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Fighting against the pandemic diseases with unique characters requires new sophisticated approaches like the artificial intelligence. This paper develops an artificial intelligence algorithm to produce multi-dimensional policies for controlling and minimizing the pandemic casualties under the limited pharmacological resources. In this respect, a comprehensive parametric model with a priority and age-specific vaccination policy and a variety of non-pharmacological policies are introduced. This parametric model is utilized for constructing an artificial intelligence algorithm by following the exact analogy of the model-based solution. Also, this parametric model is manipulated by the artificial intelligence algorithm to seek for the best multi-dimensional non-pharmacological policies that minimize the future pandemic casualties as desired. The role of the pharmacological and non-pharmacological policies on the uncertain future casualties are extensively addressed on the real data. It is shown that the developed artificial intelligence algorithm is able to produce efficient policies which satisfy the particular optimization targets such as focusing on minimization of the death casualties more than the infected casualties or considering the curfews on the people age over 65 rather than the other non-pharmacological policies. The paper finally analyses a variety of the mutant virus cases and the corresponding non-pharmacological policies aiming to reduce the morbidity and mortality rates. [ABSTRACT FROM AUTHOR]

Fighting against the pandemic diseases with unique characters requires new sophisticated approaches like the artificial intelligence. This paper develops an artificial intelligence algorithm to produce multi-dimensional policies for controlling and minimizing the pandemic casualties under the limited pharmacological resources. In this respect, a comprehensive parametric model with a priority and age-specific vaccination policy and a variety of non-pharmacological policies are introduced. This parametric model is utilized for constructing an artificial intelligence algorithm by following the exact analogy of the model-based solution. Also, this parametric model is manipulated by the artificial intelligence algorithm to seek for the best multi-dimensional non-pharmacological policies that minimize the future pandemic casualties as desired. The role of the pharmacological and non-pharmacological policies on the uncertain future casualties are extensively addressed on the real data. It is shown that the developed artificial intelligence algorithm is able to produce efficient policies which satisfy the particular optimization targets such as focusing on minimization of the death casualties more than the infected casualties or considering the curfews on the people age over 65 rather than the other non-pharmacological policies. The paper finally analyses a variety of the mutant virus cases and the corresponding non-pharmacological policies aiming to reduce the morbidity and mortality rates. [ABSTRACT FROM AUTHOR]

As an algorithmic framework, message passing is extremely powerful and has wide applications in the context of different disciplines including communications, coding theory, statistics, signal processing, artificial intelligence and combinatorial optimization. In this paper, we investigate the performance of a message-passing algorithm called min-sum belief propagation (BP) for the vertex-disjoint shortest $k$ -path problem ($k$ -VDSP) on weighted directed graphs, and derive the iterative message-passing update rules. As the main result of this paper, we prove that for a weighted directed graph $G$ of order $n$ , BP algorithm converges to the unique optimal solution of $k$ -VDSP on $G$ within $O(n^{2}w_{max})$ iterations, provided that the weight $w_{e}$ is nonnegative integral for each arc $e\in E(G)$ , where $w_{max}=\max \{w_{e}: e\in E(G)\}$. To the best of our knowledge, this is the first instance where BP algorithm is proved correct for NP-hard problems. Additionally, we establish the extensions of $k$ -VDSP to the case of multiple sources or sinks. [ABSTRACT FROM AUTHOR]

As video data are occupying an increasingly more significant portion of global data traffic, video communication has become an indispensable component for most multimedia applications. As an enabling technology of video communication, although video coding is well standardized, the strategy to control video codec is highly customized to applications. The consistency of video quality is being paid more attention in many emerging applications. For example, in video surveillance, the quality of video frames should be stable in order to ensure the performance of machine intelligence algorithm, such as object detection precision. In this paper, we focus on achieving constant objective reconstruction quality in the process of video coding. To achieve certain rate-distortion performance, bit rate and distortion metric are usually modeled as a function of video content and control parameters of codec. For content modeling in existing work, there is still room for improvement, including the design of more efficient content feature, the compensation for assumption about constant RD characteristics among consecutive frames, and the adjustment of Lagrangian multiplier $\lambda $ according to content property. The main contributions of this paper are: 1) a robust content adaptive model for residual bit rate modeling based on content statistics called mean absolute partial transformed difference (MAPTD); 2) a content-related header bit rate modeling; 3) preprocessing scheme for robust content feature estimation at scene change; 4) a distortion model consistent with local content; and 5) content adaptive $\lambda $ determination. The experimental results show that our constant quality control strategy can achieve superior performance compared with the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel adaptive importance sampling (AIS) algorithm for probabilistic inference. The sampler learns a proposal distribution adaptation strategy by framing AIS as a reinforcement learning problem. Under this structure, the proposal distribution of the sampler is treated as an agent whose state is controlled using a parameterized policy. At each iteration of the algorithm, the agent earns a reward that is related to its contribution to the variance of the AIS estimator of the normalization constant of the target distribution. Policy gradient methods are employed to learn a locally optimal policy that maximizes the expected value of the sum of all rewards. Numerical simulations on two different examples demonstrate promising results for the future application of the proposed method to complex Bayesian models. [ABSTRACT FROM AUTHOR]

