Search

Implementations that follow the functional programming paradigm are being used in more and more domains. As functional programming paradigm has mathematical reference transparency, refinement to functional programs contributes to improving the reliability of the transformation process and simplifying the refinement steps. However, it is a challenge to generate functional programs from specifications. Most existing transformation methods refine specifications into abstract algorithm-level programs based on loop invariants rather than functional programs. This paper proposes a novel functional program generation method based on the partition-recursion refinement rule. It establishes a novel program refinement framework based on functional theory for the first time. This is the first study to regard the whole program refinement process as a composition of abstract functions. This paper designs a recurrence-based algorithm design language (Radl+) and implements a software prototype to map Radl+ algorithms into executable Haskell programs. To prove the feasibility and efficiency of this method, this paper transforms the polynomial multiplication problem from a specification into an executable Haskell program. This case shows that compared with existing approaches, the proposed method can simplify the transformation steps and reduce the number of lines of generated code from 38 to 10. • Novel refinement framework provides a new approach to generating a functional program. • The composition of abstract functions explains the program refinement process. • Substitution rule and Recursion rule have none of the side effects. • Software prototype transforms the polynomial multiplication problem into Haskell program. [ABSTRACT FROM AUTHOR]

This paper concerns a new incremental approach to mining data streams. It is known that patterns in a data stream may evolve over time. In many cases, we need to track and analyze the nature of these changes. In the paper, the probabilistic neural networks are considered as basic models for tracking changes in data streams. We present globally convergent stream data mining algorithms applied to problems of regression, classification, and density estimation in a time-varying (drifting) environment. The algorithms are derived from the Parzen kernel-based probabilistic neural networks working in the online mode. For each problem, a theorem is presented ensuring the L 2 convergence of the algorithm designed for tracking drifting regression, density, or discriminant functions. Illustrative examples explain in detail how to choose the bandwidth of the Parzen kernel and the learning rate of the online algorithm. The performance of all algorithms is shown in exemplary simulations. It should be noted that this paper is one of very few, in the existing literature, presenting mathematically justified stream data mining algorithms. • The incremental version of the Generalized Regression Neural Network (IGRNN) able to track drifting regression functions. • The incremental version of the Probabilistic Neural Network (IPNN) working in non-stationary environments. • Application of IPNN for tracking drifting discriminant functions. • Mathematical proofs of the L 2 convergence of all the proposed estimators. [ABSTRACT FROM AUTHOR]

Many real-life problems can be formulated as numerical optimization problems. Such problems pose a challenge for researchers when designing efficient techniques that are capable of finding the desired solution without suffering from premature convergence. This paper proposes a novel evolutionary algorithm that blends the exploitative and explorative merits of two main evolutionary algorithms, namely the Stochastic Fractal Search (SFS) and a Differential Evolution (DE) variant. This amalgam has an effective interaction and cooperation of an ensemble of diverse strategies to derive a single framework called Iterative Cyclic Tri-strategy with adaptive Differential Stochastic Fractal Evolutionary Algorithm (Ic3-aDSF-EA). The component algorithms cooperate and compete to enhance the quality of the generated solutions and complement each other. The iterative cycles in the proposed algorithm consist of three consecutive phases. The main idea behind the cyclic nature of Ic3-aDSF-EA is to gradually emphasize the work of the best-performing algorithm without ignoring the effects of the other inferior algorithm during the search process. The cooperation of component algorithms takes place at the end of each cycle for information sharing and the quality of solutions for the next cycle. The algorithm's performance is evaluated on 43 problems from three different benchmark suites. The paper also investigates the application to a set of real-life problems. The overall results show that the proposed Ic3-aDSF-EA has a propitious performance and a reliable scalability behavior compared to other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a novel adaptive generalized Nash equilibrium (GNE) seeking algorithm is designed, in order to address the non-cooperative game with private inequality constraints under high-dimensional input dead-zone. That is to say, the dead-zone dynamics may be thought of as a generic high-dimensional convex set, and the introduction of two methods distinguishes our works in seeking the GNE of non-cooperative games. On the one hand, a two-time-scale structure based on singular perturbation method is led into the design of GNE seeking algorithm, where the fast dynamics part rapidly eliminates the influence of input dead-zone, and the slow dynamics part drives the players' action to the GNE. On the other hand, adaptive penalty method is utilized to ensure the player's action enters the inequality constraints set without a prior estimation of centralized information for penalty parameters. The algorithm in this paper realizes complete distribution and parameter independence, making it easy to apply in practical programming. At last, several numerical examples regarding the electricity markets are employed to verify the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

Telemedicine provides remote online services for digital diagnosis and treatment via the Internet. However, there is a risk of data leakage during transmission. Therefore, data protection is an important challenge for telemedicine. Chaos is widely used in image, audio, and EEG encryption because of its unique characteristics of unpredictability, nonlinearity, and sensitivity to an initial state. However, some chaotic maps have various security issues. To solve these problems, this paper proposes a K-sine-transform-based coupled chaotic system (K-STBCCS), combining any two one-dimensional chaotic mappings to generate a new chaos mapping. To demonstrate the reliability of the system, this paper generates three new chaotic mappings using K-STBCCS and analyzes their performance. Using the chaotic mapping generated by K-STBCCS, this paper further proposes an EEG signal encryption scheme based on the confusion-diffusion principle. The purpose of confusion is to separate adjacent EEG signals, while the purpose of diffusion is to change the value of EEG signals. Among them, the diffusion operation uses positive and negative diffusion to reduce the correlation between the ciphertext and the original signals. The experimental results and security analysis show that the proposed EEG signal encryption scheme performs well and passes the rigorous cryptographic security test. [ABSTRACT FROM AUTHOR]

Telemedicine provides remote online services for digital diagnosis and treatment via the Internet. However, there is a risk of data leakage during transmission. Therefore, data protection is an important challenge for telemedicine. Chaos is widely used in image, audio, and EEG encryption because of its unique characteristics of unpredictability, nonlinearity, and sensitivity to an initial state. However, some chaotic maps have various security issues. To solve these problems, this paper proposes a K-sine-transform-based coupled chaotic system (K-STBCCS), combining any two one-dimensional chaotic mappings to generate a new chaos mapping. To demonstrate the reliability of the system, this paper generates three new chaotic mappings using K-STBCCS and analyzes their performance. Using the chaotic mapping generated by K-STBCCS, this paper further proposes an EEG signal encryption scheme based on the confusion-diffusion principle. The purpose of confusion is to separate adjacent EEG signals, while the purpose of diffusion is to change the value of EEG signals. Among them, the diffusion operation uses positive and negative diffusion to reduce the correlation between the ciphertext and the original signals. The experimental results and security analysis show that the proposed EEG signal encryption scheme performs well and passes the rigorous cryptographic security test. [ABSTRACT FROM AUTHOR]

