新的基于特征关系表述的步态识别算法

为了快速有效地进行步态识别,利用特征关系非平稳分布的统计特性,提出了一种新的基于特征关系表述的步态识别算法。首先,将剪影轮廓相邻像素点间8邻域相对方向标号作为特征关系属性一,将轮廓边界点与中心点间的距离作为特征关系属性二,经直方图归一化处理,得到两种关系属性的联合概率;其次,结合主成分分析(PCA)降维的方法,提取特征主向量;最后,采用最近邻分类器进行识别分类。实验证明,该算法在CASIA步态数据库上,最高达到了90%以上的识别率,而且与传统的特征关系表述步态识别算法相比,关系属性联合概率矩阵维数由900维下降到240维,大大降低了算法的计算代价。     

多粒度粗糙集模型中属性子集序列的构造方法

针对多粒度粗糙集模型中属性子集序列的构造问题,提出一种基于属性间距离的构造方法。该方法首先引入信息系统中属性间距离的概念,并给出距离的定量计算公式;然后根据公式来计算出各个属性之间的距离;最后根据属性之间距离的远近,得到每个属性的邻域属性集,从而构造出一个属性子集序列。实验结果表明,与随机构造的属性子集序列相比,该方法构造的序列对于实验的每个对象类具有更高的近似精度。     

基于线性回归和属性集成的分类算法

对于高维度小样本数据的分类问题,高维属性的复杂性限制了分类模型预测的准确率。为了进一步提高准确率,提出了基于线性回归和属性集成的分类算法。首先,采用线性回归为每一个属性构建属性线性分类器(Attri-bute Linear Classifier,ALC);其次,为了避免因ALC数量过多而导致准确率下降,利用经验风险最小化策略中的经验损失值作为评估标准来优选ALC;最后,应用多数投票法来集成被筛选的ALC。采用高维度小样本的基因表达数据集进行实验,结果显示该算法具有比逻辑回归、支持向量机和随机森林算法更高的准确率。     

Attribute-based authentication for multi-agent systems with dynamic groups

In a multi-agent system, agents are required to interact in order to exchange information. To achieve a reliable information exchange, a sound security protection must be in place. Unfortunately, security and privacy in multi-agent systems have not drawn adequate attention. They have been actually ignored or mistreated in most proposed multi-agent protocols. We observe that security and privacy issues are indeed not trivial and cannot be resolved with traditional security mechanisms, if agents are not trusted each other and their privacy must be protected. In this paper, we propose a secure multi-agent protocol that captures several most important security properties including agent privacy, data confidentiality, and agent authenticity. Intuitionally, we allow each agent in a group to hold a set of policy attributes. To access a protected data set, an agent must hold a correct policy attribute. In other words, the private information between two agents can be exchanged, if and only if the policy attribute embedded in the transmitted message matches that held by the receiver. In case of mismatching attributes, the private information of the corresponding agent will not be revealed to their counterpart. The proposed scheme is formalized with a sound cryptographic algorithm with a rigorous security proof.     

针对混合型分类数据改进的[K]-modes算法距离公式

传统[K]-modes算法在分类属性聚类中有着广泛的应用,但是传统算法并不区分有序分类属性与无序分类属性。在区分这两种属性的基础上,提出了一种新的距离公式,并优化了算法流程。基于无序分类属性的距离数值,确定了有序分类属性相邻属性值之间距离数值的合理范围。借助有序分类属性蕴含的顺序关系,构建了有序分类属性的距离公式。计算样本点与质心距离之时,引入了簇内各属性值的比例作为总体距离公式的重要参数。综上,新的距离公式良好地刻画了有序分类属性的距离,并且平衡了两种不同分类属性距离公式之间的差异性。实验结果表明,提出的改进算法和距离公式在UCI真实数据集上比原始[K]-modes算法及其改进算法均有显著的效果。     

