Measuring moral judgement and the implications of cooperative education and rule-based learning

The Defining Issues Test (DIT), developed by Rest (1986), measures a person's level of moral development using hypothetical social dilemmas. Although the DIT is useful for measuring moral development in social settings, it might not adequately capture an individual's moral judgement abilities in solving work-related problems (Weber, 1990; Trevino, 1992; Welton et al., 1994). In the present study, the moral judgement levels of 97 accounting students were measured over a 1 year period using two separate test instruments, the DIT and a context-specific instrument developed by Welton et al. (1994). The test scores are significantly higher on the DIT than the Welton instrument (between the instruments and over time), suggesting that accounting students use higher levels of moral reasoning in resolving hypothetical social dilemmas and lower levels of moral reasoning in resolving context-specific dilemmas. The difference in test scores was highest during cooperative education (work placement programme), implying that the environment is a significant determinant on students' test scores.

Rule-based dependency models for security protocol analysis

Security protocol analysis has been discussed for quite some time in the past few years. Although formal methods have been widely used to identify various vulnerabilities, mainly susceptibility to freshness attacks and impersonation, the arisen inconsistent data between principals and collusion attacks held by a group of dishonest principals have been largely ignored. Moreover, the previous methods focus on reasoning about certain security-related properties and detecting known attacks against secure message, whereas there have been insufficient efforts to handle the above hidden but powerful attacks. In this paper, we address these critical issues and prove the efficiency and intuitiveness of rule-based dependency models in defending a protocol against the attacks. This is able to provide a numerical estimation to measure he occurrence of these attacks. It will be useful in enhancing the current protocol analysis.

A rule-based intelligent energy management system for an internal combustion engine vehicle

An intelligent energy management system (IEMS) is developed to improve fuel efficiency of an internal combustion engine vehicle. It helps determine the best approach to run the engine system through dynamically analysing various factors relating to vehicle. The energy balance technique is implemented and utilised. The simulation outcome of the IEMS is compared against that of a conventional system under the same driving factors. The results show that the IEMS reduces the fuel consumption around 5.6% for the tested conditions.

The application of a fuzzy rule based system in an exposition of the antecedents of sedge warbler song flight

The utilization of a fuzzy aspect within data analysis attempts to move from a quantitative to a more qualitative investigative environment. As such, this may allow the more non-quantitative researchers results they can use, based on sets of linguistic terms. In this paper an inductive fuzzy decision tree approach is utilized to construct a fuzzy-rule-based system for the first time in a biological setting. The specific biological problem considered attempts to identify the antecedents (conditions in the fuzzy decision rules) which characterize the length of song flight of the male sedge warbler when attempting to attract a mate. Hence, for a non-quantitative investigator the resultant set of fuzzy rules allows an insight into the linguistic interpretation on the relationship between associated characteristics and the respective song flight duration.

Potential applications of discrete-event simulation and fuzzy rule-based systems to structural reliability and availability

This chapter discusses and illustrates some potential applications of discrete-event simulation (DES) techniques in structural reliability and availability analysis, emphasizing the convenience of using probabilistic approaches in modern building and civil engineering practices. After reviewing existing literature on the topic, some advantages of probabilistic techniques over analytical ones are highlighted. Then, we introduce a general framework for performing structural reliability and availability analysis through DES. Our methodology proposes the use of statistical distributions and techniques – such as survival analysis – to model component-level reliability. Then, using failure- and repair-time distributions and information about the structural logical topology (which allows determination of the structural state from their components’ state), structural reliability, and availability information can be inferred. Two numerical examples illustrate some potential applications of the proposed methodology to achieving more reliable and structural designs. Finally, an alternative approach to model uncertainty at component level is also introduced as ongoing work. This new approach is based on the use of fuzzy rule-based systems and it allows the introduction of experts’ opinions and evaluations in our methodology.

Rule pruning in a fuzzy rule-based classification system

In this paper, we purpose a rule pruning strategy to reduce the number of rules in a fuzzy rule-based classification system.A confidence factor, which is formulated based on the compatibility of the rules with the input patterns is under deployed for rule pruning.The pruning strategy aims at reducing the complexity of the fuzzy classification system and, at the same time, maintaining the accuracy rate at a good level.To evaluate the effectiveness of the pruning strategy, two benchmark data sets are first tested. Then, a fault classification problem with real senor measurements collected from a power generation plant is evaluated.The results obtained are analyzed and explained, and implications of the proposed rule pruning strategy to the fuzzy classification system are discussed.

A new monotonicity index for fuzzy rule-based systems

A search in the literature reveals that mathematical conditions (usually sufficient conditions) for the Fuzzy Inference System (FIS) models to satisfy the monotonicity property have been developed. A monotonically-ordered fuzzy rule base is important to maintain the monotonicity property of an FIS. However, it may difficult to obtain a monotonically-ordered fuzzy rule base in practice. We have previously introduced the idea of fuzzy rule relabeling to tackle this problem. In this paper, we further propose a monotonicity index for the FIS system, which serves as a metric to indicate the degree of a fuzzy rule base fulfilling the monotonicity property. The index is useful to provide an indication whether a fuzzy rule base should (or should not) be used in practice, even with fuzzy rule relabeling. To illustrate the idea, the zero-order Sugeno FIS model is exemplified. We add noise as errors into the fuzzy rule base to formulate a set of non-monotone fuzzy rules. As such, the metric also acts as a measure of noise in the fuzzy rule base. The results show that the proposed metric is useful to indicate the degree of a fuzzy rule base fulfilling the monotonicity property.

