892 resultados para Hidden homelessness


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We revisit the venerable question of access credentials management, which concerns the techniques that we, humans with limited memory, must employ to safeguard our various access keys and tokens in a connected world. Although many existing solutions can be employed to protect a long secret using a short password, those solutions typically require certain assumptions on the distribution of the secret and/or the password, and are helpful against only a subset of the possible attackers. After briefly reviewing a variety of approaches, we propose a user-centric comprehensive model to capture the possible threats posed by online and offline attackers, from the outside and the inside, against the security of both the plaintext and the password. We then propose a few very simple protocols, adapted from the Ford-Kaliski server-assisted password generator and the Boldyreva unique blind signature in particular, that provide the best protection against all kinds of threats, for all distributions of secrets. We also quantify the concrete security of our approach in terms of online and offline password guesses made by outsiders and insiders, in the random-oracle model. The main contribution of this paper lies not in the technical novelty of the proposed solution, but in the identification of the problem and its model. Our results have an immediate and practical application for the real world: they show how to implement single-sign-on stateless roaming authentication for the internet, in a ad-hoc user-driven fashion that requires no change to protocols or infrastructure.

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In this paper, we propose a new steganalytic method to detect the message hidden in a black and white image using the steganographic technique developed by Liang, Wang and Zhang. Our detection method estimates the length of hidden message embedded in a binary image. Although the hidden message embedded is visually imperceptible, it changes some image statistic (such as inter-pixels correlation). Based on this observation, we first derive the 512 patterns histogram from the boundary pixels as the distinguishing statistic, then we compute the histogram difference to determine the changes of the 512 patterns histogram induced by the embedding operation. Finally we propose histogram quotient to estimate the length of the embedded message. Experimental results confirm that the proposed method can effectively and reliably detect the length of the embedded message.

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Process models define allowed process execution scenarios. The models are usually depicted as directed graphs, with gateway nodes regulating the control flow routing logic and with edges specifying the execution order constraints between tasks. While arbitrarily structured control flow patterns in process models complicate model analysis, they also permit creativity and full expressiveness when capturing non-trivial process scenarios. This paper gives a classification of arbitrarily structured process models based on the hierarchical process model decomposition technique. We identify a structural class of models consisting of block structured patterns which, when combined, define complex execution scenarios spanning across the individual patterns. We show that complex behavior can be localized by examining structural relations of loops in hidden unstructured regions of control flow. The correctness of the behavior of process models within these regions can be validated in linear time. These observations allow us to suggest techniques for transforming hidden unstructured regions into block-structured ones.

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This paper reports on the findings of an international telecollaboration study using Facebook, in which teachers studying in M. Ed programs in Australia and Greece, discussed the use of mobile phones in language classrooms. Results suggest that invisible barriers exist in the use of mobile phones in the classroom, including bans on use in schools, lack of familiarity with educational uses for mobile phones, and negative perceptions about mobile phones specifically in terms of classroom management.

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This research will contribute to improved responses to first time homelessness in later life by analysing the nature of the problem, the precipitating factors and successful interventions. - See more at: http://www.ahuri.edu.au/publications/projects/p21005#sthash.11zLWaMs.dpuf

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The term ‘two cultures’ was coined more than 50 years ago by scientist and novelist C.P. Snow to describe the divergence in the world views and methods of scientists and the creative sector. This divergence has meant that innovation systems and policies have focused for decades on science, engineering, technology and medicine and the industries that depend on them. The humanities, arts and social sciences have been bit players at best; their contributions hidden from research agendas, policy and program initiatives, and the public mind. But structural changes to advanced economies and societies have brought services industries and the creative sector to greater prominence as key contributors to innovation. Hidden Innovation peels back the veil, tracing the way innovation occurs through new forms of screen production enabled by social media platforms as well as in public broadcasting. It shows that creative workers are contributing fresh ideas across the economy and how creative cities debates need reframing. It traces how policies globally are beginning to catch up with the changing social and economic realities. In his new book, Cunningham argues that the innovation framework offers the best opportunity in decades to reassess and refresh the case for the public role of the humanities, particularly the media, cultural and communication studies disciplines.

