Hamiltonian-connected graphs

For a simple graph G, let . In this paper, we prove that if NCD(G)≥|V(G)|, then either G is Hamiltonian-connected, or G belongs to a well-characterized class of graphs. The former results by Dirac, Ore and Faudree et al. are extended.     

Application classification using packet size distribution and port association

Traffic classification is an essential part in common network management applications such as intrusion detection and network monitoring. Identifying traffic by looking at port numbers is only suitable to well-known applications, while signature-based classification is not applicable to encrypted messages. Our preliminary observation shows that each application has distinct packet size distribution (PSD) of the connections. Therefore, it is feasible to classify traffic by analyzing the variances of packet sizes of the connections without analyzing packet payload. In this work, each connection is first transformed into a point in a multi-dimensional space according to its PSD. Then it is compared with the representative points of pre-defined applications and recognized as the application having a minimum distance. Once a connection is identified as a specific application, port association is used to accelerate the classification by combining it with the other connections of the same session because applications usually use consecutive ports during a session. Using the proposed techniques, packet size distribution and port association, a high accuracy rate, 96% on average, and low false positive and false negative rates, 4–5%, are achieved. Our proposed method not only works well for encrypted traffic but also can be easily incorporated with a signature-based method to provide better accuracy.     

Evolving Least Squares Support Vector Machines for Stock Market Trend Mining

In this paper, an evolving least squares support vector machine (LSSVM) learning paradigm with a mixed kernel is proposed to explore stock market trends. In the proposed learning paradigm, a genetic algorithm (GA), one of the most popular evolutionary algorithms (EAs), is first used to select input features for LSSVM learning, i.e., evolution of input features. Then, another GA is used for parameters optimization of LSSVM, i.e., evolution of algorithmic parameters. Finally, the evolving LSSVM learning paradigm with best feature subset, optimal parameters, and a mixed kernel is used to predict stock market movement direction in terms of historical data series. For illustration and evaluation purposes, three important stock indices, S&P 500 Index, Dow Jones Industrial Average (DJIA) Index, and New York Stock Exchange (NYSE) Index, are used as testing targets. Experimental results obtained reveal that the proposed evolving LSSVM can produce some forecasting models that are easier to be interpreted by using a small number of predictive features and are more efficient than other parameter optimization methods. Furthermore, the produced forecasting model can significantly outperform other forecasting models listed in this paper in terms of the hit ratio. These findings imply that the proposed evolving LSSVM learning paradigm can be used as a promising approach to stock market tendency exploration.     

CoBTx-Net: A model for business collaboration reliability verification

Collaborative business process can become unreliable when business partners collaborate in a peer- based fashion without central control. Therefore, reliability checking becomes an important issue that needs to be dealt with for any generic solution in managing business collaboration. In this paper, we propose a novel Choreographical Business Transaction Net (CoBTx-Net) to model collaborative business process and to manage the collaboration by individual participants. Furthermore three reliability properties named Time-embedded dead marking freeness, Inter-organizational dead marking freeness, and Collaborative soundness are defined based on CoBTx-Net to verify (1) the violation of time constraint, (2) collaborative logic conflicts, and (3) the improper termination from individual organizations.

Credit scoring using support vector machines with direct search for parameters selection

Support vector machines (SVM) is an effective tool for building good credit scoring models. However, the performance of the model depends on its parameters’ setting. In this study, we use direct search method to optimize the SVM-based credit scoring model and compare it with other three parameters optimization methods, such as grid search, method based on design of experiment (DOE) and genetic algorithm (GA). Two real-world credit datasets are selected to demonstrate the effectiveness and feasibility of the method. The results show that the direct search method can find the effective model with high classification accuracy and good robustness and keep less dependency on the initial search space or point setting.     

Maximum Penalized Likelihood Kernel Regression for Fast Adaptation

This paper proposes a nonlinear generalization of the popular maximum-likelihood linear regression (MLLR) adaptation algorithm using kernel methods. The proposed method, called maximum penalized likelihood kernel regression adaptation (MPLKR), applies kernel regression with appropriate regularization to determine the affine model transform in a kernel-induced high-dimensional feature space. Although this is not the first attempt of applying kernel methods to conventional linear adaptation algorithms, unlike most of other kernelized adaptation methods such as kernel eigenvoice or kernel eigen-MLLR, MPLKR has the advantage that it is a convex optimization and its solution is always guaranteed to be globally optimal. In fact, the adapted Gaussian means can be obtained analytically by simply solving a system of linear equations. From the Bayesian perspective, MPLKR can also be considered as the kernel version of maximum a posteriori linear regression (MAPLR) adaptation. Supervised and unsupervised speaker adaptation using MPLKR were evaluated on the Resource Management and Wall Street Journal 5K tasks, respectively, achieving a word error rate reduction of 23.6% and 15.5% respectively over the speaker-independently model.     

