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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]

• 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]

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]

Shadowed sets, as a three-way approximation of fuzzy sets, employ two thresholds (α , β) to maintain the essence of fuzzy sets with reduced computing complexity and simpler semantic interpretation by allowing quantification of numeric membership grades into three parts: core, total exclusion, and complete uncertainty. An essential issue in inducing shadowed sets is determining the thresholds (α , β) and maintaining the consistency in fuzziness. However, it is found that the existing interval shadowed sets (ISS) have a significant fuzziness loss. In addition, the algorithm used to obtain the threshold pairs in ISS need to be further optimized. To this end, a novel method to induce interval shadowed sets based on a new objective function is proposed in this paper. Firstly, the shortcomings of the objective function and fuzziness loss of ISS are analyzed. Then, a new objective function is given, and interval shadowed sets with less fuzziness loss are induced based on this objective function. Afterwards, a fast algorithm is developed to obtain interval shadowed sets quickly. Finally, the rationality and validity of the proposed method are verified by experimental results. [ABSTRACT FROM AUTHOR]

This paper proposes two-stage continuous-time triggered algorithms for solving distributed optimization problems with inequality constraints over directed graphs. The inequality constraints are penalized by adopting log-barrier penalty method. The first stage of the proposed algorithms is capable of finding the optimal point of each local optimization problem in finite time. In the second stage of the proposed algorithms, zero-gradient-sum algorithms with time-triggered and event-triggered communication strategies are considered in order to reduce communication costs. Then, with the help of LaSalle's invariance principle, it is proved that the state solution of each agent reaches consensus at the optimal point of the considered penalty distributed optimization problem, and Zeno behavior is also excluded. Finally, numerical examples are given to illustrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper focuses on the solution to a formation-aggregation problem concerned with a cluster of quadrotors. In particular, all the quadrotors are steered towards a target region while reaching an expected formation configuration. However, only a subset of quadrotors, treated as leaders, have access to the projections onto the target region; and in the meantime, each quadrotor just exchanges information with its neighbors. It is assumed that the quadrotors without the knowledge of the target region, treated as followers, are within the convex hull of the leaders in the expected configuration. A distributed formation-aggregation control algorithm is proposed under the hierarchical framework. To be specific, resorting to an auxiliary dynamic system with a distributed idea, a command force is first synthesized in the position loop for the concerned formation-aggregation objective. Next, an applied thrust and a command attitude are extracted from the synthesized command force. Finally, an applied torque is synthesized in the attitude loop such that the attitude tracking to the command one is realized. In terms of the Lyapunov theory, it is shown that the leaders are capable of guiding the overall quadrotor cluster into the target region in the expected configuration. Simulations are carried out to validate the theoretical results. [ABSTRACT FROM AUTHOR]

More and more privacy-sensitive images have been produced daily. Chaotic encryption guarantees that only the targeted receiver can obtain the raw image. Many works perform encryption on bit-level for better permutation but incur an unbearable time consumption caused by generating additional 3D coordinates. High-dimensional chaotic system, suitable for encrypting images, needs to iterate repeatedly for several rounds, resulting in low encrypting efficiency. This paper proposes a parallel image encryption algorithm using intra bitplane scrambling. Four threads are utilized to encrypt the images in bit-level during the permutation, where each thread scrambles two bitplanes. In diffusion, we build multiple threads to generate keystream segments to reduce encryption time. We make cryptographic analyses and compare the encryption speed with the advanced works, demonstrating that our scheme is secure and efficient. • The proposed parallel bit-level permutation algorithm offers a good trade-off between efficiency and effect. • To accelerate the keystreams generation in diffusion stage, multi-threading technology is used to generate them in parallel. • The results of security analyses show the proposed algorithm has high efficiency compared with other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

