查看更多>>摘要：It is widely acknowledged that the initial spreaders play an important role in the spread of information in complex networks. Thus, a variety of centrality-based methods have been proposed for identifying the most influential spreaders. However, most existing studies overlook the fact that, in real social networks, it is more costly and difficult to convince influential individuals to act as initial spreaders, resulting in a high risk to maximal spreading. In this paper, we address this problem on the basis of the assumption that the activation of large-degree nodes carries a higher risk than that of small-degree nodes. We aim to identify the initial spreaders that most effectively maximize the spreading when considering both the activation risk and the outbreak size of the initial spreaders. Analysis of random networks reveals that the degree of the optimal initial spreaders does not correspond to the largest node degree in the network, but is instead determined by the infection probability and difference in activation risk among nodes with different degrees. We propose a risk-aware metric to identify the most effective spreaders in real networks. Numerical simulations show that this risk-aware metric outperforms the existing benchmark centralities in terms of maximizing the spreading. (c) 2021 Elsevier Inc. All rights reserved.
查看更多>>摘要：Cross-validation (CV), while being extensively used for model selection, may have three major weaknesses. The regular 10-fold CV, for instance, is often unstable in its choice of the best model among the candidates. Secondly, the CV outcome of singling out one candidate based on the total prediction errors over the different folds does not convey any sensible information on how much one can trust the apparent winner. Lastly, when only one data splitting ratio is considered, regardless of its choice, it may work very poorly for some situations. In this work, to address these shortcomings, we propose a new averaging-voting based version of cross-validation for better comparison results. Simulations and real data are used to illustrate the superiority of the new approach over traditional CV methods. (c) 2021 Elsevier Inc. All rights reserved.
查看更多>>摘要：As a concept put forward by Prof Zadeh, Z-numbers have become a research hotspot in fuzzy theory. Different from the previous fuzzy sets, Z-numbers possess a stronger ability in expressing uncertainty because of the unique structure. The chief purpose of this paper is to research linguistic uncertain Z-numbers with a rectangular coordinate system. Taking into account the shortcomings of previous studies, the rectangular coordinate system is firstly adopted to address linguistic Z-numbers. Based on the new expression, arithmetic operations are defined. After summarizing the drawbacks of the previous aggregation oper-ators, a novel approach named linguistic uncertain Z-numbers weighted averaging aggre-gation operator based on the rectangular coordinate system (LUZWAAORCS) is defined. Subsequently, the Minkowski distance measure of linguistic uncertain Z-numbers is pro-posed and the rationality is proven by a theorem. Follow that, a score function considering the Minkowski distance measure and technique for order preference by similarity to an ideal solution (TOPSIS) is suggested to quantify the information in different Z-numbers. Besides, an innovative Cosine similarity is defined to measure the similarity. Simultaneously, several examples are used to describe the proposed innovations. As far as our latest knowledge is concerned, Z-numbers have never been researched with a rect-angular coordinate system, so this may be another door to process Z-number-based information. (c) 2021 Elsevier Inc. All rights reserved.
查看更多>>摘要：In this paper, we propose adversarial predictive coding (APC), a novel method for detecting abnormal events. Abnormal event detection (AED) is to identify unobserved events from a given training dataset consisting of normal events, and it is considered as one of the most important objectives in developing intelligent surveillance systems. Given videos and motion flows of normal events, the APC derives a normal event model by applying an adversarial prediction approach on the jointly learnt latent feature space from the videos and motion flows. Since latent space requires more abstracted and noise-free information than the raw data space, the APC can derive a more discriminative model for normal events compared with other deep learning-based AED methods which directly apply uni-modal losses such as mean square error and cross-entropy to low-level data such as video frames. We demonstrate the effectiveness of our method in detecting abnormal events using UCSD-Ped, Avenue, and UCF-Crime datasets. The experimental results show that the APC surpass the existing state-of-the-art AED methods by deriving a more discriminative model for normal events. (c) 2021 Published by Elsevier Inc.
查看更多>>摘要：The loan evaluation is a fundamental task in peer-to-peer (P2P) lending. Effective loan eval-uation can help lenders make informed investment decisions. Existing methods do not con-sider the return of loans in the core learning stage and thus fail to explore the relationship between the return of loans and their final loan payoff outcomes. In this study, we propose a systematic loan evaluation framework called COst Sensitive Loan Evaluation (COSLE). Specifically, we first develop an instance-aware misclassification cost (IMCO) matrix, which specifies personalized cost for each loan. Then, we present a differential labelling algorithm called DILA cost for assigning node labels and assessing the corresponding cost. By integrating these enhancements into the tree-induction process, we construct a node splitting measurement called COG index. It exploits the relationship between the return information and the final payoff outcome. Additionally, we design the LER evaluation met-ric to measure the ability of a loan evaluation model to increase the lender & rsquo;s return. Finally, the COSLE is used to improve popular tree models. Extensive experiments based on the Lending Club dataset show that our COSLE can effectively increase the lender & rsquo;s return. (c) 2021 Elsevier Inc. All rights reserved.
