Introduction: Resistance, Recovery and Revitalisation

Aboriginal and Torres Strait Islander perspectives on contemporary cultural issues are presented in this collection of critical essays by indigenous Australians. From museums and anthropology to land rights and feminism, a range of topics are covered that touch on both indigenous and mainstream Australian history. Discussions of identity politics, the concept of Aboriginality, and aesthetic representations of indigenous people are rich with insight about the evolution of indigenous culture, with its shift from marginalization to cultural prominence in modern scholarship.

Media Economics

A concise introduction to the key ideas and issues in the study of media economics, drawing on a broad range of case studies - from Amazon and Twitter, to Apple and Netflix - to illustrate how economic paradigms are not just theories, but provide important practical insights into how the media operates today. Understanding the economic paradigms at work in media industries and markets is vitally important for the analysis of the media system as a whole. The changing dynamics of media production, distribution and consumption are stretching the capacity of established economic paradigms. In addition to succinct accounts of neo-classical and critical political economics, the text offers fresh perspectives for understanding media drawn from two 'heterodox' approaches: institutional economics and evolutionary economics. Applying these paradigms to vital topics and case studies, Media Economics stresses the value – and limits – of contending economic approaches in understanding how the media operates today. It is essential reading for all students of Media and Communication Studies, and also those from Economics, Policy Studies, Business Studies and Marketing backgrounds who are studying the media. Table of Contents: 1. Media Economics: The Mainstream Approach 2. Critical Political Economy of the Media 3. Institutional Economics 4. Evolutionary Economics 5. Case Studies and Conclusions

Digital processing of diffusion-tensor images of avascular tissues

Diffusion is the process that leads to the mixing of substances as a result of spontaneous and random thermal motion of individual atoms and molecules. It was first detected by the English botanist Robert Brown in 1827, and the phenomenon became known as ‘Brownian motion’. More specifically, the motion observed by Brown was translational diffusion – thermal motion resulting in random variations of the position of a molecule. This type of motion was given a correct theoretical interpretation in 1905 by Albert Einstein, who derived the relationship between temperature, the viscosity of the medium, the size of the diffusing molecule, and its diffusion coefficient. It is translational diffusion that is indirectly observed in MR diffusion-tensor imaging (DTI). The relationship obtained by Einstein provides the physical basis for using translational diffusion to probe the microscopic environment surrounding the molecule.

User profiling based on folksonomy information in Web 2.0 for personalized recommender systems

Information overload has become a serious issue for web users. Personalisation can provide effective solutions to overcome this problem. Recommender systems are one popular personalisation tool to help users deal with this issue. As the base of personalisation, the accuracy and efficiency of web user profiling affects the performances of recommender systems and other personalisation systems greatly. In Web 2.0, the emerging user information provides new possible solutions to profile users. Folksonomy or tag information is a kind of typical Web 2.0 information. Folksonomy implies the users‘ topic interests and opinion information. It becomes another source of important user information to profile users and to make recommendations. However, since tags are arbitrary words given by users, folksonomy contains a lot of noise such as tag synonyms, semantic ambiguities and personal tags. Such noise makes it difficult to profile users accurately or to make quality recommendations. This thesis investigates the distinctive features and multiple relationships of folksonomy and explores novel approaches to solve the tag quality problem and profile users accurately. Harvesting the wisdom of crowds and experts, three new user profiling approaches are proposed: folksonomy based user profiling approach, taxonomy based user profiling approach, hybrid user profiling approach based on folksonomy and taxonomy. The proposed user profiling approaches are applied to recommender systems to improve their performances. Based on the generated user profiles, the user and item based collaborative filtering approaches, combined with the content filtering methods, are proposed to make recommendations. The proposed new user profiling and recommendation approaches have been evaluated through extensive experiments. The effectiveness evaluation experiments were conducted on two real world datasets collected from Amazon.com and CiteULike websites. The experimental results demonstrate that the proposed user profiling and recommendation approaches outperform those related state-of-the-art approaches. In addition, this thesis proposes a parallel, scalable user profiling implementation approach based on advanced cloud computing techniques such as Hadoop, MapReduce and Cascading. The scalability evaluation experiments were conducted on a large scaled dataset collected from Del.icio.us website. This thesis contributes to effectively use the wisdom of crowds and expert to help users solve information overload issues through providing more accurate, effective and efficient user profiling and recommendation approaches. It also contributes to better usages of taxonomy information given by experts and folksonomy information contributed by users in Web 2.0.

