Description:
We consider parameterized Mathematical Programs with Complementarity Constraints arising, e.g., in modeling of deregulated electricity markets. Using the standard rules of the generalized differential calculus we analyze qualitative stability of solutions to the respective M-stationarity conditions. In particular, we provide characterizations and criteria for the isolated calmness and the Aubin properties of the stationarity map. To this end, we introduce the second-order limiting coderivative of mappings and provide formulas for this notion and for the graphical derivative of the limiting coderivative in the case of the normal cone mapping to ℝn Funding ARC- Australian Research Council
Description:
IIntroducing a new concept of (®, ¯)-fairness, which allows for a bounded fairness compromise, so that a source is allocated a rate neither less than 0 · ® · 1, nor more than ¯ ¸ 1, times its fair share, this paper provides a framework to optimize efficiency (utilization, throughput or revenue) subject to fairness constraints in a general telecommunications network for an arbitrary fairness criterion and cost functions. We formulate a non-linear program (NLP) that finds the optimal bandwidth allocation by maximizing efficiency subject to (®, ¯)-fairness constraints. This leads to what we call an efficiency-fairness function, which shows the benefit in efficiency as a function of the extent to which fairness is compromised. To solve the NLP we use two algorithms. The first is a well known branch-and-bound-based algorithm called Lipschitz Global Optimization and the second is a recently developed algorithm called Algorithm for Global Optimization Problems (AGOP). We demonstrate the applicability of the framework to a range of example from sharing a single link to efficiency fairness issues associated with serving customers in remote communities.
Description:
IIntroducing a new concept of (®, ¯)-fairness, which allows for a bounded fairness compromise, so that a source is allocated a rate neither less than 0 · ® · 1, nor more than ¯ ¸ 1, times its fair share, this paper provides a framework to optimize efficiency (utilization, throughput or revenue) subject to fairness constraints in a general telecommunications network for an arbitrary fairness criterion and cost functions. We formulate a non-linear program (NLP) that finds the optimal bandwidth allocation by maximizing efficiency subject to (®, ¯)-fairness constraints. This leads to what we call an efficiency-fairness function, which shows the benefit in efficiency as a function of the extent to which fairness is compromised. To solve the NLP we use two algorithms. The first is a well known branch-and-bound-based algorithm called Lipschitz Global Optimization and the second is a recently developed algorithm called Algorithm for Global Optimization Problems (AGOP). We demonstrate the applicability of the framework to a range of example from sharing a single link to efficiency fairness issues associated with serving customers in remote communities.
Description:
This research project into the manipulation of spatial concepts by artists on the two-dimensional surface plane, has involved a selected study into cultural and aesthetic evolution from early civilization through to the present era. I have cast a line of inquiry into eastern, western and primitive art practices, observing the journey of chance accelerated by developments in technology. Traditionally artists utilized modes of spatial convention and techniques according to the specific cultural traditions of the time and place of their production. By contrast, contemporary artists know no such boundaries, and are able to select from a range of spatial options relevant both to current forms of expression and to a personal visual language. My own art practice has been enriched and extended, increasing my ability to challenge the notion of still life composition by reversing the traditional hierarchy of form and space through the application of a series of experiments brought about by extensive research into this spatial evolution. The research project has further assisted this development in my art practice by engaging me in a new level of understanding of the topic, informing my perceptions and increasing my ability to translate a combination of forms in space with heightened emotion and personal meaning.
Description:
This research project into the manipulation of spatial concepts by artists on the two-dimensional surface plane, has involved a selected study into cultural and aesthetic evolution from early civilization through to the present era. I have cast a line of inquiry into eastern, western and primitive art practices, observing the journey of chance accelerated by developments in technology. Traditionally artists utilized modes of spatial convention and techniques according to the specific cultural traditions of the time and place of their production. By contrast, contemporary artists know no such boundaries, and are able to select from a range of spatial options relevant both to current forms of expression and to a personal visual language. My own art practice has been enriched and extended, increasing my ability to challenge the notion of still life composition by reversing the traditional hierarchy of form and space through the application of a series of experiments brought about by extensive research into this spatial evolution. The research project has further assisted this development in my art practice by engaging me in a new level of understanding of the topic, informing my perceptions and increasing my ability to translate a combination of forms in space with heightened emotion and personal meaning.
