A note on “Regular bipolar fuzzy graphs” Neural Computing and Applications 21(1) (2012) 197–205

In this note, we show by examples that Definitions 3.3, 3.5, Propositions 3.9, 3.10 and Theorem 3.17 in the paper by Akram and Dudek (Neural Comput Appl 21(1):197–205, 2012) contain some flaws, and then, we presented the updated results. Hence, we introduce generalized regular bipolar fuzzy graphs.

     

Multi-objective optimization of the heat transmission and fluid forces around a rounded cornered square cylinder

The unsteady fluid stream and warmth transmission nearby a square cylinder with sharp and rounded cornered edges are numerically examined, and then the roundness of the corner is predicted and optimized for the minimum fluid forces and maximum heat transmission rate. The roundness of the cylinder corner is changing 0.5D (circle) to 0.71D (square); D is the depth of the cylinder. The fluid flow and the heat transmission features around the sharp and curved cornered square cylinder are evaluated with the streamline, isotherm patterns, pressure coefficient, drag and lift coefficients, local Nusselt number (Nu_local) and average Nusselt number (Nu_avg) at different Re and for several roundness values. These characteristics are predicted by the gene expression programming, and then the multi-objective genetic algorithm is utilized for the optimization. A number of combinations of values of corners have been found in the form of Pareto-optimal solution to compromise the minimum fluid forces with maximum heat transfer rate.

     

New Results on Exponential Convergence for HRNNs with Continuously Distributed Delays in the Leakage Terms

This paper concerns with exponential convergence for a class of high-order recurrent neural networks with continuously distributed delays in the leakage terms. Without assuming the boundedness on the activation functions, some sufficient conditions are derived to ensure that all solutions of the networks converge exponentially to the zero point by using Lyapunov functional method and differential inequality techniques, which correct some recent results of Chen and Yang (Neural Comput Appl. doi:10.1007/s00521-012-1172-2, 2012). Moreover, we propose a new approach to prove the exponential convergence of HRNNs with continuously distributed leakage delays.     

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.     

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.

     

Stopping criteria for MAPLS-AW,a hybrid multi-objective evolutionary algorithm

Evolutionary algorithms are widely used to solve multi-objective optimization problems effectively by performing global search over the solution space to find better solutions. Hybrid evolutionary algorithms have been introduced to enhance the quality of solutions obtained. One such hybrid algorithm is memetic algorithm with preferential local search using adaptive weights (MAPLS-AW) (Bhuvana and Aravindan in Soft Comput, doi: 10.1007/s00500-015-1593-9, 2015). MAPLS-AW, a variant of NSGA-II algorithm, recognizes the elite solutions of the population and preferences are given to them for local search during the evolution. This paper proposes a termination scheme derived from the features of MAPLS-AW. The objective of the proposed scheme is to detect convergence of population without compromising quality of solutions generated by MAPLS-AW. The proposed termination scheme consists of five stopping measures, among which two are newly proposed in this paper to predict the convergence of the population. Experimental study has been carried out to analyze the performance of the proposed termination scheme and to compare with existing termination schemes. Several constrained and unconstrained multi-objective benchmark test problems are used for this comparison. Additionally, a real-time application economic emission and load dispatch has also been used to check the performance of the proposed scheme. The results show that the proposed scheme identifies convergence of population much earlier than the existing stopping schemes without compromising the quality of solutions.     

