983 resultados para procédure attribution


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Driver aggression is an increasing concern for motorists, with some research suggesting that drivers who behave aggressively perceive their actions as justified by the poor driving of others. Thus attributions may play an important role in understanding driver aggression. A convenience sample of 193 drivers (aged 17-36) randomly assigned to two separate roles (‘perpetrators’ and ‘victims’) responded to eight scenarios of driver aggression. Drivers also completed the Aggression Questionnaire and Driving Anger Scale. Consistent with the actor-observer bias, ‘victims’ (or recipients) in this study were significantly more likely than ‘perpetrators’ (or instigators) to endorse inadequacies in the instigator’s driving skills as the cause of driver aggression. Instigators were significantly more likely attribute the depicted behaviours to external but temporary causes (lapses in judgement or errors) rather than stable causes. This suggests that instigators recognised drivers as responsible for driving aggressively but downplayed this somewhat in comparison to ‘victims’/recipients. Recipients and instigators agreed that the behaviours were examples of aggressive driving but instigators appeared to focus on the degree of intentionality of the driver in making their assessments while recipients appeared to focus on the safety implications. Contrary to expectations, instigators gave mean ratings of the emotional impact of driving aggression on recipients that were higher in all cases than the mean ratings given by the recipients. Drivers appear to perceive aggressive behaviours as modifiable, with the implication that interventions could appeal to drivers’ sense of self-efficacy to suggest strategies for overcoming plausible and modifiable attributions (e.g. lapses in judgement; errors) underpinning behaviours perceived as aggressive.

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In this paper we extend the concept of speaker annotation within a single-recording, or speaker diarization, to a collection wide approach we call speaker attribution. Accordingly, speaker attribution is the task of clustering expectantly homogenous intersession clusters obtained using diarization according to common cross-recording identities. The result of attribution is a collection of spoken audio across multiple recordings attributed to speaker identities. In this paper, an attribution system is proposed using mean-only MAP adaptation of a combined-gender UBM to model clusters from a perfect diarization system, as well as a JFA-based system with session variability compensation. The normalized cross-likelihood ratio is calculated for each pair of clusters to construct an attribution matrix and the complete linkage algorithm is employed to conduct clustering of the inter-session clusters. A matched cluster purity and coverage of 87.1% was obtained on the NIST 2008 SRE corpus.

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Over the last twenty years, the use of open content licenses has become increasingly and surprisingly popular. The use of such licences challenges the traditional incentive-based model of exclusive rights under copyright. Instead of providing a means to charge for the use of particular works, what seems important is mitigating against potential personal harm to the author and, in some cases, preventing non-consensual commercial exploitation. It is interesting in this context to observe the primacy of what are essentially moral rights over the exclusionary economic rights. The core elements of common open content licences map somewhat closely to continental conceptions of the moral rights of authorship. Most obviously, almost all free software and free culture licences require attribution of authorship. More interestingly, there is a tension between social norms developed in free software communities and those that have emerged in the creative arts over integrity and commercial exploitation. For programmers interested in free software, licence terms that prohibit commercial use or modification are almost completely inconsistent with the ideological and utilitarian values that underpin the movement. For those in the creative industries, on the other hand, non-commercial terms and, to a lesser extent, terms that prohibit all but verbatim distribution continue to play an extremely important role in the sharing of copyright material. While prohibitions on commercial use often serve an economic imperative, there is also a certain personal interest for many creators in avoiding harmful exploitation of their expression – an interest that has sometimes been recognised as forming a component of the moral right of integrity. One particular continental moral right – the right of withdrawal – is present neither in Australian law or in any of the common open content licences. Despite some marked differences, both free software and free culture participants are using contractual methods to articulate the norms of permissible sharing. Legal enforcement is rare and often prohibitively expensive, and the various communities accordingly rely upon shared understandings of acceptable behaviour. The licences that are commonly used represent a formalised expression of these community norms and provide the theoretically enforceable legal baseline that lends them legitimacy. The core terms of these licences are designed primarily to alleviate risk in sharing and minimise transaction costs in sharing and using copyright expression. Importantly, however, the range of available licences reflect different optional balances in the norms of creating and sharing material. Generally, it is possible to see that, stemming particularly from the US, open content licences are fundamentally important in providing a set of normatively accepted copyright balances that reflect the interests sought to be protected through moral rights regimes. As the cost of creation, distribution, storage, and processing of expression continues to fall towards zero, there are increasing incentives to adopt open content licences to facilitate wide distribution and reuse of creative expression. Thinking of these protocols not only as reducing transaction costs but of setting normative principles of participation assists in conceptualising the role of open content licences and the continuing tensions that permeate modern copyright law.