基于COSA算法的中文文本聚类

传统聚类算法在计算两个对象间的距离时,每个属性对距离的贡献相同。COSA(Clustering On Subsets of Attributes)算法^[1]认为在不同的分组中,每个属性对计算距离所起的作用可能并不相等,因为不同分组中的对象可能在不同的属性子集上聚集。文献[1]在此基础上定义了新的距离,并提出了两种COSA算法: COSA1算法是一种分割的聚类算法;COSA2算法是一种层次聚类算法。为了对比COSA距离和传统的欧氏距离在文本聚类中的表现,本文对中文文本进行了分割聚类和层次聚类的实验。实验结果显示出COSA算法较基于欧氏距离的聚类算法有更好的性能,而且对于属性数的变化,COSA算法更加稳定。     

一种适用于混合型分类数据的聚类算法

传统的K-modes算法采用简单的属性匹配方式计算同一属性下不同属性值的距离，并且计算样本距离时令所有属性权重相等。在此基础上，综合考虑有序型分类数据中属性值的顺序关系、无序型分类数据中不同属性值之间的相似性以及各属性之间的关系等，提出一种更加适用于混合型分类数据的改进聚类算法，该算法对无序型分类数据和有序型分类数据采用不同的距离度量，并且用平均熵赋予相应的权重。实验结果表明，改进算法在人工数据集和真实数据集上均有比K-modes算法及其改进算法更好的聚类效果。     

一种属性不一致性加权的K近邻分类方法

针对传统的K近邻算法在计算样本之间相似度时将每个属性视为同等重要的问题,提出了一种基于推土机距离的方法来计算每个条件属性的权重。首先根据近邻关系划分用于比较一致性的两个分布;之后根据推土机距离设计不一致性评价函数,用于衡量每个属性下各个样本的近邻样本集与这一集合由决策属性细化的等价划分之间的不一致性程度;最后将近邻的不一致性程度转换为相应属性的重要性,用于实现属性加权K近邻分类器。通过在多个数据集上进行实验,该方法对参数的敏感程度低,在多个参数下可以显著提高K近邻的分类精度,并且在多个指标下的表现优于现有的一些分类方法。结果表明,该方法可以通过属性加权选择出更加准确的近邻样本,可广泛应用于基于近邻的机器学习方法中。     

多智能体车间调度系统研究

现在制造业所面临的动态需求使得其必须具有更加灵活的应变机制，这使得车间调度问题变得越来越复杂。本文采取多智能体系统技术（MAS）设计了一个包含四个智能体（agent）的多智能体车间调度系统，分别为车间调度智能体、任务分配智能体、车间资源智能体以及拍卖智能体。通过这四个智能体的通信、交互和合作，系统可以给出一个满足当前制造需求的调度最优结果。     

时间窗下单船岸桥调度——基于数学规划和规则的启发式算法

在考虑任务属性中的任务优先顺序和不可同时执行要求,岸桥属性中的岸桥时间窗、转移时间、初始位置、安全距离和装卸速度等因素下,以单艘船舶的最短岸桥作业时间为目标函数,建立单艘船舶岸桥调度的混合整数线性模型P1。计算数据采集于宁波某集装箱港口,通过简化模型P2求解岸桥调度模型P1的下限边界值和排程数据,在此基础上,运用基于规则的启发式算法求解模型P1的岸桥调度时序表。计算结果表示本组合算法能较好地得到满意解,而且比较符合港口实际。     

Lonely and Susceptible: The Influence of Social Exclusion and Gender on Persuasion by an Artificial Agent

Earlier research showed that artificial social agents can influence human behavior. This article argues that, especially under certain circumstances, people are sensitive to persuasion by (artificial) social agents. For example, when people feel socially excluded, they are motivated to increase their social connections with others. It was hypothesized that socially excluded people would attribute more human-likeness and be persuaded more by an artificial agent than socially included people. These hypotheses were investigated in two studies in which participants were either socially included or excluded, after which they performed an energy-saving task while receiving social feedback from an artificial agent. Results did not support the expectation that socially excluded people ascribe more human-likeness to an artificial agent, but they did show the expected effects on behavior change, indicating the importance of including a person’s psychological state in the design of human–agent interactions. Also, in line with earlier findings, female participants were more susceptible to the agent’s feedback than male participants, indicating that a user’s gender may also determine the effectiveness of persuasive technology.     

