Managing process model complexity via concrete syntax modifications

While Business Process Management (BPM) is an established discipline, the increased adoption of BPM technology in recent years has introduced new challenges. One challenge concerns dealing with the ever-growing complexity of business process models. Mechanisms for dealing with this complexity can be classified into two categories: 1) those that are solely concerned with the visual representation of the model and 2) those that change its inner structure. While significant attention is paid to the latter category in the BPM literature, this paper focuses on the former category. It presents a collection of patterns that generalize and conceptualize various existing mechanisms to change the visual representation of a process model. Next, it provides a detailed analysis of the degree of support for these patterns in a number of state-of-the-art languages and tools. This paper concludes with the results of a usability evaluation of the patterns conducted with BPM practitioners.

Why teaching about Asia matters in higher education

I commence this opinion piece with specific reference to the Gillard Government's decision to cut funding for the Australian Learning and Teaching Council (ALTC)in 2011. I then consider impact of this decision on quality teaching in higher education with specific reference to Studies of Asia. In particular, I reflect on the teaching of Asian languages and cultures in Australia since the 1970 Auchmuty report, and conclude that despite the efforts of policy makers, not much has really changed. In doing so, I emphasise the importance of quality teaching in higher education for inspiring students to challenge their cultural assumptions and to prompt them to develop new views of the world.

Policy filtering with XACML

This paper presents a modified approach to evaluate access control policy similarity and dissimilarity based on the proposal by Lin et al. (2007). Lin et al.'s policy similarity approach is intended as a filter stage which identifies similar XACML policies that can be analysed further using more computationally demanding techniques based on model checking or logical reasoning. This paper improves the approach of computing similarity of Lin et al. and also proposes a mechanism to calculate a dissimilarity score by identifying related policies that are likely to produce different access decisions. Departing from the original algorithm, the modifications take into account the policy obligation, rule or policy combining algorithm and the operators between attribute name and value. The algorithms are useful in activities involving parties from multiple security domains such as secured collaboration or secured task distribution. The algorithms allow various comparison options for evaluating policies while retaining control over the restriction level via a number of thresholds and weight factors.

The concealed difficulties of aligning CRE decisions to corporate goals

Realisation of the importance of real estate asset strategic decision making has inspired a burgeoning corporate real estate management (CREM) literature. Much of this criticises the poor alignment between strategic business direction and the ‘enabling’ physical environment. This is based on the understanding that corporate real estate assets represent the physical resource base that supports business, and can either complement or impede that business. In the hope of resolving this problem, CRE authors advocate a deeper integration of strategic and corporate real estate decisions. However this recommendation appears to be based on a relatively simplistic theoretical approach to organization where decision-making tends to be viewed as a rationally managed event rather than a complex process. Defining decision making as an isolated event has led to an uncritical acceptance of two basic assumptions: ubiquitous, conflict-free rationality and profit maximisation. These assumptions have encouraged prescriptive solutions that clearly lack the sophistication necessary to come to grips with the complexity of the built and organizational environment. Alternatively, approaching CREM decision making from a more sophisticated perspective, such as that of the “Carnegie School”, leads to conceptualise it as a ‘process’, creating room for bounded rationality, multiple goals, intra-organizational conflict, environmental matching, uncertainty avoidance and problem searching. It is reasonable to expect that such an approach will result in a better understanding of the organizational context, which will facilitate the creation of organizational objectives, assist with the formation of strategies, and ultimately will aid decision.

Regrounding critical literacy : representation, facts and reality

A discussion of the pivotal theoretical and practical issue in the teaching of critical literacies: the relationship between representation and material social, economic and ecosystemic reality. The argument here is that models of critical literacy are contingent upon a principled and grounded pursuit of material social, economic and ecological realities 'outside' of textual representation per se.

Soundness of workflow nets: classification, decidability, and analysis

Workflow nets, a particular class of Petri nets, have become one of the standard ways to model and analyze workflows. Typically, they are used as an abstraction of the workflow that is used to check the so-called soundness property. This property guarantees the absence of livelocks, deadlocks, and other anomalies that can be detected without domain knowledge. Several authors have proposed alternative notions of soundness and have suggested to use more expressive languages, e.g., models with cancellations or priorities. This paper provides an overview of the different notions of soundness and investigates these in the presence of different extensions of workflow nets.We will show that the eight soundness notions described in the literature are decidable for workflow nets. However, most extensions will make all of these notions undecidable. These new results show the theoretical limits of workflow verification. Moreover, we discuss some of the analysis approaches described in the literature.

Reduction rules for reset/inhibitor nets

Reset/inhibitor nets are Petri nets extended with reset arcs and inhibitor arcs. These extensions can be used to model cancellation and blocking. A reset arc allows a transition to remove all tokens from a certain place when the transition fires. An inhibitor arc can stop a transition from being enabled if the place contains one or more tokens. While reset/inhibitor nets increase the expressive power of Petri nets, they also result in increased complexity of analysis techniques. One way of speeding up Petri net analysis is to apply reduction rules. Unfortunately, many of the rules defined for classical Petri nets do not hold in the presence of reset and/or inhibitor arcs. Moreover, new rules can be added. This is the first paper systematically presenting a comprehensive set of reduction rules for reset/inhibitor nets. These rules are liveness and boundedness preserving and are able to dramatically reduce models and their state spaces. It can be observed that most of the modeling languages used in practice have features related to cancellation and blocking. Therefore, this work is highly relevant for all kinds of application areas where analysis is currently intractable.