Compared with other applications in computer vision, convolutional neural networks (CNNs) have underperformed on pedestrian detection. A breakthrough was made very recently using sophisticated deep CNN (DCNN) models, with a number of handcrafted features or explicit occlusion handling mechanism. In this paper, we show that by reusing the convolutional feature maps of a DCNN model as image features to train an ensemble of boosted decision models, we are able to achieve the best reported accuracy without using specially designed learning algorithms. We empirically identify and disclose important implementation details. We also show that pixel labeling may be simply combined with a detector to boost the detection performance. By adding complementary handcrafted features such as optical flow, the DCNN-based detector can be further improved. We advance the state-of-the-art results by lowering the log-average miss rate from 11.7% to 8.9% on the Caltech data set and from 11.2% to 8.6% on the Inria data set. We also achieve a comparable result to state-of-the-art approaches on the KITTI data set. [ABSTRACT FROM AUTHOR]

Due to the private ownerships of various resource and equipment in a microgrid, it is essential to design a proper power trading strategy for a mutually satisfying partnership among suppliers and users. This paper proposes a novel discounted min-consensus algorithm to discover the optimal electrical power trading route with minimal power loss in dc microgrids. The proposed distributed algorithm only requires information exchange among neighbors based on a peer-to-peer communication protocol, which is robust to topology changes and enables parallel data processing to achieve fast decision-making. The convergence and optimality of the proposed discounted min-consensus algorithm are guaranteed by rigorous analysis. Simulation results of the 10-bus, 68-bus, and 1062-bus power systems demonstrate the effectiveness of the proposed distributed optimal routing strategy for electrical power trading. Moreover, the proposed novel distributed algorithm sheds light on applying distributed control and optimization approaches to solve problems arising in artificial intelligence. [ABSTRACT FROM AUTHOR]

Simultaneous localization and planning (SLAP) is a crucial ability for an autonomous robot operating under uncertainty. In its most general form, SLAP induces a continuous partially observable Markov decision process (POMDP), which needs to be repeatedly solved online. This paper addresses this problem and proposes a dynamic replanning scheme in belief space. The underlying POMDP, which is continuous in state, action, and observation space, is approximated offline via sampling-based methods, but operates in a replanning loop online to admit local improvements to the coarse offline policy. This construct enables the proposed method to combat changing environments and large localization errors, even when the change alters the homotopy class of the optimal trajectory. It further outperforms the state-of-the-art Feedback-based Information RoadMap (FIRM) method by eliminating unnecessary stabilization steps. Applying belief space planning to physical systems brings with it a plethora of challenges. A key focus of this paper is to implement the proposed planner on a physical robot and show the SLAP solution performance under uncertainty, in changing environments and in the presence of large disturbances, such as a kidnapped robot situation. [ABSTRACT FROM AUTHOR]

Autonomous driving technologies can provide greater safety, comfort, and efficiency for future transportation systems. Until now, much of the research effort has been devoted to developing different sensing and control algorithms. However, there has been limited research on how to handle sensor errors efficiently. A simple error in the sensor may lead to an unexpected failure in the whole autonomous driving function. In those cases, the vehicle is then recommended to be sent back to the manufacturer for repair, which costs time and money. This paper introduces an efficient automatic online sensor correction method. The method includes four major functions: sensor error detection, human teaching, vehicle learning, and vehicle self-evaluation. The first function is assumed to be ready and the major contribution of this paper is the human-vehicle teaching and learning framework, which utilizes human-vehicle interaction to collaboratively adjust the parameter in the control model in order to compensate for the errors of the sensors. The self-evaluation function is also briefly introduced. The applications of this method to radar and vision sensors to recover adaptive cruise control and lane keeping functions are introduced in detail. Experimental results acquired from high-fidelity 1/10-scale autonomous driving vehicles illustrate the effectiveness and advantages of the proposed approach. [ABSTRACT FROM AUTHOR]

A power transformer is one of the most crucial items of equipment in the electricity supply chain. The reliability of this valuable asset is strongly dependent on the condition of its subsystems such as insulation, core, windings, bushings and tap changer. Integration of various measured parameters of these subsystems makes it possible to evaluate the overall health condition of an in-service transformer. This paper develops an artificially intelligent algorithm based on multiple general regression neural networks to combine the operating condition of various subsystems of a transformer to form a quantitative health index. The model is developed using a training set derived from four conditional boundaries based on IEEE standards, the literature and the knowledge of transformer experts. Performance of the proposed method is compared with expert classifications using a database of 345 power transformers. This shows that the proposed method is reliable and effective for condition assessment and is sensitive to poor condition of any single subsystem. [ABSTRACT FROM PUBLISHER]

Sparse modeling representative selection (SMRS) has been recently introduced for selecting the most relevant examples in data sets. SMRS exploits data self-representativeness coding in order to infer a coding matrix with block sparsity constraint. The relevance scores of samples are then derived from the estimated matrix of coefficients. Since SMRS is based on a linear model for data self-representation, it cannot always provide good relevant samples. Besides, most of its selected samples can be found in dense areas in input space. In this paper, we propose to overcome the SMRS method’s shortcomings that are related to the coding matrix estimation. We introduce two nonlinear data self-representativeness coding schemes that are based on Hilbert space and column generation. Experimental evaluation is carried out on summarizing a video movie and on summarizing training image data sets used for classification tasks. These experiments demonstrated that the proposed nonlinear methods can outperform state-of-the art selection methods. [ABSTRACT FROM AUTHOR]

A residual networks family with hundreds or even thousands of layers dominates major image recognition tasks, but building a network by simply stacking residual blocks inevitably limits its optimization ability. This paper proposes a novel residual network architecture, residual networks of residual networks (RoR), to dig the optimization ability of residual networks. RoR substitutes optimizing residual mapping of residual mapping for optimizing original residual mapping. In particular, RoR adds levelwise shortcut connections upon original residual networks to promote the learning capability of residual networks. More importantly, RoR can be applied to various kinds of residual networks (ResNets, Pre-ResNets, and WRN) and significantly boost their performance. Our experiments demonstrate the effectiveness and versatility of RoR, where it achieves the best performance in all residual-network-like structures. Our RoR-3-WRN58-4 + SD models achieve new state-of-the-art results on CIFAR-10, CIFAR-100, and SVHN, with the test errors of 3.77%, 19.73%, and 1.59%, respectively. RoR-3 models also achieve state-of-the-art results compared with ResNets on the ImageNet data set. [ABSTRACT FROM AUTHOR]