CAMAS: A cluster-aware multiagent system for attributed graph clustering

Attributed graphs describe nodes via attribute vectors and also relationships between different nodes via edges. To partition nodes into clusters with tighter correlations, an effective way is applying clustering techniques on attributed graphs based on various criteria such as node connectivity and/or attribute similarity. Even though clusters typically form around nodes with tight edges and similar attributes, existing methods have only focused on one of these two data modalities. In this paper, we comprehend each node as an autonomous agent and develop an accurate and scalable multiagent system for extracting overlapping clusters in attributed graphs. First, a kernel function with a tunable bandwidth factor δ is introduced to measure the influence of each agent, and those agents with highest local influence can be viewed as the “leader” agents. Then, a novel local expansion strategy is proposed, which can be applied by each leader agent to absorb the most relevant followers in the graph. Finally, we design the cluster-aware multiagent system (CAMAS), in which agents communicate with each other freely under an efficient communication mechanism. Using the proposed multiagent system, we are able to uncover the optimal overlapping cluster configuration, i.e. nodes within one cluster are not only connected closely with each other but also with similar attributes. Our method is highly efficient, and the computational time is shown that nearly linearly dependent on the number of edges when δ ∈ [0.5, 1). Finally, applications of the proposed method on a variety of synthetic benchmark graphs and real-life attributed graphs are demonstrated to verify the systematic performance.     

One Dependence Value Difference Metric

Many distance-related algorithms depend upon a good distance metric to be successful. The Value Difference Metric, simply VDM, is proposed to find reasonable distance metric between each pair of instances with nominal attribute values only. In VDM, all of the attributes are assumed to be fully independent, and the difference between two values of an attribute is only considered to be closer if they have more similar correlation with the output classes. It is obvious that the attribute independence assumption in VDM is rarely true in reality, which would harm its performance in the applications with complex attribute dependencies. In this paper, we single out an improved Value Difference Metric by relaxing its unrealistic attribute independence assumption. We call it One Dependence Value Difference Metric, simply ODVDM. In ODVDM, the structure learning algorithms for Bayesian network classifiers, such as tree augmented naive Bayes, are used to find the dependence relationships among the attributes. Our experimental results validate its effectiveness in terms of classification accuracy.     

基于属性约简集评价节点重要性研究

社会网络成员的重要性确定通常依赖结构属性对网络节点的评价。首先定义了网络中节点排序可区分以及属性约简集的概念，并在此基础上量化了属性聚类的阈值，从而确定了类别的数量。设计了网络节点重要性的属性约简集评价算法。通过与度、介数、全属性评价在人工网络、海豚网上的实现，证明了属性约简集评价节点排序的可行性。通过属性约简集在海豚网、9·11恐怖分子合作网上的节点评价值、网络鲁棒性以及节点可区分性等方面的应用对比分析，发现属性约简集评价节点重要性既兼顾了网络结构的完整性，又避免了单一属性评价的片面性和多个属性之间的属性冗余性，提高了节点评价结果的准确性，降低了算法复杂度。     

On fuzzy-rough attribute selection: Criteria of Max-Dependency,Max-Relevance,Min-Redundancy,and Max-Significance

Attribute selection is one of the important problems encountered in pattern recognition, machine learning, data mining, and bioinformatics. It refers to the problem of selecting those input attributes or features that are most effective to predict the sample categories. In this regard, rough set theory has been shown to be successful for selecting relevant and nonredundant attributes from a given data set. However, the classical rough sets are unable to handle real valued noisy features. This problem can be addressed by the fuzzy-rough sets, which are the generalization of classical rough sets. A feature selection method is presented here based on fuzzy-rough sets by maximizing both relevance and significance of the selected features. This paper also presents different feature evaluation criteria such as dependency, relevance, redundancy, and significance for attribute selection task using fuzzy-rough sets. The performance of different rough set models is compared with that of some existing feature evaluation indices based on the predictive accuracy of nearest neighbor rule, support vector machine, and decision tree. The effectiveness of the fuzzy-rough set based attribute selection method, along with a comparison with existing feature evaluation indices and different rough set models, is demonstrated on a set of benchmark and microarray gene expression data sets.     