Use of the fast Fourier transform method for analyzing linear and equispaced Fizeau fringes

The Fourier transform method is applied to analyze the initial phase of linear and equispaced Fizeau fringes. We develop an algorithm for high-precision phase measurement by using the Fourier coefficient that corresponds to the spatial frequency of the Fizeau fringes, and we describe methods for determining the fringe carrier frequency. Errors caused by carrier frequency fluctuation and data truncation are studied theoretically and by computer simulation. To demonstrate the method we apply it to the real-time calibration of a piezoelectric transducer mirror in a Twyman-Green interferometer.     

Phase distribution and associated mechanical property distribution in silicon carbide particle-reinforced aluminium fabricated by liquid metal infiltration

The reaction between silicon carbide and aluminium to form silicon and Al₄C₃ in SiC particle-reinforced aluminium fabricated by liquid aluminium infiltration was most severe near the original interface between liquid aluminium and the SiC preform. This resulted in the highest concentration of Al₄C₃ and the lowest concentrations of silicon and SiC in the part of the composite near this interface. In particular, the silicon concentration was highest in the bottom centre of the composite when infiltration occurred from the top, because silicon diffused toward the surrounding aluminium melt before solidification. These non-uniform phase distributions, as measured by X-ray diffraction and differential scanning calorimetry, did not cause any non-uniform shear strength distribution. However, excessive reaction between SiC and aluminium, as observed for an infiltration (=mould=liquid metal) temperature of 780° C, caused the tensile strength to decrease. In the case where a steel mould was used during infiltration at 780° C, iron-containing precipitates, such as ternary Al-Fe-Si, were observed in the part of the composite within 5 mm from the above-mentioned interface; their formation was related to the silicon out-diffusion in the form of liquid Al-Si; they caused the shear strength to be lower in this part of the composite; larger such precipitates (up to 100 m) were observed in the excess aluminium adjacent to the cast composite. For pure aluminium as the infiltrating metal, the optimum infiltration temperature for the highest tensile strength was 700° C. An infiltration temperature of 670° C resulted in incomplete infiltration, which was more severe when a steel mould rather than a graphite mould was used because of the higher thermal conductivity of the former.     

RADIOIMMUNOTOXICOLOGICAL EFFECT OF ENRICHED URANIUM ON CENTRAL AND PERIPHERAL IMMUNE CELLS AND THE PROTECTIVE ACTION OF IL－1 AND IL－2

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Immobilization of Pt Nanoparticles via Rapid and Reusable Electropolymerization of Dopamine on TiO2 Nanotube Arrays for Reversible SERS Substrates and Nonenzymatic Glucose Sensors

Inspired by mussel‐adhesion phenomena in nature, polydopamine (PDA) coatings are a promising route to multifunctional platforms for decorating various materials. The typical self‐polymerization process of dopamine is time‐consuming and the coatings of PDA are not reusable. Herein, a reusable and time‐saving strategy for the electrochemical polymerization of dopamine (EPD) is reported. The PDA layer is deposited on vertically aligned TiO₂ nanotube arrays (NTAs). Owing to the abundant catechol and amine groups in the PDA layer, uniform Pt nanoparticles (NPs) are deposited onto the TiO₂ NTAs and can effectively prevent the recombination of electron–hole pairs generated from photo‐electrocatalysis and transfer the captured electrons to participate in the photo‐electrocatalytic reaction process. Compared with pristine TiO₂ NTAs, the as‐prepared Pt@TiO₂ NTA composites exhibit surface‐enhanced Raman scattering sensitivity for detecting rhodamine 6G and display excellent UV‐assisted self‐cleaning ability, and also show promise as a nonenzymatic glucose biosensor. Furthermore, the mussel‐inspired electropolymerization strategy and the fast EPD‐reduced nanoparticle decorating process presented herein can be readily extended to various functional substrates, such as conductive glass, metallic oxides, and semiconductors. It is the adaptation of the established PDA system for a selective, robust, and generalizable sensing system that is the emphasis of this work.