Search

This paper presents a new Reinforcement Learning (RL)-based control approach that uses the Policy Iteration (PI) and a metaheuristic Grey Wolf Optimizer (GWO) algorithm to train the Neural Networks (NNs). Due to an efficient tradeoff to exploration and exploitation, the GWO algorithm shows good results in NN training and solving complex optimization problems. The proposed approach is compared to the classical PI RL-based control approach using the Gradient Descent (GD) algorithm, and with the RL-based control approach which uses the metaheuristic Particle Swarm Optimization (PSO) algorithm. The experiments are conducted using a nonlinear servo system laboratory equipment. Each approach evaluated on how well it solves the optimal reference tracking control for an experimental servo system position control system. The policy NNs specific to all three approaches are implemented as state feedback with integrator controllers to remove the steady-state control errors and thus ensure the convergence of the objective function. Because of the random nature of metaheuristic algorithms, the experiments for GWO and PSO algorithms are run multiple times and the results are averaged before the conclusions are presented. The experimental results shows that for the control objective considered in this paper, the GWO algorithm represents a better solution compared to GD and PSO algorithms.

The idea of computable aggregation operators was introduced as a generalization of aggregation operators, allowing the replacement of the mathematical function usually considered for aggregation, by a program that performs the aggregation process. There are different reasons to justify this extension. One of them is the interest in exploring some computational properties not directly related to the aggregation itself but to its implementation (complexity, recursivity, parallelisation, etc). Another reason, the one driving to the present paper, is the need to define a framework where the quite common process of first sampling (over a large data set) and then aggregating the sample, could be analysed as a formal aggregation process. This process does not match with the idea of an aggregation function, due to its non-deterministic nature, but could easily be adapted to that of a (non-deterministic) computable aggregation. The idea of non-deterministic aggregation requires the extension of the concept of monotonicity (a key aspect of aggregation operators) to this new framework. The present paper will explore this kind of non-deterministic aggregation processes, first from an empirical point of view and then in terms of populations, adapting the idea of monotonicity to both of them and finally defining a common framework for its analysis.

In this survey, a systematic literature review of the state-of-the-art on emotion expression recognition from facial images is presented. The paper has as main objective arise the most commonly used strategies employed to interpret and recognize facial emotion expressions, published over the past few years. For this purpose, a total of 51 papers were analyzed over the literature totaling 94 distinct methods, collected from well-established scientific databases (ACM Digital Library, IEEE Xplore, Science Direct and Scopus), whose works were categorized according to its main construction concept. From the analyzed works, it was possible to categorize them into two main trends: classical and those approaches specifically designed by the use of neural networks . The obtained statistical analysis demonstrated a marginally better recognition precision for the classical approaches when faced to neural networks counterpart, but with a reduced capacity of generalization . Additionally, the present study verified the most popular datasets for facial expression and emotion recognition showing the pros and cons each and, thereby, demonstrating a real demand for reliable data-sources regarding artificial and natural experimental environments.

This paper is concerned with the stabilization of complex-valued stochastic coupled systems possessing multiple time delays and regime-switching jump diffusion (RSJD) via periodically intermittent control. Moreover, most of the existing results about RSJD are considered in real-valued cases, which is extended to complex-valued cases in this paper. Combining the Lyapunov method with the graph theory, some sufficient conditions can be obtained, which reflect that the realization of stability is related to stochastic disturbance strength, coupling strength and control rate. Additionally, the theoretical results are applied to complex-valued stochastic coupled oscillators with multiple time delays and RSJD. In the end, some simulation results are presented to demonstrate the validity and availability of the theoretical results.

The paper deals with online distributed convex optimization over a multi-agent network, where a swarm of agents attempts to minimize coordinately a sequence of time-changing global loss functions. At per time slot, the global loss function is decomposed into a sum of several local loss functions, each of which is available sequentially and held privately by one agent over the network, and individual agent does not possess prior knowledge of the future global loss function. We are interested in a bandit setting, where only the values of local loss functions at queried points are disclosed to individual agent. Meanwhile, we consider a general multi-agent network where the agents’ communication is represented as a sequence of time-changing unbalanced digraphs and the corresponding weight matrices are only row stochastic. We investigate two bandit scenarios including one- and two-point bandit feedback, and then design two corresponding online distributed bandit algorithms for such a class of problems by exploiting the classical dual averaging method. We show that both algorithms can achieve the sub-linear expected dynamic regret provided that the accumulative variation of the benchmark sequence grows sub-linearly with time. In particular, the bounds of the expected static regret obtained in this paper can reduce the relevant results when restricted to the centralized bandit online convex optimization. In contrast to existing algorithms with gradient feedback, numerical tests verify the competitive performances of the proposed algorithms.

This paper develops a novel insight and procedure that includes a variety of algorithms for finding the best solution in a structured multi-agent system with internal communications and a global purpose. In other words, it finds the optimal communication structure among agents and the optimal policy in this structure. First, a unique reinforcement learning algorithm is proposed to find the optimal policy of each agent in a fixed structure with non-linear function approximators like artificial neural networks (ANN) and with eligibility traces. Secondly, a mechanism is presented to perform self-organization based on the information of the learned policy. Finally, an algorithm that can discover an appropriate inter-structure mapping and then can transfer the previous knowledge to the new structure is developed, which increases the speed of the learning in this new environment after self-organization. This paper is one of the first works that analyzes the problem fully theoretically and devises some algorithms to find the best solution. We use a simplified version of the distributed task allocation problem (DTAP) as our case study. The experimental results verify the stability of our approach and show the high speed of finding the optimal solution as a result of using transfer learning .

