Discovering a cover set of ARsi with hierarchy from quantitative databases

This paper extends the work on discovering fuzzy association rules with degrees of support and implication (ARsi). The effort is twofold: one is to discover ARsi with hierarchy so as to express more semantics due to the fact that hierarchical relationships usually exist among fuzzy sets associated with the attribute concerned; the other is to generate a “core” set of rules, namely the rule cover set, that are of more interest in a sense that all other rules could be derived by the cover set. Corresponding algorithms for ARsi with hierarchy and the cover set are proposed along with pruning strategies incorporated to improve the computational efficiency. Some data experiments are conducted as well to show the effectiveness of the approach.     

一种基于遗传算法的兴趣规则挖掘算法

数据挖掘是在数据中发现隐藏的结构和模式。但发现的许多模式对用卢来说可能是已知的，从而使这些模式毫无意义，毫无兴趣性。文献中多强调分类规则的准确性和可理解性，但发现兴趣规则在数据挖掘算法中依然是一个令人生畏的挑战。本文采用一种遗传数据挖掘方法，在分类规则产生的同时对其兴趣性进行度量，直接产生兴趣规则。实验表明该方法是可行的、高效的。     

Discovering driving strategies with a multiobjective optimization algorithm

When driving a vehicle along a given route, several objectives such as the traveling time and the fuel consumption have to be considered. This can be viewed as an optimization problem and solved with the appropriate optimization algorithms. The existing optimization algorithms mostly combine objectives into a weighted-sum cost function and solve the corresponding single-objective problem. Using a multiobjective approach should be, in principle, advantageous, since it enables better exploration of the multiobjective search space, however, no results about the optimization of driving with this approach have been reported yet. To test the multiobjective approach, we designed a two-level Multiobjective Optimization algorithm for discovering Driving Strategies (MODS). It finds a set of nondominated driving strategies with respect to two conflicting objectives: the traveling time and the fuel consumption. The lower-level algorithm is based on a deterministic breadth-first search and nondominated sorting, and searches for nondominated driving strategies. The upper-level algorithm is an evolutionary algorithm that optimizes the input parameters for the lower-level algorithm. The MODS algorithm was tested on data from real-world routes and compared with the existing single-objective algorithms for discovering driving strategies. The results show that the presented algorithm, on average, significantly outperforms the existing algorithms.     

Discovering physics with microcomputers

This paper describes the development and evaluation of CAL software developed for the Open University course “S271—Discovering Physics”. The programs are used by students at week-long summer schools which combine the practical experience of doing physics experiments with remedial tutorial sessions. A wide range of computer tutorials, simulated experiments and tutorial simulations as well as programs to enhance traditional physics experiments were produced. The software was implemented on the TERAK 8510/a graphics microcomputer. The project incorporated the development of an “implementation system” (called OASIS) based on UCSD Pascal to simplify such aspects as text layout, response parsing and graphics. An important part of the project was a program of testing and evaluation by staff and students at a week long summer schoolin 1981. Reactions to the programs were assessed using log sheets, interviews and questionnaires (both written and interactive using a microcomputer) as well as direct observation. The software and the associated printed material were completely redesigned on the basis of the results of this study. Another outcome of the evaluation was the acquisition of knowledge of how students use the CAL software in the environment of a physics summer school.     

Discovering Text Databases with Neural Nets

Since documents on the Web are naturally partitioned into many text databases, the efficient document retrieval process requires identifying the text databases that are most likely to provide relevant documents to the query and then searching for the identified text databases. In this paper, we propose a neural net based approach to such an efficient document retrieval. First, we present a neural net agent that learns about underlying text databases from the user's relevance feedback. For a given query, the neural net agent, which is sufficiently trained on the basis of the BPN learning mechanism, discovers the text databases associated with the relevant documents and retrieves those documents effectively. In order to scale our approach with the large number of text databases, we also propose the hierarchical organization of neural net agents which reduces the total training cost at the acceptable level. Finally, we evaluate the performance of our approach by comparing it to those of the conventional well-known approaches. Received 5 March 1999 / Revised 7 March 2000 / Accepted in revised form 2 November 2000     

Discovering admissibleWeb services with uncertain QoS

Open and dynamic environments lead to inherent uncertainty of Web service QoS (Quality of Service), and the QoS-aware service selection problem can be looked upon as a decision problem under uncertainty. We use an empirical distribution function to describe the uncertainty of scores obtained from historical transactions. We then propose an approach to discovering the admissible set of services including alternative services that are not dominated by any other alternatives according to the expected utility criterion. Stochastic dominance (SD) rules are used to compare two services with uncertain scores regardless of the distribution form of their uncertain scores. By using the properties of SD rules, an algorithm is developed to reduce the number of SD tests, by which the admissible services can be reported progressively. We prove that the proposed algorithm can be run on partitioned or incremental alternative services. Moreover, we achieve some useful theoretical conclusions for correct pruning of unnecessary calculations and comparisons in each SD test, by which the efficiency of the SD tests can be improved. We make a comprehensive experimental study using real datasets to evaluate the effectiveness, efficiency, and scalability of the proposed algorithm.     