For systems that can only be locally stabilized, control laws and their effective regions are both important. In this paper, invariant policy iteration is proposed to solve the optimal control of discrete-time systems. At each iteration, a given policy is evaluated in its invariantly admissible region, and a new policy and a new region are updated for the next iteration. Theoretical analysis shows the method is regionally convergent to the optimal value and the optimal policy. Combined with sum-of-squares polynomials, the method is able to achieve the near-optimal control of a class of discrete-time systems. An invariant adaptive dynamic programming algorithm is developed to extend the method to scenarios where system dynamics is not available. Online data are utilized to learn the near-optimal policy and the invariantly admissible region. Simulated experiments verify the effectiveness of our method. [ABSTRACT FROM AUTHOR]

The implementation of artificial intelligence (AI) in wireless networks is becoming more and more popular because of the growing number of mobile devices and the availability of huge amount of data. However, directly transmitting data for centralized learning will cause long communication latency owing to the limited communication resource and may incur severe privacy issue as well. To address these issues, we consider the federated edge learning (FEEL) system in this paper and develop an importance-aware joint data selection and resource allocation algorithm to maximize the learning efficiency. Aiming at selecting important data for local training, we first analyze the relation between loss decay and gradient norm, which indicates that larger gradient norm generally leads to faster learning speed. Based on this, a learning efficiency maximization problem is formulated by jointly considering the communication resource allocation and data selection. The closed-form results for optimal communication resource allocation and data selection are both developed, where some insights are also highlighted. Furthermore, an optimal algorithm with low computational complexity is developed to obtain the optimal end-to-end latency in one training period. We show that the sample size should be set to its upper limit in order to maximize the learning performance. Finally, we conduct extensive experiments on three popular convolutional neural network (CNN) models. The results show that the proposed algorithm can effectively reduce the training latency and improve the learning accuracy as compared with some benchmark algorithms. [ABSTRACT FROM AUTHOR]

To cope with the lack of on-device machine learning samples, this article presents a distributed data augmentation algorithm, coined federated data augmentation (FAug). In FAug, devices share a tiny fraction of their local data, i.e., seed samples, and collectively train a synthetic sample generator that can augment the local datasets of devices. To further improve FAug, we introduce a multihop-based seed sample collection method and an oversampling technique that mixes up collected seed samples. Both approaches enjoy the benefit from the crowd of devices, by hiding data privacy from preceding hops and feeding diverse seed samples. In the image classification tasks, simulations demonstrate that the proposed FAug frameworks yield stronger privacy guarantees, lower communication latency, and higher on-device ML accuracy. [ABSTRACT FROM AUTHOR]

The tensor completion issues have obtained a great deal of attention in the past few years. However, the data fidelity part minimizes a squared loss function, which may be inappropriate for the case of noisy one-bit observations. In this paper, we alleviate the mentioned difficulty by drawing on the experience of matrix scenarios. Based on the convex relation to $\ell _{1}$ norm of the tensor multi-rank, we propose a novel optimization model trying to recover the underlying tensor in case of one-bit observations. The feasibility of this model is proved by theoretical derivations. Furthermore, an alternating direction method of multipliers based algorithm is designed to find the solution. The numerical experiments demonstrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Mismatch removal is a key step in many computer vision problems that involve point matching. The existing methods for checking geometric consistency mainly focus on similarity or affine transformations. In this paper, we propose a novel mismatch removal method that can cope with the projective transformation between two corresponding point sets. Our approach is based on the augmented homogeneous coordinates matrix constructed from the coordinates of anchor matches, whose degeneracy can indicate the correctness of anchor matches. The set of anchor matches is initially all the matches and is iteratively updated by calculating the difference between the estimated matched points, which can be easily computed in a closed form, and the actually matched points and removing those with large differences. Experimental results on synthetic 2D point matching data sets and real image matching data sets verify that our method achieves the highest $F$ -score among all the methods under similarity, affine, and projective transformations with noises and outliers. Our method can also achieve faster speed than all other iterative methods. Those non-iterative methods with slight advantage in speed are not competitive in accuracy when compared with ours. We also show that the set of anchor matches is stable through the iteration and the computation time grows very slowly with respect to the number of matched points. When applied to mismatch removal in partial-duplicate image search, our method achieves the best retrieval precision, and its computing time is also highly competitive. [ABSTRACT FROM AUTHOR]

In order to enable voltage ride-through operation, fast and reliable voltage dips detection is necessary. It has been noticed by the analysis of a huge number of measurement results recorded in-field, in laboratory, and obtained by simulation that the voltage dips may be characterized by specific low-order voltage harmonics pattern, which appears at the beginning and at the end of the voltage dips. This feature is named harmonic footprint. This paper proposes a new method of voltage dips detection based on harmonic footprint as part of smart algorithm for detection and classification of power quality events. The aim is to achieve reliable smart voltage dips detection in 1 ms range. Several methods for harmonic estimation in real time were compared in order to choose the optimal one. The footprint is modeled by mathematical function and applied for voltage dips smart detection using recurrent neural network. The proposed method was tested for voltage dips detection analyzing signals from measurement in the real distribution grid (680 records), computer simulations using IEEE-13 bus test grid, and testing in the laboratory. The reliability (detection rate) and speed of voltage dips detection are tested and verified. Furthermore, the future research directions for presented method are outlined. [ABSTRACT FROM AUTHOR]

