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Exam paper generation is an indispensable part of teaching. Existing methods focus on the use of question extraction algorithms with labels for each question provided. Obviously, manual labeling is inefficient and cannot avoid label bias. Furthermore, the quality of the exam papers generated by the existing methods is not guaranteed. To address these problems, we propose a novel approach to generating exam papers based on prediction of exam performance. As such, we update the quality of the initially generated questions one by using dynamic programming, as well as in batches by using genetic algorithms. We performed the prediction task by using Deep Knowledge Tracing. Our approach considered the skill weight, difficulty, and distribution of exam scores. By comparisons, experimental results indicate that our approach performed better than the two baselines. Furthermore, it can generate exam papers with adaptive difficulties closely to the expected levels, and the related student exam scores will be guaranteed to be relatively reasonable distribution. In addition, our approach was evaluated in a real learning scenarios and shows advantages.

To compute the similarity of scientific papers, text-based similarity measures, link-based similarity measures, and hybrid methods can be applied. The text-based and link-based similarity measures take into account only a single aspect of scientific papers, content or citations, respectively. The hybrid methods consider both content and citations; however, they do not carefully consider the relation between the content of a pair of papers involved in a citation relationship. In this paper, we propose a novel method, SimCC (similarity based on content and citations), that considers both aspects, content and citations, to compute the similarity of scientific papers. Unlike previous methods, SimCC effectively reflects both content and authority of scientific papers simultaneously in similarity computation by applying a new RA (relevance and authority) weighting scheme. Also, we propose an RA+R weighting scheme to consider the recency of papers and an RA+E weighting scheme to take into account the author expertise of papers in similarity computation. The effectiveness of our proposed method is demonstrated by extensive experiments on a real-world dataset of scientific papers. The results show that our method achieves more than 100% improvement in accuracy in comparison with previous methods.

The task of assessing the similarity of research papers is of interest in a variety of application contexts. It is a challenging task, however, as the full text of the papers is often not available, and similarity needs to be determined based on the papers' abstract, and some additional features such as their authors, keywords, and the journals in which they were published. Our work explores several methods to exploit this information, first by using methods based on the vector space model and then by adapting language modeling techniques to this end. In the first case, in addition to a number of standard approaches we experiment with the use of a form of explicit semantic analysis. In the second case, the basic strategy we pursue is to augment the information contained in the abstract by interpolating the corresponding language model with language models for the authors, keywords and journal of the paper. This strategy is then extended by revealing the latent topic structure of the collection using an adaptation of Latent Dirichlet Allocation, in which the keywords that were provided by the authors are used to guide the process. Experimental analysis shows that a well-considered use of these techniques significantly improves the results of the standard vector space model approach.

In a very recently presented paper, Turky and Abdullah 5 proposed a novel multi-population harmony search with external archive (MHSA-ExtArchive) for dynamic optimization problems. In the experimental results, the authors claimed that their approach could outperform several state-of-the-art algorithms. They also showed the superiority of their method by means of numerical experiments on Moving Peaks Benchmark (MPB). Despite the interesting idea of applying multi-population scheme on harmony search and using a new type of external archive for dealing with dynamic problems, we believe that there are two very important shortcomings in the result analysis, which we point out in this short note. The main motivation of the present note is to contribute toward preventing the same mistakes from happening by the other researchers.

In this short note, we briefly present and discuss an example of page rank algorithm given in [Information Sciences 150 (2003) 165-176].

One way to achieve mutual exclusion in a distributed system is to assign votes to each site in the system. If the total number of votes is odd, the assignment is known to be nondominated, meaning that no other assignment can provide strictly greater access and still achieve mutual exclusion. We characterize in this note dominated even-totaled vote assignments. As a consequence, we obtain that the problem of determining whether an even-totaled vote assignment is dominated is trivial if each site is assigned exactly one vote; however, the problem is NP-complete in general.

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.

This paper aims to develop a new axiomatic design-based mathematical programming method for heterogeneous multi-criteria group decision making (HMCGDM) problems with linguistic fuzzy truth degrees (LFTDs). The main contributions of this paper are summarized in five aspects: (1) The information content definitions for six types of fuzzy numbers are initially provided according to axiomatic design . (2) Considering the authority of experts on different criteria and group consensus, a bi-objective programming model is constructed to derive experts’ weights by maximizing individual deviation and minimizing group discordance. (3) Each alternative is assessed on the basis of its information content to a fuzzy positive ideal solution. Information content is firstly used to define the linguistic fuzzy consistency and inconsistency indices. (4) A bi-objective linguistic fuzzy mathematic programming model is built to determine the criteria weights, which considers consistency and inconsistency indices simultaneously. This model can be dexterously transformed into a crisp linear programming model for resolution by the linguistic scale function. (5) The group information content of each alternative to fuzzy positive ideal solution is calculated to determine the ranking order of alternatives. Finally, an example of blockchain service provider selection is given to validate the proposed method.

