Neuro-fuzzy knowledge processing in intelligent learning environments for improved student diagnosis

In this paper, a neural network implementation for a fuzzy logic-based model of the diagnostic process is proposed as a means to achieve accurate student diagnosis and updates of the student model in Intelligent Learning Environments. The neuro-fuzzy synergy allows the diagnostic model to some extent "imitate" teachers in diagnosing students' characteristics, and equips the intelligent learning environment with reasoning capabilities that can be further used to drive pedagogical decisions depending on the student learning style. The neuro-fuzzy implementation helps to encode both structured and non-structured teachers' knowledge: when teachers' reasoning is available and well defined, it can be encoded in the form of fuzzy rules; when teachers' reasoning is not well defined but is available through practical examples illustrating their experience, then the networks can be trained to represent this experience. The proposed approach has been tested in diagnosing aspects of student's learning style in a discovery-learning environment that aims to help students to construct the concepts of vectors in physics and mathematics. The diagnosis outcomes of the model have been compared against the recommendations of a group of five experienced teachers, and the results produced by two alternative soft computing methods. The results of our pilot study show that the neuro-fuzzy model successfully manages the inherent uncertainty of the diagnostic process; especially for marginal cases, i.e. where it is very difficult, even for human tutors, to diagnose and accurately evaluate students by directly synthesizing subjective and, some times, conflicting judgments

Qualitative System Identification from Imperfect Data

Experience in the physical sciences suggests that the only realistic means of understanding complex systems is through the use of mathematical models. Typically, this has come to mean the identification of quantitative models expressed as differential equations. Quantitative modelling works best when the structure of the model (i.e., the form of the equations) is known; and the primary concern is one of estimating the values of the parameters in the model. For complex biological systems, the model-structure is rarely known and the modeler has to deal with both model-identification and parameter-estimation. In this paper we are concerned with providing automated assistance to the first of these problems. Specifically, we examine the identification by machine of the structural relationships between experimentally observed variables. These relationship will be expressed in the form of qualitative abstractions of a quantitative model. Such qualitative models may not only provide clues to the precise quantitative model, but also assist in understanding the essence of that model. Our position in this paper is that background knowledge incorporating system modelling principles can be used to constrain effectively the set of good qualitative models. Utilising the model-identification framework provided by Inductive Logic Programming (ILP) we present empirical support for this position using a series of increasingly complex artificial datasets. The results are obtained with qualitative and quantitative data subject to varying amounts of noise and different degrees of sparsity. The results also point to the presence of a set of qualitative states, which we term kernel subsets, that may be necessary for a qualitative model-learner to learn correct models. We demonstrate scalability of the method to biological system modelling by identification of the glycolysis metabolic pathway from data

Phase mixing of a three dimensional magnetohydrodynamic pulse

Phase mixing of a three dimensional magnetohydrodynamic (MHD) pulse is studied in the compressive, three-dimensional (without an ignorable coordinate) regime. It is shown that the efficiency of decay of an Alfvénic part of a compressible MHD pulse is related linearly to the degree of localization of the pulse in the homogeneous transverse direction. In the developed stage of phase mixing (for large times), coupling to its compressive part does not alter the power-law decay of an Alfvénic part of a compressible MHD pulse. The same applies to the dependence upon the resistivity of the Alfvénic part of the pulse. All this implies that the dynamics of Alfvén waves can still be qualitatively understood in terms of the previous 2.5D models. Thus, the phase mixing remains a relevant paradigm for the coronal heating applications in the realistic 3D geometry and compressive plasma

Observation of surface charge screening and Fermi level pinning on a synthetic, boron-doped diamond

Spectroscopic current-voltage (I-V) curves taken with a scanning tunneling microscope on a synthetic, boron-doped diamond single crystal indicate that the diamond, boiled in acid and baked to 500 °C in vacuum, does not exhibit ideal Schottky characteristics. These I-V curves taken in ultrahigh vacuum do not fit the traditional theory of thermionic emission; however, the deviation from ideal can be accounted for by charge screening at the diamond surface. At ambient pressure, the I-V curves have a sharp threshold voltage at 1.7 eV above the valence band edge indicating pinning of the Fermi energy. This measurement is in excellent agreement with the 1/3 band gap rule of Mead and Spitzer [Phys. Rev. 134, A713 (1964)]

Advisor Choice in Asia-Pacific Property Markets

This paper examines advisor choice decisions by publicly traded REITs and listed property companies in Asia-Pacific real estate markets. Using a sample of 168 firms, we find robust evidence that firms strategically evaluate and compare the increased agency costs associated with external advisement against the potential benefits associated with collocating decision rights with location specific soft information. Our empirical results reveal real estate companies tend to hire external advisors when they invest in countries: 1) that are more economically and politically unstable, 2) whose legal system is based on civil law, 3) where the level of corruption is perceived to be high, and 4) when disclosure is relatively poor. Additionally, we find the probability of retaining an external advisor is directly related to the expected agency costs. Lastly, we find evidence of return premiums in excess of 13 % for firms whose organizational structure matches their investment profile. As such, we conclude that the decision to hire an external advisor represents a value relevant trade-off between the costs and benefits of this organizational arrangement

Clawback Provisions in Real Estate Investment Trusts

Using a sample of 195 unique real estate investment trusts (REITs), we examine factors related to the adoption of clawback provisions within managerial compensation contracts. In general, we find strong and consistent empirical evidence that clawback provision are directly related to firm size, complexity, leverage, growth options, monitoring incentives, and CEO performance incentives. We also find that clawbacks are associated with enhanced market and accounting performance, with stronger performance relations observed for adoption decisions tied directly to regulatory mandates. In sum, we conclude compensation clawback provisions represent a value-relevant, strategic governance mechanism for REITs

Capital Structure and Political Risk in Asia-Pacific Real Estate Markets

This study investigates the determinants of capital structure decisions by real estate firms, with a specific focus on the impact of political risk on leverage. Using a sample of Asia-Pacific REITs and listed property trusts, we find those firms with properties located in countries characterized by relatively high degrees of political risk, such as political instability, and/or greater uncertainty in the ability to repatriate and monetize profits from international investment activities, employ less debt than their counterparts operating in more politically stable environments. This core finding remains robust to alternative sample selection criteria including the division of the sample into high versus low market-to-book value firms, and also holds within the subset of organizations that are active in raising additional capital in the secondary markets