Asynchronous stochastic gradient descent (ASGD) is a computationally efficient algorithm, which speeds up deep learning training and plays an important role in distributed deep learning. However, ASGD suffers from the stale gradient problem, i.e., the gradient of worker may mismatch the weight of parameter server. This problem seriously affects the model performance and even causes the divergence. To address this issue, this paper designs a dynamic adjustment scheme via the momentum algorithm, which uses both stale penalty and stale compensation , i.e., stale penalty is to reduce the trust in stale gradient, stale compensation is to compensate the hurt of stale gradient. Based on this dynamic adjustment scheme, this paper proposes a dynamic asynchronous stochastic gradient descent algorithm (DASGD), which dynamically adjusts the compensation factor and the penalty factor via stale size. Moreover, we prove that DASGD is convergent under some mild assumptions. Finally, we build a real distributed training cluster to evaluate our DASGD on Cifar10 and ImageNet datasets. Compared with four SOTA baselines, experiment results confirm the superior performance of DASGD. More specifically, our DASGD has nearly the same test accuracy as SGD on Cifar10 and ImageNet , and only uses around 27.6% and 40.8% training time that of SGD, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes a fast bit-level chaotic image encryption algorithm. The goal is to make the difference between 0 and 1 in the bit-level ciphertext as small as possible. By introducing the concept of the difference between 0 and 1, constantly adjust the number of binary 0 and 1 in the ciphertext, and the difference is fed back to the encryption system until the difference reaches the preset accuracy, so as to quickly achieve a secure encryption effect. To verify the security of the algorithm, this paper selects multiple chaotic systems for testing and applies different chaotic systems to the encryption algorithm. Moreover, the proposed algorithm is compared with the bit-level and non-bit-level image encryption algorithms proposed by other scholars in recent years, and the results show that the proposed algorithm has higher security than other algorithms. [ABSTRACT FROM AUTHOR]

• A many objective evolutionary algorithm based on decomposition and moth search is proposed. • Random and adaptive weights is used to break the limitation of uniform distribution weights. • Mutual evaluation value is used to evaluate the optimal individual in the neighborhood. • Improving scale factor α in MSA is to improve the performance of the proposed algorithm. Decomposition-based multi-objective evolutionary algorithm (MOEA/D) has good performance in solving multi-objective problems (MOPs) but poor performance in solving many-objective optimization problems (MaOPs). The weight vectors in MOEA/D are relatively fixed, which results in poor performance when dealing with complex MaOPs. In this paper, random and adaptive weights are introduced into MOEA/D to break the limitation of fixed weight vectors. And the moth search algorithm (MSA) is used as an operator to improve global search ability. The updating strategies in MSA are more consistent with the neighborhood learning strategy adopted in MOEA/D. In addition, to enable MSA to find the optimal solution in the neighborhood on the MaOPs to update other individuals. This paper introduces mutual evaluation value for evaluating the optimal individual in the neighborhood, and the proposed algorithm is abbreviated as MOEA/DMS. In comparative experiments on the MaF test suite, hypervolume (HV) and inverted generational distance (IGD) are used to measure MOEA/DMS and other many-objective evolutionary algorithms (MaOEAs). The results show that MOEA/DMS has an excellent performance in dealing with MaOPs. Besides, MOEA/DMS is compared with other state-of-the-art MaOEAs on two combinatorial MaOPs. The results show that MOEA/DMS also has significant advantages in dealing with combinatorial MaOPs. [ABSTRACT FROM AUTHOR]

• The initial information contribution of nodes is normalized by utilizing their degree and two network properties, and the size of the initial information contribution of nodes is adjusted with the help of a free parameter. • The average degree of the intermediate nodes between the source node and destination node is considered to achieve the global information transmission between them. • The size of the initial information contribution of nodes and three ways of information transmission are applied to construct the algorithm proposed in the paper. • The proposed algorithm has great advantage in the effectiveness, robustness, and practicability of link prediction compared with most benchmark algorithms. Many link prediction algorithms have originated from the process of information transmission between nodes in recent years. Despite these algorithms can obtain great prediction results, there may be also some limitations. For instance, the size of the initial information amount of nodes is ignored when these kinds of algorithms are constructed. Aiming at this issue, a link prediction algorithm based on the initial information contribution of nodes is proposed in this paper. First of all, the initial information contribution of nodes is quantified by utilizing some topological information around them and an adjustable parameter. In the next, three ways of bidirectional information transmission between nodes are analyzed. After that, the total information amount that received by two nodes through three ways of information transmission is applied to measure the structural similarity between them, to design the link prediction algorithm. At last, the experimental results on sixteen real-world networks demonstrate that the proposed algorithm has great advantages in effectiveness and robustness, compared with ten mainstream benchmark indices. More than that, in order to verify the application performance of the proposed algorithm in the practical scenario, our algorithm is also employed in some social domains, such as the Facebook and crime networks. [ABSTRACT FROM AUTHOR]

• Jointly consider the task dependency and edge cloud environment. • Combine deep reinforcement learning and genetic algorithm. • Generate the initial population of genetic algorithm by DQN. Edge computing can greatly decrease the delay between users and cloud servers, which can significantly improve system service performance. However, it remains challenging for more efficient scheduling and allocation of users' application demands with dependence constraints to edge cloud servers. Due to the randomness of the initial population, traditional intelligent optimization algorithms have poor convergence speed in addressing resource scheduling. Therefore, to minimize the execution time of the application, this paper proposes a hybrid algorithm to solve the resource scheduling problem with parallelism and subtask dependency. To improve the convergence speed of the algorithm, this paper makes full use of the features of deep Q networks (DQN) and genetic algorithms (GA). The initial population of GA is generated using DQN. Finally, to evaluate the effectiveness of our proposed algorithm, this paper selects three real scientific workflows for experiments. The experimental results show that the hybrid algorithm can converge quickly and improve the optimization effect in a short time. [ABSTRACT FROM AUTHOR]

