Search Results for author: Ting Wang

Found 93 papers, 29 papers with code

Don’t Miss the Potential Customers! Retrieving Similar Ads to Improve User Targeting

no code implementations Findings (EMNLP) 2021 Yi Feng, Ting Wang, Chuanyi Li, Vincent Ng, Jidong Ge, Bin Luo, Yucheng Hu, Xiaopeng Zhang

User targeting is an essential task in the modern advertising industry: given a package of ads for a particular category of products (e. g., green tea), identify the online users to whom the ad package should be targeted.

Divide and Denoise: Learning from Noisy Labels in Fine-Grained Entity Typing with Cluster-Wise Loss Correction

no code implementations ACL 2022 Kunyuan Pang, Haoyu Zhang, Jie zhou, Ting Wang

In this work, we propose a clustering-based loss correction framework named Feature Cluster Loss Correction (FCLC), to address these two problems.

Entity Typing

Semi-Automatic Line-System Provisioning with Integrated Physical-Parameter-Aware Methodology: Field Verification and Operational Feasibility

no code implementations24 Mar 2024 Hideki Nishizawa, Giacomo Borraccini, Takeo Sasai, Yue-Kai Huang, Toru Mano, Kazuya Anazawa, Masatoshi Namiki, Soichiroh Usui, Tatsuya Matsumura, Yoshiaki Sone, Zehao Wang, Seiji Okamoto, Takeru Inoue, Ezra Ip, Andrea D'Amico, Tingjun Chen, Vittorio Curri, Ting Wang, Koji Asahi, Koichi Takasugi

We propose methods and an architecture to conduct measurements and optimize newly installed optical fiber line systems semi-automatically using integrated physics-aware technologies in a data center interconnection (DCI) transmission scenario.

Recommending Missed Citations Identified by Reviewers: A New Task, Dataset and Baselines

1 code implementation4 Mar 2024 Kehan Long, Shasha Li, Pancheng Wang, Chenlong Bao, Jintao Tang, Ting Wang

To help improve citations of full papers, we first define a novel task of Recommending Missed Citations Identified by Reviewers (RMC) and construct a corresponding expert-labeled dataset called CitationR.

Citation Recommendation Recommendation Systems

APT-MMF: An advanced persistent threat actor attribution method based on multimodal and multilevel feature fusion

no code implementations20 Feb 2024 Nan Xiao, Bo Lang, Ting Wang, Yikai Chen

Cyber threat intelligence (CTI), which involves analyzing multisource heterogeneous data from APTs, plays an important role in APT actor attribution.

Attribute Graph Attention

VQAttack: Transferable Adversarial Attacks on Visual Question Answering via Pre-trained Models

no code implementations16 Feb 2024 Ziyi Yin, Muchao Ye, Tianrong Zhang, Jiaqi Wang, Han Liu, Jinghui Chen, Ting Wang, Fenglong Ma

Correspondingly, we propose a novel VQAttack model, which can iteratively generate both image and text perturbations with the designed modules: the large language model (LLM)-enhanced image attack and the cross-modal joint attack module.

Adversarial Robustness Language Modelling +3

A Proactive and Dual Prevention Mechanism against Illegal Song Covers empowered by Singing Voice Conversion

no code implementations30 Jan 2024 Guangke Chen, Yedi Zhang, Fu Song, Ting Wang, Xiaoning Du, Yang Liu

To improve the imperceptibility of perturbations, we refine a psychoacoustic model-based loss with the backing track as an additional masker, a unique accompanying element for singing voices compared to ordinary speech voices.

Voice Conversion

Automated Fusion of Multimodal Electronic Health Records for Better Medical Predictions

1 code implementation20 Jan 2024 Suhan Cui, Jiaqi Wang, Yuan Zhong, Han Liu, Ting Wang, Fenglong Ma

The widespread adoption of Electronic Health Record (EHR) systems in healthcare institutes has generated vast amounts of medical data, offering significant opportunities for improving healthcare services through deep learning techniques.

