BZPEERReading, trust and collaboration for research
Menu

Discover

GraphFollow connections around a paper, topic or saved view.

Workspaces

HomePick up where your research flow left off.InboxHandle requests, replies and notifications from one place.CollectionsCurate reading lists, evidence sets and shareable dossiers.ResearchSee your private provenance graph, trail and imports.CollaborationMove from discovery into focused discussion rooms.PublishDraft, preview and publish full-text research articles.

Network

ExpertsFind specialists worth following or asking for help.CommunitiesJoin research circles organized around shared work.PartnersSee external organizations active around the same topics.

Opportunities

ListingsBrowse roles, calls and collaborations with clearer fit signals.

Account

Log inReturn to your reading and collaboration workspace.Create accountStart saving work, following people and joining teams.
Log inCreate account

Researcher profile

Jingyi Wang

Jingyi Wang contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Machine LearningCryptography and SecurityArtificial IntelligenceSoftware Engineeringastro-ph.HEmath.OCComputationComputation and Languagecs.CYeess.ASgr-qcHardware ArchitectureInformation Theorymath.ITmath.NAmath.STMethodologyNumerical AnalysisSoundStatistics Theory

Trust snapshot

Quick read

Trust 21 - EmergingVerification L1Unclaimed author
18works
0followers
20topics
4close collaborators

Actions

Decide how to stay connected

Follow researcher0

Identity and collaboration

How to connect with this researcher

Claiming links this public author record to a researcher profile and unlocks direct collaboration workflows.

Log in to claim

Direct collaboration

Open a focused conversation when the fit is right

Claim this author entity first to unlock direct invitations.

Research graph

See the researcher in context

Open full explorer

Inspect adjacent work, topics, institutions and collaborators without jumping out to a separate graph page.

Building this graph slice

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Published work

18 published item(s)

preprint2026arXiv

Convergence Rates of Constrained Expected Improvement

Constrained Bayesian optimization (CBO) methods have seen significant success in black-box optimization with constraints. One of the most commonly used CBO methods is the constrained expected improvement (CEI) algorithm. CEI is a natural extension of expected improvement (EI) when constraints are incorporated. However, the theoretical convergence rate of CEI has not been established. In this work, we study the convergence rate of CEI by analyzing its simple regret upper bound. First, we show that when the objective function $f$ and constraint function $c$ are assumed to each lie in a reproducing kernel Hilbert space (RKHS), CEI achieves the convergence rates of $\mathcal{O} \left(t^{-\frac{1}{2}}\log^{\frac{d+1}{2}}(t) \right) \ \text{and }\ \mathcal{O}\left(t^{\frac{-ν}{2ν+d}} \log^{\fracν{2ν+d}}(t)\right)$ for the commonly used squared exponential and Matérn kernels ($ν>\frac{1}{2}$), respectively. Second, we show that when $f$ is assumed to be sampled from Gaussian processes (GPs), CEI achieves similar convergence rates with a high probability. Numerical experiments are performed to validate the theoretical analysis.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Provable Fairness Repair for Deep Neural Networks

Deep neural networks (DNNs) are suffering from ethical issues such as individual discrimination. In response, extensive NN repair techniques have been developed to adjust models and mitigate such undesired behaviors. However, existing fairness repair methods are typically data-centric, which often lack provable guarantees and generalization to unseen samples. To overcome these limitations, we propose ProF, a novel fairness repair framework with provable guarantees. The key intuition of ProF is to leverage interval bound propagation (a widely used NN verification technique) to soundly capture model outputs over the whole set $S(\mathbf{x})$ around a biased sample $\mathbf{x}$. The derived bounds are utilized to guide fairness repair which encourages the model to produce consistent outputs on $S(\mathbf{x})$. Specifically, we integrate fairness constraints and model modifications into a unified constraint-solving formulation, which can be transformed to a Mixed-Integer Linear Programming (MILP) problem solvable by off-the-shelf solvers. The solution to the MILP problem effectively induces a repaired model with guaranteed fairness over the whole set $S(\mathbf{x})$. We evaluate ProF on four widely used benchmark datasets and demonstrate that it achieves provable fairness repair, with generalization of up to 95.93\% on full datasets and 93.16\% on the entire input space. Notably, ProF can be easily configured to support multiple sensitive attributes and more practical fairness definitions, while providing provable repair guarantees and delivering around 90\% fairness improvement. Our code is available at https://github.com/nninjn/ProF.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Secure Communication via Modulation Order Confusion

