Case base adaptation using solution-space metrics

In this paper we propose a generalisation of the k-nearest neighbour (k-NN) retrieval method based on an error function using distance metrics in the solution and problem space. It is an interpolative method which is proposed to be effective for sparse case bases. The method applies equally to nominal, continuous and mixed domains, and does not depend upon an embedding n-dimensional space. In continuous Euclidean problem domains, the method is shown to be a generalisation of the Shepard's Interpolation method. We term the retrieval algorithm the Generalised Shepard Nearest Neighbour (GSNN) method. A novel aspect of GSNN is that it provides a general method for interpolation over nominal solution domains. The performance of the retrieval method is examined with reference to the Iris classification problem,and to a simulated sparse nominal value test problem. The introducion of a solution-space metric is shown to out-perform conventional nearest neighbours methods on sparse case bases.

Case base reduction using solution-space metrics

In this paper we propose a case base reduction technique which uses a metric defined on the solution space. The technique utilises the Generalised Shepard Nearest Neighbour (GSNN) algorithm to estimate nominal or real valued solutions in case bases with solution space metrics. An overview of GSNN and a generalised reduction technique, which subsumes some existing decremental methods, such as the Shrink algorithm, are presented. The reduction technique is given for case bases in terms of a measure of the importance of each case to the predictive power of the case base. A trial test is performed on two case bases of different kinds, with several metrics proposed in the solution space. The tests show that GSNN can out-perform standard nearest neighbour methods on this set. Further test results show that a caseremoval order proposed based on a GSNN error function can produce a sparse case base with good predictive power.

Case based adaptation using interpolation over nominal values

In this paper we propose a method for interpolation over a set of retrieved cases in the adaptation phase of the case-based reasoning cycle. The method has two advantages over traditional systems: the first is that it can predict “new” instances, not yet present in the case base; the second is that it can predict solutions not present in the retrieval set. The method is a generalisation of Shepard’s Interpolation method, formulated as the minimisation of an error function defined in terms of distance metrics in the solution and problem spaces. We term the retrieval algorithm the Generalised Shepard Nearest Neighbour (GSNN) method. A novel aspect of GSNN is that it provides a general method for interpolation over nominal solution domains. The method is illustrated in the paper with reference to the Irises classification problem. It is evaluated with reference to a simulated nominal value test problem, and to a benchmark case base from the travel domain. The algorithm is shown to out-perform conventional nearest neighbour methods on these problems. Finally, GSNN is shown to improve in efficiency when used in conjunction with a diverse retrieval algorithm.

Applying CBR to measure similarity of software design structures

This paper examines different ways for measuring similarity between software design models for the purpose of software reuse. Current approaches to this problem are discussed and a set of suitable similarity metrics are proposed and evaluated. Work on the optimisation of weights to increase the competence of a CBR system is presented. A graph matching algorithm and associated metrics capturing the structural similarity between UML class diagrams is presented and demonstrated through an example case.

Support vector machines for regression and applications to software quality prediction

Software metrics are the key tool in software quality management. In this paper, we propose to use support vector machines for regression applied to software metrics to predict software quality. In experiments we compare this method with other regression techniques such as Multivariate Linear Regression, Conjunctive Rule and Locally Weighted Regression. Results on benchmark dataset MIS, using mean absolute error, and correlation coefficient as regression performance measures, indicate that support vector machines regression is a promising technique for software quality prediction. In addition, our investigation of PCA based metrics extraction shows that using the first few Principal Components (PC) we can still get relatively good performance.

Integrating personnel movement simulation into preliminary ship design

Traditionally, when designing a ship the driving issues are seen to be powering, stability, strength and seakeeping. Issues related to ship operations and evolutions are investigated later in the design process, within the constraint of a fixed layout. This can result in operational inefficiencies and limitations, excessive crew numbers and potentially hazardous situations. University College London and the University of Greenwich are in the final year of a three year EPSRC funded research project to integrate the simulation of personnel movement into early stage ship design. This allows the assessment of onboard operations while the design is still amenable to change. The project brings together the University of Greenwich developed maritimeEXODUS personnel movement simulation software and the SURFCON implementation of the Design Building Block approach to early stage ship design, which originated with the UCL Ship Design Research team. Central to the success of this project is the definition of a suitable series of Naval Combatant Human Performance Metrics which can be used to assess the performance of the design in different operational scenarios. The paper outlines the progress made on deriving the human performance metric from human factors criteria measured in simulations and their incorporation into a Behavioural Matrix for analysis. It describes the production of a series of SURFCON ship designs based on the RN Type 22 Batch 3 frigate, and their analysis using the PARAMARINE and maritimeEXODUS software. Conclusions to date will be presented on the integration of personnel movement simulation into the preliminary ship design process.

Measuring similarity of software designs using graph matching for CBR

This paper examines different ways of measuring similarity between software design models for Case Based Reasoning (CBR) to facilitate reuse of software design and code. The paper considers structural and behavioural aspects of similarity between software design models. Similarity metrics for comparing static class structures are defined and discussed. A Graph representation of UML class diagrams and corresponding similarity measures for UML class diagrams are defined. A full search graph matching algorithm for measuring structural similarity diagrams based on the identification of the Maximum Common Sub-graph (MCS) is presented. Finally, a simple evaluation of the approach is presented and discussed.

