Influence of Cu<Superscript>2+</Superscript> ions on structural and magnetic properties of NiZn ferrites

The paper is devoted to the preparation of NiZn ferrite with small substitutions of copper by means of ceramic technology. The influence of small Cu substitution on the microstructural and magnetic properties of NiZn ferrites have been analysed by means of various experimental methods and interpreted from the point of view of preparation technology optimisation and possible applications of such materials. A strong correlation between the substituent content and resulting properties has been observed, thus allowing preparation of material with the properties tailored for any particular application.     

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.     

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.     

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.     

Influence of Field Effects on the Performance of InGaAs-Based Terahertz Radiation Detectors

A detailed electrical characterization of high-performance bow-tie InGaAs-based terahertz detectors is presented along with simulation results. The local surface potential and tunnelling current were scanned over the surfaces of the detectors by means of Kelvin probe force microscopy (KPFM) and scanning tunnelling microscopy (STM), which also enabled the determination of the Fermi level. Current-voltage curves were measured and modelled using the Synopsys Sentaurus TCAD package to gain deeper insight into the processes involved in detector operation. In addition, we performed finite-difference time-domain (FDTD) simulations to reveal features related to changes in the electric field due to the metal detector contacts. The investigation revealed that field-effect-induced conductivity modulation is a possible mechanism contributing to the high sensitivity of the studied detectors.     

Optimal attacks on qubit-based Quantum Key Recycling

Quantum Key Recycling (QKR) is a quantum cryptographic primitive that allows one to reuse keys in an unconditionally secure way. By removing the need to repeatedly generate new keys, it improves communication efficiency. ?kori? and de Vries recently proposed a QKR scheme based on 8-state encoding (four bases). It does not require quantum computers for encryption/decryption but only single-qubit operations. We provide a missing ingredient in the security analysis of this scheme in the case of noisy channels: accurate upper bounds on the required amount of privacy amplification. We determine optimal attacks against the message and against the key, for 8-state encoding as well as 4-state and 6-state conjugate coding. We provide results in terms of min-entropy loss as well as accessible (Shannon) information. We show that the Shannon entropy analysis for 8-state encoding reduces to the analysis of quantum key distribution, whereas 4-state and 6-state suffer from additional leaks that make them less effective. From the optimal attacks we compute the required amount of privacy amplification and hence the achievable communication rate (useful information per qubit) of qubit-based QKR. Overall, 8-state encoding yields the highest communication rates.     

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.     

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.