A wealth of empirical evidence shows that people display opposite behaviors when deciding whether to rely on an algorithm, even if it is inexpensive to do so and using the algorithm should enhance their own performance. This paper develops a formal theory to explain some of these conflicting facts and submit new testable predictions. Drawing from decision analysis, I invoke two key notions: the 'value of information' and the 'value of control'. The value of information matters to users of algorithms like recommender systems and prediction machines, which essentially provide information. I find that ambiguity aversion or a subjective cost of employing an algorithm will tend to decrease the value of algorithmic information, while repeated exposure to an algorithm might not always increase this value. The value of control matters to users who may delegate decision making to an algorithm. I model how, under partial delegation, imperfect understanding of what the algorithm actually does (so the algorithm is in fact a black box) can cause algorithm aversion. Some possible remedies are formulated and discussed. • This paper initiates a formal decision-theoretic approach to make sense of the empirical evidence concerning people's attitudes towards algorithms. • This approach exploits two fundamental notions: the value of information and the value of control. • Ambiguity aversion will tend to decrease the value of algorithmic information; repeated exposure to algorithms may not increase it. • A first model of 'black box' algorithms is developed to analyze the value of keeping versus delegating control. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive security analysis on an improved chaos-based image encryption algorithm. The initial algorithm, proposed by Li et al., involves permutation related to the sum of plaintext pixel values and diffusion associated with 9 specific pixel values in the permuted image. However, a thorough analysis conducted by Liu et al. reveals two major flaws in it: firstly, the 9 specific pixel values are not involved in the diffusion process; secondly, the permutation method exhibits significant vulnerabilities. In response to these shortcomings, Liu et al. proposed targeted improvements on it, which include incorporating a permutation step for the 9 specific pixels and enhancing the original permutation method. In this study, we analyze the improved algorithm and discover that it still possesses security vulnerabilities, rendering it susceptible to chosen-plaintext attack. By constructing three categories of special plaintexts, one can decipher the equivalent permutation and diffusion. Theoretical analysis and experimental results provide strong evidence for the effectiveness of our analysis in this paper. [ABSTRACT FROM AUTHOR]

This paper presents a novel and robust framework for state estimation of switched finite-field networks (SFFNs), which are a class of multi-valued logical networks that can model complex systems. The paper uses the semi-tensor product (STP) method to transform the dynamical model of SFFNs into an algebraic form, which can be easily manipulated and analyzed. The paper then applies a Monte-Carlo-based sequential importance sampling (SIS) filter to estimate the state of SFFNs from noisy and incomplete observations. The paper also introduces a resampling algorithm (RA) to prevent the particle degeneracy problem (PDP), which can affect the accuracy and efficiency of the filter. Finally, the paper demonstrates the performance and advantages of the proposed multi-valued particle filter (MVPF) framework through three examples. [ABSTRACT FROM AUTHOR]

This paper proposes an interactive forest algorithm for planning single-tuned harmonic filters for electric power distribution systems. The planning of harmonic filters is a complex problem that must consider multiple objectives, including minimizing the total cost of installed filters and the individual and total harmonic distortion while adhering to operational constraints, such as the voltage drop, the current loading and the harmonic limits. Moreover, system perturbations must be considered, such as frequency variations. This paper proposes a new method for multi-objective optimization of planning single-tuned harmonic filters utilizing the proposed interactive forest algorithm with a non-differentiable multi-objective function involving continuous and discrete variables. A multi-objective optimization technique based on the proposed interactive forest algorithm is proposed. In the interactive approach, planners set expectations for each objective and use the forest algorithm to figure out the optimization problems. The results of the Pareto Front are analyzed and the answers are searched through the interactive process to identify a solution that meets the planner's expectations, allowing for practical compromise or satisfactory solutions. A case study is reported to show the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to apply grey wolf optimizer (GWO) algorithm for steady state analysis of self-excited induction generators (SEIGs) supplying isolated loads. Design/methodology/approach: Taking the equivalent circuit of SEIG, the impedances representing the stator, rotor and the connected load are reduced to a single loop impedance in terms of the unknown frequency, magnetizing reactance and core loss resistance for the given rotor speed. This loop impedance is taken as the objective function and minimized using GWO to solve for the unknown parameters. By including the value of the desired voltage as a constraint, the formulated objective function is also extended for estimating the required excitation capacitance. Findings: The experimental results obtained on a three phase 415 V, 3.5 kW SEIG and the corresponding predetermined performance characteristics agree closely, thereby validating the proposed GWO method. Moreover, a comparative study of GWO with genetic algorithm and particle swarm optimization techniques reveals that GWO exhibits much quicker convergence of the objective function. Originality/value: The important contributions of this paper are as follows: for the first time, GWO has been introduced for the SEIG performance predetermination and computation of the excitation capacitance for attaining the desired terminal voltage for the given load and speed; the predicted performance accuracy is improved by considering the variable core loss of the SEIG; and GWO does not require derivations of lengthy equations for calculating the SEIG performance. [ABSTRACT FROM AUTHOR]