Personalized recommender system based on item taxonomy and folksonomy

Item folksonomy or tag information is popularly available on the web now. However, since tags are arbitrary words given by users, they contain a lot of noise such as tag synonyms, semantic ambiguities and personal tags. Such noise brings difficulties to improve the accuracy of item recommendations. In this paper, we propose to combine item taxonomy and folksonomy to reduce the noise of tags and make personalized item recommendations. The experiments conducted on the dataset collected from Amazon.com demonstrated the effectiveness of the proposed approaches. The results suggested that the recommendation accuracy can be further improved if we consider the viewpoints and the vocabularies of both experts and users.

Connecting users and items with weighted tags for personalized item recommendations

Social tags are an important information source in Web 2.0. They can be used to describe users’ topic preferences as well as the content of items to make personalized recommendations. However, since tags are arbitrary words given by users, they contain a lot of noise such as tag synonyms, semantic ambiguities and personal tags. Such noise brings difficulties to improve the accuracy of item recommendations. To eliminate the noise of tags, in this paper we propose to use the multiple relationships among users, items and tags to find the semantic meaning of each tag for each user individually. With the proposed approach, the relevant tags of each item and the tag preferences of each user are determined. In addition, the user and item-based collaborative filtering combined with the content filtering approach are explored. The effectiveness of the proposed approaches is demonstrated in the experiments conducted on real world datasets collected from Amazon.com and citeULike website.

Mining users’ opinions based on item folksonomy and taxonomy for personalized recommender systems

Item folksonomy or tag information is a kind of typical and prevalent web 2.0 information. Item folksonmy contains rich opinion information of users on item classifications and descriptions. It can be used as another important information source to conduct opinion mining. On the other hand, each item is associated with taxonomy information that reflects the viewpoints of experts. In this paper, we propose to mine for users’ opinions on items based on item taxonomy developed by experts and folksonomy contributed by users. In addition, we explore how to make personalized item recommendations based on users’ opinions. The experiments conducted on real word datasets collected from Amazon.com and CiteULike demonstrated the effectiveness of the proposed approaches.

Parallel user profiling based on folksonomy for Large Scaled Recommender Systems : an implimentation of Cascading MapReduce

The Large scaled emerging user created information in web 2.0 such as tags, reviews, comments and blogs can be used to profile users’ interests and preferences to make personalized recommendations. To solve the scalability problem of the current user profiling and recommender systems, this paper proposes a parallel user profiling approach and a scalable recommender system. The current advanced cloud computing techniques including Hadoop, MapReduce and Cascading are employed to implement the proposed approaches. The experiments were conducted on Amazon EC2 Elastic MapReduce and S3 with a real world large scaled dataset from Del.icio.us website.

A hybrid recommender systems based on weighted tags

Social tags in web 2.0 are becoming another important information source to describe the content of items as well as to profile users’ topic preferences. However, as arbitrary words given by users, tags contains a lot of noise such as tag synonym and semantic ambiguity a large number personal tags that only used by one user, which brings challenges to effectively use tags to make item recommendations. To solve these problems, this paper proposes to use a set of related tags along with their weights to represent semantic meaning of each tag for each user individually. A hybrid recommendation generation approaches that based on the weighted tags are proposed. We have conducted experiments using the real world dataset obtained from Amazon.com. The experimental results show that the proposed approaches outperform the other state of the art approaches.

Automatic domain ontology extraction for context-sensitive opinion mining

Automated analysis of the sentiments presented in online consumer feedbacks can facilitate both organizations’ business strategy development and individual consumers’ comparison shopping. Nevertheless, existing opinion mining methods either adopt a context-free sentiment classification approach or rely on a large number of manually annotated training examples to perform context sensitive sentiment classification. Guided by the design science research methodology, we illustrate the design, development, and evaluation of a novel fuzzy domain ontology based contextsensitive opinion mining system. Our novel ontology extraction mechanism underpinned by a variant of Kullback-Leibler divergence can automatically acquire contextual sentiment knowledge across various product domains to improve the sentiment analysis processes. Evaluated based on a benchmark dataset and real consumer reviews collected from Amazon.com, our system shows remarkable performance improvement over the context-free baseline.

Text mining and probabilistic language modeling for online review spam detecting

In the era of Web 2.0, huge volumes of consumer reviews are posted to the Internet every day. Manual approaches to detecting and analyzing fake reviews (i.e., spam) are not practical due to the problem of information overload. However, the design and development of automated methods of detecting fake reviews is a challenging research problem. The main reason is that fake reviews are specifically composed to mislead readers, so they may appear the same as legitimate reviews (i.e., ham). As a result, discriminatory features that would enable individual reviews to be classified as spam or ham may not be available. Guided by the design science research methodology, the main contribution of this study is the design and instantiation of novel computational models for detecting fake reviews. In particular, a novel text mining model is developed and integrated into a semantic language model for the detection of untruthful reviews. The models are then evaluated based on a real-world dataset collected from amazon.com. The results of our experiments confirm that the proposed models outperform other well-known baseline models in detecting fake reviews. To the best of our knowledge, the work discussed in this article represents the first successful attempt to apply text mining methods and semantic language models to the detection of fake consumer reviews. A managerial implication of our research is that firms can apply our design artifacts to monitor online consumer reviews to develop effective marketing or product design strategies based on genuine consumer feedback posted to the Internet.

