arXiv subjects

Lei Xu

Publications and source records attributed to Lei Xu.

At least 163 records · Page 9Linked to original sources

arXiv2023

Scalable Scheduling for Industrial Time-Sensitive Networking: A Hyper-flow Graph Based Scheme

Industrial Time-Sensitive Networking (TSN) provides deterministic mechanisms for real-time and reliable flow transmission. Increasing attention has been paid to efficient scheduling for time-sensitive flows with stringent requirements such as ultra-low latency and jitter. In TSN, the fine-grained traffic shaping protocol, cyclic queuing and forwarding (CQF), eliminates uncertain delay and frame loss by cyclic traffic forwarding and queuing. However, it inevitably causes high scheduling complexity. Moreover, complexity is quite sensitive to flow attributes and network scale. The problem stems in part from the lack of an attribute mining mechanism in existing frame-based scheduling. For time-critical industrial networks with large-scale complex flows, a so-called hyper-flow graph based scheduling scheme is proposed to improve the scheduling scalability in terms of schedulability, scheduling efficiency and latency & jitter. The hyper-flow graph is built by aggregating similar flow sets as hyper-flow nodes and designing a hierarchical scheduling framework. The flow attribute-sensitive scheduling information is embedded into the condensed maximal cliques, and reverse maps them precisely to congestion flow portions for re-scheduling. Its parallel scheduling reduces network scale induced complexity. Further, this scheme is designed in its entirety as a comprehensive scheduling algorithm GH^2. It improves the three criteria of scalability along a Pareto front. Extensive simulation studies demonstrate its superiority. Notably, GH^2 is verified its scheduling stability with a runtime of less than 100 ms for 1000 flows and near 1/430 of the SOTA FITS method for 2000 flows.

cs.NI↗

arXiv2023

Leakage-Abuse Attacks Against Forward and Backward Private Searchable Symmetric Encryption

Dynamic searchable symmetric encryption (DSSE) enables a server to efficiently search and update over encrypted files. To minimize the leakage during updates, a security notion named forward and backward privacy is expected for newly proposed DSSE schemes. Those schemes are generally constructed in a way to break the linkability across search and update queries to a given keyword. However, it remains underexplored whether forward and backward private DSSE is resilient against practical leakage-abuse attacks (LAAs), where an attacker attempts to recover query keywords from the leakage passively collected during queries. In this paper, we aim to be the first to answer this question firmly through two non-trivial efforts. First, we revisit the spectrum of forward and backward private DSSE schemes over the past few years, and unveil some inherent constructional limitations in most schemes. Those limitations allow attackers to exploit query equality and establish a guaranteed linkage among different (refreshed) query tokens surjective to a candidate keyword. Second, we refine volumetric leakage profiles of updates and queries by associating each with a specific operation. By further exploiting update volume and query response volume, we demonstrate that all forward and backward private DSSE schemes can leak the same volumetric information (e.g., insertion volume, deletion volume) as those without such security guarantees. To testify our findings, we realize two generic LAAs, i.e., frequency matching attack and volumetric inference attack, and we evaluate them over various experimental settings in the dynamic context. Finally, we call for new efficient schemes to protect query equality and volumetric information across search and update queries.

cs.CR↗

arXiv2023

An Empirical Study of AI-based Smart Contract Creation

The introduction of large language models (LLMs) like ChatGPT and Google Palm2 for smart contract generation seems to be the first well-established instance of an AI pair programmer. LLMs have access to a large number of open-source smart contracts, enabling them to utilize more extensive code in Solidity than other code generation tools. Although the initial and informal assessments of LLMs for smart contract generation are promising, a systematic evaluation is needed to explore the limits and benefits of these models. The main objective of this study is to assess the quality of generated code provided by LLMs for smart contracts. We also aim to evaluate the impact of the quality and variety of input parameters fed to LLMs. To achieve this aim, we created an experimental setup for evaluating the generated code in terms of validity, correctness, and efficiency. Our study finds crucial evidence of security bugs getting introduced in the generated smart contracts as well as the overall quality and correctness of the code getting impacted. However, we also identified the areas where it can be improved. The paper also proposes several potential research directions to improve the process, quality and safety of generated smart contract codes.