查看更多>>摘要：Assisting end users to identify desired results from a large dataset is an important problem for multi-criteria decision making. To address this problem, top -k and skyline queries have been widely adopted, but they both have inherent drawbacks, i.e., the user either has to provide a specific utility function or faces many results. The k-regret minimization query is proposed, which integrates the merits of top -k and skyline queries. Due to the NP hardness of the problem, the k-regret minimization query is time consuming and the greedy framework is widely adopted. However, formal theoretical analysis of the greedy approaches for the quality of the returned results is still lacking. In this paper, we first fill this gap by conducting a nontrivial theoretical analysis of the approximation ratio of the returned results. To speed up query processing, a sampling-based method, STOCPRESGREED, is developed to reduce the evaluation cost. In addition, a theoretical analysis of the required sample size is conducted to bound the quality of the returned results. Finally, comprehensive experiments are conducted on both real and synthetic datasets to demonstrate the efficiency and effectiveness of the proposed methods. (c) 2021 Elsevier Inc. All rights reserved. Superscript/Subscript Available</comment
查看更多>>摘要：With the widespread application of smart devices, spatial crowdsourcing (SC) has been extensively integrated into daily life. Task assignment is a crucial issue in SC and has attracted much attention. Most prior studies on task assignment ignore the importance of dependency among tasks, resulting in some ineffective matching pairs and wasting workers' time. To this end, we formulate a new problem in SC, abbreviated as multi-stage complex task assignment (MSCTA), which aims to assign workers to multi-stage complex tasks to maximize the total profit. Compared with existing studies, MSCTA can obtain more effective assignments by considering the dependency constraints among tasks. We prove that the MSCTA problem is NP-hard and propose a greedy algorithm and a game algorithm. Specifically, both algorithms iteratively utilize a filtering module to obtain a set of executable tasks (ET) for assignment. The greedy algorithm can quickly assign the most profitable workers to the subtasks in each round of ET, and obtain a prov-able approximate result. The game algorithm is proved to be convergent and can win a Nash equilibrium when processing the subtasks in each round of ET. Extensive experimen-tal results demonstrate the efficiency of our algorithm. (c) 2021 Elsevier Inc. All rights reserved.
查看更多>>摘要：In recent years, the study of linguistic steganalysis has been focused on altering network structure, such as replacing the basic neural units or increasing the model depth, which inevitably increases computational overhead and restricts further improvement in resource-constrained scenarios. In this paper, instead of relying on complex neural networks, we propose an alternative linguistic steganalysis framework based on hierarchical mutual learning to achieve higher detection accuracy with less inference time and model size. In the proposed method, networks with either identical or different structures are trained cooperatively to learn distinct text features from each other. To this end, in addition to the supervised learning loss function, we construct three mimicry loss functions at different feature extraction stages, which can integrate steganalytic features from various levels. Finally, we illustrate how the steganalysis framework can be extended from two networks to multiple networks. Four representative steganalysis networks with different structures are employed to verify the effectiveness of our framework. The experimental results show that the proposed framework can effectively assist networks with fewer resources to perform better in model size, inference time, and detection accuracy than state-of-the-art steganalysis algorithms. (c) 2021 Elsevier Inc. All rights reserved.
查看更多>>摘要：Cloud service selection (CSS) remains a strategically significant decision and has a substantial impact on an organization & rsquo;s competitive edge. Despite considerable research, the literature lacks a comprehensive unified approach to consensual CSS. Recognizing the significance of CSS decisions, in this paper, we propose a novel Cloud Service Scrutinization and Selection Framework (C3SF) that includes four interdependent phases: (1) requirements elicitation, (2) scrutinization, (3) evaluation, and (4) ranking & selection. As part of C3SF, we use conjunctive screening to scrutinize cloud services. We also propose a novel multi-criteria decision-making (MCDM) approach called the modified Best-Worst Method (MBWM), which computes the weights of criteria using early-stage consensus among decision-makers. In addition, we introduce an innovative two-step consensus process for ranking services using leading MCDM methods followed by an aggregation of ranks using a Markov chain-based approach. Moreover, to develop a broader consensus, we propose another two-stage novel mechanism comprising multi-aggregation and synthesis/fusion of rank information using a partially ordered set. We validate the performance and effectiveness of C3SF through a CSS case study using real-world data followed by a comprehensive analysis. The results show that C3SF is robust, practical, and suitable for wellinformed decision-making. (c) 2021 Elsevier Inc. All rights reserved.
查看更多>>摘要：Particle Swarm Optimization(PSO) is a well-known and powerful meta-heuristic algorithm in Swarm Intelligence (SI), and it was invented by simulating the foraging behavior of bird flock in 1995. Recently, many different PSO variants were proposed to tackle different optimization applications, however, the overall performance of these variants were not satisfactory. In this paper, a new PSO variant is advanced to tackle single-objective numerical optimization, and there are three contributions mentioned in the paper: First, a sorted particle swarm with hybrid paradigms is proposed to improve the optimization performance; Second, novel adaptation schemes both for the ratio of each paradigm and the constriction coefficients are proposed during the iteration; Third, a fully-informed search scheme based on the global optimum in each generation is proposed which helps the algorithm to jump out the local optimum and improve the overall performance. A large test suite containing benchmarks from CEC2013, CEC2014 and CEC2017 test suites on real-parameter single objective optimization is employed in the algorithm validation, and the experiment results show the competitiveness of our algorithm with the famous or recently proposed state-ofthe-art PSO variants. (c) 2021 Elsevier Inc. All rights reserved.
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