• An adaptive random competition strategy is designed. • A neighborhood crossover strategy to improve convergence speed and accuracy. • A more robust multi-strategy competitive-cooperative co-evolution algorithm is presented. In order to effectively solve multi-objective optimization problems (MOPs) and fully balance uniformity and convergence, a multi-strategy competitive-cooperative co-evolutionary algorithm based on adaptive random competition and neighborhood crossover, namely MSCOEA is developed in this paper. In the MSCOEA, a new adaptive random competition strategy is designed to determine whether one sub-population loses diversity through the performance. A random competition process is executed to increase the sub-population diversity in order to compete for participation opportunities in the next iteration. And the extra population is employed to store the found non-dominated solutions. A new neighborhood crossover strategy is designed to enhance the local search ability. Finally, three different types of multi-objective benchmark functions are selected to verify the effectiveness of the MSCOEA. The experiment results show that the MSCOEA can effectively balance convergence and uniformity, and obtains better optimization performance and robustness by comparing with other algorithms. The convergence performance of the adaptive random competition and the neighborhood crossover strategies are also analyzed in detail. [ABSTRACT FROM AUTHOR]

Dynamic multiobjective optimization problems (DMOPs) vary over time, requiring an optimization algorithm to track the position of Pareto-optimal front (PF) in a dynamic environment. To achieve that, a novel co-evolutionary algorithm based on problem analysis (CAPA) is proposed in this paper. CAPA is designed to solve DMOPs from decision space and objective space simultaneously, which is achieved by the combination of adjustable prediction (AP) and precise mapping strategy (PM). In decision space, the proposed multi-model prediction can estimate the location of new population based on the historical median points. In objective space, a novel sampling method is developed to search for sample points with better convergence or diversity. Then, mapping these sample points back to decision space based on inverse model. Through the problem analysis mechanism, the proportion of the new solutions produced by each strategy changes adaptively. CAPA is incorporated into the dynamic multiobjective evolutionary algorithm (DMOEA) based on decomposition (MOEA/D) to construct a novel algorithm. The efficacy of CAPA is validated by comparison with five state-of-the-art algorithms on 28 benchmarks. Experimental results show that CAPA has the ability to generate high quality population uniformly along PF. [ABSTRACT FROM AUTHOR]

With the proliferation of mobile technology and IT development, people can use social network services anywhere and anytime. Among many social network mining problems, identifying cohesive subgraphs has attracted extensive attention from different fields due to its numerous applications. The most widely used among many cohesive subgraph models is k -core, because of its simple and intuitive structure. In this paper, we formulate (p , n) -core in signed networks by extending the k -core model. (p , n) -core is designed to guarantee sufficient internal positive edges and deficient internal negative edges. We formally prove that finding an exact (p , n) -core is NP-hard. Hence, we propose three efficient and effective algorithms to find a solution. We demonstrate the superiority of our proposed algorithms using real-world and synthetic networks. [ABSTRACT FROM AUTHOR]

This paper is concerned with the communication protocol-based zonotopic state and fault estimation issue for a class of discrete time-varying industrial cyber-physical systems under bounded disturbances and constrained communication resources. First, in order to alleviate resource consumption, a round-robin protocol is developed to schedule the data transmission between the local sensor and the remote estimator. Second, a zonotopic set-membership estimation algorithm is developed to guarantee that the unavailable system state and fault signals could be simultaneously estimated and enclosed in a zonotope at every instant of time. Subsequently, the size of such a zonotope is minimized at each iteration by appropriately selecting a correlation matrix. Furthermore, the rigorous stability and performance analysis for the developed zonotopic estimation algorithm is provided. Finally, two simulation examples are provided to verify the obtained theoretical results. • Novel zonotopic set-membership estimation algorithm, where the zonotope guarantees to contain unknown system state and fault a rigorous stability analysis for the developed estimation scheme is provided by analyzing the boundedness of zonotope. • Finally two simulation examples are provided to show the effectiveness of the proposed estimation scheme. [ABSTRACT FROM AUTHOR]

As a robust online learning approach, adaptive filtering based on correntropy has attracted more attentions. Recently, researchers have made efforts to develop complex correntropy to deal with adaptive filtering issues in complex domain. However, most of the literature on complex correntropy lack details regarding the processing of noncircular signals. Up to now, the maximum improper complex correntropy criterion (MICCC) algorithm for noncircular and widely-linear (WL) adaptive filtering utilizes the gradient descent method to find the maximum of the objective function. However, the MICCC algorithm fails to guarantee both low steady-state weight error power and fast convergence. To overcome this defect, this paper proposes a class of adaptive filtering algorithms which are based on fixed-point instead of gradient descent. It is worth noting that we manage to avoid the massive calculation of matrix inversion in traditional fixed-point based methods by utilizing the lemma of matrix inversion properly and further provide two alternative recursive algorithms. Finally, the local convergence analysis of mean and mean square is performed, and the simulated results offer strong evidences for the superior performance of the proposed algorithms over widely-linear complex least mean square (WL-CLMS), widely-linear recursive least square (WL-RLS) and MICCC. [ABSTRACT FROM AUTHOR]

In recent years, researchers have proposed many graph-based multi-view clustering (GMC) algorithms to solve the multi-view clustering (MVC) problem. However, there are still some limitations in the existing GMC algorithm. In these algorithms, a graph is usually constructed to represent the relationship between the samples in a view; however, it cannot represent the relationship very well since it is often preconstructed. In addition, these algorithms ignore the robustness problem of each graph and high-level information between different graphs. Then, in the paper, we propose a novel MVC method, i.e., robust and optimal neighborhood graph learning for MVC (RONGL/MVC). Specifically, we first build an initial graph for each view. However, these initial graphs cannot represent the relationship between the samples in each view well, so we look for the optimal graph with k connected components in the neighborhood of each initial graph, where k is the number of clusters. Then, to improve the robustness of RONGL/MVC, we reconstruct the optimal graph with the self-representation matrix. Furthermore, we stack all the self-representation matrices into a tensor and impose the tensor low-rank constraint, which can maximize consistent features and explore the high-order relationship between optimal graphs. In addition, we provide an optimization strategy by utilizing the Augmented Lagrange Multiplier (ALM) method. Experimental results on several datasets indicate that RONGL/MVC outperforms state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Comparative view of the cost function when using the GWO, GD, and PSO algorithms. [Display omitted] • A Policy Iteration Reinforcement Learning-based control approach is proposed. • A Grey Wolf Optimizer algorithm trains the policy neural network. • A comparison with classical Gradient Descent and metaheuristic PSO is offered. • The validation is done on real-time nonlinear servo system position control. • The fast convergence of the suggested approach is proved. 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. [ABSTRACT FROM AUTHOR]