Temporal graphs are graphs whose edges are associated with timestamps. Subgraph matching on temporal graphs retrieves temporal subgraphs whose edge timestamps satisfy user-specified temporal orders. In this paper, a temporal query pattern is composed of a query graph with an arbitrary structure and a partial order on the edge set of the query graph. Moreover, the time span between the minimum and the maximum edge timestamps in a matching is required to be less than or equal to a specified threshold. This paper proposes a temporal subgraph matching algorithm based on two key techniques. First, a memory-efficient index structure called TO-tree is designed to compactly store all necessary information required for finding all temporal subgraph matchings. The TO-tree constructed for a temporal query pattern is much smaller than the temporal graph because unnecessary information is mostly excluded from the TO-tree by three powerful filters. The second technique is a temporal subgraph matching enumeration method that runs on the TO-tree instead of on the temporal graph. This enumeration method expands temporal subgraph matchings in an edge-by-edge manner. Since the TO-tree can fit into the main memory, the enumeration method runs very fast on the TO-tree. An extensive experimental evaluation has been carried out. The experimental results show that the TO-tree index structure is memory-efficient, which in turn enables a fast temporal subgraph matching enumeration. Overall, our algorithm is at least 3X faster than the baseline algorithm adapted from the state-of-the-art non-temporal subgraph matching algorithm CECI and is at least 4X faster than the temporal subgraph matching algorithm HASSE .

Evolving fuzzy systems (EFSs) are a type of adaptive fuzzy rule-based systems which can self-adapt both their structures and parameters simultaneously. However, the existing EFSs suffer from two drawbacks: 1) classical EFSs usually use all input features to model systems, resulting in lengthy fuzzy rules; 2) some redundant information in fuzzy rules may hinder high generalization . To address these two issues, a promising method is proposed in this paper by combining very sparse random projection (VSRP) with a class of EFSs based-on data clouds, called VSRP-AnYa-EFS. The proposed method introduces: 1) a random sparse-Bernoulli (RSB) matrix based-on VSRP is utilized to compress the lengthy antecedent part into a tighter form, triggering a feature-reduction mechanism. By employing VSRP in RSB matrix, some redundant information in fuzzy rules can be filtered; 2) Local learning is used for consequent parameter optimization to suit decoupled behavior of rules after redundant information between rules is deleted. By adopting VSRP and local learning, the proposed VSRP-AnYa-EFS owns a compact structure and fast learning speed. Numerical examples presented in this paper demonstrate that the proposed method can significantly reduce training time from hours to minutes while the accuracy can be improved up to 5%.

The paper explores a novel way to solve the multi-attribute decision-making issues under the hesitant fuzzy environment (HF-MADM) with three-way decision (3WD) theory. Firstly, according to the nature of the relative loss function (RLF), the RLF under the hesitant fuzzy (HF) environment is defined. Then, according to the practical significance of the loss function, this paper establishes the relationship between the loss function and the evaluation value. At the same time, considering that the HF-MADM has multiple attributes and multiple evaluation values, we provide an aggregated loss function via the hesitant fuzzy weight average (HFWA) operator to reflect the overall loss of the alternative. We establish the mixed information table (HF-MADMRLF) based on the overall loss function. Secondly, based on an outranking function and a distance from the positive ideal (PI) solution, this paper defines an estimation on the conditional probability method. Finally, we utilize the presented method to solve actual medical diagnosis of pneumonia (MDP) issue, and prove the validity and applicability of the method through comparison with several representative methods and experimental evaluations.

In recent years, many graph compression methods have been introduced. One successful category of them is based on local decompression designed to answer neighborhood queries. These techniques mainly rely on local similarities of vertices. Besides, their performance is usually a function of graph sparsity. The proposed approach, in this paper, is a lossy compression technique used to answer neighborhood queries with a more general precondition, called transitivity. The output of this method is a sparse graph optimized to keep original adjacent vertices, in at most 2-distance from each other and vice versa. In other words, by traversing a compressed graph by depth of 2, from any desired vertex, its original adjacency list is reconstructed, with an acceptable error. This paper models an optimization problem to solve the inverse problem of finding the best compressed graph in order to minimize the reconstruction error. Then, this NP problem is approximated by a heuristic with a low degree polynomial time-complexity near to the complexity of the forward problem. The results of applying the proposed method on toy and real datasets are compared with the state of the art that improves compression ratio and performance with an acceptable query response time.

Aiming at the security issues during the multi-types data storage and data transmission in autonomous Internet of Things (IoT) systems, this paper proposes an AI algorithm for data enhanced encryption used in the ends and the intermediate nodes of IoTs. The algorithm in this paper first constructs a three-dimensional Arnold transformation matrix for data unit value encryption in the end of IoTs, and designs a quantum logic intelligent mapping that effectively diffuses the encrypted data units to reduce the linear correlation of the image data and to improve the security performance of IoT edge data. Furthermore, the algorithm designs an AI access strategy for scrambling sequence nodes and builds a random-access route for the elements of the scrambling sequence which can reduce the calculation cost and improve the operating efficiency of IoT system in the ends and intermediate nodes. Finally, the data shared matrix is used to share the encrypted data to achieve the (k, n) threshold strategy. Experimental results prove that the algorithm has high plaintext and key sensitivity and can effectively resist brute force attacks, statistical analysis and differential attacks. The algorithm in this paper provides an AI solution for data security encryption in the ends and the intermediate nodes of autonomous IoT systems.