Short packet communications (SPC) will play an important role in future Internet-of-Things networks. Conventional pilot-assisted transmission (PAT) needs significant overhead to obtain accurate channel state information (CSI) for further symbol detection and bit recovery, thereby reducing the spectral efficiency of the transmission. In this paper, a machine learning framework called Label-Assisted Transmission is proposed, in which the received signals are grouped into clusters through clustering algorithms and known labels are transmitted for cluster-symbol/bits mapping. This novel framework supports bit recovery directly without requiring the bit-symbol mapping information. When such mapping information is available, modulation constrained (MC) clustering algorithms are proposed, which exploit the unique characteristics of digital communication signals. For frequency-flat channels, this novel design needs only one known label regardless of the modulation size and the missing labels can be reconstructed using a proposed label reconstruction scheme. For frequency-selective channels with $L$ -tap time domain channel responses, only $L$ known labels are needed to reconstruct the missing labels if orthogonal frequency division multiplexing technology is adopted. The proposed clustering receiver works well even when the number of clusters is much larger than the number of received samples. The performance of the proposed framework is analyzed empirically through extensive simulations, which verify that the proposed scheme approaches the maximum likelihood detector with perfect CSI. [ABSTRACT FROM AUTHOR]

Photo aesthetic quality evaluation is a challenging task in artificial intelligence systems. In this paper, we propose a biologically inspired aesthetic descriptor that mimicks humans sequentially perceiving visually/semantically salientref refid="fnote1"/ id="fnote1" asterisk="no"paraIn general, visually salient regions are perceived by low-level visual features, such as the high contrast between the foreground and the background objects; while semantically salient regions are perceived by high-level visual features such as human faces.pararegions in a photo. In particular, a weakly supervised learning paradigm is developed to project the local image descriptors into a low-dimensional semantic space. Then, each graphlet can be described by multiple types of visual features, both in low-level and in high-level. Since humans usually perceive only a few salient regions in a photo, a sparsity-constrained graphlet ranking algorithm is proposed that seamlessly integrates both the low-level and the high-level visual cues. Top-ranked graphlets are those visually/semantically prominent local aesthetic descriptors in a photo. They are sequentially linked into a path that simulates humans actively viewing process. Finally, we learn a probabilistic aesthetic measure based on such actively viewing paths (AVPs) from the training photos. Experimental results show that: 1) the AVPs are 87.65% consistent with real human gaze shifting paths, as verified by the eye-tracking data and 2) our aesthetic measure outperforms many of its competitors. [ABSTRACT FROM AUTHOR]

This paper considers the mean-reverting portfolio (MRP) design problem arising from statistical arbitrage (a.k.a. pairs trading) in the financial markets. It aims at designing a portfolio of underlying assets by optimizing the mean reversion strength of the portfolio, while taking into consideration the portfolio variance and an investment budget constraint. Several specific design problems are considered based on different mean reversion criteria. Efficient algorithms are proposed to solve the problems. Numerical results on both synthetic and market data show that the proposed MRP design methods can generate consistent profits and outperform the traditional design methods and the benchmark methods in the literature. [ABSTRACT FROM AUTHOR]

In this paper, we seek to improve deep neural networks by generalizing the pooling operations that play a central role in the current architectures. We pursue a careful exploration of approaches to allow pooling to learn and to adapt to complex and variable patterns. The two primary directions lie in: (1) learning a pooling function via (two strategies of) combining of max and average pooling, and (2) learning a pooling function in the form of a tree-structured fusion of pooling filters that are themselves learned. In our experiments every generalized pooling operation we explore improves performance when used in place of average or max pooling. We experimentally demonstrate that the proposed pooling operations provide a boost in invariance properties relative to conventional pooling and set the state of the art on several widely adopted benchmark datasets. These benefits come with only a light increase in computational overhead during training (ranging from additional 5 to 15 percent in time complexity) and a very modest increase in the number of model parameters (e.g., additional 1, 9, and 27 parameters for mixed, gated, and 2-level tree pooling operators, respectively). To gain more insights about our proposed pooling methods, we also visualize the learned pooling masks and the embeddings of the internal feature responses for different pooling operations. Our proposed pooling operations are easy to implement and can be applied within various deep neural network architectures. [ABSTRACT FROM PUBLISHER]

The modularization of process plants is regarded as a promising approach to cope with upcoming requirements in the process industry regarding flexibility. Within the Decentralized Intelligence for Modular Assets Project, a concept has been developed to overcome current deficiencies in implementing modular plants and especially their automation. On one hand, the approach considers a clear separation of engineering efforts into plant-independent module engineering and plant-specific integration engineering. On the other hand, the concept provides a method for the fast integration of a module's automation system into a higher level process control system. To do so, each module provides its process functionality in encapsulated services. The core of the integration method is a file-based description of the module including its services, operating screens, and communication variables. The so-called “module-type package” is currently under standardization by different organizations. This paper presents the general approach as well as current results of the standardization efforts. Furthermore, the results are executed on an application demonstrator to allow a practical assessment. Thus, module engineering and integration engineering, as well as corresponding software tools can be demonstrated. [ABSTRACT FROM AUTHOR]

