Flow-based reputation with uncertainty: evidence-based subjective logic

The concept of reputation is widely used as a measure of trustworthiness based on ratings from members in a community. The adoption of reputation systems, however, relies on their ability to capture the actual trustworthiness of a target. Several reputation models for aggregating trust information have been proposed in the literature. The choice of model has an impact on the reliability of the aggregated trust information as well as on the procedure used to compute reputations. Two prominent models are flow-based reputation (e.g., EigenTrust, PageRank) and subjective logic-based reputation. Flow-based models provide an automated method to aggregate trust information, but they are not able to express the level of uncertainty in the information. In contrast, subjective logic extends probabilistic models with an explicit notion of uncertainty, but the calculation of reputation depends on the structure of the trust network and often requires information to be discarded. These are severe drawbacks. In this work, we observe that the ‘opinion discounting’ operation in subjective logic has a number of basic problems. We resolve these problems by providing a new discounting operator that describes the flow of evidence from one party to another. The adoption of our discounting rule results in a consistent subjective logic algebra that is entirely based on the handling of evidence. We show that the new algebra enables the construction of an automated reputation assessment procedure for arbitrary trust networks, where the calculation no longer depends on the structure of the network, and does not need to throw away any information. Thus, we obtain the best of both worlds: flow-based reputation and consistent handling of uncertainties.     

Learning Fast Emulators of Binary Decision Processes

Computation time is an important performance characteristic of computer vision algorithms. The paper shows how existing (slow) binary decision algorithms can be approximated by a (fast) trained WaldBoost classifier. WaldBoost learning minimises the decision time of the classifier while guaranteeing predefined precision. We show that the WaldBoost algorithm together with bootstrapping is able to efficiently handle an effectively unlimited number of training examples provided by the implementation of the approximated algorithm. Two interest point detectors, the Hessian-Laplace and the Kadir-Brady saliency detectors, are emulated to demonstrate the approach. Experiments show that while the repeatability and matching scores are similar for the original and emulated algorithms, a 9-fold speed-up for the Hessian-Laplace detector and a 142-fold speed-up for the Kadir-Brady detector is achieved. For the Hessian-Laplace detector, the achieved speed is similar to SURF, a popular and very fast handcrafted modification of Hessian-Laplace; the WaldBoost emulator approximates the output of the Hessian-Laplace detector more precisely.     

Improved Approximations for Guarding 1.5-Dimensional Terrains

We present a 4-approximation algorithm for the problem of placing the fewest guards on a 1.5D terrain so that every point of the terrain is seen by at least one guard. This improves on the previous best approximation factor of 5 (see King in Proceedings of the 13th Latin American Symposium on Theoretical Informatics, pp. 629–640, 2006). Unlike most of the previous techniques, our method is based on rounding the linear programming relaxation of the corresponding covering problem. Besides the simplicity of the analysis, which mainly relies on decomposing the constraint matrix of the LP into totally balanced matrices, our algorithm, unlike previous work, generalizes to the weighted and partial versions of the basic problem.     

Recognizing malicious software behaviors with tree automata inference

We explore how formal methods and tools of the verification trade could be used for malware detection and analysis. In particular, we propose a new approach to learning and generalizing from observed malware behaviors based on tree automata inference. Our approach infers k-testable tree automata from system call dataflow dependency graphs. We show how inferred automata can be used for malware recognition and classification.     

Input‐output triggered control using ‐stability over finite horizons

This paper investigates stability of nonlinear control systems under intermittent information. Following recent results in the literature, we replace the traditional periodic paradigm, where the up‐to‐date information is transmitted and control laws are executed in a periodic fashion, with the event‐triggered paradigm. Building on the small gain theorem, we develop input–output triggered control algorithms yielding stable closed‐loop systems. In other words, based on the currently available (but outdated) measurements of the outputs and external inputs of a plant, a mechanism triggering when to obtain new measurements and update the control inputs is provided. Depending on the noise in the environment, the developed algorithm yields stable, asymptotically stable, and ‐stable (with bias) closed‐loop systems. Control loops are modeled as interconnections of hybrid systems for which novel results on ‐stability are presented. The prediction of a triggering event is achieved by employing ‐gains over a finite horizon. By resorting to convex programming, a method to compute ‐gains over a finite horizon is devised. Finally, our approach is successfully applied to a trajectory tracking problem for unicycles. Copyright © 2014 John Wiley & Sons, Ltd.     

