A quantitative solution to Constraint Satisfaction Problem (CSP)

Constraint Satisfaction Problem (CSP) is an important problem in artificial intelligence and operations research. Many practical problems can be formulated as CSP, i.e., finding a consistent value assignment to variables subject to a set of constraints. In this paper, we give a quantitative approach to solve the CSPs which deals uniformly with binary constraints as well as high order,k-ary (k ≥ 2) constraints. In this quantitative approach, using variable transformation and constraint transformation, a CSP is transformed into a satisfiability (SAT) problem. The SAT problem is then solved within a continuous search space. We will evaluate the performance of this method based on randomly generated SAT problem instances and regularly generatedk-ary (k ≥ 2) CSP problem instances.     

Undecidable goals for completed acyclic programs

We prove for a well-known acyclic logic programP that it is undecidable whether or not a given goal is a logical consequence of the completion ofP. This complements recent decidability results for acyclic programs and bounded goals.     

Granularity analysis for exploiting adaptive parallelism of declarative programs on multiprocessors

Declarative Programming Languages (DPLs) apply a grocess model of Horn clauses such as PARLOG^[8] or a reduction model of λ-calculus such as SML^[7] and are,in principle,well suited to multiprocessor implementation.However,the performance of a paralled declarative program can be impaired by a mismatch between the parallelism available in an application and the parallelism available in the architecture.A particularly attractive solution is to automatically match the parallelism of the program to the parallelism of the target hardware as a compilation step.In this paper,we present an optimizing compilation technique called granularity analysis which identifies and removes excess parallelism that would degrade performance.The main steps are:an analysis of the flow of data to form an attributed call graph between function (or predicate) arguments;and an asymptotic estimation of granularity of a function (or predicate) to generate approximate grain size.Compiled procedure calls can be annotated with grain size and a task scheduler can make scheduling decisions with the classification scheme of grains to control parallelism at run-time.The resulting granularity analysis scheme is suitable for exploiting adaptive parallelism of declarative programming languages on multiprocessors.     

Implementing specifications by dynamic inheritance

An extended form of logic programming is presented which combines SLD-resolution with a dynamic form of inheritance between modules. The resulting inference system supports the notion of ‘implementation’ as a process of computing mappings between specifications and technologies—both formulated as sets of clausal-form theories—in order to satisfy requirements posed as queries. Besides detailing the operational features of the formalism, the paper also explores its semantics and its practical value in two case studies.     

Distributed programming with Logic Tuple Spaces

From the point of view of distributed programming one of the most interesting communication mechanisms is associative tuple matching in a shared dataspace, as exemplified in the Linda coordination language. Linda has been used as a coordination layer to parallelize several sequential programming languages, such as C and Scheme. In this paper we study the combination of Linda with a logic language, whose result is the language Extended Shared Prolog (ESP). We show that ESP is based on a new programming model called PoliS, that extends Linda with Multiple Tuple Spaces. A class of applications for ESP is discussed, introducing the concept of “open multiple tuple spaces”. Finally, we show how the distributed implementation of ESP uses the network version of Linda’s tuple space.     

The learning convergence of CMAC in cyclic learning

In this paper we discuss the learning convergence of the cerebellar model articulation controller (CMAC) in cyclic learning.We prove the following results.First,if the training samples are noiseless,the training algorithm converges if and only if the learning rate is chosen from (0,2).Second,when the training samples have noises,the learning algorithm will converge with a probability of one if the learning rate is dynamically decreased.Third,in the case with noises,with a small but fixed learning rate ε the mean square error of the weight sequences generated by the CMAC learning algorithm will be bounded by O(ε).Some simulation experiments are carried out to test these results.     

Domains of attraction in autoassociative memory networks

An autoassociative memory network is constructed by storing reference pattern vectors whose components consist of a small positive number ∈ and 1-∈. Although its connection weights can not be determined only by this storing condition, it is proved that the output function of the network becomes a contraction mapping in a region around each stored pattern if ∈ is sufficiently small. This implies that the region is a domain of attraction in the network. The shape of the region is clarified in our analysis. Domains of attraction larger than this region are also found. Any noisy pattern vector in such domains, which may have real valued components, can be recognized as one of the stored patterns. We propose a method for determining connection weights of the network, which uses the shape of the domains of attraction. The model obtained by this method has symmetric connection weights and is successfully applied to character pattern recognition.     