In this paper, a unified framework is proposed to address the synchronization problem of memristor chaotic systems (MCSs) via the sliding-mode control method. By employing the presented unified framework, the finite-time and fixed-time synchronization of MCSs can be realized simultaneously. On the one hand, based on the Lyapunov stability and sliding-mode control theories, the finite-/fixed-time synchronization results are obtained. It is proved that the trajectories of error states come near and get to the designed sliding-mode surface, stay on it accordingly and approach the origin in a finite/fixed time. On the other hand, we develop an image encryption algorithm as well as its implementation process to show the application of the synchronization. Finally, the theoretical results and the corresponding image encryption application are carried out by numerical simulations and statistical performances. [ABSTRACT FROM AUTHOR]

Distributed coordination of multi-agent systems (MASs) has been investigated for many years, and fractional-order calculus has been proved that it can model the dynamics more accurately in certain circumstances. Hence, in this paper, combining the above two aspects, the distributed coordination of fractional-order MASs (FOMASs) is researched, which is a promising topic. Besides, in this paper, the uncertainty, inherent nonlinearity and external disturbances are taken into consideration, aiming at achieving the robust consensus tracking. In particular, the uncertain parameters will be identified from an optimization perspective using artificial bee colony algorithm (ABC). Firstly, to ameliorate the performance of the standard ABC, a hybrid ABC (hABC) incorporating two groups of searching mechanisms is designed, it facilitates the identification of unknown parameters. After obtaining the identified parameters, an efficient distributed nonlinear controller is raised to fulfill the robust consensus tracking. Finally, experiments prove that the designed parameters identification approach can successfully estimate the uncertain parameters with high accuracy, besides the designed control algorithm can robustly control the FOMASs. [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]

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 suggests a new hybrid algorithm by integrating two population-based algorithms: Whale Optimization Algorithm (WOA) and Flower Pollination Algorithm (FPA), to solve complex nonlinear systems and unconstrained optimization problems. WOFPA denotes the suggested algorithm, a hybrid Whale Optimization Algorithm and Flower Pollination Algorithm. Nonlinear systems can be cast into unconstrained optimization problems, called merit functions, where the optimal solutions for the merit functions are equivalent to the solutions of nonlinear systems. WOFPA aims to decrease the execution time and the complexity of WOA and FPA. WOFPA has the advantages of WOA and FPA; WOFPA is a high-quality algorithm to solve both problems, nonlinear systems and unconstrained optimization problems. For example, FPA may have a premature convergence in the local optima, and WOFPA subdues the disadvantage of FPA. Numerical experiments of 14 benchmarks nonlinear systems and 30 CEC 2014 benchmarks unconstrained optimization functions with various dimensions are employed to test the performance of WOFPA. To have a further investigation for the performance of WOFPA, WOFPA is compared with WOA, FPA, and other existing algorithms from the literature. Two non-parametric statistical tests, Wilcoxon statistical test and the Friedman test, are conducted for this study to check the performance of the proposed algorithms and other compared algorithms and the significance of our results. The experiment results demonstrate that WOFPA performs better than other algorithms in the literature by getting the optimum solutions for most nonlinear systems and optimization problems and proves its efficiency compared with other existing algorithms. • This paper proposes a new hybridization Whale Optimization Algorithm and Flower Pollination Algorithm. • We solve system of nonlinear equations and unconstrained optimization problem. • The efficiency and effectiveness of our algorithm are provided. • Experimental results prove the superiority of our algorithm over the state-of-the-arts. [ABSTRACT FROM AUTHOR]

In a previous paper based on the interpolation finite difference method, a calculation system was shown for calculating 1D (one-dimensional) Laplace's equation and Poisson's equation using high-order difference schemes. Finite difference schemes, from the usual second-order to tenth-order differences, including odd number order differences, were systematically and instantaneously derived over equally/unequally spaced grid points based on the Lagrange interpolation function. Using the direct method with the band diagonal matrix algorithm, 1D Poisson equations were numerically calculated under double precision floating arithmetic, but it became clear that high accurate calculations could not be secured in high-order differences because "digit-loss errors" caused by the finite precision of computations occurred in the calculations when using the high-order differences. The double precision calculation corresponds to 15 (significant) digit calculation. In this paper, we systematically investigate how the calculation accuracy changes by high precision calculations (30-digit, and 45-digit calculations). Under 45-digit calculation, where the digit-loss error can be almost ignored, the high-order differences enable extremely high-accurate calculations. [ABSTRACT FROM AUTHOR]

