An index structure supporting rule activation in pervasive applications

World Wide Web - Rule mechanism has been widely used in many areas, such as databases, artificial intelligent and pervasive computing. In a rule mechanism, rule activation decides which rules are...     

Heterometallic sulfide cluster [Ag6Sn6S20]10 −: Solvothermal syntheses and characterizations of silver thiostannates with lanthanide complex counter cations

Novel organic hybrid silver thiostannates [Hen]₄[Ln(en)₄]₂[Ag₆Sn₆S₂₀]·3en (Ln = Er, 1; Tm, 2; Yb, 3) were prepared by the reactions of Ln₂O₃, Ag, Sn and S in ethylenediamine (en) under solvothermal conditions. Six SnS₄ tetrahedra and six AgS₃ triangles are connected into the heterometallic sulfide cluster [Ag₆Sn₆S₂₀]^10 − via edge-sharing. In the [Ag₆Sn₆S₂₀]^10 − cluster, a hexanuclear Ag₆S₆ core is enclosed by two Sn₃S₁₀ fragments. The Ag₆S₆ core is the first As–S cluster stabilized by inorganic SnS₄ ligands. In 1–3, all Ln^3 + ions are in 8-fold coordination environments that involved four bidentate en ligands, forming bicapped trigonal prisms. Compounds 1–3 show well-defined absorption edges with band gaps in the range of 2.18–2.47 eV.     

A gradient boosting decision tree approach for insider trading identification: An empirical model evaluation of China stock market

Insider trading is a kind of criminal behavior in stock market by using nonpublic information. In recent years, it has become the major illegal activity in China’s stock market. In this study, a combination approach of GBDT (Gradient Boosting Decision Tree) and DE (Differential Evolution) is proposed to identify insider trading activities by using data of relevant indicators. First, insider trading samples occurred from year 2007 to 2017 and corresponding non-insider trading samples are collected. Next, the proposed method is trained by the GBDT, and initial parameters of the GBDT are optimized by the DE. Finally, out-of-samples are classified by the trained GBDT–DE model and its performances are evaluated. The experiment results show that our proposed method performed the best for insider trading identification under time window length of ninety days, indicating the relevant indicators under 90-days time window length are relatively more useful. Additionally, under all three time window lengths, relative importance result shows that several indicators are consistently crucial for insider trading identification. Furthermore, the proposed approach significantly outperforms other benchmark methods, demonstrating that it could be applied as an intelligent system to improve identification accuracy and efficiency for insider trading regulation in China stock market.     

论坛消息的语义漂移分析

引入多维文档模型来组织论坛消息的描述性特征和语义性特征，并在多维索引的基础上，提出了加窗分析技术。目的在于分析论坛站点中语义漂移现象，进而发现论坛中的完整语义信息单元。     

Three perspectives of data mining

This paper reviews three recent books on data mining written from three different perspectives, i.e., databases, machine learning, and statistics. Although the exploration in this paper is suggestive instead of conclusive, it reveals that besides some common properties, different perspectives lay strong emphases on different aspects of data mining. The emphasis of the database perspective is on efficiency because this perspective strongly concerns the whole discovery process and huge data volume. The emphasis of the machine learning perspective is on effectiveness because this perspective is heavily attracted by substantive heuristics working well in data analysis although they may not always be useful. As for the statistics perspective, its emphasis is on validity because this perspective cares much for mathematical soundness behind mining methods.     

Applying data mining to learn system dynamics in a biological model

Data mining consists of a set of powerful methods that have been successfully applied to many different application domains, including business, engineering, and bioinformatics. In this paper, we propose an innovative approach that uses genetic algorithms to mine a set of temporal behavior data output by a biological system in order to determine the kinetic parameters of the system. Analyzing the behavior of a biological network is a complicated task. In our approach, the machine learning method is integrated with the framework of system dynamics so that its findings are expressed in a form of system dynamics model. An application of the method to the cell division cycle model has shown that the method can discover approximate parametric values of the system and reproduce the input behavior.     

Evolving Fault-Tolerant Neural Networks

In this paper, genetic algorithm is used to help improve the tolerance of feedforward neural networks against an open fault. The proposed method does not explicitly add any redundancy to the network, nor does it modify the training algorithm. Experiments show that it may profit the fault tolerance as well as the generalisation ability of neural networks.     

Machine-learning paradigms for selecting ecologically significant input variables

Harmful algal blooms, which are considered a serious environmental problem nowadays, occur in coastal waters in many parts of the world. They cause acute ecological damage and ensuing economic losses, due to fish kills and shellfish poisoning as well as public health threats posed by toxic blooms. Recently, data-driven models including machine-learning (ML) techniques have been employed to mimic dynamics of algal blooms. One of the most important steps in the application of a ML technique is the selection of significant model input variables. In the present paper, we use two extensively used ML techniques, artificial neural networks (ANN) and genetic programming (GP) for selecting the significant input variables. The efficacy of these techniques is first demonstrated on a test problem with known dependence and then they are applied to a real-world case study of water quality data from Tolo Harbour, Hong Kong. These ML techniques overcome some of the limitations of the currently used techniques for input variable selection, a review of which is also presented. The interpretation of the weights of the trained ANN and the GP evolved equations demonstrate their ability to identify the ecologically significant variables precisely. The significant variables suggested by the ML techniques also indicate chlorophyll-a (Chl-a) itself to be the most significant input in predicting the algal blooms, suggesting an auto-regressive nature or persistence in the algal bloom dynamics, which may be related to the long flushing time in the semi-enclosed coastal waters. The study also confirms the previous understanding that the algal blooms in coastal waters of Hong Kong often occur with a life cycle of the order of 1–2 weeks.     

核诱导距离度量的鲁棒典型相关分析

典型相关分析(canonical correlation analysis,CCA)是寻找同一对象两组变量间线性相关性的一种常用的多元统计分析方法,其采用的欧氏距离度量方式导致了算法的非鲁棒性。核诱导的距离度量不仅在理论上被证明是鲁棒的,而且在(聚类)应用上获得了有效验证。将其进一步应用于CCA,发展出了核诱导距离度量的鲁棒CCA(CCA based on kernel-induced measure,KI-CCA)。该算法不仅克服了CCA非鲁棒的不足,而且使现有基于最大相关熵的鲁棒主成分分析(half-quadratic principal component analysis,HQ-PCA)成为特例,且具有非线性相关分析的能力。一方面,核的多样性使得KI-CCA也具有多样性,从而使其成为一般性的分析算法。另一方面,与CCA刻画上的相似性,使其求解可归结为广义特征值问题。在人工数据、多特征手写体数据库(multiple feature database,MFD)和人脸数据集(Yale、AR、ORL)上的实验验证了该算法的有效性。     

面向动态任务合作求解的联盟模型

多Agent系统中,Agent间通过形成联盟达到提高任务求解能力,获取更多收益的目的。主要关注联盟模型的改进和联盟形成阶段的改进,基于ARG(agent,role,group)元模型和学习机制提出了一种采用角色和学习机制的新联盟模型CLAR(coalition model based on learnin gagent and role);在采用合同网协议的CLAR联盟模型中提出了两阶段联盟形成机制;通过捕食者问题实验验证了角色和学习机制的作用,以及两阶段联盟形成机制在减少通讯代价上的作用。