With the increasing threat posed by modulation classification to wireless security, this paper proposes a secure communication framework based on modulation order confusion (MOC), which intentionally disguises the original modulation as a higher- or lower-order one to mislead eavesdroppers. For single-antenna systems, two schemes are developed: symbol random mapping and symbol time diversity, enabling modulation order confusion with customized receivers. For multi-antenna systems, receiver-transparent MOC schemes are proposed, including series-expansion-based and constellation-path-based signal designs, and are further extended to RIS-assisted systems with joint beamformer and RIS reflection design. Numerical results show that the proposed schemes effectively defeat both deep-learning-based and expert-knowledge-based modulation classifiers without degrading communication performance.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Specific Emitter Identification via Active Learning

With the rapid growth of wireless communications, specific emitter identification (SEI) is significant for communication security. However, its model training relies heavily on the large-scale labeled data, which are costly and time-consuming to obtain. To address this challenge, we propose an SEI approach enhanced by active learning (AL), which follows a three-stage semi-supervised training scheme. In the first stage, self-supervised contrastive learning is employed with a dynamic dictionary update mechanism to extract robust representations from large amounts of the unlabeled data. In the second stage, supervised training on a small labeled dataset is performed, where the contrastive and cross-entropy losses are jointly optimized to improve the feature separability and strengthen the classification boundaries. In the third stage, an AL module selects the most valuable samples from the unlabeled data for annotation based on the uncertainty and representativeness criteria, further enhancing generalization under limited labeling budgets. Experimental results on the ADS-B and WiFi datasets demonstrate that the proposed SEI approach significantly outperforms the conventional supervised and semi-supervised methods under limited annotation conditions, achieving higher recognition accuracy with lower labeling cost.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A simplified nonsmooth nonconvex bundle method with applications to security-constrained ACOPF problems

An optimization algorithm for a group of nonsmooth nonconvex problems inspired by two-stage stochastic programming problems is proposed. The main challenges for these problems include (1) the problems lack the popular lower-type properties such as prox-regularity assumed in many nonsmooth nonconvex optimization algorithms, (2) the objective can not be analytically expressed and (3) the evaluation of function values and subgradients are computationally expensive. To address these challenges, this report first examines the properties that exist in many two-stage problems, specifically upper-C^2 objectives. Then, we show that quadratic penalty method for security-constrained alternating current optimal power flow (SCACOPF) contingency problems can make the contingency solution functions upper-C^2 . Based on these observations, a simplified bundle algorithm that bears similarity to sequential quadratic programming (SQP) method is proposed. It is more efficient in implementation and computation compared to conventional bundle methods. Global convergence analysis of the algorithm is presented under novel and reasonable assumptions. The proposed algorithm therefore fills the gap of theoretical convergence for some smoothed SCACOPF problems. The inconsistency that might arise in our treatment of the constraints are addressed through a penalty algorithm whose convergence analysis is also provided. Finally, theoretical capabilities and numerical performance of the algorithm are demonstrated through numerical examples.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

An Optimization algorithm for nonsmooth nonconvex problems with upper-C^2 objective

An optimization algorithm for nonsmooth nonconvex constrained optimization problems with upper-C2 objective functions is proposed and analyzed. Upper-C2 is a weakly concave property that exists in difference of convex (DC) functions and arises naturally in many applications, particularly certain classes of solutions to parametric optimization problems [34, 4], e.g., recourse of stochastic programming [36] and projection into closed sets [34]. The algorithm can be viewed as a bundle method specialized for upper-C2 problems and is globally convergent with bounded algorithm parameters. Compared to conventional bundle methods, the proposed method is both simpler and computationally more efficient. The algorithm handles general smooth constraints similarly to sequential quadratic programming (SQP) methods and uses a line search to ensure progress. The potential inconsistencies from the linearization of the constraints are addressed through a penalty method. The capabilities of the algorithm are demonstrated by solving both simple upper-C2 problems and real-world optimal power flow problems used in current power grid industry practices.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Keyword Spotting System and Evaluation of Pruning and Quantization Methods on Low-power Edge Microcontrollers

Keyword spotting (KWS) is beneficial for voice-based user interactions with low-power devices at the edge. The edge devices are usually always-on, so edge computing brings bandwidth savings and privacy protection. The devices typically have limited memory spaces, computational performances, power and costs, for example, Cortex-M based microcontrollers. The challenge is to meet the high computation and low-latency requirements of deep learning on these devices. This paper firstly shows our small-footprint KWS system running on STM32F7 microcontroller with Cortex-M7 core @216MHz and 512KB static RAM. Our selected convolutional neural network (CNN) architecture has simplified number of operations for KWS to meet the constraint of edge devices. Our baseline system generates classification results for each 37ms including real-time audio feature extraction part. This paper further evaluates the actual performance for different pruning and quantization methods on microcontroller, including different granularity of sparsity, skipping zero weights, weight-prioritized loop order, and SIMD instruction. The result shows that for microcontrollers, there are considerable challenges for accelerate unstructured pruned models, and the structured pruning is more friendly than unstructured pruning. The result also verified that the performance improvement for quantization and SIMD instruction.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

