Soups 2006

A report of the second annual Symposium on Usable Privacy and Security (SOUPS 2006) held at Carnegie Mellon University (CMU) 12-14 July 2006.     

Oscillation in nonlinear difference equations

In this paper, we shall discuss oscillatory behavior of the solutions of difference equations, including the self-adjoint second-order linear equation and the discrete version of the nonlinear wave equation. Our work is to give sufficient conditions such that every nontrivial solution of the equations oscillates.     

Small-signal and transient analysis of a zero-voltage-switched,phase-controlled PWM converter using averaged switch model

An averaged switch model for a popular zero-voltage-switched (ZVS), phase-controlled PWM (PC-PWM) converter is developed. The model can be easily implemented in a circuit analysis program such as PSPICE to derive small-signal characteristics as well as predict large-signal transient behaviors of the converter in both continuous and discontinuous conduction modes. Using the averaged approach, the required computer time is greatly reduced. Such a model is vitally important for studying behavior of large systems such as parallel modules. A PSPICE subcircuit implementing the zero-voltage-switching PWM function is introduced. Simulation results illustrating both small and large-signal behaviors of the converter are presented     

The super-connected property of recursive circulant graphs

In a graph G, a k-container C_k(u,v) is a set of k disjoint paths joining u and v. A k-container C_k(u,v) is k∗-container if every vertex of G is passed by some path in C_k(u,v). A graph G is k∗-connected if there exists a k∗-container between any two vertices. An m-regular graph G is super-connected if G is k∗-connected for any k with 1?k?m. In this paper, we prove that the recursive circulant graphs G(2^m,4), proposed by Park and Chwa [Theoret. Comput. Sci. 244 (2000) 35-62], are super-connected if and only if m≠2.     

Indirect augmented reality

Developing augmented reality (AR) applications for mobile devices and outdoor environments has historically required a number of technical trade-offs related to tracking. One approach is to rely on computer vision which provides very accurate tracking, but can be brittle, and limits the generality of the application. Another approach is to rely on sensor-based tracking which enables widespread use, but at the cost of generally poor tracking performance. In this paper we present and evaluate a new approach, which we call Indirect AR, that enables perfect alignment of virtual content in a much greater number of application scenarios.To achieve this improved performance we replace the live camera view used in video see through AR with a previously captured panoramic image. By doing this we improve the perceived quality of the tracking while still maintaining a similar overall experience. There are some limitations of this technique, however, related to the use of panoramas. We evaluate these boundaries conditions on both a performance and experiential basis through two user studies. The result of these studies indicates that users preferred Indirect AR over traditional AR in most conditions, and when conditions do degrade to the point the experience changes, Indirect AR can still be a very useful tool in many outdoor application scenarios.     

BSPNN: boosted subspace probabilistic neural network for email security

In the modern age of Internet connectivity, advanced information systems have accumulated huge volumes of data. Such fast growing, tremendous amount of data, collected and stored in large databases has far exceeded our human ability to comprehend without proper tools. There has been a great deal of research conducted to explore the potential applications of Machine Learning technologies in Security Informatics. This article studies the Network Security Detection problems in which predictive models are constructed to detect network security breaches such as spamming. Due to overwhelming volume of data, complexity and dynamics of computer networks and evolving cyber threats, current security systems suffer limited performance with low detection accuracy and high number of false alarms. To address such performance issues, a novel Machine Learning algorithm, namely Boosted Subspace Probabilistic Neural Network (BSPNN), has been proposed which combines a Radial Basis Function Neural Network with an innovative diversity-based ensemble learning framework. Extensive empirical analyses suggested that BSPNN achieved high detection accuracy with relatively small computational complexity compared with other conventional detection methods.     

The research on mechanism of color management system based on iCAM color appearance model

Since color recording and color representing abilities of various media are very different, color information is often lost or misrepresented in the process of image transmission and reproduction. A Color Management System (CMS) based on iCAM color appearance model is presented in this paper. It can be used either in computer operation system software or application software for obtaining color consistency across different software applications, imaging devices, imaging media and viewing conditions.     

Ultrasound-modulated twin-fluid atomization of a liquid jet

A resonant liquid capillary wave theory which extends Taylor's dispersion relation to include the sheltering effect of liquid surface inclination caused by air flow is presented. The resulting dispersion curves are compared to new experimental results of how drop-size and size distributions vary with surface tension and air velocity in both airblast and ultrasound-modulated twin-fluid atomization of liquids with a constant kinematic viscosity of 2 cSt. Good agreements between the theoretical predictions of relative growth rates of the capillary waves and the experimental results of drop-size and size distributions led to the conclusion that Taylor-mode breakup of capillary waves plays a very important role in twin-fluid (airblast) atomization of a liquid jet. Thus, the ultrasound-modulated twin-fluid atomization not only verifies the capillary wave mechanism but also provides a means for controlling the drop-size and size distributions in twin-fluid atomization, which has a variety of applications in fuel combustion, spray drying, and spray coating.     

The preferences toward constructivist Internet-based learning environments among university students in Taiwan

Numerous educators have proposed the development of constructivist Internet-based learning environments for students. When creating the constructivist Internet-based learning environments, it is important for researchers to be aware of students’ preferences toward these environments. Through gathering data from 659 university students in Taiwan, this study developed a questionnaire to assess students’ preferences toward constructivist Internet-based learning environments. The questionnaire, with adequate validity and reliability, included 34 items on the following seven scales: relevance, multiple sources (and interpretations), challenge, student negotiation, cognitive apprenticeship, reflective thinking and epistemological awareness. The questionnaire responses revealed that male students tended to prefer the Internet-based learning environments where they could solve challenging problems, acquire cognitive apprenticeship and guidance from experts, and promote epistemological development than did female students. The findings also suggested that, if educators intend to develop Internet-based learning environments for more academically advanced students, such as graduate students, care should be taken to create more opportunities for them to negotiate ideas, obtain proper guidance, reflect their own thoughts, and explore epistemological issues. Finally, students with more Internet experiences tended to demand more on many features of the constructivist Internet-based learning environments than those with less Internet experiences.     

Utilize bootstrap in small data set learning for pilot run modeling of manufacturing systems

If the production process, production equipment, or material changes, it becomes necessary to execute pilot runs before mass production in manufacturing systems. Using the limited data obtained from pilot runs to shorten the lead time to predict future production is this worthy of study. Although, artificial neural networks are widely utilized to extract management knowledge from acquired data, sufficient training data is the fundamental assumption. Unfortunately, this is often not achievable for pilot runs because there are few data obtained during trial stages and theoretically this means that the knowledge obtained is fragile. The purpose of this research is to utilize bootstrap to generate virtual samples to fill the information gaps of sparse data. The results of this research indicate that the prediction error rate can be significantly decreased by applying the proposed method to a very small data set.