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Researcher profile

Yang Xiang

Yang Xiang contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Cryptography and SecurityMachine LearningComputation and Languagecond-mat.mtrl-scieess.ASmath.APSoundInformation RetrievalArtificial IntelligenceComputer Visionmath.NANumerical Analysiscond-mat.mes-hallDistributed, Parallel, and Cluster Computingmath-phmath.MPmath.OCMethodologyNeural and Evolutionary Computing

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Published work

33 published item(s)

preprint2026arXiv

Diffeomorphic Cortical Alignment via Direct Warping of Streamline Endpoints

Cortical surface registration is often driven by local geometric descriptors (e.g., sulcal depth and curvature). While this approach achieves geometric correspondence, it neglects the long-range wiring constraints imposed by white-matter anatomy. Diffusion MRI tractography offers these crucial constraints; however, prior connectivity-informed pipelines typically align precomputed connectivity matrices, making the optimization highly sensitive to connectivity estimation and its resolution. In this paper, we introduce a novel connectivity-based surface registration method that aligns cortical surfaces by operating directly on white-matter fiber-tract endpoints. We model tract endpoints as a point cloud on the product manifold $Ω\times Ω$, where $Ω$ represents the spherical domain of the inflated cortical hemispheres. Our alignment method iteratively (i) computes a small diffeomorphic warp for $Ω$ by minimizing connectivity mismatch, and (ii) updates the endpoints based on this warp. The method relies on a geometric framework that ensures output warps are diffeomorphisms and has a final goal that optimizes the matching of well-known fiber bundles. Experiments on Human Connectome Project (HCP) data demonstrate improved tract-level correspondence, achieving higher connectivity-level overlap coefficients on major fiber bundles and stronger robustness across grid resolutions for $Ω$ compared to state-of-the-art methods such as ENCORE and MSMAll.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Exploring the Translation Mechanism of Large Language Models

While large language models (LLMs) demonstrate remarkable success in multilingual translation, their internal core translation mechanisms, even at the fundamental word level, remain insufficiently understood. To address this critical gap, this work introduces a systematic framework for interpreting the mechanism behind LLM translation from the perspective of computational components. This paper first proposes subspace-intervened path patching for precise, fine-grained causal analysis, enabling the detection of components crucial to translation tasks and subsequently characterizing their behavioral patterns in human-interpretable terms. Comprehensive experiments reveal that translation is predominantly driven by a sparse subset of components: specialized attention heads serve critical roles in extracting source language, translation indicators, and positional features, which are then integrated and processed by specific multi-layer perceptrons (MLPs) into intermediary English-centric latent representations before ultimately yielding the final translation. The significance of these findings is underscored by the empirical demonstration that targeted fine-tuning a minimal parameter subset ($<5\%$) enhances translation performance while preserving general capabilities. This result further indicates that these crucial components generalize effectively to sentence-level translation and are instrumental in elucidating more intricate translation tasks.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

StablePDENet: Enhancing Stability of Operator Learning for Solving Differential Equations

Learning solution operators for differential equations with neural networks has shown great potential in scientific computing, but ensuring their stability under input perturbations remains a critical challenge. This paper presents a robust self-supervised neural operator framework that enhances stability through adversarial training while preserving accuracy. We formulate operator learning as a min-max optimization problem, where the model is trained against worst-case input perturbations to achieve consistent performance under both normal and adversarial conditions. We demonstrate that our method not only achieves good performance on standard inputs, but also maintains high fidelity under adversarial perturbed inputs. The results highlight the importance of stability-aware training in operator learning and provide a foundation for developing reliable neural PDE solvers in real-world applications, where input noise and uncertainties are inevitable.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

TeachPro: Multi-Label Qualitative Teaching Evaluation via Cross-View Graph Synergy and Semantic Anchored Evidence Encoding

Standardized Student Evaluation of Teaching often suffer from low reliability, restricted response options, and response distortion. Existing machine learning methods that mine open-ended comments usually reduce feedback to binary sentiment, which overlooks concrete concerns such as content clarity, feedback timeliness, and instructor demeanor, and provides limited guidance for instructional improvement.We propose TeachPro, a multi-label learning framework that systematically assesses five key teaching dimensions: professional expertise, instructional behavior, pedagogical efficacy, classroom experience, and other performance metrics. We first propose a Dimension-Anchored Evidence Encoder, which integrates three core components: (i) a pre-trained text encoder that transforms qualitative feedback annotations into contextualized embeddings; (ii) a prompt module that represents five teaching dimensions as learnable semantic anchors; and (iii) a cross-attention mechanism that aligns evidence with pedagogical dimensions within a structured semantic space. We then propose a Cross-View Graph Synergy Network to represent student comments. This network comprises two components: (i) a Syntactic Branch that extracts explicit grammatical dependencies from parse trees, and (ii) a Semantic Branch that models latent conceptual relations derived from BERT-based similarity graphs. BiAffine fusion module aligns syntactic and semantic units, while a differential regularizer disentangles embeddings to encourage complementary representations. Finally, a cross-attention mechanism bridges the dimension-anchored evidence with the multi-view comment representations. We also contribute a novel benchmark dataset featuring expert qualitative annotations and multi-label scores. Extensive experiments demonstrate that TeachPro offers superior diagnostic granularity and robustness across diverse evaluation settings.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

