Learning style drift : correlation between built environment students' learning styles and the learning styles of their teachers

This paper focuses on the results of a cross-curriculum learning style survey conducted in an Australian School of Architecture and Building as part of an ongoing project aimed at resolving the learning difficulties of students collaborating in multi-disciplinary and multicultural team assignments. The research was conducted to determine how learning style differences in heterogeneous design teams might be addressed through pedagogy. We will argue that the likelihood of and reasons for learning style fluidity in student design cohorts needs determining if learning style theory is to provide a workable model for informing the teaching of design.
In light of evidence in student cohorts of learning style changes as students progress through their studies (Tucker, 2007), this research discusses one explanation of what appears to belearning style fluidity in architecture student cohorts. If, as prior research has indicated, the learning styles of academics are quite different from practitioners, evidence of a learning style drift in built environment students towards the predominant learning styles of their design teachers might suggest that students are learning how to be academics rather than practitioners. This, of course, might have serious implications for built environment teaching and for practice.

High current density and drift velocity in templated conducting polymers

Conducting polymers prepared by a templated vapour phase polymerisation process involving solid phase transition metal complexes are found to produce polymers with charge carriers that exhibit maximum drift velocity in the range of 1 m/s. This super-mobility seems to be related to a high degree of ordering in the materials as evidenced by the X-ray diffraction data. This may result from a templated polymerisation process. The high mobility manifests itself as a capacity to sustain very high current densities (>10000 A/cm2); such high current densities are of importance in thin film conductor applications.

Using multiple windows to track concept drift

In this paper we present a multiple window incremental learning algorithm that distinguishes between virtual concept drift and real concept drift. The algorithm is unsupervised and uses a novel approach to tracking concept drift that involves the use of competing windows to interpret the data. Unlike previous methods which use a single window to determine the drift in the data, our algorithm uses three windows of different sizes to estimate the change in the data. The advantage of this approach is that it allows the system to progressively adapt and predict the change thus enabling it to deal more effectively with different types of drift. We give a detailed description of the algorithm and present the results obtained from its application to two real world problems: background image processing and sound recognition. We also compare its performance with FLORA, an existing concept drift tracking algorithm.

Using selective memory to track concept drift effectively

In this paper we describe a supervised learning algorithm that uses selective memory to track concept drift. Unlike previous methods to track concept drift that use window heuristics to adapt to changes, we present an improved approach that discriminates between the instances observed. The advantage of this method is that it allows the system to both adapt to and track drift more accurately as well as filter the noise in the data more effectively. We present the algorithm and compare its performance with FLORA a well known concept drift tracking algorithm.

Limited population structure, genetic drift and bottlenecks characterise an endangered bird species in a dynamic, fire-prone ecosystem

Fire is a major disturbance process in many ecosystems world-wide, resulting in spatially and temporally dynamic landscapes. For populations occupying such environments, fire-induced landscape change is likely to influence population processes, and genetic patterns and structure among populations. The Mallee Emu-wren Stipiturus mallee is an endangered passerine whose global distribution is confined to fire-prone, semi-arid mallee shrublands in south-eastern Australia. This species, with poor capacity for dispersal, has undergone a precipitous reduction in distribution and numbers in recent decades. We used genetic analyses of 11 length-variable, nuclear loci to examine population structure and processes within this species, across its global range. Populations of the Mallee Emu-wren exhibited a low to moderate level of genetic diversity, and evidence of bottlenecks and genetic drift. Bayesian clustering methods revealed weak genetic population structure across the species' range. The direct effects of large fires, together with associated changes in the spatial and temporal patterns of suitable habitat, have the potential to cause population bottlenecks, serial local extinctions and subsequent recolonisation, all of which may interact to erode and homogenise genetic diversity in this species. Movement among temporally and spatially shifting habitat, appears to maintain long-term genetic connectivity. A plausible explanation for the observed genetic patterns is that, following extensive fires, recolonisation exceeds in-situ survival as the primary driver of population recovery in this species. These findings suggest that dynamic, fire-dominated landscapes can drive genetic homogenisation of populations of species with low-mobility and specialised habitat that otherwise would be expected to show strongly structured populations. Such effects must be considered when formulating management actions to conserve species in fire-prone systems.