Multi-Dimensional,MultiStep Negotiation

We present a multi-dimensional, multi-step negotiation mechanism for task allocation among cooperative agents based on distributed search. This mechanism uses marginal utility gain and marginal utility cost to structure this search process, so as to find a solution that maximizes the agents’ combined utility. These two utility values together with temporal constraints summarize the agents’ local information and reduce the communication load. This mechanism is anytime in character: by investing more time, the agents increase the likelihood of getting a better solution. We also introduce a multiple attribute utility function into negotiations. This allows agents to negotiate over the multiple attributes of the commitment, which produces more options, making it more likely for agents to find a solution that increases the global utility. A set of protocols are constructed and the experimental result shows a phase transition phenomenon as the complexity of negotiation situation changes. A measure of negotiation complexity is developed that can be used by an agent to choose an appropriate protocol, allowing the agents to explicitly balance the gain from the negotiation and the resource usage of the negotiation.This revised version was published online in August 2005 with a corrected cover date.     

基于改进损失函数的多阶段行人属性识别方法

大量研究工作通过挖掘属性间的正相关性提高视频监控场景下的行人属性识别性能,但对属性间负相关性的探索仍存在不足.为此,文中基于深度学习理论提出多阶段行人属性识别方法,同时探索属性间的正、负相关性.第一阶段计算每个属性在训练过程中的损失值和正确率.第二阶段为平均损失较大且正确率较小的属性单独建立一个网络分支,其它属性仍保留在原分支上,然后两个分支联合预测所有属性.第三阶段新建两个网络分支,结构与第二阶段的分支相同,优化新分支的参数,使其属性识别性能优于第二阶段.最终使用第三阶段的模型进行属性预测.此外,构建增大正负样本差异的改进损失函数,应用于三个阶段的训练,进一步提升模型性能.在两个行人属性识别数据集RAP和PETA上的实验表明,文中方法性能较优.     

Image attribute learning with ontology guided fused lasso

Extended from the traditional pure statistical learning methods, we propose to augment the statistical learning methods with ontology and apply this idea for image attribute learning. In order to capture structural information among attributes, the graph-guided fused lasso model is adopted and improved by a new distance metric based on WordNet. The novelty of our method is that we find the semantic correlation with the ontology-guided attribute space and integrate inter-attribute similarity information into the learning model. The hierarchy of ImageNet is exploited to define the image attributes and a dataset from ImageNet including over 30,000 images is collected. The experimental results show that this method can both improve the accuracy and accelerate the algorithm convergency. Moreover, the learned semantic correlation owns transfer ability to related applications.     

Attribute Selection for Numerical Databases that Contain Correlations

There are many correlated attributes in a database. Conventional attribute selection methods are not able to handle such correlations and tend to eliminate important rules that exist in correlated attributes. In this paper, we propose an attribute selection method that preserves important rules on correlated attributes. We rst compute a ranking of attributes by using conventional attribute selection methods. In addition, we compute two-dimensional rules for each pair of attributes and evaluate their importance for predicting a target attribute. Then, we evaluate the shapes of important two-dimensional rules to pick up hidden important attributes that are under-estimated by conventional attribute selection methods. After the shape evaluation, we re-calculate the ranking so that we can preserve the important correlations. Intensive experiments show that the proposed method can select important correlated attributes that are eliminated by conventional methods.     

Monolithic and modular termination analyses for higher-order attribute grammars

In this paper we describe a sound, but not complete, analysis to prove the termination of higher-order attribute grammar evaluation caused by the creation of an unbounded number of (finite) trees as local tree-valued attributes, which are then themselves decorated with attributes. The analysis extracts a set of term-rewriting rules from the grammar that model creation of new syntax trees during the evaluation of higher-order attributes. If this term rewriting system terminates, then only a finite number of trees will be created during attribute grammar evaluation. The analysis places an ordering on nonterminals to handle the cases in which higher-order inherited attributes are used to ensure that a finite number of trees are created using such attributes. When paired with the traditional completeness and circularity analyses for attribute grammars and the assumption that each attribute equation defines a terminating computation, this analysis can be used to show that attribute grammar evaluation will terminate normally. This analysis can be applied to a wide range of common attribute grammar idioms and has been used to show that evaluation of our specification of Java 1.4 terminates. We also describe a modular version of the analysis that is performed on independently developed language extension grammars and the host language being extended. If the extensions individually pass the modular analysis then their composition is also guaranteed to terminate.