Usability evaluation of anti-phishing toolbars

Phishing is considered as one of the most serious threats for the Internet and e-commerce. Phishing attacks abuse trust with the help of deceptive e-mails, fraudulent web sites and malware. In order to prevent phishing attacks some organizations have implemented Internet browser toolbars for identifying deceptive activities. However, the levels of usability and user interfaces are varying. Some of the toolbars have obvious usability problems, which can affect the performance of these toolbars ultimately. For the sake of future improvement, usability evaluation is indispensable. We will discuss usability of five typical anti-phishing toolbars: built-in phishing prevention in the Internet Explorer 7.0, Google toolbar, Netcraft Anti-phishing toolbar and SpoofGuard. In addition, we included Internet Explorer plug-in we have developed, Anti-phishing IEPlug. Our hypothesis was that usability of anti-phishing toolbars, and as a consequence also security of the toolbars, could be improved. Indeed, according to the heuristic usability evaluation, a number of usability issues were found. In this article, we will describe the anti-phishing toolbars, we will discuss anti-phishing toolbar usability evaluation approach and we will present our findings. Finally, we will propose advices for improving usability of anti-phishing toolbars, including three key components of anti-phishing client side applications (main user interface, critical warnings and the help system). For example, we found that in the main user interface it is important to keep the user informed and organize settings accordingly to a proper usability design. In addition, all the critical warnings an anti-phishing toolbar shows should be well designed. Furthermore, we found that the help system should be built to assist users to learn about phishing prevention as well as how to identify fraud attempts by themselves. One result of our research is also a classification of anti-phishing toolbar applications. Linfeng Li is a student at the University of Tampere, Finland. Marko Helenius is Assistant Professor at the Department of Computer Sciences, University of Tampere, Finland.     

A novel split-and-merge algorithm for hierarchical clustering of Gaussian mixture models

The paper presents a novel split-and-merge algorithm for hierarchical clustering of Gaussian mixture models, which tends to improve on the local optimal solution determined by the initial constellation. It is initialized by local optimal parameters obtained by using a baseline approach similar to k-means, and it tends to approach more closely to the global optimum of the target clustering function, by iteratively splitting and merging the clusters of Gaussian components obtained as the output of the baseline algorithm. The algorithm is further improved by introducing model selection in order to obtain the best possible trade-off between recognition accuracy and computational load in a Gaussian selection task applied within an actual recognition system. The proposed method is tested both on artificial data and in the framework of Gaussian selection performed within a real continuous speech recognition system, and in both cases an improvement over the baseline method has been observed.     

NICE h : a higher-order explicit numerical scheme for integration of constitutive models in plasticity

The article introduces, as a result of further development of the first-order scheme NICE, a simple and efficient higher-order explicit numerical scheme for the integration of a system of ordinary differential equations which is constrained by an algebraic condition (DAE). The scheme is based on the truncated Taylor expansion of the constraint equation with order h of the scheme being determined by the highest exponent in the truncated Taylor series. The integration scheme thus conceived will be named NICE ^h , considering both principal premises of its construction. In conjunction with a direct solution technique used to solve the boundary value problem, the NICE ^h scheme is very convenient for integrating constitutive models in plasticity. The plasticity models are defined mostly by a system of algebraic and differential equations in which the yield criterion represents the constraint condition. To study the properties of the new integration scheme, which, like the forward-Euler scheme, is characterised by its implementation simplicity due to the explicitness of its formulations, a damage constitutive model (Gurson–Tvergaard–Needleman model) is considered. The general opinion that the implicit backward-Euler scheme is much more accurate than the thus-far known explicit schemes is challenged by the introduction of the NICE ^h scheme. The accuracy of the higher-order explicit scheme in the studied cases is significantly higher than the accuracy of the classical backward-Euler scheme, if we compare them under the condition of a similar CPU time consumption.     

Semantic Mediation for Standard-Based B2B Interoperability

The authors discuss a semantic-mediation architecture to advance traditional approaches for standards-based business-to-business (B2B) interoperability. The architecture is supported by the Athena Knowledge Representation and Semantics Mediation tool suite. Initial experimentations with the architecture and the toolset offer discussions of key architectural and functional aspects and suggest directions for future tools enhancements.     

Neural network Reinforcement Learning for visual control of robot manipulators

It is known that most of the key problems in visual servo control of robots are related to the performance analysis of the system considering measurement and modeling errors. In this paper, the development and performance evaluation of a novel intelligent visual servo controller for a robot manipulator using neural network Reinforcement Learning is presented. By implementing machine learning techniques into the vision based control scheme, the robot is enabled to improve its performance online and to adapt to the changing conditions in the environment. Two different temporal difference algorithms (Q-learning and SARSA) coupled with neural networks are developed and tested through different visual control scenarios. A database of representative learning samples is employed so as to speed up the convergence of the neural network and real-time learning of robot behavior. Moreover, the visual servoing task is divided into two steps in order to ensure the visibility of the features: in the first step centering behavior of the robot is conducted using neural network Reinforcement Learning controller, while the second step involves switching control between the traditional Image Based Visual Servoing and the neural network Reinforcement Learning for enabling approaching behavior of the manipulator. The correction in robot motion is achieved with the definition of the areas of interest for the image features independently in both control steps. Various simulations are developed in order to present the robustness of the developed system regarding calibration error, modeling error, and image noise. In addition, a comparison with the traditional Image Based Visual Servoing is presented. Real world experiments on a robot manipulator with the low cost vision system demonstrate the effectiveness of the proposed approach.     

