Quantum-inspired design of resilient substitution boxes: From coding to hardware implementation

S-boxes constitute a cornerstone component in symmetric-key cryptographic algorithms, such as DES and AES encryption systems. In block ciphers, they are typically used to obscure the relationship between the plaintext and the ciphertext. Non-linear and non-correlated S-boxes are the most secure against linear and differential cryptanalysis. In this paper, we focus on a twofold objective: first, we evolve regular S-boxes with high non-linearity and low auto-correlation properties; then automatically generate evolvable hardware for the obtained S-box. Targeting the former, we use a quantum-inspired evolutionary algorithm to optimize regularity, non-linearity and auto-correlation, which constitute the three main desired properties in resilient S-boxes. Pursuing the latter, we exploit the same algorithm to automatically generate the evolvable hardware designs of substitution boxes that minimize hardware space and encryption/decryption time, which form the two main hardware characteristics. We compare our results against existing and well-known designs, which were produced by using conventional methods as well as through genetic algorithm. We will show that our approach provides higher quality S-boxes coding as well as circuits.     

Improving the non-extensive medical image segmentation based on Tsallis entropy

Thresholding techniques for image segmentation is one of the most popular approaches in Computational Vision systems. Recently, M. Albuquerque has proposed a thresholding method (Albuquerque et al. in Pattern Recognit Lett 25:1059–1065, 2004) based on the Tsallis entropy, which is a generalization of the traditional Shannon entropy through the introduction of an entropic parameter q. However, the solution may be very dependent on the q value and the development of an automatic approach to compute a suitable value for q remains also an open problem. In this paper, we propose a generalization of the Tsallis theory in order to improve the non-extensive segmentation method. Specifically, we work out over a suitable property of Tsallis theory, named the pseudo-additive property, which states the formalism to compute the whole entropy from two probability distributions given an unique q value. Our idea is to use the original M. Albuquerque’s algorithm to compute an initial threshold and then update the q value using the ratio of the areas observed in the image histogram for the background and foreground. The proposed technique is less sensitive to the q value and overcomes the M. Albuquerque and k-means algorithms, as we will demonstrate for both ultrasound breast cancer images and synthetic data.     

SUTIL – Network selection based on utility function and integer linear programming

This work presents SUTIL, a mechanism for network selection in the context of next generation networks (NGN). SUTIL selection mechanism prioritizes networks with higher relevance to the application and lower energy consumption and it enables full and seamless connectivity to mobile user devices and applications. Consequently, SUTIL contributes to realize the vision of ubiquitous computing, in which services, devices, and sensor-enriched environments interact anytime, anywhere to accomplish human designed tasks. The provided solution is based on utility function and integer linear programming and it aims at: (i) maximizing the user satisfaction while meeting application QoS and (ii) minimizing the energy consumption of devices when connecting to a target network. The solution is global since it considers for a given base station all devices that are simultaneously candidate for handoff. Simulation results showed the benefits of SUTIL usage in NGN environments.     

A systematic mapping study of software product lines testing

Context

In software development, Testing is an important mechanism both to identify defects and assure that completed products work as specified. This is a common practice in single-system development, and continues to hold in Software Product Lines (SPL). Even though extensive research has been done in the SPL Testing field, it is necessary to assess the current state of research and practice, in order to provide practitioners with evidence that enable fostering its further development.

Objective

This paper focuses on Testing in SPL and has the following goals: investigate state-of-the-art testing practices, synthesize available evidence, and identify gaps between required techniques and existing approaches, available in the literature.

Method

A systematic mapping study was conducted with a set of nine research questions, in which 120 studies, dated from 1993 to 2009, were evaluated.

Results

Although several aspects regarding testing have been covered by single-system development approaches, many cannot be directly applied in the SPL context due to specific issues. In addition, particular aspects regarding SPL are not covered by the existing SPL approaches, and when the aspects are covered, the literature just gives brief overviews. This scenario indicates that additional investigation, empirical and practical, should be performed.

Conclusion

The results can help to understand the needs in SPL Testing, by identifying points that still require additional investigation, since important aspects regarding particular points of software product lines have not been addressed yet.     