Neural Architecture Search

Situation-Dependent Causal Influence-Based Cooperative Multi-agent Reinforcement Learning

no code implementations15 Dec 2023 Xiao Du, Yutong Ye, Pengyu Zhang, Yaning Yang, Mingsong Chen, Ting Wang

To this end, in this paper, we propose a novel MARL algorithm named Situation-Dependent Causal Influence-Based Cooperative Multi-agent Reinforcement Learning (SCIC), which incorporates a novel Intrinsic reward mechanism based on a new cooperation criterion measured by situation-dependent causal influence among agents.

Multi-agent Reinforcement Learning reinforcement-learning

On the Difficulty of Defending Contrastive Learning against Backdoor Attacks

no code implementations14 Dec 2023 Changjiang Li, Ren Pang, Bochuan Cao, Zhaohan Xi, Jinghui Chen, Shouling Ji, Ting Wang

Recent studies have shown that contrastive learning, like supervised learning, is highly vulnerable to backdoor attacks wherein malicious functions are injected into target models, only to be activated by specific triggers.

Contrastive Learning

Model Extraction Attacks Revisited

no code implementations8 Dec 2023 Jiacheng Liang, Ren Pang, Changjiang Li, Ting Wang

Model extraction (ME) attacks represent one major threat to Machine-Learning-as-a-Service (MLaaS) platforms by ``stealing'' the functionality of confidential machine-learning models through querying black-box APIs.

Model extraction

Blueprinting the Future: Automatic Item Categorization using Hierarchical Zero-Shot and Few-Shot Classifiers

no code implementations6 Dec 2023 Ting Wang, Keith Stelter, Jenn Floyd, Thomas O'Neill, Nathaniel Hendrix, Andrew Bazemore, Kevin Rode, Warren Newton

In testing industry, precise item categorization is pivotal to align exam questions with the designated content domains outlined in the assessment blueprint.

Improving the Robustness of Transformer-based Large Language Models with Dynamic Attention

no code implementations29 Nov 2023 Lujia Shen, Yuwen Pu, Shouling Ji, Changjiang Li, Xuhong Zhang, Chunpeng Ge, Ting Wang

Extensive experiments demonstrate that dynamic attention significantly mitigates the impact of adversarial attacks, improving up to 33\% better performance than previous methods against widely-used adversarial attacks.

Improving Image Captioning via Predicting Structured Concepts

no code implementations14 Nov 2023 Ting Wang, Weidong Chen, Yuanhe Tian, Yan Song, Zhendong Mao

Having the difficulty of solving the semantic gap between images and texts for the image captioning task, conventional studies in this area paid some attention to treating semantic concepts as a bridge between the two modalities and improved captioning performance accordingly.

Image Captioning

An Intelligent Social Learning-based Optimization Strategy for Black-box Robotic Control with Reinforcement Learning

no code implementations11 Nov 2023 Xubo Yang, Jian Gao, Ting Wang, Yaozhen He

Individuals in the learning style use the Levy flight search strategy to learn from the best performer and form the closest relationships.

Continuous Control

IMPRESS: Evaluating the Resilience of Imperceptible Perturbations Against Unauthorized Data Usage in Diffusion-Based Generative AI

1 code implementation NeurIPS 2023 Bochuan Cao, Changjiang Li, Ting Wang, Jinyuan Jia, Bo Li, Jinghui Chen

IMPRESS is based on the key observation that imperceptible perturbations could lead to a perceptible inconsistency between the original image and the diffusion-reconstructed image, which can be used to devise a new optimization strategy for purifying the image, which may weaken the protection of the original image from unauthorized data usage (e. g., style mimicking, malicious editing).

Image Generation

VLATTACK: Multimodal Adversarial Attacks on Vision-Language Tasks via Pre-trained Models

1 code implementation NeurIPS 2023 Ziyi Yin, Muchao Ye, Tianrong Zhang, Tianyu Du, Jinguo Zhu, Han Liu, Jinghui Chen, Ting Wang, Fenglong Ma

In this paper, we aim to investigate a new yet practical task to craft image and text perturbations using pre-trained VL models to attack black-box fine-tuned models on different downstream tasks.

Adversarial Robustness

MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation

no code implementations4 Oct 2023 Yuan Zhong, Suhan Cui, Jiaqi Wang, Xiaochen Wang, Ziyi Yin, Yaqing Wang, Houping Xiao, Mengdi Huai, Ting Wang, Fenglong Ma

Health risk prediction is one of the fundamental tasks under predictive modeling in the medical domain, which aims to forecast the potential health risks that patients may face in the future using their historical Electronic Health Records (EHR).

