习

正例无标记（PU）学习中的间谍技术极易受噪声和离群点干扰，导致划分的可靠正例不纯，且在初始正例中随机选择间谍样本的机制极易造成划分可靠负例时效率低下，针对这些问题提出一种结合新型间谍技术和半监督自训练的PU学习框架。首先，该框架对初始有标记样本进行聚类并选取离聚类中心较近的样本来取代间谍样本，这些样本能有效地映射出无标记样本的分布结构，从而更好地辅助选取可靠负例；然后对间谍技术划分后的可靠正例进行自训练提纯，采用二次训练的方式取回被误分为正例样本的可靠负例。该框架有效地解决了传统间谍技术在PU学习中分类效率易受数据分布干扰以及随机间谍样本影响的问题。通过9个标准数据集上的仿真实验结果表明，所提框架的平均分类准确率和F-值均高于基本PU学习算法（Basic_PU）、基于间谍技术的PU学习算法（SPY）、基于朴素贝叶斯的自训练PU学习算法（NBST）和基于迭代剪枝的PU学习算法（Pruning）。     

工作流活动多实例的调度控制

支持多实例的工作流管理系统为工作流过程处理带来极大的灵活性,活动多实例要解决的主要问题之一是多实例的调度控制.在分析了多实例的分配和汇聚等问题之后,针对过程中活动间不同活动语义的上下文,对活动多实例的活动属性进行了统一的形式描述,提出了活动多实例控制体Shell,用于控制活动多实例的分配和提交.Shell可以根据不同的活动语义,处理多实例的同步并控制整个过程的运行.Shell的提出解决了工作流执行中一个活动多个执行实例的同步执行问题.     

A novel gray-based reduced NN classification method

In pattern recognition, instance-based learning (also known as nearest neighbor rule) has become increasingly popular and can yield excellent performance. In instance-based learning, however, the storage of training set rises along with the number of training instances. Moreover, in such a case, a new, unseen instance takes a long time to classify because all training instances have to be considered when determining the ‘nearness’ or ‘similarity’ among instances. This study presents a novel reduced classification method for instance-based learning based on the gray relational structure. Here, only some training instances in the original training set are adopted for the pattern classification tasks. The relationships among instances are first determined according to the gray relational structure. In the relational structure, the inward edges of each training instance, indicating how many times each instance is considered as the nearest neighbor or neighbors in determining the class labels of other instances can be obtained. This method excludes training instances with no or few inward edges for the pattern classification tasks. By using the proposed instance pruning approach, new instances can be classified with a few training instances. Nine data sets are adopted to demonstrate the performance of the proposed learning approach. Experimental results indicate that the classification accuracy can be maintained when most of the training instances are pruned before learning. Additionally, the number of remained training instances in the proposal presented here is comparable to that of other existing instance pruning techniques.     

基于加权的冗余相似本体实例发现的研究

在开放式的环境下一般由不同的组织和人员对一个领域的本体库的知识实例进行维护和添加,这就可能出现重复描述的实例的问题,会出现对同一对象的不同实例描述,甚至是相互矛盾的,从而出现多义性。这就要通过本体的匹配发现这些重复描述的实例,并把它们合并。但是合并前需要找出这些重复的实例,文中通过对实例属性及其值的加权并通过文中提供的算法来查找出这些冗余的实例。通过实验结果可以发现,此方法可以为解决这个问题提供一种很好的解决方法。     

DBMUTE: density-based majority under-sampling technique

Class imbalance is a challenging problem that demonstrates the unsatisfactory classification performance of a minority class. A trivial classifier is biased toward minority instances because of their tiny fraction. In this paper, our density function is defined as the distance along the shortest path between each majority instance and a minority-cluster pseudo-centroid in an underlying cluster graph. A short path implies highly overlapping dense minority instances. In contrast, a long path indicates a sparsity of instances. A new under-sampling algorithm is proposed to eliminate majority instances with low distances because these instances are insignificant and obscure the classification boundary in the overlapping region. The results show predictive improvements on a minority class from various classifiers on different UCI datasets.     

A declarative language to support dynamic evolution of web service business protocols

We investigate the problem of web service instances migration in the context of business protocol evolution, i.e., how to convert active instances of web services from an old version of a business protocol into a new one? We propose a framework based on a declarative approach to support service providers in defining fine-grained migration strategies of active instances. While the existing approaches for instances migration force the migrated instances to reflect the original ones as accurately as possible, in our approach we give to service providers the ability to declaratively define the constraints that drive the instances migration process. A migration strategy is expressed as a set of instances migration rules which are specified using an instance mapping language made of a set of generic migration patterns. The proposed approach has been implemented in a software tool that provides useful functionalities for protocol managers.     

