Abstraction-carrying code: a model for mobile code safety

Proof-Carrying Code (PCC) is a general approach to mobile code safety in which programs are augmented with a certificate (or proof). The intended benefit is that the program consumer can locally validate the certificate w.r.t. the "untrustcd" program by means of a certificate checker a process which should be much simpler, efficient, and automatic than generating the original proof. The practical uptake of PCC greatly depends on the existence of a variety of enabling technologies which allow both proving programs correct and replacing a costly verification process by an efficient checking proceduri on th( consumer side. In this work we propose Abstraction- Carrying Code (ACC), a novel approach which uses abstract interpretation as enabling technology. We argue that the large body of applications of abstract interpretation to program verification is amenable to the overall PCC scheme. In particular, we rely on an expressive class of safely policies which can be defined over different abstract domains. We use an abstraction (or abstract model) of the program computed by standard static analyzers as a certificate. The validity of the abstraction on ihe consumer side is checked in a single pass by a very efficient and specialized abstract-interpreter. We believe that ACC brings the expressiveness, flexibility and automation which is inherent in abstract interpretation techniques to the area of mobile code safety.

Determinant factors to discriminate the body weight loss in overweight people

Introduction. Most studies have described how the weight loss is when different treatments are compared (1-3), while others have also compared the weight loss by sex (4), or have taken into account psychosocial (5) and lifestyle (6, 7) variables. However, no studies have examined the interaction of different variables and the importance of them in the weight loss. Objective. Create a model to discriminate the range of weight loss, determining the importance of each variable. Methods. 89 overweight people (BMI: 25-29.9 kg?m-2), aged from 18 to 50 years, participated in the study. Four types of treatments were randomly assigned: strength training (S), endurance training (E), strength and endurance training (SE), and control group (C). All participants followed a 25% calorie restriction diet. Two multivariate discriminant models including the variables age, sex, height, daily energy expenditure (EE), type of treatment (T), caloric restriction (CR), initial body weight (BW), initial fat mass (FM), initial muscle mass (MM) and initial bone mineral density (BMD) were performed having into account two groups: the first and fourth quartile of the % of weight loss in the first model; the groups above and below the mean of the % of weight loss in the second model. The discriminant models were built using the inclusion method in SPSS allowing us to find a function that could predict the body weight loss range that an overweight person could achieve in a 6 months weight loss intervention.Results. The first discriminant analysis predicted that a combination of the studied variables would discriminate between the two ranges of body weight loss with 81.4% of correct classification. The discriminant function obtained was (Wilks? Lambda=0.475, p=0.003): Discriminant score=-18.266-(0.060xage)- (1.282xsex[0=female;1=male])+(14.701xheight)+(0.002xEE)- (0.006xT[1=S;2=E;3=SE;4=C])-(0.047xCR)- (0.558xBW)+(0.475xFM)+(0.398xMM)+(3.499xBMD) The second discriminant model obtained would discriminate between the two groups of body weight loss with 74.4% of correct classification. The discriminant function obtained was (Wilks? Lambda=0.725, p=0.005): Discriminant score=-5.021-(0.052xage)- (0.543xsex[0=female;1=male])+(3.530xheight)+(0.001xEE)- (0.493xT[1=S;2=E;3=SE;4=C])+(0.003xCR)- (0.365xBW)+(0.368xFM)+(0.296xMM)+(4.034xBMD) Conclusion. The first developed model could predict the percentage of weight loss in the following way: if the discriminant score is close to 1.051, the range of weight loss will be from 7.44 to -4.64% and if it is close to - 1.003, the range will be from -11.03 to -25,00% of the initial body weight. With the second model if the discriminant score is close to 0.623 the body weight loss will be above -7.93% and if it is close to -0.595 will be below - 7.93% of the initial body weight. References. 1. Brochu M, et al. Resistance training does not contribute to improving the metabolic profile after a 6-month weight loss program in overweight and obese postmenopausal women. J Clin Endocrinol Metab. 2009 Sep;94(9):3226-33. 2. Del Corral P, et al. Effect of dietary adherence with or without exercise on weight loss: a mechanistic approach to a global problem. J Clin Endocrinol Metab. 2009 May;94(5):1602-7. 3. Larson-Meyer DE, et al. Caloric Restriction with or without Exercise: The Fitness vs. Fatness Debate. Med Sci Sports Exerc. 2010;42(1):152-9. 4. Hagan RD, et al. The effects of aerobic conditioning and/or caloric restriction in overweight men and women. Medicine & Science in Sports & Exercise. 1986;18(1):87-94. 5. Teixeira PJ, et al. Mediators of weight loss and weight loss maintenance in middle-aged women. Obesity (Silver Spring). 2010 Apr;18(4):725-35. 6. Bautista-Castano I, et al. Variables predictive of adherence to diet and physical activity recommendations in the treatment of obesity and overweight, in a group of Spanish subjects. Int J Obes Relat Metab Disord. 2004 May;28(5):697-705.