A methodology for estimating exposure-controlled crash risk using traffic Police crash data

Exposure control or case-control methodologies are common techniques for estimating crash risks, however they require either observational data on control cases or exogenous exposure data, such as vehicle-kilometres travelled. This study proposes an alternative methodology for estimating crash risk of road user groups, whilst controlling for exposure under a variety of roadway, traffic and environmental factors by using readily available police-reported crash data. In particular, the proposed method employs a combination of a log-linear model and quasi-induced exposure technique to identify significant interactions among a range of roadway, environmental and traffic conditions to estimate associated crash risks. The proposed methodology is illustrated using a set of police-reported crash data from January 2004 to June 2009 on roadways in Queensland, Australia. Exposure-controlled crash risks of motorcyclists—involved in multi-vehicle crashes at intersections—were estimated under various combinations of variables like posted speed limit, intersection control type, intersection configuration, and lighting condition. Results show that the crash risk of motorcycles at three-legged intersections is high if the posted speed limits along the approaches are greater than 60 km/h. The crash risk at three-legged intersections is also high when they are unsignalized. Dark lighting conditions appear to increase the crash risk of motorcycles at signalized intersections, but the problem of night time conspicuity of motorcyclists at intersections is lessened on approaches with lower speed limits. This study demonstrates that this combined methodology is a promising tool for gaining new insights into the crash risks of road user groups, and is transferrable to other road users.

Adapting Lyubashevsky's signature schemes to the ring signature setting

Basing signature schemes on strong lattice problems has been a long standing open issue. Today, two families of lattice-based signature schemes are known: the ones based on the hash-and-sign construction of Gentry et al.; and Lyubashevsky’s schemes, which are based on the Fiat-Shamir framework. In this paper we show for the first time how to adapt the schemes of Lyubashevsky to the ring signature setting. In particular we transform the scheme of ASIACRYPT 2009 into a ring signature scheme that provides strong properties of security under the random oracle model. Anonymity is ensured in the sense that signatures of different users are within negligible statistical distance even under full key exposure. In fact, the scheme satisfies a notion which is stronger than the classical full key exposure setting as even if the keypair of the signing user is adversarially chosen, the statistical distance between signatures of different users remains negligible. Considering unforgeability, the best lattice-based ring signature schemes provide either unforgeability against arbitrary chosen subring attacks or insider corruption in log-sized rings. In this paper we present two variants of our scheme. In the basic one, unforgeability is ensured in those two settings. Increasing signature and key sizes by a factor k (typically 80 − 100), we provide a variant in which unforgeability is ensured against insider corruption attacks for arbitrary rings. The technique used is pretty general and can be adapted to other existing schemes.

Air pollution, temperature and pediatric influenza in Brisbane, Australia

Previous studies have demonstrated the importance of weather variables in influencing the incidence of influenza. However, the role of air pollution is often ignored in identifying the environmental drivers of influenza. This research aims to examine the impacts of air pollutants and temperature on the incidence of pediatric influenza in Brisbane, Australia. Lab-confirmed daily data on influenza counts among children aged 0-14years in Brisbane from 2001 January 1st to 2008 December 31st were retrieved from Queensland Health. Daily data on maximum and minimum temperatures for the same period were supplied by the Australian Bureau of Meteorology. Winter was chosen as the main study season due to it having the highest pediatric influenza incidence. Four Poisson log-linear regression models, with daily pediatric seasonal influenza counts as the outcome, were used to examine the impacts of air pollutants (i.e., ozone (O3), particulate matter≤10μm (PM10) and nitrogen dioxide (NO2)) and temperature (using a moving average of ten days for these variables) on pediatric influenza. The results show that mean temperature (Relative risk (RR): 0.86; 95% Confidence Interval (CI): 0.82-0.89) was negatively associated with pediatric seasonal influenza in Brisbane, and high concentrations of O3 (RR: 1.28; 95% CI: 1.25-1.31) and PM10 (RR: 1.11; 95% CI: 1.10-1.13) were associated with more pediatric influenza cases. There was a significant interaction effect (RR: 0.94; 95% CI: 0.93-0.95) between PM10 and mean temperature on pediatric influenza. Adding the interaction term between mean temperature and PM10 substantially improved the model fit. This study provides evidence that PM10 needs to be taken into account when evaluating the temperature-influenza relationship. O3 was also an important predictor, independent of temperature.

