Rehabilitation, etiology, and self-regulation : the comprehensive good lives model of treatment for sexual offenders

In this paper, we draw upon two sets of theoretical resources to develop a comprehensive theory of sexual offender rehabilitation named the Good Lives Model-Comprehensive (GLM-C). The original Good Lives Model (GLM-O) forms the overarching values and principles guiding clinical practice in the GLM-C. In addition, the latest sexual offender theory (i.e., the Integrated Theory of Sexual Offending; ITSO) provides a clear etiological grounding for these principles. The result is a more substantial and improved rehabilitation model that is able to conceptually link latest etiological theory with clinical practice. Analysis of the GLM-C reveals that it also has the theoretical resources to secure currently used self-regulatory treatment practice within a meaningful structure.

The good lives model of offender rehabilitation : clinical implications

The major aim of the current paper is to expand on the practice elements of the Good Lives Model-Comprehensive (GLM-C) of offender rehabilitation and to provide a detailed examination of its assessment and treatment implications. First we discuss the notion of rehabilitation and the qualities a good theory of rehabilitation should possess. Second, the principles, etiological assumptions, and general treatment implications of the GLM-C are briefly described. Third, we outline in considerable detail the application of this novel perspective to the assessment and treatment of sexual offenders. Finally, we conclude the paper with a summary of the major benefits we envisage the GLM-C bringing to the rehabilitation of sexual offenders.

Time for a change : applying the Good Lives Model of rehabilitation to a high-risk violent offender

In this article we operationalise the theoretical concepts of the Good Lives Model (GLM) of offender rehabilitation by providing a step-by-step framework for assessment, formulation, treatment planning, and monitoring with a high-risk violent offender residing in the community. The case study illustrates how the GLM can be applied to complement and enhance traditional Risk-Management interventions and shows how the GLM's clinical relevance extends from sex offending to broader offending typologies.

Striving for a good life : the good lives model applied to released child molesters

The good lives model (GLM) is a strengths-based approach to offender rehabilitation in which treatment aims to equip offenders with the skills and resources necessary to satisfy primary goods, or basic human values, in personally meaningful and socially acceptable ways. The aim of the present research was to explore the practical utility of the GLM with a sample of released child molesters, and investigate the relationship between primary goods attainment and overall re-entry conditions (in terms of accommodation, social support and employment). Semi-structured interviews were conducted with 16 child molesters at one, three and six months following their release from prison. As expected, participants endorsed the majority of GLM primary goods with high importance, and positive re-entry experiences were associated with increased goods attainment. Implications for clinicians, policy makers and society as a whole are discussed.

Enhanced RFID mutual authentication scheme based on shared secret information

RFID is a revolutionary remote technology which has many useful implications. Large scale implementation of RFID is seeking 100% information privacy and untraceability, for users and organizations, which is suitable for low cost RFID tag (Class1). To protect users and organization we are proposing an enhanced RFID mutual authentication scheme. In this protocol we use authentication based on shared unique parameters as a method to protect privacy. This protocol will be capable of handling forward and backward security, rouge reader better than existing protocols. In our new scheme we involved RFID reader’s hardware ID in addition to other shared secret information which uses hash to protect users and industries privacy. Moreover, we used LAMED as our PRNG (Pseudorandom Number Generator) which is faster and take less computational power.

Good lives sexual offender treatment for mentally disordered offenders

Purpose – The main aim of this paper is to describe the content, structure and preliminary evaluation of a new Good Lives sexual offender treatment group (SOTG) for male mentally disordered offenders.

Design/methodology/approach – As evaluation and work on the SOTG is necessarily ongoing, case study descriptions of each patient who attended the SOTG and of their progress throughout SOTG are described.

Findings – Overall, the case study progress reports suggest that mentally disordered male patients made some notable progress on SOTG despite their differential and complex needs. In particular, attention to each patient's life goals and motivators appeared to play a key role in promoting treatment engagement. Furthermore, patients with lower intelligence quotient and/or indirect pathways required additional support to understand the links between the Good Lives Model (GLM) and their own risk for sexual offending.

Research limitations/implications – Further evaluations of SOTG groups, that incorporate higher numbers of participants and adequate control groups, are required before solid conclusions and generalisations can be made.

Practical implications – Practitioners should consider providing additional support to clients when implementing any future SOTGs for mentally disordered patients.

Originality/value – This is the first paper to outline and describe implementation of the GLM in the sexual offender treatment of mentally disordered male patients group format. As such, it will be of interest to any professionals involved in the facilitation of sexual offender treatment within this population.

Auditor independence : shared meaning between the demand and supply sides of the audit services market?

Purpose – The purpose of the paper is to examine the extent to which there is shared meaning of the concept of auditor independence between the three major groups of parties on the demand and supply sides of the audit services market – auditors, financial report preparers and financial report users.

Design/methodology/approach – The paper utilises the measurement of meaning framework (semantic differential analysis) originally proposed by Osgood et al. in 1957. The framework is used to investigate the extent to which there is shared meaning (agreement in interpretations) of the independence concept, in response to alternative audit engagement case contexts, between key parties to the financial reporting communication process. The study's research data was collected in the period March 2004-May 2005.

Findings – Findings indicate a robust and stable single-factor cognitive structure within which the research participants interpret the connotative meaning of the auditor independence concept. An analysis of the experimental cases finds similarities in connotations (interpretations) of an audit firm's independence for the participant groups for most cases, with the exception of cases involving the joint provision of audit and non-audit (taxation) services.

