Last interglacial climates of south-eastern Australia : plant and beetle-based reconstructions from Yarra Creek, King Island, Tasmania

This paper explores the palaeoclimatic significance of a fossil plant and insect record from Yarra Creek, on King Island, between Tasmania and the Australian mainland. The record dates, based upon a thermoluminescence chronology and other evidence, to Marine Isotope Stage 5 (MIS 5); the exact timing is impossible to ascertain given the resolution of the thermoluminescence results and the presence of an unconformity in the dated section. The presence of a cool-temperate rainforest flora, outside its modern range, and other independent evidence, suggest the sequence may represent the last interglacial (MIS 5e) rather than a later MIS 5 substage. Using coexistence methods that compare modern climatic ranges of the taxa in the assemblage we reconstruct independent beetle and plant based annual and seasonal temperate and precipitation parameters. The results imply the assemblage was deposited under a wetter summer climate and suggest conditions of enhanced temperature seasonality. It is probable that enhanced temperature seasonality is a methodological artefact reflecting the rarity of extremely equable climates (like King Island) in modern climate space. This would indicate a limitation of most methods of palaeoclimatic reconstruction that rely on modern datasets – it is only possible to reconstruct past climates as being within the range of values in that currently exist in modern climate space.

Negotiation strategy for mobile agent-based e-negotiation

Negotiation is a vital component of electronic trading. It is the key decision-making approach used to reach consensus between trading partners. Generally, the trading partners implement various negotiation strategies in an attempt to maximize their utilities. As negotiation strategies have impact on the outcomes of negotiation, it is imperative to have efficient negotiation strategies that truly maximize clients’ utilities. In this paper, we propose a multi-attribute mobile agent-based negotiation strategy that maximizes client’s utility. The strategy focuses on one-to-many bilateral negotiation. It considers different factors that have significant effect on the scheduling of various negotiation phases: offer collection, evaluation, negotiation, and bid settlement. The factors include offers expiry time, market search space, communication delays, processing queues, and transportation times. We reasoned about the correctness of the proposed negotiation strategy with respect to the existing negotiation strategies. The analysis showed that the proposed strategy boosts client’s utility, shortens negotiation time, and ensures adequate market search.

Touching space : using motion capture and stereo projection to create a virtual haptics of dance

This paper describes the work of a group of artists in Australia who used real-time motion capture and 3D stereo projection to create a large-scale performance environment in which dancers seemed to "touch" the volume. This project re-versions Suzanne Langer's 1950s philosophy of dance as "virtual force" to realize the idea of a "virtual haptics" of dance that extends the dancer's physical agency literally across and through the surrounding spatial volume. The project presents a vision of interactive dance performance that "touches" space by visualizing kinematics as intentionality and agency. In doing so, we suggest the possibility of new kinds of human-computer interfaces that emphasize touch as embodied, nuanced agency that is mediated by the subtle qualities of whole-body movement, in addition to more goal-oriented, task-based gestures such as pointing or clicking.

The national mapping of teacher professional learning project : a multi-dimensional space?

This essay focuses on the National Mapping of Teacher Professional Learning (2008), a report that we co-authored along with a number of other researchers on the basis of extensive surveys and interviews relating to the policies and practices of teacher professional learning in Australia. The report is an update of an earlier survey conducted by David McRae and others, entitled PD 2000, and it registers significant changes in attitudes and practices relating to professional learning across Australia in the intervening period. Perhaps the most significant development is the way professional learning is now recognized as an important vehicle for education reform by systems, schools and by teachers themselves, most notably the standards-based reforms that have such a decisive effect on the policy landscape here in Australia and in other countries. The work of the AATE in developing the Standards for Teachers of English Language and Literacy (STELLA) is mentioned in the report. It was acknowledged that STELLA provides a generative framework for professional learning, sometimes in contradiction to more managerial approaches. The question remains, however, of how English teachers as a professional community might locate themselves within the policy landscape described in this report. This essay is an attempt to promote this kind of discussion and to argue the distinctive nature of the standpoint that English teachers might bring to thinking about and planning for professional learning and practitioner inquiry.

Series feature aggregation for content-based image retrieval

Feature aggregation is a critical technique in content-based image retrieval systems that employ multiple visual features to characterize image content. One problem in feature aggregation is that image similarity in different feature spaces can not be directly comparable with each other. To address this problem, a new feature aggregation approach, series feature aggregation (SFA), is proposed in this paper. In contrast to merging incomparable feature distances in different feature spaces to get aggregated image similarity in the conventional feature aggregation approach, the series feature aggregation directly deal with images in each feature space to avoid comparing different feature distances. SFA is effectively filtering out irrelevant images using individual features in each stage and the remaining images are images that collectively described by all features. Experiments, conducted with IAPR TC-12 benchmark image collection (ImageCLEF2006) that contains over 20,000 photographic images and defined queries, have shown that SFA can outperform the parallel feature aggregation and linear distance combination schemes. Furthermore, SFA is able to retrieve more relevant images in top ranked outputs that brings better user experience in finding more relevant images quickly.

