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This study proposes a novel incremental learning algorithm called distribution matching ensemble (DME) in context of adaptive weighted ensemble learning. In particular, DME estimates the distribution of each received data block by Gaussian mixture model (GMM) and reserves the corresponding distribution information, as well it maintains a group of classifiers in a buffer. When we receive a new data block which is required to be predicted, the similarity between its distribution and each reserved distribution will be calculated by Kullback-Leibler (KL) divergence, and then the similarities can be used to guide the weight assignment of each corresponding classifier to further make adaptive ensemble decision. DME gets rid of the underlying hypothesis that the most recent labeled data block always has the most similar distribution with the current unlabeled data block. In addition, to avoid infinite extension of ensemble buffer during incremental learning, we also develop two dynamic classifier update rules. Experiments results on some synthetic and real-world streaming datasets show that the proposed DME algorithm is able to track and adapt to various types of concept drift just in time. Especially, on data stream with frequent reoccurring drifts, the DME significantly outperforms to several state-of-the-art algorithms, indicating its superiority.

We provide optimal lower and upper bounds for the augmented Kullback-Leibler divergence in terms of the augmented total variation distance between two probability measures defined on two Euclidean spaces having different dimensions. We call them refined Pinsker’s and reverse Pinsker’s inequalities, respectively.

Assessing the reliability of consumer products that are subject to random discrete damage is critical during their design. Previous studies have approximated a continuous life distribution for consumer products by treating the occurrence (cycles) of accumulating damage as a continuous random variable. However, when the lifetime of a product is only a few damage cycles (e.g., ten drop cycles of a laptop), using a discrete lifetime distribution is more accurate. Using a discrete lifetime distribution is challenging because it contains a summation term with an unknown upper bound, which makes calculating its likelihood function cumbersome. This paper proposes a method to address this issue. First, the upper bound of the summation term is approximated through a gradient descent algorithm and the Maximum Likelihood Estimation method. Then, the upper bound is fixed, and the other parameters of the reliability model are estimated using Bayesian analysis. The paper presents a hypothetical case study that shows that using the discrete model leads to a more accurate estimation of product life when dealing with a small number of cycles.

For the complex battlefield electromagnetic environment, low probability of interception (LPI) performance has become an indispensable ability for modern radars. Waveform selection is one of the most fundamental and effective technical approaches to achieve the LPI performance of radar. However, the existing LPI waveforms are often optimized by some incomplete or weak performance representation metrics of radar or passive intercept devices (PIDs), which leads to a poor LPI performance of the designed waveform. From the perspective of information flow, this paper reformulated the processes of radar target tracking and the interception and identification of PIDs. For simplifing the LPI waveform selection optimization model, the interception and identification performance optimization criterions of PIDs are well-integrated into a comprehensive metric, which is measured by Kullback-Leibler (KL) divergence. Combining it with the radar tracking performance metric which is measured by the mutual information, an LPI waveform selection optimization model is established, which gives full consideration to both radar and PIDs performance. And, a two-round selection method is proposed to solve the optimization model. In addition, a multi-sensor cooperative tracking mechanism based on the accumulated tracking error constraint is designed for radar radiation control. The optimal waveform selection in the framework of multi-sensor cooperative tracking can improve the LPI performance of radar in both the waveform domain and the energy domain. Simulation results validate the effectiveness of the performance optimization metrics of radar and PIDs, and the superiority and feasibility of the designed waveform selection method in the multi-sensor cooperative target tracking performance and LPI performance.

The problem of characterizing the passengers’ movement in a public transport system has been considered in the literature for analysis, simulation and optimization purposes. In particular, origin-destination matrices are commonly used to describe the total number of passengers that travel between two points during a given time interval. In this paper, we propose to model the instantaneous rate of arrival of passengers for the origin-destination pairs of a metro system using point processes. More specifically, we apply the Expectation-Maximization algorithm to estimate the parameters of a Gaussian mixture intensity function for the daily flow of passengers using data from multiple days provided by EFE Valparaíso. The uncertainty in the parameter estimates is quantified computing standard errors and confidence intervals. Secondly, we quantitatively analyze the similarity of the obtained intensity functions among the different origin-destination pairs. In particular, we propose a dissimilarity index based on the Kullback-Leibler divergence and we apply this index in hierarchical agglomerative and partitioning methods to cluster origin-destination pairs with similar daily flow of passengers. The obtained numerical results confirm expert knowledge about the passengers’ behavior in EFE Valparaíso metro system and, more interestingly, provide additional insights on the passengers’ behaviour for specific origin-destination pairs.