Ontogenetic development of migration : Lagrangian drift trajectories suggest a new paradigm for sea turtles

Long distance migration occurs in a wide variety of taxa including birds, insects, fishes, mammals and reptiles. Here, we provide evidence for a new paradigm for the determinants of migration destination. As adults, sea turtles show fidelity to their natal nesting areas and then at the end of the breeding season may migrate to distant foraging sites. For a major rookery in the Mediterranean, we simulated hatchling drift by releasing 288 000 numerical particles in an area close to the nesting beaches. We show that the pattern of adult dispersion from the breeding area reflects the extent of passive dispersion that would be experienced by hatchlings. Hence, the prevailing oceanography around nesting areas may be crucial to the selection of foraging sites used by adult sea turtles. This environmental forcing may allow the rapid evolution of new migration destinations if ocean currents alter with climate change.

Asymmetric self-learning for tackling Twitter spam drift

Spam has become a critical problem on Twitter. In order to stop spammers, security companies apply blacklisting services to filter spam links. However, over 90% victims will visit a new malicious link before it is blocked by blacklists. To eliminate the limitation of blacklists, researchers have proposed a number of statistical features based mechanisms, and applied machine learning techniques to detect Twitter spam. In our labelled large dataset, we observe that the statistical properties of spam tweets vary over time, and thus the performance of existing ML based classifiers are poor. This phenomenon is referred as 'Twitter Spam Drift'. In order to tackle this problem, we carry out deep analysis of 1 million spam tweets and 1 million non-spam tweets, and propose an asymmetric self-learning (ASL) approach. The proposed ASL can discover new information of changed tweeter spam and incorporate it into classifier training process. A number of experiments are performed to evaluate the ASL approach. The results show that the ASL approach can be used to significantly improve the spam detection accuracy of using traditional ML algorithms.

Post-earnings announcement drift no mercado de ações brasileiro

Este trabalho busca testar a eficiência do mercado de ações brasileiro através da identificação da existência de post-earnings announcement drift, fenômeno já bastante estudado e reproduzido no mercado norte-americano. Segundo a literatura existente a respeito do assunto, a informação contida na divulgação de resultados de uma firma é relevante para a formação de preço de suas ações. Além disso, os retornos anormais acumulados de ações de firmas que divulgam resultados com “surpresas positivas” possuem tendência positiva por algum tempo após a divulgação do resultado. Por outro lado, os retornos anormais acumulados de ações de empresas que divulgam resultados com “surpresas negativas” possuem tendência negativa por algum tempo após a divulgação do resultado. A identificação de post-earnings announcement drift no mercado acionário brasileiro pode ser de grande utilidade para a estruturação de estratégias de arbitragem e gestão de portfólios. Após uma revisão teórica, o resultado é apresentado e se mostra parcialmente consistente com a literatura existente.

Particle Competition and Cooperation in Networks for Semi-Supervised Learning with Concept Drift

Concept drift is a problem of increasing importance in machine learning and data mining. Data sets under analysis are no longer only static databases, but also data streams in which concepts and data distributions may not be stable over time. However, most learning algorithms produced so far are based on the assumption that data comes from a fixed distribution, so they are not suitable to handle concept drifts. Moreover, some concept drifts applications requires fast response, which means an algorithm must always be (re) trained with the latest available data. But the process of labeling data is usually expensive and/or time consuming when compared to unlabeled data acquisition, thus only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are also based on the assumption that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenge in machine learning. Recently, a particle competition and cooperation approach was used to realize graph-based semi-supervised learning from static data. In this paper, we extend that approach to handle data streams and concept drift. The result is a passive algorithm using a single classifier, which naturally adapts to concept changes, without any explicit drift detection mechanism. Its built-in mechanisms provide a natural way of learning from new data, gradually forgetting older knowledge as older labeled data items became less influent on the classification of newer data items. Some computer simulation are presented, showing the effectiveness of the proposed method.