Dual prototype attentive graph network for cross-market recommendation

Cross-market recommender systems (CMRS) aim to utilize historical data from mature markets to promote multinational products in emerging markets. However, existing CMRS approaches often overlook the potential for shared preferences among users in different markets, focusing primarily on modeling specific preferences within each market. In this paper, we argue that incorporating both market-specific and market-shared insights can enhance the generalizability and robustness of CMRS. We propose a novel approach called Dual Prototype Attentive Graph Network for Cross-Market Recommendation (DGRE) to address this. DGRE leverages prototypes based on graph representation learning from both items and users to capture market-specific and market-shared insights. Specifically, DGRE incorporates market-shared prototypes by clustering users from various markets to identify behavioural similarities and create market-shared user profiles. Additionally, it constructs item-side prototypes by aggregating item features within each market, providing valuable market-specific insights. We conduct extensive experiments to validate the effectiveness of DGRE on a real-world cross-market dataset, and the results show that considering both market-specific and market-sharing aspects in modelling can improve the generalization and robustness of CMRS.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

Non-Euclidean interfaces decode the continuous landscape of graphene-induced surface reconstructions

Interfacial reconstruction between two-dimensional (2D) materials and metal substrates fundamentally governs heterostructure properties, yet conventional flat substrates fail to capture the continuous crystallographic landscape. Here, we overcome this topological limitation using non-Euclidean interfaces-curved 2D graphene-copper surfaces as a model system-to traverse the infinite spectrum of lattice orientations. By integrating multimodal microscopy with a deep-learning-enhanced dimensional upscaling framework, we translate 2D scanning electron microscopy (SEM) contrast into quantitative three-dimensional (3D) morphologies with accurate facet identification. Coupling these observations with machine-learning-assisted density functional theory, we demonstrate that reconstruction is governed by a unified thermodynamic mechanism where high-index facets correspond to specific local minima in the surface energy landscape. This work resolves the long-standing complexity of graphene-copper faceting and establishes non-Euclidean surface topologies as a generalizable paradigm for decoding and controlling interfacial reconstruction in diverse metal-2D material systems.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

RDSA: A Robust Deep Graph Clustering Framework via Dual Soft Assignment

Graph clustering is an essential aspect of network analysis that involves grouping nodes into separate clusters. Recent developments in deep learning have resulted in graph clustering, which has proven effective in many applications. Nonetheless, these methods often encounter difficulties when dealing with real-world graphs, particularly in the presence of noisy edges. Additionally, many denoising graph clustering methods tend to suffer from lower performance, training instability, and challenges in scaling to large datasets compared to non-denoised models. To tackle these issues, we introduce a new framework called the Robust Deep Graph Clustering Framework via Dual Soft Assignment (RDSA). RDSA consists of three key components: (i) a node embedding module that effectively integrates the graph&#39;s topological features and node attributes; (ii) a structure-based soft assignment module that improves graph modularity by utilizing an affinity matrix for node assignments; and (iii) a node-based soft assignment module that identifies community landmarks and refines node assignments to enhance the model&#39;s robustness. We assess RDSA on various real-world datasets, demonstrating its superior performance relative to existing state-of-the-art methods. Our findings indicate that RDSA provides robust clustering across different graph types, excelling in clustering effectiveness and robustness, including adaptability to noise, stability, and scalability.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Bayesian Permutation training deep representation learning method for speech enhancement with variational autoencoder

