Indirect message injection for MAC generation

This paper presents a model for the generation of a MAC tag using a stream cipher. The input message is used indirectly to control segments of the keystream that form the MAC tag. Several recent proposals can be considered as instances of this general model, as they all perform message accumulation in this way. However, they use slightly different processes in the message preparation and finalisation phases. We examine the security of this model for different options and against different types of attack, and conclude that the indirect injection model can be used to generate MAC tags securely for certain combinations of options. Careful consideration is required at the design stage to avoid combinations of options that result in susceptibility to forgery attacks. Additionally, some implementations may be vulnerable to side-channel attacks if used in Authenticated Encryption (AE) algorithms. We give design recommendations to provide resistance to these attacks for proposals following this model.

On Presence, Collective Performance and Assumptions of Causality

The ways we assume, observe and model “presence” and its effects are the focus in this paper. Entities with selectively shared presences are the basis of any collective, and of attributions (such as “humorous”, “efficient” or “intelligent”). The subtleties of any joint presence can markedly influence potentials, perceptions and performance of the collective as demonstrated when a humorous tale is counterpoised with disciplined thought. Disciplines build on presences assumed known or knowable while fluid and interpretable presences pervade humor. Explorations in this paper allow considerations of collectives, causality and the philosophy of computing. Economics has long considered issues of collective action in ways circumscribed by assumptions about the presence of economic entities. Such entities are deemed rational but they are clearly not intelligent. To reach its potential, collective intelligence research needs more adequate considerations of alternate presences and their impacts.

Automatic classification of human epithelial type 2 cell indirect immunofluorescence images using cell pyramid matching

This paper describes a novel system for automatic classification of images obtained from Anti-Nuclear Antibody (ANA) pathology tests on Human Epithelial type 2 (HEp-2) cells using the Indirect Immunofluorescence (IIF) protocol. The IIF protocol on HEp-2 cells has been the hallmark method to identify the presence of ANAs, due to its high sensitivity and the large range of antigens that can be detected. However, it suffers from numerous shortcomings, such as being subjective as well as time and labour intensive. Computer Aided Diagnostic (CAD) systems have been developed to address these problems, which automatically classify a HEp-2 cell image into one of its known patterns (eg. speckled, homogeneous). Most of the existing CAD systems use handpicked features to represent a HEp-2 cell image, which may only work in limited scenarios. We propose a novel automatic cell image classification method termed Cell Pyramid Matching (CPM), which is comprised of regional histograms of visual words coupled with the Multiple Kernel Learning framework. We present a study of several variations of generating histograms and show the efficacy of the system on two publicly available datasets: the ICPR HEp-2 cell classification contest dataset and the SNPHEp-2 dataset.

Wheat breeding nurseries, target environments, and indirect selection for grain yield

Use of appropriate nursery environments will maximize gain from selection for yield of wheat (Triticum aestivum L.) in the target population of environments of a breeding program. The objective of this study was to investigate how well-irrigated (low-stress) nursery environments predict yield of lines in target environments that varied in degree of water limitation. Fifteen lines were sampled from the preliminary yield evaluation stage of the Queensland wheat breeding program and tested in 26 trials under on-farm conditions (Target Environments) across nine years (1985 to 1993) and also in 27 trials conducted at three research stations (Nursery Environments) in three years (1987 to 1989). The nursery environments were structured to impose different levels of water and nitrogen (N) limitation, whereas the target environments represented a random sample of on-farm conditions from the target population of environments. Indirect selection and pattern analysis methods were used to investigate selection for yield in the nursery environments and gain from selection in the target environments. Yield under low-stress nursery conditions was an effective predictor of yield under similar low-stress target environments (r = 0.89, P < 0.01). However, the value of the low-stress nursery as a predictor of yield in the water-limited target environments decreased with increasing water stress (moderate stress r = 0.53, P < 0.05, to r = 0.38, P > 0.05; severe stress r = -0.08, P > 0.05). Yield in the stress nurseries was a poor predictor of yield in the target environments. Until there is a clear understanding of the physiological-genetic basis of variation for adaptation of wheat to the water-limited environments in Queensland, yield improvement can best be achieved by selection for a combination of yield potential in an irrigated low-stress nursery and yield in on-farm trials that sample the range of water-limited environments of the target population of environments.