The protease inhibitor JO146 demonstrates a critical role for CtHtrA for Chlamydia trachomatis reversion from penicillin persistence

The Chlamydia trachomatis serine protease HtrA (CtHtrA) has recently been demonstrated to be essential during the replicative phase of the chlamydial developmental cycle. A chemical inhibition strategy (serine protease inhibitor JO146) was used to demonstrate this essential role and it was found that the chlamydial inclusions diminish in size and are lost from the cell after CtHtrA inhibition without formation of viable elementary bodies. The inhibitor (JO146) was used in this study to investigate the role of CtHtrA for penicillin persistence and heat stress model conditionscultures for Chlamydia trachomatis. JO146 addition during penicillin persistence resulted in only minor reductions (~1 log) in the final viable infectious yield after persistent Chlamydia were reverted from persistence. However, JO146 treatment during the reversion and recovery from penicillin persistence was completely lethal for Chlamydia trachomatis. JO146 was completely lethal when added either during heat stress conditions, or during the recovery from heat stress conditions. These data together indicate that CtHtrA has essential roles during some stress environments (heat shock), recovery from stress environments (heat shock and penicillin persistence), as well as the previously characterised essential role during the replicative phase of the chlamydial developmental cycle. Thus, CtHtrA is an essential protease with both replicative phase and stress condition functions for Chlamydia trachomatis.

An extensible framework for analysing resource behaviour using event logs

Business processes depend on human resources and managers must regularly evaluate the performance of their employees based on a number of measures, some of which are subjective in nature. As modern organisations use information systems to automate their business processes and record information about processes’ executions in event logs, it now becomes possible to get objective information about resource behaviours by analysing data recorded in event logs. We present an extensible framework for extracting knowledge from event logs about the behaviour of a human resource and for analysing the dynamics of this behaviour over time. The framework is fully automated and implements a predefined set of behavioural indicators for human resources. It also provides a means for organisations to define their own behavioural indicators, using the conventional Structured Query Language, and a means to analyse the dynamics of these indicators. The framework's applicability is demonstrated using an event log from a German bank.

E-motion diaries : blogging emotional responses to online learning in higher education

