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• EKRS is an intelligent research assistance system to recommend knowledge to scholars. • EKRS is formed through mapping two sets of IR and CRD. • IR and CRD were reconstructed based on the VSM. • LRA improving the solution process and decreasing the complexity of the UKVSM. Expert Knowledge Recommendation System (EKRS) is an intelligent research assistance system. The system is formed by mapping two sets of conceptual spaces through Institutional Repository (IR) and Core Resource Dataset (CRD) in 2018. The user knowledge pattern matching (UKPM) of EKRS has problems such as uncertain user knowledge text matching, slow update of expert knowledge, and inability to accurately track user knowledge. This paper establishes a user knowledge vector space reconstruction model (UKVSM) through the following steps to solve the above problems. Firstly, the text feature items of IR and CRD are reconstructed and the depth and density correction coefficient matrix of the original node of the text semantic meaning is calculated based on the similarity of feature items of the semantic layer. Secondly, in order to improve the efficiency of UKPM exact matching, the Lagrangian relaxation algorithm (LRA) is used to optimize the two sets of knowledge matching strategies. Finally, the real data set is extracted from the EKRS platform, and the model and algorithm proposed in this paper are tested and verified respectively, and compared with other methods. Experiments show that reconstruction model can improve the accuracy of user knowledge task assignment in EKRS, while LRA can improve the efficiency of model solving. [ABSTRACT FROM AUTHOR]

This paper addresses the bearing-only distributed localization, which is more accurate and more reliable than traditional localization technologies. A new bearing-only distributed localization framework is proposed to solve localization problems for both static sensor networks and mobile multi-agent systems (MASs). Based on the complex coordinate representation, a novel bearing-only distributed localization algorithm for sensor networks is proposed. The algorithm combines the orientation estimation and the position estimation to solve the orientation alignment problem, which makes the compasses no longer needed for the localized networks. The localization for the mobile MASs is also studied and the corresponding localization algorithm is designed, which is more general and more challenging. The key to obtaining positions of moving agents, is the velocity estimator in the proposed localization algorithm which makes the estimated positions and velocities converge to the true value simultaneously. A distinctive advantage of the localization framework proposed in this paper is that the bearing-only localization algorithms can be applied to more general systems, such as sensor networks and MASs. Simulations and experiment are presented to verify the proposed algorithms. [ABSTRACT FROM AUTHOR]

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

In the era of big data, the data in many business scenarios are characterized by a small number of labelled samples and a large number of unlabelled samples. It is quite difficult to classify and identify such data and provide effective decision support for a business. A commonly employed processing method in this kind of data scenario is the disagreement-based semisupervised learning method, i.e., exchanging high-confidence samples among multiple models as pseudolabel samples to improve each model's classification performance. As such pseudolabel samples inevitably contain label noise, they may interfere with the subsequent model learning and damage the robustness of the ensemble model. To solve this problem, a semisupervised classification algorithm based on noise learning theory and a disagreement cotraining framework is proposed. In this model, first, the probably approximately correct (PAC) estimation theory under label noise conditions is applied, the relationship between the label noise level and model robust estimation in the process of multiround cotraining is discussed, and a disagreement elimination algorithm framework based on multiple-model (feature argument and select (FANS) algorithm and L1 penalized logistics regression (PLR) algorithm) cotraining is constructed based on this theoretical relationship. The experimental results show that the algorithm proposed in this paper gives not only a high-confidence sample set that meets the upper bound constraint of the label noise level but also a robust ensemble model capable of resisting sampling bias. The work performed in this paper provides a new research perspective for semisupervised learning theory based on disagreement. [ABSTRACT FROM AUTHOR]

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

In this paper, a new switching sequence convex optimization (SSCO) algorithm is proposed for solving non-convex optimization problem with complex time-varying relaxation matrix structures that arises during output feedback design. Firstly, the introduced time-varying relaxation matrix combines the membership functions and the designed switching mechanism to adjust the positive and negative terms of the inequality constraints. As a result, relaxed controller design conditions with complex matrix structures are established. The proposed SSCO algorithm employs switching optimization variables and inner approximation strategy, which is able to compute non-convex optimization problems with complex matrix structures more flexibly and converge quickly. It is worth noting that the implementation of the SSCO algorithm requires a set of strictly feasible initial solutions. Therefore, an initialization iterative algorithm is proposed, which overcomes the difficulties of transforming the solving problem into a typical non-convex optimization problem and linearizing multiple different concave parts, by which a set of optimized feasible solutions are obtained. Finally, simulation examples are used to demonstrate the superiority of the design scheme proposed in this paper. [ABSTRACT FROM AUTHOR]

In this paper, we are devoted to solving the problems of designing an estimator, and determining its estimator stability and estimation performance for a system with unknown measurement losses (UML). The solutions to these problems include three steps: first, we design two measurement-loss detectors to detect the measurement losses; Then, we design a detector-based estimator for UML systems. Finally, by analyzing the upper and lower bounds of the covariances of the proposed estimator, we establish a stability condition and determine the estimation performance. Detailed findings and main contributions of this paper are summarized as follows: (i) from the estimator stability perspective, we obtain a necessary and sufficient stability condition. Specifically, the estimator is stable almost surely if and only if the measurement-loss rate is less than a critical value. (ii) From the estimation performance perspective, we prove that under some conditions, the performance of the proposed estimator is almost surely the same as that of the optimal estimator for the system with known measurement losses. • A detector-based estimator is designed for systems with unknown measurement losses (UML). • A necessary and sufficient estimator stability condition is obtained for UML systems. • The proposed estimator has almost surely the same performance as the optimal estimator with known measurement losses. [ABSTRACT FROM AUTHOR]

• Classical interval arithmetic should not be used for modeling the uncertain shape. • Modification of directed interval arithmetic reduces overestimations. • The interval parametric integral equation system method is less time-consuming. • The correctness of the interval PIES solutions are proved. This paper presents a new method for simultaneous modeling the uncertainty of measurement data (necessary to define the boundary shape and boundary conditions) in boundary problems. The interval parametric integral equation system (interval PIES) was developed for solving boundary problems with input data defined in this way. The motivation for conducting this research was that this topic (simultaneous consideration of uncertainties of all input data) has appeared sporadically in the literature (mainly with uncertainly defined boundary conditions or other parameters). In this paper, the uncertainty was defined using interval numbers and modeled using interval arithmetic. The direct application of both classical and directed interval arithmetic caused the overestimation and obtained solutions were useless in practice. Therefore, modification of the directed interval arithmetic was developed. The reliability of the interval PIES solutions obtained using such arithmetic was verified on 2D problems described by Laplace's equation. The solutions were compared with the interval analytical solutions (differently obtained), as well as with the solutions of exactly defined (without the uncertainty) numerical methods. All performed tests indicated the high potential of the method. Obtained interval solutions occurred to be less overestimated and not as time-consuming as presented alternative methods. [ABSTRACT FROM AUTHOR]