Recently, variational autoencoder (VAE), a deep representation learning (DRL) model, has been used to perform speech enhancement (SE). However, to the best of our knowledge, current VAE-based SE methods only apply VAE to the model speech signal, while noise is modeled using the traditional non-negative matrix factorization (NMF) model. One of the most important reasons for using NMF is that these VAE-based methods cannot disentangle the speech and noise latent variables from the observed signal. Based on Bayesian theory, this paper derives a novel variational lower bound for VAE, which ensures that VAE can be trained in supervision, and can disentangle speech and noise latent variables from the observed signal. This means that the proposed method can apply the VAE to model both speech and noise signals, which is totally different from the previous VAE-based SE works. More specifically, the proposed DRL method can learn to impose speech and noise signal priors to different sets of latent variables for SE. The experimental results show that the proposed method can not only disentangle speech and noise latent variables from the observed signal but also obtain a higher scale-invariant signal-to-distortion ratio and speech quality score than the similar deep neural network-based (DNN) SE method.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A deep representation learning speech enhancement method using $β$-VAE

In previous work, we proposed a variational autoencoder-based (VAE) Bayesian permutation training speech enhancement (SE) method (PVAE) which indicated that the SE performance of the traditional deep neural network-based (DNN) method could be improved by deep representation learning (DRL). Based on our previous work, we in this paper propose to use $β$-VAE to further improve PVAE&#39;s ability of representation learning. More specifically, our $β$-VAE can improve PVAE&#39;s capacity of disentangling different latent variables from the observed signal without the trade-off problem between disentanglement and signal reconstruction. This trade-off problem widely exists in previous $β$-VAE algorithms. Unlike the previous $β$-VAE algorithms, the proposed $β$-VAE strategy can also be used to optimize the DNN&#39;s structure. This means that the proposed method can not only improve PVAE&#39;s SE performance but also reduce the number of PVAE training parameters. The experimental results show that the proposed method can acquire better speech and noise latent representation than PVAE. Meanwhile, it also obtains a higher scale-invariant signal-to-distortion ratio, speech quality, and speech intelligibility.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Approximation of Functionals by Neural Network without Curse of Dimensionality

In this paper, we establish a neural network to approximate functionals, which are maps from infinite dimensional spaces to finite dimensional spaces. The approximation error of the neural network is $O(1/\sqrt{m})$ where $m$ is the size of networks, which overcomes the curse of dimensionality. The key idea of the approximation is to define a Barron spectral space of functionals.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Bunching instability and asymptotic properties in epitaxial growth with elasticity effects: continuum model

We study the continuum epitaxial model for elastic interacting atomic steps on vicinal surfaces proposed by Xiang and E (Xiang, SIAM J. Appl. Math. 63:241-258, 2002; Xiang and E, Phys. Rev. B 69:035409, 2004). The non-local term and the singularity complicate the analysis of its PDE. In this paper, we first generalize this model to the Lennard-Jones (m,n) interaction between steps. Based on several important formulations of the non-local energy, we prove the existence, symmetry, unimodality, and regularity of the energy minimizer in the periodic setting. In particular, the symmetry and unimodality of the minimizer implies that it has a bunching profile. Furthermore, we derive the minimum energy scaling law for the original continnum model. All results are consistent with the corresponding results proved for discrete models by Luo et al. (Luo et al., Multiscale Model. Simul. 14:737 - 771, 2016).

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

CATNet: Cross-event Attention-based Time-aware Network for Medical Event Prediction

Medical event prediction (MEP) is a fundamental task in the medical domain, which needs to predict medical events, including medications, diagnosis codes, laboratory tests, procedures, outcomes, and so on, according to historical medical records. The task is challenging as medical data is a type of complex time series data with heterogeneous and temporal irregular characteristics. Many machine learning methods that consider the two characteristics have been proposed for medical event prediction. However, most of them consider the two characteristics separately and ignore the correlations among different types of medical events, especially relations between historical medical events and target medical events. In this paper, we propose a novel neural network based on attention mechanism, called cross-event attention-based time-aware network (CATNet), for medical event prediction. It is a time-aware, event-aware and task-adaptive method with the following advantages: 1) modeling heterogeneous information and temporal information in a unified way and considering temporal irregular characteristics locally and globally respectively, 2) taking full advantage of correlations among different types of events via cross-event attention. Experiments on two public datasets (MIMIC-III and eICU) show CATNet can be adaptive with different MEP tasks and outperforms other state-of-the-art methods on various MEP tasks. The source code of CATNet will be released after this manuscript is accepted.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Existence, uniqueness, and energy scaling of 2+1 dimensional continuum model for stepped epitaxial surfaces with elastic effects