A Bayesian approach to forecasting daily air-pollutant levels

Forecasting air-pollutant levels is an important issue, due to their adverse effects on public health, and often a legislative necessity. The advantage of Bayesian methods is their ability to provide density predictions which can easily be transformed into ordinal or binary predictions given a set of thresholds. We develop a Bayesian approach to forecasting PM\(_{10}\) and O\(_3\) levels that efficiently deals with extensive amounts of input parameters, and test whether it outperforms classical models and experts. The new approach is used to fit models for PM\(_{10}\) and O\(_3\) level forecasting that can be used in daily practice. We also introduce a novel approach for comparing models to experts based on estimated cost matrices. The results for diverse air quality monitoring sites across Slovenia show that Bayesian models outperform classical models in both PM\(_{10}\) and O\(_3\) predictions. The proposed models perform better than experts in PM\(_{10}\) and are on par with experts in O\(_3\) predictions—where experts already base their predictions on predictions from a statistical model. A Bayesian approach—especially using Gaussian processes—offers several advantages: superior performance, robustness to overfitting, more information, and the ability to efficiently adapt to different cost matrices.     

Simulation of liquid micro-jet in free expanding high-speed co-flowing gas streams

We present the development of an experimentally validated computational fluid dynamics model for liquid micro jets. Such jets are produced by focusing hydrodynamic momentum from a co-flowing sheath of gas on a liquid stream in a nozzle. The numerical model based on laminar two-phase, Newtonian, compressible Navier–Stokes equations is solved with finite volume method, where the phase interface is treated by the volume of fluid approach. A mixture model of the two-phase system is solved in axisymmetry using?~?300,000 finite volumes, while ensuring mesh independence with the finite volumes of the size 0.25 µm in the vicinity of the jet and drops. The numerical model is evaluated by comparing jet diameters and jet lengths obtained experimentally and from scaling analysis. They are not affected by the strong temperature and viscosity changes in the focusing gas while expanding at nozzle outlet. A range of gas and liquid-operating parameters is investigated numerically to understand their influence on the jet performance. The study is performed for gas and liquid Reynolds numbers in the range 17–1222 and 110–215, and Weber numbers in the range 3–320, respectively. A reasonably good agreement between experimental and scaling results is found for the range of operating parameters never tackled before. This study provides a basis for further computational designs as well as adjustments of the operating conditions for specific liquids and gases.     

Recovery of flavonoids from grape skins by enzyme-assisted extraction

Grape skin contains large amounts of different flavonoids so it can be used for their recovery. Optimization of enzyme-assisted extraction of flavonoids was conducted using oenological enzyme preparations with respect to enzyme dosage, temperature, extraction time, pH, and enzyme preparation. Optimal conditions were obtained using enzyme preparation Lallzyme EX-V, at the temperature of 45°C, time of 3 h, pH 2.0, and enzyme dosage of 10.52 mg/g. The new optimized extraction method is less expensive, simple, accurate, and selective for the recovery of simple flavonoids. It is based on an environmentally-friendly extraction solvent which may provide a valuable alternative to conventional methods.     

(<Emphasis Type="Italic">E,E</Emphasis>)-α-Farnesene,an Alarm Pheromone of the Termite <Emphasis Type="Italic">Prorhinotermes canalifrons</Emphasis>

The behavioral and electroantennographic responses of Prorhinotermes canalifrons to its soldier frontal gland secretion, and two separated major components of the secretion, (E)-1-nitropentadec-1-ene and (E,E)-α-farnesene, were studied in laboratory experiments. Behavioral experiments showed that both the frontal gland secretion and (E,E)-α-farnesene triggered alarm reactions in P. canalifrons, whereas (E)-1-nitropentadec-1-ene did not affect the behavior of termite groups. The alarm reactions were characterized by rapid walking of activated termites and efforts to alert and activate other members of the group. Behavioral responses to alarm pheromone differed between homogeneous and mixed groups, suggesting complex interactions. Antennae of both soldiers and pseudergates were sensitive to the frontal gland secretion and to (E,E)-α-farnesene, but soldiers showed stronger responses. The dose responses to (E,E)-α-farnesene were identical for both soldiers and pseudergates, suggesting that both castes use similar receptors to perceive (E,E)-α-farnesene. Our data confirm (E,E)-α-farnesene as an alarm pheromone of P. canalifrons.     

Visualization of trends using RadViz

Data mining is sometimes treating data consisting of items representing measurements of a single property taken in different time points. In this case data can be understood as a time series of one feature. It is no exception when the clue for evaluation of such data is related to their development trends as observed in several successive time points. From the qualitative point of view one can distinguish three basic types of behaviour between two neighbouring time points: the value of the feature is stable (remains the same), it grows or it falls. This paper is concerned with identification of typical qualitative development patterns as they appear in the windows of given length in the considered time-stamped data and their utilization for specification of interesting subgroups.