• A lossy model for graph compression is presented that answers to neighborhood queries. • The output is a sparse graph with the same vertices as the original one. • Compressed graph is traversed with depth 2 to reconstruct the original graph. • An optimization problem is proposed to find the best compressed graph. • A heuristic is proposed to solve the optimization model with a low degree of complexity. 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. [ABSTRACT FROM AUTHOR]

Velocity planning serves as an important issue in motion planning for autonomous vehicles. The presented paper proposes a novel velocity planning method with minimum moving time on the basis of path curvature which is accomplished in three steps. First, the assigned path is divided into some elementary parts based on the path curvature. Second, the velocity planning is transformed into an unconstrained optimization problem by assuming the velocity of vehicle to be a specific cubic polynomial on every elementary part to avoid a sudden acceleration in path switching. Finally, we use a modified projection particle swarm optimization (PPSO) algorithm to obtain the time-optimal velocity profile. The proposed method can generate a smooth time-optimal velocity profile while considering all possible relevant constraints. Three examples are provided on different types of path to demonstrate that the final velocity profile is efficient to avoid the sudden acceleration change. Furthermore, the modified PPSO algorithm in this paper is used to solve the optimization problem with high dimensional variables when its upper bound is known, which can not be achieved by the general PPSO algorithm. [ABSTRACT FROM AUTHOR]

To address the challenge of facilitating overall problem solving and improving the overall efficiency of intelligent systems efforts through group decision making (GDM) by experts at all stages of the systems, this paper proposes an approach that focuses on improving consistency and enhancing local consensus. The proposed approach specifically considers the probabilistic linguistic preference relations (PLPRs) and incorporates personalized individual semantics (PISs) of decision makers (DMs). For the consistency procedure, we construct an expectation-additive consistency-driven semantic model to acquire PISs of different DMs. Secondly, the preference relation (PR) which does not satisfy the consistency is improved based on a minimum adjustment model. In particular, according to the expected value with PISs this paper, DMs offer PLPRs can be transformed into fuzzy preference relations (FPRs) correspondently for making decisions. For the FPRs, a virtue consensus measure is explored for the consensus reaching process (CRP) from two levels to combine the average value and variance of pair similarities, which effectively improves the accuracy of the consensus measurement. Next, a collective consensus level is calculated to replace the consensus threshold objectively, and then the DMs and alternative pairs that do not reach consensus are identified locally from two levels. Subsequently, an optimisation model that combines the two objectives is developed to improve the consensus level while ensuring consistency. Finally, our method is applied to a publicly available air quality dataset, and the consensus and ranking results are discussed in comparison with other advanced methods to confirm the superiority of the established method. [ABSTRACT FROM AUTHOR]

Multiattribute group decision-making (MAGDM) is a decision analysis method used to address decision-making problems that have multiple attributes and multiple decision alternatives. It is generally believed that multigranulation rough sets (MGRSs) are a twofold scheme to solve MAGDM problems. Thus, in this paper, we aim to generalize MGRSs and decision-theoretic rough sets (DTRSs) to intuitionistic fuzzy β -covering (IF β C) information systems and propose a novel MAGDM method to handle decision-making problems, which not only deepens the cognition of the three-way decision (3WD) model but also expands its application in decision-making problems. The paper first introduces a novel conditional probability using the ideal positive degree of intuitionistic fuzzy numbers (IFNs) and then investigates its properties. Second, in the IF β C environment, we utilize the proposed conditional probabilities to construct two types of multigranulation decision-theoretic rough set (MGDTRS) models, an optimistic strategy and a pessimistic strategy, discuss their theoretical properties and explore the relationship between the two strategies. Third, a novel MAGDM method for multi-intuitionistic β -neighborhoods is established in accordance with the proposed MGDTRS model. We also establish an algorithm for the proposed MAGDM method. Fourth, we apply the proposed method to address several MAGDM problems. By conducting comparative analysis with existing methods, we demonstrate the effectiveness of the proposed method and discuss the advantages and limitations of the proposed method. Furthermore, we conduct extensive parameter experimentation analysis on the proposed method from different perspectives. The experimental results demonstrate the stability and effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

In the realm of practical problem-solving, multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) are becoming increasingly prevalent. MOPRVIF involve determining the optimal decision variables that optimise multiple objectives, leveraging the relational data of a set of variables and multiple objectives. For these problems, this paper focuses on the following two issues: one is the demand for a unified computational model to solve this problem; the other is how to improve the algorithm's deep intelligent search capability. In this regard, this paper designs a dual data-driven multi-objective optimisation method. The method used consisted of four parts: elimination of redundant variables (ERV), objective function construction (OFC), selection evolution operator (SEO), and multi-objective evolutionary algorithm (MOEA). MOEA was the main focus of the method. ERV is data preparation and variable selection according to multiple objectives. OFC involves constructing the relationship model between variables and objectives, and a high-accuracy model is important for guaranteeing reliable results. Furthermore, SEO can adjust the evolution operator during a deep search. This is an important guarantee for deep, intelligent search. MOEA combined OFC and SEO to form the final solution algorithm—Dual Data Driven Multi-Objective Evolutionary Algorithm (DDMOEA). DDMOEA was explored using two different disciplinary problems of drug compound optimisation and wild blueberry cultivation and benchmarks were selected. The first two problem domains are distinct. The first problem is more complex than the second; however, both encompass redundant variables and indefinite objective functions. Benchmarks are utilised independently to gauge the profound intelligent search capability. The experiments affirm that the dual data-driven optimization approach proposed in this paper is effective, practical, and scalable. • Provide a unified solution for optimisation problems with redundant variables and indefinite objective functions. • Propose a data-driven multi-objective optimisation algorithm. • Designing an adaptive optimiser to enhance the algorithm's depth search capability. [ABSTRACT FROM AUTHOR]

Public bicycle sharing systems (PBSS) allow citizens to move between two points within a region using bicycles, being composed of stations where users can either pick up or return bicycles after using them. Depending on the demand for bicycles, stations may either become empty or full of bicycles. This problem is usually solved by means of a vehicle that exchanges bicycles among the stations following some balancing strategy. In this paper, we propose a model for PBSSs using generalized stochastic Petri nets with external control, a Petri net with an external agent that decides how to execute the balancing strategy. The main advantage of our model is that it allows different balancing strategies to be implemented without changing the Petri net structure. In addition, since the model is a Petri net, we can easily implement it by making a Petri net simulator built with the incidence matrix associated with the Petri net model. We also propose two balancing strategies and validate both the simulation algorithm and the balancing strategies by applying them to the performance evaluations of the model of a real system, named Integra UFRJ PBSS. The performance evaluation of the system under the balacing strategies have shown that both strategies considerably reduce the times, in average, that stations remain full or empty throughout the system operation, solving problems related to stations being empty of full; in particular, the first strategy reduced those times in such a way that no more than five percent of the users of each station had trouble returning their bicycles due to stations being full, whereas the second strategy reduced the percentage of lost users due to stations being empty to no more than five percent for each station. [ABSTRACT FROM AUTHOR]

