arXiv subjects

Wei Kang

Publications and source records attributed to Wei Kang.

At least 37 records · Page 2Linked to original sources

arXiv2025

Capacity of Hierarchical Secure Coded Gradient Aggregation with Straggling Communication Links

The growing privacy concerns in distributed learning have led to the widespread adoption of secure aggregation techniques in distributed machine learning systems, such as federated learning. Motivated by a coded gradient aggregation problem in a user-helper-master hierarchical network setting with straggling communication links, we formulate a new secure hierarchical coded gradient aggregation problem. In our setting, $ K $ users communicate with the master through an intermediate layer of $ N $ helpers, who can communicate with each other. With a resiliency threshold of $ N_r $ for straggling communication links, and at most $ T $ colluding helpers and any number of colluding users, the master aims to recover the sum of all users' gradients while remaining unaware of any individual gradient that exceeds the expected sum. In addition, helpers cannot infer more about users' gradients than what is already known by the colluding users. We propose an achievable scheme where users' upload messages are based on a globally known Vandermonde matrix, and helper communication is facilitated using an extended Vandermonde matrix with special structural properties. A matching converse bound is also derived, establishing the optimal result for this hierarchical coded gradient aggregation problem.

cs.IT↗

arXiv2025

k2SSL: A Faster and Better Framework for Self-Supervised Speech Representation Learning

Self-supervised learning (SSL) has achieved great success in speech-related tasks. While Transformer and Conformer architectures have dominated SSL backbones, encoders like Zipformer, which excel in automatic speech recognition (ASR), remain unexplored in SSL. Concurrently, inefficiencies in data processing within existing SSL training frameworks, such as fairseq, pose challenges in managing the growing volumes of training data. To address these issues, we propose k2SSL, an open-source framework that offers faster, more memory-efficient, and better-performing self-supervised speech representation learning, focusing on downstream ASR tasks. The optimized HuBERT and proposed Zipformer-based SSL systems exhibit substantial reductions in both training time and memory usage during SSL training. Experiments on LibriSpeech demonstrate that Zipformer Base significantly outperforms HuBERT and WavLM, achieving up to a 34.8% relative WER reduction compared to HuBERT Base after fine-tuning, along with a 3.5x pre-training speedup in GPU hours. When scaled to 60k hours of LibriLight data, Zipformer Large exhibits remarkable efficiency, matching HuBERT Large's performance while requiring only 5/8 pre-training steps.

eess.AS↗

arXiv2025

EPhishCADE: A Privacy-Aware Multi-Dimensional Framework for Email Phishing Campaign Detection

Phishing attacks, typically carried out by email, remain a significant cybersecurity threat with attackers creating legitimate-looking websites to deceive recipients into revealing sensitive information or executing harmful actions. In this paper, we propose {\bf EPhishCADE}, the first {\em privacy-aware}, {\em multi-dimensional} framework for {\bf E}mail {\bf Phish}ing {\bf CA}mpaign {\bf DE}tection to automatically identify email phishing campaigns by clustering seemingly unrelated attacks. Our framework employs a hierarchical architecture combining a structural layer and a contextual layer, offering a comprehensive analysis of phishing attacks by thoroughly examining both structural and contextual elements. Specifically, we implement a graph-based contextual layer to reveal hidden similarities across multiple dimensions, including textual, numeric, temporal, and spatial features, among attacks that may initially appear unrelated. Our framework streamlines the handling of security threat reports, reducing analysts' fatigue and workload while enhancing protection against these threats. Another key feature of our framework lies in its sole reliance on phishing URLs in emails without the need for private information, including senders, recipients, content, etc. This feature enables a collaborative identification of phishing campaigns and attacks among multiple organizations without compromising privacy. Finally, we benchmark our framework against an established structure-based study (WWW \textquotesingle 17) to demonstrate its effectiveness.

cs.CR↗

arXiv2025

CR-CTC: Consistency regularization on CTC for improved speech recognition

Connectionist Temporal Classification (CTC) is a widely used method for automatic speech recognition (ASR), renowned for its simplicity and computational efficiency. However, it often falls short in recognition performance. In this work, we propose the Consistency-Regularized CTC (CR-CTC), which enforces consistency between two CTC distributions obtained from different augmented views of the input speech mel-spectrogram. We provide in-depth insights into its essential behaviors from three perspectives: 1) it conducts self-distillation between random pairs of sub-models that process different augmented views; 2) it learns contextual representation through masked prediction for positions within time-masked regions, especially when we increase the amount of time masking; 3) it suppresses the extremely peaky CTC distributions, thereby reducing overfitting and improving the generalization ability. Extensive experiments on LibriSpeech, Aishell-1, and GigaSpeech datasets demonstrate the effectiveness of our CR-CTC. It significantly improves the CTC performance, achieving state-of-the-art results comparable to those attained by transducer or systems combining CTC and attention-based encoder-decoder (CTC/AED). We release our code at https://github.com/k2-fsa/icefall.