Network science is a widely studied subject and graph theory has a major role in it. This paper studies connectivity status of vertices in a fuzzy graph. This paper adopt connectivity status to build up the status sequence related to a fuzzy graph. Results on connectivity status and status sequence of different structures are obtained. On the basis of connectivity status, vertices of a fuzzy graph can be classified into connectivity status enhancing vertices, connectivity status reducing vertices and connectivity status neutral vertices. Connectivity status analysis of vertices is also carried out. Algorithms related to these concepts are provided and an application related to bandwidth allocation problem in networking is proposed.

Inspired by the structure and communication method of neural systems, a parallel computing model, spiking neural P systems (SN P systems, for short), was proposed in 2006. A new class of SN P systems, SN P systems with autapses (SNP-AU systems), is presented in this work. Autapses are a special kind of synapses, connecting the axon of a neuron onto itself. We prove that SNP-AU systems can generate Turing-computable numbers, through the simulation of the modules of universal register machines. This result improves significantly the results given by classical SN P systems in terms of the number of neurons and rules, while preserving simplicity and power to a reasonable extent. Moreover, we construct an SNP-AU system using 53 neurons, proving its universality for computing functions. Finally, going beyond the design of the building blocks of register machines, a whole universal machine is provided. Thus, a simulator is developed and used to check the correctness of two universal SNP-AU systems proposed in this paper, complementing the theoretical proof with the experimental validation of our systems with respect to the reference example appearing in the foundational paper of small register machines.

This paper proposes a new tent-multi dynamic piecewise coupled mapping lattice (TMDPCML). Through the comprehensive analysis of the performance test results, TMDPCML system increases the correlation dynamics of spatiotemporal behavior and improves the efficiency of energy diffusion between lattices. Moreover, TMDPCML system has larger parameter space, better chaos and outstanding cryptographic characteristics. Therefore, this paper proposes a privacy image encryption algorithm combined with TMDPCML system. The application of TMDPCML system in private images encryption further proves that TMDPCML system has good chaotic behavior and meets the requirements of cryptography.

Nowadays, more scholars and practitioners are committed to improving and applying type-2 fuzzy sets (T2FSs) in various domains because of the stronger ability to handle the uncertainty of fuzzy complex systems. To explore the status and internal laws of development of this field, this paper presents the development overview based on the bibliometric analysis, then the dynamic evolution of main topics and the knowledge diffusion trajectory are also displayed on the basis of the strategy diagram and main path analysis (MPA). From 1997 to 2019, 1749 documents are retrieved from Web of Science (WoS) repository for the analysis. The results show that there are four stable collaborative communities existing in the countries/regions’ collaborative network and the collaboration is affected by geographical factors to some degree. Three main evolution paths of hot topics are also presented in this paper and the multi-criteria decision-making (MCDM) has gradually developed into the moto theme. Furthermore, articles appearing on the main path mainly focus on the research of basic concept and framework, self-optimization and applications in various domains of T2FSs. In general, this paper provides a new landscape in the longitudinal research based on the development overview, thematic evolution and the knowledge diffusion trajectory.

In this paper, a novel approach of supervised feature selection is proposed based on the principle of dense subgraph discovery. To exploit dense subgraph discovery for the purpose of feature selection, the dataset is initially mapped to an equivalent weighted graph notation by considering the set of all features as its vertex set and the mutual dependency between each pair of features as the weight of the corresponding edge. The proposed feature selection algorithm proceeds in a two-phase manner. In the first stage, a dense sub-graph is first discovered so that the features within it become maximally non-redundant among each other and the averaged class relevance as well as averaged standard deviation of all these features are obtained as maximal as possible. In this regard, a novel induced degree is also defined for each feature by incorporating the aforesaid three important objectives of feature selection. In this phase, a modified version of an existing approximation algorithm is also used to find dense subgraph module. Finally, in the second stage, a floating forward–backward search is performed on the dense subgraph so obtained to reveal a better feature subset. In both stages, an existing version of the normalized mutual information score is employed to compute both the class relevance and redundancy. The main contribution of this paper is proposing a feature selection strategy by which the reduced features have the characteristics like maximal average class relevance, minimal average pairwise redundancy, and good discriminating power. The experimental results demonstrate that the proposed approach is competent with several conventional as well as state-of-art algorithms of supervised feature selection.

The influence of each entity in a network is a crucial index of the network information dissemination. Greedy influence maximization algorithms suffer from time efficiency and scalability issues. In contrast, heuristic influence maximization algorithms improve efficiency, but they cannot guarantee accurate results. Considering this, this paper proposes a Gaussian propagation model based on the social networks. Multi-dimensional space modeling is constructed by offset, motif, and degree dimensions for propagation simulation. This space’s circumstances are controlled by some influence diffusion parameters. An influence maximization algorithm is proposed under this model, and this paper uses an improved CELF algorithm to accelerate the influence maximization algorithm. Further, the paper evaluates the effectiveness of the influence maximization algorithm based on the Gaussian propagation model supported by theoretical proofs. Extensive experiments are conducted to compare the effectiveness and efficiency of a series of influence maximization algorithms. The results of the experiments demonstrate that the proposed algorithm shows significant improvement in both effectiveness and efficiency.

