Visual tracking using convolutional features with sparse coding

Artificial Intelligence Review - Visual object tracking has become one of the most active research topics in computer vision, and it has been applied in several commercial...     

Dynamics of localizable entanglement in a qutrit chain with Dzyaloshinskii–Moriya interaction

Dynamics of localizable entanglement in a qutrit chain, in the presence of the Dzyaloshinskii–Moriya (DM) interaction is studied. Three distinct initial states, namely, superposition of the ground and the first excited state (SGE), a GHZ state and a superposition of qutrit coherent states (SQCS) are considered in this investigation. While the ground and the first excited state exhibit the maximum of entanglement, the latter is diminished for any superposition of the states. In both SGE and GHZ cases, localizable entanglement (LE) oscillates and its period is a decreasing function of the ratio of the strength of DM interaction and the spin coupling constant (DS ratio), but its maximum value is independent of the latter. In SQCS case, LE also oscillates in time at small values of DS ratio; its average is reduced as the strength of the DM interaction increases and gains its maximum average and the highest peaks at a specific value of the coherence parameter.     

The use of coevolution and the artificial immune system for ensemble learning

This paper presents two new approaches for constructing an ensemble of neural networks (NN) using coevolution and the artificial immune system (AIS). These approaches are extensions of the CLONal Selection Algorithm for building ENSembles (CLONENS) algorithm. An explicit diversity promotion technique was added to CLONENS and a novel coevolutionary approach to build neural ensembles is introduced, whereby two populations representing the gates and the individual NN are coevolved. The former population is responsible for defining the ensemble size and selecting the members of the ensemble. This population is evolved using the differential evolution algorithm. The latter population supplies the best individuals for building the ensemble, which is evolved by AIS. Results show that it is possible to automatically define the ensemble size being also possible to find smaller ensembles with good generalization performance on the tested benchmark regression problems. More interestingly, the use of the diversity measure during the evolutionary process did not necessarily improve generalization. In this case, diverse ensembles may be found using only implicit diversity promotion techniques.     

An adaptive genetic-based signature learning system for intrusion detection

Rule-based intrusion detection systems generally rely on hand crafted signatures developed by domain experts. This could lead to a delay in updating the signature bases and potentially compromising the security of protected systems. In this paper, we present a biologically-inspired computational approach to dynamically and adaptively learn signatures for network intrusion detection using a supervised learning classifier system. The classifier is an online and incremental parallel production rule-based system.A signature extraction system is developed that adaptively extracts signatures to the knowledge base as they are discovered by the classifier. The signature extraction algorithm is augmented by introducing new generalisation operators that minimise overlap and conflict between signatures. Mechanisms are provided to adapt main algorithm parameters to deal with online noisy and imbalanced class data. Our approach is hybrid in that signatures for both intrusive and normal behaviours are learnt.The performance of the developed systems is evaluated with a publicly available intrusion detection dataset and results are presented that show the effectiveness of the proposed system.     

Neural-Based Learning Classifier Systems

UCS is a supervised learning classifier system that was introduced in 2003 for classification in data mining tasks. The representation of a rule in UCS as a univariate classification rule is straightforward for a human to understand. However, the system may require a large number of rules to cover the input space. Artificial neural networks (NNs), on the other hand, normally provide a more compact representation. However, it is not a straightforward task to understand the network. In this paper, we propose a novel way to incorporate NNs into UCS. The approach offers a good compromise between compactness, expressiveness, and accuracy. By using a simple artificial NN as the classifier's action, we obtain a more compact population size, better generalization, and the same or better accuracy while maintaining a reasonable level of expressiveness. We also apply negative correlation learning (NCL) during the training of the resultant NN ensemble. NCL is shown to improve the generalization of the ensemble.     

Improving Deep Attractor Network by BGRU and GMM for Speech Separation

Deep Attractor Network (DANet) is the state-of-the-art technique in speech separation field, which uses Bidirectional Long Short-Term Memory (BLSTM), but the complexity of the DANet model is very high. In this paper, a simplified and powerful DANet model is proposed using Bidirectional Gated neural network (BGRU) instead of BLSTM. The Gaussian Mixture Model (GMM) other than the k-means was applied in DANet as a clustering algorithm to reduce the complexity and increase the learning speed and accuracy. The metrics used in this paper are Signal to Distortion Ratio (SDR), Signal to Interference Ratio (SIR), Signal to Artifact Ratio (SAR), and Perceptual Evaluation Speech Quality (PESQ) score. Two speaker mixture datasets from TIMIT corpus were prepared to evaluate the proposed model, and the system achieved 12.3 dB and 2.94 for SDR and PESQ scores respectively, which were better than the original DANet model. Other improvements were 20.7% and 17.9% in the number of parameters and time training respectively. The model was applied on mixed Arabic speech signals and the results were better than that in English.     

