Formal verification of security protocol implementations: a survey

Automated formal verification of security protocols has been mostly focused on analyzing high-level abstract models which, however, are significantly different from real protocol implementations written in programming languages. Recently, some researchers have started investigating techniques that bring automated formal proofs closer to real implementations. This paper surveys these attempts, focusing on approaches that target the application code that implements protocol logic, rather than the libraries that implement cryptography. According to these approaches, libraries are assumed to correctly implement some models. The aim is to derive formal proofs that, under this assumption, give assurance about the application code that implements the protocol logic. The two main approaches of model extraction and code generation are presented, along with the main techniques adopted for each approach.     

Approximate range–sum query answering on data cubes with probabilistic guarantees

Approximate range aggregate queries are one of the most frequent and useful kinds of queries for Decision Support Systems (DSS), as they are widely used in many data analysis tasks. Traditionally, sampling-based techniques have been proposed to tackle this problem. However, their effectiveness degrade when the underlying data distribution is skewed. Another approach based on the outlier management can limit the effect of data skews but fails to address other requirements of approximate range aggregate queries, such as error guarantees and query processing efficiency. In this paper, we present a technique that provides approximate answers to range aggregate queries on OLAP data cubes efficiently, with theoretical guarantees on the errors. Our basic idea is to build different data structures to manage outliers and the rest of the data. Carefully chosen outliers are organized in a quad-tree based indexing data structure to provide efficient access for query processing. A query-workload adaptive, tree-like synopsis data structure, called T unable P artition-Tree (TP-Tree), is proposed to organize samples extracted from non-outlier data. Our experiments clearly demonstrate the merits of our technique, by comparing with previous well-known techniques.     

A new method for reliability analysis and reliability-based design optimization

Structural and Multidisciplinary Optimization - We present a novel method for reliability-based design optimization, which is based on the approximation of the safe region in the random space by a...     

Evapotranspiration on western U.S. rivers estimated using the Enhanced Vegetation Index from MODIS and data from eddy covariance and Bowen ratio flux towers

We combined remote sensing and in-situ measurements to estimate evapotranspiration (ET) from riparian vegetation over large reaches of western U.S. rivers and ET by individual plant types. ET measured from nine flux towers (eddy covariance and Bowen ratio) established in plant communities dominated by five major plant types on the Middle Rio Grande, Upper San Pedro River, and Lower Colorado River was strongly correlated with Enhanced Vegetation Index (EVI) values from the Moderate Resolution Imaging Spectrometer (MODIS) sensor on the NASA Terra satellite. The inclusion of maximum daily air temperatures (T_a) measured at the tower sites further improved this relationship. Sixteen-day composite values of EVI and T_a were combined to predict ET across species and tower sites (r² = 0.74); the regression equation was used to scale ET for 2000-2004 over large river reaches with T_a from meteorological stations. Measured and estimated ET values for these river segments were moderate when compared to historical, and often indirect, estimates and ranged from 851-874 mm yr^− 1. ET of individual plant communities ranged more widely. Cottonwood (Populus spp.) and willow (Salix spp.) stands generally had the highest annual ET rates (1100-1300 mm yr^− 1), while mesquite (Prosopis velutina) (400-1100 mm yr^− 1) and saltcedar (Tamarix ramosissima) (300-1300 mm yr^− 1) were intermediate, and giant sacaton (Sporobolus wrightii) (500-800 mm yr^− 1) and arrowweed (Pluchea sericea) (300-700 mm yr^− 1) were the lowest. ET rates estimated from the flux towers and by remote sensing in this study were much lower than values estimated for riparian water budgets using crop coefficient methods for the Middle Rio Grande and Lower Colorado River.     

Lamb Meat Quality Assessment by Support Vector Machines

The correct assessment of meat quality (i.e., to fulfill the consumer’s needs) is crucial element within the meat industry. Although there are several factors that affect the perception of taste, tenderness is considered the most important characteristic. In this paper, a Feature Selection procedure, based on a Sensitivity Analysis, is combined with a Support Vector Machine, in order to predict lamb meat tenderness. This real-world problem is defined in terms of two difficult regression tasks, by modeling objective (e.g. Warner–Bratzler Shear force) and subjective (e.g. human taste panel) measurements. In both cases, the proposed solution is competitive when compared with other neural (e.g. Multilayer Perceptron) and Multiple Regression approaches.