Knot calculation for spline fitting via sparse optimization

Curve fitting with splines is a fundamental problem in computer-aided design and engineering. However, how to choose the number of knots and how to place the knots in spline fitting remain a difficult issue. This paper presents a framework for computing knots (including the number and positions) in curve fitting based on a sparse optimization model. The framework consists of two steps: first, from a dense initial knot vector, a set of active knots is selected at which certain order derivative of the spline is discontinuous by solving a sparse optimization problem; second, we further remove redundant knots and adjust the positions of active knots to obtain the final knot vector. Our experiments show that the approximation spline curve obtained by our approach has less number of knots compared to existing methods. Particularly, when the data points are sampled dense enough from a spline, our algorithm can recover the ground truth knot vector and reproduce the spline.     

Efficient RNTS system for privacy of banking off-line customer

With the development in IT technology and with growing demands of users, a ubiquitous environment is being made. Because individual identification is important in ubiquitous environment, RFID technology would be used frequently. RFID is a radio frequency identification technology to replace bar code. The reader transmits query (request of user information) and tag-provides user information. RFID has various advantages, such as high speed identification rates, mass memory storages. However, eavesdropping is possible as well as a problem that user information is exposed (Juels et al. in Conference on Computer and Communications Security—ACM CCS, pp. 103–111, 2003; Ohkubo et al. in RFID Privacy Workshop 2003; Weis et al. in International Conference on Security in Pervasive Computing, pp. 201–212, 2003; Weis et al. in Cryptographic Hardware and Embedded Systems—CHES, pp. 454–469, 2002). Therefore, when off-line customer had visited bank for banking service, RNTS (RFID number ticket service) system provides both anonymity in customer identification and efficiency of banking service. In addition, RNTS system protects privacy of an off-line user visiting the bank and it is an efficient method offering service in order of arriving in the bank.     

Two-stage Robust Network Design with Exponential Scenarios

We study two-stage robust variants of combinatorial optimization problems on undirected graphs, like Steiner tree, Steiner forest, and uncapacitated facility location. Robust optimization problems, previously studied by Dhamdhere et al. (Proc. of 46th Annual IEEE Symposium on Foundations of Computer Science (FOCS’05), pp. 367–378, 2005), Golovin et al. (Proc. of the 23rd Annual Symposium on Theoretical Aspects of Computer Science (STACS), 2006), and Feige et al. (Proc. of the 12th International Integer Programming and Combinatorial Optimization Conference, pp. 439–453, 2007), are two-stage planning problems in which the requirements are revealed after some decisions are taken in Stage 1. One has to then complete the solution, at a higher cost, to meet the given requirements. In the robust k-Steiner tree problem, for example, one buys some edges in Stage 1. Then k terminals are revealed in Stage 2 and one has to buy more edges, at a higher cost, to complete the Stage 1 solution to build a Steiner tree on these terminals. The objective is to minimize the total cost under the worst-case scenario. In this paper, we focus on the case of exponentially many scenarios given implicitly. A scenario consists of any subset of k terminals (for k-Steiner tree), or any subset of k terminal-pairs (for k-Steiner forest), or any subset of k clients (for facility location). Feige et al. (Proc. of the 12th International Integer Programming and Combinatorial Optimization Conference, pp. 439–453, 2007) give an LP-based general framework for approximation algorithms for a class of two stage robust problems. Their framework cannot be used for network design problems like k-Steiner tree (see later elaboration). Their framework can be used for the robust facility location problem, but gives only a logarithmic approximation. We present the first constant-factor approximation algorithms for the robust k-Steiner tree (with exponential number of scenarios) and robust uncapacitated facility location problems. Our algorithms are combinatorial and are based on guessing the optimum cost and clustering to aggregate nearby vertices. For the robust k-Steiner forest problem on trees and with uniform multiplicative increase factor for Stage 2 (also known as inflation), we present a constant approximation. We show APX-hardness of the robust min-cut problem (even with singleton-set scenarios), resolving an open question of (Dhamdhere et al. in Proc. of 46th Annual IEEE Symposium on Foundations of Computer Science (FOCS’05), pp. 367–378, 2005) and (Golovin et al. in Proc. of the 23rd Annual Symposium on Theoretical Aspects of Computer Science (STACS), 2006).     