We study the 2+1 dimensional continuum model for the evolution of stepped epitaxial surface under long-range elastic interaction proposed by Xu and Xiang (SIAM J. Appl. Math. 69, 1393-1414, 2009). The long-range interaction term and the two length scales in this model makes PDE analysis challenging. Moreover, unlike in the 1+1 dimensional case, there is a nonconvexity contribution in the total energy in the 2+1 dimensional case, and it is not easy to prove that the solution is always in the well-posed regime during the evolution. In this paper, we propose a modified 2+1 dimensional continuum model based on the underlying physics. This modification fixes the problem of possible illposedness due to the nonconvexity of the energy functional. We prove the existence and uniqueness of both the static and dynamic solutions and derive a minimum energy scaling law for them. We show that the minimum energy surface profile is mainly attained by surfaces with step meandering instability. This is essentially different from the energy scaling law for the 1+1 dimensional epitaxial surfaces under elastic effects attained by step bunching surface profiles. We also discuss the transition from the step bunching instability to the step meandering instability in 2+1 dimensions.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Exploring Unfairness on Proof of Authority: Order Manipulation Attacks and Remedies

Proof of Authority (PoA) is a type of permissioned consensus algorithm with a fixed committee. PoA has been widely adopted by communities and industries due to its better performance and faster finality. In this paper, we explore the \textit{unfairness} issue existing in the current PoA implementations. We have investigated 2,500+ \textit{in the wild} projects and selected 10+ as our main focus (covering Ethereum, Binance smart chain, etc.). We have identified two types of order manipulation attacks to separately break the transaction-level (a.k.a. transaction ordering) and the block-level (sealer position ordering) fairness. Both of them merely rely on honest-but-\textit{profitable} sealer assumption without modifying original settings. We launch these attacks on the forked branches under an isolated environment and carefully evaluate the attacking scope towards different implementations. To date (as of Nov 2021), the potentially affected PoA market cap can reach up to $681,087$ million USD. Besides, we further dive into the source code of selected projects, and accordingly, propose our recommendation for the fix. To the best of knowledge, this work provides the first exploration of the \textit{unfairness} issue in PoA algorithms.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

FAAG: Fast Adversarial Audio Generation through Interactive Attack Optimisation

Automatic Speech Recognition services (ASRs) inherit deep neural networks&#39; vulnerabilities like crafted adversarial examples. Existing methods often suffer from low efficiency because the target phases are added to the entire audio sample, resulting in high demand for computational resources. This paper proposes a novel scheme named FAAG as an iterative optimization-based method to generate targeted adversarial examples quickly. By injecting the noise over the beginning part of the audio, FAAG generates adversarial audio in high quality with a high success rate timely. Specifically, we use audio&#39;s logits output to map each character in the transcription to an approximate position of the audio&#39;s frame. Thus, an adversarial example can be generated by FAAG in approximately two minutes using CPUs only and around ten seconds with one GPU while maintaining an average success rate over 85%. Specifically, the FAAG method can speed up around 60% compared with the baseline method during the adversarial example generation process. Furthermore, we found that appending benign audio to any suspicious examples can effectively defend against the targeted adversarial attack. We hope that this work paves the way for inventing new adversarial attacks against speech recognition with computational constraints.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Formal Security Analysis on dBFT Protocol of NEO

NEO is one of the top public chains worldwide. We focus on its backbone consensus protocol, called delegated Byzantine Fault Tolerance (dBFT). The dBFT protocol has been adopted by a variety of blockchain systems such as ONT. dBFT claims to guarantee the security when no more than $f = \lfloor \frac{n}{3} \rfloor$ nodes are Byzantine, where $n$ is the total number of consensus participants. However, we identify attacks to break the claimed security. In this paper, we show our results by providing a security analysis on its dBFT protocol. First, we evaluate NEO&#39;s source code and formally present the procedures of dBFT via the state machine replication (SMR) model. Next, we provide a theoretical analysis with two example attacks. These attacks break the security of dBFT with no more than $f$ nodes. Then, we provide recommendations on how to fix the system against the identified attacks. The suggested fixes have been accepted by the NEO official team. Finally, we further discuss the reasons causing such issues, the relationship with current permissioned blockchain systems, and the scope of potential influence.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Nebula-I: A General Framework for Collaboratively Training Deep Learning Models on Low-Bandwidth Cloud Clusters

