458 resultados para PENALIZED LIKELIHOOD
Resumo:
Organisations employ Enterprise Social Networks (ESNs) (such as Yammer) expecting better intra-organisational communication and collaboration. However, ESNs are struggling to gain momentum and wide adoption among users. Promoting user participation is a challenge, particularly in relation to lurkers – the silent ESN members who do not contribute any content. Building on behaviour change research, we propose a three-route model consisting of the central, peripheral and coercive routes of influence that depict users’ cognitive strategies, and we examine how management interventions (e.g. sending promotional emails) impact users’ beliefs and (consequent) posting and lurking behaviours in ESNs. Furthermore, we identify users’ salient motivations to lurk or post. We employ a multi-method research design to conceptualise, operationalise and validate the research model. This study has implications for academics and practitioners regarding the nature, patterns and outcomes of management interventions in prompting ESN.
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This paper reports profiling information for speeding offenders and is part of a larger project that assessed the deterrent effects of increased speeding penalties in Queensland, Australia, using a total of 84,456 speeding offences. The speeding offenders were classified into three groups based on the extent and severity of an index offence: once-only low-rang offenders; repeat high-range offenders; and other offenders. The three groups were then compared in terms of personal characteristics, traffic offences, crash history and criminal history. Results revealed a number of significant differences between repeat high-range offenders and those in the other two offender groups. Repeat high-range speeding offenders were more likely to be male, younger, hold a provisional and a motorcycle licence, to have committed a range of previous traffic offences, to have a significantly greater likelihood of crash involvement, and to have been involved in multiple-vehicle crashes than drivers in the other two offender types. Additionally, when a subset of offenders’ criminal histories were examined, results revealed that repeat high-range speeding offenders were also more likely to have committed a previous criminal offence compared to once only low-range and other offenders and that 55.2% of the repeat high-range offenders had a criminal history. They were also significantly more likely to have committed drug offences and offences against order than the once only low-range speeding offenders, and significantly more likely to have committed regulation offences than those in the other offenders group. Overall, the results indicate that speeding offenders are not an homogeneous group and that, therefore, more tailored and innovative sanctions should be considered and evaluated for high-range recidivist speeders because they are a high-risk road user group.
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Victorious alliances often fight about the spoils of war. This article presents an experiment on the determinants of whether alliances break up and fight internally after having defeated a joint enemy. First, if peaceful sharing yields an asymmetric rent distribution, this increases the likelihood of fighting. In turn, anticipation of the higher likelihood of internal fight reduces the alliance’s ability to succeed against the outside enemy. Second, the option to make nonbinding nonaggression declarations between alliance members does not make peaceful settlement within the alliance more likely. Third, higher differences in the alliance players’ contributions to alliance effort lead to more internal conflict and more intense fighting.
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Abnormal event detection has attracted a lot of attention in the computer vision research community during recent years due to the increased focus on automated surveillance systems to improve security in public places. Due to the scarcity of training data and the definition of an abnormality being dependent on context, abnormal event detection is generally formulated as a data-driven approach where activities are modeled in an unsupervised fashion during the training phase. In this work, we use a Gaussian mixture model (GMM) to cluster the activities during the training phase, and propose a Gaussian mixture model based Markov random field (GMM-MRF) to estimate the likelihood scores of new videos in the testing phase. Further-more, we propose two new features: optical acceleration, and the histogram of optical flow gradients; to detect the presence of any abnormal objects and speed violations in the scene. We show that our proposed method outperforms other state of the art abnormal event detection algorithms on publicly available UCSD dataset.
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Background Few studies have examined acute injuries in track and field in both elite and sub-elite athletes. Purpose To observe the absolute and relative rates of injury in track and field athletes across a wide range of competition levels and ages during three years of the Penn Relays Carnival to assist with future medical coverage planning and injury prevention strategies. Study design: Descriptive epidemiology study. Methods Over a 3-year period all injuries treated by the medical staff were recorded on a standardised injury report form. Absolute injury rates (absolute number of injuries) and relative injury rates (number of injuries per 1000 participants) were determined and odds ratios (OR) of injury rates were calculated between sexes, competition levels and events. Injuries were also broken down into major or minor medical or orthopedic injuries. Results Throughout the study period 48,473 competing athletes participated in the Penn Relays Carnival, and 436 injuries were sustained. For medical coverage purposes, the relative rate of injury subtypes was greatest for minor orthopedic injuries (5.71 injuries per 1000 participants), followed by minor medical injuries (3.42 injuries per 1000 participants), major medical injuries (0.69 injuries per 1000 participants) and major orthopedic injuries (0.18 injuries per 1000 participants). College/elite level athletes displayed the lowest relative injury rate (7.99 injuries per 1000 participants), which was significantly less than high school (9.87 injuries per 1000 participants) and masters level athletes (16.33 injuries per 1000 participants). Males displayed a greater likelihood of suffering a minor orthopedic injury compared to females (OR = 1.36, 95% CI = 1.06 to 1.75; χ2 = 5.73, p = 0.017) but were less likely to sustain a major medical injury (OR = 0.33, 95% CI = 0.15 to 0.75; χ2 = 7.75, p = 0.005). Of the three most heavily participated in events, the 4 x 400m relay displayed the greatest relative injury rate (13.6 injuries per 1000 participants) compared to the 4 x 100 and 4 x 200m relay. Conclusions Medical coverage teams for future large scale track and field events need to plan for at least two major orthopedic and seven major medical injuries per 1000 participants. Male track and field athletes, particularly masters level male athletes, are at greater risk of injury compared to other genders and competition levels.
