A new hybrid method for Bayesian network learning With dependency constraints

A Bayes net has qualitative and quantitative aspects: The qualitative aspect is its graphical structure that corresponds to correlations among the variables in the Bayes net. The quantitative aspects are the net parameters. This paper develops a hybrid criterion for learning Bayes net structures that is based on both aspects. We combine model selection criteria measuring data fit with correlation information from statistical tests: Given a sample d, search for a structure G that maximizes score(G, d), over the set of structures G that satisfy the dependencies detected in d. We rely on the statistical test only to accept conditional dependencies, not conditional independencies. We show how to adapt local search algorithms to accommodate the observed dependencies. Simulation studies with GES search and the BDeu/BIC scores provide evidence that the additional dependency information leads to Bayes nets that better fit the target model in distribution and structure.

The social network learning cloud: architectural education for the 21 century

In less than a decade, architectural education has, in some ways, significantly evolved. The advent of computation has not so much triggered the change, but Social Networks (SN) have ignited a novel way of learning, interaction and knowledge construction. SN enable learners to engage with friends, tutors, professionals and peers, form the base for learning resources, allow students to make their voices heard, to listen to other views and much more. They offer a more authentic, inter-professional and integrated problem based, Just-in-Time (JIT), Just-in-Place (JIP) learning. Online SN work in close association with offline SN to form a blended social learning realm-the Social Network Learning Cloud (SNLC)-that greatly enables and enhances students' learning in a far more influential way than any other learning means, resources or methods do. This paper presents a SNLC for architectural education that provides opportunities for linking the academic Learning Management Systems (LMS) with private or professional SN such that it enhances the learning experience and deepens the knowledge of the students. The paper proposes ways of utilising SNLC in other learning and teaching areas of the curriculum and concludes with directions of how SNLC then may be employed in professional settings.

A hybrid neural network classifier combining ordered fuzzy ARTMAP and the dynamic decay adjustment algorithm

This paper presents a novel conflict-resolving neural network classifier that combines the ordering algorithm, fuzzy ARTMAP (FAM), and the dynamic decay adjustment (DDA) algorithm, into a unified framework. The hybrid classifier, known as Ordered FAMDDA, applies the DDA algorithm to overcome the limitations of FAM and ordered FAM in achieving a good generalization/performance. Prior to network learning, the ordering algorithm is first used to identify a fixed order of training patterns. The main aim is to reduce and/or avoid the formation of overlapping prototypes of different classes in FAM during learning. However, the effectiveness of the ordering algorithm in resolving overlapping prototypes of different classes is compromised when dealing with complex datasets. Ordered FAMDDA not only is able to determine a fixed order of training patterns for yielding good generalization, but also is able to reduce/resolve overlapping regions of different classes in the feature space for minimizing misclassification during the network learning phase. To illustrate the effectiveness of Ordered FAMDDA, a total of ten benchmark datasets are experimented. The results are analyzed and compared with those from FAM and Ordered FAM. The outcomes demonstrate that Ordered FAMDDA, in general, outperforms FAM and Ordered FAM in tackling pattern classification problems.

An enhanced fuzzy min-max neural network for pattern classification

An enhanced fuzzy min-max (EFMM) network is proposed for pattern classification in this paper. The aim is to overcome a number of limitations of the original fuzzy min-max (FMM) network and improve its classification performance. The key contributions are three heuristic rules to enhance the learning algorithm of FMM. First, a new hyperbox expansion rule to eliminate the overlapping problem during the hyperbox expansion process is suggested. Second, the existing hyperbox overlap test rule is extended to discover other possible overlapping cases. Third, a new hyperbox contraction rule to resolve possible overlapping cases is provided. Efficacy of EFMM is evaluated using benchmark data sets and a real medical diagnosis task. The results are better than those from various FMM-based models, support vector machine-based, Bayesian-based, decision tree-based, fuzzy-based, and neural-based classifiers. The empirical findings show that the newly introduced rules are able to realize EFMM as a useful model for undertaking pattern classification problems.