Clustering is an important data analysis technique. However, due to the diversity of datasets, each clustering algorithm is unable to produce satisfactory results on some particular datasets. In this paper, we propose a clustering optimization method called HIAC (H ighly I mproving the A ccuracy of C lustering algorithms). By introducing gravitation, HIAC forces objects in the dataset to move towards similar objects, making the ameliorated dataset more friendly to clustering algorithms (i.e. , clustering algorithms can produce more accurate results on the ameliorated dataset). HIAC is independent of clustering principle, so it can optimize different clustering algorithms. In contrast to other similar methods, HIAC is the first to adopt the selective-addition mechanism, i.e. , only adding gravitation between valid-neighbors, to avoid dissimilar objects approaching each other. In order to identify valid-neighbors from neighbors, HIAC introduces a decision graph, from which the naked eye can observe a clear division threshold. Additionally, the decision graph can assist HIAC in reducing the negative effects that improper parameter values have on optimization. We conducted numerous experiments to test HIAC. Experiment results show that HIAC can effectively ameliorate high-dimensional datasets, Gaussian datasets, shape datasets, the datasets with outliers, and overlapping datasets. HIAC greatly improves the accuracy of clustering algorithms, and its improvement rates are far higher than that of similar methods. The average improvement rate is as high as 253.6% (except maximum and minimum). Moreover, its runtime is significantly shorter than that of most similar methods. More importantly, with different parameter values, the advantages of HIAC over similar methods are always maintained. The code of HIAC is available at https://github.com/qiqi12/HIAC. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of reliable fuzzy prognosis of decentralized fuzzy discrete event systems (FDESs) in face of possible unavailability of some local agents. Firstly, the notion of k -reliable fuzzy coprognosability is formalized for a decentralized FDES with m local agents, which requires that a k -reliable fuzzy coprognosable FDES can predict the fault to a certain degree in the manner of fuzziness even when m - k local agents are unavailable, where 1 ⩽ k ⩽ m. Then, a reliable fuzzy copredictor is constructed to perform the reliable fuzzy prognosis of decentralized FDESs, and a necessary and sufficient condition of the k -reliable fuzzy coprognosability is presented. In particular, an algorithm based on reliable fuzzy copredictor is proposed to verify the k -reliable fuzzy coprognosability of FDESs. [ABSTRACT FROM AUTHOR]

This paper considers the constrained finite-time consensus control problem for second-order multi-agent systems with leaderless and leader–follower. By using the saturated controller design method and homogenous system theory, a finite-time consensus algorithm for multi-agent systems is proposed. Compared with the existing results, the proposed consensus algorithm makes the closed-loop system not only finite-time convergence but also satisfies the velocity constraints and input saturations. Simulation examples show the effectiveness of the proposed consensus algorithms. [ABSTRACT FROM AUTHOR]

• A method for representing node performance based on multi-attribute expression. • A TOPSIS-based algorithm for evaluating the node performance. • A replica placement algorithm based on the entropy weight TOPSIS method. Performance optimization based on node attributes is of profound significance in the replica placement algorithm used in Hadoop distributed file system (HDFS). Currently, most researchers studying replica placement algorithms consider only a single attribute or a few multiple attributes. However, a single attribute cannot accurately express the performance of a node. Therefore, this paper proposes a replica placement algorithm based on the entropy weight TOPSIS (technique for order preference by similarity to ideal solution) method, called TS-REPLICA. First, a multi-attribute matrix that comprehensively reflects the performance and load of nodes is defined. Then, a TOPSIS-based algorithm is proposed to calculate the performance score of each data node. In addition, the entropy weight method is introduced to derive the weights of attributes for balancing the influence of weights of multiple attributes. Next, the comprehensive load score of each data node in the Spark cluster, the average comprehensive load score of each rack, and the average comprehensive load score of the entire cluster are calculated, and replica placement is performed based on the obtained scores. Finally, the effectiveness of the proposed algorithm is verified on various datasets and test cases. The experimental results show that the TS-REPLICA algorithm outperforms the better comparison algorithm in execution number in Spark cluster. [ABSTRACT FROM AUTHOR]

The extraction of frequent subgraphs is a basic and well studied operation on graphs. Thus, mining frequent graph patterns and problems associated with it is very important. However, the number of frequent subgraphs is potentially exponential while mining large graph patterns. This issue can be partly overcome using closed frequent graphs mining. Instead of mining all frequent subgraphs, it is more efficient to enumerate only the closed frequent graphs. Thus, in this paper, we propose a novel closed frequent subgraph mining algorithm: CFGM. In this algorithm, a stack-based architecture is used to enumerate the frequent graph represented by the depth-first search. Moreover, this algorithm defines a strict partial order among frequent graphs. We demonstrate that, with respect to this strict partial order, only maximal elements (frequent graphs) need to be discovered. A pruning strategy is developed based on this strict partial order that dramatically reduces unnecessary frequent subgraphs to be enumerated. Computational results show our algorithm displays excellent performance, especially for some large asymmetric frequent graph patterns. [ABSTRACT FROM AUTHOR]

This paper proposes an evolutionary algorithm with clustering-based selection strategies to deal with multi-objective optimization problems. In the proposed algorithm, two clustering based selection strategies, named local indicator selection and local crowding selection, have been devised to appropriately search the space. The local indicator selection is developed to select diverse and well-converged individuals for mating while the local crowding selection strategy is designed to maintain a set of evenly distributed individuals on the Pareto front for next generation of evolution. The proposed method is further enhanced by a clustering based crowding degree strategy, which is introduced to extract a uniformly distributed and convergent solutions as the final output. The performance of proposed algorithm has been evaluated on 31 benchmark problems and compared with related methods. The results clearly show the merits of proposed strategies and the proposed method could significantly outperform related methods to be compared. [ABSTRACT FROM AUTHOR]