Optimal Bayesian experimental design for models with intractable likelihoods using indirect inference applied to biological process models

This paper addresses the problem of determining optimal designs for biological process models with intractable likelihoods, with the goal of parameter inference. The Bayesian approach is to choose a design that maximises the mean of a utility, and the utility is a function of the posterior distribution. Therefore, its estimation requires likelihood evaluations. However, many problems in experimental design involve models with intractable likelihoods, that is, likelihoods that are neither analytic nor can be computed in a reasonable amount of time. We propose a novel solution using indirect inference (II), a well established method in the literature, and the Markov chain Monte Carlo (MCMC) algorithm of Müller et al. (2004). Indirect inference employs an auxiliary model with a tractable likelihood in conjunction with the generative model, the assumed true model of interest, which has an intractable likelihood. Our approach is to estimate a map between the parameters of the generative and auxiliary models, using simulations from the generative model. An II posterior distribution is formed to expedite utility estimation. We also present a modification to the utility that allows the Müller algorithm to sample from a substantially sharpened utility surface, with little computational effort. Unlike competing methods, the II approach can handle complex design problems for models with intractable likelihoods on a continuous design space, with possible extension to many observations. The methodology is demonstrated using two stochastic models; a simple tractable death process used to validate the approach, and a motivating stochastic model for the population evolution of macroparasites.

Effectiveness of a puppet show on iodine knowledge, attitudes and behaviour of elementary students and the indirect effects on their parents and households in Ho Chi Minh City : a pilot study

Deficiencies in iodine levels have been shown to seriously affect a child’s intellectual development and learning capacity.1 In South-East Asia, iodine deficiency remains a major public health concern. Approximately 30% of the region’s population of 503.6 million have insufficient iodine intake, and only 61% of households have access to iodized salt.1 For this reason, it is necessary to initiate effective, community-based health promotion activities that are targeted toward populations of various ages. A puppet show is one imaginative and entertaining method of health education that has been advocated for use in communicating positive health behaviours to children.2e5 The authors undertook a literature review and found no studies assessing the effectiveness of a puppet show to teach an iodine education programme...

Indirect effects of phytochemicals on offspring performance of Queensland fruit fly, Bactrocera tryoni (Diptera: Tephritidae)

Phytochemical lures such as methyl eugenol (ME) and cue-lure are used in the management of Bactrocera fruit flies for monitoring and control. These lures are not just attractants, but also trigger physiological changes in males that lead to enhanced mating success. Additionally, in the cue-lure-responsive Bactrocera tryoni, females mated with lure-fed males exhibit changes in fecundity, remating receptivity and longevity. While the lures show current generation effects, no research has been carried out on possible multigenerational effects, although such effects have been hypothesized within a ‘sexy-son’ sexual selection model. In this study, we test for indirect, cross-generational effects of lure exposure in F1offspring of B. tryoni females mated with cue-lure-fed, zingerone-fed and lure-unfed (=control) males. The F1 attributes we recorded were immature development time, immature survival, adult survival and adult male lure foraging. No significant differences were found between treatments for any of the three life-history measurements, except that the offspring sired by zingerone-fed males had a longer egg development time than cue-lure and control offspring. However, indirect exposure to lures significantly enhanced the lure-foraging ability of F1 adult males. More offspring of cue-lure-fed males arrived at a lure source in both large flight cages and small laboratory cages over a 2-h period than did control males. The offspring of zingerone-fed males were generally intermediate between cue-lure and control offspring. This study provides the first evidence of a next generation effect of fruit fly male lures. While the results of this study support a ‘sexy-son’ sexual selection mechanism for the evolution of lure response in Bactrocera fruit flies, our discussion urges caution in interpreting our results in this way.

Regional aviation and economic growth : cointegration and causality analysis in Australia

Air transport is a critical link to regional, rural and remote communities in Australia. Air services provide important economic and social benefits but very little research has been done on assessing the value of regional aviation. This research provides the first empirical evidence that there is short and long run causality between regional aviation and economic growth. The authors analysed 88 regional airports in Australia over a period of 1985–86 to 2010–11 to determine the catalytic impacts of regional air transport on regional economic growth. The analysis was conducted using annual data related to total airport passenger movements – for the level of airport activity, and real aggregate taxable income – to represent economic growth. A significant bi-directional relationship was established: airports have an impact on regional economic growth and the economy directly impacts regional air transport. The economic significance of regional air transport confirms the importance of the airport as infrastructure for regional councils and the need for them to maintain and develop local airports. Funding should be targeted at airports directly to support regional development.