Emotions are inherently social, and are central to learning, online interaction and literacy practices (Shen, Wang, & Shen, 2009). Demonstrating the dynamic sociality of literacy practice, we used e-motion diaries or web logs to explore the emotional states of pre-service high school teachers’ experiences of online learning activities. This is because the methods of communication used by university educators in online learning and writing environments play an important role in fulfilling students’ need for social interaction and inclusion (McInnerney & Roberts, 2004). Feelings of isolation and frustration are common emotions experienced by students in many online learning environments, and are associated with the success or failure of online interactions and learning (Su, et al., 2005). The purpose of the study was to answer the research question: What are the trajectories of pre-service teachers’ emotional states during online learning experiences? This is important because emotions are central to learning, and the current trend toward Massive Open Online Courses (MOOCs) needs research about students’ emotional connections in online learning environments (Kop, 2011). The project was conducted with a graduate class of 64 high school science pre-service teachers in Science Education Curriculum Studies in a large Australian university, including males and females from a variety of cultural backgrounds, aged 22-55 years. Online activities involved the students watching a series of streamed live lectures for the first 5 weeks providing a varied set of learning experiences, such as viewing science demonstrations (e.g., modeling the use of discrepant events). Each week, students provided feedback on learning by writing and posting an e-motion diary or web log about their emotional response. Students answered the question: What emotions did you experience during this learning experience? The descriptive data set included 284 online posts, with students contributing multiple entries. Linguistic appraisal theory, following Martin and White (2005), was used to regroup the 22 different discrete emotions reported by students into the six main affect groups – three positive and three negative: unhappiness/happiness, insecurity/security, and dissatisfaction/satisfaction. The findings demonstrated that the pre-service teachers’ emotional responses to the streamed lectures tended towards happiness, security, and satisfaction within the typology of affect groups – un/happiness, in/security, and dis/satisfaction. Fewer students reported that the streamed lectures triggered negative feelings of frustration, powerlessness, and inadequacy, and when this occurred, it often pertained to expectations of themselves in the forthcoming field experience in classrooms. Exceptions to this pattern of responses occurred in relation to the fifth streamed lecture presented in a non-interactive slideshow format that compressed a large amount of content. Many students responded to the content of the lecture rather than providing their emotional responses to this lecture, and one student felt “completely disengaged”. The social practice of online writing as blogs enabled the students to articulate their emotions. The findings primarily contribute new understanding about students' wide range of differing emotional states, both positive and negative, experienced in response to streamed live lectures and other learning activities in higher education external coursework. The is important because the majority of previous studies have focused on particular negative emotions, such as anxiety in test taking. The research also highlights the potentials of appraisal theory for studying human emotions in online learning and writing.

Phosphodiesterase PDE3, but not PDE4, reduces β1- and β2-adrenoceptor-mediated inotropic and lusitropic effects in failing ventricle from metoprolol-treated patients

Background and purpose Phosphodiesterases PDE3 and/or PDE4 control ventricular effects of catecholamines in several species but their relative effects in failing human ventricle are unknown. We investigated whether the PDE3-selective inhibitor cilostamide (0.3-1μM) or PDE4 inhibitor rolipram (1-10μM) modified the positive inotropic and lusitropic effects of catecholamines in human failing myocardium. Experimental approach Right and left ventricular trabeculae from freshly explanted hearts of 5 non-β-blocker-treated and 15 metoprolol-treated patients with terminal heart failure were paced to contract at 1Hz. The effects of (-)-noradrenaline, mediated through β1-adrenoceptors (β2-adrenoceptors blocked with ICI118551), and (-)-adrenaline, mediated through β2-adrenoceptors (β1-adrenoceptors blocked with CGP20712A), were assessed in the absence and presence of PDE inhibitors. Catecholamine potencies were estimated from –logEC50s. Key results Cilostamide did not significantly potentiate the inotropic effects of the catecholamines in non-β-blocker-treated patients. Cilostamide caused greater potentiation (P=0.037) of the positive inotropic effects of (-)-adrenaline (0.78±0.12 log units) than (-)-noradrenaline (0.47±0.12 log units) in metoprolol-treated patients. Lusitropic effects of the catecholamines were also potentiated by cilostamide. Rolipram did not affect the inotropic and lusitropic potencies of (-)-noradrenaline or (-)-adrenaline on right and left ventricular trabeculae from metoprolol-treated patients. Conclusions and implications Metoprolol induces a control by PDE3 of ventricular effects mediated through both β1- and β2-adrenoceptors, thereby further reducing sympathetic cardiostimulation in patients with terminal heart failure. Concurrent therapy with a PDE3 blocker and metoprolol could conceivably facilitate cardiostimulation evoked by adrenaline through β2-adrenoceptors. PDE4 does not appear to reduce inotropic and lusitropic effects of catecholamines in failing human ventricle.