Integrated Computer-Aided Innovation: The PROSIT approach

The paper presents a methodology aimed at the improvement of the product development cycle through the integration of Computer-Aided Innovation (CAI) with Optimization and PLM systems. The interoperability of these tools is obtained through the adoption of Optimization systems as a bridging element between CAI and PLM systems. This methodology was developed within the PROSIT project (http://www.kaemart.it/prosit).The paper describes the main issues related to the integration of these complementary instruments and the solutions proposed by the authors. More specifically, the main idea of the PROSIT project to link CAI and Optimization systems is the adoption of the latter tools not just to generate optimized solutions, but also as a design analysis tool, capable to outline critical aspects of a mechanical component in terms of conflicting design requirements or parameters. CAI systems are then applied to overcome the contradictory requirements. The second step, i.e. the integration between Optimization and PLM systems, has been obtained through the development of Knowledge-Based (KB) tools to support designer's activities. More in details, they provide means to analyze and extrapolate useful geometrical information from the results provided by the optimizer, as well as semi-automatic modelling features for some specific geometries. A detailed example related to the design of a plastic wheel for light moto-scooters clarifies the whole procedure. The paper integrates, extends and updates topics presented in Cugini et al., Barbieri et al. and Cascini et al. [U. Cugini, G. Cascini, M. Ugolotti, Enhancing interoperability in the design process—the PROSIT approach, in: Proceedings of the 2nd IFIP Working Conference on Computer-Aided Innovation, Brighton (MI), USA, October 8–9, 2007, published on Trends in Computer-Aided Innovation, Springer, ISBN 978-0-387-75455-0, pp. 189–200; L. Barbieri, F. Bruno, M. Muzzupappa, U. Cugini, Design automation tools as a support for knowledge management in topology optimization, in: Proceedings of the ASME 2008 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE 2008), Brooklyn, New York, USA, August 3–6, 2008; L. Barbieri, F. Bruno, M. Muzzupappa, U. Cugini, Guidelines for an efficient integration of topological optimization tools in the product development process, in: Third International Conference on Design Computing and Cognition, Atlanta, USA, June 23–25, 2008; G. Cascini, P. Rissone, F. Rotini, From design optimization systems to geometrical contradictions, in: Proceedings of the 7th ETRIA TRIZ Future Conference, Frankfurt, Germany, November 6–8, 2007].     

基于差分进化算法的B 样条曲线曲面拟合

应用B 样条曲线曲面拟合内在形状带有间断或者尖点的数据时,最小二乘法得到的 拟合结果往往在间断和尖点处误差较大,原因在于最小二乘法将拟合函数B 样条的节点固定。本 文在利用3 次B 样条曲线和曲面拟合数据时,应用差分进化算法设计出一种能够自适应地设置B 样条节点的方法,同时对节点的数量和位置进行优化,使得B 样条拟合曲线曲面在间断和尖点处 产生拟多重节点,实现高精度地拟合采样于带有间断或尖点的曲线和曲面数据。     

Guest editorial: On Modeling Object-Oriented Information Systems

Object-Oriented Information System (OOIS) is an information system that employs object-oriented technologies in system design and implementation. Recent research advances and industrial innovations in distributed system modeling and Internet applications have enabled OOIS design and implementation to be carried out on the basis of new technologies and platforms. This special section on Modeling Object-Oriented Information Systems presents readers with a set of best papers selected from the 7th International Conference on OOIS. Reviews of theories and applications of OOISs are also provided for predicating trends in OOIS modeling.     

Modifying free-formed NURBS curves and surfaces for offsetting without local self-intersection

This paper presents an algorithm of modifying free-formed NURBS curve/surface for offsetting without local self-intersecting. The method consists of (1) sampling a number of points from a progenitor curve/surface based on second derivatives; (2) checking the curvature or maximum curvature of the progenitor curve/surface at the sampled points; (3) inserting corresponding knots of sampled points; (4) repositioning control points till the curvature/maximum curvature of the curve/surface everywhere are less than the reciprocal of offset distance. The method is efficient and is able to obtain better offsetting results.     

