Application of Trust in Recommender Systems—Utilizing Naive Bayes Classifier

Receiving a recommendation for a certain item or a place to visit is now a common experience. However, the issue of trustworthiness regarding the recommended items/places remains one of the main concerns. In this paper, we present an implementation of the Naive Bayes classifier, one of the most powerful classes of Machine Learning and Artificial Intelligence algorithms in existence, to improve the accuracy of the recommendation and raise the trustworthiness confidence of the users and items within a network. Our approach is proven as a feasible one, since it reached the prediction accuracy of 89%, with a confidence of approximately 0.89, when applied to an online dataset of a social network. Naive Bayes algorithms, in general, are widely used on recommender systems because they are fast and easy to implement. However, the requirement for predictors to be independent remains a challenge due to the fact that in real-life scenarios, the predictors are usually dependent. As such, in our approach we used a larger training dataset; hence, the response vector has a higher selection quantity, thus empowering a higher determining accuracy.

The Response Vector for Mastery Method of Standard Setting

Proposed is a new method of standard setting referred to as response vector for mastery (RVM) method. Under the RVM method, the task of panelists that participate in the standard setting process does not involve conceptualization of a borderline examinee and probability judgments as it is the case with the Angoff and bookmark methods. Also, the RVM-based computation of a cut-score is not based on a single item (e.g., marked in an ordered item booklet) but, instead, on a response vector (1/0 scores) on items and their parameters calibrated in item response theory or under the recently developed D-scoring method. Illustrations with hypothetical and real-data scenarios of standard setting are provided and methodological aspects of the RVM method are discussed.

3D distributed and dispersed source array acquisition and data processing

Numerous field acquisition examples and case studies have demonstrated the importance of recording, processing, and interpreting broadband land data. In most seismic acquisition surveys, three main objectives should be considered: (1) dense spatial source and receiver locations to achieve optimum subsurface illumination and wavefield sampling; (2) coverage of the full frequency spectrum, i.e., broadband acquisition; and (3) cost efficiency. Consequently, an effort has been made to improve the manufacturing of seismic vibratory sources by providing the ability to emit both lower (approximately 1.5 Hz) and higher frequencies (approximately 120 Hz) and of receivers by utilizing single, denser, and lighter digital sensors. All these developments achieve both operational (i.e., weight, optimized power consumption) and geophysical benefits (i.e., amplitude and phase response, vector fidelity, tilt detection). As part of the effort to reduce the acquisition cycle time, increase productivity, and improve seismic imaging and resolution while optimizing costs, a novel seismic acquisition survey was conducted employing 24 vibrators generating two different types of sweeps in a 3D unconstrained decentralized and dispersed source array field configuration. During this novel blended acquisition design, the crew reached a maximum of 65,000 vibrator points during 24 hours of continuous recording, which represents significantly higher productivity than a conventional seismic crew operating in the same area using a nonblended centralized source mode. Applying novel and newly developed deblending algorithms, high-resolution images were obtained. In addition, two data sets (i.e., low-frequency and medium-high-frequency sources) were merged to obtain full-bandwidth broadband seismic images. Data comparisons between the distributed blended and nonblended conventional surveys, acquired by the same crew during the same time over the same area, showed that the two data sets are very similar in the poststack and prestack domains.

Non-Contact Damage Detection under Operational Conditions with Multipoint Laservibrometry

Scanning laser–Doppler vibrometry (SLDV) can localize and visualize damages in mechanical structures. In order to enable scanning, it is necessary to repeat the vibration. Therefore, this technique is not suited to detect emerging hazards in working machinery that change the vibration behavior. A common technique for such cases is monitoring the vibration excited by machine operation with accelerometers. This technique requires mechanical coupling between sensors and the measurement object, which influences the high-frequency vibration responses. However, in the low-frequency range, local damages do not shift resonances or distort operational deflection shapes (ODS) significantly. These alterations in the vibration behavior are tiny and hard to detect. This paper shows that multipoint laservibrometry (MPV) with laser excitation can measure these effects efficiently, and it further demonstrates that damages influence ODSs at frequencies above 20 kHz much stronger than at frequencies below 20 kHz. In addition, ODS-based damage indices are discussed; these are highly sensitive to minute visible changes of the ODSs. In order to enhance the sensitivity of hazard detection, the response vector assurance criterion value is computed and evaluated during operation. The capabilities and limitations of the methodology on the example of a cantilever with manually emerging damage are demonstrated.

