What's in it for me? Virtual conspicuous donation strategies as a source of value in blood donation

Providing an incentive is becoming common practice among blood service organisations. Driven by self-orientated motives rather than pure philanthropic intentions, research is showing that people increasingly want something in return for their support. It is contended that individuals donate conspicuously with the hope it will improve their social standing. Yet there is limited evidence for the effectiveness of conspicuous recognition strategies, and no studies, to the researcher’s knowledge, that have examined conspicuous donation strategies in an online social media context. There is a need to understand what value drives individuals to donate blood, and whether conspicuous donation strategies are a source of such value post blood donation. The purpose of this paper is to conceptualise how conspicuous donation strategies, in the form of virtual badges on social media sites, can be applied to the social behaviour of blood donation, as a value-adding tool, to encourage repeat behaviour.

Source apportionment of airborne particulate matter : an overview of Australian and New Zealand studies

Despite the existence of air quality guidelines in Australia and New Zealand, the concentrations of particulate matter have exceeded these guidelines on several occasions. To identify the sources of particulate matter, examine the contributions of the sources to the air quality at specific areas and estimate the most likely locations of the sources, a growing number of source apportionment studies have been conducted. This paper provides an overview of the locations of the studies, salient features of the results obtained and offers some perspectives for the improvement of future receptor modelling of air quality in these countries. The review revealed that because of its advantages over alternative models, Positive Matrix Factorisation (PMF) was the most commonly applied model in the studies. Although there were differences in the sources identified in the studies, some general trends were observed. While biomass burning was a common problem in both countries, the characteristics of this source varied from one location to another. In New Zealand, domestic heating was the highest contributor to particle levels on days when the guidelines were exceeded. On the other hand, forest back-burning was a concern in Brisbane while marine aerosol was a major source in most studies. Secondary sulphate, traffic emissions, industrial emissions and re-suspended soil were also identified as important sources. Some unique species, for example, volatile organic compounds and particle size distribution were incorporated into some of the studies with results that have significant ramifications for the improvement of air quality. Overall, the application of source apportionment models provided useful information that can assist the design of epidemiological studies and refine air pollution reduction strategies in Australia and New Zealand.

Atmospheric particles as a pollutant source in the urban water environment in Sri Lanka – current research trends and future directions

Human and ecosystem health impacts imposed by water pollution are a major problem in the urban areas of Sri Lanka. A primary source of pollutants to urban water sources is atmospheric particles. Hence, it is important to develop a detailed understanding of atmospheric particle characteristics, their sources of origin and the transport pathways. Several research studies have been conducted in Sri Lanka on atmospheric pollution and these studies have tended to differ in their scope, study region and the investigated pollutants. The objectives of this paper are: (1) to report the outcomes of a detailed state-of-art literature review of atmospheric pollution related studies in Sri Lanka to understand the current trends and (2) to discuss the future research activities necessary to generate the important knowledge required for the development of effective strategies to control the adverse impacts of atmospheric pollution on urban waterways.

Timing and source constraints on the relationship between mafic and felsic intrusions in the Emeiashan large igneous province

Several I- and A-type granite, syenite plutons and spatially associated, giant Fe–Ti–V deposit-bearing mafic ultramafic layered intrusions occur in the Pan–Xi(Panzhihua–Xichang) area within the inner zone of the Emeishan large igneous province (ELIP). These complexes are interpreted to be related to the Emeishan mantle plume. We present LA-ICP-MS and SIMS zircon U–Pb ages and Hf–Nd isotopic compositions for the gabbros, syenites and granites from these complexes. The dating shows that the age of the felsic intrusive magmatism (256.2 ± 3.0–259.8 ± 1.6 Ma) is indistinguishable from that of the mafic intrusive magmatism (255.4 ± 3.1–259.5 ± 2.7 Ma) and represents the final phase of a continuous magmatic episode that lasted no more than 10 Myr. The upper gabbros in the mafic–ultramafic intrusions are generally more isotopically enriched (lower eNd and eHf) than the middle and lower gabbros, suggesting that the upper gabbros have experienced a higher level of crustal contamination than the lower gabbros. The significantly positive eHf(t) values of the A-type granites and syenites (+4.9 to +10.8) are higher than those of the upper gabbros of the associated mafic intrusion, which shows that they cannot be derived by fractional crystallization of these bodies. They are however identical to those of the mafic enclaves (+7.0 to +11.4) and middle and lower gabbros, implying that they are cogenetic. We suggest that they were generated by fractionation of large-volume, plume-related basaltic magmas that ponded deep in the crust. The deep-seated magma chamber erupted in two stages: the first near a density minimum in the basaltic fractionation trend and the second during the final stage of fractionation when the magma was a low density Fe-poor, Si-rich felsic magma. The basaltic magmas emplaced in the shallowlevel magma chambers differentiated to form mafic–ultramafic layered intrusions accompanied by a small amount of crustal assimilation through roof melting. Evolved A-type granites (synenites and syenodiorites) were produced dominantly by crystallization in the deep crustal magma chamber. In contrast, the I-type granites have negative eNd(t) [-6.3 to -7.5] and eHf(t) [-1.3 to -6.7] values, with the Nd model ages (T Nd DM2) of 1.63-1.67 Ga and Hf model ages (T Hf DM2) of 1.56-1.58 Ga, suggesting that they were mainly derived from partial melting of Mesoproterozoic crust. In combination with previous studies, this study also shows that plume activity not only gave rise to reworking of ancient crust, but also significant growth of juvenile crust in the center of the ELIP.