Distributed public-key cryptography from weak secrets

We introduce the notion of distributed password-based public-key cryptography, where a virtual high-entropy private key is implicitly defined as a concatenation of low-entropy passwords held in separate locations. The users can jointly perform private-key operations by exchanging messages over an arbitrary channel, based on their respective passwords, without ever sharing their passwords or reconstituting the key. Focusing on the case of ElGamal encryption as an example, we start by formally defining ideal functionalities for distributed public-key generation and virtual private-key computation in the UC model. We then construct efficient protocols that securely realize them in either the RO model (for efficiency) or the CRS model (for elegance). We conclude by showing that our distributed protocols generalize to a broad class of “discrete-log”-based public-key cryptosystems, which notably includes identity-based encryption. This opens the door to a powerful extension of IBE with a virtual PKG made of a group of people, each one memorizing a small portion of the master key.

Truncated differential analysis of reduced-round LBlock

In this paper we present truncated differential analysis of reduced-round LBlock by computing the differential distribution of every nibble of the state. LLR statistical test is used as a tool to apply the distinguishing and key-recovery attacks. To build the distinguisher, all possible differences are traced through the cipher and the truncated differential probability distribution is determined for every output nibble. We concatenate additional rounds to the beginning and end of the truncated differential distribution to apply the key-recovery attack. By exploiting properties of the key schedule, we obtain a large overlap of key bits used in the beginning and final rounds. This allows us to significantly increase the differential probabilities and hence reduce the attack complexity. We validate the analysis by implementing the attack on LBlock reduced to 12 rounds. Finally, we apply single-key and related-key attacks on 18 and 21-round LBlock, respectively.

The role of parents and non-parents in the supervision of learner drivers in Australia

The purpose of this study was to contrast the role of parental and non-parental (sibling, other family and non-family) supervisors in the supervision of learner drivers in graduated driver licensing systems. The sample consisted of 522 supervisors from the Australian states of Queensland (n = 204, 39%) and New South Wales (n = 318, 61%). The learner licence requirements in these two states are similar, although learners in Queensland are required to accrue 100 h of supervision in a log book while those in New South Wales are required to accrue 120 h. Approximately 50 per cent of the sample (n = 255) were parents of the learner driver while the remainder of the sample were either siblings (n = 72, 13.8%), other family members (n = 153, 29.3%) or non-family (n = 114, 21.8%). Parents were more likely than siblings, other family or non-family members to be the primary supervisor of the learner driver. Siblings provided fewer hours of practice when compared with other supervisor types while the median and mode suggest that parents provided the most hours of practice to learner drivers. This study demonstrates that non-parental supervisors, such as siblings, other family members and non-family, at least in jurisdictions that require 100 or 120 h of practice, are important in facilitating learner drivers to accumulate sufficient supervised driving practice.

Tracking spatio temporal movement of human in terms of space utilization using media access control address data

Using Media-Access-Control (MAC) address for data collection and tracking is a capable and cost effective approach as the traditional ways such as surveys and video surveillance have numerous drawbacks and limitations. Positioning cell-phones by Global System for Mobile communication was considered an attack on people's privacy. MAC addresses just keep a unique log of a WiFi or Bluetooth enabled device for connecting to another device that has not potential privacy infringements. This paper presents the use of MAC address data collection approach for analysis of spatio-temporal dynamics of human in terms of shared space utilization. This paper firstly discuses the critical challenges and key benefits of MAC address data as a tracking technology for monitoring human movement. Here, proximity-based MAC address tracking is postulated as an effective methodology for analysing the complex spatio-temporal dynamics of human movements at shared zones such as lounge and office areas. A case study of university staff lounge area is described in detail and results indicates a significant added value of the methodology for human movement tracking. By analysis of MAC address data in the study area, clear statistics such as staff’s utilisation frequency, utilisation peak periods, and staff time spent is obtained. The analyses also reveal staff’s socialising profiles in terms of group and solo gathering. The paper is concluded with a discussion on why MAC address tracking offers significant advantages for tracking human behaviour in terms of shared space utilisation with respect to other and more prominent technologies, and outlines some of its remaining deficiencies.