Generating new test instances by evolving in instance space

Our confidence in the future performance of any algorithm, including optimization algorithms, depends on how carefully we select test instances so that the generalization of algorithm performance on future instances can be inferred. In recent work, we have established a methodology to generate a 2-d representation of the instance space, comprising a set of known test instances. This instance space shows the similarities and differences between the instances using measurable features or properties, and enables the performance of algorithms to be viewed across the instance space, where generalizations can be inferred. The power of this methodology is the insights that can be generated into algorithm strengths and weaknesses by examining the regions in instance space where strong performance can be expected. The representation of the instance space is dependent on the choice of test instances however. In this paper we present a methodology for generating new test instances with controllable properties, by filling observed gaps in the instance space. This enables the generation of rich new sets of test instances to support better the understanding of algorithm strengths and weaknesses. The methodology is demonstrated on graph colouring as a case study.     

过程实例跃迁正确性判定准则研究

过程模型的动态更改会引起运行中的过程实例向更改后的过程模型跃迁,必须保证过程实例跃迁后运行的一致性。由于多分支结构的影响,不同过程实例的跃迁条件不易确定。研究了过程模型更改机制,通过删除、插入、修改三类元更改操作构造更改域。将过程执行历史能否重放作为过程实例跃迁正确性的判定准则,提出一种高效的过程实例精确跃迁条件评判方法,可以确定拥有不同路由结构、不同运行状态的过程实例的跃迁策略。该方法能适应多分支的过程模型,灵活性高,便于实现过程模型动态更改和实例跃迁的自动化。     

MILD: Multiple-Instance Learning via Disambiguation

In multiple-instance learning (MIL), an individual example is called an instance and a bag contains a single or multiple instances. The class labels available in the training set are associated with bags rather than instances. A bag is labeled positive if at least one of its instances is positive; otherwise, the bag is labeled negative. Since a positive bag may contain some negative instances in addition to one or more positive instances, the true labels for the instances in a positive bag may or may not be the same as the corresponding bag label and, consequently, the instance labels are inherently ambiguous. In this paper, we propose a very efficient and robust MIL method, called Multiple-Instance Learning via Disambiguation (MILD), for general MIL problems. First, we propose a novel disambiguation method to identify the true positive instances in the positive bags. Second, we propose two feature representation schemes, one for instance-level classification and the other for bag-level classification, to convert the MIL problem into a standard single-instance learning (SIL) problem that can be solved by well-known SIL algorithms, such as support vector machine. Third, an inductive semi-supervised learning method is proposed for MIL. We evaluate our methods extensively on several challenging MIL applications to demonstrate their promising efficiency, robustness, and accuracy.     

A Comparative Evaluation of Curriculum Learning with Filtering and Boosting in Supervised Classification Problems

Not all instances in a data set are equally beneficial for inferring a model of the data, and some instances (such as outliers) can be detrimental. Several machine learning techniques treat the instances in a data set differently during training such as curriculum learning, filtering, and boosting. However, it is difficult to determine how beneficial an instance is for inferring a model of the data. In this article, we present an automated method that orders the instances in a data set by complexity based on their likelihood of being misclassified (instance hardness) for supervised classification problems that generates a hardness ordering. The underlying assumption of this method is that instances with a high likelihood of being misclassified represent more complex concepts in a data set. Using a hardness ordering allows a learning algorithm to focus on the most beneficial instances. We integrate a hardness ordering into the learning process using curriculum learning, filtering, and boosting. We find that focusing on the simpler instances during training significantly increases generalization accuracy. Also, the effects of curriculum learning depend on the learning algorithm that is used. In general, filtering and boosting outperform curriculum learning, and filtering has the most significant effect on accuracy. © 2014 Wiley Periodicals, Inc.     

Selecting services in the cloud: a decision support methodology focused on infrastructure-as-a-service context

Growing demand for reduced local hardware infrastructure is driving the adoption of Cloud Computing. In the Infrastructure-as-a-Service model, service providers offer virtualized computational resources in the form of virtual machine instances. The existence of a large variety of providers and instances makes the decision-making process a difficult task for users, especially as factors such as the datacenter location - where the virtual machine is hosted - have a direct influence on the price of instances. The same instance may present price differences when hosted in different geographically distributed datacenters and, because of that, the datacenter location needs to be taken into account through the decision-making process. Given this problem, we propose the D-AHP, a methodology to aid decision-making based on Pareto Dominance and Analytic Hierarchy Process (AHP). In the D-AHP, the dominance concept is applied to reduce the number of instances to be compared; the instances selection is based on a set of objectives, while AHP ranks the selected ones from a set of criteria and sub-criteria, among them the datacenter location. The results from case studies show that differences may arise in the results, regarding which instance is more suitable for the user, when considering the datacenter location as a criterion to choose an instance. This fact highlights the need to consider this factor during the process of migrating applications to the Cloud. In addition, Pareto Dominance applied early over the set of total instances has proved to be efficient, once it significantly reduces the number of instances to be compared and ordered by the AHP by excluding instances with less computational resources and higher cost in the decision-making process, mainly for larger application workloads.