Parallel implementations of incremental clustering have been provided to increase performances of data stream processing in smart factories, to enable real-time anomaly detection, remote diagnosis, condition-based monitoring of Cyber-Physical Systems. Incremental clustering algorithms iteratively extract and update over time clusters of data points (often denoted as micro-clusters) whose maximum number is bounded. However, the capability of controlling costs derived from the exploitation of computational resources on the distributed architecture is challenging to enable a sustainable processing of massive data streams. In this paper, we present a multi-level parallelization approach for clustering massive data streams based on an horizontal scaling platform for Big Data processing. In particular, the following levels are considered: (i) a first parallelization level is based on a multi-dimensional model with exploration facets used to perform a first, coarse-grained partition of data streams, according to a divide-and-conquer strategy; (ii) a second parallelization level is based on a buffering mechanism, that splits the data stream into portions of data points on which processing is performed in parallel; (iii) the third level of parallelization is defined over the set of micro-clusters that are generated and change over time. The approach is conceived for anomaly detection in smart manufacturing, where the concept of data relevance, defined in terms of distance from critical conditions of monitored systems, is used in order to force a stronger parallelization (and therefore higher resource usage) only when necessary, that is, when approaching to critical conditions. The scalability and efficiency of the approach are evaluated using a real dataset in a smart factory scenario. In particular, experiments demonstrated that when the maximum number of allowed micro-clusters decreases and the buffer size increases, parallelization based on buffering does not ensure good scalability. Additionally, as the number of features (that is, the complexity of data stream) increases, the parallelization based on buffering may present scalability issues. This paves the way to the advantages of tuning different parallelization levels according to the approach proposed in this paper.

In this paper, the cluster synchronization problem of a heterogeneous second-order leader-following multi-agent system with nonlinear dynamics, actuator faults, and integral quadratic constraints (IQCs) under a directed topology with a directed spanning tree is investigated. Based on the local topology information, two adaptive fault-tolerant pinning control strategies with fixed and adaptive pinning gains are proposed to guarantee cluster synchronization in finite time. An adaptive input compensation is developed to attenuate the adverse effects of actuator faults. It is worth mentioning that just one parameter needs to be estimated for each agent in this compensation, which implies that the strategies designed in this paper can effectively reduce the computational cost. Furthermore, the use of the pinning control method instead of the fully equipped control method makes the strategies more cost-effective for large-scale multi-agent systems. Finally, numerical simulation examples are introduced to demonstrate the effectiveness and advantages of the proposed strategies.

In this paper, we study a wireless networked control system (WNCS) with N ⩾ 2 sub-systems sharing a common wireless channel. Each sub-system consists of a plant and a controller and the control message must be delivered from the controller to the plant through the shared wireless channel. The wireless channel is unreliable due to shadowing and fading. As a result, a packet can be successfully delivered in a slot with a certain probability. A network scheduling policy determines how to transmit those control messages generated by such N sub-systems and directly influences the transmission delay of control messages. We first consider the case that all sub-systems have the same sampling period. We characterize the stability condition of such a WNCS under the joint design of the control policy and the network scheduling policy by means of 2N linear inequalities . For scalar systems, we further simplify the stability condition into only one linear inequality for two special cases: the perfect-channel case and the symmetric-structure case. One main technical contribution of this paper is to introduce the recent results on the network scheduling policy design for delay-constrained wireless communications into the analysis of WNCSs. In addition, we have applied our theory to a practical problem of stabilizing multiple pendulum-cart sub-systems over a shared wireless channel. Simulations show that our joint design effectively achieves better performance than existing baseline.

Many identity-based digital signature schemes are proved secure in the random oracle model. However, the application of the random oracle may lead to security risks. The used hash function is specific and the response result of the query is not always random, hence it may cause the insecurity of the scheme. To solve the above issues, this paper presents an efficient identity-based signature scheme which is proved secure under the standard model. The security of the proposed scheme is reduced to the well-known computational Diffie–Hellman (CDH) assumption. Furthermore, compared with the related identity-based signature schemes, the scheme proposed in this paper has great advantages in the computation cost of signing and verification.

In a previous paper citepkim2019revealing, an analytical framework based on the constraint satisfaction problems was proposed to reveal the characteristics of households using event logs from smart door lock systems. This work provides a more rigorous justification for the previous approach. This paper proposes a novel parameter estimation method called the maximum feasibility estimation (MFE). The MFE does not rely on any assumption about the parametric family of probability densities from which a random observation is drawn. Instead, we assume that constraints are imposed on observations and that some of the constraints are a function of a parameter of interest. The proposed estimator maximizes the feasible region, a set of all possible observations that satisfy those constraints. The method proposed is validated using synthetic data as well as real streaming event log data.

Real-world multi-objective optimization problems encounter different types of uncertainty that may affect the quality of solutions. One common type is the stochastic noise that contaminates the objective functions. Another type of uncertainty is the different forms of dynamism including changes in the objective functions. Although related work in the literature targets only a single type, in this paper, we study Dynamic Multi-objective Optimization problems (DMOPs) contaminated with stochastic noises by dealing with the two types of uncertainty simultaneously. In such problems, handling uncertainty becomes a critical issue since the evolutionary process should be able to distinguish between changes that come from noise and real environmental changes that resulted from different forms of dynamism. To study both noisy and dynamic environments, we propose a flexible mechanism to incorporate noise into the DMOPs. Two novel techniques called Multi-Sensor Detection Mechanism (MSD) and Welford-Based Detection Mechanism (WBD) are proposed to differentiate between real change points and noise points. The proposed techniques are incorporated into a set of Dynamic Multi-objective Evolutionary Algorithms (DMOEAs) to analyze their impact. Our empirical study reveals the effectiveness of the proposed techniques for isolating noise from real dynamic changes and diminishing the noise effect on performance.