Research on injury compensation and health outcomes : ignoring the problem of reverse causality led to a biased conclusion

Objective This study highlights the serious consequences of ignoring reverse causality bias in studies on compensation-related factors and health outcomes and demonstrates a technique for resolving this problem of observational data. Study Design and Setting Data from an English longitudinal study on factors, including claims for compensation, associated with recovery from neck pain (whiplash) after rear-end collisions are used to demonstrate the potential for reverse causality bias. Although it is commonly believed that claiming compensation leads to worse recovery, it is also possible that poor recovery may lead to compensation claims—a point that is seldom considered and never addressed empirically. This pedagogical study compares the association between compensation claiming and recovery when reverse causality bias is ignored and when it is addressed, controlling for the same observable factors. Results When reverse causality is ignored, claimants appear to have a worse recovery than nonclaimants; however, when reverse causality bias is addressed, claiming compensation appears to have a beneficial effect on recovery, ceteris paribus. Conclusion To avert biased policy and judicial decisions that might inadvertently disadvantage people with compensable injuries, there is an urgent need for researchers to address reverse causality bias in studies on compensation-related factors and health.

Prevalence of retinopathy among adults with self-reported diabetes mellitus: the Sri Lanka diabetes and Cardiovascular Study

Background: At present there are no large scale nationally-representative studies from Sri Lanka on the prevalence and associations of Diabetic Retinopathy (DR). The present study aims to evaluate the prevalence and risk factors for DR in a community-based nationally-representative sample of adults with self-reported diabetes mellitus from Sri Lanka. Methods: A cross-sectional community-based national study among 5,000 adults (≥18 years) was conducted in Sri Lanka, using a multi-stage stratified cluster sampling technique. An interviewer-administered questionnaire was used to collect data. Ophthalmological evaluation of patients with ‘known’ diabetes (previously diagnosed at a government hospital or by a registered medical practitioner) was done using indirect ophthalmoscopy. A binary-logistic regression analysis was performed with ‘presence of DR’ as the dichotomous dependent variable and other independent covariates. Results: Crude prevalence of diabetes was 12.0%(n=536),of which 344 were patients with ‘known’ diabetes.Mean age was 56.4 ± 10.9 years and 37.3% were males. Prevalence of any degree of DR was 27.4% (Males-30.5%, Females-25.6%; p = 0.41). In patients with DR, majority had NPDR (93.4%), while 5.3% had maculopathy. Patients with DR had a significantly longer duration of diabetes than those without. In the binary-logistic regression analysis in all adults duration of diabetes (OR:1.07), current smoking (OR:1.67) and peripheral neuropathy (OR:1.72)all were significantly associated with DR. Conclusions: Nearly 1/3rd of Sri Lankan adults with self-reported diabetes are having retinopathy. DR was associated with diabetes duration, cigarette smoking and peripheral neuropathy. However, further prospective follow up studies are required to establish causality for identified risk factors

Using genomic data to make indirect (and unauthorized) estimates of disease risk

The number of genetic factors associated with common human traits and disease is increasing rapidly, and the general public is utilizing affordable, direct-to-consumer genetic tests. The results of these tests are often in the public domain. A combination of factors has increased the potential for the indirect estimation of an individual's risk for a particular trait. Here we explain the basic principals underlying risk estimation which allowed us to test the ability to make an indirect risk estimation from genetic data by imputing Dr. James Watson's redacted apolipoprotein E gene (APOE) information. The principles underlying risk prediction from genetic data have been well known and applied for many decades, however, the recent increase in genomic knowledge, and advances in mathematical and statistical techniques and computational power, make it relatively easy to make an accurate but indirect estimation of risk. There is a current hazard for indirect risk estimation that is relevant not only to the subject but also to individuals related to the subject; this risk will likely increase as more detailed genomic data and better computational tools become available.