This paper addresses the distributed H ∞ state estimation problem for a class of sensor networks with switching characteristics, where the switchings of parameters are presumed to obey persistent dwell-time switching mechanism rather than dwell time or average dwell-time ones in the discrete-time context. For the purpose of tracking the unavailable state of the target plant, a sensor network is formed by employing multiple sensor nodes distributed in space and worked cooperatively under a specific connection topology. The intention of the paper mainly centers on deriving some sufficient criteria for the addressed model to achieve the exponential mean-square stability with a prescribed H ∞ performance, and the estimator gains corresponding to differently constructed estimators are further solved by means of the convex optimization method. Finally, the validity of the proposed approach is illustrated by a numerical example.

This paper focuses on improving the accuracy of image matching by eliminating the residual mismatches in the matching results of standard RANSAC. Based on pixel shift clustering and RANSAC algorithms, a matching optimization algorithm called pixel shift clustering RANSAC, PSC-RANSAC in short, is proposed in this paper. Firstly, the pixel shift model of space point from two perspectives are established by parallax principle and camera projection model. Then, based on the established pixel shift model, density peaks clustering (DPC) algorithm is used to select the mismatches out to enhance the accuracy of image matching. Meanwhile the comparisons among PSC-RANSAC, standard RANSAC, progressive sample consensus and graph-cut RANSAC show that PSC-RANSAC can more effectively and robustly eliminate the residual mismatches in initial matching results. The proposed method provides an effective tool for optimization on image matching.

Surrogate-assisted evolutionary algorithms (SAEAs), which use efficient surrogate models or meta-models to approximate the fitness function in evolutionary algorithms (EAs), are effective and popular methods for solving computationally expensive optimization problems. During the past decades, a number of SAEAs have been proposed by combining different surrogate models and EAs. This paper dedicates to providing a more systematical review and comprehensive empirical study of surrogate models used in single-objective SAEAs. A new taxonomy of surrogate models in SAEAs for single-objective optimization is introduced in this paper. Surrogate models are classified into two major categories: absolute fitness models, which directly approximate the fitness function values of candidate solutions, and relative fitness models, which estimates the relative rank or preference of candidates rather than their fitness values. Then, the characteristics of different models are analyzed and compared by conducting a series of experiments in terms of time complexity (execution time), model accuracy, parameter influence, and the overall performance when used in EAs. The empirical results are helpful for researchers to select suitable surrogate models when designing SAEAs. Open research questions and future work are discussed at the end of the paper.

Knowledge graphs (KGs) are one of the most common frameworks for knowledge representation. However, they suffer from a severe scalability problem that hinders their usage. KG embedding aims to provide a solution to this issue. Nonetheless, general approaches are incapable of representing and reasoning about information not previously contained in the graph. This paper proposes to leverage semantic and ontological information for a significant benefit of knowledge graph completion, focusing on triple classification. The goal of this task is to determine whether a given fact holds. Furthermore, this paper also considers the classification of facts that include entities that have not been seen during training, denoted out-of-knowledge-base or OOKB entities. An incremental method is presented, composed of six stages. Although the proposal can be applied to any KG embedding model, this work focuses on its application for semantic matching models, such as ComplEx and DistMult. Compared to other approaches, our proposal is model-agnostic, computationally inexpensive, and does not require retraining. The results show that triple classification accuracy scales up to 15% with the proposed approach, as well as accelerating the convergence of the model to its optimal solution. Furthermore, facts containing OOKB entities can be classified with a reasonable accuracy.

This paper proposes an adaptive denoising methodology for noisy signals that employs a novel adaptive complementary ensemble empirical mode decomposition (NACEEMD) and a peak statistics (PS)-based thresholding technique. The key idea in this paper is the peak statistics (PS)-based thresholding technique,which breaks the traditional strategy with respect to selecting more accurate and more adaptive thresholds. The NACEEMD algorithm is proposed to decompose the noisy signal into a series of intrinsic mode functions (IMFs). At the same time, NACEEMD is also used to verify the applicability of the PS-based thresholding technique in different decomposition algorithms. The PS-based threshold is used to remove the noise inherent in noise-dominant IMFs, and the denoised signal is reconstructed by combining the denoised noise-dominant IMFs and the signal-dominant IMFs. This paper uses a various of simulated signals in various noisy environments for experiments, the experimental results indicate that the proposed algorithm outperforms traditional threshold denoising methodologies in terms of signal-to-noise ratio, root mean square error, and percent root distortion. Moreover, through real ECG signal and multi-sensor data fusion experiments, the application of the proposed algorithm in the field of engineering is explored and expanded.