Search Results for author: Dan Li

Found 33 papers, 10 papers with code

Face It Yourselves: An LLM-Based Two-Stage Strategy to Localize Configuration Errors via Logs

1 code implementation31 Mar 2024 Shiwen Shan, Yintong Huo, Yuxin Su, Yichen Li, Dan Li, Zibin Zheng

Based on the insights gained from the preliminary study, we propose an LLM-based two-stage strategy for end-users to localize the root-cause configuration properties based on logs.

A Variational Autoencoder Framework for Robust, Physics-Informed Cyberattack Recognition in Industrial Cyber-Physical Systems

no code implementations10 Oct 2023 Navid Aftabi, Dan Li, Paritosh Ramanan

This data-driven framework considers the temporal behavior of a generic physical system that extracts features from the time series of the sensor measurements that can be used for detecting covert attacks, distinguishing them from equipment faults, as well as localize the attack/fault.

Time Series

Adversarial Client Detection via Non-parametric Subspace Monitoring in the Internet of Federated Things

no code implementations2 Oct 2023 Xianjian Xie, Xiaochen Xian, Dan Li, Andi Wang

The Internet of Federated Things (IoFT) represents a network of interconnected systems with federated learning as the backbone, facilitating collaborative knowledge acquisition while ensuring data privacy for individual systems.

Adversarial Attack Federated Learning

Corporate Credit Rating: A Survey

no code implementations19 Sep 2023 Bojing Feng, Xi Cheng, Dan Li, Zeyu Liu, Wenfang Xue

Corporate credit rating (CCR) plays a very important role in the process of contemporary economic and social development.

Mapping effective connectivity by virtually perturbing a surrogate brain

1 code implementation31 Dec 2022 Zixiang Luo, Kaining Peng, Zhichao Liang, Shengyuan Cai, Chenyu Xu, Dan Li, Yu Hu, Changsong Zhou, Quanying Liu

Effective connectivity (EC), indicative of the causal interactions between brain regions, is fundamental to understanding information processing in the brain.

Eco-driving for Electric Connected Vehicles at Signalized Intersections: A Parameterized Reinforcement Learning approach

no code implementations24 Jun 2022 Xia Jiang, Jian Zhang, Dan Li

This paper proposes an eco-driving framework for electric connected vehicles (CVs) based on reinforcement learning (RL) to improve vehicle energy efficiency at signalized intersections.

Reinforcement Learning (RL)

Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review

no code implementations7 May 2022 Leon Witt, Mathis Heyer, Kentaroh Toyoda, Wojciech Samek, Dan Li

This is the first systematic literature review analyzing holistic FLFs in the domain of both, decentralized and incentivized federated learning.

Federated Learning

A Novel Splitting Criterion Inspired by Geometric Mean Metric Learning for Decision Tree

no code implementations23 Apr 2022 Dan Li, Songcan Chen

Decision tree (DT) attracts persistent research attention due to its impressive empirical performance and interpretability in numerous applications.

Metric Learning

Improve Deep Image Inpainting by Emphasizing the Complexity of Missing Regions

no code implementations13 Feb 2022 Yufeng Wang, Dan Li, Cong Xu, Min Yang

Deep image inpainting research mainly focuses on constructing various neural network architectures or imposing novel optimization objectives.

Image Inpainting

BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing

no code implementations16 Dec 2021 Tianfeng Liu, Yangrui Chen, Dan Li, Chuan Wu, Yibo Zhu, Jun He, Yanghua Peng, Hongzheng Chen, Hongzhi Chen, Chuanxiong Guo

Extensive experiments on various GNN models and large graph datasets show that BGL significantly outperforms existing GNN training systems by 20. 68x on average.

Graph Property Prediction Node Classification +1

VIRT: Improving Representation-based Models for Text Matching through Virtual Interaction

no code implementations8 Dec 2021 Dan Li, Yang Yang, Hongyin Tang, Jingang Wang, Tong Xu, Wei Wu, Enhong Chen

With the booming of pre-trained transformers, representation-based models based on Siamese transformer encoders have become mainstream techniques for efficient text matching.

Text Matching

Paint4Poem: A Dataset for Artistic Visualization of Classical Chinese Poems

1 code implementation23 Sep 2021 Dan Li, Shuai Wang, Jie Zou, Chang Tian, Elisha Nieuwburg, Fengyuan Sun, Evangelos Kanoulas

We create abenchmark for Paint4Poem: we train two representative text-to-image generation models: AttnGAN and MirrorGAN, and evaluate theirperformance regarding painting pictorial quality, painting stylistic relevance, and semantic relevance between poems and paintings. The results indicate that the models are able to generate paintings that have good pictorial quality and mimic Feng Zikai's style, but thereflection of poem semantics is limited.