cs.SE↗

arXiv2023

Trapping and sympathetic cooling of conformationally selected molecular ions

We report the generation, trapping and sympathetic cooling of individual conformers of molecular ions with the example of cis- and trans-meta-aminostyrene. Following conformationally selective photoionization, the incorporation of the conformers into a Coulomb crystal of laser-cooled calcium ions was confirmed by fluorescence imaging, mass spectrometry and molecular dynamics simulations. We deduce the molecules to be stable in the trap environment for more than ten minutes. The present results pave the way for the spectroscopy and controlled chemistry of distinct ionic conformers in traps.

physics.chem-ph↗

arXiv2023

Decentralized Translator of Trust: Supporting Heterogeneous TEE for Critical Infrastructure Protection

Trusted execution environment (TEE) technology has found many applications in mitigating various security risks in an efficient manner, which is attractive for critical infrastructure protection. First, the natural of critical infrastructure requires it to be well protected from various cyber attacks. Second, performance is usually important for critical infrastructure and it cannot afford an expensive protection mechanism. While a large number of TEE-based critical infrastructure protection systems have been proposed to address various security challenges (e.g., secure sensing and reliable control), most existing works ignore one important feature, i.e., devices comprised the critical infrastructure may be equipped with multiple incompatible TEE technologies and belongs to different owners. This feature makes it hard for these devices to establish mutual trust and form a unified TEE environment. To address these challenges and fully unleash the potential of TEE technology for critical infrastructure protection, we propose DHTee, a decentralized coordination mechanism. DHTee uses blockchain technology to support key TEE functions in a heterogeneous TEE environment, especially the attestation service. A Device equipped with one TEE can interact securely with the blockchain to verify whether another potential collaborating device claiming to have a different TEE meets the security requirements. DHTee is also flexible and can support new TEE schemes without affecting devices using existing TEEs that have been supported by the system.

cs.CR↗

arXiv2023

The chromatic index of finite projective spaces

A line coloring of PG$(n,q)$, the $n$-dimensional projective space over GF$(q)$, is an assignment of colors to all lines of PG$(n,q)$ so that any two lines with the same color do not intersect. The chromatic index of PG$(n,q)$, denoted by $χ'(PG(n,q))$, is the least number of colors for which a coloring of PG$(n,q)$ exists. This paper translates the problem of determining the chromatic index of PG$(n,q)$ to the problem of examining the existences of PG$(3,q)$ and PG$(4,q)$ with certain properties. In particular, it is shown that for any odd integer $n$ and $q\in\{3,4,8,16\}$, $χ'(PG(n,q))=(q^n-1)/(q-1)$, which implies the existence of a parallelism of PG$(n,q)$ for any odd integer $n$ and $q\in\{3,4,8,16\}$.

math.CO↗

arXiv2023

Cross Chain Bribery Contracts: Majority vs Mighty Minority

Bribery is a perilous issue in the real world, especially in an economical aspect. This fraudulence is unavoidable, and more importantly, it is more difficult to trace in case smart contracts are utilized for bribing on a distributed public blockchain. In our paper, we propose a new threat to the security of a blockchain system, cross-chain bribery using smart contracts. An arbitrary wealthy briber can utilize cross-chain smart contracts to manipulate a consensus mechanism on a victim's blockchain or to disgrace a victim's blockchain. To better understand this threat, our paper proposes a framework to analyze bribery using cross-chain smart contracts. We analyze the amount of incentive to bribe rational miners in a victim's blockchain and also a full cost of conducting a cross-chain bribery attack. The result is that such attacks can be carried out with a reasonable amount of money or cryptocurrencies.