K-nearest neighbors search (K-NNS) is a fundamental problem in many areas of machine learning and data mining. In an attempt to solve NNS problems by locality-sensitive hashing (LSH)-based algorithms and avoid the F1-trap, in this paper, we theoretically prove the necessity of maintaining close edges of each dimension, the independence of the partition location and the precision of retrieval. Based on the theories, a new method is proposed for constructing the hash function family. Then, a volume-balancing locality-sensitive hashing (VBLSH) algorithm is implemented. In this algorithm, the problem of constructing the hash function family is transformed into an optimization problem under a fixed number of hypercubes. By solving the optimization problem, different hyperplane intervals are set for each dimension to ensure that the hash functions can evenly segment the data space, where each segmented hypercube has a close volume and close side lengths. Finally, the analysis of the algorithm shows that the VBLSH algorithm has a high index coding efficiency and hash code calculation efficiency. The experimental results show that the VBLSH algorithm can simultaneously take into account the efficiency, precision and recall rate, and avoid the F1-trap; it shows excellent effectiveness on 4 datasets.

Traditional topic classification usually adopts the closed-world assumption that all the test topics have been seen in training. However, in open dynamic environments, the potential new topics may appear in testing due to the evolution of text data over time. Considering the uncertainty and multi-granularity of dynamic text data, such open topic classification needs to detect unseen topics by mining the boundary region continually, and incrementally update the previous models by knowledge accumulation. To address these challenge issues, this paper introduces a unified framework of three-way multi-granularity learning to open topic classification based on the fusion of three-way decision and granular computing. First, we propose the multilevel granular structure of tasks from the temporal-spatial multi-granularity perspective. Then, we construct an adaptive decision boundary and use the centroids and the corresponding radius to discover unknowns by the reject option. Subsequently, we further explore the unknown topics by three-way enhanced clustering and the uncertain instances will be re-investigated in the next stage. Besides, we design a built-in knowledge base represented as the centroid of each topic to store the topic knowledge. Finally, the experiments are conducted to compare the performances of proposed models and the efficiency of knowledge accumulation with classic models.

It is widely acknowledged that the initial spreaders play an important role in the spread of information in complex networks. Thus, a variety of centrality-based methods have been proposed for identifying the most influential spreaders. However, most existing studies overlook the fact that, in real social networks, it is more costly and difficult to convince influential individuals to act as initial spreaders, resulting in a high risk to maximal spreading. In this paper, we address this problem on the basis of the assumption that the activation of large-degree nodes carries a higher risk than that of small-degree nodes. We aim to identify the initial spreaders that most effectively maximize the spreading when considering both the activation risk and the outbreak size of the initial spreaders. Analysis of random networks reveals that the degree of the optimal initial spreaders does not correspond to the largest node degree in the network, but is instead determined by the infection probability and difference in activation risk among nodes with different degrees. We propose a risk-aware metric to identify the most effective spreaders in real networks. Numerical simulations show that this risk-aware metric outperforms the existing benchmark centralities in terms of maximizing the spreading.

A multi-party quantum key distribution protocol based on repetitive code is designed for the first time in this paper. First we establish a classical (t, n) threshold protocol which can authenticate the identity of the participants, and encode the classical key sequence in accordance with this repetitive code. Then unitary transformation of the quantum state sequence corresponding to this encoded sequence is carried out by using the parameters from this (t, n) threshold protocol. Furthermore, we derive two thresholds for whether or not reserving the measured values of the received sequence, and extract the classical subkey sequence from the measured values conforming to these two threshold conditions. This protocol can authenticate the identity of the participant, resist the attack from the internal and external participants, and do not need the decoy state particles when testing the eavesdropper, which is more efficient than the similar protocols, and also saves the quantum resources.

In this paper, the problem of tracking control of networked control systems with false data injection attacks is studied. A new terminal integral adaptive sliding mode control method is proposed. Firstly, the mathematical model of networked control system with the actuator and sensor false data injection attacks is modeled. Secondly, an augmented state observer is designed in order to estimate the augmented states, in which, a discontinuous input term is designed to defenses the actuator attack, and the convergence of the estimation error is proved. Thirdly, the tracking output errors are introduced to design the terminal integral sliding mode control method. Furthermore, in order to reduce the oscillation induced by the sensor false data injection attack, the terminal integral adaptive sliding mode control algorithm using the estimation error as adaptive factor is structured, and the stability of the algorithm are proved by using Lyapunov theory. Finally, a numerical simulation and a practical experiment are executed to verify the superiority of proposed control strategies.

Aspect-level sentiment classification aims to obtain fine-grained sentiment polarities of different aspects in one sentence. Most existing approaches handle the classification by acquiring the importance of context words towards each given aspect individually, and ignore the benefits brought by aspect relations. Since the sentiment of one aspect can be deduced through their relationship according to other aspects, in this paper, we propose a novel relation construction multi-task learning network (RMN), which is the first attempt to extract aspect relations as an auxiliary classification task. RMN generates aspect representations through graph convolution networks with a semantic dependency graph and utilizes the bi-attention mechanism to capture the relevance between the aspect and the context. Unlike conventional multi-task learning methods that need extra datasets, we construct an auxiliary relation-level classification task that extracts aspect relations from the original dataset with shared parameters. Extensive experiments on five public datasets from SemEval 14, 15, 16 and MAMS show that our RMN improves about 0.09% to 0.8% on accuracy and about 0.04% to 1.19% on F1 score, compared to several comparative baselines.