Memristor-based memory technology is one of the emerging memory technologies, which is a potential candidate to replace traditional memories. Efficient test solutions are required to enable the quality and reliability of such products. In previous works, fault models are caused by open, short and bridge defects and parametric variations during the fabrication. However, these fault models cannot describe the bridge defects that cause the state of the faulty cell to an undefined state. In this paper, we analyze the different effects of bridge defects and aggregate their faulty behavior into new fault models, undefined coupling fault and dynamic undefined coupling fault. In addition, an enhanced March algorithm is designed to detect all the modeled faults. In one resistor crossbar with $N$ memristors, the enhanced March algorithm requires $8N$ write and $7N$ read operations with negligible hardware overhead. To reduce the test time, a March RC algorithm is proposed based on read operations with new reference currents, which requires $4N+2$ write and $6N$ read operations. Analytical results show that the proposed test algorithms can detect all the modeled faults outperforming all the previous methods. Subsequently, a Design-for-Testability scheme is proposed to implement March RC algorithm with a little area overhead. [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]

In this paper, we deal with a specific type of quasilinear boundary value problem with Dirichlet boundary conditions and with p -Laplacian. We show two ways of proving the existence of nontrivial weak solutions. The first one uses the mountain pass theorem, the other one is based on our new minimax theorem. This method is novel even for p = 2. In the paper, we also present a numerical algorithm based on the introduced approach. The suggested algorithm is illustrated on numerical examples and compared with a current approach to demonstrate its efficiency. [ABSTRACT FROM AUTHOR]

The paper describes the FPGA implementation of a novel position evaluation algorithm based on the time difference of arrival (TDOA) principle that combines the characteristics of an Orthogonal Frequency Division Modulation (OFDM) symbol with the properties of the Zadoff-Chu mathematical sequences. The resulting system is highly scalable and its characteristics are easily adaptable to different operating scenarios. The algorithm has been implemented using the Stratix IV-E EP4SGX70HF35C3 FPGA, requiring about 112k bit of memory and less than 44k logic elements of which about 16k are registers. The paper describes the algorithm and its FPGA implementation along with experimental results validating the system performance even in the presence of multipath interferences and showing precision in target position estimation that is better than 2 cm. [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]

For the operation of the salp swarm algorithm(SSA), the leaders fall into local optimum, which leads to the population falling into local optimum problem. In this paper, a salp swarm algorithm based on Harris Eagle foraging strategy is proposed(ISSAHF). Harris hawk optimization algorithms of four types of feeding mechanism are integrated into salp location update process optimization algorithm follower. To strengthen the exploitation of potential areas, complemented by a multi-point leadership crossover strategy to maintain a balance between exploitation and exploration. The performance of the proposed ISSAHF was compared, and Wilcoxon's statistical analysis was performed on 20 benchmark functions including unimodal, multimodal, and partial CEC2014. Finally, ISSAHF is applied to three practical engineering optimization problems to evaluate its optimization performance further. [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]

In this paper, we propose the full-rank and reduced-rank relaxed gradient-based iterative algorithms for solving the generalized coupled Sylvester-transpose matrix equations. We provide analytically the necessary and sufficient condition for the convergence of the proposed iterative algorithm and give explicitly the optimal step size such that the convergence rate of the algorithm is maximized. Some numerical examples are examined to confirm the feasibility and efficiency of the proposed algorithms. [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]

• 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]

A recently developed metaheuristic optimization algorithm, Salp Swarm Algorithm (SSA), has manifested its capability in solving various optimization problems and many real-life applications. SSA is based on salps' swarming behaviour when finding their way and searching for food in the oceans. Nonetheless, like most metaheuristic algorithms, SSA experiences low convergence and stagnation in local optima and rate. There is a need to enhance SSA to speed its convergence and effectiveness to solve complex problems. In the present study, we will introduce chaos into SSA (CSSA) to increase its global search mobility for robust global optimization. Detailed studies are carried out on real-world nonlinear benchmark systems and CEC 2013 benchmark functions with chaotic map (Tent). Here, the algorithm utilizes a Tent map to tune the salp leaders' attractive movement around food sources. The experimental results, considering both convergence and accuracy simultaneously, demonstrate the effectiveness of CSSA for 12 nonlinear systems and 28 unconstrained optimization problems CEC 2013. Two nonparametric statistical tests, the Friedman test and Wilcoxon Signed-Rank Test, are conducted to show the superiority of CSCA over other states of the art algorithms and our results' significance. • This paper introduces chaos into Salp Swarm Algorithm. • Solve 28 unconstrained optimization problems, CEC and 12 nonlinear systems. • The effectiveness and efficiency of our algorithm are provided. • Experimental results prove superiority of our algorithm over the state-of-the-arts. [ABSTRACT FROM AUTHOR]

