arXiv subjects

Yi Tang

Publications and source records attributed to Yi Tang.

31 records · Page 2Linked to original sources

arXiv2021

Website fingerprinting on early QUIC traffic

Cryptographic protocols have been widely used to protect the user's privacy and avoid exposing private information. QUIC (Quick UDP Internet Connections), including the version originally designed by Google (GQUIC) and the version standardized by IETF (IQUIC), as alternatives to the traditional HTTP, demonstrate their unique transmission characteristics: based on UDP for encrypted resource transmitting, accelerating web page rendering. However, existing encrypted transmission schemes based on TCP are vulnerable to website fingerprinting (WFP) attacks, allowing adversaries to infer the users' visited websites by eavesdropping on the transmission channel. Whether GQUIC and IQUIC can effectively resist such attacks is worth investigating. In this paper, we study the vulnerabilities of GQUIC, IQUIC, and HTTPS to WFP attacks from the perspective of traffic analysis. Extensive experiments show that, in the early traffic scenario, GQUIC is the most vulnerable to WFP attacks among GQUIC, IQUIC, and HTTPS, while IQUIC is more vulnerable than HTTPS, but the vulnerability of the three protocols is similar in the normal full traffic scenario. Features transferring analysis shows that most features are transferable between protocols when on normal full traffic scenario. However, combining with the qualitative analysis of latent feature representation, we find that the transferring is inefficient when on early traffic, as GQUIC, IQUIC, and HTTPS show the significantly different magnitude of variation in the traffic distribution on early traffic. By upgrading the one-time WFP attacks to multiple WFP Top-a attacks, we find that the attack accuracy on GQUIC and IQUIC reach 95.4% and 95.5%, respectively, with only 40 packets and just using simple features, whereas reach only 60.7% when on HTTPS. We also demonstrate that the vulnerability of IQUIC is only slightly dependent on the network environment.

cs.CR↗

arXiv2021

Video Salient Object Detection via Adaptive Local-Global Refinement

Video salient object detection (VSOD) is an important task in many vision applications. Reliable VSOD requires to simultaneously exploit the information from both the spatial domain and the temporal domain. Most of the existing algorithms merely utilize simple fusion strategies, such as addition and concatenation, to merge the information from different domains. Despite their simplicity, such fusion strategies may introduce feature redundancy, and also fail to fully exploit the relationship between multi-level features extracted from both spatial and temporal domains. In this paper, we suggest an adaptive local-global refinement framework for VSOD. Different from previous approaches, we propose a local refinement architecture and a global one to refine the simply fused features with different scopes, which can fully explore the local dependence and the global dependence of multi-level features. In addition, to emphasize the effective information and suppress the useless one, an adaptive weighting mechanism is designed based on graph convolutional neural network (GCN). We show that our weighting methodology can further exploit the feature correlations, thus driving the network to learn more discriminative feature representation. Extensive experimental results on public video datasets demonstrate the superiority of our method over the existing ones.

cs.CV↗

arXiv2021

Fast Video Salient Object Detection via Spatiotemporal Knowledge Distillation

Since the wide employment of deep learning frameworks in video salient object detection, the accuracy of the recent approaches has made stunning progress. These approaches mainly adopt the sequential modules, based on optical flow or recurrent neural network (RNN), to learn robust spatiotemporal features. These modules are effective but significantly increase the computational burden of the corresponding deep models. In this paper, to simplify the network and maintain the accuracy, we present a lightweight network tailored for video salient object detection through the spatiotemporal knowledge distillation. Specifically, in the spatial aspect, we combine a saliency guidance feature embedding structure and spatial knowledge distillation to refine the spatial features. In the temporal aspect, we propose a temporal knowledge distillation strategy, which allows the network to learn the robust temporal features through the infer-frame feature encoding and distilling information from adjacent frames. The experiments on widely used video datasets (e.g., DAVIS, DAVSOD, SegTrack-V2) prove that our approach achieves competitive performance. Furthermore, without the employment of the complex sequential modules, the proposed network can obtain high efficiency with 0.01s per frame.