In this paper, we introduce an algorithm for computing all the shortest reducts in a decision system. The proposed algorithm is based on determining the size of the shortest reducts using a small super-reduct and some new pruning methods. Once the size of the shortest reduct is determined, all other reducts of the same size are found applying the new pruning methods. The results of our experiments using several synthetic and real-world decision systems show that the proposed algorithm is, in most cases, faster than the state of the art algorithms for computing all the shortest reducts reported in the literature.

The contradiction separation (CS) based automated deduction is an extension of the binary resolution principle for deductive inference rules, whose CSE prover and combined versions have unique performance among others in CADE ATP system competitions since their first appearances in 2018. The contradictions constructing method is one of the most important factors to determine the efficiency of the CS based deduction, which is more complex compared to the complementary pair finding in the binary resolution. To get structures and concrete forms of contradictions, this paper examines two types of standard contradictions, i.e., the regular standard contradictions and the standard contradictions for non-unit clauses sets. The concepts of the regular standard contradiction and its extended form are presented. Some properties of them are discussed. In order to get simpler contradiction separation clauses in every deductive step, four types of structures for standard contradictions obtained by adding related literals in the regular standard contradiction and its extended form are gotten. Two structures for the standard contradictions for non-unit clauses set are obtained. Two unified algorithms for constructing standard contradictions are also contrived.

As a concept put forward by Prof Zadeh, Z-numbers have become a research hotspot in fuzzy theory. Different from the previous fuzzy sets, Z-numbers possess a stronger ability in expressing uncertainty because of the unique structure. The chief purpose of this paper is to research linguistic uncertain Z-numbers with a rectangular coordinate system. Taking into account the shortcomings of previous studies, the rectangular coordinate system is firstly adopted to address linguistic Z-numbers. Based on the new expression, arithmetic operations are defined. After summarizing the drawbacks of the previous aggregation operators, a novel approach named linguistic uncertain Z-numbers weighted averaging aggregation operator based on the rectangular coordinate system (LUZWAAORCS) is defined. Subsequently, the Minkowski distance measure of linguistic uncertain Z-numbers is proposed and the rationality is proven by a theorem. Follow that, a score function considering the Minkowski distance measure and technique for order preference by similarity to an ideal solution (TOPSIS) is suggested to quantify the information in different Z-numbers. Besides, an innovative Cosine similarity is defined to measure the similarity. Simultaneously, several examples are used to describe the proposed innovations. As far as our latest knowledge is concerned, Z-numbers have never been researched with a rectangular coordinate system, so this may be another door to process Z-number-based information.

Since multimodal imaging technology is able to provide multiple perspectives on the lesion, it has become increasingly important in clinical diagnosis and treatment planning. In this paper, a novel medical image fusion using gradient domain-guided filter random walk (GDGFRW) and side window filtering (SWF) in the framelet transform (FT) domain is presented. Firstly, FT is performed on the original multimodal source images to obtain the corresponding approximate and residual representations. Secondly, a novel model—GDGFRW, which combines the superiorities of both gradient domain guided filtering and random walks—is constructed to interpret the approximate sub-bands, while the residual sub-bands are fused by SWF. Finally, the fused approximate sub-bands and residual sub-bands undergo inverse FT to generate the final fused image. To verify its effectiveness, the proposed method was tested on different categories of multimodal medical image fusion issues, in more than 40 pairs of source images. The proposed method outperforms the current representative ones in terms of both subjective visual performance and objective assessment.

In this paper, the issues of both fault estimation and accommodation are studied for a class of continuous-time Markov jump linear systems under actuator fault, sensor fault and external disturbance in the framework of finite frequency domain. The imprecise statistic of modes transitions are considered here, which means that the transition rates are uncertain. Firstly, the sensor fault is defined as a new state, and then an adaptive observer is developed to estimate the actuator fault and the newly defined state with the aid of the descriptor system approach. By using the obtained fault estimation, a novel fault accommodation scheme is proposed based on the sample point controller design approach to satisfy the stochastic stability requirement of the closed-loop system with a prescribed H ∞ performance bound. Moreover, for the convenience of design, the presented sample point controller design is successfully transformed into resolving a class of input-delay control issues. Finally, two numerical examples, including a practical F-404 aircraft engine system, are illustrated to show the effectiveness and applicability of the developed design methods.

Time-series data are widespread in real-world industrial scenarios. To recover and infer missing information in real-world applications, the problem of time-series prediction has been widely studied as a classical research topic in data mining. Deep learning architectures have been viewed as next-generation time-series prediction models. However, recent studies have shown that deep learning models are vulnerable to adversarial attacks. In this study, we prospectively examine the problem of time-series prediction adversarial attacks and propose an attack strategy for generating an adversarial time series by adding malicious perturbations to the original time series to deteriorate the performance of time-series prediction models. Specifically, a perturbation-based adversarial example generation algorithm is proposed using the gradient information of the prediction model. In practice, unlike the imperceptibility to humans in the field of image processing, time-series data are more sensitive to abnormal perturbations and there are more stringent requirements regarding the amount of perturbations. To address this challenge, we craft an adversarial time series based on the importance measurement to slightly perturb the original data. Based on comprehensive experiments conducted on real-world time-series datasets, we verify that the proposed adversarial attack methods not only effectively fool the target time-series prediction model LSTNet, they also attack state-of-the-art CNN-, RNN-, and MHANET-based models. Meanwhile, the results show that the proposed methods achieve a good transferability. That is, the adversarial examples generated for a specific prediction model can significantly affect the performance of the other methods. Moreover, through a comparison with existing adversarial attack approaches, we can see that much smaller perturbations are sufficient for the proposed importance-measurement based adversarial attack method. The methods described in this paper are significant in understanding the impact of adversarial attacks on a time-series prediction and promoting the robustness of such prediction technologies.

