Control flow-based malware variant detection

Static detection of malware variants plays an important role in system security and control flow has been shown as an effective characteristic that represents polymorphic malware. In our research, we propose a similarity search of malware to detect these variants using novel distance metrics. We describe a malware signature by the set of control flowgraphs the malware contains. We use a distance metric based on the distance between feature vectors of string-based signatures. The feature vector is a decomposition of the set of graphs into either fixed size k-subgraphs, or q-gram strings of the high-level source after decompilation. We use this distance metric to perform pre-filtering. We also propose a more effective but less computationally efficient distance metric based on the minimum matching distance. The minimum matching distance uses the string edit distances between programs' decompiled flowgraphs, and the linear sum assignment problem to construct a minimum sum weight matching between two sets of graphs. We implement the distance metrics in a complete malware variant detection system. The evaluation shows that our approach is highly effective in terms of a limited false positive rate and our system detects more malware variants when compared to the detection rates of other algorithms. © 2013 IEEE.

Uses of management control and performance measurement systems by Deans and Heads in Australian universities: their effects on research, teaching and networking capabilities

Invoking a resource-based view (RBV), this study investigates relationships between management control systems (MCSs) use, including information use from performance measurement systems (PMSs), and organisational capabilities in the context of academic units of Australian universities. Increased competition and attention to distinctive capabilities amongst universities, particularly at their strategic operating unit level of a Faculty1 or School2, provides the setting for application of this theoretic perspective. Based on a questionnaire survey of all Faculty Deans and Heads of Schools in all 39 universities in Australia, evidence is provided on relationships between diagnostic and interactive use of MCSs, attention given to imposed and discretionary types of PMS information, the strength of capabilities of the academic unit and, in turn, overall performance of the academic unit. Highlights of findings are that Heads/Deans conceived capabilities of their unit in functional dimensions, not in generic dimensions as found in prior literature; interactive MCS use and imposed performance measures, respectively, direct relate to several types of capabilities and indirectly to performance of the academic unit, but diagnostic MCS use does not. The findings have practical implications for styles of control systems use and performance information use by management in universities.

Assessing the economic benefits of starling detection and control to Western Australia

The common starling (Sturnus vulgaris) has a proven invasion history in many countries, and at a continental scale in North America and Australasia. In Australia, starlings are firmly established throughout the eastern states and Tasmania. Incursions of starlings into Western Australia (WA) represent a significant threat to this State’s agricultural, public amenity and biodiversity assets. We present models of starling population dynamics that incorporate environmental and control effort variability. We incorporate knowledge of starling ecology with economic data to assess the potential economic cost of starlings establishing in WA, evaluating the cost–benefits for each management scenario. We calculated starling population size will approach carrying capacity in WA within as little as 30 years if left unchecked. A population of this size could cost the WA economy up to $43.7 million annually in 2011/2012 dollars. Over a 50 year horizon, the conservative benefit–cost ratio for ongoing detection and control at the current level of expenditure is 6.03:1. However, even under current levels of control, starling numbers are projected to increase to almost 11 million by 2061. Further improvements in the efficiency of starling detection and control and/or an increased level of expenditure on detection and control are required.

Nutritional intervention to prevent weight gain in patients commenced on olanzapine: a randomized controlled trial

Objective: Olanzapine is the most commonly prescribed atypical antipsychotic medication in Australia. Research reports an average weight gain of between 4.5 and 7 kg in the 3 months following its commencement. Trying to minimize this weight gain in a population with an already high prevalence of obesity, mortality and morbidity is of clinical and social importance. This randomized controlled trial investigated the impact of individual nutrition education provided by a dietitian on weight gain in the 3 and 6 months following the commencement of olanzapine.


Method: Fifty-one individuals (29 females, 22 males) who had started on olanzapine in the previous 3 months (mean length of 27 days ± 20) were recruited through Peninsula Health Psychiatric Services and were randomly assigned to either the intervention (n = 29) or the control group (n = 22). Individuals in the intervention group received six 1 hour nutrition education sessions over a 3-month period. Weight, waist circumference, body mass index (BMI) and qualitative measures of exercise levels, quality of life, health and body image were collected at baseline at 3 and 6 months.


Results: After 3 months, the control group had gained significantly more weight than the treatment group (6.0 kg vs 2.0 kg, p ≤ 0.002). Weight gain of more than 7% of initial weight occurred in 64% of the control group compared to 13% of the treatment group. The control group's BMI increased significantly more than the treatment group's (2 kg/m2vs 0.7 kg/m2, p ≤ 0.03). The treatment group reported significantly greater improvements in moderate exercise levels, quality of life, health and body image compared to the controls. At 6 months, the control group continued to show significantly more weight gain since baseline than the treatment group (9.9 kg vs 2.0 kg, p ≤ 0.013) and consequently had significantly greater increases in BMI (3.2 kg/m2vs 0.8 kg/m2, p ≤ 0.017).

Long-term cost-effectiveness of weight management in primary care

Background:  As obesity prevalence and health-care costs increase, Health Care providers must prevent and manage obesity cost-effectively.

Methods:  Using the 2006 NICE obesity health economic model, a primary care weight management programme (Counterweight) was analysed, evaluating costs and outcomes associated with weight gain for three obesity-related conditions (type 2 diabetes, coronary heart disease, colon cancer). Sensitivity analyses examined different scenarios of weight loss and background (untreated) weight gain.

