Is a cooperative approach to seaweed farming effectual? An analysis of the seaweed cluster project (SCP), Malaysia

Seaweed (Kappaphycus spp.) farming has been practised in Malaysia since the late 1970s following government policy incentives (training and farming inputs). However, numerous governance, economic, environmental, technological and sociocultural challenges have limited the industry from achieving its full potential. The Seaweed Cluster Project (SCP) was introduced in 2012 to address some of these challenges. We sought to evaluate the effectiveness of the SCP in delivering its central objectives of increasing seaweed production, optimising the farming area, improving seaweed quality and farming efficiency, raising farmers’ income, and reducing the environmental impact of seaweed farming. Community and industry perceptions of the SCP were obtained from seven communities using a mixed-methods approach based on face-to-face semi-structured interviews, focus group discussions, household surveys, observation and secondary data. Views on the SCP outcomes were generally negative, including low take-up rates by indigenous people, poor stakeholder participation in decision-making, limited acceptance of new technologies, economic vulnerability, a complex marketing system, and low social cohesion of seaweed farming communities. Positive perceptions included recognition that the SCP confers high social status upon a community, reduces operating costs, and facilitates the production of certified seaweed. The SCP’s problems are linked to poor multi-level governance, weak market mechanisms and unintegrated community development. The study concludes with five recommendations to improve the SCP: promote the participation of indigenous people; legalise existing migrant farmers; strengthen local seaweed cooperative organisations; provide entrepreneurship skills to farmers; and fully integrate stakeholders into decision-making

Internet: Culture Diversity and Unification

Culture specifics of the Internet usage is analysed. The analysis done is a preliminary work for the application of the socio-historical theory of human mental development. The practice of the Internet usage is ambigious as it gives rise to both the unification and the diversity. The parameters analysed include the techniques of the hypertexts browsing,\ud and the status/position/rank of the communicators - its influence on holding the floor and turntaking rules, the ways the emotions are expressed while Internet communication, and the way the English language serves the functions of world-wide medium

Polar Fusion Technique Analysis for Evaluating the Performances of Image Fusion of Thermal and Visual Images for Human Face Recognition

This paper presents a comparative study of two different methods, which are based on fusion and polar transformation of visual and thermal images. Here, investigation is done to handle the challenges of face recognition, which include pose variations, changes in facial expression, partial occlusions, variations in illumination, rotation through different angles, change in scale etc. To overcome these obstacles we have implemented and thoroughly examined two different fusion techniques through rigorous experimentation. In the first method log-polar transformation is applied to the fused images obtained after fusion of visual and thermal images whereas in second method fusion is applied on log-polar transformed individual visual and thermal images. After this step, which is thus obtained in one form or another, Principal Component Analysis (PCA) is applied to reduce dimension of the fused images. Log-polar transformed images are capable of handling complicacies introduced by scaling and rotation. The main objective of employing fusion is to produce a fused image that provides more detailed and reliable information, which is capable to overcome the drawbacks present in the individual visual and thermal face images. Finally, those reduced fused images are classified using a multilayer perceptron neural network. The database used for the experiments conducted here is Object Tracking and Classification Beyond Visible Spectrum (OTCBVS) database benchmark thermal and visual face images. The second method has shown better performance, which is 95.71% (maximum) and on an average 93.81% as correct recognition rate.Comment: Proceedings of IEEE Workshop on Computational Intelligence in Biometrics and Identity Management (IEEE CIBIM 2011), Paris, France, April 11 - 15, 201

The Anarchist in the Coffee House: A Brief Consideration of Local Culture, the Free Culture Movement, and Prospects for a Global Public Sphere

Jürgen Habermas\u27 influential historical work, The Structural Transformation of the Public Sphere, describes a moment in the social and political history of Europe in which a rising bourgeoisie was able to gather in salons and cafes to discuss matters of public concern. The public sphere represented a set of sites and conventions in the eighteenth century in which (almost exclusively male) members of the bourgeoisie could forge a third space to mediate between domestic concerns and matters of state. Here, Vaidhyanathan examines one particular Public Sphere experiment--the rise of a global Free Culture Movement that aims to limit the spread of strong intellectual property regimes by considering the complications encountered by the movement when it crosses a very different value set at work in global cultural policy debates--the protection of native or local culture exemplified by the Native Culture Movement

Quadratic Projection Based Feature Extraction with Its Application to Biometric Recognition

This paper presents a novel quadratic projection based feature extraction framework, where a set of quadratic matrices is learned to distinguish each class from all other classes. We formulate quadratic matrix learning (QML) as a standard semidefinite programming (SDP) problem. However, the con- ventional interior-point SDP solvers do not scale well to the problem of QML for high-dimensional data. To solve the scalability of QML, we develop an efficient algorithm, termed DualQML, based on the Lagrange duality theory, to extract nonlinear features. To evaluate the feasibility and effectiveness of the proposed framework, we conduct extensive experiments on biometric recognition. Experimental results on three representative biometric recogni- tion tasks, including face, palmprint, and ear recognition, demonstrate the superiority of the DualQML-based feature extraction algorithm compared to the current state-of-the-art algorithm