查看更多>>摘要：Human personality plays a crucial role in decision-making and it has paramount importance when individuals negotiate with each other to reach a common group decision. Such situations are conceivable, for instance, when a group of individuals want to watch a movie together. It is well known that people influence each other's decisions, the more assertive a person is, the more influence they will have on the final decision. In order to obtain a more realistic group recommendation system (GRS), we need to accommodate the assertiveness of the different group members' personalities. Although pairwise preferences are long-established in group decision-making (GDM), they have received very little attention in the recommendation systems community. Driven by the advantages of pairwise preferences on ratings in the recommendation systems domain, we have further pursued this approach in this paper, however we have done so for GRS. We have devised a three-stage approach to GRS in which we 1) resort to three binary matrix factorization methods, 2) develop an influence graph that includes assertiveness and cooperativeness as personality traits, and 3) apply an opinion dynamics model in order to reach consensus. We have shown that the final opinion is related to the stationary distribution of a Markov chain associated with the influence graph. Our experimental results demonstrate that our approach results in high precision and fairness. (C) 2022 The Authors. Published by Elsevier Inc.
查看更多>>摘要：The exaggerated use of smartphones and growing informatization of the environment allows modeling people's behavior as a process, namely, a social workflow, where both individual actions and interactions with other people are captured. This modelling includes actions that are part of an individual's routine, as well as less frequent events. Although infrequent actions may provide relevant information, it is routine behaviors that characterize users. However, the extraction of this knowledge is not simple. Current process mining techniques face problems when analyzing large amounts of traces generated by many users. When very different behavioral patterns are integrated, the resulting social workflow does not clearly depict their behavior, either individually or as a group. Proposals based on frequent pattern mining aim to distinguish traces that characterize frequent behaviors from the rest. However, tools that allow grouping/filtering of users with a common behavior pattern are needed beforehand, to analyze each of these groups separately. This study presents the so-called federated process mining and an associated tool, SOWCompact, based on this concept. Its potential is validated through the case study called activities of daily living (ADL). Using federated process mining, along with current process mining techniques, more compact processes using only the social workflow's most relevant information are obtained, while allowing (event enabling) the analysis of these social workflows. (C) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：This paper is devoted to the study of intermittent H-infinity control problem for discrete-time switched interval type-2 fuzzy systems (DSIT2FSs) under minimum dwell time switching. A novel virtual-clock-dependent H-infinity intermittent interval type-2 (IT2) fuzzy controller design scheme is proposed. Firstly, we transform the N-mode DSIT2FS with intermittent control inputs into a new one with N controller-busy modes and N controller-free modes. Then, by constructing a class of non-increasing virtual-clock-dependent multiple quadratic Lyapunov functions (VMQLFs), sufficient conditions on the existence of the H-infinity intermittent IT2 fuzzy controllers are obtained for DSIT2FSs with dwell time constraints. The controller gains can be gotten by solving a set of strict linear matrix inequalities (LMIs). The obtained results are also applicable to non-switched IT2 fuzzy systems. It is shown that the designed IT2 fuzzy controllers work intermittently but still can guarantee stability and H 1 performance of the close-loop systems. Finally, a tunnel diode circuit system model and a single link robot arm model are provided to illustrate the effectiveness and advantages of the results. (C) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：The secret sharing (SS) scheme has been widely used as the de facto paradigm for group key management in cryptography and distributed computation. An SS scheme for a general access structure (GSS) has drawn more and more attention since it allows flexible access control. However, previous solutions to GSS need to either assign multiple shares to each participant or to solve a complex nonlinear integer programming. In this paper, we propose a novel strategy to address the two problems simultaneously based on the Chinese Remainder Theorem (CRT) for a polynomial ring over a finite field. We classify general access structures satisfying the monotone property into the two families of maximum forbidden subsets and minimum qualified subsets conforming to the security condition and the revealing condition. The moduli used in the scheme are pairwise coprime polynomials and can take the irreducible polynomials. To find the degrees of these polynomials, we only need to solve an integer linear programming (ILP) by minimizing the sum of the degrees of all the moduli. The proposed scheme is inherently a weighted SS scheme and may have no solution which commonly exists in all GSS schemes. To ensure a solution, we put forth a preprocessing algorithm which separates the original access structure into several subaccess structures based on graph theory. The proposed scheme only assigns a share for each participant and achieves perfect security. It has good generalization and provides a universal approach to program the scheme construction for SS schemes with general access structures, threshold access structures and weighted access structures. (C) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：In recent years, class imbalance learning (CIL) has become an important branch of machine learning. The Synthetic Minority Oversampling TEchnique (SMOTE) is considered to be a benchmark algorithm among CIL techniques. Although the SMOTE algorithm performs well on the vast majority of class-imbalance tasks, it also has the inherent drawback of noise propagation. Many SMOTE-variants have been proposed to address this problem. Generally, the improved solutions conduct a hybrid sampling procedure, i.e., carrying out an undersampling process after SMOTE to remove noises. However, owing to the complexity of data distribution, it is sometimes difficult to accurately identify real instances of noise, resulting in low modeling quality. In this paper, we propose a more robust