Hydromagnetic nanofluid flow past a stretching cylinder embedded in non-Darcian Forchheimer porous media

The present article presents the hydromagnetic nanofluid flow past a stretching cylinder embedded in non-Darcian Forchheimer porous media by using Buongiorno’s mathematical model (Buongiorno in J Heat Transf 128:240–250, 2006; Nadeem et al. in J Taiwan Inst Chem Eng 45:121, 2014, Nadeem et al. Appl Nanosci 4:625–631, 2014). Thermal radiation via Roseland’s approximation (Akbar et al. in Chin J Aeronaut 26:1389–1397, 2013; Nadeem and Haq in J Aerosp Eng 28:04014061, 2012), Brownian motion, thermophoresis and Joule heating effects are also considered. To explore thermal characteristics, prescribed heat flux and prescribed mass flux boundary conditions are deployed. Governing flow problem consists of PDEs in the cylindrical form, which are converted into system of nonlinear ODEs by applying applicable similarity transforms. ODEs are tackled by RK–Fehlberg fourth–fifth-order numerical integration scheme with shooting algorithm. Impact of numerous involving physical parameters on flow features like temperature distribution, velocity distribution, Sherwood number, local Nusselt number and skin friction coefficient is shown through graphs and tables.

     

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).     

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.     

A Kernel-based sparse representation method for face recognition

Sparse Representation Method has been proved to outperform conventional face recognition (FR) methods and is widely applied in recent years. A novel Kernel-based Sparse Representation Method (KBSRM) is proposed in this paper. In order to cope with the possible complex variation of the face images caused by varying facial expression and pose, the KBSRM first uses a kernel-induced distance to determine N nearest neighbors of the testing sample from all the training samples. Then, in the second step, the KBSRM represents the testing sample as a linear combination of the determinate N nearest neighbors and performs the classification by the representation result. It can be inferred that the N nearest training samples selected are closer to the test sample than the rest, so using the N nearest neighbors to represent the testing sample can make the ultimate classification more accurate. A number of FR experiments show that the KBSRM can achieve a better classification result than the algorithm mentioned in Xu et al. (Neural Comput Appl doi:10.1007/s00521-012-0833-5).     

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.     

A complete ranking of trapezoidal fuzzy numbers and its applications to multi-criteria decision making

The problem (or scenario) involving qualitative or imprecise information is not solvable by classical set theory. To overcome the shortcoming of classical set theory, Zadeh (Inf Control 8(3):338–356, 26) introduced the concept of fuzzy sets that generalizes the concept of classical sets. Fuzzy set theory allows modelling and handling of imprecise information in an effective way. As a special class of fuzzy sets, fuzzy numbers (FN) which are very much important in decision making was introduced by Dubois and Prade (Int J Syst Sci 9:631–626, 12). The available methods for solving multi-criteria decision making problems (MCDM) are problem dependent in nature due to the partial ordering on the class of FN. Total ordering on the class of FN by countable number of real-valued parameters was achieved by Wang and Wang (Fuzzy Sets Syst 243:131–141, 21). A complete ranking on the class of trapezoidal fuzzy numbers (TrFNs) using finite number of score functions is achieved in this paper. In this paper, a new ranking procedure (complete) on the class of TrFNs using the concepts of mid-point, radius, left and right fuzziness of TrFN is proposed and further we introduce a method for solving fuzzy multi-criteria decision making (Fuzzy MCDM) problem. Finally, comparisons of our proposed method with familiar existing methods are listed.

     

Combining bag-of-words and sentiment features of annual reports to predict abnormal stock returns

Automated textual analysis of firm-related documents has become an important decision support tool for stock market investors. Previous studies tended to adopt either dictionary-based or machine learning approach. Nevertheless, little is known about their concurrent use. Here we use the combination of financial indicators, readability, sentiment categories, and bag-of-words (BoW) to increase prediction accuracy. This paper aims to extract both sentiment and BoW information from the annual reports of US firms. The sentiment analysis is based on two commonly used dictionaries, namely a general dictionary Diction 7.0 and a finance-specific dictionary proposed by Loughran and McDonald (J Finance 66:35–65, 2011. doi:10.1111/j.1540-6261.2010.01625.x). The BoW are selected according to their tf–idf. We combine these features with financial indicators to predict abnormal stock returns using a multilayer perceptron neural network with dropout regularization and rectified linear units. We show that this method performs similarly as naïve Bayes and outperforms other machine learning algorithms (support vector machine, C4.5 decision tree, and k-nearest neighbour classifier) in predicting positive/negative abnormal stock returns in terms of ROC. We also show that the quality of the prediction significantly increased when using the correlation-based feature selection of BoW. This prediction performance is robust to industry categorization and event window.