Iterative weighted least squares algorithms for neural networks classifiers

This paper discusses learning algorithms of layered neural networks from the standpoint of maximum likelihood estimation. At first we discuss learning algorithms for the most simple network with only one neuron. It is shown that Fisher information of the network, namely minus expected values of Hessian matrix, is given by a weighted covariance matrix of input vectors. A learning algorithm is presented on the basis of Fisher's scoring method which makes use of Fisher information instead of Hessian matrix in Newton's method. The algorithm can be interpreted as iterations of weighted least squares method. Then these results are extended to the layered network with one hidden layer. Fisher information for the layered network is given by a weighted covariance matrix of inputs of the network and outputs of hidden units. Since Newton's method for maximization problems has the difficulty when minus Hessian matrix is not positive definite, we propose a learning algorithm which makes use of Fisher information matrix, which is non-negative, instead of Hessian matrix. Moreover, to reduce the computation of full Fisher information matrix, we propose another algorithm which uses only block diagonal elements of Fisher information. The algorithm is reduced to an iterative weighted least squares algorithm in which each unit estimates its own weights by a weighted least squares method. It is experimentally shown that the proposed algorithms converge with fewer iterations than error back-propagation (BP) algorithm.     

On pseudo-BL-algebras and pseudo-hoops with normal maximal filters

We study the class of pseudo-BL-algebras whose every maximal filter is normal. We present an equational base for this class and we extend these results for the class of basic pseudo hoops with fixed strong unit. This is a continuation of the research from Botur et al. (Soft Comput 16:635–644, doi: 10.1007/s00500-011-0763-7, 2012).     

A hybrid deep learning neural approach for emotion recognition from facial expressions for socially assistive robots

We have recently seen significant advancements in the development of robotic machines that are designed to assist people with their daily lives. Socially assistive robots are now able to perform a number of tasks autonomously and without human supervision. However, if these robots are to be accepted by human users, there is a need to focus on the form of human–robot interaction that is seen as acceptable by such users. In this paper, we extend our previous work, originally presented in Ruiz-Garcia et al. (in: Engineering applications of neural networks: 17th international conference, EANN 2016, Aberdeen, UK, September 2–5, 2016, proceedings, pp 79–93, 2016. https://doi.org/10.1007/978-3-319-44188-7_6), to provide emotion recognition from human facial expressions for application on a real-time robot. We expand on previous work by presenting a new hybrid deep learning emotion recognition model and preliminary results using this model on real-time emotion recognition performed by our humanoid robot. The hybrid emotion recognition model combines a Deep Convolutional Neural Network (CNN) for self-learnt feature extraction and a Support Vector Machine (SVM) for emotion classification. Compared to more complex approaches that use more layers in the convolutional model, this hybrid deep learning model produces state-of-the-art classification rate of \(96.26\%\), when tested on the Karolinska Directed Emotional Faces dataset (Lundqvist et al. in The Karolinska Directed Emotional Faces—KDEF, 1998), and offers similar performance on unseen data when tested on the Extended Cohn–Kanade dataset (Lucey et al. in: Proceedings of the third international workshop on CVPR for human communicative behaviour analysis (CVPR4HB 2010), San Francisco, USA, pp 94–101, 2010). This architecture also takes advantage of batch normalisation (Ioffe and Szegedy in Batch normalization: accelerating deep network training by reducing internal covariate shift. http://arxiv.org/abs/1502.03167, 2015) for fast learning from a smaller number of training samples. A comparison between Gabor filters and CNN for feature extraction, and between SVM and multilayer perceptron for classification is also provided.

     

Two-factor authentication for the Bitcoin protocol

We show how to realize two-factor authentication for a Bitcoin wallet. To do so, we explain how to employ an ECDSA adaption of the two-party signature protocol by MacKenzie and Reiter (Int J Inf Secur 2(3–4):218–239, 2004. doi: 10.1007/s10207-004-0041-0) in the context of Bitcoin and present a prototypic implementation of a Bitcoin wallet that offers both: two-factor authentication and verification over a separate channel. Since we use a smart phone as the second authentication factor, our solution can be used with hardware already available to most users and the user experience is quite similar to the existing online banking authentication methods.     

An extended Hamiltonian QR algorithm

An extended QR algorithm specifically tailored for Hamiltonian matrices is presented. The algorithm generalizes the customary Hamiltonian QR algorithm with additional freedom in choosing between various possible extended Hamiltonian Hessenberg forms. We introduced in Ferranti et al. (Calcolo, 2015. doi: 10.1007/s10092-016-0192-1) an algorithm to transform certain Hamiltonian matrices to such forms. Whereas the convergence of the classical QR algorithm is related to classical Krylov subspaces, convergence in the extended case links to extended Krylov subspaces, resulting in a greater flexibility, and possible enhanced convergence behavior. Details on the implementation, covering the bidirectional chasing and the bulge exchange based on rotations are presented. The numerical experiments reveal that the convergence depends on the selected extended forms and illustrate the validity of the approach.     