Knowledge based quality-driven architecture design and evaluation

Modelling and evaluating quality properties of software is of high importance, especially when our every day life depends on the quality of services produced by systems and devices embedded into our surroundings. This paper contributes to the body of research in quality and model driven software engineering. It does so by introducing; (1) a quality aware software architecting approach and (2) a supporting tool chain. The novel approach with supporting tools enables the systematic development of high quality software by merging benefits of knowledge modelling and management, and model driven architecture design enhanced with domain-specific quality attributes. The whole design flow of software engineering is semi-automatic; specifying quality requirements, transforming quality requirements to architecture design, representing quality properties in architectural models, predicting quality fulfilment from architectural models, and finally, measuring quality aspects from implemented source code. The semi-automatic design flow is exemplified by the ongoing development of a secure middleware for peer-to-peer embedded systems.     

A study of moderately thick quadrilateral plate elements based on the absolute nodal coordinate formulation

Finite element analysis using plate elements based on the absolute nodal coordinate formulation (ANCF) can predict the behaviors of moderately thick plates subject to large deformation. However, the formulation is subject to numerical locking, which compromises results. This study was designed to investigate and develop techniques to prevent or mitigate numerical locking phenomena. Three different ANCF plate element types were examined. The first is the original fully parameterized quadrilateral ANCF plate element. The second is an update to this element that linearly interpolates transverse shear strains to overcome slow convergence due to transverse shear locking. Finally, the third is based on a new higher order ANCF plate element that is being introduced here. The higher order plate element makes it possible to describe a higher than first-order transverse displacement field to prevent Poisson thickness locking. The term “higher order” is used, because some nodal coordinates of the new plate element are defined by higher order derivatives. The performance of each plate element type was tested by (1) solving a comprehensive set of small deformation static problems, (2) carrying out eigenfrequency analyses, and (3) analyzing a typical dynamic scenario. The numerical calculations were made using MATLAB. The results of the static and eigenfrequency analyses were benchmarked to reference solutions provided by the commercially available finite element software ANSYS. The results show that shear locking is strongly dependent on material thickness. Poisson thickness locking is independent of thickness, but strongly depends on the Poisson effect. Poisson thickness locking becomes a problem for both of the fully parameterized element types implemented with full 3-D elasticity. Their converged results differ by about 18 % from the ANSYS results. Corresponding results for the new higher order ANCF plate element agree with the benchmark. ANCF plate elements can describe the trapezoidal mode; therefore, they do not suffer from Poisson locking, a reported problem for fully parameterized ANCF beam elements. For cases with shear deformation loading, shear locking slows solution convergence for models based on either the original fully parameterized plate element or the newly introduced higher order plate element.     

Neural Net Based Hybrid Modeling of the Methanol Synthesis Process

A Hybrid modeling approach, combining an analytical model with a radial basis function neural network is introduced in this paper. The modeling procedure is combined with genetic algorithm based feature selection designed to select informative variables from the set of available measurements. By only using informative inputs, the model's generalization ability can be enhanced. The approach proposed is applied to modeling of the liquid–phase methanol synthesis. It is shown that a hybrid modeling approach exploiting available a priori knowledge and experimental data can considerably outperform a purely analytical approach.     

Stereoselective hybrid catalysts: new opportunities

The combination of biotransformations and metal‐assisted catalysis within hybrid systems represents a promising strategy to extend the repertoire of reactions enabled in a biomolecular environment. Both covalent catalytic entities and supramolecular assemblies have been created recently to mediate transformations otherwise difficult to perform in ‘classical’ biocatalysis. In this context, various biopolymers can serve as ligand spheres simultaneously acting as chiral inducers. In particular, modular approaches embedding novel metal‐based catalytic centers via an affinity interaction are highly appealing, as this technology may serve as a versatile platform to incorporate diverse reactivities in a biological matrix in a stereoselective fashion. Copyright © 2007 Society of Chemical Industry     

Is cluster policy useful for the energy sector? Assessing self-declared hydrogen clusters in the Netherlands

Research on science-based industries has shown that it is important for organisations to be active in interorganisational networks. Cluster policy has been developed as a means to stimulate the development of these networks and thereby the success rate of these industries. Cluster policy is however not a common policy instrument in the energy sector. In this paper, we focus on three self-declared clusters active in hydrogen-related R&D in the Netherlands and address several characteristics of these clusters. We conclude that cluster policy is a useful addition to existing energy R&D policies but that monitoring whether self-declared clusters actually function as clusters and what their contribution is to the overall system is pivotal in reaping the benefits of cluster policy.