Search

Fractional-order derivative is attracting more and more attention of researchers in image processing because of its better property in restoring more texture than the total variation. To improve the performance of fractional-order variation model in image restoration, a truncated fractional-order variation model was proposed in Chan and Liang [Truncated fractional-order variation model for image restoration, J. Oper. Res. Soc. China]. In this paper, we propose a dual approach to solve this truncated fractional-order variation model on noise removal. The proposed algorithm is based on the dual approach proposed by Chambolle [An algorithm for total variation minimisation and applications, J. Math Imaging Vis. 20 (2004), pp. 89–97]. Conversely, the Chambolle's dual approach can be treated as a special case of the proposed algorithm with fractional order α = 1. The work of this paper modifies the result in Zhang et al. [Adaptive fractional-order multi-scale method for image denoising, J. Math. Imaging Vis. 43(1) (2012), pp. 39–49. Springer Netherlands 0924–9907, Computer Science, pp. 1–11, 2011], where the convergence is not analysed. Based on the truncation, the convergence of the proposed dual method can be analysed and the convergence criteria can be provided. In addition, the accuracy of the reconstruction is improved after the truncation is taken. [ABSTRACT FROM AUTHOR]

Moth-flame Optimisation Algorithm (MFO) is a new metaheuristics optimisation algorithm presented by Mirjalili in 2015 which inspired by the navigation method of moths in nature. It has gained a huge interest due to its impressive characteristics mainly: no derivation information needed in the starting phase, few numbers of parameters, simple in implementation, scalable and flexible. Till now, different variants to solve various optimisation problems such as binary, real(continuous), constraint, single-objective, multi-objective, and multimodal MFO has been introduced. Many research papers have been presented and summarised. In this review, a general overview of MFO is presented at first. Then, different variants of MFO are described which are classified into three classes: modified, hybridised, and multi-objective. Furthermore, applications of MFO in Engineering, Computer Science, Wireless Sensor Networks, and other fields are discussed. Finally, many possible and future directions are provided. [ABSTRACT FROM AUTHOR]

Reversing is the notion of thinking or working in reverse. Computer science textbooks and tutors recognize it primarily in the form of recursion. However, recursion is only one form of reversing. Reversing appears in the computer science curriculum in many other forms, at various intellectual levels, in a variety of fundamental courses. As such, it is too important a notion to be left unexplored. In this paper we discuss reversing in the context of two theoretical frameworks - as a fundamental idea in computer science, in the context of Schwill's model of fundamental ideas, and as a powerful problem solving means, in the context of Schoenfeld's cognitive model of problem solving. We recognize seven different forms of reversing, and demonstrate the essential role of each in algorithm or proof design. Our focus in the paper is conceptual, yet we briefly note our initial experimental experience, with student difficulties in both the invocation and utilization of reversing. We then discuss cognitive aspects of the teaching of reversing and offer ways to assimilate its awareness and elaborate its fundamental role in computer science. [ABSTRACT FROM AUTHOR]

In this paper, we present an interesting modification to the heap structure which yields a better comparison-based in-place heapsort algorithm. The number of comparisons is shown to be bounded by nlogn+nlog*n−0.546871n which is 0.853129n+nlog*n away from the optimal theoretical bound of nlogn−1.44n. [ABSTRACT FROM PUBLISHER]

Detection of copositivity plays an important role in combinatorial and quadratic optimization. Recently, an algorithm for copositivity detection by simplicial partition has been proposed. In this paper, we develop an improved depth-first simplicial partition algorithm which reduces memory requirements significantly and therefore enables copositivity checks of much larger matrices - of size up to a few thousands instead of a few hundreds. The algorithm has been investigated experimentally on a number of MaxClique problems as well as on generated random problems. We present numerical results showing that the algorithm is much faster than a recently published linear algebraic algorithm for copositivity detection based on traditional ideas - checking properties of principal sub-matrices. We also show that the algorithm works very well for solving MaxClique problems through copositivity checks. [ABSTRACT FROM AUTHOR]

The Data Encryption Standard (DES) is no longer secure because of its small key size. One way to augment the keyspace is to perform multiple encryptions with DES. This paper examines the cryptographic consequences of multiple encryptions with DES through two simplified DES algorithms. Both simplified algorithms, under multiple encryptions, generate the alternating group of permutations on the message space. Moreover, equivalent keys and key pairs are rare in both cases. These facts support past work that has shown that performing multiple encryptions with DES adds keyspace by demonstrating that the increase in keyspace is significant. [ABSTRACT FROM AUTHOR]

The following paper explores the fundamental aesthetic importance of algorithms in musical practice. It focuses upon areas of potentially far-reaching aesthetic consequence that currently seem to pose some problems/misunderstandings/misconceptions or an awkwardness of interface between the disciplines of computer science and music composition—including approaches to symbolic manipulation systems, and concepts such as optimization, population, fitness and search space. [ABSTRACT FROM AUTHOR]

ion is a major concept in computer science and serves as a powerful tool in software development. Pattern-oriented instruction (POI) is a pedagogical approach that incorporates patterns in an introductory computer science course in order to structure the learning of algorithmic problem solving. This paper examines abstraction processes in the course of solving an algorithmic problem and highlights three distinct, although interrelated, facets of abstraction: pattern recognition, black-boxing, and structure identification. A study that examined the influence of the POI approach on students' abstraction skills is described; students who learned according to the POI approach were compared with students who learned in a traditional manner with regard to analogical reasoning and problem decomposition and solution, as well as verbal expression. The three facets of abstraction were used to analyze students' abstraction skills and their competency in algorithmic problem solving. [ABSTRACT FROM AUTHOR]

