“How do you know that I don’t understand?” A look at the future of intelligent tutoring systems

Many software systems would significantly improve performance if they could adapt to the emotional state of the user, for example if Intelligent Tutoring Systems (ITSs), ATM’s, ticketing machines could recognise when users were confused, frustrated or angry they could guide the user back to remedial help systems so improving the service. Many researchers now feel strongly that ITSs would be significantly enhanced if computers could adapt to the emotions of students. This idea has spawned the developing field of affective tutoring systems (ATSs): ATSs are ITSs that are able to adapt to the affective state of students. The term “affective tutoring system” can be traced back as far as Rosalind Picard’s book Affective Computing in 1997.This paper presents research leading to the development of Easy with Eve, an ATS for primary school mathematics. The system utilises a network of computer systems, mainly embedded devices to detect student emotion and other significant bio-signals. It will then adapt to students and displays emotion via a lifelike agent called Eve. Eve’s tutoring adaptations are guided by a case-based method for adapting to student states; this method uses data that was generated by an observational study of human tutors. This paper presents the observational study, the case-based method, the ATS itself and its implementation on a distributed computer systems for real-time performance, and finally the implications of the findings for Human Computer Interaction in general and e-learning in particular. Web-based applications of the technology developed in this research are discussed throughout the paper.     

A fast and noise resilient cluster-based anomaly detection

Clustering, while systematically applied in anomaly detection, has a direct impact on the accuracy of the detection methods. Existing cluster-based anomaly detection methods are mainly based on spherical shape clustering. In this paper, we focus on arbitrary shape clustering methods to increase the accuracy of the anomaly detection. However, since the main drawback of arbitrary shape clustering is its high memory complexity, we propose to summarize clusters first. For this, we design an algorithm, called Summarization based on Gaussian Mixture Model (SGMM), to summarize clusters and represent them as Gaussian Mixture Models (GMMs). After GMMs are constructed, incoming new samples are presented to the GMMs, and their membership values are calculated, based on which the new samples are labeled as “normal” or “anomaly.” Additionally, to address the issue of noise in the data, instead of labeling samples individually, they are clustered first, and then each cluster is labeled collectively. For this, we present a new approach, called Collective Probabilistic Anomaly Detection (CPAD), in which, the distance of the incoming new samples and the existing SGMMs is calculated, and then the new cluster is labeled the same as of the closest cluster. To measure the distance of two GMM-based clusters, we propose a modified version of the Kullback–Libner measure. We run several experiments to evaluate the performances of the proposed SGMM and CPAD methods and compare them against some of the well-known algorithms including ABACUS, local outlier factor (LOF), and one-class support vector machine (SVM). The performance of SGMM is compared with ABACUS using Dunn and DB metrics, and the results indicate that the SGMM performs superior in terms of summarizing clusters. Moreover, the proposed CPAD method is compared with the LOF and one-class SVM considering the performance criteria of (a) false alarm rate, (b) detection rate, and (c) memory efficiency. The experimental results show that the CPAD method is noise resilient, memory efficient, and its accuracy is higher than the other methods.     

A novel image encryption/decryption scheme based on chaotic neural networks

This paper presents a novel image encryption/decryption algorithm based on chaotic neural network (CNN). The employed CNN is comprised of two 3-neuron layers called chaotic neuron layer (CNL) and permutation neuron layer (PNL). The values of three RGB (Red, Green and Blue) color components of image constitute inputs of the CNN and three encoded streams are the network outputs. CNL is a chaotic layer where, three well-known chaotic systems i.e. Chua, Lorenz and Lü systems participate in generating weights and biases matrices of this layer corresponding to each pixel RGB features. Besides, a chaotic tent map is employed as the activation function of this layer, and makes the relationship between the plain image and cipher image nonlinear. The output of CNL, i.e. the diffused information, is the input of PNL, where three-dimensional permutation is applied to the diffused information. The overall process is repeated several times to make the encryption process more robust and complex. A 160-bit-long authentication code has been used to generate the initial conditions and the parameters of the CNL and PNL. Some security analysis are given to demonstrate that the key space of the new algorithm is large enough to make brute-force attacks infeasible and simulations have been carried out with detailed numerical analysis, demonstrating the high security of the new image encryption scheme.     