Dimensionality reduction techniques including partial least-squares (PLS) and principal component analysis (PCA) have been widely applied for data-driven process monitoring. However, the objectives of PCA- and PLS-based techniques are not specific for fault detection where a superior detection performance results from a large divergence (i.e., difference) between normal operating data and faulty data. In this article, a maximized divergence analysis (MDA) method is proposed to detect faults in industrial systems. The objective of MDA is to directly maximizes the Kullback-Leibler (KL) divergence corresponding to the distributions of normal operating data and faulty data during the procedure of dimensionality reduction. An algorithm using eigenvalue-decomposition technique is put forward to efficiently solve the optimization problem of maximizing KL-divergence. Two-dimensional synthetic data and Tennessee Eastman process are used to demonstrate the effectiveness of the proposed MDA-based detection approach.

The constrained discrete optimization (CDO) problems pose an immense challenge to solve with provable accuracy and computational efficiency. Dynamic programming (DP) is an elegant technique that is used to solve a class of such problems with linear constraints that follow a particular structure, namely Bellman’s principle of optimality (BPO). Unfortunately, many of the CDO problems do not fall into this category. This work focuses on solving a class of CDO problems, which we call problem class $H$ , that do not satisfy BPO if the constraint functions are considered. There are no conditions placed on the constraint functions of $H$ . However, the objective function alone satisfies the BPO. Such problems are ubiquitous in wireless communication, signal processing, and machine learning. These problems are, in general, NP-Hard. This paper attempts to unify this class of problems to be solvable using the DP framework. Using the theory of multi-objective optimization and assisted by an information-theoretic measure, we establish provable near-optimality guarantees with reduced computational complexity. We describe two algorithms to solve $H$ . We support our claims by solving the power-constrained analog-to-digital converter bit allocation (BA) problem in massive Multiple-Input Multiple-Output (MaMIMO) receivers. The optimal BA thus obtained ensures the maximum energy efficiency of the MaMIMO receiver.

Power inspection plays an important role in ensuring the normal operation of the power grid. However, inspection of transmission lines in an unoccupied area is time-consuming and labor-intensive. Recently, unmanned aerial vehicle (UAV) inspection has attracted remarkable attention in the space-ground collaborative smart grid, where UAVs are able to provide full converge of patrol points on transmission lines without the limitation of communication and manpower. Nevertheless, how to schedule UAVs to traverse numerous, dispersed target nodes in a vast area with the least cost (e.g., time consumption and total distance) has rarely been studied. In this paper, we focus on this challenging and practical issue which can be considered as a family of vehicle routing problems (VRPs) with regard to different constraints, and propose a Diverse Trajectory-driven Deep Reinforcement Learning (DT-DRL) approach with encoder-decoder scheme to tackle it. First, we bring in a threshold unit in our encoder for better state representation. Secondly, we realize that the already visited nodes have no impact on future decisions, and then devise a dynamic-aware context embedding which removes irrelevant nodes to trace the current graph. Finally, we introduce multiply decoders with identical structure but unshared parameters, and design a Kullback-Leibler divergence based regular term to enforce decoders to output diverse trajectories, which expands the search space and enhances the routing performance. Comprehensive experiments on five types of routing problems show that our approach consistently outperforms both DRL and heuristic methods by a clear margin.