cs.CR↗

arXiv2023

Transporting cold atoms towards a GaN-on-sapphire chip via an optical conveyor belt

Trapped atoms on photonic structures inspire many novel quantum devices for quantum information processing and quantum sensing. Here, we have demonstrated a hybrid photonic-atom chip platform based on a GaN-on-sapphire chip and the transport of an ensemble of atoms from free space towards the chip with an optical conveyor belt. The maximum transport efficiency of atoms is about 50% with a transport distance of 500 $\mathrm{μm}$. Our results open up a new route toward the efficiently loading of cold atoms into the evanescent-field trap formed by the photonic integrated circuits, which promises strong and controllable interactions between single atoms and single photons.

physics.atom-ph↗

arXiv2023

Conformational and state-specific effects in reactions of 2,3-dibromobutadiene with Coulomb-crystallized calcium ions

Recent advances in experimental methodology enabled studies of the quantum-state and conformational dependence of chemical reactions under precisely controlled conditions in the gas phase. Here, we generated samples of selected gauche and s-trans 2,3-dibromobutadiene (DBB) by electrostatic deflection in a molecular beam and studied their reaction with Coulomb crystals of laser-cooled $\mathrm{Ca^{+}}$ ions in an ion trap. The rate coefficients for the total reaction were found to strongly depend on both the conformation of DBB and the electronic state of $\mathrm{Ca^{+}}$. In the $\mathrm{(4p)~^{2}P_{1/2}}$ and $\mathrm{(3d)~^{2}D_{3/2}}$ excited states of $\mathrm{Ca^{+}}$, the reaction is capture-limited and faster for the gauche conformer due to long-range ion-dipole interactions. In the $\mathrm{(4s)~^{2}S_{1/2}}$ ground state of $\mathrm{Ca^{+}}$, the reaction rate for s-trans DBB still conforms with the capture limit, while that for gauche DBB is strongly suppressed. The experimental observations were analysed with the help of adiabatic capture theory, ab-initio calculations and reactive molecular dynamics simulations on a machine-learned full-dimensional potential energy surface of the system. The theory yields near-quantitative agreement for s-trans-DBB, but overestimates the reactivity of the gauche-conformer compared to the experiment. The present study points to the important role of molecular geometry even in strongly reactive exothermic systems and illustrates striking differences in the reactivity of individual conformers in gas-phase ion-molecule reactions.

physics.chem-ph↗

arXiv2023

Mitigating the Effect of Class Imbalance in Fault Localization Using Context-aware Generative Adversarial Network

Fault localization (FL) analyzes the execution information of a test suite to pinpoint the root cause of a failure. The class imbalance of a test suite, i.e., the imbalanced class proportion between passing test cases (i.e., majority class) and failing ones (i.e., minority class), adversely affects FL effectiveness. To mitigate the effect of class imbalance in FL, we propose CGAN4FL: a data augmentation approach using Context-aware Generative Adversarial Network for Fault Localization. Specifically, CGAN4FL uses program dependencies to construct a failure-inducing context showing how a failure is caused. Then, CGAN4FL leverages a generative adversarial network to analyze the failure-inducing context and synthesize the minority class of test cases (i.e., failing test cases). Finally, CGAN4FL augments the synthesized data into original test cases to acquire a class-balanced dataset for FL. Our experiments show that CGAN4FL significantly improves FL effectiveness, e.g., promoting MLP-FL by 200.00%, 25.49%, and 17.81% under the Top-1, Top-5, and Top-10 respectively.

cs.SE↗

arXiv2022

Linking Sketch Patches by Learning Synonymous Proximity for Graphic Sketch Representation

