Collaboration on social network sites: Amateurs, professionals and celebrities

Amateurs are found in arts, sports, or entertainment, where they are linked with professional counterparts and inspired by celebrities. Despite the growing number of CSCW studies in amateur and professional domains, little is known about how technologies facilitate collaboration between these groups. Drawing from a 1.5-year field study in the domain of bodybuilding, this paper describes the collaboration between and within amateurs, professionals, and celebrities on social network sites. Social network sites help individuals to improve their performance in competitions, extend their support network, and gain recognition for their achievements. The findings show that amateurs benefit the most from online collaboration, whereas collaboration shifts from social network sites to offline settings as individuals develop further in their professional careers. This shift from online to offline settings constitutes a novel finding, which extends previous work on social network sites that has looked at groups of amateurs and professionals in isolation. As a contribution to practice, we highlight design factors that address this shift to offline settings and foster collaboration between and within groups.

Being online, living offline: The influence of social ties over the appropriation of social network sites

Research on social network sites has examined how people integrate offline and online life, but with a particular emphasis on their use by friendship groups. We extend earlier work by examining a case in which offline ties are non-existent, but online ties strong. Our case is a study of bodybuilders, who explore their passion with like-minded offline 'strangers' in tightly integrated online communities. We show that the integration of offline and online life supports passion-centric activities, such as bodybuilding.

Uncertainty based online planning for UAV target finding in cluttered and GPS-denied environments

There are some scenarios in which Unmmaned Aerial Vehicle (UAV) navigation becomes a challenge due to the occlusion of GPS systems signal, the presence of obstacles and constraints in the space in which a UAV operates. An additional challenge is presented when a target whose location is unknown must be found within a confined space. In this paper we present a UAV navigation and target finding mission, modelled as a Partially Observable Markov Decision Process (POMDP) using a state-of-the-art online solver in a real scenario using a low cost commercial multi rotor UAV and a modular system architecture running under the Robotic Operative System (ROS). Using POMDP has several advantages to conventional approaches as they take into account uncertainties in sensor information. We present a framework for testing the mission with simulation tests and real flight tests in which we model the system dynamics and motion and perception uncertainties. The system uses a quad-copter aircraft with an board downwards looking camera without the need of GPS systems while avoiding obstacles within a confined area. Results indicate that the system has 100% success rate in simulation and 80% rate during flight test for finding targets located at different locations.

Human action recognition from video sequences

This PhD research has proposed new machine learning techniques to improve human action recognition based on local features. Several novel video representation and classification techniques have been proposed to increase the performance with lower computational complexity. The major contributions are the construction of new feature representation techniques, based on advanced machine learning techniques such as multiple instance dictionary learning, Latent Dirichlet Allocation (LDA) and Sparse coding. A Binary-tree based classification technique was also proposed to deal with large amounts of action categories. These techniques are not only improving the classification accuracy with constrained computational resources but are also robust to challenging environmental conditions. These developed techniques can be easily extended to a wide range of video applications to provide near real-time performance.

Fraudsters change tactics as a crackdown cuts some losses due to online scams

The amount of financial loss from online fraud suffered by people in Western Australia has almost halved, dropping from A$16.8 million in 2014 to A$9.8 million for 2015, according to a statement this January from the state’s Attorney General and Minister for Commerce, Michael Mischin. In addition, the minister noted that losses from relationship and dating fraud have fallen by 55%, to A$4.9 million lost last year. These are both impressive claims, and at face value, there is truth to the statistics. Both assertions are based on data received by WA’s Scamnet, which is the public interface between consumer protection and citizens. While it is good to see a reduction in the number of losses overall, particularly to relationship and dating fraud, it is highly unlikely that the statistics tell the full story.

Elliptical summary randomisation for sensor-based human activity recognition

Many conventional statistical machine learning al- gorithms generalise poorly if distribution bias ex- ists in the datasets. For example, distribution bias arises in the context of domain generalisation, where knowledge acquired from multiple source domains need to be used in a previously unseen target domains. We propose Elliptical Summary Randomisation (ESRand), an efficient domain generalisation approach that comprises of a randomised kernel and elliptical data summarisation. ESRand learns a domain interdependent projection to a la- tent subspace that minimises the existing biases to the data while maintaining the functional relationship between domains. In the latent subspace, ellipsoidal summaries replace the samples to enhance the generalisation by further removing bias and noise in the data. Moreover, the summarisation enables large-scale data processing by significantly reducing the size of the data. Through comprehensive analysis, we show that our subspace-based approach outperforms state-of-the-art results on several activity recognition benchmark datasets, while keeping the computational complexity significantly low.