CARSA : A context-aware reasoning-based service agent model for AI planning of web service compostion

In order to achieve automatic and more intelligent service composition, dynamic description logic (DDL) is proposed and utilized as one emerging logic-level solution. However, reasoning optimization and utilization in such DDL-related solutions is still an open problem. In this paper, we propose the context-aware reasoning-based service agent model (CARSA) which exploits the relationships among different service consumers and providers, together with the corresponding optimization approach to strengthen the effectiveness of Web service composition. Through the model, two reasoning optimization methods are proposed based on the substitute relationship and the dependency relationship, respectively, so irrelevant actions can be filtered out of the reasoning space before the DDL reasoning process is carried out. The case study and experimental analysis demonstrates the capability of the proposed approach.

A class of optimal fault tolerant Jacobian for minimal redundant manipulators based on symmetric geometries

Design of locally optimal fault tolerant manipulators has been recently addressed via using the constraints of the desired null space for the Jacobian matrix of the manipulators. In the present paper the Jacobian matrices for optimal fault tolerance are presented based on geometric properties of column vectors instead of the null space. They are equally fault tolerant to a single joint failure from the worst-case relative manipulability and worst-case dexterity points of view. The optimality is achieved through a symmetric distribution of points on spheres.

Human action recognition based on 3D SIFT and LDA model

How to recognize human action from videos captured by modern cameras efficiently and effectively is a challenge in real applications. Traditional methods which need professional analysts are facing a bottleneck because of their shortcomings. To cope with the disadvantage, methods based on computer vision techniques, without or with only a few human interventions, have been proposed to analyse human actions in videos automatically. This paper provides a method combining the three dimensional Scale Invariant Feature Transform (SIFT) detector and the Latent Dirichlet Allocation (LDA) model for human motion analysis. To represent videos effectively and robustly, we extract the 3D SIFT descriptor around each interest point, which is sampled densely from 3D Space-time video volumes. After obtaining the representation of each video frame, the LDA model is adopted to discover the underlying structure-the categorization of human actions in the collection of videos. Public available standard datasets are used to test our method. The concluding part discusses the research challenges and future directions.

Human action recognition based on pyramid histogram of oriented gradients

Human action recognition has been attracted lots of interest from computer vision researchers due to its various promising applications. In this paper, we employ Pyramid Histogram of Orientation Gradient (PHOG) to characterize human figures for action recognition. Comparing to silhouette-based features, the PHOG descriptor does not require extraction of human silhouettes or contours. Two state-space models, i.e.; Hidden Markov Model (HMM) and Conditional Random Field (CRF), are adopted to model the dynamic human movement. The proposed PHOG descriptor and the state-space models with respect to different parameters are tested using a standard dataset. We also testify the robustness of the method with respect to various unconstrained conditions and viewpoints. Promising experimental result demonstrates the effectiveness and robustness of our proposed method.

Hierarchical directory-based shared memory architecture

We describe the design of a directory-based shared memory architecture on a hierarchical network of hypercubes. The distributed directory scheme comprises two separate hierarchical networks for handling cache requests and transfers. Further, the scheme assumes a single address space and each processing element views the entire network as contiguous memory space. The size of individual directories stored at each node of the network remains constant throughout the network. Although the size of the directory increases with the network size, the architecture is scalable. The results of the analytical studies demonstrate superior performance characteristics of our scheme compared with those of other schemes.

Reliable aggregation on network traffic for web based knowledge discovery

The web is a rich resource for information discovery, as a result web mining is a hot topic. However, a reliable mining result depends on the reliability of the data set. For every single second, the web generate huge amount of data, such as web page requests, file transportation. The data reflect human behavior in the cyber space and therefore valuable for our analysis in various disciplines, e.g. social science, network security. How to deposit the data is a challenge. An usual strategy is to save the abstract of the data, such as using aggregation functions to preserve the features of the original data with much smaller space. A key problem, however is that such information can be distorted by the presence of illegitimate traffic, e.g. botnet recruitment scanning, DDoS attack traffic, etc. An important consideration in web related knowledge discovery then is the robustness of the aggregation method , which in turn may be affected by the reliability of network traffic data. In this chapter, we first present the methods of aggregation functions, and then we employe information distances to filter out anomaly data as a preparation for web data mining.