TESTSGD: Interpretable Testing of Neural Networks Against Subtle Group Discrimination

Discrimination has been shown in many machine learning applications, which calls for sufficient fairness testing before their deployment in ethic-relevant domains such as face recognition, medical diagnosis and criminal sentence. Existing fairness testing approaches are mostly designed for identifying individual discrimination, i.e., discrimination against individuals. Yet, as another widely concerning type of discrimination, testing against group discrimination, mostly hidden, is much less studied. To address the gap, in this work, we propose TESTSGD, an interpretable testing approach which systematically identifies and measures hidden (which we call `subtle' group discrimination} of a neural network characterized by conditions over combinations of the sensitive features. Specifically, given a neural network, TESTSGDfirst automatically generates an interpretable rule set which categorizes the input space into two groups exposing the model's group discrimination. Alongside, TESTSGDalso provides an estimated group fairness score based on sampling the input space to measure the degree of the identified subtle group discrimination, which is guaranteed to be accurate up to an error bound. We evaluate TESTSGDon multiple neural network models trained on popular datasets including both structured data and text data. The experiment results show that TESTSGDis effective and efficient in identifying and measuring such subtle group discrimination that has never been revealed before. Furthermore, we show that the testing results of TESTSGDcan guide generation of new samples to mitigate such discrimination through retraining with negligible accuracy drop.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Towards Comprehensively Understanding the Run-time Security of Programmable Logic Controllers: A 3-year Empirical Study

Programmable Logic Controllers (PLCs) are the core control devices in Industrial Control Systems (ICSs), which control and monitor the underlying physical plants such as power grids. PLCs were initially designed to work in a trusted industrial network, which however can be brittle once deployed in an Internet-facing (or penetrated) network. Yet, there is a lack of systematic empirical analysis of the run-time security of modern real-world PLCs. To close this gap, we present the first large-scale measurement on 23 off-the-shelf PLCs across 13 leading vendors. We find many common security issues and unexplored implications that should be more carefully addressed in the design and implementation. To sum up, the unsupervised logic applications can cause system resource/privilege abuse, which gives adversaries new means to hijack the control flow of a runtime system remotely (without exploiting memory vulnerabilities); 2) the improper access control mechanisms bring many unauthorized access implications; 3) the proprietary or semi-proprietary protocols are fragile regarding confidentiality and integrity protection of run-time data. We empirically evaluated the corresponding attack vectors on multiple PLCs, which demonstrates that the security implications are severe and broad. Our findings were reported to the related parties responsibly, and 20 bugs have been confirmed with 7 assigned CVEs.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Two-Sample Test for High-Dimensional Covariance Matrices: a normal-reference approach

Testing the equality of the covariance matrices of two high-dimensional samples is a fundamental inference problem in statistics. Several tests have been proposed but they are either too liberal or too conservative when the required assumptions are not satisfied which attests that they are not always applicable in real data analysis. To overcome this difficulty, a normal-reference test is proposed and studied in this paper. It is shown that under some regularity conditions and the null hypothesis, the proposed test statistic and a chi-square-type mixture have the same limiting distribution. It is then justified to approximate the null distribution of the proposed test statistic using that of the chi-square-type mixture. The distribution of the chi-square-type mixture can be well approximated using a three-cumulant matched chi-square-approximation with its approximation parameters consistently estimated from the data. The asymptotic power of the proposed test under a local alternative is also established. Simulation studies and a real data example demonstrate that in terms of size control, the proposed test outperforms the existing competitors substantially.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