Clustering and visualization of failure modes using an evolving tree

Despite the popularity of Failure Mode and Effect Analysis (FMEA) in a wide range of industries, two well-known shortcomings are the complexity of the FMEA worksheet and its intricacy of use. To the best of our knowledge, the use of computation techniques for solving the aforementioned shortcomings is limited. As such, the idea of clustering and visualization pertaining to the failure modes in FMEA is proposed in this paper. A neural network visualization model with an incremental learning feature, i.e., the evolving tree (ETree), is adopted to allow the failure modes in FMEA to be clustered and visualized as a tree structure. In addition, the ideas of risk interval and risk ordering for different groups of failure modes are proposed to allow the failure modes to be ordered, analyzed, and evaluated in groups. The main advantages of the proposed method lie in its ability to transform failure modes in a complex FMEA worksheet to a tree structure for better visualization, while maintaining the risk evaluation and ordering features. It can be applied to the conventional FMEA methodology without requiring additional information or data. A real world case study in the edible bird nest industry in Sarawak (Borneo Island) is used to evaluate the usefulness of the proposed method. The experiments show that the failure modes in FMEA can be effectively visualized through the tree structure. A discussion with FMEA users engaged in the case study indicates that such visualization is helpful in comprehending and analyzing the respective failure modes, as compared with those in an FMEA table. The resulting tree structure, together with risk interval and risk ordering, provides a quick and easily understandable framework to elucidate important information from complex FMEA forms; therefore facilitating the decision-making tasks by FMEA users. The significance of this study is twofold, viz., the use of a computational visualization approach to tackling two well-known shortcomings of FMEA; and the use of ETree as an effective neural network learning paradigm to facilitate FMEA implementations. These findings aim to spearhead the potential adoption of FMEA as a useful and usable risk evaluation and management tool by the wider community.

Developing and asynchronous learning network

Deakin University, Australia, has committed resources over a number of years to developing the use of information and communication technologies in all aspects of teaching and learning. This paper focuses on the development over a four year period of an Asynchronous Learning Network (ALN) for distance education students studying undergraduate introductory macroeconomics. The research is based on quantitative and qualitative data gained from student evaluations, academic staff interviews, participation levels and an analysis of the online communication. Key findings from the research relate to the quality of the learning environment, the level of communication, and the role of academic staff in the learning experience. Strategies discussed for the successful use of an ALN include the nurturing of a collaborative learning environment, the adaptation of curriculum and pedagogy, the role of assessment, and the role of academic staff training and development.

Evolutionary structure learning algorithm for Bayesian network and penalized mutual information metric

This paper formulates the problem of learning Bayesian network structures from data as determining the structure that best approximates the probability distribution indicated by the data. A new metric, Penalized Mutual Information metric, is proposed, and a evolutionary algorithm is designed to search for the best structure among alternatives. The experimental results show that this approach is reliable and promising.

Actor network theory and the study of online learning: new perspectives on quality

This paper describes an approach to studying innovation and change that is taken from the field of Science and Technology Studies. Actor-network theory draws attention to the performative nature of the implementation of new technologies like quality systems and on1ine teaching. The theory posits that the world is not populated with entities that possess certain essences in and of themselves, but rather that the world is a texture of relations-a network which occasionally produces the effect of stabilised entities. We examine the consequences of producing durable forms of online teaching and quality assurance and argue that achieving durable performances requires a conformity to existing performances of a university thus reproducing current patterns of inequity.

A self-learning system for identifying harmful network information

Network information identification is a “hot” topic currently. This paper designs a self-learning system using neural network algorithm for identifying the harmful network messages of both Chinese and English languages. The system segments the message into words and creates key word vector which characterizes the harmful network information. The BP algorithm is taken advantage of to train the neural network. The result of training and studying of the neural network can be applied onto many network applications based on message identification. The result of experiments demonstrates that our system has a high degree of accuracy.