Unsupervised domain adaptation is an important branch of transfer learning, which mainly solves the generalization problem of models from fully labeled datasets to unlabeled datasets. Unsupervised domain adaptation mainly aims to make full use of domain features, suppress inter domain distribution shift, and improve the domain generalization ability of the model. In this paper, we propose a two-branch symmetric domain adaptation neural network. In each independent domain adaptation branch, we use the method of adversarial learning to realize the embedding and alignment of domain features, and use a new generalized autoencoder model based on Ulam stability theory to enhance the feature extraction ability of the network. A fusion network is adapted to fuse the features extracted from each branch of the feature subspace to further improve the domain adaptability and generalization ability of the model. Experimental results show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The traditional output tracking problem for a system is to design a controller such that the output of the system tracks a constant signal or a network after a finite time. However, when the output of the system can not completely track the reference signal, there is no result to show how to deal with this situation. To solve such problem, we investigate the optimal output tracking problem of Boolean control networks (BCNs) with a constant reference signal in this paper, that is, to design an appropriate control scheme such that each state will evolve to their optimal output tracking trajectory. Firstly, the optimal output tracking cycle (OTC) of each state is defined. Secondly, a method is presented to compute the cycle with certain length in the system, and the least upper bound of the length for the optimal OTC is derived. Finally, a constructive algorithm is presented to design the state feedback controllers that enable the system to realize optimal output tracking. Moreover, an illustrative example is given to show the obtained results. [ABSTRACT FROM AUTHOR]

This paper proposes an active learning (AL) algorithm to solve regression problems based on inverse-distance weighting functions for selecting the feature vectors to query. The algorithm has the following features: (i) supports both pool-based and population-based sampling; (ii) is not tailored to a particular class of predictors; (iii) can handle known and unknown constraints on the queryable feature vectors; and (iv) can run either sequentially, or in batch mode, depending on how often the predictor is retrained. The potentials of the method are shown in numerical tests on illustrative synthetic problems and real-world datasets. An implementation of the algorithm, that we call IDEAL (Inverse-Distance based Exploration for Active Learning), is available at http://cse.lab.imtlucca.it/bemporad/ideal. [ABSTRACT FROM AUTHOR]

• We propose a novel information completion strategy for GNNs to learn low-degree node representation better. • We realize the information completion strategy as the general extension of GNNs in an end-to-end manner, named AIC-GNN, with rich experimental results. • We propose novel dual node embedding alignment mechanisms as supplements for AIC-GNN, which could effectively facilitate simulating the node missing information distribution. Graph neural networks (GNNs) have attracted extensive attention due to their demonstrated powerful performance in various graph mining tasks. The implicit assumption of GNNs being able to work is that all nodes have adequate information for meaningful aggregation. However, this is not easy to satisfy because the degrees of a real-world graph commonly follow the power-law distribution, where most nodes belong to low-degree nodes with limited neighborhoods. In this paper, we argue that to make GNNs better handle the low-degree node representation learning is the key to solving the above problem and propose a pluggable framework named Adversarial Information Completion Graph Neural Networks (AIC-GNN). It introduces a novel Graph Information Generator to fit adaptively the node missing information distribution. Then, the Graph Embedding Discriminator distinguishes between the node embeddings with the ideal information and the node embeddings after information completion. The representational capacity of the model is enhanced by adversarial training between the Generator and Discriminator. Meanwhile, the dual node embedding alignment mechanisms are employed to guide the high quality of predicted information. Extensive experiments demonstrate that AIC-GNN outperforms state-of-the-art methods on four real-world graphs. [ABSTRACT FROM AUTHOR]

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

For imbalanced binary classification, most methods classify data in the original space by balancing data or modifying classification algorithms. However, for datasets with severe overlaps and complex class boundaries, the classification performance is limited by the ability of the classifier to fit the decision boundary. A practical alternative is to map the data to a latent space with lower classification difficulty. In this paper, we propose an imbalanced binary classification method via space mapping using normalizing flows with class discrepancy constraints. The flow-based model is employed to map the original data to a latent space, ensuring the accuracy of the mapping and the authenticity of the mapped distribution. To solve the problem of overlaps and within-class imbalance, class discrepancy constraints, including global and local constraints, are proposed to modify the flow-based model. The former maps different subclusters to the same cluster of the latent space, and the latter increases the separability of different classes. Classification thus can be carried out in the latent space with simpler distribution and high separability to achieve better classification performance. Experimental results on 35 KEEL and UCI public datasets indicate that the proposed method outperforms other state-of-the-art methods on F1-measure and G-mean. [ABSTRACT FROM AUTHOR]

Partition-based clustering methods, such as point center-based and plane center-based clustering techniques, have drawn much attention due to their simplicity and effectiveness in general clustering tasks. However, most of these methods use an unbounded distance during clustering, which may cause their performance to be sensitive to the defined infinite measure, and almost all of them cannot automatically identify halos. To solve these problems, by adopting a locally finite capped l 2 , 1 -norm distance in clustering, this paper proposes a novel clustering method named locally finite distance clustering with discriminative information (LFDC). The LFDC effectively solves the above problems and realizes robust clustering by solving a series of eigenvalue problems. We test the effectiveness of the LFDC on a number of different data, including artificial data, benchmark data, and image segmentation data. The experimental results show that the LFDC is more robust than the compared methods. [ABSTRACT FROM AUTHOR]

With the ubiquitous of GPS-equipped devices, spatial crowdsourcing (SC) technology has been widely utilized in our daily life. As a novel computing paradigm, it hires mobile users as workers who physically move to the location of the task and perform the task. Task assignment is a fundamental issue in SC. In real life, there are many complex tasks requiring different workers, among which the quality of worker cooperation and the price satisfaction of users should not be ignored. Hence, this paper examines a satisfaction-aware task assignment (SATA) problem with the goal of maximizing overall user satisfaction, where the user satisfaction integrates the satisfaction towards price and cooperation quality. The SATA problem has been proved to be NP-hard by reducing it from the k -set packing problem. In addition, two algorithms, namely, conflict-aware greedy (CAG) algorithm and game theoretic (GT) algorithm with an optimization strategy, are proposed for solving the SATA problem. The CAG algorithm can efficiently obtain a result with provable approximate bound, while the GT algorithm is proven to be convergent which can find a Nash equilibrium. Extensive experiments have demonstrated the effectiveness and efficiency of our proposed approaches on both real and synthetic datasets. [ABSTRACT FROM AUTHOR]