Description:
Vibration testing is an essential component in Structural Health Monitoring (SHM). It can provide vital information regarding the integrity of critical structure; for instance, it can provide information on progressive failure monitoring of composites structure in the aerospace industry. Over the past decade, there have been many successful researches showing extraordinary ability of Fiber Bragg grating (FBG) sensors as a dynamic sensor. Ability of acquiring both static and dynamic strain measurements, make FBG sensor as a good alternative to replace the conventional vibration sensors. In addition the physical size of FBG sensor provides greater access to embed them in composite structures without affecting to any properties of the composite. However, in most applications to date, people have used only the FBG with wavelength 1550 nm. Moreover, FBG sensors with this wavelength are commonly use in industries such as telecommunications and medical industries. However, there is an option of using near infra-red (NIR) FBG range which comparably cheap in term of total system design. This paper details the use of near infra-red (NIR) FBGs as dynamic sensors; a part of SHM system for the monitoring of the damages in a thin glass fiber composite plates. Results reveal that the NIR FBG range gives good response to an impact and; also to applied high frequency vibrations.
Description:
During the past decade, many successful studies have evidently shown remarkable capability of Fiber Bragg Gratings (FBG) sensor for dynamic sensing. Most of the research works utilized the 1550 nm wavelength range of FBG sensors. However near infra-red (NIR) FBG sensors can offer the lower cost of Structural health Monitoring (SHM) systems which uses cheaper silicon sources and detectors. Unfortunately, the excessive noise levels that experienced in NIR wavelengths have caused the rejection of sensor that operating in this range of wavelengths for SHM systems. However, with the appropriate use of signal processing tools, these noisy signals can be easily ‘cleaned’. Wavelet analysis is one of the powerful signal processing tools nowadays, not only for time-frequency analysis but also for signal denoising. This present study revealed that the NIR FBG range gave good response to impact signals. Furthermore, these ‘noisy’ signals’ response were successfully filtered using one dimensional wavelet analysis.
Description:
In this paper we use an example of when students appear not to have learnt from their experience to examine some of the 'orthodoxies' of experiential education. This frames a discussion exploring how it is possible for a teacher to declare that students have got it 'wrong' in relation to learning from experience. It is argued that both learning through experience and who has experiences is viewed through specific forms of reason within contemporary experiential education. The paper concludes with a challenge to open reason up to greater scrutiny in experiential education and consider the possibilities that emerge through the indeterminacy of relationships inherent in experience and education. [ABSTRACT FROM AUTHOR]
Description:
"The aim of this study was to examine the effect of consecutive bouts of acute exhaustive exercise in a hot ambient environmemt on baroreflex gain, plasma volume and heart variability one week post-exercise in trained individuals." Problem with pages xviii-xx Pages 9-37 missing.
Description:
"The aim of this study was to examine the effect of consecutive bouts of acute exhaustive exercise in a hot ambient environmemt on baroreflex gain, plasma volume and heart variability one week post-exercise in trained individuals." Problem with pages xviii-xx Pages 9-37 missing.