eess.AS↗

arXiv2024

LibriheavyMix: A 20,000-Hour Dataset for Single-Channel Reverberant Multi-Talker Speech Separation, ASR and Speaker Diarization

The evolving speech processing landscape is increasingly focused on complex scenarios like meetings or cocktail parties with multiple simultaneous speakers and far-field conditions. Existing methodologies for addressing these challenges fall into two categories: multi-channel and single-channel solutions. Single-channel approaches, notable for their generality and convenience, do not require specific information about microphone arrays. This paper presents a large-scale far-field overlapping speech dataset, crafted to advance research in speech separation, recognition, and speaker diarization. This dataset is a critical resource for decoding ``Who said What and When'' in multi-talker, reverberant environments, a daunting challenge in the field. Additionally, we introduce a pipeline system encompassing speech separation, recognition, and diarization as a foundational benchmark. Evaluations on the WHAMR! dataset validate the broad applicability of the proposed data.

cs.SD↗

arXiv2024

Enhanced Safety in Autonomous Driving: Integrating Latent State Diffusion Model for End-to-End Navigation

With the advancement of autonomous driving, ensuring safety during motion planning and navigation is becoming more and more important. However, most end-to-end planning methods suffer from a lack of safety. This research addresses the safety issue in the control optimization problem of autonomous driving, formulated as Constrained Markov Decision Processes (CMDPs). We propose a novel, model-based approach for policy optimization, utilizing a conditional Value-at-Risk based Soft Actor Critic to manage constraints in complex, high-dimensional state spaces effectively. Our method introduces a worst-case actor to guide safe exploration, ensuring rigorous adherence to safety requirements even in unpredictable scenarios. The policy optimization employs the Augmented Lagrangian method and leverages latent diffusion models to predict and simulate future trajectories. This dual approach not only aids in navigating environments safely but also refines the policy's performance by integrating distribution modeling to account for environmental uncertainties. Empirical evaluations conducted in both simulated and real environment demonstrate that our approach outperforms existing methods in terms of safety, efficiency, and decision-making capabilities.

cs.AI↗

arXiv2024

Demand Private Coded Caching: the Two-File Case

We investigate the demand private coded caching problem, which is an $(N,K)$ coded caching problem with $N$ files, $K$ users, each equipped with a cache of size $M$, and an additional privacy constraint on user demands. We first present a new virtual-user-based achievable scheme for arbitrary number of users and files. Then, for the case of 2 files and arbitrary number of users, we derive some new converse bounds. As a result, we obtain the exact memory-rate tradeoff of the demand private coded caching problem for 2 files and 3 users. As for the case of 2 files and arbitrary number of users, the exact memory-rate tradeoff is characterized for $M\in [0,\frac{2}{K}] \cup [\frac{2(K-1)}{K+1},2]$.

cs.IT↗

arXiv2024

Zipformer: A faster and better encoder for automatic speech recognition

The Conformer has become the most popular encoder model for automatic speech recognition (ASR). It adds convolution modules to a transformer to learn both local and global dependencies. In this work we describe a faster, more memory-efficient, and better-performing transformer, called Zipformer. Modeling changes include: 1) a U-Net-like encoder structure where middle stacks operate at lower frame rates; 2) reorganized block structure with more modules, within which we re-use attention weights for efficiency; 3) a modified form of LayerNorm called BiasNorm allows us to retain some length information; 4) new activation functions SwooshR and SwooshL work better than Swish. We also propose a new optimizer, called ScaledAdam, which scales the update by each tensor's current scale to keep the relative change about the same, and also explictly learns the parameter scale. It achieves faster convergence and better performance than Adam. Extensive experiments on LibriSpeech, Aishell-1, and WenetSpeech datasets demonstrate the effectiveness of our proposed Zipformer over other state-of-the-art ASR models. Our code is publicly available at https://github.com/k2-fsa/icefall.

eess.AS↗

arXiv2024

Rolling bearing fault diagnosis method based on generative adversarial enhanced multi-scale convolutional neural network model