Modeling wine preferences by data mining from physicochemical properties

We propose a data mining approach to predict human wine taste preferences that is based on easily available analytical tests at the certification step. A large dataset (when compared to other studies in this domain) is considered, with white and red vinho verde samples (from Portugal). Three regression techniques were applied, under a computationally efficient procedure that performs simultaneous variable and model selection. The support vector machine achieved promising results, outperforming the multiple regression and neural network methods. Such model is useful to support the oenologist wine tasting evaluations and improve wine production. Furthermore, similar techniques can help in target marketing by modeling consumer tastes from niche markets.     

On graph reasoning

In this paper, we study the (positive) graph relational calculus. The basis for this calculus was introduced by Curtis and Lowe in 1996 and some variants, motivated by their applications to semantics of programs and foundations of mathematics, appear scattered in the literature. No proper treatment of these ideas as a logical system seems to have been presented. Here, we give a formal presentation of the system, with precise formulation of syntax, semantics, and derivation rules. We show that the set of rules is sound and complete for the valid inclusions, and prove a finite model result as well as decidability. We also prove that the graph relational language has the same expressive power as a first-order positive fragment (both languages define the same binary relations), so our calculus may be regarded as a notational variant of the positive existential first-order logic of binary relations. The graph calculus, however, has a playful aspect, with rules easy to grasp and use. This opens a wide range of applications which we illustrate by applying our calculus to the positive relational calculus (whose set of valid inclusions is not finitely axiomatizable), obtaining an algorithm for deciding the valid inclusions and equalities of the latter.     

Traffic analysis and synthetic models of second life

Virtual Worlds (VW), such as Massive Multiplayer Online Social Games, have been gaining increasing attention in the last few years, mainly due to the new way users interact with them. However, little effort has been devoted to understand their traffic profile and the implications to the traffic management area. With the current growing rate of VWs’ usage, their traffic demand could eventually impose a significant burden on the operation of a typical Internet Service Provider (ISP) network. In this paper, we seek to understand the traffic behavior of an increasingly popular VW application, namely Second Life (SL), from both the connection and network level perspectives. We also show results of a traffic analysis of a SL client, when an avatar performs different actions in the VW, at different places and under different network conditions. Our results show that SL makes intensive use of network resources (mostly bandwidth), since the capacity needed for having a full SL experience (listening to live music) may reach 500 kbps. We then extend the traffic analysis work on SL by providing analytical models for its traffic profile. We aim at helping the network management and planning area to estimate the impact of an intense use of such VW on access links or core networks. Such synthetic models are also useful to the networking research community for the use in network simulations.     

Fixed-point DSP implementation of nonlinear controller for large gap electromagnetic suspension system

Electromagnetic suspension systems are inherently nonlinear and often face hardware limitation when digitally controlled. The main contributions of this paper are: the design of a nonlinear H_∞ controller, including dynamic weighting functions, applied to a large gap electromagnetic suspension system and the presentation of a procedure to implement this controller on a fixed-point DSP, through a methodology able to translate a floating-point algorithm into a fixed-point algorithm by using l_∞ norm minimization due to conversion error. Experimental results are also presented, in which the performance of the nonlinear controller is evaluated specifically in the initial suspension phase.     

Boundary Detection in Particle‐based Fluids

This paper presents a novel method to detect free‐surfaces on particle‐based volume representation. In contrast to most particle‐based free‐surface detection methods, which perform the surface identification based on physical and geometrical properties derived from the underlying fluid flow simulation, the proposed approach only demands the spatial location of the particles to properly recognize surface particles, avoiding even the use of kernels. Boundary particles are identified through a Hidden Point Removal (HPR) operator used for visibility test. Our method is very simple, fast, easy to implement and robust to changes in the distribution of particles, even when facing large deformation of the free‐surface. A set of comparisons against state‐of‐the‐art boundary detection methods show the effectiveness of our approach. The good performance of our method is also attested in the context of fluid flow simulation involving free‐surface, mainly when using level‐sets for rendering purposes.