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This paper investigates compressed sensing using hidden Markov models (HMMs) and hence provides an extension of recent single frame, bounded error sparse decoding problems into a class of sparse estimation problems containing both temporal evolution and stochastic aspects. This paper presents two optimal estimators for compressed HMMs. The impact of measurement compression on HMM filtering performance is experimentally examined in the context of an important image based aircraft target tracking application. Surprisingly, tracking of dim small-sized targets (as small as 5-10 pixels, with local detectability/SNR as low as − 1.05 dB) was only mildly impacted by compressed sensing down to 15% of original image size.

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In this paper, a novel data-driven approach to monitoring of systems operating under variable operating conditions is described. The method is based on characterizing the degradation process via a set of operation-specific hidden Markov models (HMMs), whose hidden states represent the unobservable degradation states of the monitored system while its observable symbols represent the sensor readings. Using the HMM framework, modeling, identification and monitoring methods are detailed that allow one to identify a HMM of degradation for each operation from mixed-operation data and perform operation-specific monitoring of the system. Using a large data set provided by a major manufacturer, the new methods are applied to a semiconductor manufacturing process running multiple operations in a production environment.

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This research first asks ‘What happens when young people leave state care?’ in respect of Victoria and Queensland and second ‘What are the service support implications of this?’ A number of methods were used to explore these questions including semi-structured interviews with 27 young adults aged 19-23 years who had been homeless or at risk of homelessness, and focus groups with young people and service providers. This study provides support for the proposition that young people should be proactively and voluntarily involved in periodic monitoring of their lived experience post care and linkage of this monitoring to the activation of timely support. The great majority of young people involved in this study thought this was not only desirable but important. Whilst some young people will be in close contact with leaving care services many others will not. New research is recommended to develop a mentoring and support activation process using participatory monitoring and action research methods. This type of approach reflects the importance of utilising processes with young people in care and leaving care which acknowledge their personhood and capacity to contribute voluntarily to the processes which seek to support them.

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“Guardians of Hidden Universes” is a playful story for experiencing on your phone in the street. You’re a Guardian of Hidden Universes, and when you check your Communication Terminal you find the Guild has made a terrible mistake they need you to repair. The experience lasts about 15-25 minutes, and takes you around whatever city you happen to be in. That’s right – you can play it in any city in the world! The app was launched at Popup Playground‘s 2013 Fresh Air Festival – a new pervasive gaming festival in Melbourne, Australia. This playful story is inspired by the idea of creating a locative-experience that is not site-specific. How can we have an experience than engages with the environment that also can work worldwide? The story is also playing with the idea of engaging with a character and a space and intuition within a short period of time. The short and playful experience is a Universe Creation 101 production. It was conceived, written, and designed by Christy Dena; with artwork, coding, and interface design by Elroy.

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This paper develops maximum likelihood (ML) estimation schemes for finite-state semi-Markov chains in white Gaussian noise. We assume that the semi-Markov chain is characterised by transition probabilities of known parametric from with unknown parameters. We reformulate this hidden semi-Markov model (HSM) problem in the scalar case as a two-vector homogeneous hidden Markov model (HMM) problem in which the state consist of the signal augmented by the time to last transition. With this reformulation we apply the expectation Maximumisation (EM ) algorithm to obtain ML estimates of the transition probabilities parameters, Markov state levels and noise variance. To demonstrate our proposed schemes, motivated by neuro-biological applications, we use a damped sinusoidal parameterised function for the transition probabilities.

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In this paper we propose and study low complexity algorithms for on-line estimation of hidden Markov model (HMM) parameters. The estimates approach the true model parameters as the measurement noise approaches zero, but otherwise give improved estimates, albeit with bias. On a nite data set in the high noise case, the bias may not be signi cantly more severe than for a higher complexity asymptotically optimal scheme. Our algorithms require O(N3) calculations per time instant, where N is the number of states. Previous algorithms based on earlier hidden Markov model signal processing methods, including the expectation-maximumisation (EM) algorithm require O(N4) calculations per time instant.