Unsupervised deep learning approach for network intrusion detection combining convolutional autoencoder and one-class SVM

Applied Intelligence - With the rapid advancement in network technologies, the need for cybersecurity has gained increasing momentum in recent years. As a primary defense mechanism, an intrusion...     

Single mask fabrication process for movable MEMS devices

The current work reports on the realization of movable micromachining devices using self-aligned single-mask fabrication process. Only dry etching process utilizing inductively coupled plasma reactive ion etching was used to release 3D micro structures from single crystal silicon substrate. No wet etching process is required to release the structures as is the case with silicon on insulator (SOI) wafers. Also the developed process does not require an SOI substrate and accordingly dispensing with the application of a wet etching step, thus yielding uniform structures without stiction. The optimized process was applied to realize thermally actuated microgrippers. The article presents the development of the fabrication process and demonstrates the operation of the fabricated device. The optimized process provides an avenue for low cost fabrication of movable micromachining devices without the use of complicated wet etching steps typically associated with SOI substrates.     

Ultramicroscopy of association colloids formed from block copolymers

Ultramicroscopy studies have been made of micelle formation by two poly(styrene)-poly(isoprene) block copolymers in organic solvents (N,N-dimethylacetamide and n-decane respectively) and a poly(l-glutamic acid)-poly(l-leucine) block copolymer in an aqueous solution of 0.2M NaCl at pH = 4.0. The technique provides a method of determining the number-average translational diffusion coefficient) $\text{D}\text{?}{}_{\mathrm{n}}$, of association colloids and leads, via the Stokes-Einstein relation, to a measure of the number-average of the reciprocal hydrodynamic radius $\text{(R}{}_{\mathrm{<mtext>D</mtext><mtext>?</mtext>}}{}^1\text{)}{}_{\mathrm{n}}$ for spherical particles. Particles having a radius less than approximately 30 nm were too small to be detected by the technique.The ultramicroscopy results were compared with data obtained by laser light scattering photon correlation spectroscopy which provides a measure of the z-average translational diffusion coefficient. $\text{D}\text{?}{}_{\mathrm{z}}$. An additional comparison was made by carrying out measurements on two well-characterized poly(styrene/divinyl benzene) latices.     

Perfect secret sharing scheme based on vertex domination set

Due to the fast development in data communication systems and computer networks in recent years, the necessity to protect the secret data has become extremely imperative. Several methods have been proposed to protect the secret data; one of them is the secret sharing scheme. It is a method of distributing a secret K among a finite set of participants, in such a way that only predefined subset of participant is enabled to reconstruct a secret from their shares. A secret sharing scheme realizing uniform access structure described by a graph has received a considerable attention. In this scheme, each vertex represents a participant and each edge represents a minimum authorized subset. In this paper, an independent dominating set of vertices in a graph G is introduced and applied as a novel idea to construct a secret sharing scheme such that the vertices of the graph represent the participants and the dominating set of vertices in G represents the minimal authorized set. While most of the previous schemes were based on the principle of adjacent vertices, the proposed scheme is based upon the principle of non-adjacent vertices. We prove that the scheme is perfect, and the lower bound of the information rate of this new construction is improved when compared to some well-known previous constructions. We include an experiment involving security threats to demonstrate the effectiveness of the proposed scheme.     

Eliminating Selective Dropping Attack in Mobile Ad Hoc Network

Wireless Personal Communications - In this paper, we propose a new reputation approach, called I-WD (improved WatchDog). We attempt to eliminate selective dropping attack that ensue when malicious...     

An Iterative Method for Deciding SVM and Single Layer Neural Network Structures

We present two new classifiers for two-class classification problems using a new Beta-SVM kernel transformation and an iterative algorithm to concurrently select the support vectors for a support vector machine (SVM) and the hidden units for a single hidden layer neural network to achieve a better generalization performance. To construct the classifiers, the contributing data points are chosen on the basis of a thresholding scheme of the outputs of a single perceptron trained using all training data samples. The chosen support vectors are used to construct a new SVM classifier that we call Beta-SVN. The number of chosen support vectors is used to determine the structure of the hidden layer in a single hidden layer neural network that we call Beta-NN. The Beta-SVN and Beta-NN structures produced by our method outperformed other commonly used classifiers when tested on a 2-dimensional non-linearly separable data set.     