Graphic sketch representations are effective for representing sketches. Existing methods take the patches cropped from sketches as the graph nodes, and construct the edges based on sketch's drawing order or Euclidean distances on the canvas. However, the drawing order of a sketch may not be unique, while the patches from semantically related parts of a sketch may be far away from each other on the canvas. In this paper, we propose an order-invariant, semantics-aware method for graphic sketch representations. The cropped sketch patches are linked according to their global semantics or local geometric shapes, namely the synonymous proximity, by computing the cosine similarity between the captured patch embeddings. Such constructed edges are learnable to adapt to the variation of sketch drawings, which enable the message passing among synonymous patches. Aggregating the messages from synonymous patches by graph convolutional networks plays a role of denoising, which is beneficial to produce robust patch embeddings and accurate sketch representations. Furthermore, we enforce a clustering constraint over the embeddings jointly with the network learning. The synonymous patches are self-organized as compact clusters, and their embeddings are guided to move towards their assigned cluster centroids. It raises the accuracy of the computed synonymous proximity. Experimental results show that our method significantly improves the performance on both controllable sketch synthesis and sketch healing.

cs.CV↗

arXiv2022

The role of drop shape in impact and splash

The impact and splash of liquid drops on solid substrates are ubiquitous in many important fields. However, previous studies have mainly focused on spherical drops while the non-spherical situations, such as raindrops, charged drops, oscillating drops, and drops affected by electromagnetic field, remain largely unexplored. Using ferrofluid, we realize various drop shapes and illustrate the fundamental role of shape in impact and splash. Experiments show that different drop shapes produce large variations in spreading dynamics, splash onset, and splash amount. However, underlying all these variations we discover universal mechanisms across various drop shapes: the impact dynamics is governed by the superellipse model, the splash onset is triggered by the Kelvin-Helmholtz instability, and the amount of splash is determined by the energy dissipation before liquid taking off. Our study generalizes the drop impact research beyond the spherical geometry, and reveals the potential of using drop shape to control impact and splash.

physics.flu-dyn↗

arXiv2022

Diffusion-Dominated Pinch-Off of Ultralow Surface Tension Fluids

We study the breakup of a liquid thread inside another liquid at different surface tensions. In general, the pinch-off of a liquid thread is governed by the dynamics of fluid flow. However, when the interfacial tension is ultralow (2 to 3 orders lower than normal liquids), we find that the pinch-off dynamics can be governed by bulk diffusion. By studying the velocity and the profile of the pinch-off, we explain why the diffusion-dominated pinch-off takes over the conventional breakup at ultralow surface tensions.

cond-mat.soft↗

arXiv2022

Q-factor mediated quasi-BIC resonances coupling in asymmetric dimer lattices

Resonance coupling in the regime of bound states in the continuum (BICs) provides an efficient method for engineering nanostructure's optical response with various lineshape while maintaining an ultra-narrow linewidth feature, where the quality factor of resonances plays a crucial role. Independent manipulation of the Q factors of BIC resonances enables full control of interaction behavior as well as both near- and far-field light engineering. In this paper, we harness reflection symmetry (RS) and translational symmetry (TS) protected BIC resonances supported in an asymmetric dimer lattice and investigate Q-factor-mediated resonance coupling behavior under controlled TS and RS perturbations. We focus on in-plane electrical dipole BIC (EDi-BIC) and magnetic dipole BIC (MD-BIC) which are protected by RS, and out-of-plane electrical dipole BIC (EDo-BIC) protected by TS. The coupling between EDi-BIC and EDo-BIC exhibits a resonance crossing behavior where the transmission spectrum at the crossing could be tuned flexibly, showing an electromagnetically induced transparency lineshape or satisfying the lattice Kerker condition with pure phase modulation capability depending on TS and RS perturbed Q factors. While the coupling between MD-BIC and EDo-BIC shows an avoided resonance crossing behavior, where the strongly coupled resonances would lead to the formation of a Friedrich-Wintgen BICs whose spectral position could also be shifted by tuning the Q factors. Our results suggest an intriguing platform to explore BIC resonance interactions with independent Q factor manipulation capability for realizing multi-functional meta-devices.