Fitness inheritance in multiple objective evolutionary algorithms: A test bench and real-world evaluation

In many real-world applications of evolutionary algorithms, the fitness of an individual has to be derived using complex models and time-consuming computations. Especially in the case of multiple objective optimisation problems, the time needed to evaluate these individuals increases exponentially with the number of objectives due to the ‘curse of dimensionality’ [J. Chen, D.E. Goldberg, S. Ho, K. Sastry, Fitness inheritance in multi-objective optimization, in: W.B. Langdon et al. (Eds.), GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, July 9–13, Morgan Kaufmann Publishers, New York, 2002, pp. 319–326]. This in turn leads to a slower convergence of the evolutionary algorithms. It is not feasible to use time-consuming models with large population sizes unless the time to evaluate the objective functions is reduced. Fitness inheritance is an efficiency enhancement technique that was originally proposed by Smith et al. [R.E. Smith, B.A. Dike, S.A. Stegmann, Fitness inheritance in genetic algorithms, in: Proceedings of the 1995 ACM Symposium on Applied Computing, February 26–28, ACM, Nashville, TN, USA, 1995] to improve the performance of genetic algorithms. Sastry et al. [K. Sastry, D.E. Goldberg, M. Pelikan, Don’t evaluate, inherit, in: L. Spector et al. (Eds.), GECCO 2001: Proceedings of the Genetic and Evolutionary Computation Conference, Morgan Kaufmann Publishers, San Francisco, 2001, pp. 551–558] and Chen et al. [J. Chen, D.E. Goldberg, S. Ho, K. Sastry, Fitness inheritance in multi-objective optimization, in: W.B. Langdon et al. (Eds.), GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, July 9–13, Morgan Kaufmann Publishers, New York, 2002, pp. 319–326] have developed analytical models for fitness inheritance. In this paper, the usefulness of fitness inheritance for a set of popular and separable multiple objective test functions as well as a non-separable real-world problem is evaluated based on unary performance measures testing closeness to the Pareto-optimal front, uniform distribution along and extent of the obtained Pareto front. A statistical evaluation of the performance of an NSGA-II like algorithm on the basis of these unary performance measures suggests that especially for non-convex or non-continuous problems the use of fitness inheritance negatively affects the closeness to the Pareto-optimal front.     

Shape-sensitive MLS deformation

This work presents methods for deforming meshes in a shape-sensitive way using Moving Least Squares (MLS) optimization. It extends an approach for deforming space (Cuno et al. in Proceedings of the 27th Computer Graphics International Conference, pp. 115–122, 2007) by showing how custom distance metrics may be used to achieve deformations which preserve the overall mesh shape. Several variant formulations are discussed and demonstrated, including the use of geodesic distances, distances constrained to paths contained in the mesh, the use of skeletons, and a reformulation of the MLS scheme which makes it possible to affect the bending behavior of the deformation. Finally, aspects of the implementation of these techniques in parallel architectures such as GPUs (graphics processing units) are described and compared with CPU-only implementations.     

Data fitting with a spline using a real-coded genetic algorithm

To obtain a good approximation for data fitting with a spline, frequently we have to deal with knots as variables. The problem to be solved then becomes a continuous nonlinear and multivariate optimization problem with many local optima. Therefore, it is difficult to obtain the global optimum. In this paper, we propose a method for solving this problem by using a real-coded genetic algorithm. Our method can treat not only data with a smooth underlying function, but also data with an underlying function having discontinuous points and/or cusps. We search for the best model among candidate models by using the Bayes Information Criterion (BIC). With this, we can appropriately determine the number and locations of knots automatically and simultaneously. Five examples of data fitting are given to show the performance of our method.     

B-Spline Curve Approximation by Utilizing Big Bang-Big Crunch Method

The location of knot points and estimation of the number of knots are undoubtedly known as one of the most difficult problems in B-Spline curve approximation. In the literature, different researchers have been seen to use more than one optimization algorithm in order to solve this problem. In this paper, Big Bang-Big Crunch method (BB-BC) which is one of the evolutionary based optimization algorithms was introduced and then the approximation of B-Spline curve knots was conducted by this method. The technique of reverse engineering was implemented for the curve knot approximation. The detection of knot locations and the number of knots were randomly selected in the curve approximation which was performed by using BB-BC method. The experimental results were carried out by utilizing seven different test functions for the curve approximation. The performance of BB-BC algorithm was examined on these functions and their results were compared with the earlier studies performed by the researchers. In comparison with the other studies, it was observed that though the number of the knot in BB-BC algorithm was high, this algorithm approximated the B-Spline curves at the rate of minor error.     

