arXiv subjects

Yadong Zhou

Publications and source records attributed to Yadong Zhou.

6 recordsLinked to original sources

arXiv2024

Cross-Domain Transfer Learning Method for Thermal Adaptive Behavior Recognition with WiFi

A reliable comfort model is essential to improve occupant satisfaction and reduce building energy consumption. As two types of the most common and intuitive thermal adaptive behaviors, precise recognition of dressing and undressing can effectively support thermal comfort prediction. However, traditional activity recognition suffers from shortcomings in privacy, cost, and performance. To address the above issues, this study proposes a cross-domain transfer learning method for human dressing and undressing adaptive behavior recognition with WiFi. First, we determine the activity interval by calculating the sliding variance for denoised WiFi signals. Subsequently, short-time Fourier transform and discrete wavelet transform are performed to extract action information on the basis of time-frequency analysis. Ultimately, an efficient 1D CNN pre-trained model is integrated with the SVM algorithm as a hybrid model to enhance the identification robustness in new scenarios. Experiment results show that the hybrid model based on transfer learning provides a more accurate prediction for the adaptative behavior of target subjects, achieving 96.9% and 94.9% accuracy in two cases, respectively.

cs.HC↗

arXiv2023

The role of hydrodynamics for the spatial distribution of high-temperature hydrothermal vent-endemic fauna in the deep ocean environment

Active hydrothermal vents provide the surrounding submarine environment with substantial amounts of matter and energy, thus serving as important habitats for diverse megabenthic communities in the deep ocean and constituting a unique, highly productive chemosynthetic ecosystem on Earth. Vent-endemic biological communities gather near the venting site and are usually not found beyond a distance of the order of 100 m from the vent. This is surprising because one would actually expect matter ejected from high-temperature vents, which generate highly turbulent buoyancy plumes, to be suspended and carried far away by the plume flows and deep-sea currents. Here, we study this problem from a fluid dynamics perspective by simulating the vent hydrodynamics using a numerical model that couples the plume flow with induced matter and energy transport. We find that both low- and high-temperature vents deposit most vent matter relatively close to the plume. In particular, the tendency of turbulent buoyancy plumes to carry matter far away is strongly counteracted by generated entrainment flows back into the plume stem. The deposition ranges of organic and inorganic hydrothermal particles obtained from the simulations for various natural high-temperature vents are consistent with the observed maximum spatial extent of biological communities, evidencing that plume hydrodynamics exercises strong control over the spatial distribution of vent-endemic fauna. While other factors affecting the spatial distribution of vent-endemic fauna, such as geology and geochemistry, are site-specific, the main physical features of plume hydrodynamics unraveled in this study are largely site-unspecific and therefore universal across vent sites on Earth.

physics.flu-dyn↗

arXiv2021

Infer-AVAE: An Attribute Inference Model Based on Adversarial Variational Autoencoder

User attributes, such as gender and education, face severe incompleteness in social networks. In order to make this kind of valuable data usable for downstream tasks like user profiling and personalized recommendation, attribute inference aims to infer users' missing attribute labels based on observed data. Recently, variational autoencoder (VAE), an end-to-end deep generative model, has shown promising performance by handling the problem in a semi-supervised way. However, VAEs can easily suffer from over-fitting and over-smoothing when applied to attribute inference. To be specific, VAE implemented with multi-layer perceptron (MLP) can only reconstruct input data but fail in inferring missing parts. While using the trending graph neural networks (GNNs) as encoder has the problem that GNNs aggregate redundant information from neighborhood and generate indistinguishable user representations, which is known as over-smoothing. In this paper, we propose an attribute \textbf{Infer}ence model based on \textbf{A}dversarial \textbf{VAE} (Infer-AVAE) to cope with these issues. Specifically, to overcome over-smoothing, Infer-AVAE unifies MLP and GNNs in encoder to learn positive and negative latent representations respectively. Meanwhile, an adversarial network is trained to distinguish the two representations and GNNs are trained to aggregate less noise for more robust representations through adversarial training. Finally, to relieve over-fitting, mutual information constraint is introduced as a regularizer for decoder, so that it can make better use of auxiliary information in representations and generate outputs not limited by observations. We evaluate our model on 4 real-world social network datasets, experimental results demonstrate that our model averagely outperforms baselines by 7.0$\%$ in accuracy.

cs.LG↗

arXiv2020

Learning by Sampling and Compressing: Efficient Graph Representation Learning with Extremely Limited Annotations

Graph convolution network (GCN) attracts intensive research interest with broad applications. While existing work mainly focused on designing novel GCN architectures for better performance, few of them studied a practical yet challenging problem: How to learn GCNs from data with extremely limited annotation? In this paper, we propose a new learning method by sampling strategy and model compression to overcome this challenge. Our approach has multifold advantages: 1) the adaptive sampling strategy largely suppresses the GCN training deviation over uniform sampling; 2) compressed GCN-based methods with a smaller scale of parameters need fewer labeled data to train; 3) the smaller scale of training data is beneficial to reduce the human resource cost to label them. We choose six popular GCN baselines and conduct extensive experiments on three real-world datasets. The results show that by applying our method, all GCN baselines cut down the annotation requirement by as much as 90$\%$ and compress the scale of parameters more than 6$\times$ without sacrificing their strong performance. It verifies that the training method could extend the existing semi-supervised GCN-based methods to the scenarios with the extremely small scale of labeled data.

cs.LG↗

arXiv2015

An Inference Attack Model for Flow Table Capacity and Usage: Exploiting the Vulnerability of Flow Table Overflow in Software-Defined Network

As the most competitive solution for next-generation network, software-defined network (SDN) and its dominant implementation OpenFlow, are attracting more and more interests. But besides convenience and flexibility, SDN/OpenFlow also introduces new kinds of limitations and security issues. Of these limitations, the most obvious and maybe the most neglected one, is the flow table capacity of SDN/OpenFlow switches. In this paper, we proposed a novel inference attack targeting at SDN/OpenFlow network, which is motivated by the limited flow table capacities of SDN/OpenFlow switches and the following measurable network performance decrease resulting from frequent interactions between data plane and control plane when the flow table is full. To our best knowledge, this is the first proposed inference attack model of this kind for SDN/OpenFlow. We also implemented an inference attack framework according to our model and examined its efficiency and accuracy. The simulation results demonstrate that our framework can infer the network parameters(flow table capacity and flow table usage) with an accuracy of 80% or higher. These findings give us a deeper understanding of SDN/OpenFlow limitations and serve as guidelines to future improvements of SDN/OpenFlow.

cs.NI↗

arXiv2014

A Feasible Graph Partition Framework for Random Walks Implemented by Parallel Computing in Big Graph

Graph partition is a fundamental problem of parallel computing for big graph data. Many graph partition algorithms have been proposed to solve the problem in various applications, such as matrix computations and PageRank, etc., but none has pay attention to random walks. Random walks is a widely used method to explore graph structure in lots of fields. The challenges of graph partition for random walks include the large number of times of communication between partitions, lots of replications of the vertices, unbalanced partition, etc. In this paper, we propose a feasible graph partition framework for random walks implemented by parallel computing in big graph. The framework is based on two optimization functions to reduce the bandwidth, memory and storage cost in the condition that the load balance is guaranteed. In this framework, several greedy graph partition algorithms are proposed. We also propose five metrics from different perspectives to evaluate the performance of these algorithms. By running the algorithms on the big graph data set of real world, the experimental results show that these algorithms in the framework are capable of solving the problem of graph partition for random walks for different needs, e.g. the best result is improved more than 70 times in reducing the times of communication.

cs.SI↗