The L 1 -regularized regression with Kullback–Leibler divergence (KL- L 1 R) is a popular regression technique. Although many efforts have been devoted to its efficient implementation, it remains challenging when the number of features is extremely large. In this paper, to accelerate KL- L 1 R, we introduce a novel and fast sequential safe feature elimination rule (FER) based on its sparsity, local regularity properties, and duality theory. It takes negligible time to select and delete most redundant features before and during the training process. Only one reduced model needs to be solved, which makes the computational time shortened. To further speed up the reduced model, the Newton coordinate descent method (Newton-CDM) is chosen as a solver. The superiority of FER is safety, i.e., its solution is exactly the same as the original KL- L 1 R. Numerical experiments on three artificial datasets, five real-world datasets, and one handwritten digit dataset demonstrate the feasibility and validity of our FER. [ABSTRACT FROM AUTHOR]

In this paper, the design of low probability of intercept (LPI) radar waveforms considers not only the performance of passive interception systems (PISs), but also radar detection and resolution performance. Waveform design is an important considerations for the LPI ability of radar. Since information theory has a powerful performance-bound description ability from the perspective of information flow, LPI waveforms are designed in this paper within the constraints of the detection performance metrics of radar and PISs, both of which are measured by the Kullback–Leibler divergence, and the resolution performance metric, which is measured by joint entropy. The designed optimization model of LPI waveforms can be solved using the sequential quadratic programming (SQP) method. Simulation results verify that the designed LPI waveforms not only have satisfactory target-detecting and resolution performance, but also have a superior low interception performance against PISs. [ABSTRACT FROM AUTHOR]

At the start of new operation, a production process is commonly unstable. Then, a process condition is into being stable gradually over time. For the purpose of shifting to mass production, the stability in a process should be evaluated. This paper proposes a method of evaluating the process stability based on Bayesian statistics and information theory. In Bayesian statistics, the knowledge for a process is renewed by deriving posterior distribution based on current prior distribution and observations.Here, equivalency between prior and posterior distributions could be considered to be a criterion for the stability in a process. It is needed to define difference between prior and posterior distributions in order to evaluate the equivalency between prior and posterior distributions. We first formulate the relation between the prior distribution and the posterior distribution for process mean using Bayesian statistics. Secondly, we evaluate the difference between the both distributions based on Kullback-Leibler (K-L) divergence in information theory. Finally, some numerical examples in the method of evaluating the stability in processmean are illustrated. Also, we discuss about a decision rule for the stability in process mean through the numerical investigation. [ABSTRACT FROM AUTHOR]

The family of visibility algorithms provides a new point of view to describe time series by transforming them into networks. In this paper, we propound a new visibility algorithm named Temporal Vector Visibility graph ( TVV g) , which maps the multivariate time series to a directed network. Computed by the i n g o i n g and o u t g o i n g degree distributions obtained from the TVV g , these statistics such as the Kullback–Leibler divergence (KLD), the normalized Shannon entropy and the statistical complexity measure, are introduced to assess the complexity of time series. Furthermore, we also apply the Multivariate Multiscale Entropy Plane (MMEP) to evaluate the complexity of multivariate time series. The experimental results of eight different types of time series verify the effectiveness of our method. Subsequently, this method is employed to explore the complexity characteristics of financial time series and classify different stock markets. Our research reveals that this method is capable of investigating the physical structures of financial time series. [ABSTRACT FROM AUTHOR]

The Chernoff information between two probability measures is a statistical divergence measuring their deviation defined as their maximally skewed Bhattacharyya distance. Although the Chernoff information was originally introduced for bounding the Bayes error in statistical hypothesis testing, the divergence found many other applications due to its empirical robustness property found in applications ranging from information fusion to quantum information. From the viewpoint of information theory, the Chernoff information can also be interpreted as a minmax symmetrization of the Kullback–Leibler divergence. In this paper, we first revisit the Chernoff information between two densities of a measurable Lebesgue space by considering the exponential families induced by their geometric mixtures: The so-called likelihood ratio exponential families. Second, we show how to (i) solve exactly the Chernoff information between any two univariate Gaussian distributions or get a closed-form formula using symbolic computing, (ii) report a closed-form formula of the Chernoff information of centered Gaussians with scaled covariance matrices and (iii) use a fast numerical scheme to approximate the Chernoff information between any two multivariate Gaussian distributions. [ABSTRACT FROM AUTHOR]