How Optimal Depth Cue Integration Depends on the Task

The aim of this paper is to explore a linear geometric algorithm for recovering the three dimensional motion of a moving camera from image velocities. Generic similarities and differences between the discrete approach and the differential approach are clearly revealed through a parallel development of an analogous motion estimation theory previously explored in Vieville, T. and Faugeras, O.D. 1995. In Proceedings of Fifth International Conference on Computer Vision, pp. 750–756; Zhuang, X. and Haralick, R.M. 1984. In Proceedings of the First International Conference on Artificial Intelligence Applications, pp. 366–375. We present a precise characterization of the space of differential essential matrices, which gives rise to a novel eigenvalue-decomposition-based 3D velocity estimation algorithm from the optical flow measurements. This algorithm gives a unique solution to the motion estimation problem and serves as a differential counterpart of the well-known SVD-based 3D displacement estimation algorithm for the discrete case. Since the proposed algorithm only involves linear algebra techniques, it may be used to provide a fast initial guess for more sophisticated nonlinear algorithms (Ma et al., 1998c. Electronic Research Laboratory Memorandum, UC Berkeley, UCB/ERL(M98/37)). Extensive simulation results are presented for evaluating the performance of our algorithm in terms of bias and sensitivity of the estimates with respect to different noise levels in image velocity measurements.     

Discrete Jaya with refraction learning and three mutation methods for the permutation flow shop scheduling problem

The Permutation Flow Shop Scheduling Problem (PFSSP) is an interesting scheduling problem that has many real-world applications. It has been widely used as a benchmark to prove the efficiency of many discrete optimization algorithms. The DJaya algorithm is a discrete variation of the Jaya algorithm that has been recently proposed for solving discrete real-world problems. However, DJaya may get stuck in a local optima because of some limitations in its optimization operators. In this paper, we propose a new discrete optimization algorithm called Discrete Jaya with Refraction Learning and Three Mutation Methods (DJRL3M) for solving the PFSSP. DJRL3M incorporates five modifications into DJaya. First, it utilizes Refraction Learning (RL), which is a special type of opposition learning, to generate a diverse initial population of solutions. Second, it uses three mutation methods to explore the search space of a problem: DJaya mutation, highly disruptive polynomial mutation and Pitch Adjustment mutation. Third, it employs RL at each iteration to generate the opposite solutions of the best and worst solutions in an attempt to jump out local optima. Fourth, it uses the abandon method at the end of each iteration to discard a predefined percentage of the worst solutions and generate new random solutions. Finally, it uses the smallest position value to determine the correct values of the decision variables in a given candidate solution. The performance of DJRL3M was evaluated and compared with six well-recognized optimization algorithms [(New Cuckoo Search (NCS) (Wang et al. in SC 21:4297–4307, 2017), DJaya (Gao et al. in ITC 49:1944–1955, 2018), Hybrid Harmony Search (HHS) (Zhao et al. in EAAI 65:178-199, 2017), Modified Genetic algorithm (MGA) (Mumtaz et al. in: Advances in Manufacturing Technology XXXII: Proceedings of the 16th International Conference on Manufacturing Research, incorporating the 33rd National Conference on Manufacturing Research, 2018), Generalised Accelerations for Insertion-based Heuristics (GAIbH) (Fernandez-Viagas et al. in EJOR 282:858–872, 2020), Memetic algorithm with novel semi-constructive crossover and mutation operators (MASC) (Kurdi in ASC 94:106548, 2020)] using a set of Taillard’s benchmark instances. The experimental and statistical results show that DJRL3M obtains better performance than the performance of NCS, DJaya, HHS and MGA and exhibits competitive performance compared to the performance of MASC and GAIbH.