A Boolean function in n variables is 2-rotation symmetric if it is invariant under even powers of ρ (x 1 , ... , x n) = (x 2 , ... , x n , x 1) , but not under the first power (ordinary rotation symmetry); we call such a function a 2-function. A 2-function is called monomial rotation symmetric (MRS) if it is generated by applying powers of ρ 2 to a single monomial. If the quartic MRS 2-function in 2 n variables has a monomial x 1 x q x r x s , then we use the notation 2 - (1 , q , r , s) 2 n for the function. A detailed theory of equivalence of quartic MRS 2-functions in 2 n variables was given in a 2020 paper by Cusick, Cheon and Dougan. This theory divides naturally into two classes, called mf 1 and mf 2 in the paper. Next to describing the equivalence classes, the second major problem is giving details of the linear recursions that the Hamming weights for any sequence of functions 2 - (1 , q , r , s) 2 n (with q < r < s , say), n = s , s + 1 , ... can be shown to satisfy. This problem was solved for the mf 1 case only in the 2020 paper. In this paper the problem for the mf 2 case is solved, using new ideas about short functions, as defined in this paper. These short functions are also of independent interest, and further results about them are in the paper. [ABSTRACT FROM AUTHOR]

Advertising using the word-of-mouth effect is quite effective in promoting products. In the last decade, there has been intensive research studying the influence maximization problem in marketing. The problem of influence maximization aims to identify a small group of people in the social network as seeds such that eventually, they will trigger the largest influence spread or product adoption in the network. In practical scenarios of online marketing, it is common that there are competitions among similar products in the network and the promotion is targeted at specific groups of users. For instance, an event organizer disseminates an event ad on a social platform hoping to attract attention of the most number of local residents. Meanwhile, there are multiple competing events being promoted on the social platform. In this paper, we formulate such problem as Targeted Influence Maximization in Competitive social networks (TIMC). To model the influence diffusion, we combine the target nodes and competitive relationships into an independent cascade model. We propose a Reverse Reachable set-based Greedy (RRG) algorithm to solve the TIMC problem and theoretically proved its approximation ratio. We also design a pruning strategy to further speed up the performance of the proposed algorithm. Extensive experiments have confirmed the efficiency of the proposed RRG algorithm. We also find that the algorithm works particularly well for sparse large networks with strong competition. [ABSTRACT FROM AUTHOR]

• A succinct competitive model on social networks is constructed. • A cost-effective competition (CEC) problem on social networks is proposed. • A multi-objective optimization approach (MOCEC) is developed to address the CEC problem. • Theoretical analysis and experimental results demonstrate the effectiveness of MOCEC. The competition on social networks has attracted widespread attention in recent years. Despite the great progress in this domain, acquiring more votes for competitors with fewer recruiting costs is still a research blank. This paper addresses this issue from three aspects: modeling, proposition, and methodology. First, a succinct competitive model is built to clear the final beliefs of normal agents. Then, a cost-effective competition (CEC) problem is proposed by formulating the number of votes and the recruiting costs as two optimization objectives. Seeing the cost constraints, a theoretical cost budget is also determined. Next, a multi-objective optimization approach (MOCEC) with theoretical guarantees is developed to solve the CEC problem. In the experiments, MOCEC is compared with seven prevalent competitive methods on ten real-world social networks. We show that MOCEC can achieve the best trade-offs between maximizing the number of votes with minimizing the recruiting costs in all test environments. Especially when the number of votes reaches the maximum value, MOCEC can save up to 50% of the recruiting costs than the best baseline. [ABSTRACT FROM AUTHOR]

• Our method maintains the flexibility of interval arithmetic and interval reasoning as major objectives where FIs are represented by their profiles, viewed as gradual numbers. • Our method is based on interval relations to determine a generic formulation of the intersection operation between type-2 fuzzy intervals (FIs). • The proposed method is applied to a risk decision-making problem. • Our method offers a convenient tool, which enables decision-makers to manage their judgment in the possible uncertain domain of decision. • The Bellman-Zadeh decision-making problem using membership functions can be transformed into an interval arithmetic problem using the FI profiles. This paper proposes an interval-based computational formulation of the Bellman-Zadeh decision-making approach when the handled information (goals and constraints) is represented by type-2 fuzzy intervals (FIs). Our method, which maintains the flexibility of interval arithmetic and interval reasoning as major objectives, consists of representing an FI by its profiles, which are considered gradual numbers. The developed reflection is based on interval relations to determine a generic formulation of the intersection operation between type-2 FIs, where a computational mechanism can be easily derived. This intersection area is considered an uncertain decision domain that is represented by lower type-1 FI situations and upper type-1 FI bounds that are considered extreme situations in adverse situations and favorable situations, respectively. In this framework, any FI between these FI bounds can be chosen by decision makers as an optimal solution according to a specified decision criterion. In this paper, a risk decision-making criterion is considered; however, other decision criteria can be employed in a similar manner. The proposed vision offers a convenient tool that enables decision makers to manage their judgment in the possible uncertain domain of a decision. The interest of the proposed approach is the extension of inter-interval relations to type-1 and type-2 FIs, where the Bellman-Zadeh decision-making problem using membership functions can be transformed into an interval arithmetic problem using the FI profiles. [ABSTRACT FROM AUTHOR]

• This paper is to find the truth Z of events in social sensing with information flows. • A truth discovery model is presented which considers the individuals' reliability R. • The model also considers the dependency D between individuals' observations. • An iterative expectation maximization is presented to infer both Z, R and D. • Extensive experiments demonstrate the effectiveness of the presented algorithm. Social sensing relies on a large number of observations reported by different, possibly unreliable, agents to determine if an event has occurred or not. In this paper, we consider the truth discovery problem in social sensing, in which an agent may receive another agent's observation (known as an information flow), and may change its observation to match the observation it receives. If an agent's observation is influenced by another agent, we say that the former is a dependent agent. We propose an Iterative Expectation Maximization algorithm for Truth Discovery (IEMTD) in social sensing with dependent agents. Compared with other popular truth discovery approaches, which assume either the agents' observations are independent, or their dependency is known a priori, IEMTD allows to infer each agent's reliability, the observations' dependency and the events' truth jointly. Simulation results on synthetic data and three real world data sets demonstrate that in almost all our experiments, IEMTD achieves a higher truth discovery accuracy than the existing algorithms when dependencies exist between agents' observations. [ABSTRACT FROM AUTHOR]

