Learning a subclass of k-quasi-Horn formulas with membership queries

Boolean formulas have been widely studied in the field of learning theory. We focus on the model of learning with queries, and study a restriction of the class of k-quasi-Horn formulas, that is, conjunctive normal form formulas where the number of unnegated literals per clause is at most k. This class is known to be as hard to learn as the general class CNF of conjunctive normal form formulas. By imposing some constraints, we define a fragment of this logic that can be learned using only membership queries. Also we prove that none of these constraints makes by itself the class learnable.     

P2P soft security: On evolutionary dynamics of P2P incentive mechanism

This paper thoroughly investigates the evolutionary dynamics of soft security mechanism, namely, reciprocity-based incentive mechanism, in P2P systems based on Evolutionary Game Theory (EGT). By soft security mechanism, it means social control mechanisms to overcome peers’ selfish (rational) behaviors, and encourage cooperation in P2P systems. Specifically, there exist three strategies in P2P systems: always cooperative (ALLC), always defect (ALLD) and reciprocator (R). Instead of existing work which take it for granted that, like ALLC users, R users did not bear any information-seeking cost, we assume small reciprocation cost, and study generalized mutation-selection dynamics. Our contributions are threefold: firstly, we prove and illustrate that, in a well-mixed P2P structure, ALLD is the only strict Nash equilibrium; secondly, we infer the specific condition under which evolution dynamics exhibits rock-scissors-paper oscillation in a structured P2P population. That is, the population cycles from ALLD to R to ALLC and back to ALLD; finally, we theoretically illustrate that the intensity of selection plays an important role in the evolutionary dynamics of P2P incentive mechanism. That is, when the intensity of selection is relatively weak and reciprocation cost limits to zero, the time average can be mostly concentrated on reciprocator. In brief, considering the existence of reciprocation cost and the small mutation in P2P incentive mechanisms, unlike existing work, it is impossible to simply achieve the “absolute cooperative” in P2P incentive mechanisms. On the other hand, stochastic evolution in P2P incentive mechanism with finite population and network structure still favor reciprocation.     

Rough set based approaches to feature selection for Case-Based Reasoning classifiers

This paper investigates feature selection based on rough sets for dimensionality reduction in Case-Based Reasoning classifiers. In order to be useful, Case-Based Reasoning systems should be able to manage imprecise, uncertain and redundant data to retrieve the most relevant information in a potentially overwhelming quantity of data. Rough Set Theory has been shown to be an effective tool for data mining and for uncertainty management. This paper has two central contributions: (1) it develops three strategies for feature selection, and (2) it proposes several measures for estimating attribute relevance based on Rough Set Theory. Although we concentrate on Case-Based Reasoning classifiers, the proposals are general enough to be applicable to a wide range of learning algorithms. We applied these proposals on twenty data sets from the UCI repository and examined the impact of feature selection over classification performance. Our evaluation shows that all three proposals benefit the basic Case-Based Reasoning system. They also present robustness in comparison to well-known feature selection strategies.     

Extending the notion of rationality of selfish agents: Second Order Nash equilibria

Motivated by the increasing interest of the Computer Science community in the study and understanding of non-cooperative systems, we present a novel model for formalizing the rational behavior of agents with a more farsighted view of the consequences of their actions. This approach yields a framework creating new equilibria, which we call Second Order equilibria, starting from a ground set of traditional ones. By applying our approach to pure Nash equilibria, we define the set of Second Order pure Nash equilibria and present their applications to the Prisoner’s Dilemma game, to an instance of Braess’s Paradox in the Wardrop model and to the KP model with identical machines.     

精确计算氢键结合能的相关一致性基函数两点外推法

为了获得精确的氢键结合能并减小计算需求,在CCSD(T)水平下使用了一种基于相关一致性基函数的简单两点外推公式。为了提高氢键结合能的精确度,在公式中增加了一个变量。根据10个氢键体系分析的结果显示,使用基函数aug-cc-pVDZ和aug-cc-pVTZ计算所得总能量之差与变量存在线性关系,进而得到新的外推公式。从用于测试的18个氢键体系的数据显示,由这种外推方法获得的氢键结合能与使用基函数aug-cc-pVQZ计算所得结果能够很好的吻合。     

粗集理论中的容错关系及其构造方法

粗集理论（Rough Sets Theory,RST）在许多领域已得到成功的应用,它在处理不完全、不精确、不确定性智能信息问题中表现出优秀品质。然而在标准RST理论中存在有局限性,比如对等价关系的定义使得知识约简相对复杂化。论文提出了基于RST的容错粗集的概念,给出了容错粗集上的性质,使得在进行知识约简时无需进行量化。从而大大提高了知识发现的效率。     

多种学习理论视野下的计算机辅助教学设计探究

计算机辅助教学与传统的课堂教学相比,的确具有其自身的优越性,它改变了以往传统电教手段相对呆板的教学方式,在文字、图形的基础上增加了图象,动画、声音、影像等,加强了表现效果;另一方面,当前的计算机辅助教学存在着严重的问题。要解决这些问题,必须做好教学设计工作我们如果想以某一种学习理论来指导我们的教学,解决教学中的一切问题,那也是行不通的。科学的做法是,以多种学习理论为指导,吸收其长处,克服其不足,对教学进行具体分析,鉴别不同的学习类型,再去寻找合适的指导思想、教学方法与策略。     

基于建构主义理论的《计算机组成原理》课程教学研究

根据《计算机组成原理》课程的特点,在对建构主义理论分析的基础上,将《计算机组成原理》教学与建构主义理论相结合,对基于建构主义理论的《计算机组成原理》课程的教学改革进行了探讨和分析,有利于进一步提高教学质量,培养学生良好的《计算机组成原理》教学素养和创新意识。     

Primal or dual: which promises faster spatiotemporal search?

The existing predictive spatiotemporal indexes can be classified into two categories, depending on whether they are based on the primal or dual methodology. Although we have gained considerable empirical knowledge about various access methods, currently there is only limited understanding on the theoretical characteristics of the two methodologies. In fact, the experimental results in different papers even contradict each other, regarding the relative superiority of the primal and dual techniques. This paper presents a careful study on the query performance of general primal and dual indexes, and reveals important insight into the behavior of each technique. In particular, we mathematically establish the conditions that determine the superiority of each methodology, and provide rigorous justification for well-known observations that have not been properly explained in the literature. Our analytical findings also resolve the contradiction in the experiments of previous work.     

A Theory of Constraints approach to interorganizational systems implementation

Interorganizational systems (IOS) may provide substantial benefits, however many organizations are reluctant to implement them. This empirical research takes a novel approach and develops a model of IOS feasibility, which is based on the Theory of Constraints (TOC). It introduces the notion of maximal infeasibility, which is the highest among the values of five factors: economic infeasibility, organizational infeasibility, technological infeasibility, risks, and lack of financial resources. The highest value was selected because implementation is hindered even if only one of the feasibility requirements is not fulfilled. Data collected from 139 medium and large Israeli business organizations validated the model, and indicated that strategic motivation is the main driving force for an organization to initiate or to join an IOS, while the main barriers are organizational infeasibility issues such as lack of management support or uninterested potential partners. Adopting a TOC approach to IOS implementation may assist organizations to overcome these obstacles and increase the chance of a successful implementation. An earlier shorter version of this paper was presented at WEB 2006, a pre-ICIS workshop on e-business, in Milwaukee, WI.