The ever-growing model size and scale of compute have attracted increasing interests in training deep learning models over multiple nodes. However, when it comes to training on cloud clusters, especially across remote clusters, huge challenges are faced. In this work, we introduce a general framework, Nebula-I, for collaboratively training deep learning models over remote heterogeneous clusters, the connections between which are low-bandwidth wide area networks (WANs). We took natural language processing (NLP) as an example to show how Nebula-I works in different training phases that include: a) pre-training a multilingual language model using two remote clusters; and b) fine-tuning a machine translation model using knowledge distilled from pre-trained models, which run through the most popular paradigm of recent deep learning. To balance the accuracy and communication efficiency, in Nebula-I, parameter-efficient training strategies, hybrid parallel computing methods and adaptive communication acceleration techniques are jointly applied. Meanwhile, security strategies are employed to guarantee the safety, reliability and privacy in intra-cluster computation and inter-cluster communication. Nebula-I is implemented with the PaddlePaddle deep learning framework, which can support collaborative training over heterogeneous hardware, e.g. GPU and NPU. Experiments demonstrate that the proposed framework could substantially maximize the training efficiency while preserving satisfactory NLP performance. By using Nebula-I, users can run large-scale training tasks over cloud clusters with minimum developments, and the utility of existed large pre-trained models could be further promoted. We also introduced new state-of-the-art results on cross-lingual natural language inference tasks, which are generated based upon a novel learning framework and Nebula-I.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Stochastic Continuum Models for High--Entropy Alloys with Short-range Order

High entropy alloys (HEAs) are a class of novel materials that exhibit superb engineering properties. It has been demonstrated by extensive experiments and first principles/atomistic simulations that short-range order in the atomic level randomness strongly influences the properties of HEAs. In this paper, we derive stochastic continuum models for HEAs with short-range order from atomistic models. A proper continuum limit is obtained such that the mean and variance of the atomic level randomness together with the short-range order described by a characteristic length are kept in the process from the atomistic interaction model to the continuum equation. The obtained continuum model with short-range order is in the form of an Ornstein--Uhlenbeck (OU) process. This validates the continuum model based on the OU process adopted phenomenologically by Zhang et al. [Acta Mater., 166 (2019), pp. 424--434] for HEAs with short-range order. We derive such stochastic continuum models with short-range order for both elasticity in HEAs without defects and HEAs with dislocations (line defects). The obtained stochastic continuum models are based on the energy formulations, whose variations lead to stochastic partial differential equations.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Video is All You Need: Attacking PPG-based Biometric Authentication

Unobservable physiological signals enhance biometric authentication systems. Photoplethysmography (PPG) signals are convenient owning to its ease of measurement and are usually well protected against remote adversaries in authentication. Any leaked PPG signals help adversaries compromise the biometric authentication systems, and the advent of remote PPG (rPPG) enables adversaries to acquire PPG signals through restoration. While potentially dangerous, rPPG-based attacks are overlooked because existing methods require the victim&#39;s PPG signals. This paper proposes a novel spoofing attack approach that uses the waveforms of rPPG signals extracted from video clips to fool the PPG-based biometric authentication. We develop a new PPG restoration model that does not require leaked PPG signals for adversarial attacks. Test results on state-of-art PPG-based biometric authentication show that the signals recovered through rPPG pose a severe threat to PPG-based biometric authentication.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Weak solutions to an initial-boundary value problem for a continuum equation of motion of grain boundaries

We investigate an initial-(periodic-)boundary value problem for a continuum equation, which is a model for motion of grain boundaries based on the underlying microscopic mechanisms of line defects (disconnections) and integrated the effects of a diverse range of thermodynamic driving forces. We first prove the global-in-time existence and uniqueness of weak solution to this initial-boundary value problem in the case with positive equilibrium disconnection density parameter B, and then investigate the asymptotic behavior of the solutions as B goes to zero. The main difficulties in the proof of main theorems are due to the degeneracy of B=0, a non-local term with singularity, and a non-smooth coefficient of the highest derivative associated with the gradient of the unknown. The key ingredients in the proof are the energy method, an estimate for a singular integral of the Hilbert type, and a compactness lemma.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Well-posedness of a modified degenerate Cahn-Hilliard model for surface diffusion

We study the well-posedness of a modified degenerate Cahn-Hilliard type model for surface diffusion. With degenerate phase-dependent diffusion mobility and additional stabilizing function, this model is able to give the correct sharp interface limit. We introduce a notion of weak solutions for the nonlinear model. The existence result is obtained by approximations of the proposed model with nondegenerate mobilities. We also employ this method to prove existence of weak solutions to a related model where the chemical potential contains a nonlocal term originated from self-climb of dislocations in crystalline materials.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A New Formulation of Coupling and Sliding Motions of Grain Boundaries Based on Dislocation Structure