Biometrics has nowadays become a preferred solution for systems requiring secure authentication. However, the usage of biometric characteristics raises significant concerns regarding personal data protection and privacy. Several template protection schemes have been therefore proposed to conceal the employed identifiers, while still ensuring the ability to efficiently recognise users. In this paper, we propose and analyse three different approaches generating cancelable templates from finger vein features. A thorough analysis of the considered methods is conducted to investigate their impact on the achievable recognition performance, as well as their security in terms of renewability and unlinkability. Furthermore, a specific attack is designed to evaluate the irreversibility of the protection scheme providing the best recognition performance.

The missing data problem - attributed to malfunctioning detectors, packet loss during transmission, or data removed by quality control procedures - is unavoidable in most traffic-related datasets. However, this problem has adversely affected traffic engineering applications as they heavily rely on accurate and comprehensive data. This study aims to impute missing loop detector data in order to improve the estimation results of traffic flow analysis. This paper presents a statistically principled methodology that focuses not only on proposing a computationally efficient imputation approach, but also on assessing the uncertainty associated with imputed values. The proposed methodology quantifies the accuracy of imputation and estimation of uncertainty for a range of challenging patterns of missing loop detector data, and compares them with existing methods. The results of the analysis demonstrate that the performance of the proposed approach remains unaffected by the presence of a large number of missing patterns and reflects the true statistical properties of the principal data. The proposed approach is also comparatively less computationally complex than the existing methods. Further, the comparative analysis of the proposed estimator shows that the generated prediction intervals are reasonably accurate and conform to the desired confidence levels with relatively small interval width.

In this paper, we investigate the distributed consensus problem for a class of nonlinear multi-agent systems. Firstly, the uniform quantizer with scaling function is considered in the communication channels to reduce the communication burden. Secondly, noting that the state’ information of each agent cannot be directly obtained, we design a suitable observer to rebuild the states of the nonlinear systems with the help of dynamic gain function. Since the information of communication quantization and state observation is coupled to each other, it is necessary to jointly design uniform quantizer with scaling function and the observer. Based on this, a distributed control protocol is proposed, which leads to a distributed asymptotic consensus of overall nonlinear systems with either known or unknown Lipschitz constant through ⌈ log 2 ( 2 R ) ⌉ -bit information exchange between each pair of agents. Finally, to validity the feasibility of the control scheme, a simulation example is provided.

The pattern recognition of surface electromyography (sEMG) signal is an important application in the realization of human-machine interface. However, due to the disturbance of human body, sensors and environment, sEMG signal usually contains lots of noise, which brings great challenges to the high-accuracy sEMG pattern recognition. In addition, embedded human wearable devices are becoming more and more popular nowadays. How to realize the sEMG recognition method with low power consumption and high noise immunity has also become a difficult and very meaningful research topic. In this paper, a spiking neural network (SNN) classification method based on second-order information bottleneck training is proposed. Firstly, the training loss function for classification neural networks is constructed based on the proposed second-order information bottleneck. The method is used to train the conventional continuous-valued neural network and convert it into an SNN model with equivalent structure and connection weights. Then, the converted SNN is used to classify the sEMG signal patterns. Through a series of theoretical analysis and experimental results, it is proved that this method has significant advantages in terms of generalization of network determination and computational efficiency. The experimental code can be accessed from https://github.com/anvien/2OIB-for-sEMG-Recognition.

Cloud service selection (CSS) remains a strategically significant decision and has a substantial impact on an organization’s competitive edge. Despite considerable research, the literature lacks a comprehensive unified approach to consensual CSS. Recognizing the significance of CSS decisions, in this paper, we propose a novel Cloud Service Scrutinization and Selection Framework (C3SF) that includes four interdependent phases: (1) requirements elicitation, (2) scrutinization, (3) evaluation, and (4) ranking & selection. As part of C3SF, we use conjunctive screening to scrutinize cloud services. We also propose a novel multi-criteria decision-making (MCDM) approach called the modified Best-Worst Method (MBWM), which computes the weights of criteria using early-stage consensus among decision-makers. In addition, we introduce an innovative two-step consensus process for ranking services using leading MCDM methods followed by an aggregation of ranks using a Markov chain-based approach. Moreover, to develop a broader consensus, we propose another two-stage novel mechanism comprising multi-aggregation and synthesis/fusion of rank information using a partially ordered set. We validate the performance and effectiveness of C3SF through a CSS case study using real-world data followed by a comprehensive analysis. The results show that C3SF is robust, practical, and suitable for well-informed decision-making.

The forecasting of time series provides great convenience in our daily life. Studies of time series forecasting have been used in many fields such as financial models, weather, and traffic patterns. In this paper, we propose a model fusion-based time series forecasting to improve the forecasting accuracy and efficiency. We propose a time series forecasting scheme based on a multivariate grey model and uses artificial fish swarm algorithm to optimize the settings. We then propose two fusion models with the grey model-based schemes on two different perspectives: data decomposition, and weighted summation. We conduct evaluations based on real data series and compared them with other forecasting models. Results show that our model can achieve good prediction accuracy and efficiency, which can be used for time series forecasting in different scenarios.