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Australia has a significantly higher suicide rate than England. Rather than accepting that this ‘statistical fact’ is a direct reflection of some positivist truth, this paper begins with the premise that how suicide is counted depends upon what counts as suicide. This study involves semi-structured interviews with coroners both in Australia and England, as well as observations at inquests. Important differences between the two coronial systems include: first, quite different logics of operation; second, the burden of proof for reaching a finding of suicide is significantly higher in England; and third, the presence of family members at English inquests results in far greater pressure being brought to bear upon coroners. These combined factors result in a reduced likelihood of English coroners reaching a finding of suicide. The conclusions are twofold. First, this research supports existing criticisms of comparative suicide statistics. Second, this research adds theoretical weight to criticisms of positivist analyses of social phenomena.
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Impaired driver alertness increases the likelihood of drivers’ making mistakes and reacting too late to unexpected events while driving. This is particularly a concern on monotonous roads, where a driver’s attention can decrease rapidly. While effective countermeasures do not currently exist, the development of in-vehicle sensors opens avenues for monitoring driving behavior in real-time. The aim of this study is to predict drivers’ level of alertness through surrogate measures collected from in-vehicle sensors. Electroencephalographic activity is used as a reference to evaluate alertness. Based on a sample of 25 drivers, data was collected in a driving simulator instrumented with an eye tracking system, a heart rate monitor and an electrodermal activity device. Various classification models were tested from linear regressions to Bayesians and data mining techniques. Results indicated that Neural Networks were the most efficient model in detecting lapses in alertness. Findings also show that reduced alertness can be predicted up to 5 minutes in advance with 90% accuracy, using surrogate measures such as time to line crossing, blink frequency and skin conductance level. Such a method could be used to warn drivers of their alertness level through the development of an in-vehicle device monitoring, in real-time, drivers' behavior on highways.
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Rapid recursive estimation of hidden Markov Model (HMM) parameters is important in applications that place an emphasis on the early availability of reasonable estimates (e.g. for change detection) rather than the provision of longer-term asymptotic properties (such as convergence, convergence rate, and consistency). In the context of vision- based aircraft (image-plane) heading estimation, this paper suggests and evaluates the short-data estimation properties of 3 recursive HMM parameter estimation techniques (a recursive maximum likelihood estimator, an online EM HMM estimator, and a relative entropy based estimator). On both simulated and real data, our studies illustrate the feasibility of rapid recursive heading estimation, but also demonstrate the need for careful step-size design of HMM recursive estimation techniques when these techniques are intended for use in applications where short-data behaviour is paramount.
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Roundabouts reduce the frequency and severity of motor vehicle crashes and therefore the number installed has increased dramatically in the last 20 years in many countries. However, the safety impacts of roundabouts for bicycle riders are a source of concern, with many studies reporting lower injury reductions for cyclists than car occupants. This paper summarises the results of a project undertaken to provide guidance on how cyclist safety could be improved at existing roundabouts in Queensland, Australia, where cyclist crashes have been increasing and legislation gives motor vehicles priority over cyclists and pedestrians at roundabouts. The review of international roundabout design guidelines identified two schools of design: tangential roundabouts (common in English-speaking countries, including Australia), which focus on minimising delay to motor vehicles, and radial roundabouts (common in continental Europe), which focus on speed reduction and safety. While it might be expected that radial roundabouts would be safer for cyclists, there have been no studies to confirm this view. Most guidelines expect cyclists to act as vehicle traffic in single-lane, typically low-speed, roundabouts. Some jurisdictions do not permit cyclists to travel on multi-lane roundabouts, and recommend segregated bicycle facilities because of their lowest crash risk for cyclists. Given that most bicycle-vehicle crashes at roundabouts involve an entering vehicle and a circulating cyclist, the greatest challenges appear to be reducing the speed of motor vehicles on the approach/entry to roundabouts and other ways of maximizing the likelihood that cyclists will be seen. Lower entry speeds are likely to underpin the greater safety of compact roundabouts for cyclists and, conversely, the higher than expected crash rates at two-lane roundabouts. European research discourages the use of bike lanes in roundabouts which position cyclists at the edge of the road and contributes to cyclists being less likely to be noticed by drivers.
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Leptospirosis outbreaks have been associated with many common water events including water consumption, water sports, environmental disasters and occupational exposure. The ability of leptospires to survive in moist environments makes them a high risk agent for infection following contact with any contaminated water source. Water treatment processes reduce the likelihood of leptospirosis or other microbial agents causing infection provided they do not malfunction and the distribution networks are maintained. Notably, there are many differences in water treatment systems around the world, particularly between developing and developed countries. Detection of leptospirosis in water samples is uncommonly performed by molecular methods.