A continuum model of the two dimensional low angle grain boundary motion and the dislocation structure evolution on the grain boundaries has been developed in Ref. [48]. The model is based on the motion and reaction of the constituent dislocations of the grain boundaries. The long-range elastic interaction between dislocations is included in the continuum model, and it maintains a stable dislocation structure described by the Frank&#39;s formula for grain boundaries. In this paper, we develop a new continuum model for the coupling and sliding motions of grain boundaries that avoids the time-consuming calculation of the long-range elastic interaction. In this model, the long-range elastic interaction is replaced by a constraint of the Frank&#39;s formula. The constrained evolution problem in our new continuum model is further solved by using the projection method. Effects of the coupling and sliding motions in our new continuum model and relationship with the classical motion by curvature model are discussed. The continuum model is validated by comparisons with discrete dislocation dynamics model and the early continuum model [48] in which the long-range dislocation interaction is explicitly calculated.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A Speech Enhancement Algorithm based on Non-negative Hidden Markov Model and Kullback-Leibler Divergence

In this paper, we propose a novel supervised single-channel speech enhancement method combing the the Kullback-Leibler divergence-based non-negative matrix factorization (NMF) and hidden Markov model (NMF-HMM). With the application of HMM, the temporal dynamics information of speech signals can be taken into account. In the training stage, the sum of Poisson, leading to the KL divergence measure, is used as the observation model for each state of HMM. This ensures that a computationally efficient multiplicative update can be used for the parameter update of the proposed model. In the online enhancement stage, we propose a novel minimum mean-square error (MMSE) estimator for the proposed NMF-HMM. This estimator can be implemented using parallel computing, saving the time complexity. The performance of the proposed algorithm is verified by objective measures. The experimental results show that the proposed strategy achieves better speech enhancement performance than state-of-the-art speech enhancement methods. More specifically, compared with the traditional NMF-based speech enhancement methods, our proposed algorithm achieves a 5\% improvement for short-time objective intelligibility (STOI) and 0.18 improvement for perceptual evaluation of speech quality (PESQ).

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Analysis of Trending Topics and Text-based Channels of Information Delivery in Cybersecurity

Computer users are generally faced with difficulties in making correct security decisions. While an increasingly fewer number of people are trying or willing to take formal security training, online sources including news, security blogs, and websites are continuously making security knowledge more accessible. Analysis of cybersecurity texts can provide insights into the trending topics and identify current security issues as well as how cyber attacks evolve over time. These in turn can support researchers and practitioners in predicting and preparing for these attacks. Comparing different sources may facilitate the learning process for normal users by persisting the security knowledge gained from different cybersecurity context. Prior studies neither systematically analysed the wide-range of digital sources nor provided any standardisation in analysing the trending topics from recent security texts. Although LDA has been widely adopted in topic generation, its generated topics cannot cover the cybersecurity concepts completely and considerably overlap. To address this issue, we propose a semi-automated classification method to generate comprehensive security categories instead of LDA-generated topics. We further compare the identified 16 security categories across different sources based on their popularity and impact. We have revealed several surprising findings. (1) The impact reflected from cyber-security texts strongly correlates with the monetary loss caused by cybercrimes. (2) For most categories, security blogs share the largest popularity and largest absolute/relative impact over time. (3) Websites deliver security information without caring about timeliness much, where one third of the articles do not specify the date and the rest have a time lag in posting emerging security issues.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Catering to Your Concerns: Automatic Generation of Personalised Security-Centric Descriptions for Android Apps

Android users are increasingly concerned with the privacy of their data and security of their devices. To improve the security awareness of users, recent automatic techniques produce security-centric descriptions by performing program analysis. However, the generated text does not always address users&#39; concerns as they are generally too technical to be understood by ordinary users. Moreover, different users have varied linguistic preferences, which do not match the text. Motivated by this challenge, we develop an innovative scheme to help users avoid malware and privacy-breaching apps by generating security descriptions that explain the privacy and security related aspects of an Android app in clear and understandable terms. We implement a prototype system, PERSCRIPTION, to generate personalised security-centric descriptions that automatically learn users&#39; security concerns and linguistic preferences to produce user-oriented descriptions. We evaluate our scheme through experiments and user studies. The results clearly demonstrate the improvement on readability and users&#39; security awareness of PERSCRIPTION&#39;s descriptions compared to existing description generators.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Daedalus: Breaking Non-Maximum Suppression in Object Detection via Adversarial Examples