Rule-based extraction of goal-use case models from text

Goal and use case modeling has been recognized as a key approach for understanding and analyzing requirements. However, in practice, goals and use cases are often buried among other content in requirements specifications documents and written in unstructured styles. It is thus a time-consuming and error-prone process to identify such goals and use cases. In addition, having them embedded in natural language documents greatly limits the possibility of formally analyzing the requirements for problems. To address these issues, we have developed a novel rule-based approach to automatically extract goal and use case models from natural language requirements documents. Our approach is able to automatically categorize goals and ensure they are properly specified. We also provide automated semantic parameterization of artifact textual specifications to promote further analysis on the extracted goal-use case models. Our approach achieves 85% precision and 82% recall rates on average for model extraction and 88% accuracy for the automated parameterization.

Neutrosophic information fusion applied to the options market

In this paper we basically make two propositions - firstly a non-linear process that is primarily fuelled by mass cognitive dissonance could generate systematic deviations between the theoretical and market prices of long-term options, and secondly such deviations are best reconciled in terms of neutrosophic rather than ruled-based reasoning, especially in the context of the users of automated trading systems designed to generate trading signals based on analysis of information  from conflicting sources.

Automatic rule induction in Arabic to English machine translation framework

This chapter addresses the exploitation of a supervised machine learning technique to automatically induce Arabic-to-English transfer rules from chunks of parallel aligned linguistic resources. The induced structural transfer rules encode the linguistic translation knowledge for converting an Arabic syntactic structure into a target English syntactic structure. These rules are going to be an integral part of an Arabic-English transfer-based machine translation. Nevertheless, a novel morphological rule induction method is employed for learning Arabic morphological rules that are applied in our Arabic morphological analyzer. To demonstrate the capability of the automated rule induction technique, we conducted rule-based translation experiments that use induced rules from a relatively small data set. The translation quality of the hybrid translation experiments achieved good results in terms of WER.

Anisotropic hardening model based on non-associated flow rule and combined nonlinear kinematic hardening for sheet materials

 A material model for more effective analysis of plastic deformation of sheet materials is presented in this paper. The model is capable of considering the following aspects of plastic deformation behavior of sheet materials: the anisotropy in yielding stresses in different directions by using a quadratic yield function (based on Hill’s 1948 model and stress ratios), the anisotropy in work hardening by introducing non-constant flow stress hardening in different directions, the anisotropy in plastic strains in different directions by using a quadratic plastic potential function and non-associated flow rule (based on Hill’s 1948 model and plastic strain ratios, r-values), and finally some of the cyclic hardening phenomena such as Bauschinger’s effect and transient behavior for reverse loading by using a coupled nonlinear kinematic hardening (so-called Armstrong-Frederick-Chaboche model). Basic fundamentals of the plasticity of the model are presented in a general framework. Then, the model adjustment procedure is derived for the plasticity formulations. Also, a generic numerical stress integration procedure is developed based on backward-Euler method (so-called multistage return mapping algorithm). Different aspects of the model are verified for DP600 steel sheet. Results show that the new model is able to predict the sheet material behavior in both anisotropic hardening and cyclic hardening regimes more accurately. By featuring the above-mentioned facts in the presented constitutive model, it is expected that more accurate results can be obtained by implementing this model in computational simulations of sheet material forming processes. For instance, more precise results of springback prediction of the parts formed from highly anisotropic hardened materials or that of determining the forming limit diagrams is highly expected by using the developed material model.

Multi-output interval type-2 fuzzy logic system for protein secondary structure prediction

A new multi-output interval type-2 fuzzy logic system (MOIT2FLS) is introduced for protein secondary structure prediction in this paper. Three outputs of the MOIT2FLS correspond to three structure classes including helix, strand (sheet) and coil. Quantitative properties of amino acids are employed to characterize twenty amino acids rather than the widely used computationally expensive binary encoding scheme. Three clustering tasks are performed using the adaptive vector quantization method to construct an equal number of initial rules for each type of secondary structure. Genetic algorithm is applied to optimally adjust parameters of the MOIT2FLS. The genetic fitness function is designed based on the Q3 measure. Experimental results demonstrate the dominance of the proposed approach against the traditional methods that are Chou-Fasman method, Garnier-Osguthorpe-Robson method, and artificial neural network models.

The use of prolog in the realisation of digital transfer functions

Systems characterised by general graphs are very flexible because of a given application they offer a great number of degrees of freedom and consequently a great possibility of choice to the designer. This can become a drawback in practice when myriads of cases have to be examined analytically and most of them rejected for a variety of heterogeneous reasons. For such design problems, it appears that logic programming languages are ideally suited because they can implement the heterogeneous rules which describe the desired system in a natural way. An application of the Prolog language to the design of active RC biquad circuits investigated by Mouly in his doctoral thesis illustrates the advantages of such a rule-based approach.