Discovering episodes with compact minimal windows

Discovering the most interesting patterns is the key problem in the field of pattern mining. While ranking or selecting patterns is well-studied for itemsets it is surprisingly under-researched for other, more complex, pattern types. In this paper we propose a new quality measure for episodes. An episode is essentially a set of events with possible restrictions on the order of events. We say that an episode is significant if its occurrence is abnormally compact, that is, only few gap events occur between the actual episode events, when compared to the expected length according to the independence model. We can apply this measure as a post-pruning step by first discovering frequent episodes and then rank them according to this measure. In order to compute the score we will need to compute the mean and the variance according to the independence model. As a main technical contribution we introduce a technique that allows us to compute these values. Such a task is surprisingly complex and in order to solve it we develop intricate finite state machines that allow us to compute the needed statistics. We also show that asymptotically our score can be interpreted as a $P$ value. In our experiments we demonstrate that despite its intricacy our ranking is fast: we can rank tens of thousands episodes in seconds. Our experiments with text data demonstrate that our measure ranks interpretable episodes high.     

Discovering Explanations

Minds and Machines -     

个性化智能信息提取中的用户兴趣发现

1 引言 1990年,WWW(World Wide Web)出现,在随后的几年中它获得了空前的发展,Internet上的信息量以指数形式飞速增长,现在,Internet已成为一个浩瀚的海量信息源。但由于Internet是一个具有开放性、动态性和异构性的全球分布式网络,资源分布很分散,且     

Exact solutions of a KdV equation hierarchy with variable coefficients

In this paper, a nonisospectral and variable-coefficient KdV equation hierarchy with self-consistent sources is derived from the related linear spectral problem. Exact solutions of the KdV equation hierarchy are obtained through the inverse scattering transformation (IST). It is shown that the IST is an effective mathematical tool for solving the whole hierarchy of nonisospectral nonlinear partial differential equations with self-consistent sources.     

Discovering injective episodes with general partial orders

Frequent episode discovery is a popular framework for temporal pattern discovery in event streams. An episode is a partially ordered set of nodes with each node associated with an event type. Currently algorithms exist for episode discovery only when the associated partial order is total order (serial episode) or trivial (parallel episode). In this paper, we propose efficient algorithms for discovering frequent episodes with unrestricted partial orders when the associated event-types are unique. These algorithms can be easily specialized to discover only serial or parallel episodes. Also, the algorithms are flexible enough to be specialized for mining in the space of certain interesting subclasses of partial orders. We point out that frequency alone is not a sufficient measure of interestingness in the context of partial order mining. We propose a new interestingness measure for episodes with unrestricted partial orders which, when used along with frequency, results in an efficient scheme of data mining. Simulations are presented to demonstrate the effectiveness of our algorithms.     

Institutions with a hierarchy of authorities in distributed dynamic environments

A single global authority is not sufficient to regulate heterogenous agents in multiagent systems based on distributed architectures, due to idiosyncratic local situations and to the need to regulate new issues as soon as they arise. On the one hand institutions should be structured as normative systems with a hierarchy of authorities able to cope with the dynamics of local situations, but on the other hand higher authorities should be able to delimit the autonomy of lower authorities to issue valid norms. In this paper, we study the interplay of obligations and strong permissions in the context of hierarchies of authorities using input/output logic, because its explicit norm base facilitates reasoning about norm base maintenance, and it covers a variety of conditional obligations and permissions. We combine the logic with constraints, priorities and hierarchies of authorities. In this setting, we observe that Makinson and van der Torre’s notion of prohibition immunity for permissions is no longer sufficient, and we introduce a new notion of permission as exception and a new distinction between static and dynamic norms. We show how strong permissions can dynamically change an institution by adding exceptions to obligations, provide an explicit representation of what is permitted to the subjects of the normative system and allow higher level authorities to limit the power of lower level authorities to change the normative system.