The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network

Sample complexity results from computational learning theory, when applied to neural network learning for pattern classification problems, suggest that for good generalization performance the number of training examples should grow at least linearly with the number of adjustable parameters in the network. Results in this paper show that if a large neural network is used for a pattern classification problem and the learning algorithm finds a network with small weights that has small squared error on the training patterns, then the generalization performance depends on the size of the weights rather than the number of weights. For example, consider a two-layer feedforward network of sigmoid units, in which the sum of the magnitudes of the weights associated with each unit is bounded by A and the input dimension is n. We show that the misclassification probability is no more than a certain error estimate (that is related to squared error on the training set) plus A3 √((log n)/m) (ignoring log A and log m factors), where m is the number of training patterns. This may explain the generalization performance of neural networks, particularly when the number of training examples is considerably smaller than the number of weights. It also supports heuristics (such as weight decay and early stopping) that attempt to keep the weights small during training. The proof techniques appear to be useful for the analysis of other pattern classifiers: when the input domain is a totally bounded metric space, we use the same approach to give upper bounds on misclassification probability for classifiers with decision boundaries that are far from the training examples.

Rademacher and Gaussian complexities: Risk bounds and structural results

We investigate the use of certain data-dependent estimates of the complexity of a function class, called Rademacher and Gaussian complexities. In a decision theoretic setting, we prove general risk bounds in terms of these complexities. We consider function classes that can be expressed as combinations of functions from basis classes and show how the Rademacher and Gaussian complexities of such a function class can be bounded in terms of the complexity of the basis classes. We give examples of the application of these techniques in finding data-dependent risk bounds for decision trees, neural networks and support vector machines.

Doorway to research : Issues in the development of an interactive website for international graduate students

This paper describes results of a study evaluating the content, functionality and design features of an innovative online website called the Doorway to Research (http://rsc.acid.net.au/Main.aspx) , which was developed to support international graduate students studying at universities in Australia. First, the key features of the website are described. Second, the result of a pilot study involving 12 students and faculty members who tested key aspects of the design, content and functionality of the website and provided written and oral feedback base on task-based questions and focus group discussions are explored. Finally, recommendations for future development are presented. Results of the study indicate general student satisfaction with the website and its design, content and functionality, with specific areas identified for further development.

On the data consumption benefits of accepting increased uncertainty

In the context of learning paradigms of identification in the limit, we address the question: why is uncertainty sometimes desirable? We use mind change bounds on the output hypotheses as a measure of uncertainty and interpret ‘desirable’ as reduction in data memorization, also defined in terms of mind change bounds. The resulting model is closely related to iterative learning with bounded mind change complexity, but the dual use of mind change bounds — for hypotheses and for data — is a key distinctive feature of our approach. We show that situations exist where the more mind changes the learner is willing to accept, the less the amount of data it needs to remember in order to converge to the correct hypothesis. We also investigate relationships between our model and learning from good examples, set-driven, monotonic and strong-monotonic learners, as well as class-comprising versus class-preserving learnability.

Binang gurri: turning a deaf ear to indigenous hearing loss

Objective: This paper asks whether Indigenous health policies might be improved if governments listened to Indigenous voices, both Australian and those who drafted the Declaration on the Rights of Indigenous Peoples, 2007. Methods: A fundamental tenet of the Declaration, which Australia endorsed in 2009, is respect for Indigenous knowledge and voice. The author analyses legal, cultural and historical sources for evidence of this respect. The metaphorical and empirical framework of the analysis is the epidemic of otitis media among Indigenous children. Results: A survey of Indigenous advice about health clearly demonstrates that access to their land and respect for the diversity of Indigenous cultures should inform health policies. Despite, however, claiming to consult Indigenous peoples, policy-makers have not been listening. In many Indigenous languages not listening, or ‘bad ears’, has connotations of disrespect. Conclusions: By turning a deaf ear to Indigenous knowledge governments are undermining any respect Indigenous peoples may have for them and their policies. A new approach is needed. Implications: The Declaration on the Rights of Indigenous Peoples can provide federal, state and territory governments with benchmarks against which health policy can be developed and implemented. Authentic consultation could restore Indigenous confidence in government policies.

High-probability regret bounds for bandit online linear optimization

We present a modification of the algorithm of Dani et al. [8] for the online linear optimization problem in the bandit setting, which with high probability has regret at most O ∗ ( √ T) against an adaptive adversary. This improves on the previous algorithm [8] whose regret is bounded in expectation against an oblivious adversary. We obtain the same dependence on the dimension (n 3/2) as that exhibited by Dani et al. The results of this paper rest firmly on those of [8] and the remarkable technique of Auer et al. [2] for obtaining high probability bounds via optimistic estimates. This paper answers an open question: it eliminates the gap between the high-probability bounds obtained in the full-information vs bandit settings.

REGAL : a regularization based algorithm for reinforcement learning in weakly communicating MDPs

We provide an algorithm that achieves the optimal regret rate in an unknown weakly communicating Markov Decision Process (MDP). The algorithm proceeds in episodes where, in each episode, it picks a policy using regularization based on the span of the optimal bias vector. For an MDP with S states and A actions whose optimal bias vector has span bounded by H, we show a regret bound of ~ O(HS p AT ). We also relate the span to various diameter-like quantities associated with the MDP, demonstrating how our results improve on previous regret bounds.