A class of models for activity-driven networks is proposed in which nodes vary in two states： active and inactive. Only active nodes can receive links from others which represent instantaneous dynamical interactions....
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A class of models for activity-driven networks is proposed in which nodes vary in two states： active and inactive. Only active nodes can receive links from others which represent instantaneous dynamical interactions. The evolution of the network couples the addition of new nodes and state transitions of old ones. The active group changes with activated nodes entering and deactivated ones leaving. A general differential equation framework is developed to study the degree distribution of nodes of integrated networks where four different schemes are formulated.
In local differential privacy(LDP), a challenging problem is the ability to generate highdimensional data while efficiently capturing the correlation between attributes in a dataset. Existing solutions for low-dimensi...
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In local differential privacy(LDP), a challenging problem is the ability to generate highdimensional data while efficiently capturing the correlation between attributes in a dataset. Existing solutions for low-dimensional data synthesis, which partition the privacy budget among all attributes, cease to be effective in high-dimensional scenarios due to the large-scale noise and communication cost caused by the high dimension. In fact, the high-dimensional characteristics not only bring challenges but also make it possible to apply some technologies to break this bottleneck. This paper presents Sam Priv Syn for high-dimensional data synthesis under LDP, which is composed of a marginal sampling module and a data generation *** marginal sampling module is used to sample from the original data to obtain two-way marginals. The sampling process is based on mutual information, which is updated iteratively to retain, as much as possible,the correlation between attributes. The data generation module is used to reconstruct the synthetic dataset from the sampled two-way marginals. Furthermore, this study conducted comparison experiments on the real-world datasets to demonstrate the effectiveness and efficiency of the proposed method, with results proving that Sam PrivSyn can not only protect privacy but also retain the correlation information between the attributes.
The weighted sampling methods based on k-nearest neighbors have been demonstrated to be effective in solving the class imbalance problem. However,they usually ignore the positional relationship between a sample and th...
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The weighted sampling methods based on k-nearest neighbors have been demonstrated to be effective in solving the class imbalance problem. However,they usually ignore the positional relationship between a sample and the heterogeneous samples in its neighborhood when calculating sample weight. This paper proposes a novel neighborhood-weighted based sampling method named NWBBagging to improve the Bagging algorithm's performance on imbalanced datasets. It considers the positional relationship between the center sample and the heterogeneous samples in its neighborhood when identifying critical samples. And a parameter reduction method is proposed and combined into the ensemble learning framework, which reduces the parameters and increases the classifier's diversity. We compare NWBBagging with some state-of-the-art ensemble learning algorithms on 34 imbalanced datasets, and the result shows that NWBBagging achieves better performance.
This letter deals with an interesting intersection phenomenon of prescribed-time stability (PTSta) for dynamical systems, and develops a novel switching control scheme to investigate prescribed-time synchronization (P...
This letter deals with an interesting intersection phenomenon of prescribed-time stability (PTSta) for dynamical systems, and develops a novel switching control scheme to investigate prescribed-time synchronization (PTSyn) for multiweighted and directly coupled complex networks. Different from most previous works that scholars only pay attention to designing the regulation function to ensure PTSta, we aim to select various parameters and uncover the mathematical mechanism of intersecting system state curves. We rigorously prove that, if the settling time is larger than 1, then no matter what the initial value is, the intersection exists only once before the settling time; otherwise, there is no intersection. Moreover, an energy consumption evaluation function is also put forward for PTSta, whose exact value is also calculated via exponential integrals. Then, this intersection theory is applied on the PTSyn of multiweighted complex networks, which is beneficial to construct the switching control scheme or choose the optimal parameters to reduce the energy cost. The rearranging variables’ order technique is utilized to conduct the multiweighted complex networks and obtain the synchronization criterion. Finally, four simulations are presented to verify theoretical results.
Accurate traffic prediction is crucial for urban traffic management. Spatial-temporal graph neural networks, which combine graph neural networks with time series processing, have been extensively employed in traffic p...