• The novel transformation and comparison methods for MGPLZNs are developed. • DEA is employed to solve the normalized priority weight vectors for MGPLZNPRs. • Expert credibility that reflects the certainty, ambivalence, and hesitancy of experts is defined. • The subjectively determined expert weights are adjusted according to the expert credibility. • Two case examples are used to illustrate the validity of the proposed method. Multi-granularity probabilistic linguistic Z-number (PLZN) is an effective and flexible tool for expressing expert opinions. For group decision-making (GDM) problems with multi-granularity PLZN preference relations (PLZNPRs), this paper proposes a GDM method based on data envelopment analysis cross-evaluation (DEA-CE) model and expert credibility. First, as the foundation for solving GDM problems with multi-granularity PLZNPRs, this paper reveals the inadequacies of existing methods on transformation and comparison between multi-granularity PLZNs through arithmetic examples. After analyzing these inadequacies, this paper proposes the novel transformation and comparison methods with better performances. Second, to obtain the priorities of multi-granularity PLZNPRs more efficiently, DEA-CE model is introduced and the effectiveness of introducing DEA-CE model is demonstrated. The superiority of introducing DEA-CE model is that it requires no further adjustment of preference relations at any consistency level, which helps to maximize the retention of original information. Third, the subjective expert weights determined based on realistic situations are objectively adjusted using the expert credibility calculated according to the information quality, making the results more reasonable. Finally, this paper illustrates by two case examples that the proposed model can be well adapted to GDM problems with all sizes, regardless of the numbers of decision variables and experts. [ABSTRACT FROM AUTHOR]

This paper solves the problem about the practical fixed-time consensus for heterogeneous nonlinear multiagent systems (MASs) with one-order dynamic models. First, the event trigger functions are introduced to reduce the communication burden of the network. And the adaptive laws are present which aim to deal with the instability caused by the uncertain parameters. By combining them, the control input is designed to make the system practical fixed-time consensus. Then we prove the boundedness of the variables and there is no Zeno phenomenon in the discussed systems under the control scheme. Comparing with the existing works, this paper focus on the distribute uncertain heterogeneous nonlinear MASs, has a practical fixed-time consensus and takes a balance between communication burden and accuracy. Finally, a comparison experiment is illustrated. [ABSTRACT FROM AUTHOR]

Finding the longest common subsequence of multiple sequences (i.e., the MLCS problem) is a key issue in data mining, such as pattern recognition of bio-sequences (e.g., DNA and protein sequences), diagnosis of diseases, and deciphering human genetic codes. The length of the longest common subsequence is often used to measure the similarity between sequences. Current state-of-the-art algorithms for the MLCS problem often transform the problem into finding the longest path in a directed acyclic graph (DAG); however, their fatal flaw is that the time cost or constructed DAG is too large. Aiming at solving the problem of the existing algorithms, this paper proposes a time-efficient upper bound estimation method that can estimate the upper bound for the length of any path through a node in much less time, and then proposes an adaptive lower bound estimation strategy that can estimate the more precise lower bound for the length of the longest common subsequences. This study also describes a distributed storage scheme that stores most parts of the DAG in external devices and only a few parts in memory. A comparison with several state-of-the-art algorithms in the experiments indicates that the proposed algorithm is more efficient and effective. [ABSTRACT FROM AUTHOR]

• Prior Knowledge Encoding. We propose a novel knowledge-aware prompt-tuning into verbalizer for biomedical relation extraction that the rich semantic knowledge to solve the problem, which simultaneously transfers entity-node-level and relation-link-level structures across graphs. • Efficient Prompt Design. To the best of our knowledge, this is the first work to successfully perform the knowledge jointly optimizing the representation of a learnable words and learnable relation words in the biomedical graphs. • SOTA Results. BioKnowPrompt outperforms baseline models in five relation extraction benchmark datasets in the biomedical domain, showing its potential to serve as an effective general-purpose representation learning algorithm for biomedical graph data. Domain tuning pre-trained language models (PLMs) with task-specific prompts have achieved great success in different domains. By using cloze-style language prompts to stimulate the versatile knowledge of PLMs, which directly bridges the gap between pre-training tasks and various downstream tasks. Large unlabelled corpora in the biomedical domain have been created in the last decade(i.e., PubMed, PMC, MIMIC, and ScienceDirect). In this paper, we introduce BioKnowPrompt, a prompt-tuning PLMs model that has been incorporating imprecise knowledge into verbalizer for biomedical text relation extraction. In particular, we use learnable words and learnable relation words to infuse entity and relation information into quick creation, and we use biomedical domain knowledge constraints to synergistically improve their representation. By using additional prompts to fine-tune PLMs, we can further stimulate the rich knowledge distributed in PLMs to better serve downstream tasks such as relation extraction. BioKnowPrompt has a lot of significant potential in few-shot learning, which outperforms the previous models and achieves state-of-the-art on the 5 datasets. [ABSTRACT FROM AUTHOR]

Federated learning is a privacy-preserving machine learning paradigm that can train a model with decentralized data. Classical federated learning systems are vulnerable to attacks from malicious clients. Although a number of efforts have been made to improve robustness of federated learning, existing solutions are either too costly or attack-specific. In this paper, we propose the Robust Ternary Gradients Aggregation (RTGA) algorithm, which can efficiently handle different attacks using two novel mechanisms. At the client-side, the ternary quantization mechanism compresses gradients using only two bits to store each coordinate of the gradient vector. We adopt the error-feedback mechanism to compensate for the difference between the compressed and actual gradients. At the server-side, the robust aggregation mechanism is designed to detect malicious clients according to the risk score of each client. The risk score is calculated by solving an optimization problem of the divergence among clients. Furthermore, we provide analysis of the compression and robustness properties of our proposed algorithm. Extensive experiments show that, compared with existing algorithms, RTGA can mitigate different types of attacks while costing much less communications. [ABSTRACT FROM AUTHOR]

• The notion of heterogeneous diversified attribute multi-decision systems is presented. • A composite binary relation is defined based on attribute semantics. • Four kinds of variable precision multi-decision multi-granulation rough sets are established. • The established models are applied to medical diagnosis. • The feasibility, effectiveness, and superiority of the established models are verified. Rough set theory has become an effective tool to address uncertain decision-making problems. Nevertheless, existing rough set-based decision-making methods cannot effectively deal with the management decision-making problems with heterogeneity, multi-decision, and diversified attributes simultaneously. Given this, the purpose of this paper is to explore an effective classification solution for solving decision-making problems with the above-described characteristics from the perspective of granular computing. First, we present the notion of heterogeneous diversified attribute multi-decision systems. Because attribute semantics is crucial to multi-attribute decision analysis, we then divide an attribute set into nine subsets according to attribute semantics. Further, a composite binary relation that fits the realistic attribute features is constructed. Subsequently, considering different decision scenarios, we establish four kinds of variable precision multi-decision multi-granulation rough set models. Meanwhile, the related properties and relationships of these models are explored. We find that the established models are extensions of the existing classical rough sets. In the end, we apply the established models to an illustrative example of medical diagnosis. The results of the case and comparative analyses demonstrate the models' feasibility, effectiveness, and superiority. As a result, our work provides a kind of new decision thinking with reasonable semantic analysis for solving complex management decision-making problems in reality. [ABSTRACT FROM AUTHOR]