To alleviate the restriction of system model on control design, data-driven model-free adaptive control (MFAC) is an excellent alternative to model-based control methods. This paper studies event-triggered data-driven control for switched systems over a vulnerable and resource-constrained network. The system is transformed into an equivalent switched data model through dynamic linearization. Resource constraints and denial of service (DoS) attacks in the network are concerned, and a novel joint anti-attack method including resilient event-triggering mechanism and prediction scheme is presented. Furthermore, new event-triggered MFAC algorithms are proposed. In this scenario, by constructing a Lyapunov functional on tracking error, sufficient conditions to ensure its boundedness are derived. This is the first time in the literature to give a complete solution to data-driven control of switched systems. At last, the validity of new algorithms and theoretical results is confirmed by simulations. [ABSTRACT FROM AUTHOR]

This paper focuses on constructing a conjugate gradient-based (CGB) method to solve the generalized periodic coupled Sylvester matrix equations in complex space. The presented method is developed from a point of conjugate gradient methods. It is proved that the presented method can find the solution of the considered matrix equations within finite iteration steps in the absence of round-off errors by theoretical derivation. Some numerical examples are provided to verify the convergence performance of the presented method, which is superior to some existing numerical algorithms both in iteration steps and computation time. [ABSTRACT FROM AUTHOR]

Three-way concept analysis is a research hotspot. Inspired by multi-scale information system, some scholars put forward and researched multi-granularity formal context. Most of the existing algorithms for constructing three-way concept lattice take care of the static formal contexts and can not deal with the multi-granularity formal context. To address this problem, this paper primarily focuses on a constructing approach to object-induced three-way concept (OE-concept) lattices for multi-granularity formal contexts. Firstly, transformation relationships between OE-concepts based on different granularities are researched. Secondly, the mutual transformation between OE-concept lattices under different granularities is proposed via these relationships, and then corresponding algorithms are proposed. Finally, several groups of datasets are selected from UCI for comparative experiments. Experimental results exhibit that our algorithms are more effective and advantageous than the latest construction algorithms. [ABSTRACT FROM AUTHOR]

Recovering a low-rank matrix and a sparse matrix from an observed matrix, known as sparse and low-rank decomposition (SLRD), is becoming a hot topic in recent years. The most popular model for SLRD is to use the ℓ 1 norm and nuclear norm for the sparse and low-rank approximation. Since this convex model has certain limitations, various nonconvex models have been explored and found to be very promising. In this paper, we introduce a generalized nonconvex nonsmooth model for SLRD which covers a wide range of nonconvex surrogate functions that are continuous, concave and monotonically increasing on [ 0 , ∞) to approximate both the ℓ 0 norm and the rank function, such as ℓ p norm (0 < p < 1), Logarithm, Geman, SCAD and MCP functions. The choice of the nonconvex surrogates for the sparse and low-rank components can be different. Due to the nonconvexity and extensive options of the surrogates, the optimization problem is untractable. Based on the majorization-minimization (MM) algorithm, we propose a unified framework named MM-ADMM algorithm to solve this problem, which can be applied to all eligible surrogates as long as their supergradients are available. The constrained majorizing problems established under the MM framework can be easily solved by the alternating direction method of multipliers (ADMM). The theoretical convergence properties are investigated and proved, including the convergence of the sequence of objective function values generated by the designed algorithm and a weak convergence result related to the inner ADMM-iterations. Experiments on the synthetic data and real-world applications demonstrate the effectiveness of our designed MM-ADMM algorithm. [ABSTRACT FROM AUTHOR]

In this paper, the distributed adaptive fault estimation issue using practical fixed-time design is investigated for attitude synchronization control systems. A distributed fault estimation observer is proposed based on the fixed-time technique. Meanwhile, a novel fixed-time adaptive fault estimation algorithm is also constructed to guarantee convergence rate and improve estimation rapidity. The fault estimation error is uniformly ultimately bounded and is practically fixed-time stable, which converges to the neighborhood of the origin in a fixed time. Finally, simulation results of an attitude synchronization control system are presented to verify the effectiveness of proposed techniques. [ABSTRACT FROM AUTHOR]