In order to solve the problem that current convolutional neural networks can not capture the correlation features between the time domain signals of rolling bearings effectively, and the model accuracy is limited by the number and quality of samples, a rolling bearing fault diagnosis method based on generative adversarial enhanced multi-scale convolutional neural network model is proposed. Firstly, Gram angular field coding technique is used to encode the time domain signal of the rolling bearing and generate the feature map to retain the complete information of the vibration signal. Then, the re-sulting data is divided into a training set, a validation set, and a test set. Among them, the training set is input into the gradient penalty Wasserstein distance generation adversarial network to complete the training, and a new sample with similar features to the training sample is obtained, and then the original training set is expanded. Next, multi-scale convolution is used to extract the fault features of the extended training set, and the feature graph is normalized by example to overcome the influence of the difference in feature distribution. Finally, the attention mechanism is applied to the adaptive weighting of normalized features and the extraction of deep features, and the fault diagnosis is completed by the softmax classifier. Compared with ResNet method, the experimental results show that the proposed method has better generalization performance and anti-noise performance.

eess.SP↗

arXiv2024

A characteristics-based method for shock-ramp data analysis

For the data analysis problem of shock-ramp compression, i.e., ramp compression after a relatively strong initial shock, a characteristics-based method that strictly deals with the initial hydrodynamic shock is described in detail. Validation of this analysis method using simulated shock-ramp data generated by molecular dynamics and one-dimensional radiation hydrodynamic code is also presented.

cs.CE↗

arXiv2024

One-to-Multiple Clean-Label Image Camouflage (OmClic) based Backdoor Attack on Deep Learning

Image camouflage has been utilized to create clean-label poisoned images for implanting backdoor into a DL model. But there exists a crucial limitation that one attack/poisoned image can only fit a single input size of the DL model, which greatly increases its attack budget when attacking multiple commonly adopted input sizes of DL models. This work proposes to constructively craft an attack image through camouflaging but can fit multiple DL models' input sizes simultaneously, namely OmClic. Thus, through OmClic, we are able to always implant a backdoor regardless of which common input size is chosen by the user to train the DL model given the same attack budget (i.e., a fraction of the poisoning rate). With our camouflaging algorithm formulated as a multi-objective optimization, M=5 input sizes can be concurrently targeted with one attack image, which artifact is retained to be almost visually imperceptible at the same time. Extensive evaluations validate the proposed OmClic can reliably succeed in various settings using diverse types of images. Further experiments on OmClic based backdoor insertion to DL models show that high backdoor performances (i.e., attack success rate and clean data accuracy) are achievable no matter which common input size is randomly chosen by the user to train the model. So that the OmClic based backdoor attack budget is reduced by M$\times$ compared to the state-of-the-art camouflage based backdoor attack as a baseline. Significantly, the same set of OmClic based poisonous attack images is transferable to different model architectures for backdoor implant.

cs.CR↗

arXiv2024

PromptASR for contextualized ASR with controllable style

Prompts are crucial to large language models as they provide context information such as topic or logical relationships. Inspired by this, we propose PromptASR, a framework that integrates prompts in end-to-end automatic speech recognition (E2E ASR) systems to achieve contextualized ASR with controllable style of transcriptions. Specifically, a dedicated text encoder encodes the text prompts and the encodings are injected into the speech encoder by cross-attending the features from two modalities. When using the ground truth text from preceding utterances as content prompt, the proposed system achieves 21.9% and 6.8% relative word error rate reductions on a book reading dataset and an in-house dataset compared to a baseline ASR system. The system can also take word-level biasing lists as prompt to improve recognition accuracy on rare words. An additional style prompt can be given to the text encoder and guide the ASR system to output different styles of transcriptions. The code is available at icefall.

eess.AS↗

arXiv2024

Libriheavy: a 50,000 hours ASR corpus with punctuation casing and context

In this paper, we introduce Libriheavy, a large-scale ASR corpus consisting of 50,000 hours of read English speech derived from LibriVox. To the best of our knowledge, Libriheavy is the largest freely-available corpus of speech with supervisions. Different from other open-sourced datasets that only provide normalized transcriptions, Libriheavy contains richer information such as punctuation, casing and text context, which brings more flexibility for system building. Specifically, we propose a general and efficient pipeline to locate, align and segment the audios in previously published Librilight to its corresponding texts. The same as Librilight, Libriheavy also has three training subsets small, medium, large of the sizes 500h, 5000h, 50000h respectively. We also extract the dev and test evaluation sets from the aligned audios and guarantee there is no overlapping speakers and books in training sets. Baseline systems are built on the popular CTC-Attention and transducer models. Additionally, we open-source our dataset creatation pipeline which can also be used to other audio alignment tasks.