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This paper examines a case of academic plagiarism and the subsequent treatment of the issues across several academic institutions. It calls for academic leaders in universities to act on what constitutes a serious breach of standards, engendered in part by broader institutional norms and values promoting the need for publications in a ‘publish or perish’ environment. While universities often promote high-sounding ideals and would generally wish to be seen to uphold high academic standards, it is argued that silence and complicity surround the way in which instances of plagiarism in academic publications are often dealt with. Actions (and inaction) by academic leaders in universities in dealing with cases of academic plagiarism speak volumes in terms of the values academic institutions profess, and those they actually uphold. The paper prompts readers to consider the need for a more consistent and proactive stance on the part of their own institutions to exercise ethical leadership in identifying and addressing academic plagiarism when it occurs.

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In this paper we propose and evaluate a speaker attribution system using a complete-linkage clustering method. Speaker attribution refers to the annotation of a collection of spoken audio based on speaker identities. This can be achieved using diarization and speaker linking. The main challenge associated with attribution is achieving computational efficiency when dealing with large audio archives. Traditional agglomerative clustering methods with model merging and retraining are not feasible for this purpose. This has motivated the use of linkage clustering methods without retraining. We first propose a diarization system using complete-linkage clustering and show that it outperforms traditional agglomerative and single-linkage clustering based diarization systems with a relative improvement of 40% and 68%, respectively. We then propose a complete-linkage speaker linking system to achieve attribution and demonstrate a 26% relative improvement in attribution error rate (AER) over the single-linkage speaker linking approach.

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This research makes a major contribution which enables efficient searching and indexing of large archives of spoken audio based on speaker identity. It introduces a novel technique dubbed as “speaker attribution” which is the task of automatically determining ‘who spoke when?’ in recordings and then automatically linking the unique speaker identities within each recording across multiple recordings. The outcome of the research will also have significant impact in improving the performance of automatic speech recognition systems through the extracted speaker identities.

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Speaker attribution is the task of annotating a spoken audio archive based on speaker identities. This can be achieved using speaker diarization and speaker linking. In our previous work, we proposed an efficient attribution system, using complete-linkage clustering, for conducting attribution of large sets of two-speaker telephone data. In this paper, we build on our proposed approach to achieve a robust system, applicable to multiple recording domains. To do this, we first extend the diarization module of our system to accommodate multi-speaker (>2) recordings. We achieve this through using a robust cross-likelihood ratio (CLR) threshold stopping criterion for clustering, as opposed to the original stopping criterion of two speakers used for telephone data. We evaluate this baseline diarization module across a dataset of Australian broadcast news recordings, showing a significant lack of diarization accuracy without previous knowledge of the true number of speakers within a recording. We thus propose applying an additional pass of complete-linkage clustering to the diarization module, demonstrating an absolute improvement of 20% in diarization error rate (DER). We then evaluate our proposed multi-domain attribution system across the broadcast news data, demonstrating achievable attribution error rates (AER) as low as 17%.

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Phishing and related cybercrime is responsible for billions of dollars in losses annually. Gartner reported more than 5 million U.S. consumers lost money to phishing attacks in the 12 months ending in September 2008 (Gartner 2009). This paper asks whether the majority of organised phishing and related cybercrime originates in Eastern Europe rather than elsewhere such as China or the USA. The Russian “Mafiya” in particular has been popularised by the media and entertainment industries to the point where it can be hard to separate fact from fiction but we have endeavoured to look critically at the information available on this area to produce a survey. We take a particular focus on cybercrime from an Australian perspective, as Australia was one of the first places where Phishing attacks against Internet banks were seen. It is suspected these attacks came from Ukrainian spammers. The survey is built from case studies both where individuals from Eastern Europe have been charged with related crimes or unsolved cases where there is some nexus to Eastern Europe. It also uses some earlier work done looking at those early Phishing attacks, archival analysis of Phishing attacks in July 2006 and new work looking at correlation between the Corruption Perception Index, Internet penetration and tertiary education in Russia and the Ukraine. The value of this work is to inform and educate those charged with responding to cybercrime where a large part of the problem originates and try to understand why.

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Three studies investigated moderators of the tendency to attribute greater humanness to the self than to others, an interpersonal counterpart of outgroup infra-humanization. Study 1 demonstrated that this self-humanizing effect is reduced when the other is the focus of comparison. Study 2 showed that the effect is reduced when the other is individuated. Study 3 indicated that empathy does not moderate self-humanizing: Self-humanizing failed to correlate negatively with dispositional empathy or perspective-taking. Study 3 also indicated that abstract construal moderates the self-humanizing effect using a temporal comparison. Participants rated their future self, but not their past self, as less human than their present self. Studies 1 and 3 also showed that selfhumanizing is greater for undesirable traits: People may view their failings as “only human.” All findings were distinct from those attributable to self-enhancement. Self-humanizing may reflect a combination of egocentrism, focalism, abstract representation of others, and motivated processes.

