API-Based and Information-Theoretic Metrics for Measuring the Quality of Software Modularization

An entropy-based metric is presented that can be used for assessing the quality of a solution set as obtained from multi-objective optimization techniques. This metric quantifies the “goodness” of a set of solutions in terms of distribution quality over the Pareto frontier. The metric can be used to compare the performance of different multi-objective optimization techniques. In particular, the metric can be used in analysis of multi-objective evolutionary algorithms, wherein the capabilities of such techniques to produce and maintain diversity among different solution points are desired to be compared on a quantitative basis. An engineering test example, the multi-objective design optimization of a speed-reducer, is provided to demonstrate an application of the proposed entropy metric.

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Notice of Violation of IEEE Publication Principles: Measuring the Quality of Software Modularization Using Coupling-Based Structural Metrics for an OOS System

10.1109/icetet.2008.76 ◽

2008 ◽

Author(s):

M. Aruna ◽

M.P. Suguna Devi ◽

M. Deepa

Keyword(s):

Software Modularization ◽

Structural Metrics

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Misuse of Information-Theoretic Dispersion Measures as Design Complexity Metrics

Volume 9: 23rd International Conference on Design Theory and Methodology; 16th Design for Manufacturing and the Life Cycle Conference ◽

10.1115/detc2011-48295 ◽

2011 ◽

Cited By ~ 4

Author(s):

Jami J. Shah ◽

George Runger

Keyword(s):

Common Sense ◽

Design Theory ◽

Goodness Of Fit ◽

Axiomatic Theory ◽

Design Complexity ◽

Information Theoretic ◽

Complexity Metrics ◽

Mathematical Properties ◽

Dispersion Measures

Complexity is defined as a quality of an object with many interwoven elements, aspects, details, or attributes that makes the whole object difficult to understand in a collective sense. Many measures of design complexity have been proposed in the literature. Of these the most popular are Information-theoretic metrics, such as Information Content based on Suh’s Axiomatic Theory and Entropy based on Shannon’s Information Theory. In this paper we will show that not only these metrics do not provide common sense measures of complexity, but they also do not possess proper mathematical properties. At best, they are geared towards measuring a designs goodness of fit rather than its complexity. It is hoped that this paper will generate some debate on strongly held beliefs in the design theory community.

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The Statistical Derivation of Revealed Spatial Preference and Spatial Equity Functions

Environment and Planning A Economy and Space ◽

10.1068/a090521 ◽

1977 ◽

Vol 9 (5) ◽

pp. 521-528 ◽

Cited By ~ 6

Author(s):

J L Girt

Keyword(s):

Spatial Interaction ◽

Personal Attributes ◽

Individual Choice ◽

Interaction Data ◽

Information Theoretic ◽

Spatial Preference ◽

Spatial Equity ◽

Ratio Measure ◽

Choice Probabilities

Revealed spatial preference scales are the most general way of measuring the degree of individual choice unanimity between alternatives in revealed spatial interaction data. From an examination of the information theoretic content of these scales, a continuous transform of scale differences into predicted choice probabilities is derived, as well as a ratio measure of spatial equity, or a measure of the differences in the quality of facility provision for groups of individuals. It is not necessary to assume that the functions involved are monotonic, additive, or independent of other choices that are known to have been made, and the personal attributes of the decisionmakers.

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INFORMATION-THEORETIC DATA-HIDING: RECENT ACHIEVEMENTS AND OPEN PROBLEMS

International Journal of Image and Graphics ◽

10.1142/s0219467805001641 ◽

2005 ◽

Vol 05 (01) ◽

pp. 5-35 ◽

Cited By ~ 8

Author(s):

SVIATOSLAV VOLOSHYNOVSKIY ◽

FREDERIC DEGUILLAUME ◽

OLEKSIY KOVAL ◽

THIERRY PUN

Keyword(s):

Data Hiding ◽

Channel Coding ◽

Source Coding ◽

Side Information ◽

Digital Data ◽

Open Problems ◽

Information Theoretic ◽

Public Network ◽

Digital Data Hiding

In this paper we introduce and develop a framework for visual data-hiding technologies that aim at resolving emerging problems of modern multimedia networking. First, we introduce the main open issues of public network security, quality of services control and secure communications. Secondly, we formulate digital data-hiding into visual content as communications with side information and advocate an appropriate information-theoretic framework for the analysis of different data-hiding methods in various applications. In particular, Gel'fand-Pinsker channel coding with side information at the encoder and Wyner-Ziv source coding with side information at the decoder are used for this purpose. Finally, we demonstrate the possible extensions of this theory for watermark-assisted multimedia processing and indicate its perspectives for distributed communications.

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Adaptive Multiobjective Memetic Optimization

International Journal of Cognitive Informatics and Natural Intelligence ◽

10.4018/ijcini.2016100102 ◽

2016 ◽

Vol 10 (4) ◽

pp. 21-58 ◽

Cited By ~ 1

Author(s):

Hieu V. Dang ◽

Witold Kinsner

Keyword(s):

Optimization Problems ◽

Image Watermarking ◽

Optimization Algorithms ◽

Test Problems ◽

Stopping Criterion ◽

Adaptive Selection ◽

Information Theoretic ◽

Conflicting Objectives ◽

Adaptive Processes

Multiobjective memetic optimization algorithms (MMOAs) are recently applied to solve nonlinear optimization problems with conflicting objectives. An important issue in an MMOA is how to identify the relative best solutions to guide its adaptive processes. In this paper, the authors introduce a framework of adaptive multiobjective memetic optimization algorithms (AMMOA) with an information theoretic criterion for guiding the adaptive selection, clustering, local learning processes, and a robust stopping criterion of AMMOA. The implementation of AMMOA is applied to several benchmark test problems with remarkable results. The paper also presents the application of AMMOA in designing an optimal image watermarking to maximize the quality of the watermarked images and the robustness of the watermark.

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Statistical inference of protein structural alignments using information and compression

10.1101/056598 ◽

2016 ◽

Author(s):

James H. Collier ◽

Lloyd Allison ◽

Arthur M. Lesk ◽

Peter J. Stuckey ◽

Maria Garcia de la Banda ◽

...

Keyword(s):

Protein Complexes ◽

Structural Alignment ◽

Explanatory Power ◽

Three Dimensional ◽

Computational Techniques ◽

Information Theoretic ◽

Alignment Problem ◽

Comparable Quality ◽

Structural Alignments

AbstractStructural molecular biology depends crucially on computational techniques that compare protein three-dimensional structures and generate structural alignments (the assignment of one-to-one correspondences between subsets of amino acids based on atomic coordinates.) Despite its importance, the structural alignment problem has not been formulated, much less solved, in a consistent and reliable way. To overcome these difficulties, we present here a framework for precise inference of structural alignments, built on the Bayesian and information-theoretic principle of Minimum Message Length (MML). The quality of any alignment is measured by its explanatory power - the amount of lossless compression achieved to explain the protein coordinates using that alignment. We have implemented this approach in the program MMLignerhttp://lcb.infotech.monash.edu.au/mmligner to distinguish statistically significant alignments, not available elsewhere. We also demonstrate the reliability of MMLigner’s alignment results compared with the state of the art. Importantly, MMLigner can also discover different structural alignments of comparable quality, a challenging problem for oligomers and protein complexes.

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