Determinants of online sperm donor success: How women choose

Because the worldwide demand for sperm donors is much higher than the actual supply available through fertility clinics, an informal online market has emerged for sperm donation. Very little empirical evidence exists, however, on this newly formed market and even less on the characteristics that lead to donor success. This article therefore explores the determinants of online sperm donors’ selection success, which leads to the production of offspring via informal donation. We find that donor age and income play a significant role in donor success as measured by the number of times selected, even though there is no requirement for ongoing paternal investment. Donors with less extroverted and lively personality traits who are more intellectual, shy and systematic are more successful in realizing offspring via informal donation. These results contribute to both the economic literature on human behaviour and on large-scale decision-making.

Sign and share: What influences our participation in online microvolunteering

Microvolunteering is bite-size volunteering with no commitment to repeat and minimum formality, involving short and specific actions. Online microvolunteering occurs through an internet-connected device. University students' online microvolunteering decisions were investigated using an extended theory of planned behavior (TPB) comprising attitudes and normative and control perceptions, with the additional variables of moral norm and group norm. Participants (N = 303) completed the main TPB questionnaire and 1-month follow-up survey (N = 171) assessing engagement in online microvolunteering. Results generally supported standard and additional TPB constructs predicting intention. Intention predicted behavior. The findings suggest an important role for attitudes and moral considerations in understanding what influences this increasingly popular form of online activity.

Fighting like a girl … or a boy? An analysis of videos of violence between young girls posted on online fight websites

How young women engage in physical violence with other young women is an issue that raises specific concerns in both criminological literature and theories. Current theoretical explanations construct young women’s violence in one of two ways: young women are not physically violent at all, and adhere to an accepted performance of hegemonic femininity; or young women reject accepted performances of hegemonic femininity in favour of a masculine gendered performance to engage in violence successfully. This article draws on qualitative and quantitative data obtained from a structured observation and thematic analysis of 60 online videos featuring young women’s violent altercations. It argues that, contrary to this dichotomous construction, there appears to be a third way young women are performing violence, underpinned by masculine characteristics of aggression but upholding a hegemonic feminine gender performance. In making this argument, this article demonstrates that a more complex exploration and conceptualisation of young women’s violence, away from gendered constructs, is required for greater understanding of the issue.

Google, online search and consumer confusion in Australia

The legality of the operation of Google’s search engine, and its liability as an Internet intermediary, has been tested in various jurisdictions on various grounds. In Australia, there was an ultimately unsuccessful case against Google under the Australian Consumer Law relating to how it presents results from its search engine. Despite this failed claim, several complex issues were not adequately addressed in the case including whether Google sufficiently distinguishes between the different parts of its search results page, so as not to mislead or deceive consumers. This article seeks to address this question of consumer confusion by drawing on empirical survey evidence of Australian consumers’ understanding of Google’s search results layout. This evidence, the first of its kind in Australia, indicates some level of consumer confusion. The implications for future legal proceedings in against Google in Australia and in other jurisdictions are discussed.

Place categorization and semantic mapping on a mobile robot

In this paper we focus on the challenging problem of place categorization and semantic mapping on a robot with-out environment-specific training. Motivated by their ongoing success in various visual recognition tasks, we build our system upon a state-of-the-art convolutional network. We overcome its closed-set limitations by complementing the network with a series of one-vs-all classifiers that can learn to recognize new semantic classes online. Prior domain knowledge is incorporated by embedding the classification system into a Bayesian filter framework that also ensures temporal coherence. We evaluate the classification accuracy of the system on a robot that maps a variety of places on our campus in real-time. We show how semantic information can boost robotic object detection performance and how the semantic map can be used to modulate the robot’s behaviour during navigation tasks. The system is made available to the community as a ROS module.