In recent years, many applications are using various forms of deep learning models. Such methods are usually based on traditional learning paradigms requiring the consistency of properties among the feature spaces of the training and test data and also the availability of large amounts of training data, e.g., for performing supervised learning tasks. However, many real-world data do not adhere to such assumptions. In such situations transfer learning can provide feasible solutions, e.g., by simultaneously learning from data-rich source data and data-sparse target data to transfer information for learning a target task. In this paper, we survey deep transfer learning models with a focus on applications to text data. First, we review the terminology used in the literature and introduce a new nomenclature allowing the unequivocal description of a transfer learning model. Second, we introduce a visual taxonomy of deep learning approaches that provides a systematic structure to the many diverse models introduced until now. Furthermore, we provide comprehensive information about text data that have been used for studying such models because only by the application of methods to data, performance measures can be estimated and models assessed. publishedVersion

This papers addresses the sliding mode control design problem for the flexible spacecraft attitude tracking control system with Takagi-Sugeno fuzzy model based method. In this design, the external disturbance and actuator input dead-zone are considered simultaneously, and the signals between sensors and controller are quantized before transmission over digital communication channels. A quantized adaptive integral sliding mode control law is proposed to ensure that the sliding motion is globally asymptotically stable, and the effects of unknown external disturbances, actuator nonlinearity and quantization errors are compensated via the online adaptive mechanism simultaneously. Moreover, under the proposed digital control law the finite-time reachability of the designed sliding surface can be guaranteed strictly. Finally, simulation results are presented to illustrate the feasibility and effectiveness of the developed quantized spacecraft attitude control method.

This paper deals with fault detection problem of delayed semi-Markov jump systems with disturbances. More specifically, the asynchronous phenomenon is taken into account between modes of linear and nonlinear dynamics. Accordingly, an efficient solution of fault detection is developed based on asynchronous mode-dependent fault filter with conditional probability. On the basis of constructing appropriate Lyapunov-Krasovskii functions, sufficient criteria are established such that H ∞ performance can be satisfied for filter error with residual signal. Furthermore, the devised filter gains are computed in terms of convex optimization. Eventually, the applicability of our proposed design is verified by an illustrative example.

Multi-modal multi-objective optimization problem (MMOPs) has attracted more and more attention in evolutionary computing recently. It is not easy to solve these problems using the existing evolutionary algorithms. The non-dominated solution sorting genetic algorithm (NSGA-II) has poor PS distribution and convergence. In this paper, an enhanced fast NSGA-II based on a special congestion strategy and adaptive crossover strategy, namely ASDNSGA-II is proposed. In the ASDNSGA-II, the strategy with a special congestion degree is used to improve the selection strategy. Then a new adaptive crossover strategy is designed by evaluating the advantages and disadvantages of the SBX crossover strategy with the ability to solve high dimensions and the BLX-α with the ability of Pareto solution to produce offspring solutions. These can ensure the generation of offspring solutions around individuals with large crowding degrees and balance the convergence and diversity of decision space and object space. It can improve PS distribution and convergence and maintain PF precision. Eight functions of MMF1-MMF8 from the CEC2020 are selected to prove the effectiveness of the ASDNSGA-II. By comparing several latest multi-modal multi-objective evolutionary algorithms, the results show that the ASDNSGA-II can effectively find the global Pareto solution set and improve the distribution and convergence of PS.

In the constrained multi-objective optimization problems, the pursuit of feasibility could improve convergence but will lead to the loss of diversity. For optimization algorithm, balancing the weight between convergence and diversity dynamically is a challenge, especially in problems with low proportion of feasible regions. In this paper, a constrained multi-objective optimization algorithm is proposed based on a hybrid driven strategy to enhance both the feasibility and diversity performance of the approximate Pareto solutions. The proposed algorithm contains two archives, that one is driven by feasibility information and the other is driven by diversity information. A self-adaptive archive selection mechanism and a conditional tournament selection strategy are proposed to provide mating parent solutions according to the evolutionary stage. Moreover, in the update of the feasibility archive, an evolutionary direction prediction mechanism is proposed and adopted to improve the evolutionary efficiency. Compared to four other multi-objective algorithms on three benchmark suits of different types, the performance of the proposed algorithm is better than the peer algorithms, especially in large-infeasible-regions multi-objective optimization problems.

The emergence and maintenance of cooperation is a popular topic in studies of information sciences and evolutionary game theory. In two-player iterated games, memory in terms of the outcome of previous interactions and the strategy choices of co-players are of great referential significance for subsequent strategy actions. It is generally recognized that there is no single simple and overarching strategy whereby one player X can unilaterally achieve a higher payoff than his opponent Y, irrespective of Y’s strategy and response. In this paper, we demonstrate that such strategies do exist in diverse networked populations. More precisely, (i) such strategies can obtain a low payoff for the focal player, however, they also lead to an even lower payoff for that player’s partner, in turn lowering benefits of the overall populations; (ii) they are capable of winning with a high probability against opponents with an unknown strategy; and (iii) they have a survival advantage and robust fitness in terms of evolutionary processes. We refer to these as the “self-bad, partner-worse” (SBPW) strategies. Results presented here add to previous studies on strategy evolution in the context of social dilemmas and hint at some insights concerning cooperation promotion mechanisms among networked populations.

The main objective of the study is to investigate the Takagi-Sugeno fuzzy-model-based networked control system under the fuzzy event-triggered H ∞ control scheme. Instead of considering the conventional network transmission delay, the paper introduces the probability distribution based network transmission delay, which is more compatible with real time-simulations. Due to distributed transmission delay, the closed-loop network control system has become distributed delay system. To make use of full information about the membership function, the Lyapunov function candidate is designed as fuzzy-membership-dependent Lyapunov function. A switching concept with respect to the rate of changes in membership functions is introduced to design the optimal control gain matrices for the considered system. The main advantage of the fuzzy-membership-dependent Lyapunov function is that it can ensure the less conservatism of the result without increasing the number of decision variables. To validate the effectiveness of the proposed approach, two kinds are real-time systems are taken into account, one is nonlinear mass-spring-damper model, followed by an industrial example, say, surface mounted permanent magnet synchronous motor model. The corresponding simulation results show the superiority of the proposed results over the existing works.