     

Using Biologically Inspired Features for Face Processing

In this paper, we show that a new set of visual features, derived from a feed-forward model of the primate visual object recognition pathway proposed by Riesenhuber and Poggio (R&P Model) (Nature Neurosci. 2(11):1019–1025, 1999) is capable of matching the performance of some of the best current representations for face identification and facial expression recognition. Previous work has shown that the Riesenhuber and Poggio Model features can achieve a high level of performance on object recognition tasks (Serre, T., et al. in IEEE Comput. Vis. Pattern Recognit. 2:994–1000, 2005). Here we modify the R&P model in order to create a new set of features useful for face identification and expression recognition. Results from tests on the FERET, ORL and AR datasets show that these features are capable of matching and sometimes outperforming other top visual features such as local binary patterns (Ahonen, T., et al. in 8th European Conference on Computer Vision, pp. 469–481, 2004) and histogram of gradient features (Dalal, N., Triggs, B. in International Conference on Computer Vision & Pattern Recognition, pp. 886–893, 2005). Having a model based on shared lower level features, and face and object recognition specific higher level features, is consistent with findings from electrophysiology and functional magnetic resonance imaging experiments. Thus, our model begins to address the complete recognition problem in a biologically plausible way.     

Blind signature and ring signature schemes: Rehabilitation and attack

Blind signature and ring signature are two signature schemes with privacy concern. Zhang [Jianhong Zhang, Linkability analysis of some blind signature schemes, In International Conference on Computational Intelligence and Security 2006, IEEE, vol. 2, 2006, pp. 1367–1370, (Available at http://dx.doi.org/10.1109/ICCIAS.2006.295283.)] analyzed the unlinkability of Zhang and Kim [Fangguo Zhang, Kwangjo Kim, ID-based blind signature and ring signature from pairings, in: Yuliang Zheng (Ed.), Advances in Cryptology — ASIACRYPT 2002, 8th International Conference on the Theory and Application of Cryptology and Information Security, Queenstown, New Zealand, December 1–5, 2002, Proceedings, Lecture Notes in Computer Science, vol. 2501, Springer, 2002, pp. 533–547], Huang et al. [Zhenjie Huang, Kefei Chen, Yumin Wang, Efficient identity-based signatures and blind signatures, in: Yvo Desmedt, Huaxiong Wang, Yi Mu, Yongqing Li (Eds.), Cryptology and Network Security, 4th International Conference, CANS 2005, Xiamen, China, December 14–16, 2005, Proceedings, Lecture Notes in Computer Science, vol. 3810, Springer, 2005, pp. 120–133] and Wu et al. [Qianhong Wu, Willy Susilo, Yi Mu, Fangguo Zhang, Efficient partially blind signatures with provable security, in: Osvaldo Gervasi, Marina L. Gavrilova, (Eds.), Computational Science and Its Applications — ICCSA 2007, International Conference, Kuala Lumpur, Malaysia, August 26–29, 2007. Proceedings. Part III, Lecture Notes in Computer Science, vol. 4707, Springer, 2007, pp. 1096–1105] and claimed that they are indeed linkable. On the other hand, Gamage et al. [Chandana Gamage, Ben Gras, Bruno Crispo, Andrew S. Tanenbaum, An identity-based ring signature scheme with enhanced privacy, Securecomm and Workshops 2006, IEEE, 2006, pp. 1–5, (Available at http://dx.doi.org/10.1109/SECCOMW.2006.359554)] claimed that the scheme of Chow et al. [Sherman S.M. Chow, Siu-Ming Yiu, Lucas Chi Kwong Hui, Efficient identity based ring signature, in: John Ioannidis, Angelos D. Keromytis, Moti Yung (Eds.), Applied Cryptography and Network Security, Third International Conference, ACNS 2005, New York, NY, USA, June 7–10, 2005, Proceedings, Lecture Notes in Computer Science, vol. 3531, 2005, pp. 499–512] is vulnerable to key exposure attack. This paper shows that all these claims are incorrect. Furthermore, we show that the scheme proposed by Gamage et al. [Chandana Gamage, Ben Gras, Bruno Crispo, Andrew S. Tanenbaum, An identity-based ring signature scheme with enhanced privacy, Securecomm and Workshops 2006, IEEE, 2006, pp. 1–5, (Available at http://dx.doi.org/10.1109/SECCOMW.2006.359554)] which aimed to provide enhanced privacy actually has privacy level reduced. We hope this work can pinpoint the standard one should use when analyzing the unlinkability of blind signatures and the anonymity of ring signatures.     