An economical cognitive approach for bi-objective optimization using bliss points, visualization, and interaction

When an optimization problem encompasses multiple objectives, it is usually difficult to define a single optimal solution. The decision maker plays an important role when choosing the final single decision. Pareto-based evolutionary multiobjective optimization (EMO) methods are very informative for the decision making process since they provide the decision maker with a set of efficient solutions to choose from. Despite that the set of efficient solutions may not be the global efficient set, we show in this paper that the set can still be informative when used in an interactive session with the decision maker. We use a combination of EMO and single objective optimization methods to guide the decision maker in interactive sessions.     

Empirically Verified Thermodynamic Model of Gate Capacitance and Threshold Voltage of Nanoelectronic MOS Devices With Applications to HfO2 and ZrO2 Gate Insulators

A thermodynamic variational model derived by minimizing the Helmholtz free energy of the MOS device is presented. The model incorporates an anisotropic permittivity tensor and accommodates a correction for quantum-mechanical charge confinement at the dielectric/substrate interface. The energy associated with the fringe field that is adjacent to the oxide is of critical importance in the behavior of small devices. This feature is explicitly included in our model. The model is verified using empirical and technology-computer-aided-design-generated capacitance-voltage data obtained on MOS devices with ZrO₂, HfO₂, and SiO₂ gate insulators. The model includes considerations for an interfacial low-k interface layer between the silicon substrate and the high-k dielectric. This consideration enables the estimation of the equivalent oxide thickness. The significance of sidewall capacitance effects is apparent in our modeling of the threshold voltage (V_th) for MOS capacitors with effective channel length at 30 nm and below. In these devices, a variation in high-k permittivity produces large differences in V_th. This effect is also observed in the variance of V_th, due to dopant fluctuation under the gate.     

Web-conference supervision for advanced psychotherapy training: A practical guide.

The advent of readily accessible, inexpensive Web-conferencing applications has opened the door for distance psychotherapy supervision, using video recordings of treated clients. Although relatively new, this method of supervision is advantageous given the ease of use and low cost of various Internet applications. This method allows periodic supervision from point to point around the world, with no travel costs and no long gaps between direct training contacts. Web-conferencing permits face-to-face training so that the learner and supervisor can read each other's emotional responses while reviewing case material. It allows group learning from direct supervision to complement local peer-to-peer learning methods. In this article, we describe the relevant literature on this type of learning method, the practical points in its utilization, its limitations, and its benefits. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Overdose among youth in Bahrain: psycho-social characteristics, contact with helping agencies and problems

The in vitro minimal inhibitory concentrations (MIC) and minimal bactericidal concentration (MBC) of roxithromycin and erythromycin against Actinobacillus actinomycetemcomitans were evaluated. Sixty-seven different A. actinomycetemcomitans isolated from periodontal pockets of 101 subjects with different forms of early-onset and adult periodontitis and three reference strains of A. actinomycetemcomitans (ATCC 29522, ATCC 29523, and NCTC 9710) were included in this study. Erythromycin showed poor in vitro activity against A. actinomycetemcomitans; roxithromycin, on the contrary, exhibited good in vitro activity. Moreover, roxithromycin showed the best in vitro antimicrobial activity against 17 serotype a and 12 serotype c subpopulations of A. actinomycetemcomitans; against 38 serotype b subpopulation of A. actinomycetemcomitans, roxithromycin was consistently active. Roxithromycin exhibited MBC values usually equal to, or one-fold higher than MIC values. All the MBC values of erythromycin were three- to four-fold higher than the respective MIC result. Since roxithromycin is characterized by high concentrations in serum and good penetration and diffusion into gingival tissue, it could be expected to pass into the gingival crevicular fluid at levels sufficiently high to inhibit A. actinomycetemcomitans in vivo. These data indicate that roxithromycin might be a potential candidate for therapeutic trials in patients with A. actinomycetemcomitans-associated periodontitis.