A robust optimization approach for imprecise data envelopment analysis

Crisp input and output data are fundamentally indispensable in traditional data envelopment analysis (DEA). However, the input and output data in real-world problems are often imprecise or ambiguous. Some researchers have proposed interval DEA (IDEA) and fuzzy DEA (FDEA) to deal with imprecise and ambiguous data in DEA. Nevertheless, many real-life problems use linguistic data that cannot be used as interval data and a large number of input variables in fuzzy logic could result in a significant number of rules that are needed to specify a dynamic model. In this paper, we propose an adaptation of the standard DEA under conditions of uncertainty. The proposed approach is based on a robust optimization model in which the input and output parameters are constrained to be within an uncertainty set with additional constraints based on the worst case solution with respect to the uncertainty set. Our robust DEA (RDEA) model seeks to maximize efficiency (similar to standard DEA) but under the assumption of a worst case efficiency defied by the uncertainty set and it’s supporting constraint. A Monte-Carlo simulation is used to compute the conformity of the rankings in the RDEA model. The contribution of this paper is fourfold: (1) we consider ambiguous, uncertain and imprecise input and output data in DEA; (2) we address the gap in the imprecise DEA literature for problems not suitable or difficult to model with interval or fuzzy representations; (3) we propose a robust optimization model in which the input and output parameters are constrained to be within an uncertainty set with additional constraints based on the worst case solution with respect to the uncertainty set; and (4) we use Monte-Carlo simulation to specify a range of Gamma in which the rankings of the DMUs occur with high probability.     

Theory and methodology on the global optimal solution to a General Reverse Logistics Inventory Model for deteriorating items and time-varying rates

In this paper, we present a unified general inventory model for integrated production of new items and remanufacturing of returned items for an infinite planning horizon. Our model considers a production environment that consists of three shops. The first shop is for remanufacturing returned items, the second shop is for manufacturing new items, while the third shop is for collecting returned items to be remanufactured in the first shop. The system is subject to joint production and remanufacturing options, the first one is to produce new items while the second one is to reproduce/recycle the returned items “as-good-as new”. Items deteriorate while they are in storage, and production, remanufacturing, demand, return, and deterioration rates are arbitrary functions of time. A closed form for the total relevant costs as well as a rigorous mathematical proof, which shows the global optimality of the solution to the underlying inventory system are introduced. Illustrative examples, which explain the application of the theoretical results as well as their numerical verifications, are also given.     

The effect of nonhuman’s versus human’s external regulation on children’s speech use, manifested self-regulation, and satisfaction during learning tasks

Because of several analytical and methodological critiques on the findings and contexts of children’s private speech (PS), self-regulation learning (SRL), and thinking aloud (TA), the present study was conducted to shed new light on the effect of the nonhuman’s/computer’s versus human’s/teacher’s intervention (C-Condition versus T-Condition) on young children’s speech use, SRL, and satisfaction during learning tasks. Four developmental measurements with novel criteria were used to measure: (1) speech analysis, (3) SRL as a function of task level selection, (3) SRL as a function of task precision, and (4) a friendly-chat questionnaire to measure children’s satisfaction. Two types of intervention (enacted versus verbal encouragement) were applied through computer-based learning environment and investigated by forty preschool children divided by their teachers between the two conditions equivalently. It was hypothesized that children who acted alone (C-Condition) were more PS productive, manifested higher SRL, task performance, and satisfaction. The results confirmed the hypothesis with no significant differential effect of the gender on performance, showed that the injudicious use of encouragement hindered the children’s regulation behavior, and proved that PS and TA elicitation were fully different. However, the results were not confirmed Vygotsky’s view and simultaneously not fully inline with Piaget’s view of self-regulation development.