Data Augmentation

PETA: Parameter-Efficient Trojan Attacks

no code implementations1 Oct 2023 Lauren Hong, Ting Wang

Parameter-efficient fine-tuning (PEFT) enables efficient adaptation of pre-trained language models (PLMs) to specific tasks.

Bilevel Optimization

ReMasker: Imputing Tabular Data with Masked Autoencoding

1 code implementation25 Sep 2023 Tianyu Du, Luca Melis, Ting Wang

We present ReMasker, a new method of imputing missing values in tabular data by extending the masked autoencoding framework.

Imputation

Fast WDM provisioning with minimal probing: the first field experiments for DC exchanges

no code implementations14 Sep 2023 Hideki Nishizawa, Toru Mano, Thomas Ferreira de Lima, Yue-Kai Huang, Zehao Wang, Wataru Ishida, Masahisa Kawashima, Ezra Ip, Andrea D'Amico, Seiji Okamoto, Takeru Inoue, Kazuya Anazawa, Vittorio Curri, Gil Zussman, Daniel Kilper, Tingjun Chen, Ting Wang, Koji Asahi, Koichi Takasugi

Then, using field fibers deployed in the NSF COSMOS testbed (deployed in an urban area), a Linux-based transmission device software architecture, and coherent transceivers with different optical frequency ranges, modulators, and modulation formats, the fast WDM provisioning of an optical path was completed within 6 minutes (with a Q-factor error of about 0. 7 dB).

Federated Linear Bandit Learning via Over-the-Air Computation

no code implementations25 Aug 2023 Jiali Wang, Yuning Jiang, Xin Liu, Ting Wang, Yuanming Shi

In this context, we propose a customized federated linear bandits scheme, where each device transmits an analog signal, and the server receives a superposition of these signals distorted by channel noise.

Federated Reinforcement Learning for Electric Vehicles Charging Control on Distribution Networks

no code implementations17 Aug 2023 Junkai Qian, Yuning Jiang, Xin Liu, Qing Wang, Ting Wang, Yuanming Shi, Wei Chen

To effectively learn the optimal EV charging control strategy, a federated deep reinforcement learning algorithm named FedSAC is further proposed.

reinforcement-learning

Hardware Accelerators in Autonomous Driving

no code implementations11 Aug 2023 Ken Power, Shailendra Deva, Ting Wang, Julius Li, Ciarán Eising

Computing platforms in autonomous vehicles record large amounts of data from many sensors, process the data through machine learning models, and make decisions to ensure the vehicle's safe operation.

Autonomous Driving Decision Making

Robust retrieval of material chemical states in X-ray microspectroscopy

no code implementations8 Aug 2023 Ting Wang, Xiaotong Wu, Jizhou Li, Chao Wang

X-ray microspectroscopic techniques are essential for studying morphological and chemical changes in materials, providing high-resolution structural and spectroscopic information.

Retrieval

Retrieval-augmented GPT-3.5-based Text-to-SQL Framework with Sample-aware Prompting and Dynamic Revision Chain

no code implementations11 Jul 2023 Chunxi Guo, Zhiliang Tian, Jintao Tang, Shasha Li, Zhihua Wen, Kaixuan Wang, Ting Wang

Prompt learning with large language models (LLMs) has emerged as a recent approach, which designs prompts to lead LLMs to understand the input question and generate the corresponding SQL.

Retrieval Text-To-SQL

Address Matching Based On Hierarchical Information

no code implementations10 May 2023 Chengxian Zhang, Jintao Tang, Ting Wang, Shasha Li

There is evidence that address matching plays a crucial role in many areas such as express delivery, online shopping and so on.

On the Security Risks of Knowledge Graph Reasoning

1 code implementation3 May 2023 Zhaohan Xi, Tianyu Du, Changjiang Li, Ren Pang, Shouling Ji, Xiapu Luo, Xusheng Xiao, Fenglong Ma, Ting Wang

Knowledge graph reasoning (KGR) -- answering complex logical queries over large knowledge graphs -- represents an important artificial intelligence task, entailing a range of applications (e. g., cyber threat hunting).