Multi-Task Learning (MTL) can enhance a classifier’s generalization performance by learning multiple related tasks simultaneously. Conventional MTL works under the offline or batch setting, and suffers from expensive training cost and poor scalability. To address such inefficiency issues, online learning techniques have been applied to solve MTL problems. However, most existing algorithms of online MTL constrain task relatedness into a presumed structure via a single weight matrix, which is a strict restriction that does not always hold in practice. In this paper, we propose a robust online MTL framework that overcomes this restriction by decomposing the weight matrix into two components: The first one captures the low-rank common structure among tasks via a nuclear norm and the second one identifies the personalized patterns of outlier tasks via a group lasso. Theoretical analysis shows the proposed algorithm can achieve a sub-linear regret with respect to the best linear model in hindsight. Even though the above framework achieves good performance, the nuclear norm that simply adds all nonzero singular values together may not be a good low-rank approximation. To improve the results, we use a log-determinant function as a non-convex rank approximation. The gradient scheme is applied to optimize log-determinant function and can obtain a closed-form solution for this refined problem. Experimental results on a number of real-world applications verify the efficacy of our method. [ABSTRACT FROM PUBLISHER]

Bearing is an important machine detail that appears in almost all mechanical systems. Estimating its operating condition online in order to hold the initiative in exploiting the systems, therefore, is one of the most urgent requirements. In this paper, we propose an online bearing damage identifying method named ASSBDIM based on adaptive neuro-fuzzy inference system (ANFIS), singular spectrum analysis (SSA), and sparse filtering (SF). It is an online process with offline and online phases. In the offline phase, by applying SSA and SF to the measured data stream typed big data with noise, both preprocessing data and extracting valuable information are implemented to build two offline databases signed Off_DaB and Off_testDaB. The ANFIS identifies the dynamic response of the mechanical system via Off_DaB. Based on Off_testDaB, the parameters of the ASSBDIM are then optimized. In the online phase, at each time, another database called On_DaB is built in the way similar to the one used for building the input space of the two offline databases. On_DaB participates as inputs of the ANFIS to estimate its outputs, which are then compared with the corresponding encoded outputs to specify bearing real status at this time. Survey results based on different data sources showed the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a computationally efficient algorithm for Bayesian joint change point detection (CPD) in multiple time series is presented. The data generation model includes a number of change configurations (CC), each affecting a unique subset of the time series, which introduces correlation between the positions of change points (CPs) in the monitored time series. The inference objective is to identify joint changes and the associated CC. The algorithm consists of two stages: First, a univariate CPD algorithm is applied separately to each of the involved time series. The outcomes of this step are maximum a posteriori (MAP) detected CPs and posterior distributions of CPs conditioned on the MAP CPs. These outcomes are used in combination to approximate the posterior for the CCs. In the second algorithm stage, dynamic programming is used to find the maxima of this approximate CC posterior. The algorithm is applied to synthetic data, and it is shown to be both significantly faster and more accurate compared to a previously proposed algorithm designed to solve similar problems. Also, the initial algorithm is extended with steps from the maximization–maximization algorithm, which allows the hyperparameters of the data generation model to be estimated jointly with the CCs, and we show that these estimates coincide with estimates obtained from a Markov chain Monte Carlo algorithm. [ABSTRACT FROM AUTHOR]

Advanced persistent threats (APTs) are a major threat to cyber-security, causing significant financial and privacy losses each year. In this paper, cumulative prospect theory (CPT) is applied to study the interactions between a cyber system and an APT attacker when each of them makes subjective decisions to choose their scan interval and attack interval, respectively. Both the probability distortion effect and the framing effect are applied to model the deviation of subjective decisions of end-users from the objective decisions governed by expected utility theory, under uncertain attack durations in a pure-strategy game and scan interval in a mixed-strategy game. The CPT-based APT detection game incorporates both the probability weighting distortion and the framing effect of the subjective attacker and security agent of the cyber system, rather than discrete decision weights, as in earlier prospect theoretic study of APT detection. The Nash equilibria of the APT detection game are derived, showing that a subjective attacker becomes risk-seeking if the frame of reference for evaluating the utility is large, and becomes risk-averse if the frame of reference for evaluating the utility is small. A policy hill-climbing (PHC) based detection scheme is proposed to increase the policy uncertainty to fool the attacker in the dynamic game, and a “hotbooting” technique that exploits experiences in similar scenarios to initialize the quality values is developed to accelerate the learning speed of PHC-based detection. A practical example of a mobile network is presented to evaluate the performance of the proposed detection strategy. Simulation results show that the proposed strategy can improve detection performance with a higher data protection level and utilities of the cloud in the presence of an attacker compared with a standard Q-learning strategy. [ABSTRACT FROM AUTHOR]

In several multirobot applications in which communication is limited, the mission could require the robots to iteratively take coordinated joint decisions on how to spread out in the environment and on how to reconnect with each other to share data and compute plans. Exploration and surveillance are examples of these applications. In this paper, we consider the problem of computing robots’ paths on a graph-represented environment for restoring connections at minimum traveling cost. We call it the multirobot reconnection problem, we show its NP-hardness and hardness of approximation on some important classes of graphs, and we provide optimal and heuristic algorithms to solve it in practical settings. The techniques we propose are then exploited to derive a new efficient planning algorithm for a relevant connectivity-constrained multirobot planning problem addressed in the literature, the multirobot informative path planning with periodic connectivity problem. [ABSTRACT FROM AUTHOR]