In this paper, a distributed algorithm based on the alternating direction method of multipliers is proposed to solve an unconstrained consensus optimization problem. The network topology among the agents is assumed to be connected and self-loops of each agent are considered. This algorithm takes less time than sequential algorithms because the optimization variables of the agents can be updated synchronously. In addition, it is shown that the algorithm is actually a Jacobi-proximal ADMM algorithm and the requirement of the proximal matrix is relaxed. A convergence analysis of the algorithm is provided, and its convergence rate is found to be O (1 / k). Finally, the characteristics of the proposed algorithm are illustrated by numerical experiments, and comparisons are made with other ADMM algorithms in solving a least-squares problem. [ABSTRACT FROM AUTHOR]

The maximal flow problems (MFPs) are among the most significant optimization problems in network flow theory with widespread and diverse applications. To represent qualitative aspects of uncertainty in the maximal flow model, which asks for the largest amount of flow transported from one vertex to another, the use of linguistic variables has effective means for experts in expressing their views. In this paper, we first define trapezoidal Pythagorean fuzzy numbers (TrPFNs) along with some new arithmetic operations which cover the gaps in previously defined operations. For defuzzification of TrPFNs, we introduce a ranking procedure based on value and ambiguity indices. This work puts forward a theoretical framework for a new Pythagorean fuzzy maximal flow algorithm (PFMFA), which helps to solve different optimization problems with PF information by considering linguistic capacities and flows. The implementation of the algorithm is elaborated by considering two case studies. Firstly, we examine the maximum flow of a water distribution pipeline network in Pyigyitagon Township, Mandalay, Myanmar. Secondly, we compute maximum PF power flow in a 14-bus electricity network provided by the IEEE working group, concerning the example data from the University of Washington. The results illustrate the superiority of the proposed method and give a detailed analysis of flow connected with several practical performances. In addition, the Pythagorean fuzzy optimal flows corresponding to each network arc are compared and performance comparison of our method is investigated which shows the increasing and decreasing trends of backward and forward arcs of the network, respectively. Moreover, the runtime analysis of existing well-known maximal flow algorithms is provided. Finally, we present the advantages of our technique to promote its cogency. [ABSTRACT FROM AUTHOR]

Partial Label Learning (PLL) is a weakly supervised learning framework where each training instance is associated with more than one candidate label. However, label ambiguity in the case of label imbalance has not been well solved. In this paper, a novel label imbalance-aware algorithm named PL-LIMAP is proposed to effectively solve this problem. Firstly, a partial manifold constrained low-rank representation method is proposed to learn the instance affinity matrix, which can identify instances with the same label by fully mining the manifold structure of the data. Moreover, for making full use of the information hidden in candidate labels, the Jaccard distance is introduced to measure the similarity between candidate labels to obtain the label affinity matrix. The combination of these two affinity matrices can achieve the purpose of disambiguation through complementary information. Meanwhile, aiming to determine the imbalance of ambiguous labels, specialized measures are proposed to evaluate the imbalance level in PLL. According to the imbalance level of different labels, an alternative weighting scheme is proposed to mitigate the imbalance of ambiguous labels. As a consequence, the ground-truth label of an instance can be found out even though its label information is not available in advance. Experiments reveal the overwhelming performances of PL-LIMAP. [ABSTRACT FROM AUTHOR]

Nowadays, organizations are seeking professionals who can both excel in their areas of expertise and collaborate effectively across different disciplines. This demand has given rise to the concept of T-shaped experts who possess a deep understanding of one topic domain and a broad knowledge of several others. This combination allows these professionals to be more creative, flexible, and adaptable in problem-solving by leveraging their diverse perspectives and experiences. To find T-shaped experts in any skill area, we need to measure how deep and wide their knowledge is. In this paper, we present a novel translation-based method to estimate each user's depth of knowledge in a given skill area. The proposed method leverages a self-attention-based multi-label classification network to identify the most relevant translations for each skill that belongs to the given skill area. We utilize two new methods based on binary cross-entropy and focal loss to determine whether a user's expertise shape matches the T-shaped. We evaluate the proposed method using the standard benchmark datasets. The experimental results on three collections of the StackOverflow dataset demonstrate the superiority of the proposed methods in comparison with existing baselines. [ABSTRACT FROM AUTHOR]

For existing imbalanced ensemble (IE) methods, the sample subsets are constructed from the same dataset, which usually suffer from low quality (diversity and separability) of the subsets, so a manifold neighboring envelope sample generation mechanism (MNESG) and an imbalanced ensemble algorithm based on the mechanism (MNESG-IE) are proposed to solve this problem. First, for the original balanced subsets (OBS), a manifold neighboring sample envelope projection mechanism (MNSEP) is designed to mine the local correlation information between the samples and their nearest neighbors in the subsets. Second, the fuzzy c-means clustering (FCM) is used to further mine the global correlation information among similar samples in the subsets. Third, the sample distribution consistency preservation mechanism (SDCPM) is designed to enhance the consistency of the sample distribution before and after clustering. To better reduce the three accumulated losses above, the three steps are conducted simultaneously, thereby realizing the MNESG, which can transform the OBS into two new types of high quality envelope sample subsets − neighboring envelope sample (NES) subsets and neighboring cluster envelope sample (NCES) subsets. Finally, base classifiers are trained on the NES subsets and NCES subsets, and then fused by a two-dimensional sparse fusion mechanism (2D-SFM). Various representative IE algorithms on over thirty benchmark datasets are considered for verification. The results show that compared with the state-of-the-art IE algorithms, MNESG-IE achieves 17.79%, 17.90%, 23.61%, 18.08% improvement in terms of ACC, AUC, F-M and G-M, respectively. The major originality of the paper is: (a) proposing the MNSEP to mine the local correlation information for improving the quality of the subsets; (b) proposing the MNESG to generate high quality subsets by mining local and global correlation information simultaneously; and (c)forming an IE algorithm to better solve the imbalanced classification problem. [ABSTRACT FROM AUTHOR]

Multi-agent systems are widely used in practice. The existing results on the consensus of multi-agent systems mainly focus on the complete consensus, which can not deal with the situation of the system in the face of multiple tasks. In this paper, the multi-group consensus problem of multi-agent systems with semi-Markov jump topologies against hybrid cyber-attacks is studied. The purpose is to ensure that the multi-agent systems can achieve different consensus goals when they encounter hybrid cyber-attacks, and to solve the problem of time-varying communication topology between agents modeled by the semi-Markov process. Additionally, two types of cyber-attacks are taken into account in a single framework through two independent sets of Bernoulli sequences. Subsequently, a necessary condition is established to guide the system toward achieving the multi-group consensus objective. Lastly, a series of illustrative examples are presented to underscore the validity of the designed controller. [ABSTRACT FROM AUTHOR]