Knowledge Graphs

Green Federated Learning Over Cloud-RAN with Limited Fronthual Capacity and Quantized Neural Networks

no code implementations30 Apr 2023 Jiali Wang, Yijie Mao, Ting Wang, Yuanming Shi

We rigorously develop an energy consumption model for the local training at devices through the use of QNNs and communication models over Cloud-RAN.

Federated Learning

Prompting GPT-3.5 for Text-to-SQL with De-semanticization and Skeleton Retrieval

no code implementations26 Apr 2023 Chunxi Guo, Zhiliang Tian, Jintao Tang, Pancheng Wang, Zhihua Wen, Kang Yang, Ting Wang

Text-to-SQL is a task that converts a natural language question into a structured query language (SQL) to retrieve information from a database.

Informativeness Retrieval +2

ChatGPT and a New Academic Reality: Artificial Intelligence-Written Research Papers and the Ethics of the Large Language Models in Scholarly Publishing

no code implementations21 Mar 2023 Brady Lund, Ting Wang, Nishith Reddy Mannuru, Bing Nie, Somipam Shimray, Ziang Wang

Potential ethical issues that could arise with the emergence of large language models like GPT-3, the underlying technology behind ChatGPT, and its usage by academics and researchers, are discussed and situated within the context of broader advancements in artificial intelligence, machine learning, and natural language processing for research and scholarly publishing.

Chatbot Ethics

FreeEagle: Detecting Complex Neural Trojans in Data-Free Cases

1 code implementation28 Feb 2023 Chong Fu, Xuhong Zhang, Shouling Ji, Ting Wang, Peng Lin, Yanghe Feng, Jianwei Yin

Thus, in this paper, we propose FreeEagle, the first data-free backdoor detection method that can effectively detect complex backdoor attacks on deep neural networks, without relying on the access to any clean samples or samples with the trigger.

Backdoor Attack

AutoML in The Wild: Obstacles, Workarounds, and Expectations

no code implementations21 Feb 2023 Yuan Sun, Qiurong Song, Xinning Gui, Fenglong Ma, Ting Wang

Automated machine learning (AutoML) is envisioned to make ML techniques accessible to ordinary users.

AutoML

Graph based Environment Representation for Vision-and-Language Navigation in Continuous Environments

no code implementations11 Jan 2023 Ting Wang, Zongkai Wu, Feiyu Yao, Donglin Wang

First, we propose an Environment Representation Graph (ERG) through object detection to express the environment in semantic level.

Object object-detection +2

Hijack Vertical Federated Learning Models As One Party

no code implementations1 Dec 2022 Pengyu Qiu, Xuhong Zhang, Shouling Ji, Changjiang Li, Yuwen Pu, Xing Yang, Ting Wang

Vertical federated learning (VFL) is an emerging paradigm that enables collaborators to build machine learning models together in a distributed fashion.

Vertical Federated Learning

SportsTrack: An Innovative Method for Tracking Athletes in Sports Scenes

1 code implementation14 Nov 2022 Jie Wang, Yuzhou Peng, Xiaodong Yang, Ting Wang, YanMing Zhang

The SportsMOT dataset aims to solve multiple object tracking of athletes in different sports scenes such as basketball or soccer.

Multiple Object Tracking

Neural Architectural Backdoors

no code implementations21 Oct 2022 Ren Pang, Changjiang Li, Zhaohan Xi, Shouling Ji, Ting Wang

This paper asks the intriguing question: is it possible to exploit neural architecture search (NAS) as a new attack vector to launch previously improbable attacks?

Neural Architecture Search

An Embarrassingly Simple Backdoor Attack on Self-supervised Learning

3 code implementations ICCV 2023 Changjiang Li, Ren Pang, Zhaohan Xi, Tianyu Du, Shouling Ji, Yuan YAO, Ting Wang

As a new paradigm in machine learning, self-supervised learning (SSL) is capable of learning high-quality representations of complex data without relying on labels.

Adversarial Robustness Backdoor Attack +2

Label Inference Attacks Against Vertical Federated Learning

2 code implementations USENIX Security 22 2022 Chong Fu, Xuhong Zhang, Shouling Ji, Jinyin Chen, Jingzheng Wu, Shanqing Guo, Jun Zhou, Alex X. Liu, Ting Wang

However, we discover that the bottom model structure and the gradient update mechanism of VFL can be exploited by a malicious participant to gain the power to infer the privately owned labels.