Image Colorization Based on a Generalization of the Low Dimensional Manifold Model

In this paper, we introduce a novel model that restores a color image from a grayscale image with color values given in small regions. The model is based on the idea of the generalization of the low dimensional manifold model (Shi et al. in J Sci Comput, 2017.  https://doi.org/10.1007/s10915-017-0549-x) and the YCbCr color space. It involves two prior terms, a weighted nonlocal Laplacian (WNLL) and a weighted total variation (WTV). The WNLL allows regions without color information to be interpolated smoothly from given sparse color data, while the WTV assists to inhibit the diffusion of color values across edges. To cope with various types of sampled data, we introduce an updating rule for the weight function in the WNLL. Furthermore, we present an efficient iterative algorithm for solving the proposed model. Lastly, numerical experiments validate the superior performance of the proposed model over that of the other state-of-the-art models.     

Comments on “A privacy preserving three-factor authentication protocol for e-health clouds”

The security provisioning of increasing healthcare data is of critical importance. The e-health clouds can be seen as a move towards an efficient management of such a big volume of healthcare data. Many schemes have been presented to bring more security and privacy along with efficiency, in the handling of data for booming e-health industry. Recently, in this connection, Jiang et al. (J Supercomput 1–24 doi: 10.1007/s11227-015-1610-x, 2016) presented a three-factor authentication protocol for e-health clouds. In this letter, we identify a serious flaw in the mutual authentication phase of the scheme, since an adversary may launch a denial-of-service attack (DoS) against the service providing server. Finally, we suggest a modification in the scheme to overcome the DoS attack.     

Modeling and Simulation of Laser Processing of Particulate-Functionalized Materials

The objective of this paper is to focus on one of the “building blocks” of additive manufacturing technologies, namely selective laser-processing of particle-functionalized materials. Following a series of work in Zohdi (Int J Numer Methods Eng 53:1511–1532, 2002; Philos Trans R Soc Math Phys Eng Sci 361(1806):1021–1043, 2003; Comput Methods Appl Mech Eng 193(6–8):679–699, 2004; Comput Methods Appl Mech Eng 196:3927–3950, 2007; Int J Numer Methods Eng 76:1250–1279, 2008; Comput Methods Appl Mech Eng 199:79–101, 2010; Arch Comput Methods Eng 1–17. doi: 10.1007/s11831-013-9092-6, 2013; Comput Mech Eng Sci 98(3):261–277, 2014; Comput Mech 54:171–191, 2014; J Manuf Sci Eng ASME doi: 10.1115/1.4029327, 2015; CIRP J Manuf Sci Technol 10:77–83, 2015; Comput Mech 56:613–630, 2015; Introduction to computational micromechanics. Springer, Berlin, 2008; Introduction to the modeling and simulation of particulate flows. SIAM (Society for Industrial and Applied Mathematics), Philadelphia, 2007; Electromagnetic properties of multiphase dielectrics: a primer on modeling, theory and computation. Springer, Berlin, 2012), a laser-penetration model, in conjunction with a Finite Difference Time Domain Method using an immersed microstructure method, is developed. Because optical, thermal and mechanical multifield coupling is present, a recursive, staggered, temporally-adaptive scheme is developed to resolve the internal microstructural fields. The time step adaptation allows the numerical scheme to iteratively resolve the changing physical fields by refining the time-steps during phases of the process when the system is undergoing large changes on a relatively small time-scale and can also enlarge the time-steps when the processes are relatively slow. The spatial discretization grids are uniform and dense enough to capture fine-scale changes in the fields. The microstructure is embedded into the spatial discretization and the regular grid allows one to generate a matrix-free iterative formulation which is amenable to rapid computation, with minimal memory requirements, making it ideal for laptop computation. Numerical examples are provided to illustrate the modeling and simulation approach, which by design, is straightforward to computationally implement, in order to be easily utilized by researchers in the field. More advanced conduction models, based on thermal-relaxation, which are a key feature of fast-pulsing laser technologies, are also discussed.     

Stochastic C-Stability and B-Consistency of Explicit and Implicit Milstein-Type Schemes

This paper focuses on two variants of the Milstein scheme, namely the split-step backward Milstein method and a newly proposed projected Milstein scheme, applied to stochastic differential equations which satisfy a global monotonicity condition. In particular, our assumptions include equations with super-linearly growing drift and diffusion coefficient functions and we show that both schemes are mean-square convergent of order 1. Our analysis of the error of convergence with respect to the mean-square norm relies on the notion of stochastic C-stability and B-consistency, which was set up and applied to Euler-type schemes in Beyn et al. (J Sci Comput 67(3):955–987, 2016. doi: 10.1007/s10915-015-0114-4). As a direct consequence we also obtain strong order 1 convergence results for the split-step backward Euler method and the projected Euler–Maruyama scheme in the case of stochastic differential equations with additive noise. Our theoretical results are illustrated in a series of numerical experiments.     