CFD Modeling of Micromixing and Velocity Distribution in a 1.7‐MHz Tubular Sonoreactor

The effect of high‐frequency (1.7 MHz) ultrasound waves on the mixing rate in a new continuous tubular sonoreactor was investigated by CFD modeling. Modeling of piezoelectric transducer (PZT) vibrations was done based on the dynamic mesh model. Results indicate that the acoustic streams were in the direction of wave propagation and their maximum velocity near the PZT surface agreed well with experimental measurements. The micromixing efficiency of the sonoreactor was studied by adopting the Villermaux/Dushman reaction in the modeling. Comparison of the calculated relative segregation index from modeling results with experimental data revealed reasonable accordance.     

Inverse optimality in the class of Hopfield neural networks with input nonlinearity

This paper presents the chaos suppression problem in the class of Hopfield neural networks (HNNs) with input nonlinearity using inverse optimality approach. Using the inverse optimality technique and based on Lyapunov stability theory, a stabilizing control law, which is optimal with respect to meaningful cost functional, is determined to achieve global asymptotically stability in the closed-loop system. Numerical simulation is performed on a four-dimensional hyper-chaotic HNN to demonstrate the effectiveness of the proposed method.     

Propagation rates in the anionic polymerization of α-methyl-α-butyl-β-propiolactam

Summary The anionic polymerization of -methyl--butyl--propiolactam, MBPL, was studied for the effect of the lactam salt coinitiator on the rate of propagation. It was found that the lithium and tetramethylammonium salts gave essentially identical rate constants but the potassium salt gave a slightly higher value indicating more free ion character for the latter. However, the very small variation in rates observed as a function of the counterion suggests that anion solvation is the most important factor in these reactions, as previously proposed.Prof. Dr.Dr.h.c.mult. G. V. Schulz dedicated on the occasion of his 75th birthday     

Reverse iodine transfer polymerization of vinyl acetate and vinyl benzoate: synthesis and characterization of homo‐ and copolymers

Homo‐ and copolymers of vinyl esters including vinyl acetate (VAc) and vinyl benzoate (VBz) were synthesized via the reverse iodine transfer radical polymerization technique. Polymerization was carried out in the presence of iodine as the in situ generator of the transfer agent and 2,2′‐azobis(isobutyronitrile) as the initiator at 70 °C. Reverse iodine transfer radical homopolymerization of VAc and VBz led to conversions of 76 and 57%, number‐average molecular weights of 8266 and 9814 g mol^?1 and molecular weight distributions of 1.58 and 1.49, respectively. The microstructure of the synthesized polymers was investigated in detail using gel permeation chromatography, ¹H NMR, ¹³C NMR and distortionless enhancement of polarization transfer (135° decoupler pulse) techniques. Relatively narrow molecular weight distribution and controlled and predictable trend of molecular weight versus conversion were observed for the synthesized polymers, showing that reverse iodine transfer radical homo‐ and copolymerization of VAc and VBz proceeded with controlled characteristics. Results of molecular weight and its distribution along with the ¹H NMR spectra recorded for homo‐ and copolymers indicated that side reactions can occur during the course of polymerization with a significant contribution when VAc, even in a small amount, was present in the reaction mixture. This can result in polymer chains with aldehyde dead end and broadening of the molecular weight distribution. © 2015 Society of Chemical Industry     

Simplified modeling and generalized predictive position control of an ultrasonic motor

Ultrasonic motors (USM's) possess heavy nonlinear and load dependent characteristics such as dead-zone and saturation reverse effects, which vary with driving conditions. In this paper, behavior of an ultrasonic motor is modeled using Hammerstein model structure and experimental measurements. Also, model predictive controllers are designed to obtain precise USM position control. Simulation results indicate improved performance of the motor for both set point tracking and disturbance rejection.     

Alkoxysilane modification of a Ti‐based catalyst system for ethylene dimerization: A step forward in enhancing productivity and selectivity

Alkoxysilanes were used as novel enhancing agents in the Ti‐based catalyst for the highly selective ethylene dimerization to butene‐1. The dimerization of ethylene was carried out using the homogeneous Ti(OBu)₄/THF/TEA/alkoxysilane catalyst system, where Ti(OBu)₄, THF (tetrahydrofuran), TEA (triethylaluminum), and alkoxysilane were used as catalyst, additive, activator, and modifier, respectively. The nature and concentration of alkoxysilanes on the dimerization rate, catalyst yield, by‐products production, and selectivity to butene‐1 were investigated in detail. It was found that the performance of alkoxysilanes assisted with the class of the Ti‐based catalyst system, developed in this work, has been furthered by high productivity and selectivity with respect to the bare catalyst system. It proved that alkoxysilanes could play an excellent improving role in the selective ethylene dimerization process. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017 , 134, 44615.