• A comprehensive study for the semi-external cutnode problem is presented. • A novel semi-external cutnode algorithm SECN is developed. • SECN is a linear algorithm only consuming O(m + n) time and O(m/B) I/Os. • Extensive experiments are conducted on both real and synthetic datasets. In the literature, many algorithms have been proposed for finding cutnodes on undirected graphs, since cutnodes are crucial to graph connectivity. Here, a cutnode of an undirected graph G is a node of G , whose deletion will cause a reachable pair of the other nodes in G to be unreachable. Currently, the difficulty of maintaining the entire G in the main memory makes researchers pay attention to compute cutnodes on semi-external memory model. This paper shows that traditional semi-external algorithms are limited by their unbounded time and I/O consumption, making them impractical when G is relatively large or complex. Thus, we propose a linear s emi- e xternal c ut n ode computation algorithm, named SECN. Assuming that G has n nodes and m edges, and B is the disk block size. SECN is the first that can find all the cutnodes of G in O (m + n) time and with O (m B) I/O cost on the semi-external memory model, as far as we know. SECN also has a smaller minimum memory space requirement than traditional algorithms. Our experimental evaluation conducted on both synthetic and real graphs confirms that SECN significantly outperforms existing algorithms (up to 10 3 times faster). [ABSTRACT FROM AUTHOR]

• The problem is formulated in a general setting investigating pairwise comparisons matrices. • Innovative concepts of consistency, intensity and coherence of FPCMs are introduced. • New algorithm for calculating the priority vector is proposed. • Two illustrating examples of the proposed algorithm are presented and discussed. Applying the Analytic Hierarchy Process (AHP) in a decision making (DM) problem, fuzzy elements are appropriate whenever the decision maker is uncertain about the value of his/her evaluation of the relative importance of the elements in question, i.e., criteria and/or alternatives. The method, often called the fuzzy AHP, is also used when aggregating crisp pairwise comparisons of a group of decision makers in the group DM problem. In this paper, the DM problem is formulated in a general setting using pairwise comparisons matrices with elements from an Abelian linearly ordered group (alo-group). Such an approach enables extensions of traditional multiplicative, additive, or fuzzy approaches. Here, we propose some desirable properties (consistency, coherency, and intensity) of priority vectors, and derive sufficient conditions for the existence of priority vectors with those properties. In general, the most popular methods for deriving the priority vector – the Eigenvector Method and the Geometric Mean Method – do not always provide priority vectors having these desirable properties. Here, we formulate a new solution algorithm for deriving the priority vector based on a specific optimization problem satisfying the desirable properties under appropriate assumptions. Two illustrating examples of the algorithm are presented and discussed. [ABSTRACT FROM AUTHOR]

• The existing methods may lead to a contradictory in the transitivity. • A novel neighborhood geometric similarity was firstly defined. • Four new kinds of neighborhood similarities were proposed. • A new VPNRS model is constructed, and a novel decision tree algorithm is proposed. The decision tree algorithm has been widely used in data mining and machine learning due to its high accuracy, low computational cost and high interpretability. However, when dealing with the continuous data, the classical decision tree algorithm needs to replace continuous attributes with discretized attributes by the strategy of discretization. Discretization may cause a loss of information structure, which will affect the performance of classification. To tackle this problem, many researchers have proposed different decision tree methods based on variable precision neighborhood rough sets. However, these methods do not consider the geometric structure of neighborhood systems, which may lead to a contradiction in the transitivity of the equivalence relation. In this paper, we first define a novel neighborhood geometric similarity in a neighborhood system from the perspective of geometry. Second, by combining the neighborhood geometric similarity and the neighborhood algebraic similarity, we propose four new kinds of neighborhood similarities, which can solve the contradictory transitivity of the equivalence relation. Third, a variable precision neighborhood rough set model is constructed using the new similarities, and a novel decision tree algorithm is proposed based on this model, where the degree of attribute dependence is used as the partition measure. Experimental results on 14 selected datasets from the UCI Machine Learning Repository show that our algorithm is effective. The average accuracy of our algorithm is over 90%, which is 10% higher than the classical decision tree algorithms, and the number of leaf nodes increases slightly. [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]

Blockchain distributed ledger technology (DLT) has widespread applications in society 5.0 because it improves service efficiency and significantly reduces labor costs. However, employing blockchain DLT entails considerable energy consumption in the mining process. This paper proposes a blockchain accelerator (BCA) with ultralow power consumption and a high processing rate to address the problem. The BCA focuses on accelerating the double secure hash algorithm (SHA) 256 function required in the mining process at a system-on-chip (SoC) level. We propose three ideas, namely, multiple local memories (multimem), double-cell processing element (D-PE), and nonce autoupdate (NAU), to reduce the external data transfer time and improve the BCA hardware efficiency. We propose a cascaded multiple BCA chip model to enhance the system throughput by several-fold. Our experiments on an ASIC and FPGA prove that the proposed BCA successfully performs the mining process for multiple blockchain networks with much lower power consumption than that of the state-of-the-art CPUs and GPUs. The BCA is laid out with Renesas 65 nm technology with a chip area of $25~mm^{2}$ and consumes $530~mW$ at 100 MHz. The power efficiency of the layout chip is improved by 2428 and 143 times compared with that of the fastest CPU Intel i9-10940X and GPU RTX 3090, respectively. [ABSTRACT FROM AUTHOR]