eess.AS↗

arXiv2023

Multiple Threshold Schemes Under The Weak Secure Condition

In this paper, we consider the case that sharing many secrets among a set of participants using the threshold schemes. All secrets are assumed to be statistically independent and the weak secure condition is focused on. Under such circumstances we investigate the infimum of the (average) information ratio and the (average) randomness ratio for any structure pair which consists of the number of the participants and the threshold values of all secrets. For two structure pairs such that the two numbers of the participants are the same and the two arrays of threshold values have the subset relationship, two leading corollaries are proved following two directions. More specifically, the bound related to the lengths of shares, secrets and randomness for the complex structure pair can be truncated for the simple one; and the linear schemes for the simple structure pair can be combined independently to be a multiple threshold scheme for the complex one. The former corollary is useful for the converse part and the latter one is helpful for the achievability part. Three new bounds special for the case that the number of secrets corresponding to the same threshold value $ t $ is lager than $ t $ and two novel linear schemes modified from the Vandermonde matrix for two similar cases are presented. Then come the optimal results for the average information ratio, the average randomness ratio and the randomness ratio. We introduce a tiny example to show that there exists another type of bound that may be crucial for the information ratio, to which we only give optimal results in three cases.

cs.IT↗

arXiv2023

A Cyclic Placement Strategy for Multi-access Coded Caching

We investigate the multi-access coded caching problem, which involves $N$ files, $K$ users, and $K$ caches in this paper. Each user can access $L$ adjacent caches in a cyclic manner. We present a coded placement scheme for the case of cache $M=\frac{K-1}{KL}$, when $\frac{K-1}{L}$ is an integer. The scheme is based on coded placement and involves cyclic placement in caches. In many parameter settings, our scheme achieves a lower transmission rate compared to schemes without coded placement. Additionally, the achieved transmission rate of the proposed scheme is optimal when $L=K-1$ and $N\leq K$.

cs.IT↗

arXiv2023

A composite ansatz for the calculation of dynamical structure factor

We propose an ansatz without adjustable parameters for the calculation of dynamical structure factor. The ansatz combines quasi-particle Green's function, especially the contribution from the renormalization factor, and the exchange-correlation kernel from time-dependent density functional theory together, verified for typical metals and semiconductors from plasmon excitation regime to Compton scattering regime. It has the capability to reconcile both small-angle and large-angle x-ray scattering (IXS) signals with much improved accuracy, which can be used, as the theoretical base model, in inversely inferring electronic structures of condensed matter from IXS experimental signals directly. It may also used to diagnose thermal parameters, such as temperature and density, of dense plasmas in x-ray Thomson scattering experiments.

cond-mat.mtrl-sci↗

arXiv2023

Three-user D2D Coded Caching with Two Random Requesters and One Sender

In device-to-device (D2D) coded caching problems, it is possible that not all users will make file requests in the delivery phase. Hence, we propose a new D2D centralized coded caching problem, named the 3-user D2D coded caching with two random requesters and one sender (2RR1S), where in the delivery phase, any two of the three users will make file requests, and the user that does not make any file request is the designated sender. We find the optimal caching and delivery scheme, denoted as the 2RRIS scheme, for any number of files N by proving matching converse and achievability results. It is shown that coded cache placement is needed to achieve the optimal performance. Furthermore, the optimal rate-memory tradeoff has a uniform expression for N>=4 and different expressions for N=2 and 3. To examine the usefulness of the proposed model and scheme, we adapt the 2RR1S scheme to three scenarios. The first one is the 3-user D2D coded caching model proposed by Ji et al. By characterizing the optimal rate-memory tradeoff for the 3-user D2D coded caching when N=2, which was previously unknown, we show that the adapted 2RR1S scheme is in fact optimal for the 3-user D2D coded caching problem when N=2 and the cache size is medium. The benefit comes from coded cache placement which is missing from existing D2D coded caching schemes. The second scenario is where in the delivery phase, each user makes a file request randomly and independently with the same probability p. We call this model the request-random D2D coded caching problem. Adapting the 2RR1S scheme to this scenario, we show the superiority of our adapted scheme over other existing D2D coded caching schemes for medium to large cache size. The third scenario is the K-user D2D coded caching with K-s random requesters and s senders problem, for which an achievability result is obtained by generalizing the 2RR1S scheme.

cs.IT↗

arXiv2023

A Surrogate Data Assimilation Model for the Estimation of Dynamical System in a Limited Area

We propose a novel learning-based surrogate data assimilation (DA) model for efficient state estimation in a limited area. Our model employs a feedforward neural network for online computation, eliminating the need for integrating high-dimensional limited-area models. This approach offers significant computational advantages over traditional DA algorithms. Furthermore, our method avoids the requirement of lateral boundary conditions for the limited-area model in both online and offline computations. The design of our surrogate DA model is built upon a robust theoretical framework that leverages two fundamental concepts: observability and effective region. The concept of observability enables us to quantitatively determine the optimal amount of observation data necessary for accurate DA. Meanwhile, the concept of effective region substantially reduces the computational burden associated with computing observability and generating training data.

math.NA↗