Democracy, Crime and Justice. Annals of the American Academy of Political and Social Sciences

As a growing number of nations embark on a path to democracy, criminologists have become increasingly interested and engaged in the challenges, concerns, and questions connecting democracy with both crime and criminal justice. Rising levels of violence and street crime, white collar crime and corruption both in countries where democracy is securely in place and where it is struggling, have fuelled a deepening skepticism as to the capacity of democracy to deliver on its promise of security and justice for all citizens. What role does crime and criminal justice play in the future of democracy and for democratic political development on a global level? The editors of this special volume of The Annals realized the importance of collecting research from a broad spectrum of countries and covering a range of problems that affect citizens, politicians, and criminal justice officials. The articles here represent a solid balance between mature democracies like the U.S. and U.K. as well as emerging democracies around the globe – specifically in Latin America, Africa and Eastern Europe. They are based on large and small cross-national samples, regional comparisons, and case studies. Each contribution addresses a seminal question for the future of democratic political development across the globe. What is the role of criminal justice in the process of building democracy and instilling confidence in its institutions? Is there a role for unions in democratizing police forces? What is the impact of widespread disenfranchisement of felons on democratic citizenship and the life of democratic institutions? Under what circumstances do mature democracies adopt punitive sentencing regimes? Addressing sensitive topics such as relations between police and the Muslim communities of Western Europe in the wake of terrorist attacks, this volume also sheds light on the effects of terrorism on mature democracies under increasing pressure to provide security for their citizens. By taking a broad vantage point, this collection of research delves into complex topics such as the relationship between the process of democratization and violent crime waves; the impact of rising crime rates on newly established as well as secure democracies; how crime may endanger the transition to democracy; and how existing practices of criminal justice in mature democracies affect their core values and institutions. The collection of these insightful articles not only begins to fill a gap in criminological research but also addresses issues of critical interest to political scientists as well as other social and behavioral scientists and scholars. Taking a fresh approach to the intersection of crime, criminal justice, and democracy, this volume of The Annals is a must-read for criminologists and political scientists and provides a solid foundation for further interdisciplinary research.

Criminology: A Sociological Introduction

This sociological introduction provides a much-needed textbook for an increasingly popular area of study. Written by a team of authors with a broad range of teaching and individual expertise, it covers almost every module offered in UK criminological courses and will be valuable to students of criminology worldwide. It covers: - key traditions in criminology, their critical assessment and more recent developments; - new ways of thinking about crime and control, including crime and emotions, drugs and alcohol, from a public health perspective; - different dimensions of the problem of crime and misconduct, including crime and sexuality, crimes against the environment, crime and human rights and organizational deviance; - key debates in criminological theory; - the criminal justice system; - new areas such as the globalization of crime, and crime in cyberspace. Specially designed to be user-friendly, each chapter contains boxed material on current controversies, key thinkers and examples of crime and criminal justice around the world with statistical tables, maps, summaries, critical thinking questions, annotated references and a glossary of key terms, as well as further reading sections and additional resource information as weblinks.

The verdine facies off French Guiana

This chapter reviews green grains from the shelf of French Guiana as a regional example of sedimentologic process occurring on the whole stable continental margin from the Amazon to the Orinoco River. Green grains have been observed and analyzed off the Orinoco delta and on the continental shelf of Surinam. These green grains were identified as “chamosite” and “glauconite.” The muddy coast of French Guiana is generally very flat and occupied by wet swamps and mangrove as a result of the equatorial climate. Most green grains on the continental shelf represent the verdine facies. Green grains are ubiquitous on the shelf and top of the slope off French Guiana. Two sedimentological facies exist: glaucony deeper than 150 m and verdine at shallower depths. The verdine facies has mainly developed from mineral debris and especially chloritized biotite. Carbonate bioclasts and faecal pellets are also utilized. The mica flakes were never wholly replaced by authigenic clay and the phenomenon leads to mixed grains where authigenic and substrate remains are recognizable. Carbonate substrates lead to mainly clay pure green grains becasue the initial carbonate has been dissolved. The formation of verdine can be located in a general marine environment at a comparatively warm sea-water temperature and at a depth probably shallower than 60 m.