Public-key authenticated encryption with keyword search (PAEKS) is an important cryptographic primitive for realizing effective and confidential searches on encrypted data in cloud computing. Several PAEKS schemes with various appealing functionalities have been proposed in the literature. Nevertheless, most of them are based on the classical number-theoretic assumptions and are impossible to resist quantum attacks. Their security is only proven against chosen-keyword attacks and is considered insufficient. Fortunately, ring-LWE and ring-ISIS problems reduced from the worst-case ideal lattices are believed to be post-quantum secure and could be improved computational efficiency in constructions. In this paper, we propose a scalable PAEKS scheme based on ring-LWE and ring-ISIS by adopting the ring analog of lattice algorithms of both digital signature and searchable encryption schemes, in which signature algorithms are used to authenticate ciphertext, making our PAEKS resistant to insider keyword guessing attacks. The scheme is also proved secure against chosen-ciphertext attacks and token privacy attacks. In addition, we extend the PAEKS scheme to conjunctive keywords and multi-user settings to allow for realistic promotion. We finally implement our schemes and compare them with related counterparts, which shows that our constructions are efficient in practical applications. [ABSTRACT FROM AUTHOR]

• Using machine learning to recommend appropriate parameters and operators for differential evolution algorithm. • The fitness landscape features are used as a basis for recommending parameters for the optimization problems. • A mutation operator selector based on AdaBoost & decision tree, and a parameter selector based on BP neural network are established for the differential evolution algorithm. Due to the problems of low accuracy and increasing control parameters in the existing parameter adaptive methods of differential evolution (DE) algorithm, in this paper a mutation operator selector and a parameter selector are proposed through Fitness Landscape (FL) analysing. At first, the performance differences of the two categories of mutation operators named DE / b e s t / 1 and DE / c u r r e n t - t o - r a n d / 1 were analyzed on many test problems. Secondly, the relationship between the FL and mutation operator is founded by using ensemble learning and decision tree, and achieved a classifier named mutation operator selector. Thirdly, the relationship between the FL and algorithm parameters is founded by using a neural network, and then a classifier named parameter selector is achieved. Finally, the improved DE algorithm equip with the two selectors is tested on the CEC2017 benchmark set. The results show that the proposed improved DE algorithm is outperforms both the basis DE algorithm and other three state-of-the-arts algorithms. [ABSTRACT FROM AUTHOR]

Automatic clustering and dimension reduction are two of the most intriguing topics in the field of clustering. Affinity propagation (AP) is a representative graph-based clustering algorithm in unsupervised learning. However, extracting features from high-dimensional data and providing satisfactory clustering results is a serious challenge for the AP algorithm. Besides, the clustering performance of the AP algorithm is sensitive to preference. In this paper, an improved affinity propagation based on optimization of preference (APBOP) is proposed for automatic clustering on high-dimensional data. This method is optimized to solve the difficult problem of determining the preference of affinity propagation and the poor clustering effect for non-convex data distribution. First, t-distributed stochastic neighbor embedding is introduced to reduce the dimensionality of the original data to solve the redundancy problem caused by excessively high dimensionality. Second, an improved hybrid equilibrium optimizer based on the crisscross strategy (HEOC) is proposed to optimize preference selection. HEOC introduces the crisscross strategy to enhance local search and convergence efficiency. The benchmark function experiments indicate that the HEOC algorithm has better accuracy and convergence rate than other swarm intelligence algorithms. Simulation experiments on high-dimensional and real-world datasets show that APBOP has better effectiveness. [ABSTRACT FROM AUTHOR]

Various learning problems can be represented as certain canonical forms of orthogonal matrix nearness problems under the unitarily invariant norm. Since varying unitarily invariant norms favor different structured learning of input data, it is crucial to construct a unified scheme to learn encouraging distributions via any unitarily invariant norm. In this paper, we find that the orthogonal matrix nearness problem can be generalized to a common architecture of the unitarily invariant norm minimization, thus further exploit iterative and closed-form solutions. Firstly, we start with several special circumstances of orthogonal matrix nearness problems, where the unitarily invariant norm resorts to Frobenius norm. Secondly, a general scheme for orthogonal matrix nearness problems under any unitarily invariant norm is addressed and the corresponding iterative algorithm is proposed. Thirdly, feature representation problems are formulated as certain forms of orthogonal matrix nearness, indicating a joint framework for learning low-dimensional features with closed-form solutions. Finally, comprehensive experiments on real-world data sets demonstrate the effectiveness of the proposed method against compared state-of-the-art feature representation approaches. In particular, the proposed method gains about 15% improvements of clustering accuracy on BASEHOCK, CNAE and RCV1 data sets than benchmarks, and achieves at least 3% improvements on other data sets. [ABSTRACT FROM AUTHOR]

• Explainable framework for meta-learning. • Efficiency and high causality. • Intervention and counterfactual. With the growing convergence of artificial intelligence and daily life scenarios, the application scenarios for intelligent decision methods are becoming increasingly complex. The development of various machine learning algorithms has benefited all disciplines of study, but choosing which algorithm is most suitable for a certain problem among a large number of algorithms is a challenge that every field must overcome. Another challenge at the practical application level is that machine learning algorithms currently trained with large amounts of data are primarily black-box and uninterpretable. This indicates that these methods pose potential risks and are difficult to rely on, thus hindering their application in sensitive fields such as finance and healthcare. The first challenge can be overcome by using meta-learning to combine data and prior knowledge to efficiently and automatically select the machine learning models. The second challenge remains to be addressed due to the lack of interpretability of traditional meta-learning techniques and deficiencies in transparency and fairness. Achieving the interpretability of meta-learning in autonomous algorithm selection for classification is crucial to balance the need for high accuracy and transparency of machine learning models in practical application scenarios. This paper proposes EFFECT , an interpretable meta-learning framework that can explain the recommendation results of meta-learning algorithm selection and provide a more complete and accurate explanation of the recommendation algorithm's performance on specific datasets combined with business scenarios. Extensive experiments have demonstrated the validity and correctness of this framework. [ABSTRACT FROM AUTHOR]