Learning online, clock time and network time? How students use and perceive time when studying online.

Though technology has increased opportunities for students to study online, many students continue to complain of lack of time to study and learn. Using the concepts of clock time and network time, the project combines interview, survey and Australian Bureau of Statistics time diary results to investigate student use and perceptions of their available time to study and how the technologies used in online learning affect this. We concentrate on the amount of time students think they have when studying online, how much time they really use, and what affects this perception of time. Deakin University has specialised in distance education/online learning since its inception in 1974 and long time use of technologies and pedagogies allows widespread and diverse experiences for our students, both on campus and off campus. We study student cohorts of up to 1700 students studying in a single subject online learning space, and note that students in much smaller subject cohorts have similar complaints about time.

Study of the Geelong Local Learning and Employment Network

In common with many Western nations, Australian governments, both state and federal, have increasingly embraced network-based approaches in responding to the effects of globalisation. Since 2001, thirty one Local Learning and Employment Networks (LLEN) have been established across all areas of Victoria, Australia in line with recommendations of a Ministerial Review into Post Compulsory Education and Training Pathways. That review reported that, in the globalised context, youth in transition from schooling to independence faced persistent and severe difficulties unknown to previous generations; it also found problems were frequently concentrated in particular groups and regions. LLEN bring together the expertise and experience of local education providers, industry, community organisations, individuals and government organisations. As a result of their local decisions, collaboration and community building efforts it is intended that opportunities for young people will be enhanced. My research was conducted within an Australian Research Council Linkage Project awarded to Deakin University Faculty of Education in partnership with the Smart Geelong Region LLEN (SGR LLEN). The Linkage Project included two separate research components one of which forms my thesis: a case study of SGR LLEN. My data was generated through participant observation in SGR LLEN throughout 2004 and 2005 and through interviews, reflective writing and archival review. In undertaking my analysis and presenting my thesis I have chosen to weave a series of panels whose orientation is poststructural. This approach was based in my acceptance that all knowledge is partial and fragmentary and, accordingly, researchers need to find ways that highlight the intersections in and indeterminacy of their empirical data. The LLEN is -by its nature as a network -more than the contractual entity that gains funding from government, acts as the administrative core and occupies the LLEN office. As such I have woven firstly the formation and operational structure of the bounded entity that is SGR LLEN before weaving a series of six images that portray the unbounded LLEN as an instance-in-action. The thesis draws its theoretical inspiration from the work of Deleuze and Guattari (1987). Despite increased use of notions of networks, local decision-making and community building by governments there had been little empirical research that explored stakeholder understandings of networks and their role in community building as well as a lack of theorisation of how networks actually ‘work.’ My research addresses this lack and suggests an instituted network can function as a learning community capable of fostering systemic change in the post compulsory education training and employment sector and thereby contributing to better opportunities for young people. However the full potential of the policy is undermined by the reluctance of governments to follow through on the implications of their policies and, in particular, to confront the limiting effects of performativity at all levels.

The social network virtual design studio : integrated design learning using blended learning environments

Online communications, multimedia, mobile computing and face-to-face learning create blended learning environments to which some Virtual Design Studios (VDS) have reacted to. Social Networks (SN), as instruments for communication, have provided a potentially fruitful operative base for VDS. These technologies transfer communication, leadership, democratic interaction, teamwork, social engagement and responsibility away from the design tutors to the participants. The implementation of Social Network VDS (SNVDS) moved the VDS beyond its conventional realm and enabled students to develop architectural design that is embedded into a community of learners and expertise both online and offline. Problem-based learning (PBL) becomes an iterative and reflexive process facilitating deep learning. The paper discusses details of the SNVDS, its pedagogical implications to PBL, and presents how the SNVDS is successful in enabling architectural students to collaborate and communicate design proposals that integrate a variety of skills, deep learning, knowledge and construction with a rich learning experience.