cs.CV↗

arXiv2020

Incorporating Gas Pipeline Leakage Failure Modes in Risk Evaluation of Electricity-Gas Integrated Energy Systems

In the existing literatures for the risk evaluation of electricity-gas integrated energy system (EGIES), the impacts of gas leakage in pipelines are ignored. This paper presents a method to incorporate the failure modes of gas pipeline leakage in EGIES risk evaluation. A Markov state transition model of gas pipeline with multi-state and multi-mode transition process, and a bi-level Monte Carlo sampling method for this model are developed. A stochastic topology change based network model of EGIES considering the pipeline leakage failure modes is presented. The risk indices for EGIES based on the load shedding, including those specifically for gas leakage risks, are also proposed. An EGIES with a modified RBTS and a 7-node gas system was used to demonstrate an application of the proposed method and models. The results indicate that pipeline leakage failures have significant impacts on the risk of EGIES. Ignoring pipeline leakage failures in the risk evaluation of EGIES will result in an overly underestimation of system risk and most likely a misleading conclusion in system planning.

eess.SY↗

arXiv2020

Continuous LWE

We introduce a continuous analogue of the Learning with Errors (LWE) problem, which we name CLWE. We give a polynomial-time quantum reduction from worst-case lattice problems to CLWE, showing that CLWE enjoys similar hardness guarantees to those of LWE. Alternatively, our result can also be seen as opening new avenues of (quantum) attacks on lattice problems. Our work resolves an open problem regarding the computational complexity of learning mixtures of Gaussians without separability assumptions (Diakonikolas 2016, Moitra 2018). As an additional motivation, (a slight variant of) CLWE was considered in the context of robust machine learning (Diakonikolas et al.~FOCS 2017), where hardness in the statistical query (SQ) model was shown; our work addresses the open question regarding its computational hardness (Bubeck et al.~ICML 2019).

cs.CC↗

arXiv2020

Nearly Optimal Embeddings of Flat Tori

We show that for any $n$-dimensional lattice $\mathcal{L} \subseteq \mathbb{R}^n$, the torus $\mathbb{R}^n/\mathcal{L}$ can be embedded into Hilbert space with $O(\sqrt{n\log n})$ distortion. This improves the previously best known upper bound of $O(n\sqrt{\log n})$ shown by Haviv and Regev (APPROX 2010) and approaches the lower bound of $Ω(\sqrt{n})$ due to Khot and Naor (FOCS 2005, Math. Annal. 2006).

math.MG↗

arXiv2019

A Simplified Dynamical Model for Tuned Wireless Power Transfer Systems

Dynamical models of wireless power transfer (WPT) systems are of primary importance for the dynamical behavior studies and controller design. However, the existing dynamical models usually suffer from high orders and complicated forms due to the complex nature of the coupled resonances and switched-mode power converters in WPT systems. This letter finds that a well-tuned WPT system can be accurately described by a much simpler dynamical model. Specifically, at the tuned condition, the existing dynamical model can be decomposed into two parts. One is controllable and the other one is uncontrollable. The former should be considered in the modeling while the latter can be ignored because it always exponentially converges to zero. For illustration, the recently proposed zero-voltage-switching full-bridge pulse-density modulation WPT system is modeled as an example since such a system can efficiently operate at the tuned condition with soft switching and control capabilities. The derived model was verified in experiments by time-domain and frequency-domain responses.

eess.SP↗

arXiv2018

Fermion Self-energy and Pseudovector Condensate in NJL Model with External Magnetic Field

In this paper, we aim to study the complete self-energy in the fermion propagator within two-flavor NJL model in the case of finite temperature, chemical potential and external magnetic field. Through Fierz transformation we prove that the self-energy is not simply proportional to dynamical mass in the presence of chemical potential, moreover, it contains four kinds of condensates after introducing external magnetic field. We find out the appropriate and complete form of self-energy and establish new gap equations. We take two of the four condensates (scalar and pseudovector condensates) to make an approximation and simplify the gap equations. The numerical results show that not only the dynamical mass get quantitative modification, but also the properties of Nambu phase and Wigner phase are significantly different with classic results. Instead of classic Wigner phase with zero dynamic mass in the massless NJL model, we propose a new phase - quasi-Wigner phase in this article, it has small but nonzero dynamic mass, with increasing chemical potential, eventually Nambu phase will turn into quasi-Wigner phase with first-order phase transition, therefore the chiral symmetry can never be fully restored but be partially restored. Furthermore, we prove that pseudovector condensate in self-energy can generate energy splitting in dispersion relation, it will cause minor differences of particle numbers with the split energy levels.