VeriFi: Towards Verifiable Federated Unlearning

Federated learning (FL) is a collaborative learning paradigm where participants jointly train a powerful model without sharing their private data. One desirable property for FL is the implementation of the right to be forgotten (RTBF), i.e., a leaving participant has the right to request to delete its private data from the global model. However, unlearning itself may not be enough to implement RTBF unless the unlearning effect can be independently verified, an important aspect that has been overlooked in the current literature. In this paper, we prompt the concept of verifiable federated unlearning, and propose VeriFi, a unified framework integrating federated unlearning and verification that allows systematic analysis of the unlearning and quantification of its effect, with different combinations of multiple unlearning and verification methods. In VeriFi, the leaving participant is granted the right to verify (RTV), that is, the participant notifies the server before leaving, then actively verifies the unlearning effect in the next few communication rounds. The unlearning is done at the server side immediately after receiving the leaving notification, while the verification is done locally by the leaving participant via two steps: marking (injecting carefully-designed markers to fingerprint the leaver) and checking (examining the change of the global model's performance on the markers). Based on VeriFi, we conduct the first systematic and large-scale study for verifiable federated unlearning, considering 7 unlearning methods and 5 verification methods. Particularly, we propose a more efficient and FL-friendly unlearning method, and two more effective and robust non-invasive-verification methods. We extensively evaluate VeriFi on 7 datasets and 4 types of deep learning models. Our analysis establishes important empirical understandings for more trustworthy federated unlearning.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

A Deep Graph Wavelet Convolutional Neural Network for Semi-supervised Node Classification

Graph convolutional neural network provides good solutions for node classification and other tasks with non-Euclidean data. There are several graph convolutional models that attempt to develop deep networks but do not cause serious over-smoothing at the same time. Considering that the wavelet transform generally has a stronger ability to extract useful information than the Fourier transform, we propose a new deep graph wavelet convolutional network (DeepGWC) for semi-supervised node classification tasks. Based on the optimized static filtering matrix parameters of vanilla graph wavelet neural networks and the combination of Fourier bases and wavelet ones, DeepGWC is constructed together with the reuse of residual connection and identity mappings in network architectures. Extensive experiments on three benchmark datasets including Cora, Citeseer, and Pubmed are conducted. The experimental results demonstrate that our DeepGWC outperforms existing graph deep models with the help of additional wavelet bases and achieves new state-of-the-art performances eventually.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Repairing Adversarial Texts through Perturbation

It is known that neural networks are subject to attacks through adversarial perturbations, i.e., inputs which are maliciously crafted through perturbations to induce wrong predictions. Furthermore, such attacks are impossible to eliminate, i.e., the adversarial perturbation is still possible after applying mitigation methods such as adversarial training. Multiple approaches have been developed to detect and reject such adversarial inputs, mostly in the image domain. Rejecting suspicious inputs however may not be always feasible or ideal. First, normal inputs may be rejected due to false alarms generated by the detection algorithm. Second, denial-of-service attacks may be conducted by feeding such systems with adversarial inputs. To address the gap, in this work, we propose an approach to automatically repair adversarial texts at runtime. Given a text which is suspected to be adversarial, we novelly apply multiple adversarial perturbation methods in a positive way to identify a repair, i.e., a slightly mutated but semantically equivalent text that the neural network correctly classifies. Our approach has been experimented with multiple models trained for natural language processing tasks and the results show that our approach is effective, i.e., it successfully repairs about 80\% of the adversarial texts. Furthermore, depending on the applied perturbation method, an adversarial text could be repaired in as short as one second on average.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

RobOT: Robustness-Oriented Testing for Deep Learning Systems

Recently, there has been a significant growth of interest in applying software engineering techniques for the quality assurance of deep learning (DL) systems. One popular direction is deep learning testing, where adversarial examples (a.k.a.~bugs) of DL systems are found either by fuzzing or guided search with the help of certain testing metrics. However, recent studies have revealed that the commonly used neuron coverage metrics by existing DL testing approaches are not correlated to model robustness. It is also not an effective measurement on the confidence of the model robustness after testing. In this work, we address this gap by proposing a novel testing framework called Robustness-Oriented Testing (RobOT). A key part of RobOT is a quantitative measurement on 1) the value of each test case in improving model robustness (often via retraining), and 2) the convergence quality of the model robustness improvement. RobOT utilizes the proposed metric to automatically generate test cases valuable for improving model robustness. The proposed metric is also a strong indicator on how well robustness improvement has converged through testing. Experiments on multiple benchmark datasets confirm the effectiveness and efficiency of RobOT in improving DL model robustness, with 67.02% increase on the adversarial robustness that is 50.65% higher than the state-of-the-art work DeepGini.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Relativistic reflection and reverberation in GX 339-4 with NICER and NuSTAR