physics.optics↗

arXiv2022

On the Lp Gaussian Minkowski problem

Existence of symmetric (resp. asymmetric) solutions to the $L_p$ Gaussian Minkowski problem for $p\leq 0$ (resp. $p\geq 1$) will be provided. Moreover, existence and uniqueness of smooth solutions to the problem for $p>n$ will also be proved without the restriction that the Gaussian volumes of convex bodies are not less than one-second.

math.AP↗

arXiv2022

Rewriting Meaningful Sentences via Conditional BERT Sampling and an application on fooling text classifiers

Most adversarial attack methods that are designed to deceive a text classifier change the text classifier's prediction by modifying a few words or characters. Few try to attack classifiers by rewriting a whole sentence, due to the difficulties inherent in sentence-level rephrasing as well as the problem of setting the criteria for legitimate rewriting. In this paper, we explore the problem of creating adversarial examples with sentence-level rewriting. We design a new sampling method, named ParaphraseSampler, to efficiently rewrite the original sentence in multiple ways. Then we propose a new criteria for modification, called a sentence-level threaten model. This criteria allows for both word- and sentence-level changes, and can be adjusted independently in two dimensions: semantic similarity and grammatical quality. Experimental results show that many of these rewritten sentences are misclassified by the classifier. On all 6 datasets, our ParaphraseSampler achieves a better attack success rate than our baseline.

cs.CL↗

arXiv2022

R&R: Metric-guided Adversarial Sentence Generation

Adversarial examples are helpful for analyzing and improving the robustness of text classifiers. Generating high-quality adversarial examples is a challenging task as it requires generating fluent adversarial sentences that are semantically similar to the original sentences and preserve the original labels, while causing the classifier to misclassify them. Existing methods prioritize misclassification by maximizing each perturbation's effectiveness at misleading a text classifier; thus, the generated adversarial examples fall short in terms of fluency and similarity. In this paper, we propose a rewrite and rollback (R&R) framework for adversarial attack. It improves the quality of adversarial examples by optimizing a critique score which combines the fluency, similarity, and misclassification metrics. R&R generates high-quality adversarial examples by allowing exploration of perturbations that do not have immediate impact on the misclassification metric but can improve fluency and similarity metrics. We evaluate our method on 5 representative datasets and 3 classifier architectures. Our method outperforms current state-of-the-art in attack success rate by +16.2%, +12.8%, and +14.0% on the classifiers respectively. Code is available at https://github.com/DAI-Lab/fibber

cs.CL↗

arXiv2022

Audit and Improve Robustness of Private Neural Networks on Encrypted Data

Performing neural network inference on encrypted data without decryption is one popular method to enable privacy-preserving neural networks (PNet) as a service. Compared with regular neural networks deployed for machine-learning-as-a-service, PNet requires additional encoding, e.g., quantized-precision numbers, and polynomial activation. Encrypted input also introduces novel challenges such as adversarial robustness and security. To the best of our knowledge, we are the first to study questions including (i) Whether PNet is more robust against adversarial inputs than regular neural networks? (ii) How to design a robust PNet given the encrypted input without decryption? We propose PNet-Attack to generate black-box adversarial examples that can successfully attack PNet in both target and untarget manners. The attack results show that PNet robustness against adversarial inputs needs to be improved. This is not a trivial task because the PNet model owner does not have access to the plaintext of the input values, which prevents the application of existing detection and defense methods such as input tuning, model normalization, and adversarial training. To tackle this challenge, we propose a new fast and accurate noise insertion method, called RPNet, to design Robust and Private Neural Networks. Our comprehensive experiments show that PNet-Attack reduces at least $2.5\times$ queries than prior works. We theoretically analyze our RPNet methods and demonstrate that RPNet can decrease $\sim 91.88\%$ attack success rate.

cs.LG↗