This paper addresses the finite-time and fixed-time bipartite average tracking (BAT) problems in multi-agent systems (MASs) subject to bounded disturbances. A novel non-smooth protocol is first constructed on the basis of utilizing the finite-time control method, which can realize the finite-time BAT with structurally balanced graph. Then, the fixed-time BAT problem is dealt with nonlinear fixed-time algorithm, the setting time of which is obtained without depending on initial states. By employing Lyapunov stability theory, some sufficient criteria are obtained to achieve the finite-time and fixed-time BAT for MASs with disturbances. Numerical examples are finally developed for illustration. [ABSTRACT FROM AUTHOR]

This paper presents a novel hardware architecture of the Tensorial Support Vector Machine (TSVM) based on Shallow Neural Networks (NN) for the Single Value Decomposition (SVD) computation. The proposed NN achieves a comparable Mean Squared Error and Cosine Similarity to the widely used one-sided Jacobi algorithm. When implemented on an FPGA, the NN offers $324\times $ faster computations than the one-sided Jacobi with reductions up to 58% and 67% in terms of hardware resources and power consumption respectively. When validated on a touch modality classification problem, the NN-based TSVM implementation has achieved a real-time operation while consuming about 88% less energy per classification than the Jacobi-based TSVM with an accuracy loss of at most 3%. Such results offer the ability to deploy intelligence on resource-limited platform for energy-constrained applications. [ABSTRACT FROM AUTHOR]

Piecewise affine (PWA) systems are attractive models that can represent various hybrid systems with local affine subsystems and polyhedral regions due to their universal approximation properties. The identification problem of PWA systems amounts to estimating the number of subsystems, parameters of each subsystem, and the corresponding polyhedral partitions via state-input vectors. In this paper, we propose a novel approach to address the online identification problem of continuous-time PWA systems in state-space form. Specifically, an online active mode recognition algorithm and a generalized integral concurrent learning identifier are presented to acquire the number of subsystems, the switching sequence, and the parameter of each subsystem. In addition, we develop the optimization problem for the polyhedral partition estimation, which is solved by using the estimated switching sequence and subsystem parameters. The effectiveness of the proposed identification approach is demonstrated via simulation results. [ABSTRACT FROM AUTHOR]

Deep Neural Networks (DNNs) are increasingly being used in a variety of applications. However, DNNs have huge computational and memory requirements. One way to reduce these requirements is to sparsify DNNs by using smoothed LASSO (Least Absolute Shrinkage and Selection Operator) functions. In this paper, we show that irrespective of error profile, the sparsity values obtained using various smoothed LASSO functions are similar, provided the maximum error of these functions with respect to the LASSO function is the same. We also propose a layer-wise DNN pruning algorithm, where the layers are pruned based on their individual allocated accuracy loss budget, determined by estimates of the reduction in number of multiply-accumulate operations (in convolutional layers) and weights (in fully connected layers). Further, the structured LASSO variants in both convolutional and fully connected layers are explored within the smoothed LASSO framework and the tradeoffs involved are discussed. The efficacy of proposed algorithm in enhancing the sparsity within the allowed degradation in DNN accuracy and results obtained on structured LASSO variants are shown on MNIST, SVHN, CIFAR-10, and Imagenette datasets and on larger networks such as ResNet-50 and Mobilenet. [ABSTRACT FROM AUTHOR]