The performance of differential evolution (DE) significantly depends on the settings of mutation strategies and control parameters. Inappropriate settings may cause an imbalance between exploration and exploitation of the algorithm, thus resulting in two extremes: premature convergence and failure to converge. In this paper, we propose a Differential Evolution using Leader-Adjoint populations (LADE), which simultaneously integrates four mutation strategies to meet the needs of exploration and exploitation at different evolutionary stages. In LADE, the population in each generation is divided into leader population and adjoint population by using a leader-adjoint model. The leader population adopts two mutation strategies with strong exploration ability to maintain the diversity and avoid premature convergence, while the adjoint population uses the other two mutation strategies with strong exploitation ability to promote convergence and avoid stagnation. In addition, the interaction and collaboration between both populations are achieved through the sharing between their individuals, thus achieving a good trade-off between exploration and exploitation. The performance of LADE is evaluated on single-objective benchmark problems of the 2017 Institute of Electrical and Electronics Engineers Congress on Evolutionary Computation (IEEE CEC2017). Experimental results indicate that LADE shows competitive convergence performance, and outperforms various state-of-the-art DE variants and two well-known metaheuristics. [ABSTRACT FROM AUTHOR]

Recently, it has been found that cloud storage still has security risks, and research on the security and privacy of user data and information is still in the early stage. This paper studies the security risks of cloud data, and designs an image encryption scheme based on neural networks. First, the existing neural network model is improved to obtain a new bidirectional activation (BA) neural network, to establish a many-to-one mapping relationship between the key and the chaotic initial value, to hide the original key of the cloud encryption system, and to improve the security and randomness of the key system. Then, a medical image encryption scheme based on dynamic index scrambling and the M-semitensor product diffusion is proposed. Dynamic index scrambling is more flexible than the traditional approach, and its security and efficiency are improved. The diffusion algorithm adopts the semi tensor product operation, and one of the product matrices is composed of a unitary matrix after Schur decomposition of a plaintext image to effectively resist a selective plaintext attack. Performance analysis shows that the encryption algorithm has high security. [ABSTRACT FROM AUTHOR]

• Explainable framework for meta-learning. • Efficiency and high causality. • Intervention and counterfactual. With the growing convergence of artificial intelligence and daily life scenarios, the application scenarios for intelligent decision methods are becoming increasingly complex. The development of various machine learning algorithms has benefited all disciplines of study, but choosing which algorithm is most suitable for a certain problem among a large number of algorithms is a challenge that every field must overcome. Another challenge at the practical application level is that machine learning algorithms currently trained with large amounts of data are primarily black-box and uninterpretable. This indicates that these methods pose potential risks and are difficult to rely on, thus hindering their application in sensitive fields such as finance and healthcare. The first challenge can be overcome by using meta-learning to combine data and prior knowledge to efficiently and automatically select the machine learning models. The second challenge remains to be addressed due to the lack of interpretability of traditional meta-learning techniques and deficiencies in transparency and fairness. Achieving the interpretability of meta-learning in autonomous algorithm selection for classification is crucial to balance the need for high accuracy and transparency of machine learning models in practical application scenarios. This paper proposes EFFECT , an interpretable meta-learning framework that can explain the recommendation results of meta-learning algorithm selection and provide a more complete and accurate explanation of the recommendation algorithm's performance on specific datasets combined with business scenarios. Extensive experiments have demonstrated the validity and correctness of this framework. [ABSTRACT FROM AUTHOR]

The performance of differential evolution (DE) significantly depends on the settings of mutation strategies and control parameters. Inappropriate settings may cause an imbalance between exploration and exploitation of the algorithm, thus resulting in two extremes: premature convergence and failure to converge. In this paper, we propose a Differential Evolution using Leader-Adjoint populations (LADE), which simultaneously integrates four mutation strategies to meet the needs of exploration and exploitation at different evolutionary stages. In LADE, the population in each generation is divided into leader population and adjoint population by using a leader-adjoint model. The leader population adopts two mutation strategies with strong exploration ability to maintain the diversity and avoid premature convergence, while the adjoint population uses the other two mutation strategies with strong exploitation ability to promote convergence and avoid stagnation. In addition, the interaction and collaboration between both populations are achieved through the sharing between their individuals, thus achieving a good trade-off between exploration and exploitation. The performance of LADE is evaluated on single-objective benchmark problems of the 2017 Institute of Electrical and Electronics Engineers Congress on Evolutionary Computation (IEEE CEC2017). Experimental results indicate that LADE shows competitive convergence performance, and outperforms various state-of-the-art DE variants and two well-known metaheuristics. [ABSTRACT FROM AUTHOR]

Recently, it has been found that cloud storage still has security risks, and research on the security and privacy of user data and information is still in the early stage. This paper studies the security risks of cloud data, and designs an image encryption scheme based on neural networks. First, the existing neural network model is improved to obtain a new bidirectional activation (BA) neural network, to establish a many-to-one mapping relationship between the key and the chaotic initial value, to hide the original key of the cloud encryption system, and to improve the security and randomness of the key system. Then, a medical image encryption scheme based on dynamic index scrambling and the M-semitensor product diffusion is proposed. Dynamic index scrambling is more flexible than the traditional approach, and its security and efficiency are improved. The diffusion algorithm adopts the semi tensor product operation, and one of the product matrices is composed of a unitary matrix after Schur decomposition of a plaintext image to effectively resist a selective plaintext attack. Performance analysis shows that the encryption algorithm has high security. [ABSTRACT FROM AUTHOR]

Table summarization can be of great help, which generates a concise and informative overview of a table to assist users to understand the table easily and unambiguously. A high-quality summary needs to have two desirable properties: presenting notable entities in the table and achieving broad coverage and high diversity on domains. However, notability and domain are often neglected in table summarization. Thus in this paper, we present a framework of domain-aware table summarization that is able to: (1) identify notable entities using a popularity-sensitive notability evaluation algorithm, (2) find core domains with a measurement of domain centrality, (3) and output the final high-quality summary using a three-phase clustering based algorithm. The experimental results show that our summarization method outperforms state-of-the-art methods by 9.62%, 2.78% and 6.77% on metrics coverage, diversity, and notability, respectively. We also conduct a user study to demonstrate that people can improve the accuracy of understanding tables by 17% with the help of our summarization technique. [ABSTRACT FROM AUTHOR]