hep-th↗

arXiv2013

Robustness of Link-prediction Algorithm Based on Similarity and Application to Biological Networks

Many algorithms have been proposed to predict missing links in a variety of real networks. These studies focus on mainly both accuracy and efficiency of these algorithms. However, little attention is paid to their robustness against either noise or irrationality of a link existing in almost all of real networks. In this paper, we investigate the robustness of several typical node-similarity-based algorithms and find that these algorithms are sensitive to the strength of noise. Moreover, we find that it also depends on networks' structure properties, especially on network efficiency, clustering coefficient and average degree. In addition, we make an attempt to enhance the robustness by using link weighting method to transform un-weighted network to weighted one and then make use of weights of links to characterize their reliability. The result shows that proper link weighting scheme can enhance both robustness and accuracy of these algorithms significantly in biological networks while it brings little computational effort.

physics.soc-ph↗

arXiv2012

Link Prediction in Complex Networks by Multi Degree Preferential-Attachment Indices

In principle, the rules of links formation of a network model can be considered as a kind of link prediction algorithm. By revisiting the preferential attachment mechanism for generating a scale-free network, here we propose a class of preferential attachment indices which are different from the previous one. Traditionally, the preferential attachment index is defined by the product of the related nodes degrees, while the new indices will define the similarity score of a pair of nodes by either the maximum in the two nodes degrees or the summarization of their degrees. Extensive experiments are carried out on fourteen real-world networks. Compared with the traditional preferential attachment index, the new ones, especially the degree-summarization similarity index, can provide more accurate prediction on most of the networks. Due to the improved prediction accuracy and low computational complexity, these proposed preferential attachment indices may be of help to provide an instruction for mining unknown links in incomplete networks.

physics.soc-ph↗

arXiv2011

Immunization for complex network based on the effective degree of vertex

The basic idea of many effective immunization strategies is first to rank the importance of vertices according to the degrees of vertices and then remove the vertices from highest importance to lowest until the network becomes disconnected. Here we define the effective degrees of vertex, i.e., the number of its connections linking to un-immunized nodes in current network during the immunization procedure, to rank the importance of vertex, and modify these strategies by using the effective degrees of vertices. Simulations on both the scale-free network models with various degree correlations and two real networks have revealed that the immunization strategies based on the effective degrees are often more effective than those based on the degrees in the initial network.

physics.soc-ph↗

arXiv2011

Evolving network models under a dynamic growth rule

Evolving network models under a dynamic growth rule which comprises the addition and deletion of nodes are investigated. By adding a node with a probability $P_a$ or deleting a node with the probability $P_d=1-P_a$ at each time step, where $P_a$ and $P_d$ are determined by the Logistic population equation, topological properties of networks are studied. All the fat-tailed degree distributions observed in real systems are obtained, giving the evidence that the mechanism of addition and deletion can lead to the diversity of degree distribution of real systems. Moreover, it is found that the networks exhibit nonstationary degree distributions, changing from the power-law to the exponential one or from the exponential to the Gaussian one. These results can be expected to shed some light on the formation and evolution of real complex real-world networks.

physics.soc-ph↗

arXiv2001

Dynamics of Bright Soliton in Optical Fiber

The bright soliton in optical fiber is generally investigated via its spatial evolution in the time domain, where its waveform is considered in many studies. To be consistent with the well-established picture of the dynamics of solitons in other systems, in this letter, we propose it is helpful to study the temporal evolution of the bright soliton by examining its waveshape propagating along the space coordinate axis. We develop a singular theory. Equations governing the evolution of the parameters of the bright soliton in the slow time and the radiated field are explicitly formulated for the first time. In addition, localized modes are found to appear.

physics.optics↗