Object detection plays an important role in intelligent transportation systems and intelligent vehicles. Although the topic of object detection has been studied for decades, it is still challenging to accurately detect objects under complex scenarios. The contributing factors for challenges include diversified object and background appearance, motion blur, adverse weather conditions, and complex interactions among objects. In this paper, we propose a new convolutional neural network (CNN) model for traffic object detection, by using multi-scale local and global feature representation (MFR). The proposed model consists of two components: a region proposal network that generates candidate object regions and an object detection network that incorporates multi-scale features and global information, namely MFR-CNN. These two components are jointly optimized. Once the system is trained, it can detect real-world traffic objects accurately, especially small objects and heavily occluded objects. We evaluate the proposed method on four benchmark datasets, achieving consistent improvements over the state of the art. [ABSTRACT FROM AUTHOR]

Controllable building loads have the potential to increase the flexibility of power systems. A key step in developing effective and attainable load control policies is modeling the set of feasible building load profiles. In this paper, we consider buildings whose source of flexibility is their HVAC loads. We propose a data-driven method to empirically estimate a robust feasible region of the load using coarse data, that is, using only total building load and average indoor temperatures. The proposed method uses easy-to-gather coarse data and can be adapted to buildings of any type. The resulting feasible region model is robust to temperature prediction errors and is described by linear constraints. The mathematical simplicity of these constraints makes the proposed model adaptable to many power system applications, for example, economic dispatch, and optimal power flow. We validate our model using data from EnergyPlus and demonstrate its usefulness through a case study in which flexible building loads are used to balance errors of wind power forecasts. [ABSTRACT FROM AUTHOR]

In this paper, a novel rate-control algorithm based on the region adaptive R- $\lambda $ model is proposed for depth maps coding. First, in order to obtain an accurate rate control for depth maps coding, a modified frame level bit allocation method based on coding bits statistical distribution of depth maps is proposed. Second, considering that different areas in a depth map have an imparity effect on virtual view rendering, the blocks of the depth map are divided into two types, namely, interested blocks for virtual view rending (IBV) and noninterested blocks for virtual view rending (NIBV). Then, two different R- $\lambda $ models are derived for IBV and NIBV, respectively. The optimal bitrates for IBV and NIBV are determined by solving an optimization problem. After that, based on the regional R- $\lambda $ models, the optimal Lagrange multipliers are calculated for both IBV and NIBV. Finally, the largest coding unit (LCU) level rate control is performed by adaptively adjusting the Lagrange multiplier to avoid blocking artifacts and smooth the quality of coding. Experimental results demonstrate that the proposed method can achieve considerable BD-PSNR gains compared with the unified rate-quantization model and conventional R- $\lambda $ model-based algorithms in terms of rendered virtual views quality. [ABSTRACT FROM AUTHOR]

This paper compares the application of two deterministic and three nondeterministic optimization algorithms to global transformer design optimization (TDO). Two deterministic optimization algorithms (mixed-integer nonlinear programming and heuristic algorithm) are compared to three nondeterministic approaches (harmony search, differential evolution, and genetic algorithm). All these algorithms are integrated in design optimization software applied and verified in the manufacturing industry. The comparison yields significant conclusions on the efficiency of the algorithms and the selection of the most suitable one for the TDO problem. [ABSTRACT FROM AUTHOR]

Function point analysis (FPA) is a standardized method to systematically measure the functional size of software. This method is proposed by an international organization and it is currently recommended by governments and organizations as a standard method to be adopted for this type of measurement. This paper presents a compilation of improvements, focused on increasing the accuracy of the FPA method, which have been proposed over the past 13 years. The methodology used was a systematic literature review (SLR), which was conducted with four research questions aligned with the objectives of this study. As a result of the SLR, of the 1600 results returned by the search engines, 454 primary studies were preselected according to the criteria established for the SLR. Among these studies, only 18 specifically referred to accuracy improvements for FPA, which was the goal of this study. The low number of studies that propose FPA improvements might demonstrate the maturity of the method in the current scenario of software metrics. Specifically in terms of found issues, it was found that the step for calculating the functional size exhibited the highest number of problems, indicating the need to revise FPA in order to encompass the possible improvements suggested by the researchers. [ABSTRACT FROM PUBLISHER]

We propose an online tracking algorithm for multiple target tracking with multiple cameras. In this paper, we suggest a multiple hypothesis tracking (MHT) framework to find an unknown number of multiple tracks through the spatio-temporal association between tracklets generated from multiple cameras. In this framework, the MHT is realized online by solving the maximum weighted clique problem (MWCP) at every frame to estimate the 3D trajectories of the targets. To handle the NP-hard issue of the MWCP, we propose a novel online scheme that formulates the MWCP using feedback information from the previous frame’s result to find optimal tracks at every frame. This scheme enables the MWCP to be formulated by multiple subproblems and will significantly reduce the computation. The experiments show that the proposed algorithm performs comparably with the state-of-the-art batch algorithms, even though it adopts an online scheme. [ABSTRACT FROM PUBLISHER]

The papers in this special section are focused on one aspect of the current technological wave. As we all know, nature-inspired (NI) computing and algorithms play an ever increasing role in problem solving by way of optimization, machine intelligence, data mining, and resource management. Nature has evolved over millions of years under a variety of harsh and severe environments and, thus, provides a rich source of inspiration for designing algorithms and approaches to tackle real-world challenging EMC and Signal & Power Integrity (SI/PI) problems. [ABSTRACT FROM AUTHOR]

Classification of power-quality (PQ)-related voltage and current waveform disturbances is a key task for power system monitoring. A new method based on the optimized time-frequency representation (TFR) has been proposed in the first paper [1] of this two-paper series. This paper (the second paper) presents a case study of PQ event classification with the proposed method. The classification algorithm has been successfully tested with $60 real world PQ events that cover five classes, achieving a recognition rate of 98%. The algorithm is implemented on a digital signal processor (DSP) based hardware system and optimized according to the DSP architecture to meet the hard real-time constraints. The DSP-based system is capable of processing a five-cycle (83.3 ms) PQ waveform within 11.2 ms. The real-time computing capability of the algorithm has been verified with this result. The scalability of this method is also discussed. [ABSTRACT FROM AUTHOR]