• The constrained clustering problem is studied. • An efficient local search algorithm is proposed. • A node filtering strategy is introduced for improving efficiency. • The proposed algorithm is more effective than state-of-the-art heuristics. Constrained clustering extends clustering by integrating user constraints, and aims to determine an optimal assignment under the constraints. In this paper, we propose a local search algorithm called FastCCP to solve the constrained clustering problem. In the algorithm, instances connected by must-link constraints are first merged into nodes, and then, a local search method is performed to handle the cannot-link constraints while minimizing the Within-Cluster Sum of Squares (WCSS). Several strategies are proposed to enhance the solution diversity and achieve a trade-off between constraint satisfaction and WCSS minimization during the search. Furthermore, a node-filtering strategy is proposed to improve the efficiency of the algorithm. Experiments are performed on benchmark datasets to evaluate our algorithm. The comparative results indicate that our algorithm outperforms state-of-the-art algorithms in terms of both the solution quality and CPU runtime. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• Formulate a problem on three-way concept lattice incremental construction. • Develop AE/OE concept lattices incremental construction algorithms. • Use 3WCA to explore the knowledge discovery of social networks. Three-way concept analysis (3WCA), a combination of three-way decision and formal concept analysis, is widely used in the field of knowledge discovery. Generally, constructing three-way concept lattices requires the original formal context and its complement context simultaneously. Additionally, the existing three-way concept lattice construction algorithms focus on the static formal context, and cannot cope with the dynamic formal context that is an essential representation in social networks. Toward this end, this paper pioneers a novel problem and method for the incremental construction of three-way concept lattice for knowledge discovery in social networks. Aiming to facilitate the construction efficiency, this paper firstly investigates the three-way concept lattice construction for attribute-incremental/object-incremental formal contexts, respectively. Then, the dynamic formal context of a social network can be viewed as a special formal context with both attribute-increment and object-increment. Further, we develop the AE/OE concept lattice incremental construction algorithms, called SNS-AE and SNS-OE. Extensive experiments are conducted on various formal contexts to evaluate the effectiveness of our incremental algorithms. The experimental results demonstrate that our proposed incremental algorithms can significantly decrease the construction time of three-way concept lattice compared to the non-incremental algorithm. [ABSTRACT FROM AUTHOR]

• An AI algorithm for data enhanced encryption at the end and the intermediate nodes of autonomous IoT systems is proposed. • An AI access strategy is designed for reducing the calculation cost of data encryption. • A data shared matrix is proposed to share the encrypted data to achieve the (k, n) threshold strategy. 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. [ABSTRACT FROM AUTHOR]

Supervised Learning (SL) celebrates a lot of research topics in machine learning (ML) and provides a large number of applications in multimedia. The effectiveness and performance of supervised learning are highly dependent on the quality of labeled samples, and poor or mislabeled labels always damage pre-knowledge and produce disastrous results. One of the notable works is the development of a model for detecting noisy labels in large-scale data sets for supervised classification. This paper introduces a new method for noisy label detection that combines the support vector data description (SVDD) model with manifold regularization. The proposed method is able to detect the noisy label in the testing set. In addition, we adopt K nearest neighbors to obtain the K -nearest neighbor graph, and the k -core algorithm is used to select noise nodes by selecting the influential nodes with larger k values in the K -NN graph. Experimental results over UCI data sets show that the manifold regularized SVDD can effectively detect the noise labels data, and presents better performance over most methods in terms of the outlier detection accuracy, and the average accuracy is mostly above 50 % , even 100 %. Moreover, manifold regularized SVDD has good performance in detecting the most influential top- k nodes compared with other methods. [ABSTRACT FROM AUTHOR]

Frequent pattern mining (FM) has a wide range of applications in the real world. But FM sometimes discovers many uninteresting patterns at the same time. Constraint-based FM, especially periodic constraint FM, becomes of interest, and it reduces redundant patterns. Most of the state-of-the-art algorithms for mining periodic frequent patterns (PFPs) are designed to find PFPs only in binary temporal databases. However, vast information in real situations is more suitable to be modelized as quantitative databases, in which most existing methods are inapplicable. Patterns obtained by those methods may change due to a tiny perturbation in the database, which is not expected in many situations. In this paper, we propose an algorithm (FP2M) to mine fuzzy-driven periodic frequent patterns and an algorithm (SFP2M) to find stable fuzzy-driven periodic frequent patterns in quantitative temporal databases. Fuzzy sets are used in our algorithms to deal with quantitative data since they provide a relaxed interval segmentation and are easy to implement. Novel pruning strategies and a new structure called Estimated Period Co-occurrence Structure (EPCS) are designed to speed up the mining process and improve efficiency. An improved method (SFP2M) is also presented by using the lability measurement to find stable patterns. Experimental evaluations on both real and synthetic datasets show good performance of the designed algorithms. [ABSTRACT FROM AUTHOR]

• A kernel-based feature selection algorithm is proposed, based on primary normalized cross-covariance operator (NOCCO). • A fast version is implemented via combining approximated NOCCO with column recursive method for Moore-Penrose inverse matrix. • Their effectiveness and efficiency are validated using detailed experimental comparison and analysis for nine benchmark data sets. Supervised feature selection (FS) for classification aims at finding a more discriminative subset from original features, to facilitate classifier training, improve classification performance and enhance model interpretability. Primary normalized cross-covariance operator (NOCCO) is a nonlinear kernel-based dependency measure between features and labels, including two inverse matrices, and its approximated version (ANOCCO) is simplified via exploiting linear kernel for features, delta kernel for labels and Moore–Penrose inverse. In this paper, we apply NOCCO and ANOCCO to FS task. According to sequential forward selection, a forward NOCCO-based FS algorithm (i.e., FoNOCCO) is directly implemented via various accelerating strategies, but its computational complexity is extremely high. To this end, we propose its fast version via maximizing ANOCCO (i.e., FoANOCCO), where each candidate feature is evaluated efficiently according to column recursive algorithm for Moore–Penrose inverse. Theoretical analysis shows that evaluating a candidate feature needs a time complexity of O (N 2) in FoANOCCO and O (N 3) in FoNOCCO, where N is the number of instances. On eight small-size high-dimensional benchmark data sets, our detailed experiments illustrate that our two forward FS algorithms are superior to six state-of-the-art FS methods with four baseline classifiers, and FoANOCCO runs 70 times faster than FoNOCCO with a 1.79 % accuracy drop. [ABSTRACT FROM AUTHOR]

