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#1Yaliang ZhaoH-Index: 2
#2Samwel K. Tarus (HUST: Huazhong University of Science and Technology)
Last.Jinke Wang (Henan University)
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Abstract Clustering technique plays a critical role in data mining, and has received great success to solve application problems like community analysis, image retrieval, personalized recommendation, activity prediction, etc. This paper first reviews the traditional clustering and the emerging multiple clustering methods, respectively. Although the existing methods have superior performance on some small or certain datasets, they fall short when clustering is performed on CPSS big data because o...
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Abstract Recent advances suggest that adversarial domain adaptation has been embedding into deep neural networks to learn domain-transferable representations, which reduces distribution divergence in both the training and test samples. However, previous adversarial learning algorithms only resort to learn domain-transferable feature representation by bounding the feature distribution discrepancy cross-domain. These approaches, however, may lead to misalignment and poor generalization results due...
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Abstract Discovering frequent itemsets is essential for finding association rules, yet too computational expensive using existing algorithms. It is even more challenging to find frequent itemsets upon streaming numeric data. The streaming characteristic leads to a challenge that streaming numeric data cannot be scanned repetitively. The numeric characteristic requires that streaming numeric data should be pre-processed into itemsets, e.g., fuzzy-set methods can transform numeric data into itemse...
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Abstract Most existing next POI recommendation studies rely on users’ certain check-ins at individual POIs (e.g., Italian restaurant). In reality, users may leave some uncertain check-ins in the places (e.g., shopping mall), which are named as collective POIs. It indicates that we cannot always access users’ precise check-ins at collective POIs, thus existing approaches fail to work well. To this end, we propose a new research problem, that aims to recommend next individual POIs with uncertain c...
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Abstract This paper focuses on the finite-region dissipative dynamic output feedback control problem for a class of discrete-time two-dimensional (2-D) Fornasini-Marchesini (FM) systems with missing measurements. Firstly, the definitions of finite-region boundedness (FRB) and finite-region (T, S, R)-ϑ-dissipativity are introduced for 2-D FM systems. Secondly, sufficient conditions on the FRB and finite-region (T, S, R)-ϑ-dissipativity of the resultant closed-loop 2-D systems are obtained by the ...
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Abstract We propose a descriptor based on binary patterns extracted from network-automata time-evolution patterns (TEP) aiming to characterize networks. More, in particular, we explore TEPs descriptors from the Life-Like Network Automata (LLNA), a cellular automaton inspired by the rules of the “Life-Like” family that uses a network as tessellation, and based on its dynamics to extract features for network characterization. In recent work, the LLNA has been introduced as a pattern recognition to...
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Abstract Low rank is an important but ill-posed problem in the development of nonnegative matrix factorization (NMF) algorithms because the essential information is often encoded in a low-rank intrinsic data matrix, whereas noise and outliers are contained in a residue matrix. Most existing NMF approaches achieve low rank by directly specifying the dimensions of the factor matrices. A few others impose the low rank constraint on the factor matrix and use the alternating direction method of multi...
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Abstract We propose the first method for aggregating infinite sequences of intuitionistic fuzzy sets in the literature. This new tool allows us to aggregate an infinite list of intuitionistic fuzzy sets over time –a particular case of temporal intuitionistic fuzzy sets– into a traditional intuitionistic fuzzy set. As an application, we define scores and accuracy degrees of temporal intuitionistic fuzzy elements. Then we use these tools to solve the decision making problem where data come in the ...
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Abstract We present a simple yet highly dimensional hybrid diode bridge circuit network that can exhibit complex chaotic behaviours. Further, since our network is characterised by smooth fourth-order exponential nonlinearity, we employ a distinctive approach to assess its different properties: we examine the circuit stability near fixed points; we evaluate dynamic complexity using the Lyaponov spectrum analysis, bifurcation analysis and phase space trajectories; additionally, we assess coexistin...
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Abstract This paper proposes a vehicle detection method using transfer learning for two cameras with different focal lengths. A detected vehicle region in an image of one camera is transformed into a binary map. After that, the map is used to filter convolutional neural network (CNN) feature maps which are computed for the other camera’s image. We also introduce a robust evolutionary algorithm that is used to compute the relationship between the two cameras in an off-line mode efficiently. We ca...
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Discrete mathematics
Machine learning
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