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The goal of this study was to examine the role of organizational causal attribution in understanding the relation of work stressors (work-role overload, excessive role responsibility, and unpleasant physical environment) and personal resources (social support and cognitive coping) to such organizational-attitudinal outcomes as work engagement, turnover intention, and organizational identification. In some analyses, cognitive coping was also treated as an organizational outcome. Causal attribution was conceptualized in terms of four dimensions: internality-externality, attributing the cause of one’s successes and failures to oneself, as opposed to external factors, stability (thinking that the cause of one’s successes and failures is stable over time), globality (perceiving the cause to be operative on many areas of one’s life), and controllability (believing that one can control the causes of one’s successes and failures). Several hypotheses were derived from Karasek’s (1989) Job Demands–Control (JD-C) model and from the Job Demands–Resources (JD-R) model (Demerouti, Bakker, Nachreiner & Schaufeli, 2001). Based on the JD-C model, a number of moderation effects were predicted, stating that the strength of the association of work stressors with the outcome variables (e.g. turnover intentions) varies as a function of the causal attribution; for example, unpleasant work environment is more strongly associated with turnover intention among those with an external locus of causality than among those with an internal locuse of causality. From the JD-R model, a number of hypotheses on the mediation model were derived. They were based on two processes posited by the model: an energy-draining process in which work stressors along with a mediating effect of causal attribution for failures deplete the nurses’ energy, leading to turnover intention, and a motivational process in which personal resources along with a mediating effect of causal attribution for successes foster the nurses’ engagement in their work, leading to higher organizational identification and to decreased intention to leave the nursing job. For instance, it was expected that the relationship between work stressors and turnover intention could be explained (mediated) by a tendency to attribute one’s work failures to stable causes. The data were collected from among Finnish hospital nurses using e-questionnaires. Overall 934 nurses responded the questionnaires. Work stressors and personal resources were measured by five scales derived from the Occupational Stress Inventory-Revised (Osipow, 1998). Causal attribution was measured using the Occupational Attributional Style Questionnaire (Furnham, 2004). Work engagement was assessed through the Utrecht Work Engagement Scale (Schaufeli & al., 2002), turnover intention by the Van Veldhoven & Meijman (1994) scale, and organizational identification by the Mael & Ashforth (1992) measure. The results provided support for the function of causal attribution in the overall work stress process. Findings related to the moderation model can be divided into three main findings. First, external locus of causality along with job level moderated the relationship between work overload and cognitive coping. Hence, this interaction was evidenced only among nurses in non-supervisory positions. Second, external locus of causality and job level together moderated the relationship between physical environment and turnover intention. An opposite pattern of interaction was found for this interaction: among nurses, externality exacerbated the effect of perceived unpleasantness of the physical environment on turnover intention, whereas among supervisors internality produced the same effect. Third, job level also disclosed a moderation effect for controllability attribution over the relationship between physical environment and cognitive coping. Findings related to the mediation model for the energetic process indicated that the partial model in which work stressors have also a direct effect on turnover intention fitted the data better. In the mediation model for the motivational process, an intermediate mediation effect in which the effects of personal resources on turnover intention went through two mediators (e.g., causal dimensions and organizational identification) fitted the data better. All dimensions of causal attribution appeared to follow a somewhat unique pattern of mediation effect not only for energetic but also for motivational processes. Overall findings on mediation models partly supported the two simultaneous underlying processes proposed by the JD-R model. While in the energetic process the dimension of externality mediated the relationship between stressors and turnover partially, all the dimensions of causal attribution appeared to entail significant mediator effects in the motivational process. The general findings supported the moderation effect and the mediation effect of causal attribution in the work stress process. The study contributes to several research traditions, including the interaction approach, the JD-C, and the JD-R models. However, many potential functions of organizational causal attribution are yet to be evaluated by relevant academic and organizational research. Keywords: organizational causal attribution, optimistic / pessimistic attributional style, work stressors, organisational stress process, stressors in nursing profession, hospital nursing, JD-R model, personal resources, turnover intention, work engagement, organizational identification.

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Detecting and quantifying the presence of human-induced climate change in regional hydrology is important for studying the impacts of such changes on the water resources systems as well as for reliable future projections and policy making for adaptation. In this article a formal fingerprint-based detection and attribution analysis has been attempted to study the changes in the observed monsoon precipitation and streamflow in the rain-fed Mahanadi River Basin in India, considering the variability across different climate models. This is achieved through the use of observations, several climate model runs, a principal component analysis and regression based statistical downscaling technique, and a Genetic Programming based rainfall-runoff model. It is found that the decreases in observed hydrological variables across the second half of the 20th century lie outside the range that is expected from natural internal variability of climate alone at 95% statistical confidence level, for most of the climate models considered. For several climate models, such changes are consistent with those expected from anthropogenic emissions of greenhouse gases. However, unequivocal attribution to human-induced climate change cannot be claimed across all the climate models and uncertainties in our detection procedure, arising out of various sources including the use of models, cannot be ruled out. Changes in solar irradiance and volcanic activities are considered as other plausible natural external causes of climate change. Time evolution of the anthropogenic climate change ``signal'' in the hydrological observations, above the natural internal climate variability ``noise'' shows that the detection of the signal is achieved earlier in streamflow as compared to precipitation for most of the climate models, suggesting larger impacts of human-induced climate change on streamflow than precipitation at the river basin scale.