Few-Shot Learning Text-to-Image Generation

Missingness Augmentation: A General Approach for Improving Generative Imputation Models

1 code implementation31 Jul 2021 Yufeng Wang, Dan Li, Cong Xu, Min Yang

However, data augmentation, as a simple yet effective method, has not received enough attention in this area.

Data Augmentation Imputation

Reward-Based 1-bit Compressed Federated Distillation on Blockchain

no code implementations27 Jun 2021 Leon Witt, Usama Zafar, KuoYeh Shen, Felix Sattler, Dan Li, Wojciech Samek

The recent advent of various forms of Federated Knowledge Distillation (FD) paves the way for a new generation of robust and communication-efficient Federated Learning (FL), where mere soft-labels are aggregated, rather than whole gradients of Deep Neural Networks (DNN) as done in previous FL schemes.

Federated Learning Knowledge Distillation

Online Optimization and Learning in Uncertain Dynamical Environments with Performance Guarantees

no code implementations18 Feb 2021 Dan Li, Dariush Fooladivanda, Sonia Martinez

We propose a new framework to solve online optimization and learning problems in unknown and uncertain dynamical environments.

Electrocardiogram Classification and Visual Diagnosis of Atrial Fibrillation with DenseECG

no code implementations19 Jan 2021 Dacheng Chen, Dan Li, Xiuqin Xu, Ruizhi Yang, See-Kiong Ng

We trained our model using the publicly available dataset from 2017 PhysioNet Computing in Cardiology(CinC) Challenge containing 8528 single-lead ECG recordings of short-term heart rhythms (9-61s).

Classification Feature Engineering +1

AUL is a better optimization metric in PU learning

no code implementations1 Jan 2021 Shangchuan Huang, Songtao Wang, Dan Li, Liwei Jiang

Recent works try to recover the unbiased result by estimating the proportion of positive samples with mixture proportion estimation (MPE) algorithms, but the model performance is still limited and heavy computational cost is introduced (particularly for big datasets).

Binary Classification

Adversarial Momentum-Contrastive Pre-Training

1 code implementation24 Dec 2020 Cong Xu, Dan Li, Min Yang

Recently proposed adversarial self-supervised learning methods usually require big batches and long training epochs to extract robust features, which will bring heavy computational overhead on platforms with limited resources.

Contrastive Learning Data Augmentation +1

PC-GAIN: Pseudo-label Conditional Generative Adversarial Imputation Networks for Incomplete Data

1 code implementation16 Nov 2020 Yufeng Wang, Dan Li, Xiang Li, Min Yang

Further, this classifier is incorporated into the generative adversarial framework to help the generator to yield higher quality imputation results.

Imputation Pseudo Label

Deep Learning based Covert Attack Identification for Industrial Control Systems

no code implementations25 Sep 2020 Dan Li, Paritosh Ramanan, Nagi Gebraeel, Kamran Paynabar

This data-driven framework considers the temporal behavior of a generic physical system that extracts features from the time series of the sensor measurements that can be used for detecting covert attacks, distinguishing them from equipment faults, as well as localize the attack/fault.

Time Series Time Series Analysis

Query Resolution for Conversational Search with Limited Supervision

1 code implementation24 May 2020 Nikos Voskarides, Dan Li, Pengjie Ren, Evangelos Kanoulas, Maarten de Rijke

Context from the conversational history can be used to arrive at a better expression of the current turn query, defined as the task of query resolution.

Conversational Search Passage Retrieval +1

Improving Positive Unlabeled Learning: Practical AUL Estimation and New Training Method for Extremely Imbalanced Data Sets

no code implementations21 Apr 2020 Liwei Jiang, Dan Li, Qisheng Wang, Shuai Wang, Songtao Wang

Secondly, we propose ProbTagging, a new training method for extremely imbalanced data sets, where the number of unlabeled samples is hundreds or thousands of times that of positive samples.

