查看更多>>摘要：Since the intuitionistic fuzzy set theory was proposed, it has received widespread attention. To get a deeper understand of this field, this paper analyzes a total of 2913 papers downloaded from the Web of Science (WoS) from 1984 to 2019 from different perspectives. On the one hand, this research identifies important themes and their interrelationships, showing the thematic evolution of this field vividly. On the other hand, this research digs out the knowledge diffusion paths of this domain with the help of global and key-route main paths. From the perspective of the thematic evolution, five thematic area are detected including Intuitionistic fuzzy relation, IVIFS-Similarity, MCDM/MADM-GDM, Aggregation operator and Extensions of IFS. Among them, MCDM/MADM-GDM has evolved from an emerging theme to a motor theme being the most important topic and GDM integrating themes MADM.MCDM and Aggregation operator shows continuous growth. From the perspective of knowledge diffusion paths, research topic has gradually translated from theorical construction to practical application, and several factors such as self-citation, indirect citation, hierarchy and references that may influence the result of the main path are discussed. In general, this research systematically provides scholars with the development process in this domain, which is conductive to them to fully grasp the state-of-the-art research. (C) 2022 Published by Elsevier Inc.
查看更多>>摘要：Recommendation systems are often challenged by the existence of cold-start items for which no previous rating is available. The standard content-based or collaborative-filtering recommendation approaches may address this problem by asking users to share their data with a central (cloud-based) server, which uses machine learning to predict appropriate ratings on such items. But users may be reluctant to have their (confidential) data shared. Federated learning has been lately capitalized on to address the privacy concerns by enabling an on-device distributed training of a single machine learning model. In this work, we propose a federated learning-based approach to address the item cold-start problem in recommendation systems. The originality of our solution compared to existing federated learning-based solutions comes from (1) applying federated learning specifically to the cold-start problem; (2) proposing a trust mechanism to derive trust scores for the potential recommenders, followed by a double deep Q learning scheduling approach that relies on the trust and energy levels of the recommenders to select the best candidates. Simulations on the MovieLens 1M and Epinions datasets suggest that our solution improves the accuracy of recommending cold-start items and reduces the RMSE, MAE and running time compared to five benchmark approaches. (C) 2022 Elsevier Inc. All rights reserved.
Kumar, ChintooChowdary, C. RavindranathShukla, Deepika
17页
查看更多>>摘要：Recommender systems provide personalized content from various choices by mining users' past preferences. Recommendation helps to overcome the information overload problem as the available alternatives consume a large amount of data. In some applications, a group of people are involved in the process of generating a recommendation. This paper focuses on "automatically detected groups" formation for order and flexible preferences in group recommendation using locality-sensitive hashing. The MinHash technique is applied on a characteristic matrix to generate the signature matrix. The signature matrix is the reduced representation of the characteristic matrix and preserves the Jaccard similarity to a great extent. Locality-sensitive hashing is applied on the signature matrix to determine similar users efficiently. Similar users will be the members of an automatically identified group. Therefore, the group members are maximally satisfied with recommended items. This work also studies the performance of benchmark clustering approaches in group formation. We experimented on real-world datasets and found that the proposed models to identify communities in group recommendation maximizes consensus among users in a group. (C) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：In view of the high propagation space and the complex networking of rumors, this paper proposes a group behavior prediction model based on sparse representation and interaction of complex messages. Firstly, to solve the difficulty in model training caused by the high dimension and complexity of rumor space, sparse representation is considered as the theoretical basis to construct sparse vectors for user node features, and to construct the node feature prediction submodel. Secondly, aiming at the dynamic interactive behavior among complex messages in the rumor space, the driving force of complex messages is quantified with the evolutionary game, the dynamic rumor propagation network is reconstructed, and the structure attribute prediction submodel is constructed. Finally, considering the advantages of model fusion in improving the generalization ability of the single model, the node feature prediction submodel-Submodel Based on SRC and the structure attribute prediction submodel-Submodel Based on Node2Vec are fused. Meanwhile, a dynamic group behavior prediction model under the influence of complex messages is constructed for the time-sensitive nature of rumor propagation. The experimental results show that the model not only effectively explores the interaction between complex messages but also accurately predicts the group behavior and depicts the rules of rumor propagation. (C) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：Collaborative filtering (CF) is the most successful approach of Recommender Systems that has been applied in a wide range of applications. In this approach, historical rating data is exploited to calculate similarity between pairs of users and predict user preference over unseen items. The similarity is calculated using users' global preferences estimated from their previous interactions with items. To this end, commonly rated items by a given pair of users are utilized to calculate the similarity. In a sparse dataset, there are limited corated items. In addition, inconsistent behaviours of users in rating items may add some noise to data, which makes the ratings more untrustworthy leading to less accuraterecommendations. In this manuscript, we introduce an individual ratings confidence measure (IRC) to calculate the confidence of a given rating to each item by the target user. IRC consists of 5 factors that help to have more information about the interest and boredom of users. User ratings are denoised by prioritizing extreme and high-confidence ratings, and finally, the denoised ratings are used to obtain similarity values. This approach leads to a more accurate neighbourhood selection and rating prediction. We show the superiority of the proposed method when compared with state-of-the-art recommender algorithms over some well-known benchmark datasets. (C) 2022 Published by Elsevier Inc.
