The artist in a networked society

Artists are under pressure from two conflicting sets of sociocultural expectations. On the one hand, they are expected to conform to the historically grounded myth of the artist as heroic genius. On the other hand, they must meet the expectations of the state (plus ensure their own survival) as economic contributors. One way  that these conflicting pressures are managed by artists working within the traditional art world is by separating the creator from the labourer through the use of intermediaries such as dealer galleries, critics, publishers and agents. This allows the artist to symbolically distance themselves from the economic structures that allow them to continue to work. However, for those artists working outside of these systems of support, legitimization and representation, the positioning of the individual as ‘artist’ becomes a much more complex task.

The construction of artists persona in online spaces can be seen most clearly in those artists who operate outside of the traditional art world. Lacking the symbolic distance between the economic producer and the bohemian, mythical genius, these individual artists instead negotiate a place to stand in direct relation to  their audience of fans, followers and audiences. Using examples from a range of fringe, alternative or counter-culture creative practice, this paper investigates artistic persona by linking the artist myth, economic considerations, and networked society to explore current presentation strategies. 

International encyclopedia of men and masculinities

Offers a comprehensive guide to the current state of scholarship about men, masculinities and gender around the world. Michael Flood from La Trobe University, and Bob Pease from Deakin University.

Aquaculture effluent : impacts and remedies for protecting the environment and human health

Knowledge in the field of environmental health is growing rapidly. Within the context of external factors that define its boundaries, environmental health has evolved over time into a complex, multidisciplinary and ill-defined field with uncertain solutions. Many of the key determinants and solutions to environmental health lie outside the direct realm of health and are strongly dependent on environmental changes, water and sanitation, industrial development, education, employment, trade, tourism, agriculture, urbanization, energy, housing and national security. Environmental risks, vulnerability and variability manifest themselves in different ways and at different time scales. While there are shared global and transnational problems, each community, country or region faces its own unique environmental health problems, the solution of which depends on circumstances surrounding the resources, customs, institutions, values and environmental vulnerability. This work will contain critical reviews and assessments of environmental health practices and research that have worked in places and thus can guide programs and economic development in other countries or regions.

Joint learning and dictionary construction for pattern recognition

We propose a joint representation and classification framework that achieves the dual goal of finding the most discriminative sparse overcomplete encoding and optimal classifier parameters. Formulating an optimization problem that combines the objective function of the classification with the representation error of both labeled and unlabeled data, constrained by sparsity, we propose an algorithm that alternates between solving for subsets of parameters, whilst preserving the sparsity. The method is then evaluated over two important classification problems in computer vision: object categorization of natural images using the Caltech 101 database and face recognition using the Extended Yale B face database. The results show that the proposed method is competitive against other recently proposed sparse overcomplete counterparts and considerably outperforms many recently proposed face recognition techniques when the number training samples is small.

Enabling fine-grained multi-keyword search supporting classified sub-dictionaries over encrypted cloud data

Using cloud computing, individuals can store their data on remote servers and allow data access to public users through the cloud servers. As the outsourced data are likely to contain sensitive privacy information, they are typically encrypted before uploaded to the cloud. This, however, significantly limits the usability of outsourced data due to the difficulty of searching over the encrypted data. In this paper, we address this issue by developing the fine-grained multi-keyword search schemes over encrypted cloud data. Our original contributions are three-fold. First, we introduce the relevance scores and preference factors upon keywords which enable the precise keyword search and personalized user experience. Second, we develop a practical and very efficient multi-keyword search scheme. The proposed scheme can support complicated logic search the mixed “AND”, “OR” and “NO” operations of keywords. Third, we further employ the classified sub-dictionaries technique to achieve better efficiency on index building, trapdoor generating and query. Lastly, we analyze the security of the proposed schemes in terms of confidentiality of documents, privacy protection of index and trapdoor, and unlinkability of trapdoor. Through extensive experiments using the real-world dataset, we validate the performance of the proposed schemes. Both the security analysis and experimental results demonstrate that the proposed schemes can achieve the same security level comparing to the existing ones and better performance in terms of functionality, query complexity and efficiency.