The LPN problem, lying at the core of many cryptographic constructions for lightweight and post-quantum cryptography, has recently received much attention. Accordingly, BKW, Gauss algorithm, and their improved or hybrid variants have been proposed to solve the LPN problem. In this paper, we propose an intelligent method for LPN solving from a wholly new perspective by iterative correcting the secret, which can successfully reduce the dimension of the LPN problem. While the challenge is that there are so many independent secret variables of the given checking parity equations with noise in LPN problems, a set of rules should be developed to effectively direct and correct the secret. To solve this problem, we skillfully introduce the genetic algorithm to simulate the process of iterative correction and further add vaccination technology to guide and speed up the iterative process. Owing to the small memory and data consumption of our algorithm, we conducted experiments and, for the first time, solved the largest practical LPN (256,1/8) instance in 30 days, which shows the superiority of our method. To the best of our knowledge, this is the first time the iterative correction method and the intelligent algorithm have been successfully applied to LPN problems. [ABSTRACT FROM AUTHOR]

The metaheuristic method is effective in solving complex optimization problems. Among these methods, the Salp Swarm algorithm (SSA), inspired by the sailing and foraging behavior of the deep-sea salps population, has proven to be an effective method. However, in practice, SSA is prone to the problems of reduced population diversity and falling into local minima. In order to solve this problem, this paper combines several new techniques and introduces an improved version of SSA called Genetic Salp Swarm Algorithm (GSSA). Specifically, GSSA generates an initial population using a chaotic dyad-based learning method, reduces the dimensionality by a population partitioning technique and performs crossover and mutation operations on the reduced subspace, and finally acts on the optimal solution through three mutation operators to prevent the algorithm from stagnating in the local optimum. This novel GSSA algorithm is tested on 23 benchmark function test sets, CEC2017 and CEC2022. The results show that the GSSA algorithm converges faster and has higher accuracy. [ABSTRACT FROM AUTHOR]

• Propose a rough integrated asymmetric cloud (RIAC) model. • Put forward a new AC score function, distance, and similarity. • Expert weights are measured from the perspective of cooperative game. • Establish a trust propagation model through the social network and RIAC. • Comparison analysis is performed to validate the superiority of our approach. Large group decision-making often contains strong uncertainty and randomness due to the complexity of decision-making problems. Moreover, existing methods about large group decision-making usually assume that the relationships among decision-makers are mutually independent, which will neglect the relevance of decision information to some extent. To address these limitations, this paper proposes a novel rough integrated asymmetric cloud model to tackle large group decision-making under multi-granularity linguistic environment. This model can not only flexibly portray decision-makers' preferences, but also objectively handle the uncertainty and randomness in large group decision-making through the relevance of preferences. Firstly, a trust propagation model based on rough integrated asymmetric cloud is raised to consider the impact of uncertainty and randomness in the trust propagation on large group decision-making. Secondly, a new asymmetric cloud distance and similarity are proposed to overcome some defects of existing methods. Thirdly, a decision-maker weight calculation method based on the Shapley function is advanced to enhance the decision-making reasonableness. Next, an investment problem is solved by the proposed method. Finally, the effectiveness and superiority of the proposed method are demonstrated by the sensitivity and comparison analyses. [ABSTRACT FROM AUTHOR]

Solving nonlinear equation systems (NESs) is one of the most important tasks in numerical computing. The NESs usually have multiple roots, and quickly locating their roots in a single algorithm run with a limited number of iterations has always been the algorithm improvement direction. In order to further enhance the efficiency of the existing methods, a density clustering-based differential evolution algorithm (DCDE) for the NESs problem solving is proposed in this paper. Firstly, density-based spatial clustering of applications with noise (DBSCAN) is used to divide the population into clusters and noises, which provides a better direction for population evolution. Secondly, a cluster evaluation factor is proposed, which not only divides the clusters into excellent clusters and non-excellent clusters, but also prevents the migration of excellent clusters and maintain the diversity of populations. Then, a migration strategy and individual generation mechanism are proposed to guide non-excellent clusters to migrate to promising regions. Finally, combined with an archive technique, the individuals which satisfy the solution requirements are archived and randomly initialized to further improve population diversity. To verify the effectiveness of the proposed algorithm, comparative experimental results of the propsoed DCDE with other state-of-the-art algorithms for thirty NESs problems solving show that the proposed DCDE is the most effective algorithm to locate more roots in a single run. [ABSTRACT FROM AUTHOR]

In clustering process, fuzzy partition performs better than hard partition when the boundaries between clusters are vague. Whereas, traditional fuzzy clustering algorithms produce less interpretable results, limiting their application in security, privacy, and ethics fields. To that end, this paper proposes an interpretable fuzzy clustering algorithm—fuzzy decision tree-based clustering which combines the flexibility of fuzzy partition with the interpretability of the decision tree. We constructed an unsupervised multi-way fuzzy decision tree to achieve the interpretability of clustering, in which each cluster is determined by one or several paths from the root to leaf nodes. The proposed algorithm comprises three main modules: feature and cutting point-selection, node fuzzy splitting, and cluster merging. The first two modules are repeated to generate an initial unsupervised decision tree, and the final module is designed to combine similar leaf nodes to form the final compact clustering model. Our algorithm optimizes an internal clustering validation metric to automatically determine the number of clusters without their initial positions. The synthetic and benchmark datasets were used to test the performance of the proposed algorithm. Furthermore, we provided two examples demonstrating its interest in solving practical problems. [ABSTRACT FROM AUTHOR]

Graph convolution networks (GCNs) have achieved remarkable success in processing non-Euclidean data. GCNs update the feature representations of each sample by aggregating the structure information from K -order (layer) neighborhood samples. Existing GCNs variants rely heavily on the K -th layer semantic information with K -order neighborhood information aggregating. However, semantic features from different convolution layers have distinct sample attributes. The single-layer semantic feature is only a one-sided feature representation. Besides, the semantic features of traditional GCNs will be oversmoothing with multi-layer structure information aggregates. In this paper, to solve the above-mentioned problem, we propose adaptive graph convolutional collaboration networks (AGCCNs) for the semi-supervised classification task. AGCCNs can fully use the different scales of discrimination information contained in the different convolutional layers. Specifically, AGCCNs utilize the attention mechanism to learn the relevance (contribution) coefficient of the deep semantic features from different convolution layers for the task, which aims to effectively discriminant their importance. After multiple optimizations, AGCCNs can adaptively learn the robust deep semantic features via the effective semantic fusion process between multi-layer semantic information. Compared with GCNs that only utilize the K -th layer semantic features, AGCCNs make the learned deep semantic features contain richer and more robust semantic information. What is more, our proposed AGCCNs can aggregate the appropriate K -order neighborhood information for each sample, which can relieve the oversmoothing issue of traditional GCNs and better generalize shallow GCNs to more deep layers. Abundant experimental results on several popular datasets demonstrate the superiority of our proposed AGCCNs compared with traditional GCNs. [ABSTRACT FROM AUTHOR]