This paper demonstrates that Non-Maximum Suppression (NMS), which is commonly used in Object Detection (OD) tasks to filter redundant detection results, is no longer secure. Considering that NMS has been an integral part of OD systems, thwarting the functionality of NMS can result in unexpected or even lethal consequences for such systems. In this paper, an adversarial example attack which triggers malfunctioning of NMS in end-to-end OD models is proposed. The attack, namely \texttt{Daedalus}, compresses the dimensions of detection boxes to evade NMS. As a result, the final detection output contains extremely dense false positives. This can be fatal for many OD applications such as autonomous vehicles and surveillance systems. The attack can be generalised to different end-to-end OD models, such that the attack cripples various OD applications. Furthermore, a way to craft robust adversarial examples is developed by using an ensemble of popular detection models as the substitutes. Considering the pervasive nature of model reusing in real-world OD scenarios, Daedalus examples crafted based on an \textit{ensemble of substitutes} can launch attacks without knowing the parameters of the victim models. Experimental results demonstrate that the attack effectively stops NMS from filtering redundant bounding boxes. As the evaluation results suggest, Daedalus increases the false positive rate in detection results to $99.9\%$ and reduces the mean average precision scores to $0$, while maintaining a low cost of distortion on the original inputs. It is also demonstrated that the attack can be practically launched against real-world OD systems via printed posters.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Defending against Adversarial Attack towards Deep Neural Networks via Collaborative Multi-task Training

Deep neural networks (DNNs) are known to be vulnerable to adversarial examples which contain human-imperceptible perturbations. A series of defending methods, either proactive defence or reactive defence, have been proposed in the recent years. However, most of the methods can only handle specific attacks. For example, proactive defending methods are invalid against grey-box or white-box attacks, while reactive defending methods are challenged by low-distortion adversarial examples or transferring adversarial examples. This becomes a critical problem since a defender usually does not have the type of the attack as a priori knowledge. Moreover, existing two-pronged defences (e.g., MagNet), which take advantages of both proactive and reactive methods, have been reported as broken under transferring attacks. To address this problem, this paper proposed a novel defensive framework based on collaborative multi-task training, aiming at providing defence for different types of attacks. The proposed defence first encodes training labels into label pairs and counters black-box attacks leveraging adversarial training supervised by the encoded label pairs. The defence further constructs a detector to identify and reject high-confidence adversarial examples that bypass the black-box defence. In addition, the proposed collaborative architecture can prevent adversaries from finding valid adversarial examples when the defence strategy is exposed. In the experiments, we evaluated our defence against four state-of-the-art attacks on $MNIST$ and $CIFAR10$ datasets. The results showed that our defending method achieved up to $96.3\%$ classification accuracy on black-box adversarial examples, and detected up to $98.7\%$ of the high confidence adversarial examples. It only decreased the model accuracy on benign example classification by $2.1\%$ for the $CIFAR10$ dataset.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Hybrid Neural Tagging Model for Open Relation Extraction

Open relation extraction (ORE) remains a challenge to obtain a semantic representation by discovering arbitrary relation tuples from the unstructured text. Conventional methods heavily depend on feature engineering or syntactic parsing, they are inefficient or error-cascading. Recently, leveraging supervised deep learning structures to address the ORE task is an extraordinarily promising way. However, there are two main challenges: (1) The lack of enough labeled corpus to support supervised training; (2) The exploration of specific neural architecture that adapts to the characteristics of open relation extracting. In this paper, to overcome these difficulties, we build a large-scale, high-quality training corpus in a fully automated way, and design a tagging scheme to assist in transforming the ORE task into a sequence tagging processing. Furthermore, we propose a hybrid neural network model (HNN4ORT) for open relation tagging. The model employs the Ordered Neurons LSTM to encode potential syntactic information for capturing the associations among the arguments and relations. It also emerges a novel Dual Aware Mechanism, including Local-aware Attention and Global-aware Convolution. The dual aware nesses complement each other so that the model can take the sentence-level semantics as a global perspective, and at the same time implement salient local features to achieve sparse annotation. Experimental results on various testing sets show that our model can achieve state-of-the-art performances compared to the conventional methods or other neural models.

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preprint2020arXiv

Incorporating Uncertain Segmentation Information into Chinese NER for Social Media Text

Chinese word segmentation is necessary to provide word-level information for Chinese named entity recognition (NER) systems. However, segmentation error propagation is a challenge for Chinese NER while processing colloquial data like social media text. In this paper, we propose a model (UIcwsNN) that specializes in identifying entities from Chinese social media text, especially by leveraging ambiguous information of word segmentation. Such uncertain information contains all the potential segmentation states of a sentence that provides a channel for the model to infer deep word-level characteristics. We propose a trilogy (i.e., candidate position embedding -> position selective attention -> adaptive word convolution) to encode uncertain word segmentation information and acquire appropriate word-level representation. Experiments results on the social media corpus show that our model alleviates the segmentation error cascading trouble effectively, and achieves a significant performance improvement of more than 2% over previous state-of-the-art methods.