The detection of underwater objects in sonar imagery is a key enabling technique, for applications ranging from mine hunting and seabed characterization to marine archaeology. Owing to the nonhomogeneity of the sonar imagery, the majority of detection approaches are geared toward the detection of features in the spatial domain to identify anomalies in the seabed’s background. Yet, when the seabed is complex and includes rocks and sand ripples, spatial features are hard to discriminate, leading to high false alarm rates. With the aim of validating the detection of man-made objects in complex environments, we utilize the expected spectral diversity of reflections. This way, we can differentiate man-made objects’ reflections from the relatively flat frequency response of natural objects’ reflections, such as rocks. Our solution merges a set of preregistered sonar images of the same scene that are obtained at a different center frequency. For low- or high-resolution sonar images, we apply the Jain’s fairness index or the Kullback–Leibler divergence, respectively, to evaluate the spectral diversity of the reflections of a given region of interest and, thus, detect anomalies across the spectrum domain. We test our algorithm over simulated data and over images collected in three designated sea experiments: a data set that we share with the community. The results show that, compared with benchmark schemes, our solution achieves lower false alarm rates while preserving high detection level. [ABSTRACT FROM AUTHOR]

In this paper, we propose a supervised single-channel speech enhancement method that combines Kullback-Leibler (KL) divergence-based non-negative matrix factorization (NMF) and a hidden Markov model (NMF-HMM). With the integration of the HMM, the temporal dynamics information of speech signals can be taken into account. This method includes a training stage and an enhancement stage. In the training stage, the sum of the Poisson distribution, leading to the KL divergence measure, is used as the observation model for each state of the HMM. This ensures that a computationally efficient multiplicative update can be used for the parameter update of this model. In the online enhancement stage, a novel minimum mean square error estimator is proposed for the NMF-HMM. This estimator can be implemented using parallel computing, reducing the time complexity. Moreover, compared to the traditional NMF-based speech enhancement methods, the experimental results show that our proposed algorithm improved the short-time objective intelligibility and perceptual evaluation of speech quality by 5% and 0.18, respectively. [ABSTRACT FROM AUTHOR]

The framework of this article is to introduce a new efficient Blind Source Separation (BSS) method that handles mixtures of noise-contaminated independent / dependent sources. In order to achieve that, one can minimize a criterion that fuses a separating part, based on Kullback–Leibler divergence to set apart the observed mixtures of either dependent or independent sources, with a regularization part that employs the bilateral total variation (BTV) for the purpose of denoising the observations. The proposed algorithm utilizes a primal-dual algorithm to remove the noise, while a gradient descent method is implemented to retrieve the source signals. Our algorithm has shown its effectiveness and efficiency toward the noisy dependent / independent sources and also surpassed the standard BSS algorithms through different experimental results. [ABSTRACT FROM AUTHOR]

This paper aims at certain theoretical studies and additional computational analysis on symmetry and its lack in Kullback-Leibler and Jeffreys probabilistic divergences related to some engineering applications. As it is known, the Kullback-Leibler distance in between two different uncertainty sources exhibits a lack of symmetry, while the Jeffreys model represents its symmetrization. The basic probabilistic computational implementation has been delivered in the computer algebra system MAPLE 2019®, whereas engineering illustrations have been prepared with the use of the Finite Element Method systems Autodesk ROBOT® & ABAQUS®. Determination of the first two probabilistic moments fundamental in the calculation of both relative entropies has been made (i) analytically, using a semi-analytical approach (based upon the series of the FEM experiments), and (ii) the iterative generalized stochastic perturbation technique, where some reference solutions have been delivered using (iii) Monte-Carlo simulation. Numerical analysis proves the fundamental role of computer algebra systems in probabilistic entropy determination and shows remarkable differences obtained with the two aforementioned relative entropy models, which, in some specific cases, may be neglected. As it is demonstrated in this work, a lack of symmetry in probabilistic divergence may have a decisive role in engineering reliability, where extreme and admissible responses cannot be simply replaced with each other in any case. [ABSTRACT FROM AUTHOR]