A smart device such as a television, tablet, and smartphone, often displays a compound image consisting of various types of sub-images, including texts and pictures. A text image displayed on a smart device is composed of red, green, and blue (RGB) sub-pixels having strong correlation among them. This paper proposes a new compression algorithm, called sub-pixel gradient compression (SPGC), that makes use of the correlation in a text in order to improve its compression efficiency. The initial step of the proposed algorithm converts a text into a de-colorized image in which RGB sub-pixels vary gradually. The de-colorization is a necessary step to further enhance the correlation among RGB sub-pixels, and thereby improving the compression efficiency. From the de-colorized text, the next step estimates the slopes of the gradual variations among sub-pixels and then encodes the slopes and their associated information as a bitstream. SPGC is a relatively simple algorithm because its main operation is the estimation of sub-pixel gradients and the precise estimation of the gradients makes it possible to reduce a degradation of the quality of the texts. Experimental results show that SPGC maintains lower complexity and a higher compression ratio than the compression standards including JPEG, JPEG-2000, H.264, or HEVC. [ABSTRACT FROM AUTHOR]

This paper presents a neural network-based method to generate multiple images with different exposures from a single input low dynamic range (LDR) image for improved high dynamic range (HDR) imaging. The proposed algorithm consists of three steps: 1) 2-D histogram estimation; 2) neural network-based LDR images estimation; and 3) generation of an optimal set of differently exposed images. The proposed method first generates image features by estimating a patched-based 2-D histogram. The extracted features are used in an input layer of the neural network, which plays a role to select an optimal set of LDR images. A set of LDR images is generated using a curvature-based contrast enhancement method. Experimental results show that the proposed method can generate an optimal set of LDR images using neural network and provide improved HDR images. In addition, the proposed method can be implemented as a preprocessing step in most existing HDR frameworks. The proposed HDR approach is considered as a single-input method that gives almost the same performance to multiple image-based HDR method. [ABSTRACT FROM PUBLISHER]

The integration of artificial-intelligence techniques in traditional real-time systems is a promising approach to cope with the growing complexity of real-world applications. Real-time expert systems are online knowledge-based systems that combine analytical process models with conventional process control to monitor complex industrial processes and to assist in problem identification. The expert system interfaces with the external distributed control system (DCS) via an object linking and embedding for process control module which acquires measurement data and identifies processes alarms for diagnosis. This paper proposes generating new rules from the plant measurement data using a learning engine. We present an efficient algorithm that generates all significant rules based on the data. The association-based algorithms were compared and those best suited for this process application were selected. The application for the learning system is studied in a powerplant application This innovative approach should assist in sustainable growth of automation in the power sector. [ABSTRACT FROM PUBLISHER]

Constrained interval type-2 (CIT2) fuzzy sets have been introduced to preserve interpretability when moving from type-1 to interval type-2 (IT2) membership functions. Although they can be used to produce type-2 fuzzy systems with enhanced explainability, so far, the latter comes at the expense of high computational cost. Specifically, the exhaustive type-reduction method for CIT2 Mamdani systems has been shown to be too slow to be used in practical applications and even the current approximation procedure is much slower than modern type-reduction algorithms used for IT2 fuzzy sets. In this article, a novel type-reduction procedure for CIT2 sets is presented, based on the concept of switch indices. The algorithm is applied on a real-world classification problem and compared to other type-reduction approaches used in IT2 and CIT2 systems. In the case studies presented, the new algorithm is significantly faster than the exhaustive and sampling CIT2 approaches while keeping the high level of interpretability of the type-reduction operation that characterizes CIT2 fuzzy sets. [ABSTRACT FROM AUTHOR]

As an enhanced system for marine monitoring and autonomous navigation, the space-based automatic identification system (AIS) has attracted extensive attention and become a hot topic for research. However, it encounters the problem of Doppler shift stemmed from the relative motion between satellites and ships, which leads to performance degradation. To circumvent this issue, in this correspondence, we propose an improved Doppler shift estimation algorithm for down-link signals of space-based AIS, utilizing the calculation of the autocorrelation and ratio on the Rice factor. Specifically, the addressed method is robust to the non-Gaussian noise. Further, the suggested approach has low complexity compared with the existing algorithm. Finally, numerical simulations, such as the standard deviation of the estimated Doppler shift versus signal-to-noise ratio, Rice factor and non-Gaussian noise, and the complexity comparisons, are carried out to validate the theoretical analysis, and demonstrate the superior performance of the proposed estimation approach. [ABSTRACT FROM AUTHOR]

Drive cycle identification and future energy demand prediction are advantageous when developing hybrid propulsion systems. They are applicable to vehicles that are driven along the same route every day, such as busses, refuse-collecting vehicles (RCVs), or delivery vehicles. Drive cycle identification can be used to identify what power transients can be expected to prepare the power train to operate under these conditions. If the energy management algorithm of a hybrid vehicle can account for future energy demand, then it can be arranged in such a way that the non-fossil-fuel energy sources are fully depleted at the end of the drive cycle. Given that RCVs always drive in similar drive cycles, a drive cycle has been modeled and its main characteristics parameterized. The model is separated into different drive cycles that are related to different power consumption modes. In this paper, a new method to identify drive cycles and the energy left to finish a route is proposed. The drive cycle identification is based on artificial intelligence algorithms, which have been trained and tested with real data with an average efficiency in drive cycle identification of over 90%. The energy necessary to finish the route is based on vehicle energy models and statistical analysis. This method can be used in the daily management of fleet vehicles to replace fossil fuel by electric energy, as is demonstrated in the proposed examples. [ABSTRACT FROM AUTHOR]