基于DISCOVERER的Petri网不变式自动生成*

在Petri网的验证中,代数不变式起着非常重要的作用。将Petri网建模为半代数变迁系统,提出了自动生成不变式的算法,该不变式有助于更好地分析Petri网可达空间。算法首先将Petri网的不变式假定为一个含参数系统,然后通过求解半代数系统来求解不变式中的参数;最后,基于DISCOVERER和QEPCAD等Maple软件包实现了该算法,并通过实例说明了算法的有效性。     

Distribution of cryptographic keys in systems with a hierarchy of objects

An algorithm for the distribution of cryptographic keys is presented, which is based on the hierarchical structure of objects in the system. The performance of the algorithm for some types of the graph of the object hierarchy is substantiated. A mechanism for setting mandatory and discretionary access differentiation is proposed using the algorithm.     

Discovering early aspects

Aspect-oriented software development has focused on the software life cycle's implementation phase: developers identify and capture aspects mainly in code. But aspects are evident earlier in the life cycle, such as during requirements engineering and architecture design. Early aspects are concerns that crosscut an artifact's dominant decomposition or base modules derived from the dominant separation-of-concerns criterion, in the early stages of the software life cycle. In this article, we describe how to identify and capture early aspects in requirements and architecture activities and how they're carried over from one phase to another. We'll focus on requirements and architecture design activities to illustrate the points, but the same ideas apply in other phases as well, such as domain analysis or in the fine-grained design activities that lie between architecture and implementation.     

Order-sorted logic programming with predicate hierarchy

Order-sorted logic has been formalized as first-order logic with sorted terms where sorts are ordered to build a hierarchy (called a sort-hierarchy). These sorted logics lead to useful expressions and inference methods for structural knowledge that ordinary first-order logic lacks. Nitta et al. pointed out that for legal reasoning a sort-hierarchy (or a sorted term) is not sufficient to describe structural knowledge for event assertions, which express facts caused at some particular time and place. The event assertions are represented by predicates with n arguments (i.e., n-ary predicates), and then a particular kind of hierarchy (called a predicate hierarchy) is built by a relationship among the predicates. To deal with such a predicate hierarchy, which is more intricate than a sort-hierarchy, Nitta et al. implemented a typed (sorted) logic programming language extended to include a hierarchy of verbal concepts (corresponding to predicates). However, the inference system lacks a theoretical foundation because its hierarchical expressions exceed the formalization of order-sorted logic. In this paper, we formalize a logic programming language with not only a sort-hierarchy but also a predicate hierarchy. This language can derive general and concrete expressions in the two kinds of hierarchies. For the hierarchical reasoning of predicates, we propose a manipulation of arguments in which surplus and missing arguments in derived predicates are eliminated and supplemented. As discussed by Allen, McDermott and Shoham in research on temporal logic and as applied by Nitta et al. to legal reasoning, if each predicate is interpreted as an event or action (not as a static property), then missing arguments should be supplemented by existential terms in the argument manipulation. Based on this, we develop a Horn clause resolution system extended to add inference rules of predicate hierarchies. With a semantic model restricted by interpreting a predicate hierarchy, the soundness and completeness of the Horn-clause resolution is proven.     

Discovering top-k patterns with differential privacy-an accurate approach

Frequent pattern mining discovers sets of items that frequently appear together in a transactional database; these can serve valuable economic and research purposes. However, if the database contains sensitive data (e.g., user behavior records, electronic health records), directly releasing the discovered frequent patterns with support counts will carry significant risk to the privacy of individuals. In this paper, we study the problem of how to accurately find the top-k frequent patterns with noisy support counts on transactional databases while satisfying differential privacy. We propose an algorithm, called differentially private frequent pattern (DFP-Growth), that integrates a Laplace mechanism and an exponential mechanism to avoid privacy leakage. We theoretically prove that the proposed method is (λ, δ)-useful and differentially private. To boost the accuracy of the returned noisy support counts, we take consistency constraints into account to conduct constrained inference in the post-processing step. Extensive experiments, using several real datasets, confirm that our algorithm generates highly accurate noisy support counts and top-k frequent patterns.     

Dynamic average consensus with topology balancing under a directed graph

In this paper, the distributed average tracking problem is studied on the premise of a strongly connected directed graph. To this end, we propose a weight balance strategy that could potentially make the adjacency matrix doubly stochastic for any strongly connected directed graph. The proposed scheme is fully distributive with finite time convergence and we again prove that network connectivity (described by the first left eigenvector) is instrumental in networked control systems. Then, a discrete‐time average tracking observer is introduced to ensure that all networked systems can track the average of the reference signals with bounded error. Simulation results verify the effectiveness of the proposed methods.