An architecture of multiple heterogeneous case-based reasoning employing agent technologies

This paper presents an investigation into applying Case-Based Reasoning to Multiple Heterogeneous Case Bases using agents. The adaptive CBR process and the architecture of the system are presented. A case study is presented to illustrate and evaluate the approach. The process of creating and maintaining the dynamic data structures is discussed. The similarity metrics employed by the system are used to support the process of optimisation of the collaboration between the agents which is based on the use of a blackboard architecture. The blackboard architecture is shown to support the efficient collaboration between the agents to achieve an efficient overall CBR solution, while using case-based reasoning methods to allow the overall system to adapt and “learn” new collaborative strategies for achieving the aims of the overall CBR problem solving process.

Call admission control algorithms in OFDM-based wireless multiservice networks

Orthogonal frequency division multiplexing(OFDM) is becoming a fundamental technology in future generation wireless communications. Call admission control is an effective mechanism to guarantee resilient, efficient, and quality-of-service (QoS) services in wireless mobile networks. In this paper, we present several call admission control algorithms for OFDM-based wireless multiservice networks. Call connection requests are differentiated into narrow-band calls and wide-band calls. For either class of calls, the traffic process is characterized as batch arrival since each call may request multiple subcarriers to satisfy its QoS requirement. The batch size is a random variable following a probability mass function (PMF) with realistically maximum value. In addition, the service times for wide-band and narrow-band calls are different. Following this, we perform a tele-traffic queueing analysis for OFDM-based wireless multiservice networks. The formulae for the significant performance metrics call blocking probability and bandwidth utilization are developed. Numerical investigations are presented to demonstrate the interaction between key parameters and performance metrics. The performance tradeoff among different call admission control algorithms is discussed. Moreover, the analytical model has been validated by simulation. The methodology as well as the result provides an efficient tool for planning next-generation OFDM-based broadband wireless access systems.

Relational dimensions of corporate governance in translation: networked governance in China and India

The effectiveness of corporate governance mechanisms has been a subject of academic research for many decades. Although the large majority of corporate governance studies prior to mid 1990s were based on data from developed market economies such as the U.S., U.K. and Japan, in recent years researchers have begun examining corporate governance in transition economies. A comparison of China and India offers a unique environment for analyzing the effectiveness of corporate governance. First, both countries state-owned enterprise (SOE) reform strategies hinges on the Modern Enterprise System characterized by the separation of ownership and control. Ownership of an SOE’s assets is distributed among the government, institutional investors, managers, employees, and private investors. Effective control rights are assigned to management, which generally has a very small, or even nonexistent ownership stake. This distinctive shareholding structure creates conflict of interest not only between management (insiders) and outside investors but also between large shareholders and minority investors. Moreover, because both governments desire to retain some control—in part through partial retained ownership of commercialized SOEs, further conflicts arise between politicians and firms. Second, directors in publicly listed firms in both countries are predominantly drawn from institutions with significant non-market objectives: the government and other state enterprises, particularly in China, and extended families, particularly in India. As a result, the effectiveness of internal governance mechanisms, such as the number of independent directors on the board and the number of independent supervisors on the supervisory committee, are likely to be quiet limited, although this has yet to be fully evaluated. Third, because of the political nature of the privatization process itself, typical external governance mechanisms, such as debt (in conjunction with appropriate bankruptcy procedures), takeover threats, legal protection of investors, product market competition, etc., have not been effective. Bank loans have traditionally been viewed as grants from the state designed to bail out failing firms. State-owned banks retain monopoly or quasi-monopoly positions in the banking sector and profit is not their overriding objective. If political favor is deemed appropriate, subsidized loans, rescheduling of overdue debt or even outright transfer of funds can be arranged with SOEs (soft budget constraints). In addition, a market for private, non-bank debt is limited in India and has yet to be established China. There is no active merger or takeover activity in Chinese stock markets to discipline management. Information available in the capital markets is insufficient to keep at arm’s length of the corporate decisions. In light of the above peculiarities, China and India share many of the typical institutional characteristics as a transition economy, including poor legal protection of creditors and investors, the absence of an effective takeover market, an underdeveloped capital market, a relative inefficient banking system and significant interference of politicians in firm management. Su (2005) finds that the extent of political interference, managerial entrenchment and institutional control can help explain corporate dividend policies and post-IPO financing choices in this situation. Allen et al. (2005) demonstrate that standard corporate governance mechanisms are weak and ineffective for publicly listed firms while alternative governance mechanisms based on reputation and relationship have been remarkably effective in the private sector. Because the peculiarities are significant in this context, the differences in the political-economies of the two countries are likely to be evident in such relational terms. In this paper we explore the peculiarities of corporate governance in this transitional environment through a systematic examination of certain aspects of these reputational and relationship dimensions. Utilising the methods of social network analysis we identify the inter-organisational relationships at board level formed by equity holdings and by shared directors. Using data drawn from the Orbis database we map these relations among the 3700 largest firms in India and China respectively and identify the roles played in these relational networks by the particularly characteristic institutions in each case. We find greatly different social network structures in each case with some support in these relational dimensions for their distinctive features of governance. Further, the social network metrics allow us to considerably refine proxies for political interference, managerial entrenchment and institutional control used in earlier econometric analysis.