An Improved Optimal Adaptive Control Method for MIMO Sine Vibration Control of a Multichannel Coupled System

This paper proposes an improved optimal adaptive control algorithm to accelerate convergence for sine control of general multichannel coupled system, as well as enhance the stability. First of all, the convergence of traditional multi-input multi-output (MIMO) sine control method is analytically investigated in the presence of frequency response function (FRF) error. Then, the controller with the improved optimal adaptive control algorithm is developed, where a high-precision algorithm for amplitude and phase estimation is proposed to guarantee the accuracy of the response vector calculation. Numerical simulation results show that the proposed method possess excellent performance with fast convergence rate and strong robustness.

Inference in multiscale geographically weighted regression

A recent paper (Fotheringham et al. 2017) expands the well-known Geographically Weighted Regression (GWR) framework significantly by allowing the bandwidth or smoothing factor in GWR to be derived separately for each covariate in the model – a framework referred to as Multiscale GWR (MGWR). However, one limitation of the MGWR framework is that, until now, no inference about the local parameter estimates was possible. Formally, the so-called “hat matrix,” which projects the observed response vector into the predicted response vector, was available in GWR but not in MGWR. This paper addresses this limitation by reframing GWR as a Generalized Additive Model (GAM), extending this framework to MGWR and then deriving standard errors for the local parameters in MGWR. In addition, we also demonstrate how the effective number of parameters (ENP) can be obtained for the overall fit of an MGWR model and for each of the covariates within the model. This statistic is essential for comparing model fit between MGWR, GWR, and traditional global models, as well as adjusting for multiple hypothesis tests. We demonstrate these advances to the MGWR framework with both a simulated data set and a real-world data set.

Fast Estimation Method of Space-Time Two-Dimensional Positioning Parameters Based on Hadamard Product

The estimation speed of positioning parameters determines the effectiveness of the positioning system. The time of arrival (TOA) and direction of arrival (DOA) parameters can be estimated by the space-time two-dimensional multiple signal classification (2D-MUSIC) algorithm for array antenna. However, this algorithm needs much time to complete the two-dimensional pseudo spectral peak search, which makes it difficult to apply in practice. Aiming at solving this problem, a fast estimation method of space-time two-dimensional positioning parameters based on Hadamard product is proposed in orthogonal frequency division multiplexing (OFDM) system, and the Cramer-Rao bound (CRB) is also presented. Firstly, according to the channel frequency domain response vector of each array, the channel frequency domain estimation vector is constructed using the Hadamard product form containing location information. Then, the autocorrelation matrix of the channel response vector for the extended array element in frequency domain and the noise subspace are calculated successively. Finally, by combining the closed-form solution and parameter pairing, the fast joint estimation for time delay and arrival direction is accomplished. The theoretical analysis and simulation results show that the proposed algorithm can significantly reduce the computational complexity and guarantee that the estimation accuracy is not only better than estimating signal parameters via rotational invariance techniques (ESPRIT) algorithm and 2D matrix pencil (MP) algorithm but also close to 2D-MUSIC algorithm. Moreover, the proposed algorithm also has certain adaptability to multipath environment and effectively improves the ability of fast acquisition of location parameters.

Hybrid Uncertain Analysis for Exterior Acoustic Field Prediction with Interval Random Parameters

For exterior acoustic field problems that lack sufficient information to construct precise probability distributions, an interval random model is introduced to deal with the uncertain parameters. In the interval random model, the probability variables are employed to treat the uncertain parameters, whereas some distribution parameters of random variables are modeled as interval variables instead of precise values. Based on the interval random model, the interval random finite element equation for exterior acoustic fields is established and a hybrid uncertain analysis method is presented to solve the exterior acoustic field problem with interval random variables. In the presented method, by temporarily neglecting the uncertainties of interval variables, a first-order stochastic perturbation method is adopted to calculate the expectation and the variance of the response vector. According to the monotonicity of the expectation and variance of the response vector with respect to the interval variables, the lower and upper bounds of the expectation and variance of the response vector can be calculated by the vertex method. In addition, in order to ensure accuracy of the proposed method, the subinterval technique is introduced and investigated. The numerical example of a square flexible shell model is presented to demonstrate the effectiveness of the proposed method.