Anthony McCall's solid light film Line Describing a Cone (1973) is about the emergence of dimensionality in space. This paper uses Line Describing a Cone to discuss emergence as a material algorithmic process occurring across the media of informatic systems and installation art. Evolutionary models of emergence trace patterns, whether behavioural, spatial or genetic. Line Describing a Cone suggests the emergence of a new kind of mobilized viewer within gallery spaces who does not necessarily 'evolve' but who (through interruption and noise) becomes an interactive emergent part of the material processes of the work. Noise travels and generates the excess dimensionality within which an emergent material process can occur. It is contested here that emergence can only occur within noisy environments. Emergence then, introduces a process within which viewers interact with and experience art installations. [ABSTRACT FROM AUTHOR]

In this paper, we present a new, easy to implement algorithm for detecting the termination of a parallel asynchronous computation on distributed-memory MIMD computers. We demonstrate that it operates concurrently with the main computation, adding minimal overhead, and we prove that it correctly detects termination when it occurs. Experimental results confirm that the termination detection routine imposes an overhead smaller than the experimental uncertainty. [ABSTRACT FROM AUTHOR]

Combined input-crosspoint-queued (CICQ) switch structure decouples the inputs and outputs matching and enables totally distributed arbitration. CICQ switch cannot achieve 100% throughput under non-uniform traffic if Round-Robin (RR-RR) algorithm is used. The other existing schemes require quite a bit of hardware and time complexity. In this paper, we theoretically prove that the RR-RR can achieve 100% throughput under uniform traffic, but it would be instable under non-uniform traffic. Our quantitative analysis also points out that the throughput will be f = 0.5 and k = 2, where f is the packet arrival rate along the main diagonal of the traffic matrix and k is the number of buffers used in each crosspoint. Moreover, our formula shows that the k must be at least 4 and 24, respectively, if we wish to improve the throughput to 95 and 99%. Based on our theoretical study, we propose the Differential Round-Robin (DRR) algorithm. Simulations have demonstrated that DRR can achieve 100% throughput under arbitrary traffic using only one buffer cell in each crosspoint. DRR algorithm keeps the high simplicity and efficiency of RR-RR with O (1) complexity while overcoming the instability problem of RR-RR. [ABSTRACT FROM AUTHOR]

The paper is intended to be a survey of all the important aspects and results that have shaped the field of quantum computation and quantum information. The reader is first familiarized with those features and principles of quantum mechanics providing a more efficient and secure information processing. Their applications to the general theory of information, cryptography, algorithms, computational complexity and error-correction are then discussed. Prospects for building a practical quantum computer are also analyzed. [ABSTRACT FROM AUTHOR]

Learning, i.e. estimation of weights and biases in neural networks, involves the minimization of an output error criterion, a problem which is usually solved using back-propagation algorithms. This paper aims to assess the potential of simultaneous perturbation stochastic approximation (SPSA) algorithms to handle this minimization problem. In particular, a variation of the first-order SPSA algorithm that makes use of several numerical artifices including adaptive gain sequences, gradient smoothing and a step rejection procedure is developed. For illustration purposes, several application examples in the identification and control of nonlinear dynamic systems are presented. This numerical evaluation includes the development of neural network models as well as the design of a model-based predictive neural PID controller. [ABSTRACT FROM AUTHOR]

A suite of five short papers which aim to provide an overview of several aspects of CS education research, especially: previous work of interest, current projects and results; suggestions and resources for getting started in CS education research, and for forming and entering research communities. [ABSTRACT FROM AUTHOR]

Cryptography via public key cryptosystems (PKC) has been widely used for providing services such as confality, authentication, integrity and non-repudiation. Other than security, computational efficiency is another major issue of concern. And for PKC, it is largely controlled by either modular exponentiation or scalar multiplication operations such that found in RSA and elliptic curve cryptosystem (ECC), respectively. One approach to address this operational problem is via concept of addition chain (AC), in which the exhaustive single operation involving large integer is reduced into a sequence of operations consisting of simple multiplications or additions. Existing techniques manipulate the representation of integer into binary and m-ary prior performing the series of operations. This paper proposes an iterative variant of sliding window method (SWM) form of m-ary family, for shorter sequence of multiplications corresponding to the modular exponentiation. Thus, it is called an iterative SWM. Moreover, specific for ECC that imposes no extra resource for point negation, the paper proposes an iterative recoded SWM, operating on integers recoded using a modified non-adjacent form (NAF) for speeding up the scalar multiplication. The relative behaviour is also examined, of number of additions in scalar multiplications, with the integers hamming weight. The proposed iterative SWM methods reduce the number of operations by up to 6% than the standard SWM heuristic. They result to even shorter chains of operations than ones returned by many metaheuristic algorithms for the AC. [ABSTRACT FROM AUTHOR]