Among the existing bearing faults, ball ones are known to be the most difficult to detect and classify. In this work, we propose a diagnosis methodology for these incipient faults' classification using time series of vibration signals and their decomposition. Firstly, the vibration signals were decomposed using empirical mode decomposition (EMD). Time series of intrinsic mode functions (IMFs) were then obtained. Through analysing the energy content and the components' sensitivity to the operating point variation, only the most relevant IMFs were retained. Secondly, a statistical analysis based on statistical moments and the Kullback–Leibler divergence (KLD) was computed allowing the extraction of the most relevant and sensitive features for the fault information. Thirdly, these features were used as inputs for the statistical clustering techniques to perform the classification. In the framework of this paper, the efficiency of several family of techniques were investigated and compared including linear, kernel-based nonlinear, systematic deterministic tree-based, and probabilistic techniques. The methodology's performance was evaluated through the training accuracy rate (TrA), testing accuracy rate (TsA), training time (Trt) and testing time (Tst). The diagnosis methodology has been applied to the Case Western Reserve University (CWRU) dataset. Using our proposed method, the initial EMD decomposition into eighteen IMFs was reduced to four and the most relevant features identified via the IMFs' variance and the KLD were extracted. Classification results showed that the linear classifiers were inefficient, and that kernel or data-mining classifiers achieved 100 % classification rates through the feature fusion. For comparison purposes, our proposed method demonstrated a certain superiority over the multiscale permutation entropy. Finally, the results also showed that the training and testing times for all the classifiers were lower than 2 s, and 0.2 s, respectively, and thus compatible with real-time applications. [ABSTRACT FROM AUTHOR]

Multiple Importance Sampling (MIS) combines the probability density functions (pdf) of several sampling techniques. The combination weights depend on the proportion of samples used for the particular techniques. Weights can be found by optimization of the variance, but this approach is costly and numerically unstable. We show in this paper that MIS can be represented as a divergence problem between the integrand and the pdf, which leads to simpler computations and more robust solutions. The proposed idea is validated with 1D numerical examples and with the illumination problem of computer graphics. [ABSTRACT FROM AUTHOR]

This paper investigates the degree of efficiency for the Moscow Stock Exchange. A market is called efficient if prices of its assets fully reflect all available information. We show that the degree of market efficiency is significantly low for most of the months from 2012 to 2021. We calculate the degree of market efficiency by (i) filtering out regularities in financial data and (ii) computing the Shannon entropy of the filtered return time series. We developed a simple method for estimating volatility and price staleness in empirical data in order to filter out such regularity patterns from return time series. The resulting financial time series of stock returns are then clustered into different groups according to some entropy measures. In particular, we use the Kullback–Leibler distance and a novel entropy metric capturing the co-movements between pairs of stocks. By using Monte Carlo simulations, we are then able to identify the time periods of market inefficiency for a group of 18 stocks. The inefficiency of the Moscow Stock Exchange that we have detected is a signal of the possibility of devising profitable strategies, net of transaction costs. The deviation from the efficient behavior for a stock strongly depends on the industrial sector that it belongs to. [ABSTRACT FROM AUTHOR]

With the popularization of portable devices such as mobile phones and cameras, digital images have been widely disseminated in human life. However, due to factors such as photoaging, shooting environment, etc., images will encounter some defects. To restore these defective images quickly and realistically, image inpainting technology emerges as the times require, and digital image processing technology has been rapidly developed. In recent years, thanks to the rapid development of deep learning, which has been gradually applied to image inpainting and has shown excellent performance. For the image inpainting problem, because the damaged images usually have a large number of missing areas, the inpainting models are required to have a stronger ability to mine the global correlation of the image itself and maintain the global consistency of the restoration patches. Given the above shortcomings, a self-attention-based Wasserstein generative adversarial networks (WGAN) single image inpainting method is proposed. First, WGAN is introduced, in which, the global consistency of the inpainting region is ensured through the learning of a generative adversarial model. Second, the proposed model uses the Wasserstein distance (Earth mover distance) to measure the similarity of the two distributions. Compared with the Kullback–Leibler (KL) divergence and the Jensen–Shannon (JS) divergence, even if the support sets of the two distributions do not overlap or overlap very little, the Wasserstein distance can still reflect the distance between the two distributions, and it is conducive to ensuring the stability of GAN. Third, self-attention is introduced to exploit the self-similarity of local features of images fully. The experimental results show that the proposed method can mine the global correlation of the image itself better than the compared methods in quantitative as well as qualitative assessments. [ABSTRACT FROM AUTHOR]