Description:
This thesis is a narrative inquiry to investigate teachers’ meanings for imagination and its potency for teaching and learning. Six teachers who identified it as central to their practice shared stories of how imagination is an effective pedagogy through in-depth, semi-structured interviews. Imagination is a living, mercurial phenomenon contested in philosophical circles yet taken-for-granted amongst the populace. Consequently, imagination in teaching and learning is under researched and widely regarded as mere decoration - helpful for engagement but unrelated to cognition. The literature review situates the research in international discussions concerning imagination’s value for teaching and learning. Several conceptualisations of meaning for imagination lead to a theoretical framework which re-conceptualises Bakhtin’s dialogic imagination and combines his philosophy of discourse with Ricoeur’s philosophy of imagination, and Brockmeier’s narrative imagination. Data analysis to compare and contrast the teachers’ meanings to the framework strongly suggests that, contrary to existing stereotypes, imagination is cognitive: it catalyses metaphoric meaning-making events as dialogic imagination. Since an open living discourse and narrative imagination are conditions for such meaning events, the teachers’ pedagogical choices are consequently rational and supportive of learning. Australian educational policy-makers have increasingly leveraged a closed classroom discourse over past decades: teachers must ensure students comply with national testing regimes that demand monologic responses tied to finalised syllabus requirements. Over that period students’ accomplishment has either seriously declined or flatlined. The teachers in this narrative inquiry keep living discourse and imagination open and alive but in spite of, not because of existing policy: the research presented here permits their understandings and professional art to be given voice in educational debates on effective teaching. I conclude policy makers might seriously consider the impacts of policy dynamics and whether they are slowly suffocating opportunities for a living atmosphere that invites imagination – a powerhouse of learning – into their lives.
Description:
This thesis is a narrative inquiry to investigate teachers’ meanings for imagination and its potency for teaching and learning. Six teachers who identified it as central to their practice shared stories of how imagination is an effective pedagogy through in-depth, semi-structured interviews. Imagination is a living, mercurial phenomenon contested in philosophical circles yet taken-for-granted amongst the populace. Consequently, imagination in teaching and learning is under researched and widely regarded as mere decoration - helpful for engagement but unrelated to cognition. The literature review situates the research in international discussions concerning imagination’s value for teaching and learning. Several conceptualisations of meaning for imagination lead to a theoretical framework which re-conceptualises Bakhtin’s dialogic imagination and combines his philosophy of discourse with Ricoeur’s philosophy of imagination, and Brockmeier’s narrative imagination. Data analysis to compare and contrast the teachers’ meanings to the framework strongly suggests that, contrary to existing stereotypes, imagination is cognitive: it catalyses metaphoric meaning-making events as dialogic imagination. Since an open living discourse and narrative imagination are conditions for such meaning events, the teachers’ pedagogical choices are consequently rational and supportive of learning. Australian educational policy-makers have increasingly leveraged a closed classroom discourse over past decades: teachers must ensure students comply with national testing regimes that demand monologic responses tied to finalised syllabus requirements. Over that period students’ accomplishment has either seriously declined or flatlined. The teachers in this narrative inquiry keep living discourse and imagination open and alive but in spite of, not because of existing policy: the research presented here permits their understandings and professional art to be given voice in educational debates on effective teaching. I conclude policy makers might seriously consider the impacts of policy dynamics and whether they are slowly suffocating opportunities for a living atmosphere that invites imagination – a powerhouse of learning – into their lives.
Description:
The detection of Distributed Denial of Service (DDos) attacks is very important for maintaining the security of networks and the Internet. This paper introduces a novel iterative consensus process based on Hybrid Bipartite Graph Formulation (HGBF) consensus function for DDos attacks detection. First, the features are extracted during feature extraction process based on the analysis of network traffic. Second, several clustering algorithms are applied in combination with the silhouette index to obtain a collection of independent initial clusterings. Third, the HGBF consensus function and silhouette index are used to find an appropriate consensus clustering of the initial clusterings. Fourth, this new consensus clustering is added to the pool of initial clusterings replacing another clustering with the worst Silhouette index. Fifth, the process continues iteratively until the Silhouette index of the resulting consensus clusterings stabilizes. This iterative consensus clustering process can improve the quality of the clusters. The experimental results demonstrate that our iterative consensus process is effective and can be used in practice for detecting the separate phased of DDos attacks.