Adaptive knot placement using a GMM-based continuous optimization algorithm in B-spline curve approximation

One of the key problems in using B-splines successfully to approximate an object contour is to determine good knots. In this paper, the knots of a parametric B-spline curve were treated as variables, and the initial location of every knot was generated using the Monte Carlo method in its solution domain. The best km knot vectors among the initial candidates were searched according to the fitness. Based on the initial parameters estimated by an improved k-means algorithm, the Gaussian Mixture Model (GMM) for every knot was built according to the best km knot vectors. Then, the new generation of the population was generated according to the Gaussian mixture probabilistic models. An iterative procedure repeating these steps was carried out until a termination criterion was met. The GMM-based continuous optimization algorithm could determine the appropriate location of knots automatically. A set of experiments was then implemented to evaluate the performance of the new algorithm. The results show that the proposed method achieves better approximation accuracy than methods based on artificial immune system, genetic algorithm or squared distance minimization (SDM).     

Approximate merging of B-spline curves via knot adjustment and constrained optimization

This paper addresses the problem of approximate merging of two adjacent B-spline curves into one B-spline curve. The basic idea of the approach is to find the conditions for precise merging of two B-spline curves, and perturb the control points of the curves by constrained optimization subject to satisfying these conditions. To obtain a merged curve without superfluous knots, we present a new knot adjustment algorithm for adjusting the end k knots of a kth order B-spline curve without changing its shape. The more general problem of merging curves to pass through some target points is also discussed.     

二次曲面体中的斜对称面检测

为了扩大3D重建实体的覆盖域,通过对Sugimoto等的斜对称面检测算法(Sugimoto K, Tomita F. Detection of skewed-symmetrical shape. Proceedings of the IEEE International Conference on Image Processing, Austin, 1994: 696-700)进行改进,实现了包括双曲线、抛物线等在内的所有类型的二次曲线的斜对称检测.在曲面的重建过程中,采用参数化方法实现了空间二次曲线的投影匹配,利用斜对称检测算法检测出投影曲线的斜对称轴,并由检测到的斜对称轴生成相应的平面即二次曲面体的斜对称面;同时研究了错解产生的原因.算例验证表明,采用该算法能够对含有对称部分的二次曲面体进行精确的斜对称面检测.     

Initial approaches to the application of islands-based parallel EDAs in continuous domains

Estimation of distribution algorithms (EDAs) are a wide-ranging family of evolutionary algorithms whose common feature is the way they evolve by learning a probability distribution from the best individuals in a population and sampling it to generate the next one. Although they have been widely applied to solve combinatorial optimization problems, there are also extensions that work with continuous variables. In this paper [this paper is an extended version of delaOssa et al. Initial approaches to the application of islands-based parellel EDAs in continuous domains, in: Proceedings of the 34th International Conference on Parallel Processing Workshops (ICPP 2005 Workshops), Oslo, 2005, pp. 580–587] we focus on the solution of the latter by means of island models. Besides evaluating the performance of traditional island models when applied to EDAs, our main goal consists in achieving some insight about the behavior and benefits of the migration of probability models that this framework allow.     

Activity preserving graph simplification

We study the problem of simplifying a given directed graph by keeping a small subset of its arcs. Our goal is to maintain the connectivity required to explain a set of observed traces of information propagation across the graph. Unlike previous work, we do not make any assumption about an underlying model of information propagation. Instead, we approach the task as a combinatorial problem. We prove that the resulting optimization problem is $\mathbf{NP}$ NP -hard. We show that a standard greedy algorithm performs very well in practice, even though it does not have theoretical guarantees. Additionally, if the activity traces have a tree structure, we show that the objective function is supermodular, and experimentally verify that the approach for size-constrained submodular minimization recently proposed by Nagano et al. (28th International Conference on Machine Learning, 2011) produces very good results. Moreover, when applied to the task of reconstructing an unobserved graph, our methods perform comparably to a state-of-the-art algorithm devised specifically for this task.