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This thesis proposes three novel models which extend the statistical methodology for motor unit number estimation, a clinical neurology technique. Motor unit number estimation is important in the treatment of degenerative muscular diseases and, potentially, spinal injury. Additionally, a recent and untested statistic to enable statistical model choice is found to be a practical alternative for larger datasets. The existing methods for dose finding in dual-agent clinical trials are found to be suitable only for designs of modest dimensions. The model choice case-study is the first of its kind containing interesting results using so-called unit information prior distributions.
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Most of the existing algorithms for approximate Bayesian computation (ABC) assume that it is feasible to simulate pseudo-data from the model at each iteration. However, the computational cost of these simulations can be prohibitive for high dimensional data. An important example is the Potts model, which is commonly used in image analysis. Images encountered in real world applications can have millions of pixels, therefore scalability is a major concern. We apply ABC with a synthetic likelihood to the hidden Potts model with additive Gaussian noise. Using a pre-processing step, we fit a binding function to model the relationship between the model parameters and the synthetic likelihood parameters. Our numerical experiments demonstrate that the precomputed binding function dramatically improves the scalability of ABC, reducing the average runtime required for model fitting from 71 hours to only 7 minutes. We also illustrate the method by estimating the smoothing parameter for remotely sensed satellite imagery. Without precomputation, Bayesian inference is impractical for datasets of that scale.
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The study investigated the adsorption and bioavailability characteristics of traffic generated metals common to urban land uses, in road deposited solids particles. To validate the outcomes derived from the analysis of field samples, adsorption and desorption experiments were undertaken. The analysis of field samples revealed that metals are selectively adsorbed to different charge sites on solids. Zinc, copper, lead and nickel are adsorbed preferentially to oxides of manganese, iron and aluminium. Lead is adsorbed to organic matter through chemisorption. Cadmium and chromium form weak bonding through cation exchange with most of the particle sizes. Adsorption and desorption experiments revealed that at high metal concentrations, chromium, copper and lead form relatively strong bonds with solids particles while zinc is adsorbed through cation exchange with high likelihood of being released back into solution. Outcomes from this study provide specific guidance for the removal of metals from stormwater based on solids removal.
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Targeted monitoring of threatened species within plantations is becoming more important due to forest certification programmes’ requirement to consider protection of threatened species, and to increase knowledge of the distribution of species. To determine patterns of long-tailed bat (Chalinolobus tuberculatus) activity in different habitat structures, with the aim of improving the likelihood of detection by targeting monitoring, we monitored one stand of 26 year-old Pinus radiata over seven months between December 2007 and June 2008 in Kinleith Forest, an exotic plantation forest centred around Tokoroa, South Waikato, New Zealand. Activity was determined by acoustic recording equipment, which is able to detect and record bats’ echolocation calls. We monitored activity from sunset to sunrise along a road through the stand, along stand edges, and in the interior of the stand. Bats were recorded on 80% of the 35 nights monitored. All activity throughout the monitoring period was detected on the edge of the stand or along the road. No bats were detected within the interior of the stand. Bat activity was highest along the road through the stand (40.4% of all passes), followed by an edge with stream running alongside (35.2%), along the road within a skidsite (19.8%), and along an edge without a stream (4.6%). There was a significant positive relationship between bat pass rate (bat passes h-1) and the feeding buzz rate (feeding buzzes h-1) indicating that bat activity was associated with feeding and not just commuting. Bat feeding activity was also highest along the road through the stand (59.2% of feeding buzzes), followed by the road within the skidsite (30.6%), and along the stream-side edge (10.2%). No feeding buzzes were recorded in either the interior or along the edge without the stream. Differences in overall feeding activity were significant only between the road and edge and between edges with and without a stream. Bat activity was detected each month and always by the second night of monitoring, and in this stand was highest during April. We recommend targeted monitoring for long-tailed bats be focused on road-side and stand edge habitat, and along streams, and that monitoring take place for at least three nights to maximise probability of detection.
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Road collisions negatively affect the lives of hundreds of Canadians per year. Unfortunately, safety has been typically neglected from management systems. It is common to find that a great deal of effort has been devoted to develop and implement systems capable of achieving and sustaining good levels of condition. It is relatively recent that road safety has become an important objective. Managing a network of roads is not an easy task; it requires long, medium and short term plans to maintain, rehabilitate and upgrade aging assets, reduce and mitigate accident exposure, likelihood and severity. This thesis presents a basis for incorporating road safety into road management systems; two case studies were developed; one limited by available data and another from sufficient information. A long term analysis was used to allocate improvements for condition and safety of roads and bridges, at the network level. It was confirmed that a safety index could be used to obtain a first cut model; meanwhile potential for improvement which is a difference between observed and predicted number of accidents was capable of capturing the degree of safety of individual segments. It was found that the completeness of the system resulted in savings because of the economies obtained from trade-off optimization. It was observed that safety improvements were allocated at the beginning of the analysis in order to reduce the extent of issues, which translated into a systematic reduction of potential for improvement up to a point of near constant levels, which were hypothesized to relate to those unavoidable collisions from human error or vehicle failure.