We analyze seven NICER and NuSTAR epochs of the black hole X-ray binary GX 339-4 in the hard state during its two most recent hard-only outbursts in 2017 and 2019. These observations cover the 1-100 keV unabsorbed luminosities between 0.3% and 2.1% of the Eddington limit. With NICER's negligible pile-up, high count rate and unprecedented time resolution, we perform a spectral-timing analysis and spectral modeling using relativistic and distant reflection models. Our spectral fitting shows that as the inner disk radius moves inwards, the thermal disk emission increases in flux and temperature, the disk becomes more highly ionized and the reflection fraction increases. This coincides with the inner disk increasing its radiative efficiency around ~1% Eddington. We see a hint of hysteresis effect at ~0.3% of Eddington: the inner radius is significantly truncated during the rise ($>49R_{g}$), while only a mild truncation ($\sim5R_g$) is found during the decay. At higher frequencies ($2-7$~Hz) in the highest luminosity epoch, a soft lag is present, whose energy dependence reveals a thermal reverberation lag, with an amplitude similar to previous findings for this source. We also discuss the plausibility of the hysteresis effect and the debate of the disk truncation problem in the hard state.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Testing the Kerr metric using X-ray reflection spectroscopy: spectral analysis of GX 339-4

Signatures of X-ray reprocessing (reflection) out of an accretion disk are commonly observed in the high-energy spectrum of accreting black holes, and can be used to probe the strong gravity region around these objects. In this paper, we extend previous work in the literature and we employ a full emission model for relativistic reflection in non-Kerr spacetime to demonstrate an approach that tests the Kerr black hole hypothesis. We analyze a composite spectrum obtained with the Proportional Counter Array in the Rossi X-ray Timing Explorer (RXTE), of the stellar-mass black hole GX 339-4 in its brightest hard state. With a remarkable sensitivity of ~0.1% and 40 million counts in the 3-45 keV band to capture the faint features in the reflection spectrum, we demonstrate that it is possible with existing data and an adequate model to place constraints on the black hole spin $a_*$ and the deformation parameter that quantifies the departure from the Kerr metric. Our measurement obtained with the best fit model, which should be regarded as principally a proof of concept, is $a_*=0.92^{+0.07}_{-0.12}$ and $α_{13}=-0.76^{+0.78}_{-0.60}$ with a 90% confidence level and is consistent with the hypothesis that the compact object in GX 339-4 is a Kerr black hole. We also discuss how the physical model choice and the emissivity profile adopted could make an impact on the constraints of $α_{13}$ and spin. To enable Kerr metric test using X-ray reflection spectroscopy, it is essential to improve our astrophysical understanding of accreting black holes, e.g., the natures of accretion flow and corona.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Towards Plausible Differentially Private ADMM Based Distributed Machine Learning

The Alternating Direction Method of Multipliers (ADMM) and its distributed version have been widely used in machine learning. In the iterations of ADMM, model updates using local private data and model exchanges among agents impose critical privacy concerns. Despite some pioneering works to relieve such concerns, differentially private ADMM still confronts many research challenges. For example, the guarantee of differential privacy (DP) relies on the premise that the optimality of each local problem can be perfectly attained in each ADMM iteration, which may never happen in practice. The model trained by DP ADMM may have low prediction accuracy. In this paper, we address these concerns by proposing a novel (Improved) Plausible differentially Private ADMM algorithm, called PP-ADMM and IPP-ADMM. In PP-ADMM, each agent approximately solves a perturbed optimization problem that is formulated from its local private data in an iteration, and then perturbs the approximate solution with Gaussian noise to provide the DP guarantee. To further improve the model accuracy and convergence, an improved version IPP-ADMM adopts sparse vector technique (SVT) to determine if an agent should update its neighbors with the current perturbed solution. The agent calculates the difference of the current solution from that in the last iteration, and if the difference is larger than a threshold, it passes the solution to neighbors; or otherwise the solution will be discarded. Moreover, we propose to track the total privacy loss under the zero-concentrated DP (zCDP) and provide a generalization performance analysis. Experiments on real-world datasets demonstrate that under the same privacy guarantee, the proposed algorithms are superior to the state of the art in terms of model accuracy and convergence rate.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Water Supply Prediction Based on Initialized Attention Residual Network

Real-time and accurate water supply forecast is crucial for water plant. However, most existing methods are likely affected by factors such as weather and holidays, which lead to a decline in the reliability of water supply prediction. In this paper, we address a generic artificial neural network, called Initialized Attention Residual Network (IARN), which is combined with an attention module and residual modules. Specifically, instead of continuing to use the recurrent neural network (RNN) in time-series tasks, we try to build a convolution neural network (CNN)to recede the disturb from other factors, relieve the limitation of memory size and get a more credible results. Our method achieves state-of-the-art performance on several data sets, in terms of accuracy, robustness and generalization ability.

0 citations0 reviewsNo code linkedOpen access