Negative correlation learning (NCL) is a neural network ensemble learning algorithm that introduces a correlation penalty term to the cost function of each individual network so that each neural network minimizes its mean square error/MSEI together with the correlation of the ensemble. This paper analyzes NCL and reveals that the training of NCL (when λ = 1) corresponds to training the entire ensemble as a single learning machine that only minimizes the MSE without regularization. This analysis explains the reason why NCL is prone to overtitting the noise in the training set. This paper also demonstrates that tuning the correlation parameter λ in NCL by cross validation cannot overcome the overfitting problem. The paper analyzes this problem and proposes the regularized negative correlation learning (RNCL) algorithm which incorporates an additional regularization term for the whole ensemble. RNCL decomposes the ensemble's training objectives, including MSE and regularization, into a set of sub-objectives, and each sub-objective is implemented by an individual neural network. In this paper, we also provide a Bayesian interpretation for RNCL and provide an automatic algorithm to optimize regularization parameters based on Bayesian inference. The RNCL formulation is applicable to any nonlinear estimator minimizing the MSE. The experiments on synthetic as well as real-world data sets demonstrate that RNCL achieves better performance than NCL, especially when the noise level is nontrivial in the data set. [ABSTRACT FROM AUTHOR]

Original Hebbian encoding scheme of bidirectional associative memory (BAM) provides a poor pattern capacity and recall performance. Based on Rosenblatt's perceptron learning algorithm, the pattern capacity of BAM is enlarged, and perfect recall of all training pattern pairs is guaranteed. However, these methods put their emphases on pattern capacity, rather than error correction capability which is another critical point of BAM. This paper analyzes the sensitivity to noise in RAM and obtains an interesting idea to improve noise immunity of BAM. Some researchers have found that the noise sensitivity of BAM relates to the minimum absolute value of net inputs (MAV). However, in this paper, the analysis on failure association shows that it is related not only to MAV but also to the variance of weights associated with synapse connections. In fact, it is a positive monotone increasing function of the quotient of MAV divided by the variance of weights. This idea provides an useful principle of improving error correction capability of RAM. Some revised encoding schemes, such as small variance learning for RAM (SVBAM), evolutionary pseudorelaxation learning for BAM (EPRLAB) and evolutionary bidirectional learning (EBL), have been introduced to illustrate the performance of this principle. All these methods perform better than their original versions in noise immunity. Moreover, these methods have no negative effect on the pattern capacity of BAM. The convergence of these methods is also discussed in this paper. If there exist solutions, EPRLAB and EBL always converge to a global optimal solution in the senses of both, pattern capacity and noise immunity. However, the convergence of SVBAM may be affected by a preset function. [ABSTRACT FROM AUTHOR]

Under-complete models, which derive lower dimensional representations of input data, are valuable in domains in which the number of input dimensions is very large, such as data consisting of a temporal sequence of images. This paper presents the under-complete product of experts (UPoE), where each expert models a one-dimensional projection of the data. Maximum-likelihood learning rules for this model constitute a tractable and exact algorithm for learning under-complete independent components. The learning rules for this model coincide with approximate learning rules proposed earlier for under-complete independent component analysis (UICA) models. This paper also derives an efficient sequential learning algorithm from this model and discusses its relationship to sequential independent component analysis (ICA), projection pursuit density estimation, and feature induction algorithms for additive random field models. This paper demonstrates the efficacy of these novel algorithms on high-dimensional continuous datasets. [ABSTRACT FROM AUTHOR]

Advanced Chemical-mechanical polishing (CMP) process not only needs to maintain stable run-to-run thickness control but also achieve better within wafer/within chip planarization performance. Furthermore, slurries or other consumable parts, like PAD and Disks selection are also the keys for CMP process optimization. The most difficult thing in CMP process is to have capability to predict and cover the various topologies and layout densities patterned wafers and preventing the hot spots occurrences. In this study, different Neural-Network algorithm with data pre-processing models are implemented to the in-line CMP CLC tuning and dishing/erosion prediction at various topology/pattern density test vehicle pattern wafers. Transfer learning technique is implemented on the original Neural -Network algorithm model, the behavior of real product can be simulated and predicted based on the knowledge of test vehicle database successfully. With the aid of multiple layer erosion/ dishing Neural-Network algorithm model prediction, the potential high risky hot spots can be highlighted at the initial layout design stage, then further shorten the turn-around time of design layout validation. [ABSTRACT FROM AUTHOR]

Through well-designed optimization paradigm and deep neural networks as feature extractor, deep reinforcement learning (DRL) algorithms learn optimal policy on discrete and continuous action space. However, such capability is restricted by the low sampling efficiency. By inspecting the importance of feature extraction in DRL, we find that state feature learning is one of the key obstacles for sampling efficiently. To this end, we propose a new state representation learning scheme with adjacent state consistency loss (ASC loss). The loss is based on the hypothesis that the distance between adjacent states is smaller than that of far apart ones since scenes in videos generally evolve smoothly. We exploit ASC loss as an assistant of RL loss in the training phase to boost the state feature learning, and make evaluation on existing DRL algorithms as well as behavioral cloning algorithm. Experiments on Atari games and MuJoCo continuous control tasks demonstrate the effectiveness of our scheme. [ABSTRACT FROM AUTHOR]