Description:
The detection of Distributed Denial of Service (DDos) attacks is very important for maintaining the security of networks and the Internet. This paper introduces a novel iterative consensus process based on Hybrid Bipartite Graph Formulation (HGBF) consensus function for DDos attacks detection. First, the features are extracted during feature extraction process based on the analysis of network traffic. Second, several clustering algorithms are applied in combination with the silhouette index to obtain a collection of independent initial clusterings. Third, the HGBF consensus function and silhouette index are used to find an appropriate consensus clustering of the initial clusterings. Fourth, this new consensus clustering is added to the pool of initial clusterings replacing another clustering with the worst Silhouette index. Fifth, the process continues iteratively until the Silhouette index of the resulting consensus clusterings stabilizes. This iterative consensus clustering process can improve the quality of the clusters. The experimental results demonstrate that our iterative consensus process is effective and can be used in practice for detecting the separate phased of DDos attacks.
Description:
One pass learning updates a model with only a single scan of the dataset, without storing historical data. Previous studies focus on classification tasks with a fixed class set, and will perform poorly in an open dynamic environment when new classes emerge in a data stream. The performance degrades because the classifier needs to receive a sufficient number of instances from new classes to establish a good model. This can take a long period of time. In order to reduce this period to deal with any-time prediction task, we introduce a framework to handle emerging new classes called One-Pass Class Incremental Learning (OPCIL). The central issue in OPCIL is: how to effectively adapt a classifier of existing classes to incorporate emerging new classes. We call it the new class adaptation issue, and propose a new approach to address it, which requires only one new class instance. The key is to generate pseudo instances which are optimized to satisfy properties that produce a good discriminative classifier. We provide the necessary propertiesand optimization procedures required to address this issue. Experiments validate the effectiveness of this approach.
Description:
Multi-instance multi-label learning (MIML) is a learning paradigm where an object is represented by a bag of instances and each bag is associated with multiple labels. Ordinary MIML setting assumes a fixed target label set. In real applications, multiple novel labels may exist outside this set, but hidden in the training data and unknown to the MIML learner. Existing MIML approaches are unable to discover the hidden novel labels, let alone predicting these labels in the previously unseen test data. In this paper, we propose the first approach to discover multiple novel labels in MIML problem using an efficient augmented lagrangian optimization, which has a bag-dependent loss term and a bag-independent clustering regularization term, enabling the known labels and multiple novel labels to be modeled simultaneously. The effectiveness of the proposed approach is validated in experiments.
Description:
In a multi-label learning task, an object possesses multiple concepts where each concept is represented by a class label. Previous studies on multi-label learning have focused on a fixed set of class labels, i.e., the class label set of test data is the same as that in the training set. In many applications, however, the environment is dynamic and new concepts may emerge in a data stream. In order to maintain a good predictive performance in this environment, a multi-label learning method must have the ability to detect and classify instances with emerging new labels. To this end, we propose a new approach called Multi-label learning with Emerging New Labels (MuENL). It has three functions: classify instances on currently known labels, detect the emergence of a new label, and construct a new classifier for each new label that works collaboratively with the classifier for known labels. In addition, we show that MuENL can be easily extended to handle sparse high dimensional data streams by simply reducing the original dimensionality, and then applying MuENL on the reduced dimensional space. Our empirical evaluation shows the effectiveness of MuENL on several benchmark datasets and MuENLHD on the sparse high dimensional Weibo dataset.
Description:
In a multi-label learning task, an object possesses multiple concepts where each concept is represented by a class label. Previous studies on multi-label learning have focused on a fixed set of class labels, i.e., the class label set of test data is the same as that in the training set. In many applications, however, the environment is dynamic and new concepts may emerge in a data stream. In order to maintain a good predictive performance in this environment, a multi-label learning method must have the ability to detect and classify instances with emerging new labels. To this end, we propose a new approach called Multi-label learning with Emerging New Labels (MuENL). It has three functions: classify instances on currently known labels, detect the emergence of a new label, and construct a new classifier for each new label that works collaboratively with the classifier for known labels. In addition, we show that MuENL can be easily extended to handle sparse high dimensional data streams by simply reducing the original dimensionality, and then applying MuENL on the reduced dimensional space. Our empirical evaluation shows the effectiveness of MuENL on several benchmark datasets and MuENLHD on the sparse high dimensional Weibo dataset.