查看更多>>摘要：Deep learning technologies have achieved remarkable success in various tasks, ranging from computer vision, object detection to natural language processing. Unfortunately, state-of-the-art deep learning technologies are vulnerable to adversarial examples and backdoor attacks, where an adversary destroys the model's integrity. The obstacles have urged intensive research on improving the ability of deep learning technologies to resist integrity attacks. However, existing defense methods are either incomplete (i.e., only a single attack can be detected) or expensive computing resources. It requires the defense method to have universal property, which can effectively and efficiently detect multiple integrity attacks. To this end, we propose a similarity-based integrity protection method for deep learning systems (IPDLS), which is provided with the universal property. IPDLS realizes anomaly detection by measuring the similarity between suspicious samples and samples in a preset verification set. We empirically evaluate IPDLS on the MNIST and CIFAR10 datasets. Experimental results have verified the effectiveness of IPDLS, which can detect adversarial examples and backdoor attacks simultaneously. (C) 2022 Elsevier Inc. All rights reserved.
Bartesaghi, PaoloClemente, Gian PaoloGrassi, Rosanna
19页
查看更多>>摘要：Big data and the use of advanced technologies are relevant topics in the financial market. In this context, complex networks became extremely useful in describing the structure of complex financial systems. In particular, the time evolution property of the stock markets have been described by temporal networks. However, these approaches fail to consider the interactions over time between assets. To overcome this drawback, financial markets can be described by multiplex networks where the different relations between nodes can be conveniently expressed structuring the network through different layers. To catch this kind of interconnections we provide new local clustering coefficients for multiplex networks, looking at the network from different perspectives depending on the node position, as well as a global clustering coefficient for the whole network. We also prove that all the wellknown expressions for clustering coefficients existing in the literature, suitably extended to the multiplex framework, may be unified into our proposal. By means of an application to the multiplex temporal financial network, based on the returns of the S&P100 assets, we show that the proposed measures prove to be effective in describing dependencies between assets over time. (C) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：In the open-world environment, the incremental updating approaches to attribute reduction based on rough sets are efficient and effective to evaluate and search an optimal subset of attributes from two-dimensionally time-evolving data, which can be interpreted as the complex changes of dynamic data, i.e., four types of combinations induced by the insertion/deletion of objects and the addition/remove of attributes. To avoid the time-consuming and repetitive computation from scratch in such dynamic data, this paper mainly focuses on constructing a unified incremental framework to attribute reduction by the matrix-based accelerated updating strategies. We systematically discuss and present a series of incremental updating mechanisms and algorithms of approximation quality in the neighborhood-based probabilistic rough sets. Besides, a unified framework of dynamic attribute reduction in four situations of changes is proposed to develop the performance of updating reduct. Finally, we report the comparative experiments between the nonincremental and incremental algorithms of reduct to demonstrate the feasibility and efficiency of proposed approaches. (C) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：In this paper, we propose the advanced intuitionistic fuzzy Heronian mean (AIFHM) aggregation operator (AO) and the advanced intuitionistic fuzzy weighted Heronian mean (AIFWHM) AO of intuitonistic fuzzy values (IFVs). The proposed AIFHM AO and the proposed AIFWHM AO of IFVs have the advantage of condidering interrelationships among aggregating inputs. We also explore some properties of the proposed AIFHM AO and the proposed AIFWHM AO of IFVs. Furthermore, based on the proposed AIFWHM AO of IFVs, we propose a new group decision making (GDM) method. We also provide some examples to illustrate that the proposed GDM method can overcome the drawbacks of the existing GDM methods. The proposed GDM method offers us a very useful approach to deal with GDM problems in intuitionistic fuzzy environments. (c) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：Complex multi-dimensional integrals are widely used in various engineering problems. This paper proposes a novel numerical integration method based on stochastic configuration networks (SCNs), which is constructed by learning the integrand function. A corresponding primitive function based on a simple functional expression of the trained SCN can be analytically derived, and a general functional relation between the integrand and the primitive function is established based on SCN parameters. By repeatedly applying the derived functional relations, we can successfully evaluate many complex multidimensional integrals. The SCN-based numerical integral method provides a powerful tool for solving complex multi-dimensional integrals. Effectiveness of the proposed method in terms of both computational accuracy and stability is demonstrated through numerical experiments.(c) 2022 Elsevier Inc. All rights reserved.
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