The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up

4 code implementations9 Feb 2020 Razvan V. Marinescu, Neil P. Oxtoby, Alexandra L. Young, Esther E. Bron, Arthur W. Toga, Michael W. Weiner, Frederik Barkhof, Nick C. Fox, Arman Eshaghi, Tina Toni, Marcin Salaterski, Veronika Lunina, Manon Ansart, Stanley Durrleman, Pascal Lu, Samuel Iddi, Dan Li, Wesley K. Thompson, Michael C. Donohue, Aviv Nahon, Yarden Levy, Dan Halbersberg, Mariya Cohen, Huiling Liao, Tengfei Li, Kaixian Yu, Hongtu Zhu, Jose G. Tamez-Pena, Aya Ismail, Timothy Wood, Hector Corrada Bravo, Minh Nguyen, Nanbo Sun, Jiashi Feng, B. T. Thomas Yeo, Gang Chen, Ke Qi, Shiyang Chen, Deqiang Qiu, Ionut Buciuman, Alex Kelner, Raluca Pop, Denisa Rimocea, Mostafa M. Ghazi, Mads Nielsen, Sebastien Ourselin, Lauge Sorensen, Vikram Venkatraghavan, Keli Liu, Christina Rabe, Paul Manser, Steven M. Hill, James Howlett, Zhiyue Huang, Steven Kiddle, Sach Mukherjee, Anais Rouanet, Bernd Taschler, Brian D. M. Tom, Simon R. White, Noel Faux, Suman Sedai, Javier de Velasco Oriol, Edgar E. V. Clemente, Karol Estrada, Leon Aksman, Andre Altmann, Cynthia M. Stonnington, Yalin Wang, Jianfeng Wu, Vivek Devadas, Clementine Fourrier, Lars Lau Raket, Aristeidis Sotiras, Guray Erus, Jimit Doshi, Christos Davatzikos, Jacob Vogel, Andrew Doyle, Angela Tam, Alex Diaz-Papkovich, Emmanuel Jammeh, Igor Koval, Paul Moore, Terry J. Lyons, John Gallacher, Jussi Tohka, Robert Ciszek, Bruno Jedynak, Kruti Pandya, Murat Bilgel, William Engels, Joseph Cole, Polina Golland, Stefan Klein, Daniel C. Alexander

TADPOLE's unique results suggest that current prediction algorithms provide sufficient accuracy to exploit biomarkers related to clinical diagnosis and ventricle volume, for cohort refinement in clinical trials for Alzheimer's disease.

Alzheimer's Disease Detection Disease Prediction

Nanoconfined, dynamic electrolyte gating and memory effects in multilayered graphene-based membranes

no code implementations29 Nov 2019 Jing Xiao, Hualin Zhan, Zaiquan Xu, Xiao Wang, Ke Zhang, Zhiyuan Xiong, George P. Simon, Zhe Liu, Dan Li

Multilayered graphene-based nanoporous membranes with electrolyte incorporated between individual sheets is a unique nano-heterostructure system in which nanoconfined electrons in graphene and ions confined in between sheets are intimately coupled throughout the entire membrane.

Mesoscale and Nanoscale Physics Materials Science Soft Condensed Matter Applied Physics Chemical Physics

A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection

no code implementations18 Feb 2019 Baihong Jin, Yuxin Chen, Dan Li, Kameshwar Poolla, Alberto Sangiovanni-Vincentelli

The One-Class Support Vector Machine (OC-SVM) is a popular machine learning model for anomaly detection and hence could be used for identifying change points; however, it is sometimes difficult to obtain a good OC-SVM model that can be used on sensor measurement time series to identify the change points in system health status.

Anomaly Detection Change Point Detection +2

Detecting and Diagnosing Incipient Building Faults Using Uncertainty Information from Deep Neural Networks

no code implementations18 Feb 2019 Baihong Jin, Dan Li, Seshadhri Srinivasan, See-Kiong Ng, Kameshwar Poolla, Alberto~Sangiovanni-Vincentelli

Early detection of incipient faults is of vital importance to reducing maintenance costs, saving energy, and enhancing occupant comfort in buildings.

Fault Detection

MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks

1 code implementation15 Jan 2019 Dan Li, Dacheng Chen, Lei Shi, Baihong Jin, Jonathan Goh, See-Kiong Ng

The prevalence of networked sensors and actuators in many real-world systems such as smart buildings, factories, power plants, and data centers generate substantial amounts of multivariate time series data for these systems.

Anomaly Detection BIG-bench Machine Learning +2

Anomaly Detection with Generative Adversarial Networks for Multivariate Time Series

2 code implementations13 Sep 2018 Dan Li, Dacheng Chen, Jonathan Goh, See-Kiong Ng

We used LSTM-RNN in our GAN to capture the distribution of the multivariate time series of the sensors and actuators under normal working conditions of a CPS.

Anomaly Detection Time Series +1

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