It is time to drop the “R” from NURBS

NURBS were introduced into CAD/CAM systems predominantly for the representation of conventional objects, such as conics and quadrics. Among these, the circle played a critical role in representing a myriad of parts used in the every-day practice. Being the most universally used object, the circle has enjoyed the most popularity in science and engineering. It is an essential entity in both design as well as manufacturing and hence, its representation within CAD/CAM systems requires careful attention. Although the circle enjoys both smoothness as well as a uniform parametrization, its de facto mathematical form, the NURBS form, does not provide either sufficient smoothness or uniform parametrization. On top of all this, NURBS are rational forms requiring homogeneous coordinates in the four-dimensional space, whereas the engineering entity is only Euclidean in 3D. This multiple representational glitch, 3D presence and 4D storage, has given rise to enough headache to warrant reconsideration of the validity of the rational form in engineering design. This paper argues that it is time to drop the “R” from NURBS and to switch to integral splines with approximations where necessary. We also argue that it is time to bury dumb algorithms and consider human-based computing, i.e. our algorithms should be biologically inspired and not based on pure number crunching.     

Fuzzy Structure of Complex Belief Systems: Fuzzy Relations and Fuzzy Belief Sets

In previous work, Nescolarde-Selva and Usó-Doménech (2014a Nescolarde-Selva, J. and J. L. Usó-Doménech. “Semiotic Vision of Ideologies.” Found Sci 19 (2014a): 263–282.[Crossref], [Web of Science ®] , [Google Scholar], b) discussed the theory that complex belief systems have a topological structure. In this article it is suggested that this structure is also fuzzy. We introduce the concepts of fuzzy sets in the context of beliefs (substantive and derived), and between derived beliefs themselves. Also introduced are the concepts of fuzzy covering and fuzzy invariance and the relationships between them.     

Towards an integrated formal method for verification of liveness properties in distributed systems: with application to population protocols

State-based formal methods [e.g. Event-B/RODIN (Abrial in Modeling in Event-B—system and software engineering. Cambridge University Press, Cambridge, 2010; Abrial et al. in Int J Softw Tools Technol Transf (STTT) 12(6):447–466, 2010)] for critical system development and verification are now well established, with track records including tool support and industrial applications. The focus of proof-based verification, in particular, is on safety properties. Liveness properties, which guarantee eventual, or converging computations of some requirements, are less well dealt with. Inductive reasoning about liveness is not explicitly supported. Liveness proofs are often complex and expensive, requiring high-skill levels on the part of the verification engineer. Fairness-based temporal logic approaches have been proposed to address this, e.g. TLA Lamport (ACM Trans Program Lang Syst 16(3):872–923, 1994) and that of Manna and Pnueli (Temporal verification of reactive systems—safety. Springer, New York, 1995). We contribute to this technology need by proposing a fairness-based method integrating temporal and first-order logic, proof and tools for modelling and verification of safety and liveness properties. The method is based on an integration of Event-B and TLA. Building on our previous work (Méry and Poppleton in Integrated formal methods, 10th international conference, IFM 2013, Turku, Finland, pp 208–222, 2013. doi: 10.1007/978-3-642-38613-8_15), we present the method via three example population protocols Angluin et al. (Distrib Comput 18(4):235–253, 2006). These were proposed as a theoretical framework for computability reasoning about Wireless Sensor Network and Mobile Ad-Hoc Network algorithms. Our examples present typical liveness and convergence requirements. We prove convergence results for the examples by integrated modelling and proof with Event-B/RODIN and TLA. We exploit existing proof rules, define and apply three new proof rules; soundness proofs are also provided. During the process we observe certain repeating patterns in the proofs. These are easily identified and reused because of the explicit nature of the reasoning.     

Prediction and optimization of unsteady forced convection around a rounded cornered square cylinder in the range of Re

An unsteady two-dimensional laminar forced convection heat transfer around a square cylinder with the rounded corner edge is numerically investigated for Re = 80–180 and non-dimensional corner radius, r = 0.50–0.71 at Pr = 0.71 (Air). A structured non-uniform mesh is used for the computational domain discretization, and the finite-volume-method-based commercial code FLUENT is used for numerical simulation. The heat transfer characteristics over the rounded corner square cylinder are analyzed with average Nusselt number (Nu _avg) at various Re and various corner radii. The heat transfer characteristic is predicted by gene expression programming (GEP), and the GEP-generated explicit equation of Nu _avg is utilized in particle swarm optimization to optimize the corner radii for maximum heat transfer rate. The data required for the training the GEP model have been collected from the authors’ recent published article (Neural Comput Appl, 2015. doi:10.1007/s00521-015-2023-8). It is found that the heat transfer rate of a circular cylinder can be enhanced 12 % by introducing a new cylinder geometry of corner radius r = 0.51.