Large-scale analysis of formations in soccer

Due to the demand for better and deeper analysis in sports, organizations (both professional teams and broadcasters) are looking to use spatiotemporal data in the form of player tracking information to obtain an advantage over their competitors. However, due to the large volume of data, its unstructured nature, and lack of associated team activity labels (e.g. strategic/tactical), effective and efficient strategies to deal with such data have yet to be deployed. A bottleneck restricting such solutions is the lack of a suitable representation (i.e. ordering of players) which is immune to the potentially infinite number of possible permutations of player orderings, in addition to the high dimensionality of temporal signal (e.g. a game of soccer last for 90 mins). Leveraging a recent method which utilizes a "role-representation", as well as a feature reduction strategy that uses a spatiotemporal bilinear basis model to form a compact spatiotemporal representation. Using this representation, we find the most likely formation patterns of a team associated with match events across nearly 14 hours of continuous player and ball tracking data in soccer. Additionally, we show that we can accurately segment a match into distinct game phases and detect highlights. (i.e. shots, corners, free-kicks, etc) completely automatically using a decision-tree formulation.

Predicting shot locations in tennis using spatiotemporal data

Over the past decade, vision-based tracking systems have been successfully deployed in professional sports such as tennis and cricket for enhanced broadcast visualizations as well as aiding umpiring decisions. Despite the high-level of accuracy of the tracking systems and the sheer volume of spatiotemporal data they generate, the use of this high quality data for quantitative player performance and prediction has been lacking. In this paper, we present a method which predicts the location of a future shot based on the spatiotemporal parameters of the incoming shots (i.e. shot speed, location, angle and feet location) from such a vision system. Having the ability to accurately predict future short-term events has enormous implications in the area of automatic sports broadcasting in addition to coaching and commentary domains. Using Hawk-Eye data from the 2012 Australian Open Men's draw, we utilize a Dynamic Bayesian Network to model player behaviors and use an online model adaptation method to match the player's behavior to enhance shot predictability. To show the utility of our approach, we analyze the shot predictability of the top 3 players seeds in the tournament (Djokovic, Federer and Nadal) as they played the most amounts of games.

Semi-binary based video features for activity representation

Efficient and effective feature detection and representation is an important consideration when processing videos, and a large number of applications such as motion analysis, 3D scene understanding, tracking etc. depend on this. Amongst several feature description methods, local features are becoming increasingly popular for representing videos because of their simplicity and efficiency. While they achieve state-of-the-art performance with low computational complexity, their performance is still too limited for real world applications. Furthermore, rapid increases in the uptake of mobile devices has increased the demand for algorithms that can run with reduced memory and computational requirements. In this paper we propose a semi binary based feature detectordescriptor based on the BRISK detector, which can detect and represent videos with significantly reduced computational requirements, while achieving comparable performance to the state of the art spatio-temporal feature descriptors. First, the BRISK feature detector is applied on a frame by frame basis to detect interest points, then the detected key points are compared against consecutive frames for significant motion. Key points with significant motion are encoded with the BRISK descriptor in the spatial domain and Motion Boundary Histogram in the temporal domain. This descriptor is not only lightweight but also has lower memory requirements because of the binary nature of the BRISK descriptor, allowing the possibility of applications using hand held devices.We evaluate the combination of detectordescriptor performance in the context of action classification with a standard, popular bag-of-features with SVM framework. Experiments are carried out on two popular datasets with varying complexity and we demonstrate comparable performance with other descriptors with reduced computational complexity.

