Probabilistic framework for image understanding applications using Bayesian Networks

Machine learning algorithms have been successfully utilized in various systems/devices. They have the ability to improve the usability/quality of such systems in terms of intelligent user interface, fast performance, and more importantly, high accuracy. In this research, machine learning techniques are used in the field of image understanding, which is a common research area between image analysis and computer vision, to involve higher processing level of a target image to make sense of the scene captured in it. A general probabilistic framework for image understanding where topics associated with (i) collection of images to generate a comprehensive and valid database, (ii) generation of an unbiased ground-truth for the aforesaid database, (iii) selection of classification features and elimination of the redundant ones, and (iv) usage of such information to test a new sample set, are discussed. Two research projects have been developed as examples of the general image understanding framework; identification of region(s) of interest, and image segmentation evaluation. These techniques, in addition to others, are combined in an object-oriented rendering system for printing applications. The discussion included in this doctoral dissertation explores the means for developing such a system from an image understanding/ processing aspect. It is worth noticing that this work does not aim to develop a printing system. It is only proposed to add some essential features for current printing pipelines to achieve better visual quality while printing images/photos. Hence, we assume that image regions have been successfully extracted from the printed document. These images are used as input to the proposed object-oriented rendering algorithm where methodologies for color image segmentation, region-of-interest identification and semantic features extraction are employed. Probabilistic approaches based on Bayesian statistics have been utilized to develop the proposed image understanding techniques

Exposing the myth: object-relational impedance mismatch is a wicked problem

Addressing a problem of software integration is a fact of life for those involved in software development. The popularity of both object and relational technologies means that they will inevitably be used together. However, the combination of these two technologies introduces problems. These problems are referred to collectively as the object-relational impedance mismatch. A mismatch is addressed using one or more mapping strategies, typically embodied in a pattern. A strategy is concerned with correspondence between the schema of a relational database and an object-oriented program. Such strategies are employed in mapping tools such as Hibernate and TopLink, and reinforce the received wisdom that the problem of object-relational impedance mismatch has been solved. In this paper, we observe that it is not clear whether each strategy, as one possible solution, addresses the cause or a symptom of a mismatch. We argue that the problem is not tame and easily resolved; rather it is complex and wicked. We introduce a catalogue of problem themes that demonstrate the complex nature of the problem and provide a way both to talk about the problem and to understand its complexity. In the future, as software systems become more complex and more connected, it will be important to learn from past endeavours. Our catalogue of problem themes represents a shift, in thinking about the problem of object-relational impedance mismatch, from issues of implementation towards an analysis of cause and effect. Such a shift has implications for those involved in the design of current and future software architectures. Because we have questioned the received wisdom, we are now in a position to work toward an appropriate solution to the problem of object-relational impedance mismatch

Object Orientation, Open Regional Science, and Cumulative Knowledge Building

Despite the growing need for an improved understanding of complex relationships among interacting systems, critical air, water, energy and socio-economic system research is carried out independently far too often. When it is comprehensively approached within integrated modeling environments, research teams often must recreate modeling foundations on which to base their own research, often because they are unable to access similar foundations already established by others. Moreover, there is an increasing awareness that energy, water, and environmental issues are best studied at the regional level, and many of the most relevant human-environmental interactions are tied to production and consumption technologies that themselves are tightly bound to regional economic systems that comprise national economies. We need to integrate and model these interacting systems comprehensively, and in an open access environment that promotes interaction among scholars, and database and model sharing to eliminate wasteful and redundant foundation infrastructure building. The pace of new knowledge development can be advanced radically by adopting a common and well-tested integrated systems modeling approach for widespread scientific use and development, supporting a research community that spans a wide range of problem domains. The future of regional science research thus lies in the integrated and comprehensive modeling of interacting systems. This paper describes our vision of this open science future, which we believe will rest on an open source and object-oriented foundation. We describe OASIS, a specific exemplar project now underway designed to fill the current integrated systems science infrastructure void with a framework whose evolutionary character will ultimately reflect the conceptual strengths and contributions of a large community of scholars. The result will be distinguished not only by the collective wisdom of the modeling community, but also by careful attention to the mechanisms that support replication and reproducibility. With the advantage of 21st century technology, object oriented open source open science will deepen our understanding and radically accelerate the pace of knowledge building in coming decades. We see this as a fundamentally new knowledge building paradigm that will dominate future integrated systems research

An empirical study evaluating depth of inheritance on the maintainability of object-oriented software

This empirical research was undertaken as part of a multi-method programme of research to investigate unsupported claims made of object-oriented technology. A series of subject-based laboratory experiments, including an internal replication, tested the effect of inheritance depth on the maintainability of object-oriented software. Subjects were timed performing identical maintenance tasks on object-oriented software with a hierarchy of three levels of inheritance depth and equivalent object-based software with no inheritance. This was then replicated with more experienced subjects. In a second experiment of similar design, subjects were timed performing identical maintenance tasks on object-oriented software with a hierarchy of five levels of inheritance depth and the equivalent object-based software. The collected data showed that subjects maintaining object-oriented software with three levels of inheritance depth performed the maintenance tasks significantly quicker than those maintaining equivalent object-based software with no inheritance. In contrast, subjects maintaining the object-oriented software with five levels of inheritance depth took longer, on average, than the subjects maintaining the equivalent object-based software (although statistical significance was not obtained). Subjects' source code solutions and debriefing questionnaires provided some evidence suggesting subjects began to experience diffculties with the deeper inheritance hierarchy. It is not at all obvious that object-oriented software is going to be more maintainable in the long run. These findings are sufficiently important that attempts to verify the results should be made by independent researchers

The future of technology enhanced active learning – a roadmap

The notion of active learning refers to the active involvement of learner in the learning process, capturing ideas of learning-by-doing and the fact that active participation and knowledge construction leads to deeper and more sustained learning. Interactivity, in particular learnercontent interaction, is a central aspect of technology-enhanced active learning. In this roadmap, the pedagogical background is discussed, the essential dimensions of technology-enhanced active learning systems are outlined and the factors that are expected to influence these systems currently and in the future are identified. A central aim is to address this promising field from a best practices perspective, clarifying central issues and formulating an agenda for future developments in the form of a roadmap