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Accurate traffic prediction is crucial for urban traffic management. Spatial-temporal graph neural networks, which combine graph neural networks with time series processing, have been extensively employed in traffic prediction. However, traditional graph neural networks only capture pairwise spatial relationships between road network nodes, neglecting high-order interactions among multiple nodes. Meanwhile, most work for extracting temporal dependencies suffers from implicit modeling and overlooks the internal and external dependencies of time series. To address these challenges, we propose a Geometric Algebraic Multi-order Graph Neural Network (GA-MGNN). Specifically, in the temporal dimension, we design a convolution kernel based on the rotation matrix of geometric algebra, which not only learns internal dependencies between different time steps in time series but also external dependencies between time series and convolution kernels. In the spatial dimension, we construct a tokenized hypergraph and integrate dynamic graph convolution with attention hypergraph convolution to comprehensively capture multi-order spatial dependencies. Additionally, we design a segmented loss function based on traffic periodic information to further improve prediction accuracy. Extensive experiments on seven real-world datasets demonstrate that GAMGNN outperforms state-of-the-art baselines IEEE
An effective prognostic program is crucial to the predictive maintenance of complex equipment since it can improve productivity, prolong equipment life, and enhance system safety. This paper proposes a novel technique...
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An effective prognostic program is crucial to the predictive maintenance of complex equipment since it can improve productivity, prolong equipment life, and enhance system safety. This paper proposes a novel technique for accurate failure prognosis based on back propagation neural network and quantum multi-agent algorithm. Inspired by the extensive research of quantum computing theory and multi-agent systems, the technique employs a quantum multi-agent strategy, with the main characteristics of quantum agent representation and several operations including fitness evaluation, cooperation, crossover and mutation, for parameters optimization of neural network to avoid the deficiencies such as slow convergence and liability of getting stuck to local minima. To validate the feasibility of the proposed approach, several numerical approximation experiments were firstly designed, after which real vibrational data of bearings from the laboratory of Cincinnati University were analyzed and used to assess the health condition for a given future point. The results were rather encouraging and indicated that the presented forecasting method has the potential to be utilized as an estimation tool for failure prediction in industrial machinery.
The passwords for unlocking the mobile devices are relatively simple,easier to be stolen,which causes serious potential security *** important research direction of identity authentication is to establish user behavio...
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The passwords for unlocking the mobile devices are relatively simple,easier to be stolen,which causes serious potential security *** important research direction of identity authentication is to establish user behavior models to authenticate *** this paper,a mobile terminal APP browsing behavioral authentication system architecture which synthesizes multiple factors is *** architecture is suitable for users using the mobile terminal APP in the daily *** architecture includes data acquisition,data processing,feature extraction,and sub model *** can use this architecture for continuous authentication when the user uses APP at the mobile terminal.
With the proliferation of IoT devices, there is an escalating demand for enhanced computing and communication capabilities. Mobile Edge computing (MEC) addresses this need by relocating computing resources to the netw...
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For rate control (RC) of hierarchical structure coding, an independent rate-quantization (R-Q) model was proposed based on mean absolute differences (MADs) in different temporal levels (TLs). In the proposed R-Q model...
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For rate control (RC) of hierarchical structure coding, an independent rate-quantization (R-Q) model was proposed based on mean absolute differences (MADs) in different temporal levels (TLs). In the proposed R-Q model, a novel MAD model was developed according to the hierarchical structure. The experimental results demonstrate that the proposed algorithm provides better performance, in terms of average peak signal-to-noise ratio (PSNR) and quality smoothness, than the H.264 reference model, JM14.2, under various sequences.
Geographic routing has been introduced in mobile ad hoc networks and sensor networks. But its per-formance suffers greatly from mobility-induced location errors that can cause Lost Link （LLNK） and LOOP problems. Thu...
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Geographic routing has been introduced in mobile ad hoc networks and sensor networks. But its per-formance suffers greatly from mobility-induced location errors that can cause Lost Link （LLNK） and LOOP problems. Thus various mobility prediction algorithms have been proposed to mitigate the errors, but sometimes their prediction errors are substantial. A novel mobility prediction technique that incorpo-rates both mobile positioning information and road topology knowledge was presented. Furthermore, the performance of the scheme was evaluated via simulations, along with two other schemes, namely, Linear Velocity Prediction （LVP） and Weighted Velocity Prediction （WVP） for comparison purpose. The results of simulation under Manhattan mobility model show that the proposed scheme could track the movement of a node well and hence provide noticeable improvement over LVP and MVP.
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