Results:  Mean weight changes in Counterweight attenders was −3 kg and −2.3 kg at 12 and 24 months, both 4 kg below the expected 1 kg/year background weight gain. Counterweight delivery cost was £59.83 per patient entered. Even assuming drop-outs/non-attenders at 12 months (55%) lost no weight and gained at the background rate, Counterweight was ‘dominant’ (cost-saving) under ‘base-case scenario’, where 12-month achieved weight loss was entirely regained over the next 2 years, returning to the expected background weight gain of 1 kg/year. Quality-adjusted Life-Year cost was £2017 where background weight gain was limited to 0.5 kg/year, and £2651 at 0.3 kg/year. Under a ‘best-case scenario’, where weights of 12-month-attenders were assumed thereafter to rise at the background rate, 4 kg below non-intervention trajectory (very close to the observed weight change), Counterweight remained ‘dominant’ with background weight gains 1 kg, 0.5 kg or 0.3 kg/year.

Conclusion:  Weight management for obesity in primary care is highly cost-effective even considering only three clinical consequences. Reduced healthcare resources use could offset the total cost of providing the Counterweight Programme, as well as bringing multiple health and Quality of Life benefits.

Influence of body mass index on prescribing costs and potential cost savings of a weight management programme in primary care

Objectives Prescribed medications represent a high and increasing proportion of UK health care funds. Our aim was to quantify the influence of body mass index (BMI) on prescribing costs, and then the potential savings attached to implementing a weight management intervention.

Methods Paper and computer-based medical records were reviewed for all drug prescriptions over an 18-month period for 3400 randomly selected adult patients (18–75 years) stratified by BMI, from 23 primary care practices in seven UK regions. Drug costs from the British National Formulary at the time of the review were used. Multivariate regression analysis was applied to estimate the cost for all drugs and the ‘top ten’ drugs at each BMI point. This allowed the total and attributable prescribing costs to be estimated at any BMI. Weight loss outcomes achieved in a weight management programme (Counterweight) were used to model potential effects of weight change on drug costs. Anticipated savings were then compared with the cost programme delivery. Analysis was carried out on patients with follow-up data at 12 and 24 months as well as on an intention-to-treat basis. Outcomes from Counterweight were based on the observed lost to follow-up rate of 50%, and the assumption that those patients would continue a generally observed weight gain of 1 kg per year from baseline.

Results The minimum annual cost of all drug prescriptions at BMI 20 kg/m2 was £50.71 for men and £62.59 for women. Costs were greater by £5.27 (men) and £4.20 (women) for each unit increase in BMI, to a BMI of 25 (men £77.04, women £78.91), then by £7.78 and £5.53, respectively, to BMI 30 (men £115.93 women £111.23), then by £8.27 and £4.95 to BMI 40 (men £198.66, women £160.73). The relationship between increasing BMI and costs for the top ten drugs was more pronounced. Minimum costs were at a BMI of 20 (men £8.45, women £7.80), substantially greater at BMI 30 (men £23.98, women £16.72) and highest at BMI 40 (men £63.59, women £27.16). Attributable cost of overweight and obesity accounted for 23% of spending on all drugs with 16% attributable to obesity. The cost of the programme was estimated to be approximately £60 per patient entered. Modelling weight reductions achieved by the Counterweight weight management programme would potentially reduce prescribing costs by £6.35 (men) and £3.75 (women) or around 8% of programme costs at one year, and by £12.58 and £8.70, respectively, or 18% of programme costs after two years of intervention. Potential savings would be increased to around 22% of the cost of the programme at year one with full patient retention and follow-up.

Conclusion Drug prescriptions rise from a minimum at BMI of 20 kg/m2 and steeply above BMI 30 kg/m2. An effective weight management programme in primary care could potentially reduce prescription costs and lead to substantial cost avoidance, such that at least 8% of the programme delivery cost would be recouped from prescribing savings alone in the first year.

A unified mixed-integer programming model for simultaneous fluence weight and aperture optimization in VMAT, tomotherapy, and cyberknife

All rights reserved. In this paper, we propose and study a unified mixed-integer programming model that simultaneously optimizes fluence weights and multi-leaf collimator (MLC) apertures in the treatment planning optimization of VMAT, Tomotherapy, and CyberKnife. The contribution of our model is threefold: (i) Our model optimizes the fluence and MLC apertures simultaneously for a given set of control points. (ii) Our model can incorporate all volume limits or dose upper bounds for organs at risk (OAR) and dose lower bound limits for planning target volumes (PTV) as hard constraints, but it can also relax either of these constraint sets in a Lagrangian fashion and keep the other set as hard constraints. (iii) For faster solutions, we propose several heuristic methods based on the MIP model, as well as a meta-heuristic approach. The meta-heuristic is very efficient in practice, being able to generate dose- and machinery-feasible solutions for problem instances of clinical scale, e.g., obtaining feasible treatment plans to cases with 180 control points, 6750 sample voxels and 18,000 beamlets in 470 seconds, or cases with 72 control points, 8000 sample voxels and 28,800 beamlets in 352 seconds. With discretization and down-sampling of voxels, our method is capable of tackling a treatment field of 8000-64,000cm3, depending on the ratio of critical structure versus unspecified tissues.

Control and performance in IS projects: a meta-analysis of hierarchical and market-based control relationships

Literature on IS project control distinguishes between hierarchical and market-based control relationships. Prior studies typically investigate one of these two forms of control relationships in isolation. Hence, little is known about the differences between hierarchical and market-based control relationships. Responding to this gap, we analyze how the effects of control modes on IS project performance differ in hierarchical compared with market-based control relationships. Specifically, we conduct a metaanalysis to compare the effects of control modes on IS project performance reported in research on hierarchical and market-based control relationships. The results suggest that the effects of behavior and self-control on performance differ between these two forms of control relationships. Based on our results, we derive implications for complementary and substitutive effects between control modes, and for interrelations among hierarchical and market-based control relationships.