Algebraic properties of cryptosystem PGM

In the late 1970s Magliveras invented a private-key cryptographic system calledPermutation Group Mappings (PGM). PGM is based on the prolific existence of certain kinds of factorization sets, calledlogarithmic signatures, for finite permutation groups. PGM is an endomorphic system with message space ℤ_|G| for a given finite permutation groupG. In this paper we prove several algebraic properties of PGM. We show that the set of PGM transformations ℐ _G is not closed under functional composition and hence not a group. This set is 2-transitive on ℤ_|G| if the underlying groupG is not hamiltonian and not abelian. Moreover, if the order ofG is not a power of 2, then the set of transformations contains an odd permutation. An important consequence of these results is that the group generated by the set of transformations is nearly always the symmetric group ℒ_|G|. Thus, allowing multiple encryption, any permutation of the message space is attainable. This property is one of the strongest security conditions that can be offered by a private-key encryption system. S. S. Magliveras was supported in part by NSF/NSA Grant Number MDA904-82-H0001, by U.S. West Communications, and by the Center for Communication and Information Science of the University of Nebraska.     

Investigations on the adhesion and interfacial properties of polyurethane foam/thermoplastic materials

The adhesion and interfacial properties of polyurethane (PU) foams with thermoplastic (TP) materials were investigated using different techniques. The adhesion performance of PU foam with TP materials was evaluated using the peel test method, and the adhesion durability was checked after different climate treatments. X‐ray photoelectron spectroscopy (XPS), atomic force microscopy (AFM), and contact angle measurements were used to study the surface and interface morphology of PU foam and TP material system. Three types of PU foam samples which differ in their composition and also five commercially available TP blends systems, based on poly(carbonate), poly(styrene‐co‐maleic anhydride), poly(acrylonitrile‐butadiene‐styrene), and silicone acrylate rubber have been used. The slow reacting foam shows the best adhesion properties with all the TP materials. The climate treatments strongly effected the PU foam adhesion durability with poly(carbonate) containing TP materials (70–80% loss in adhesion), but nearly no effect with poly(styrene‐co‐maleic anhydride). The samples with lowered adhesion could be separated by peeling without visible foam residues on the TP surface. AFM, XPS, and surface tension studies have shown that the surface properties of the TP material are still governed by the PU foam. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 104: 479–488, 2007     

Fuzzy triangulation signature for detection of change in human emotion from face video image sequence

The present article proposes a geometry-based fuzzy relational technique for capturing gradual change in human emotion over time available from relevant face image sequences. As associated features, we make use of fuzzy membership arising out of five triangle signatures such as - (i) Fuzzy Isosceles Triangle Signature (FIS), (ii) Fuzzy Right Triangle Signature (FRS), (iii) Fuzzy Right Isosceles Triangle Signature (FIRS), (iv) Fuzzy Equilateral Triangle Signature (FES), and (v) Other Fuzzy Triangles Signature (OFS) to achieve the task of appropriate classification of facial transition from neutrality to one among the six expressions viz. anger (AN), disgust (DI), fear (FE), happiness (HA), sadness (SA) and surprise (SU). The effectiveness of the Multilayer Perceptron (MLP) classifier is tested and validated through 10 fold cross-validation method on three benchmark image sequence datasets namely Extended Cohn-Kanade (CK+), M&M Initiative (MMI), and Multimedia Understanding Group (MUG). Experimental outcomes are found to have achieved accuracy to the tune of 98.47%, 93.56%, and 99.25% on CK+, MMI, and MUG respectively vindicating the effectiveness by exhibiting the superiority of our proposed technique in comparison to other state-of-the-art methods in this regard.

     

Deep features-based speech emotion recognition for smart affective services

Emotion recognition from speech signals is an interesting research with several applications like smart healthcare, autonomous voice response systems, assessing situational seriousness by caller affective state analysis in emergency centers, and other smart affective services. In this paper, we present a study of speech emotion recognition based on the features extracted from spectrograms using a deep convolutional neural network (CNN) with rectangular kernels. Typically, CNNs have square shaped kernels and pooling operators at various layers, which are suited for 2D image data. However, in case of spectrograms, the information is encoded in a slightly different manner. Time is represented along the x-axis and y-axis shows frequency of the speech signal, whereas, the amplitude is indicated by the intensity value in the spectrogram at a particular position. To analyze speech through spectrograms, we propose rectangular kernels of varying shapes and sizes, along with max pooling in rectangular neighborhoods, to extract discriminative features. The proposed scheme effectively learns discriminative features from speech spectrograms and performs better than many state-of-the-art techniques when evaluated its performance on Emo-DB and Korean speech dataset.