In evolutionary multiobjective optimization, effectiveness refers to how an evolutionary algorithm performs in terms of converging its solutions into the Pareto front and also diversifying them over the front. This is not an easy job, particularly for optimization problems with more than three objectives, dubbed many-objective optimization problems. In such problems, classic Pareto-based algorithms fail to provide sufficient selection pressure towards the Pareto front, whilst recently developed algorithms, such as decomposition-based ones, may struggle to maintain a set of well-distributed solutions on certain problems (e.g., those with irregular Pareto fronts). Another issue in some many-objective optimizers is rapidly increasing computational requirement with the number of objectives, such as hypervolume-based algorithms and shift-based density estimation (SDE) methods. In this paper, we aim to address this problem and develop an effective and efficient evolutionary algorithm (E3A) that can handle various many-objective problems. In E3A, inspired by SDE, a novel population maintenance method is proposed to select high-quality solutions in the environmental selection procedure. We conduct extensive experiments and show that E3A performs better than 11 state-of-the-art many-objective evolutionary algorithms in quickly finding a set of well-converged and well-diversified solutions. [ABSTRACT FROM AUTHOR]

Explaining what a reinforcement learning (RL) control agent learns play a crucial role in the safety critical control domain. Most of the approaches in the state-of-the-art focused on imitation learning methods that uncover the hidden reward function of a given control policy. However, these approaches do not uncover what the RL agent learns effectively from the agent-environment interaction. The policy learned by the RL agent depends in how good the state transition mapping is inferred from the data. When the state transition mapping is wrongly inferred implies that the RL agent is not learning properly. This can compromise the safety of the surrounding environment and the agent itself. In this paper, we aim to uncover the elements learned by data-driven RL control agents in a special class of discrete-time linear autonomous systems. Here, the approach aims to add a new explainable dimension to data-driven control approaches to increase their trust and safe deployment. We focus on the classical data-driven Q-learning algorithm and propose an explainable Q-learning (XQL) algorithm that can be further expanded to other data-driven RL control agents. Simulation experiments are conducted to observe the effectiveness of the proposed approach under different scenarios using several discrete-time models of autonomous platforms. [ABSTRACT FROM AUTHOR]

• A NLPSR scheme is proposed to balance the exploration and exploitation. • An AAU mechanism is proposed to avoid overuse of the archive. • An iRSP strategy is proposed for improving the diversity of the population. As one of the best differential evolution (DE) variants, jSO has attracted the attention of many researchers. However, jSO still has a few drawbacks that limit its performance, it fails to fully exploit the potential of dominant individuals, it overuses of archives, and it cannot effectively balance convergence and diversity. In order to effectively overcome these deficiencies, this paper proposes a novel jSO variant called NLPSR-jSO. NLPSR-jSO introduces a novel Non-Linear Population Size Reduction scheme to balance exploration and exploitation performance while adaptively allocating more evaluation resources to dominant individuals to fully realize their potential. Moreover, NLPSR-jSO presents a new mechanism named Adaptive Archive Usage, which prevents archive overuse and improves the accuracy of the solution. A modified Rank-Based Selective Pressure strategy is integrated into the mutation strategy to increase the diversity and prevent premature convergence. The strategies proposed by NLPSR-jSO effectively solve the above-mentioned problems of jSO, and the cooperation of these strategies significantly improves the performance of jSO. The proposed NLPSR-jSO was compared with seven state-of-the-art DE variants using the CEC 2018 test suite. The experimental results show that the solution accuracy and convergence speed of NLPSR-jSO are significantly better than those of the compared algorithms. [ABSTRACT FROM AUTHOR]

Organizations execute decisions within business processes on a daily basis whilst having to take into account multiple stakeholders who might require multiple point of views of the same process. Moreover, the complexity of the information systems running these business processes is generally high as they are linked to databases storing all the relevant data and aspects of the processes. Given the presence of multiple objects within an information system which support the processes in their enactment, decisions are naturally influenced by both these perspectives, logged in object-centric process logs. However, the discovery of such decisions from object-centric process logs is not straightforward as it requires to correctly link the involved objects whilst considering the sequential constraints that business processes impose as well as correctly discovering what a decision actually does. This paper proposes the first object-centric decision-mining algorithm called Integrated Object-centric Decision Discovery Algorithm (IODDA). IODDA is able to discover how a decision is structured as well as how a decision is made. Moreover, IODDA is able to discover which activities and object types are involved in the decision-making process. Next, IODDA is demonstrated with the first artificial knowledge-intensive process logs whose log generators are provided to the research community. • First discovery algorithm of DMN models from object-centric event logs. • First public knowledge-intensive object-centric process event logs. • Python implementation and demonstration of the algorithm. [ABSTRACT FROM AUTHOR]

Semi-supervised graph clustering with constraints has received considerable attention in the last decade. They use pre-given constraints to guide the clustering process and improve the performance. Nonetheless, most of related research works still face two main issues: (1) cannot-link problem: how to ensure that instances under the cannot-link constraint are located in different clusters as much as possible. (2) The high computational cost caused by eigendecomposition. To tackle these problems, this paper proposes a novel method for directly solving the clustering indicator matrix without high complexity computational steps. Then, we propose an effective and simple algorithm guided by the pre-given constraints, which is able to simultaneously optimize the associated multiple pairs of constraints, allowing the associated constraints to achieve the related co-optimal solution. Extensive experiments are conducted to verify that our approach significantly reduces the violation rate of the prior constraints and the clustering performance of the proposed method outperforms seven other state-of-the-art semi-supervised clustering methods on 16 real benchmark datasets. [ABSTRACT FROM AUTHOR]

The task of mining high-utility itemsets in a database given a minimum threshold is attracting more and more interest due to its many applications. Existing algorithms such as the vertical ones have the advantages of high scalability, efficiency and extensibility. However, they depend on a costly join operation to generate new itemsets. To overcome this limitation, this paper proposes a novel vertical algorithm, FOTH (Fast sOrted iTemset searcH), which employs a novel effective data structure, the IndexSet. The IndexSet self-propagates to produce sub-IndexSets, eliminating the need to perform join operations, which considerably reduces the memory and computation requirements. Experiments were conducted on eight benchmark databases to compare the performance of FOTH with four state-of-the-art list-based algorithms. The results show that FOTH outperforms the other algorithms on dense databases. [ABSTRACT FROM AUTHOR]