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preprint2020arXiv

Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease prediction

Deep learning (DL) based predictive models from electronic health records (EHR) deliver impressive performance in many clinical tasks. Large training cohorts, however, are often required to achieve high accuracy, hindering the adoption of DL-based models in scenarios with limited training data size. Recently, bidirectional encoder representations from transformers (BERT) and related models have achieved tremendous successes in the natural language processing domain. The pre-training of BERT on a very large training corpus generates contextualized embeddings that can boost the performance of models trained on smaller datasets. We propose Med-BERT, which adapts the BERT framework for pre-training contextualized embedding models on structured diagnosis data from 28,490,650 patients EHR dataset. Fine-tuning experiments are conducted on two disease-prediction tasks: (1) prediction of heart failure in patients with diabetes and (2) prediction of pancreatic cancer from two clinical databases. Med-BERT substantially improves prediction accuracy, boosting the area under receiver operating characteristics curve (AUC) by 2.02-7.12%. In particular, pre-trained Med-BERT substantially improves the performance of tasks with very small fine-tuning training sets (300-500 samples) boosting the AUC by more than 20% or equivalent to the AUC of 10 times larger training set. We believe that Med-BERT will benefit disease-prediction studies with small local training datasets, reduce data collection expenses, and accelerate the pace of artificial intelligence aided healthcare.

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preprint2020arXiv

Security Analysis on Tangle-based Blockchain through Simulation

The Tangle-based structure becomes one of the most promising solutions when designing DAG-based blockchain systems. The approach improves the scalability by directly confirming multiple transactions in parallel instead of single blocks in linear. However, the performance gain may bring potential security risks. In this paper, we construct three types of attacks with comprehensive evaluations, namely parasite attack (PS), double spending attack (DS), and hybrid attack (HB). To achieve that, we deconstruct the Tangle-based projects (e.g. IOTA) and abstract the main components to rebuild a simple but flexible network for the simulation. Then, we informally define three smallest actions to build up the attack strategies layer by layer. Based on that, we provide analyses to evaluate different types of attacks. To the best of our knowledge, this is the first study to provide a comprehensive security analysis of Tangle-based blockchains.

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preprint2020arXiv

SIAT: A Systematic Inter-Component Communication Analysis Technology for Detecting Threats on Android

In this paper, we present the design and implementation of a Systematic Inter-Component Communication Analysis Technology (SIAT) consisting of two key modules: \emph{Monitor} and \emph{Analyzer}. As an extension to the Android operating system at framework layer, the \emph{Monitor} makes the first attempt to revise the taint tag approach named TaintDroid both at method-level and file-level, to migrate it to the app-pair ICC paths identification through systemwide tracing and analysis of taint in intent both at the data flow and control flow. By taking over the taint logs offered by the \emph{Monitor}, the \emph{Analyzer} can build the accurate and integrated ICC models adopted to identify the specific threat models with the detection algorithms and predefined rules. Meanwhile, we employ the models&#39; deflation technology to improve the efficiency of the \emph{Analyzer}. We implement the SIAT with Android Open Source Project and evaluate its performance through extensive experiments on well-known datasets and real-world apps. The experimental results show that, compared to state-of-the-art approaches, the SIAT can achieve about 25\%$\sim$200\% accuracy improvements with 1.0 precision and 0.98 recall at the cost of negligible runtime overhead. Moreover, the SIAT can identify two undisclosed cases of bypassing that prior technologies cannot detect and quite a few malicious ICC threats in real-world apps with lots of downloads on the Google Play market.

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preprint2020arXiv

Stochastic Peierls-Nabarro Model for Dislocations in High Entropy Alloys

High entropy alloys (HEAs) are single phase crystals that consist of random solid solutions of multiple elements in approximately equal proportions. This class of novel materials have exhibited superb mechanical properties, such as high strength combined with other desired features. The strength of crystalline materials is associated with the motion of dislocations. In this paper, we derive a stochastic continuum model based on the Peierls-Nabarro framework for inter-layer dislocations in a bilayer HEA from an atomistic model that incorporates the atomic level randomness. We use asymptotic analysis and limit theorem in the convergence from the atomistic model to the continuum model. The total energy in the continuum model consists of a stochastic elastic energy in the two layers, and a stochastic misfit energy that accounts for the inter-layer nonlinear interaction. The obtained continuum model can be considered as a stochastic generalization of the classical, deterministic Peierls-Nabarro model for the dislocation core and related properties. This derivation also validates the stochastic model adopted by Zhang et al. (Acta Mater. 166, 424-434, 2019).

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