Swimmer localization from a moving camera

At the highest level of competitive sport, nearly all performances of athletes (both training and competitive) are chronicled using video. Video is then often viewed by expert coaches/analysts who then manually label important performance indicators to gauge performance. Stroke-rate and pacing are important performance measures in swimming, and these are previously digitised manually by a human. This is problematic as annotating large volumes of video can be costly, and time-consuming. Further, since it is difficult to accurately estimate the position of the swimmer at each frame, measures such as stroke rate are generally aggregated over an entire swimming lap. Vision-based techniques which can automatically, objectively and reliably track the swimmer and their location can potentially solve these issues and allow for large-scale analysis of a swimmer across many videos. However, the aquatic environment is challenging due to fluctuations in scene from splashes, reflections and because swimmers are frequently submerged at different points in a race. In this paper, we temporally segment races into distinct and sequential states, and propose a multimodal approach which employs individual detectors tuned to each race state. Our approach allows the swimmer to be located and tracked smoothly in each frame despite a diverse range of constraints. We test our approach on a video dataset compiled at the 2012 Australian Short Course Swimming Championships.

μAV - Design and implementation of an open source micro quadrotor

This paper presents the design of μAV, a palm size open source micro quadrotor constructed on a single Printed Circuit Board. The aim of the micro quadrotor is to provide a lightweight (approximately 86g) and cheap robotic research platform that can be used for a range of robotic applications. One possible application could be a cheap test bed for robotic swarm research. The goal of this paper is to give an overview of the design and capabilities of the micro quadrotor. The micro quadrotor is complete with a 9 Degree of Freedom Inertial Measurement Unit, a Gumstix Overo® Computer-On-Module which can run the widely used Robot Operating System (ROS) for use with other research algorithms.

A recommendation approach dealing with multiple market segments

A new community and communication type of social networks - online dating - are gaining momentum. With many people joining in the dating network, users become overwhelmed by choices for an ideal partner. A solution to this problem is providing users with partners recommendation based on their interests and activities. Traditional recommendation methods ignore the users’ needs and provide recommendations equally to all users. In this paper, we propose a recommendation approach that employs different recommendation strategies to different groups of members. A segmentation method using the Gaussian Mixture Model (GMM) is proposed to customize users’ needs. Then a targeted recommendation strategy is applied to each identified segment. Empirical results show that the proposed approach outperforms several existing recommendation methods.

Integrating time forgetting mechanisms into topic-based user interest profiling

The rapid development of the World Wide Web has created massive information leading to the information overload problem. Under this circumstance, personalization techniques have been brought out to help users in finding content which meet their personalized interests or needs out of massively increasing information. User profiling techniques have performed the core role in this research. Traditionally, most user profiling techniques create user representations in a static way. However, changes of user interests may occur with time in real world applications. In this research we develop algorithms for mining user interests by integrating time decay mechanisms into topic-based user interest profiling. Time forgetting functions will be integrated into the calculation of topic interest measurements on in-depth level. The experimental study shows that, considering temporal effects of user interests by integrating time forgetting mechanisms shows better performance of recommendation.

Acquiring user information needs for recommender systems

Most recommender systems attempt to use collaborative filtering, content-based filtering or hybrid approach to recommend items to new users. Collaborative filtering recommends items to new users based on their similar neighbours, and content-based filtering approach tries to recommend items that are similar to new users' profiles. The fundamental issues include how to profile new users, and how to deal with the over-specialization in content-based recommender systems. Indeed, the terms used to describe items can be formed as a concept hierarchy. Therefore, we aim to describe user profiles or information needs by using concepts vectors. This paper presents a new method to acquire user information needs, which allows new users to describe their preferences on a concept hierarchy rather than rating items. It also develops a new ranking function to recommend items to new users based on their information needs. The proposed approach is evaluated on Amazon book datasets. The experimental results demonstrate that the proposed approach can largely improve the effectiveness of recommender systems.

Ontology-based product's reputation model

Different reputation models are used in the web in order to generate reputation values for products using uses' review data. Most of the current reputation models use review ratings and neglect users' textual reviews, because it is more difficult to process. However, we argue that the overall reputation score for an item does not reflect the actual reputation for all of its features. And that's why the use of users' textual reviews is necessary. In our work we introduce a new reputation model that defines a new aggregation method for users' extracted opinions about products' features from users' text. Our model uses features ontology in order to define general features and sub-features of a product. It also reflects the frequencies of positive and negative opinions. We provide a case study to show how our results compare with other reputation models.