     

Diagnosis of Alzheimer's disease using Naive Bayesian Classifier

In the world of modern medicine, though there is lot of medical achievements, some diseases still continue to pest the human race. Unfortunately, dementia is one such disease. All over the world, a large number of people are suffering from dementia. Dementia is a brain-related disease. Diagnosis of the disease at the earlier stage is the requirement of the day. Alzheimer's disease (AD) is one of the types of the dementia, and around 60 % of demented are affected from Alzheimer’s disease (Salmon and Bondi in Neuro psychological assessment of dementia. National Institutes of Health, 2010). All over the world, there are around 35 million people suffering from AD and this number is expected to double by 2030 and more than triple by 2050, that is to 115 million (Prince et al. in World Alzheimer report 2013: journey of caringan analysis of long-term care for dementia. Kings College, London, 2013). Diagnosis of this disease at an early stage will help the patients to lead a quality life for the remaining tenure of their life. In this paper, the authors have collected data of 466 subjects by conducting neuropsychological tests. The authors focus on diagnosis of AD for neuropsychological tests using Naive Bayes.

     

Kinetic boundary conditions for vapor–gas binary mixture

Using molecular dynamics simulations, the present study investigated the precise characteristics of the binary mixture of condensable gas (vapor) and non-condensable gas (NC gas) molecules creating kinetic boundary conditions (KBCs) at a gas–liquid interface in equilibrium. We counted the molecules utilizing the improved two-boundary method proposed in previous studies by Kobayashi et al. (Heat Mass Trans 52:1851–1859, 2016. doi: 10.1007/s00231-015-1700-6). In this study, we employed Ar for the vapor molecules, and Ne for the NC gas molecules. The present method allowed us to count easily the evaporating, condensing, degassing, dissolving, and reflecting molecules in order to investigate the detailed motion of the molecules, and also to evaluate the velocity distribution function of the KBCs at the interface. Our results showed that the evaporation and condensation coefficients for vapor and NC gas molecules decrease with the increase in the molar fraction of the NC gas molecules in the liquid. We also found that the KBCs can be specified as a function of the molar fraction and liquid temperature. Furthermore, we discussed the method to construct the KBCs of vapor and NC gas molecules.     

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.     

Analogue algorithm for parallel factorization of an exponential number of large integers: I. Theoretical description

We describe a novel analogue algorithm that allows the simultaneous factorization of an exponential number of large integers with a polynomial number of experimental runs. It is the interference-induced periodicity of “factoring” interferograms measured at the output of an analogue computer that allows the selection of the factors of each integer. At the present stage, the algorithm manifests an exponential scaling which may be overcome by an extension of this method to correlated qubits emerging from n-order quantum correlations measurements. We describe the conditions for a generic physical system to compute such an analogue algorithm. A particular example given by an “optical computer” based on optical interference will be addressed in the second paper of this series (Tamma in Quantum Inf Process 11128:1189, 2015).

     

Fabrication of microfluidic structures in quartz via micro machining technologies

Microfluidic channels have been created for quartz material using micromechanical manufacturing technologies such as micro laser machining, micro ultrasonic machining, and ultra-precision machining. Ultra-precision machining has been used to manufacture cross-junction channels 14 µm wide and 28 µm deep with a three-dimensional triangle cross-section. Micro laser machining has been used to manufacture U-shaped and -shaped microfluidic channels. Deep holes and microfluidic channels with a high slenderness ratio (width/depth) can be obtained by using micro ultrasonic machining technology. These three machining techniques are compared with respect to surface profiles and machining quality.

     

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.