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Sensor reconfigurability is the basis of the fault tolerance of a system. In order to improve the fault tolerance and reliability of sensors in the system, a method of quantitative evaluation of sensor reconfigurability based on a data-driven method is proposed. In this method, an analytic redundancy analysis of sensors in the system is carried out, and the analytic redundancy model is established. Based on this, the quantitative evaluation index of sensor reconfigurability is given by using the similarity evaluation method. Because the possible inaccuracy of the evaluation index results in uncertainties of the system, an adaptive threshold is designed to improve the evaluation accuracy. This method can quantitatively evaluate the reconfigurability of the sensor without depending on the system model, which provides a theoretical basis for improving the fault tolerance of the system at the design stage. [ABSTRACT FROM AUTHOR]

Divergence is used to measure the difference of two systems, and it is widely applied in many fields. To solve this problem more efficiently, Dempster–Shafer evidence theory has been proposed, different from the traditional probability distribution, and because of its processing advantages of uncertainty, has been widely used in many aspects of reality. In this paper, a new method of belief divergence measure of mass functions is proposed, named as Earth Mover’s divergence of belief function, which is a generalization of Earth Mover’s distance (Wasserstein distance). Compared with other existing methods of divergence measuring, the EM divergence can show good performance in the presence of higher degrees of uncertainty and more conflicts. Numerical examples help have a better understanding of the Earth Mover’s divergence of belief function. Based on the new method of belief divergence measure, there is a combination model proposed to address the problem of data fusion. Application in target recognition is used to show the efficiency of the proposed method of divergence measure. [ABSTRACT FROM AUTHOR]

Background: The standard approach to local inconsistency assessment typically relies on testing the conflict between the direct and indirect evidence in selected treatment comparisons. However, statistical tests for inconsistency have low power and are subject to misinterpreting a p-value above the significance threshold as evidence of consistency., Methods: We propose a simple framework to interpret local inconsistency based on the average Kullback-Leibler divergence (KLD) from approximating the direct with the corresponding indirect estimate and vice versa. Our framework uses directly the mean and standard error (or posterior mean and standard deviation) of the direct and indirect estimates obtained from a local inconsistency method to calculate the average KLD measure for selected comparisons. The average KLD values are compared with a semi-objective threshold to judge the inconsistency as acceptably low or material. We exemplify our novel interpretation approach using three networks with multiple treatments and multi-arm studies., Results: Almost all selected comparisons in the networks were not associated with statistically significant inconsistency at a significance level of 5%. The proposed interpretation framework indicated 14%, 66%, and 75% of the selected comparisons with an acceptably low inconsistency in the corresponding networks. Overall, information loss was more notable when approximating the posterior density of the indirect estimates with that of the direct estimates, attributed to indirect estimates being more imprecise., Conclusions: Using the concept of information loss between two distributions alongside a semi-objectively defined threshold helped distinguish target comparisons with acceptably low inconsistency from those with material inconsistency when statistical tests for inconsistency were inconclusive., (© 2024. The Author(s).)

Two typical fixed-length random number generation problems in information theory are considered for general sources. One is the source resolvability problem and the other is the intrinsic randomness problem. In each of these problems, the optimum achievable rate with respect to the given approximation measure is one of our main concerns and has been characterized using two different information quantities: the information spectrum and the smooth Rényi entropy. Recently, optimum achievable rates with respect to f -divergences have been characterized using the information spectrum quantity. The f -divergence is a general non-negative measure between two probability distributions on the basis of a convex function f . The class of f -divergences includes several important measures such as the variational distance, the KL divergence, the Hellinger distance and so on. Hence, it is meaningful to consider the random number generation problems with respect to f -divergences. However, optimum achievable rates with respect to f -divergences using the smooth Rényi entropy have not been clarified yet in both problems. In this paper, we try to analyze the optimum achievable rates using the smooth Rényi entropy and to extend the class of f -divergence. To do so, we first derive general formulas of the first-order optimum achievable rates with respect to f -divergences in both problems under the same conditions as imposed by previous studies. Next, we relax the conditions on f -divergence and generalize the obtained general formulas. Then, we particularize our general formulas to several specified functions f . As a result, we reveal that it is easy to derive optimum achievable rates for several important measures from our general formulas. Furthermore, a kind of duality between the resolvability and the intrinsic randomness is revealed in terms of the smooth Rényi entropy. Second-order optimum achievable rates and optimistic achievable rates are also investigated.