Research limitations/implications – The usual external validity threat that applies to experimental research generally applies to the study. That is, the results may not be generalisable to settings beyond those examined in the study. An important implication of the study is that it emphasises the continuing problematic nature of the joint provision of audit and non-audit services, even in situations where the non-audit services comprise only traditional taxation services.

Originality/value – The study is the first to examine the concept of auditor independence by means of the Osgood et al. measurement of meaning research framework using, as research participants, the three major groups on the demand and supply sides of the audit services market.

Advancing Australian 'shared security' : secular-religious peacebuilding networks

The role of religious leaders in promoting social cohesion and ‘shared security’ is increasingly being examined by scholars, as is the growing multifaith movement. The VIIIth World Assembly of Religions for Peace first proposed the notion of ‘shared security’ and the importance of religious leaders’ role in advancing such a concept in Kyoto 2006. A recent study, Managing the Impact of Global Crisis Events on Community Relations in Multicultural Australia (Bouma et al. 2007) has documented the impacts of international crisis events and discourses of exclusion on religiously diverse communities in Australia, in particular rising Islamophobia, migrantophobia and attacks on multiculturalism. Religious communities have been far from passive in their responses to the impact of these events initiating dialogue and educational activities to dispel negative stereotypes and attitudes. State actors, including police, have prioritized engagement with religious leaders resulting in a rise of state supported multifaith and secular-religious peacebuilding activities. This paper argues that, in response to global risks of terror and exclusion, secular-religious networks including religious leaders, state actors, educators and the media have the potential to advance ‘shared security’ in multifaith societies, by drawing on Australian experiences documented in the Global Crisis Events study.

Hierarchical directory-based shared memory architecture

We describe the design of a directory-based shared memory architecture on a hierarchical network of hypercubes. The distributed directory scheme comprises two separate hierarchical networks for handling cache requests and transfers. Further, the scheme assumes a single address space and each processing element views the entire network as contiguous memory space. The size of individual directories stored at each node of the network remains constant throughout the network. Although the size of the directory increases with the network size, the architecture is scalable. The results of the analytical studies demonstrate superior performance characteristics of our scheme compared with those of other schemes.

Regularized nonnegative shared subspace learning

Joint modeling of related data sources has the potential to improve various data mining tasks such as transfer learning, multitask clustering, information retrieval etc. However, diversity among various data sources might outweigh the advantages of the joint modeling, and thus may result in performance degradations. To this end, we propose a regularized shared subspace learning framework, which can exploit the mutual strengths of related data sources while being immune to the effects of the variabilities of each source. This is achieved by further imposing a mutual orthogonality constraint on the constituent subspaces which segregates the common patterns from the source specific patterns, and thus, avoids performance degradations. Our approach is rooted in nonnegative matrix factorization and extends it further to enable joint analysis of related data sources. Experiments performed using three real world data sets for both retrieval and clustering applications demonstrate the benefits of regularization and validate the effectiveness of the model. Our proposed solution provides a formal framework appropriate for jointly analyzing related data sources and therefore, it is applicable to a wider context in data mining.

Nonnegative shared subspace learning and its application to social media retrieval

Although tagging has become increasingly popular in online image and video sharing systems, tags are known to be noisy, ambiguous, incomplete and subjective. These factors can seriously affect the precision of a social tag-based web retrieval system. Therefore improving the precision performance of these social tag-based web retrieval systems has become an increasingly important research topic. To this end, we propose a shared subspace learning framework to leverage a secondary source to improve retrieval performance from a primary dataset. This is achieved by learning a shared subspace between the two sources under a joint Nonnegative Matrix Factorization in which the level of subspace sharing can be explicitly controlled. We derive an efficient algorithm for learning the factorization, analyze its complexity, and provide proof of convergence. We validate the framework on image and video retrieval tasks in which tags from the LabelMe dataset are used to improve image retrieval performance from a Flickr dataset and video retrieval performance from a YouTube dataset. This has implications for how to exploit and transfer knowledge from readily available auxiliary tagging resources to improve another social web retrieval system. Our shared subspace learning framework is applicable to a range of problems where one needs to exploit the strengths existing among multiple and heterogeneous datasets.

Content structure discovery in educational videos using shared structures in the hierarchical hidden Markov models

In this paper, we present an application of the hierarchical HMM for structure discovery in educational videos. The HHMM has recently been extended to accommodate the concept of shared structure, ie: a state might multiply inherit from more than one parents. Utilising the expressiveness of this model, we concentrate on a specific class of video -educational videos - in which the hierarchy of semantic units is simpler and clearly defined in terms of topics and its subunits. We model the hierarchy of topical structures by an HHMM and demonstrate the usefulness of the model in detecting topic transitions.

A Bayesian framework for learning shared and individual subspaces from multiple data sources

This paper presents a novel Bayesian formulation to exploit shared structures across multiple data sources, constructing foundations for effective mining and retrieval across disparate domains. We jointly analyze diverse data sources using a unifying piece of metadata (textual tags). We propose a method based on Bayesian Probabilistic Matrix Factorization (BPMF) which is able to explicitly model the partial knowledge common to the datasets using shared subspaces and the knowledge specific to each dataset using individual subspaces. For the proposed model, we derive an efficient algorithm for learning the joint factorization based on Gibbs sampling. The effectiveness of the model is demonstrated by social media retrieval tasks across single and multiple media. The proposed solution is applicable to a wider context, providing a formal framework suitable for exploiting individual as well as mutual knowledge present across heterogeneous data sources of many kinds.