and universal SMOTE hybrid variant algorithm named SMOTE-reverse k-nearest neighbors (SMOTE-RkNN). The proposed algorithm identifies noise based on probability density but not local neighborhood information. Specifically, the probability density information of each instance is provided by RkNN, a well-known KNN variant. Noisy instances are found and deleted according to their relevant probability density. In experiments on 46 classimbalanced data sets, SMOTE-RkNN showed promising results in comparison with several popular SMOTE hybrid variant algorithms. (C) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：Breast cancer is a malignant tumor that seriously threatens women's health. Although classic multi-attribute decision-making (MADM) techniques can handle this kind of medical problem, a decision-maker (DM) can only collect sample data under various indicators for result ranking and analysis. The three-way decision (3WD) theory further supplements a classification scheme. In addition, a DM's own limited rationality and personality traits have a strong impact on decision-making results. By using a dominance-based rough set approach (DRSA), a new 3WD method based on the regret theory (RT) with optimistic, neutral and pessimistic strategies (3WD-RT-OEP) is constructed in a fuzzy environment, so as to prevent diseases in advance and improve the survival rate of patients. First, attribute weights are calculated based on the similarity between the defined attributes of dominance classes, and a new method for calculating conditional probabilities is proposed to enhance the objectivity of the method by the similarity between object dominance classes and decision classes. Second, the score functions of three strategies are proposed by considering regret and rejoicing values in RT, and specific steps and algorithms of the 3WDRT-OEP method are given as well. Finally, the validity and rationality of the constructed method are proved by experimental analysis with the support of case studies and supplementary data sets. (C) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：Multilayer networks are used to encode multiple types of relations arising in complex systems and have received significant attention in recent years. Community detection in multilayer networks is an important issue in various fields; hence, stochastic block models have emerged as a popular probabilistic framework over the past decades. However, stochastic block models are suited to binary networks rather than weighted networks. A generalised stochastic block model is proposed herein to address multilayer sparse or dense weighted networks. A variational expectation-maximisation algorithm is derived to estimate the parameters of interest. In addition, an upper bound is derived for the probability of misclassification, which is governed by the Renyi divergence of order 12. Furthermore, our model is compared with four competing methods on synthetic networks. Finally, our approach is examined on financial markets and bicycle sharing systems. (C) 2021 Elsevier Inc. All rights reserved.
查看更多>>摘要：Reversible data hiding (RDH) in JPEG images aims at correctly extracting hidden data and correctly recovering the cover image. Existing methods for RDH, however, may not attain a good balance between good visual quality and small file size increment of the embedded image. To address the problem, we design a new RDH scheme by minimizing the additive distortion in JPEG images. Our method firstly designs different additive functions to calculate DCT block cost and DCT frequency cost, and then combines these two cost functions to minimize the additive distortion for a given embedding capacity. After obtaining the optimal embedding positions for each DCT block, we employ an improved two-dimensional histogram shifting strategy for data embedding, which significantly reduces the number of invalid DCT coefficient modifications. Extensive experiments demonstrate that our method outperforms existing JPEG RDH methods with better visual quality and smaller file size increment of the marked image. (C) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：This study investigates the deterministic learning and control issues for uncertain sampled-data nonlinear systems (SDNSs). The problem of how to acquire/learn knowledge from adaptive control for SDNSs with uncertain affine terms is studied. To be specific, an appropriate neural network-based (NNB) control strategy is first presented to ensure tracking performance. To further realize learning, the exponential stability (ES) of the integrated closed-loop system coupled with the estimation error of the NN weights is considered. As the uncertain affine term prevents learning from occurring, the integrated system is converted into a discrete linear time-varying (DLTV) perturbed system by employing the state conversion technique. Tracking convergence allows the persistent excitation condition (PEC) of the NNs to be established, which guarantees the ES of the integrated DLTV system. Thus, accurate modeling of closed-loop sampled dynamics is obtained. By reutilizing the experiential knowledge obtained, a knowledge-based controller is constructed for high-performance control. Finally, simulations are performed to verify the presented strategy. (C) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：Because a knowledge graph's huge amount of item information can help recommender systems develop user/item representations, it has become the most important source of side information. Regardless of the numerous types of user/item representation approaches used in knowledge graph-based recommendation scenarios, they all have problems. In this paper, we propose a knowledge graph-based multi-context-aware recommendation algorithm for learning user/item representations that combines the advantages of path-based and propagation-based methods. A new concept (i.e., rule) is proposed first, which can be a useful way to characterize the user's preferences. Next, based on user-item interactions, an automatic rule discovery algorithm is proposed that can automatically select the most representative user preferences templates in a given recommendation scenario based on the knowledge graph and user behaviors. Then, the learning of high-order connectivity between long-distance user-item pairs is realized according to these templates. After that, a feature representation method of the local neighborhood characteristics of users and items is introduced to compensate for the defect that the path-based method can only catch the high-order connectivity. The experimental results demonstrate MANN's superiority over eight state-of-the-art baselines. (C) 2022 Elsevier Inc. All rights reserved.
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