In this paper, we propose a new multiple attribute group decision making (MAGDM) method based on the proposed advanced linguistic intuitionistic fuzzy weighted averaging (ALIFWA) aggregation operator of linguistic intuitionistic fuzzy numbers (LIFNs). The proposed ALIFWA aggregation operator of LIFNs can overcome the shortcomings of the linguistic intuitionistic fuzzy weighted averaging (LIFWA) aggregation operator, the improved LIFWA (ILIFWA) aggregation operator, and the linguistic intuitionistic fuzzy Einstein weighted averaging (LIFEWA) aggregation operator of LIFNs, which have the shortcomings that the membership degrees and the non-membership degrees of their obtained aggregated LIFNs are indeterminated in some situations. Based on the proposed ALIFWA aggregation operator of LIFNs, we propose a new MAGDM method in linguistic intuitionistic fuzzy environments. The proposed MAGDM method can overcome the drawbacks of the existing MAGDM methods. It offers us a very useful way for handling MAGDM problems in linguistic intuitionistic fuzzy environments.

Semi-supervised nonnegative matrix factorization (NMF) has received considerable attention in machine learning and data mining. A new semi-supervised NMF method, called dual semi-supervised convex nonnegative matrix factorization (DCNMF), is proposed in this paper for fully using the limited label information. Specifically, DCNMF simultaneously incorporates the pointwise and pairwise constraints of labeled samples as dual supervisory information into convex NMF, which results in a better low-dimensional data representation. Moreover, DCNMF imposes the nonnegative constraint only on the coefficient matrix but not on the base matrix. Consequently, DCNMF can process mixed-sign data, and hence enlarge the range of applications. We derive an efficient alternating iterative algorithm for DCNMF to solve the optimization, and analyze the proposed DCNMF method in terms of the convergence and computational complexity. We also discuss the relationships between DCNMF and several typical NMF based methods. Experimental results illustrate that DCNMF outperforms the related state-of-the-art NMF methods on nonnegative and mixed-sign datasets for clustering applications. This work is supported in part by the National Nature Science Foundation of China (No. U1701266, 91648208, 61976175), and Guangdong Intellectual Property Big Data Key Laboratory (No. 2018B030322016).

In this paper, we extend the concept of Dempster-Shafer Belief Structures to the case of Linguistic Belief Structures, whose focal elements and probability mass assignments are linguistic, i.e. words modeled by Interval Type-2 Fuzzy Sets. We show that Linguistic Weighted Averages are pertinent tools for derivation of lower and upper probabilities from such Belief Structures, especially when words describing probability masses and focal elements are modeled by Interval Type-2 Fuzzy Sets synthesized by collecting data from subjects. We moreover introduce methods for performing operations on Linguistic Belief Structures as well as combining them. We demonstrate how Linguistic Belief Structures can be used to represent uncertainties in natural languages and present methods for inference from them.

Cooperative problems under continuous control have always been the focus of multi-agent reinforcement learning. Existing algorithms suffer from the problem of uneven learning degree with the increase of the number of agents. In this paper, a new structure for a multi-agent actor critic is proposed, and the self-attention mechanism is applied in the critic network and the value decomposition method used to solve the uneven problem. The proposed algorithm makes full use of the samples in the replay memory buffer to learn the behavior of a class of agents. First, a new update method is proposed for policy networks that promotes learning efficiency. Second, the utilization of samples is improved, at the same time reflecting the ability of perspective-taking among groups. Finally, the ”deceptive signal” in training is eliminated and the learning degree among agents is more uniform than in the existing methods. Multiple experiments were conducted in two typical scenarios of a multi-agent particle environment. Experimental results show that the proposed algorithm can perform better than the state-of-the-art ones, and that it exhibits higher learning efficiency with an increasing number of agents.

In this paper, we propose adversarial predictive coding (APC), a novel method for detecting abnormal events. Abnormal event detection (AED) is to identify unobserved events from a given training dataset consisting of normal events, and it is considered as one of the most important objectives in developing intelligent surveillance systems. Given videos and motion flows of normal events, the APC derives a normal event model by applying an adversarial prediction approach on the jointly learnt latent feature space from the videos and motion flows. Since latent space requires more abstracted and noise-free information than the raw data space, the APC can derive a more discriminative model for normal events compared with other deep learning-based AED methods which directly apply uni-modal losses such as mean square error and cross-entropy to low-level data such as video frames. We demonstrate the effectiveness of our method in detecting abnormal events using UCSD-Ped, Avenue, and UCF-Crime datasets. The experimental results show that the APC surpass the existing state-of-the-art AED methods by deriving a more discriminative model for normal events.

Assisting end users to identify desired results from a large dataset is an important problem for multi-criteria decision making. To address this problem, top-k and skyline queries have been widely adopted, but they both have inherent drawbacks, i.e., the user either has to provide a specific utility function or faces many results. The k-regret minimization query is proposed, which integrates the merits of top-k and skyline queries. Due to the NP-hardness of the problem, the k-regret minimization query is time consuming and the greedy framework is widely adopted. However, formal theoretical analysis of the greedy approaches for the quality of the returned results is still lacking. In this paper, we first fill this gap by conducting a nontrivial theoretical analysis of the approximation ratio of the returned results. To speed up query processing, a sampling-based method, S toc P res G reed , is developed to reduce the evaluation cost. In addition, a theoretical analysis of the required sample size is conducted to bound the quality of the returned results. Finally, comprehensive experiments are conducted on both real and synthetic datasets to demonstrate the efficiency and effectiveness of the proposed methods.