• A novel approach is discussed to incorporate fuzzy algorithm into a single board microcomputer based processor for intelligent relaying operation. • A fastest intelligent relaying algorithm reported by validating the same under IEEE 13 Bus test system with respect to performed simulation result analysis. • A proper validation of an idea authenticated based on distinguished result obtained from both the Fuzzy based as well as Non Fuzzy based systems. Protection and reliability enactment of electrical systems are important and emerging in power system research. Nowadays, it is very evident that the implementation of an intelligent algorithm is found in the field of substation equipment protection and relaying purposes. Majority of the researches are based on single load connected to a single feeder line and validated using simulation. A hardware based implementation and validation system will be an additional aspects. In this paper, we have discussed an expert system based intelligent relaying scheme by incorporating fuzzy algorithm in microcontroller. Purpose is to control the moving contacts of the breaker part for controlling multiple loads connected to a single feeder line. This paper reports the entire performance of intelligent relaying mechanism only considering stage - I with respect to non-fuzzy based relaying scheme and successfully achieved fastest coordination time after validating it under IEEE 13 Bus system. We have also validated the cascaded fuzzy based system and a non-fuzzy based system using ATMEL microcontroller. [ABSTRACT FROM AUTHOR]

In this paper, we address the distributed convex optimization problems for multi-agent systems. In our research, it is assumed that each agent in the multi-agent system could merely interact with its neighbors via a directed graph and is available to its own cost function. We then utilize the push-sum distributed dual averaging (PS-DDA) algorithm to tackle with the distributed optimization problem. However, we consider that there exist subgradient delays in PS-DDA algorithm. The proof of the main result which shows that the PS-DDA algorithm with subgradient delays converges and the error possesses sublinear growth of a rate O (τ 2 T − 0.5) , where T denotes the total amount of iterations, is detailed presented in this paper. Finally, a numerical example is simulated to show the performance of the algorithm we study in this paper. [ABSTRACT FROM AUTHOR]

The supersingular isogeny key encapsulation (SIKE) protocol, as one of the post-quantum protocol candidates, is widely regarded as the best alternative for curve-based cryptography. However, the long latency, caused by the serial large-degree isogeny computation which is dominated by modular multiplications, has made it less competitive than most popular post-quantum candidates. In this paper, we propose a high-speed and low-latency architecture for our recently presented optimized SIKE algorithm. Firstly, we design a new field arithmetic logic unit (FALU) with many algorithmic transformations and architectural optimizations. Especially, for the FALU, an extremely low-latency modular multiplier is devised based on a modified algorithm by fully parallelizing and highly optimizing the small-size multipliers and the reduction submodules. Secondly, we develop a compact control logic and update the instructions based on the benchmark provided in the newest SIKE library, fitting well with our design. Thirdly, an efficient memory access method is proposed by scheduling the input and output of the arithmetic logic unit (ALU) in two identical RAMs, which can significantly reduce the latency. Finally, we code the proposed architectures using the Verilog language and integrate them into the SIKE library. The implementation results on a Xilinx Virtex-7 FPGA show that for SIKEp751, our design only costs 9.3 ms with a frequency of 155.8 MHz, about 2× faster than the state-of-the-art, and achieves the best area efficiency among existing works. Particularly, the modular multiplier merely needs 16 clock cycles, reducing the delay by nearly one order of magnitude with a small factor of increase in hardware resource. [ABSTRACT FROM AUTHOR]

This paper presents a novel switching predefined-time parameter identification algorithm with a relaxed excitation condition based on the dynamic regressor extension and mixing (DREM) method. DREM often requires the persistent excitation (PE) of the extended square regressor's determinant to ensure exponential parameter convergence. Unlike the classical DREM method, a new parameter identification algorithm configured with a two-layer filter technique is proposed under a relaxed initial excitation (IE) condition, rather than strict PE. A key point in choosing IE instead of PE is the introduction of a smooth switching function that dominates the pure integral action and filter behavior of the extended square regressor. The proposed algorithm relies on the predefined-time stability theorem and the settling-time of the identification algorithm is set a priori as a system parameter. The contributions of this paper are a novel switching predefined-time parameter estimation algorithm that 1) relaxes the stringent PE condition, 2) achieves predefined-time convergence, and 3) guarantees the monotonicity of each element of the parameter error inherited from the classical DREM method. Comparative simulation results are presented to illustrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

• A new tent-multi dynamic piecewise coupled mapping lattice (TMDPCML) was proposed. • The private-information of face which is extracted by image recognition and non-private information are encrypted by different algorithms. • Using the strategy of scrambling and diffusion at the same time. • The security of information can be guaranteed by one-time encryption. • The simulation experiment of the proposed algorithm is good by testing in USC-SIPI "miscellaneous" image database. 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. [ABSTRACT FROM AUTHOR]

• A Gaussian propagation model is explored to improve Influence Maximization. • Multi-dimensional space modeling is constructed by offset, motif, and degree dimensions. • An improved CELF algorithm to accelerate the efficiency of the greedy algorithm. 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. [ABSTRACT FROM AUTHOR]