     

基于隶属度的命名实体自动获取研究

利用维基百科(Wikipedia)和已有命名实体资源,提出维基百科类的隶属度计算方法,通过匹配、计算、过滤、扩展、去噪五个步骤构建出具有较高质量和较大规模的命名实体实例集.在英语维基百科数据上进行实验,结果显示,基于隶属度方法自动获取的人名实例规模较DBpedia抽取出的人名实例规模高出近10倍,通过对不同隶属度区间的抽取实例进行人工检验,发现抽取出的前15000个维基百科类的准确率达到99％左右,能够有效支持命名实体类实例的扩充.     

针对标记数据不足的数据流分类器

大部分数据流分类算法解决了数据流无限长度和概念漂移这两个问题。但是,这些算法需要人工专家将全部实例都标记好作为训练集来训练分类器,这在数据流高速到达并需要快速分类的环境中是不现实的,因为标记实例需要时间和成本。此时,如果采用监督学习的方法来训练分类器,由于标记数据稀少将得到一个弱分类器。提出一种基于主动学习的数据流分类算法,该算法通过选择全部实例中的一小部分来人工标记,其中这小部分实例是分类置信度较低的样本,从而可以极大地减少需要人工标记的实例数量。实验结果表明,该算法可以在数据流存在概念漂移情况下,使用较少的标记数据对数据流训练出分类器,并且分类效果良好。     

Missing data imputation by utilizing information within incomplete instances

This paper proposes to utilize information within incomplete instances (instances with missing values) when estimating missing values. Accordingly, a simple and efficient nonparametric iterative imputation algorithm, called the NIIA method, is designed for iteratively imputing missing target values. The NIIA method imputes each missing value several times until the algorithm converges. In the first iteration, all the complete instances are used to estimate missing values. The information within incomplete instances is utilized since the second imputation iteration. We conduct some experiments for evaluating the efficiency, and demonstrate: (1) the utilization of information within incomplete instances is of benefit to easily capture the distribution of a dataset; and (2) the NIIA method outperforms the existing methods in accuracy, and this advantage is clearly highlighted when datasets have a high missing ratio.     

多示例学习的示例层次覆盖算法

在多示例学习（Multi-instance learning,MIL）中,核心示例对于包类别的预测具有重要作用。若两个示例周围分布不同数量的同类示例,则这两个示例的代表程度不同。为了从包中选出最具有代表性的示例组成核心示例集,提高分类精度,本文提出多示例学习的示例层次覆盖算法（Multi-instance learning with instance_level covering algorithm,MILICA）。该算法首先利用最大Hausdorff距离和覆盖算法构建初始核心示例集,然后通过覆盖算法和反验证获得最终的核心示例集和各覆盖包含的示例数,最后使用相似函数将包转为单示例。在两类数据集和多类图像数据集上的实验证明,MILICA算法具有较好的分类性能。     

基于一致性假设的偏标记学习算法*

解决偏标记问题的基本策略是消歧,现有的消歧策略大都分别对每个示例单独进行消歧,并未充分利用示例之间的相关性.基于此原因,文中提出一致性偏标记学习算法(COPAL).该算法基于一个基本假设:相似示例的标记也应该有相关性.基于该假设,COPAL在消歧过程中同时考虑样本自身及其近邻样本的标记信息.实验表明,在人工合成的UCI数据集和真实数据集上,COPAL均取得较好的泛化性能.     

基于可达子网跟踪的业务实例性能分析

业务实例的时间性能是业务过程实时管理和调度的重要依据.业务实例的可达子网随着执行推进不断变化,其中各个活动分别处于完成、执行和等待等多种不同的状态,业务实例的性能估计只能针对该子网进行.本文首先提出执行中和等待中活动的时间性能等价模型,提出实例的归属子图集概念以跟踪实例的可达子网的动态变化,并将复杂的实例时间性能求解问题转换为更为简单的归属子图的性能求解问题,用一个例子演示了业务实例性能分析过程.