     

A Study of Web Services Projects in the Financial Services Industry

This study examines the significance of technological, methodological, and business factors in contributing to the success of initial Web Services projects. Focusing on four case studies from the financial services sector, the authors' findings suggest that a strong focus on business factors is associated with successful Web Services strategies.     

Κ‐CN gene polymorphism in Nili‐ravi buffalo,Achai and Sahiwal cattle of Pakistan

Kappa‐casein (κ‐CN) is the subtype of casein protein, an important constituent of bovine milk protein. The current study was undertaken to investigate the genetic polymorphism in κ‐CN gene of Nili‐ravi buffalo, Achai and Sahiwal cattle of Pakistan using polymerase chain reaction–restriction fragment length polymorphism (PCR‐RFLP) technique. The Nili‐ravi buffalo was found to be monomorphic (genotype BB only) for κ‐CN gene. Achai cattle were polymorphic for κ‐CN (having three genotypes AA, AB and BB) with a frequency of 0.70, 0.18 and 0.12, respectively, while in Sahiwal cattle, both the genotypes AA and AB were found with genotypic frequencies of 0.92 and 0.08, respectively. The presence of genotype BB in Achai cattle is surprising as it is absent in most of the cattle breeds worldwide.     

Characterization of acoustic signals due to surface discharges on H.V. glass insulators using wavelet radial basis function neural networks

A hybrid model incorporating wavelet and radial basis function neural network is presented which is used to detect, identify and characterize the acoustic signals due to surface discharge activity and hence differentiate abnormal operating conditions from the normal ones. The tests were carried out on cleaned and polluted high voltage glass insulators by using surface tracking and erosion test procedure of international electrotechnical commission 60587. A laboratory experiment was conducted by preparing the prototypes of the discharges. This study suggests a feature extraction and classification algorithm for surface discharge classification, which when combined together reduced the dimensionality of the feature space to a manageable dimension, by “marrying” the wavelet to radial basis function neural network very high levels of classification are achieved. Wavelet signal treatment toolbox is used to recover the surface discharge acoustic signals by eliminating the noisy portion and to reduce the dimension of the feature input vector. A radial basis function neural network classifier was used to classify the surface discharge and assess the suitability of this feature vector in classification. This learning method is proved to be effective by applying the wavelet radial basis function neural network in the classification of surface discharge fault data set. The test results show that the proposed approach is efficient and reliable.     

Synthesis of Au/UiO-66-NH2/Graphene composites as efficient visible-light photocatalysts to convert CO2

The production of new solar fuel through CO₂ photocatalytic reduction has aroused tremendous attention in recent years because of the increased demand of global energy sources and global warming caused by the mass concentration of CO₂ in the earth's atmosphere. In this work, UiO-66-NH₂ was co-modified by the Au nanoparticles (Au-NPs) and Graphene (GR). The resultant nanocomposite exhibits a strong absorption edge in visible light owing to the surface plasmon resonance (SPR) of Au-NPs. More attractively, Au/UiO-66-NH₂/GR displays much higher photocatalytic activity (49.9 μmol) and selectivity (80.9%) than that of UiO-66-NH₂/GR (selectivity: 71.6%) and pure UiO-66-NH₂ (selectivity: 38.3%) for the CO₂ reduction under visible light. The enhanced photocatalytic performance is primarily dued to the surface plasmon resonance (SPR) of Au-NPs, which could enhance the visible light absorption. The GR sheets could play as an electron acceptor with superior conductivity and thus suppress the recombination of electrons and holes. Besides, the GR could also improve the dispersibility of UiO-66-NH₂ so as to expose more active sites and strengthen the capture of CO₂. The contact effect and synergy effect among different samples are strengthened in the ternary composites and the photocatalytic performance is therefore improved. This study demonstrates a MOF based hybrid composite for efficient photocatalytic CO₂ reduction, the findings not only prove great potential for the design and application of MOFs-based materials but also bring light to novel chances in the development of new high performance photocatalysts.