Modern societies use machine learning techniques to support complex decision-making processes (e.g., renewable energy and power demand forecasting in energy systems). Data fuels these techniques, so the quality of the data fed into them determines the accuracy of the results. While the amount of data is increasing with the adoption of internet-of-things, most of it is still private. Availability of data limits the application of machine learning. Scientists and industry pioneers are proposing a model that relies on the economics of data markets, where private data can be traded for a price. Cybersecurity analyses of such markets are lacking. In this context, our study makes two contributions. First, it designs a data market for electric vehicle charging stations, which aims to improve the accuracy of electric vehicle charging demand forecasts. Accurate demand forecasts are essential for sustainable operations of the electric vehicle - charging station - power grid ecosystem, which, in turn, facilitates the electrification and decarbonization of the transportation sector. On the other hand, erroneous demand forecasts caused by malicious cyberattacks impose operational challenges to the ecosystem. Thus, the second contribution of our study is to examine the feasibility of false data injection attacks on the data market for electric vehicle charging stations and to propose a defense mechanism against such attacks. We illustrate our results using data from electric vehicle charging stations in Manhattan, New York. We demonstrate that the data market improves forecasting accuracy of charging stations and reduces the effectiveness of false data injection attacks. The purpose of this work is not only to inform electric vehicle charging stations about the economic benefits of data markets, but to promote cyber awareness among data market pioneers and stakeholders.

The variational Bayesian method solves nonlinear estimation problems by iteratively computing the integral of the marginal density. Many researchers have demonstrated the fact its performance depends on the linear approximation in the computation of the variational density in the iteration and the degree of nonlinearity of the underlying scenario. In this paper, two methods for computing the variational density, namely, the natural gradient method and the simultaneous perturbation stochastic method, are used to implement a variational Bayesian Kalman filter for maneuvering target tracking using Doppler measurements. The latter are collected from a set of sensors subject to single-hop network constraints. We propose a distributed fusion variational Bayesian Kalman filter for a networked maneuvering target tracking scenario and both of the evidence lower bound and the posterior Cramér–Rao lower bound of the proposed methods are presented. The simulation results are compared with centralized fusion in terms of posterior Cramér–Rao lower bounds, root-mean-squared errors and the 3σ bound.

We introduce a quantile-based multivariate log-normal distribution, providing a new multivariate skewed distribution with positive support. The parameters of this distribution are interpretable in terms of quantiles of marginal distributions and associations between pairs of variables, a desirable feature for statistical modeling purposes. We derive statistical properties of the quantile-based multivariate log-normal distribution involving the transformations, closed-form expressions for the mixed moments, expected value, covariance matrix, mode, Shannon entropy, and Kullback–Leibler divergence. We also present results on marginalization, conditioning, and independence. Additionally, we discuss parameter estimation and verify its performance through simulation studies. We evaluate the model fitting based on Mahalanobis-type distances. An application to children data is presented.

This paper introduces a unified Bayesian approach for testing various hypotheses related to multinomial distributions. The method calculates the Kullback–Leibler divergence between two specified multinomial distributions, followed by comparing the change in distance from the prior to the posterior through the relative belief ratio. A prior elicitation algorithm is used to specify the prior distributions. To demonstrate the effectiveness and practical application of this approach, it has been applied to several examples.

In the field of human–computer interaction (HCI), text entry methods can be evaluated through controlled user experiments or predictive modeling techniques. While the modeling approach requires a language model, the empirical approach necessitates representative text phrases for the experimental stimuli. In this context, finding a phrase set with the best language representativeness belongs to the class of optimization problems in which a solution is sought in a large search space. We propose a genetic algorithm (GA)-based method for extracting a target phrase set from the available text corpus, optimizing its language representativeness. Kullback–Leibler divergence is utilized to evaluate candidates, considering the digram probability distributions of both the source corpus and the target sample. The proposed method is highly customizable, outperforms typical random sampling, and exhibits language independence. The representative phrase sets generated by the proposed solution facilitate a more valid comparison of the results from different text entry studies. The open source implementation enables the easy customization of the GA-based sampling method, promotes its immediate utilization, and facilitates the reproducibility of this study. In addition, we provide heuristic guidelines for preparing the text entry experiments, which consider the experiment’s intended design and the phrase set to be generated with the proposed solution.