Decision models based on value function can help learn the preferences of decision makers using historical data. However, inconsistency of the preferences learned from data with the real preferences of decision makers may occur. Suppose decision data or scenarios in the target domain are insufficient to guarantee the consistency of learning preferences, and there exists one similar source domain with sufficient effective data. Then, knowledge from the source domain can help learn preferences in the target domain. Following this idea, this paper proposes a cross-domain decision making method using a parameter transfer strategy with homogeneous and heterogeneous criteria. A decision model based on the value function is constructed using a generalized additive model to ensure the interpretability of the model. The marginal value functions are adjusted using Sturm's theorem to keep the monotonicity of the learned marginal value functions on the criteria. Then, the parameter transfer strategy is adopted to transfer the obtained value functions to help learn the real preferences of the decision maker for the decision problem where preferences in historical data may not be consistent. The effectiveness of the proposed method is validated by solving the problem of diagnosing breast lesions in a hospital. [ABSTRACT FROM AUTHOR]

Positive and unlabeled learning (PU learning) is a kind of problem whose goal is learning a two-classes classifier with little proportion of positive samples and numerous unlabeled samples. A series of studies focus on how to extract most likely negative samples from the unlabeled samples, and then train a classifier with the labeled samples as supervised learning. Previous PU learning methods always ignore the additional information called privileged information, which is just provided during the training process while unavailable during testing. In this paper, we propose a novel self-paced algorithm for PU learning with privileged information (SPUPI). The proposed SPUPI extracts some reliable negative samples from unlabeled samples at first, and then generates weights for the unlabeled samples according to the similarity with each class. After that, it builds a more accurate classifier based on privileged information and similarity weights by self-paced learning. By taking the self-paced learning into training, we can build the model with a few labeled samples from easy to complex. We also solve the problem by transforming the primal problem of the proposed model into its dual problem and achieving the PU classifier. Various experiments on the practical datasets indicate that the SPUPI has a better performance compared with previous methods. [ABSTRACT FROM AUTHOR]

[Display omitted] • A two-phase optimization system for solving classification problems. • Combining Auto-ML pipelines to improve overall performance. • Intelligent selection of ensemble methods. • Best results delivered by double-fault measure and 20 or 50 maximum number of base models. Automatic Machine Learning (Auto-ML) tools enable the automatic solution of real-world problems through machine learning techniques. These tools tend to be more time consuming than standard machine learning libraries, therefore, exploiting all the available resources to the full is a valuable feature. This paper presents a two-phase optimization system for solving classification problems. The system is designed to produce more robust classifiers by exploiting the different architectures that are generated while solving classification problems with Auto-ML tools, particularly AutoGOAL. In the first phase, the system follows a probabilistic strategy to find the best combination of algorithms and hyperparameters to generate a collection of base models according to certain diversity criteria; and in the second, it follows similar Auto-ML strategies to ensemble those models. The HAHA 2019 challenge corpus and the Adult dataset were used to evaluate the system. The experimental results show that: i) a better solution can be built by ensembling a subset of the already tested models; ii) the performance of ensemble methods depends on the collection of base models used; and, iii) ensuring diversity using the double-fault measure produces better results than the disagreement measure. The source code is available online for the research community. [ABSTRACT FROM AUTHOR]

Multi-attribute decision-making (MADM) aims to rank alternatives based on their attributes and evaluation information, which provides decision support for decision-makers. Most existing MADM methods can only obtain ranking results, decision-makers usually need to subjectively choose priority alternatives to make a decision based on the preset evaluation level and ranking results, which does not meet the requirements of complex decision situations and uncertain information processing. A method is urgently needed, which can objectively produce classification results and automatically provide priority objects for decisions. Three-way decisions (3WDs) offer an effective research technique to solve decision-making problems under uncertainty and risk by objectively dividing a universal set into three pairwise disjoint parts: acceptance, non-commitment and rejection, and implementing the corresponding strategy for each part. Considering that it is difficult to accurately determine the exact value of attributes in some cases, interval numbers can be a useful concept to flexibly describe uncertain information and satisfy the decision-maker's cognition. Therefore, under an interval-valued MADM environment, this paper proposes an evaluation-based interval-valued multi-attribute three-way decision (IVMA3WD) model from an optimization viewpoint. The model achieves an effective fusion of 3WDs and MADM problems, and further expands evaluation-based 3WD models on the totally ordered set. Specifically speaking, in order to depict uncertain information in decision-making, interval numbers are used to describe the evaluation information included in the multi-attribute evaluation matrix. Then, considering a general case that there are no decision attributes in the multi-attribute evaluation matrix, we determine the evaluation function by use of the idea of the interval-valued TOPSIS method. In addition, unlike the fixed-valued loss functions in the majority of 3WD models, the relative loss function in this article is not provided straightly but computed by attribute evaluation values expressed by interval numbers. Furthermore, based on an optimization viewpoint, we construct two optimization models and attempt to find the optimal solution to obtain the threshold pair. A housing market investment decision problem is utilized as an illustrative example to find out the applicability of our built IVMA3WD model. In the end, we adopt a sensitivity analysis and a comparative analysis to demonstrate our proposed model's characteristics and advantages. [ABSTRACT FROM AUTHOR]

Recognizing the subjectivity in human judgment and the limits to human cognition, fuzzy linguistic variables and uncertain linguistic variables are widely used to express the preference information of the decision experts in Multi-Attribute Group Decision Making (MAGDM). This paper develops an MAGDM method to solve a decision problem in which the attribute values are fuzzy numbers characterized by uncertain linguistic variables, that is, dual uncertain Z -numbers. The notion of the dual uncertain Z -number is first proposed based on the classical Z -number. Next, the cloud model is introduced, together with a new notion of the dual comprehensive cloud. A method of transforming the dual uncertain Z -numbers into dual comprehensive clouds is proposed. Third, a new dual comprehensive cloud-weighted averaging operator (DCC-WA) is presented to aggregate the multiple dual comprehensive clouds. Fourth, a new dual comprehensive cloud-grey relational degree (DCC-GRD) is supplied, and a novel MAGDM method based on the DCC-GRD is proposed. Finally, a group decision making case of sustainable supplier selection is provided to validate the proposed method. A sensitivity analysis and comparison with several congeneric methods are conducted. [ABSTRACT FROM AUTHOR]