A participatory framework for researching and evaluating communication for development and social change

The growing dominance of project planning cycles and results-based management in development over the past 20 years has significant implications for the effective evaluation of communication for development and social change and the sustainability of these processes. These approaches to development and evaluation usually give priority to the linear, logical framework (or log frame) approach promoted by many development institutions. This tends to emphasize upward accountability approaches to development and its evaluation, so that development is driven by exogenous rather than endogenous models of development and social change. Such approaches are underpinned by ideas of preplanning, and predetermination of what successful out -comes look like. In this way, outcomes of complex interventions tend to be reduced to simple, cause-effect processes and the categorization of things, including people (Chambers and Pettit 2004; Eyben 2011). This runs counter to communication for development approaches, which prioritize engagement, relationships, empowerment and dialogue as important components for positive social change.

Controlled automated discovery of collections of business process models

Automated process discovery techniques aim at extracting process models from information system logs. Existing techniques in this space are effective when applied to relatively small or regular logs, but generate spaghetti-like and sometimes inaccurate models when confronted to logs with high variability. In previous work, trace clustering has been applied in an attempt to reduce the size and complexity of automatically discovered process models. The idea is to split the log into clusters and to discover one model per cluster. This leads to a collection of process models – each one representing a variant of the business process – as opposed to an all-encompassing model. Still, models produced in this way may exhibit unacceptably high complexity and low fitness. In this setting, this paper presents a two-way divide-and-conquer process discovery technique, wherein the discovered process models are split on the one hand by variants and on the other hand hierarchically using subprocess extraction. Splitting is performed in a controlled manner in order to achieve user-defined complexity or fitness thresholds. Experiments on real-life logs show that the technique produces collections of models substantially smaller than those extracted by applying existing trace clustering techniques, while allowing the user to control the fitness of the resulting models.

Process compliance analysis based on behavioural profiles

Process compliance measurement is getting increasing attention in companies due to stricter legal requirements and market pressure for operational excellence. In order to judge on compliance of the business processing, the degree of behavioural deviation of a case, i.e., an observed execution sequence, is quantified with respect to a process model (referred to as fitness, or recall). Recently, different compliance measures have been proposed. Still, nearly all of them are grounded on state-based techniques and the trace equivalence criterion, in particular. As a consequence, these approaches have to deal with the state explosion problem. In this paper, we argue that a behavioural abstraction may be leveraged to measure the compliance of a process log – a collection of cases. To this end, we utilise causal behavioural profiles that capture the behavioural characteristics of process models and cases, and can be computed efficiently. We propose different compliance measures based on these profiles, discuss the impact of noise in process logs on our measures, and show how diagnostic information on non-compliance is derived. As a validation, we report on findings of applying our approach in a case study with an international service provider.

Causal behavioural profiles : efficient computation, applications, and evaluation

Analysis of behavioural consistency is an important aspect of software engineering. In process and service management, consistency verification of behavioural models has manifold applications. For instance, a business process model used as system specification and a corresponding workflow model used as implementation have to be consistent. Another example would be the analysis to what degree a process log of executed business operations is consistent with the corresponding normative process model. Typically, existing notions of behaviour equivalence, such as bisimulation and trace equivalence, are applied as consistency notions. Still, these notions are exponential in computation and yield a Boolean result. In many cases, however, a quantification of behavioural deviation is needed along with concepts to isolate the source of deviation. In this article, we propose causal behavioural profiles as the basis for a consistency notion. These profiles capture essential behavioural information, such as order, exclusiveness, and causality between pairs of activities of a process model. Consistency based on these profiles is weaker than trace equivalence, but can be computed efficiently for a broad class of models. In this article, we introduce techniques for the computation of causal behavioural profiles using structural decomposition techniques for sound free-choice workflow systems if unstructured net fragments are acyclic or can be traced back to S- or T-nets. We also elaborate on the findings of applying our technique to three industry model collections.