Vertical Federated Learning

Reasoning over Multi-view Knowledge Graphs

no code implementations27 Sep 2022 Zhaohan Xi, Ren Pang, Changjiang Li, Tianyu Du, Shouling Ji, Fenglong Ma, Ting Wang

(ii) It supports complex logical queries with varying relation and view constraints (e. g., with complex topology and/or from multiple views); (iii) It scales up to KGs of large sizes (e. g., millions of facts) and fine-granular views (e. g., dozens of views); (iv) It generalizes to query structures and KG views that are unobserved during training.

Knowledge Graphs Representation Learning

Multi-Document Scientific Summarization from a Knowledge Graph-Centric View

1 code implementation COLING 2022 Pancheng Wang, Shasha Li, Kunyuan Pang, Liangliang He, Dong Li, Jintao Tang, Ting Wang

Multi-Document Scientific Summarization (MDSS) aims to produce coherent and concise summaries for clusters of topic-relevant scientific papers.

Descriptive Knowledge Graphs

Confidence Matters: Inspecting Backdoors in Deep Neural Networks via Distribution Transfer

no code implementations13 Aug 2022 Tong Wang, Yuan YAO, Feng Xu, Miao Xu, Shengwei An, Ting Wang

Existing defenses are mainly built upon the observation that the backdoor trigger is usually of small size or affects the activation of only a few neurons.

Backdoor Attack backdoor defense

FedEntropy: Efficient Device Grouping for Federated Learning Using Maximum Entropy Judgment

1 code implementation24 May 2022 Zhiwei Ling, Zhihao Yue, Jun Xia, Ming Hu, Ting Wang, Mingsong Chen

Along with the popularity of Artificial Intelligence (AI) and Internet-of-Things (IoT), Federated Learning (FL) has attracted steadily increasing attentions as a promising distributed machine learning paradigm, which enables the training of a central model on for numerous decentralized devices without exposing their privacy.

Federated Learning

Neural Copula: A unified framework for estimating generic high-dimensional Copula functions

no code implementations23 May 2022 Zhi Zeng, Ting Wang

In this method, a hierarchical unsupervised neural network is constructed to estimate the marginal distribution function and the Copula function by solving differential equations.

Model-Contrastive Learning for Backdoor Defense

1 code implementation9 May 2022 Zhihao Yue, Jun Xia, Zhiwei Ling, Ming Hu, Ting Wang, Xian Wei, Mingsong Chen

Due to the popularity of Artificial Intelligence (AI) techniques, we are witnessing an increasing number of backdoor injection attacks that are designed to maliciously threaten Deep Neural Networks (DNNs) causing misclassification.

Backdoor Attack backdoor defense +1

Eliminating Backdoor Triggers for Deep Neural Networks Using Attention Relation Graph Distillation

1 code implementation21 Apr 2022 Jun Xia, Ting Wang, Jiepin Ding, Xian Wei, Mingsong Chen

Due to the prosperity of Artificial Intelligence (AI) techniques, more and more backdoors are designed by adversaries to attack Deep Neural Networks (DNNs). Although the state-of-the-art method Neural Attention Distillation (NAD) can effectively erase backdoor triggers from DNNs, it still suffers from non-negligible Attack Success Rate (ASR) together with lowered classification ACCuracy (ACC), since NAD focuses on backdoor defense using attention features (i. e., attention maps) of the same order.

backdoor defense Knowledge Distillation +1

Transfer Attacks Revisited: A Large-Scale Empirical Study in Real Computer Vision Settings

no code implementations7 Apr 2022 Yuhao Mao, Chong Fu, Saizhuo Wang, Shouling Ji, Xuhong Zhang, Zhenguang Liu, Jun Zhou, Alex X. Liu, Raheem Beyah, Ting Wang

To bridge this critical gap, we conduct the first large-scale systematic empirical study of transfer attacks against major cloud-based MLaaS platforms, taking the components of a real transfer attack into account.

Over-the-Air Federated Learning via Second-Order Optimization

1 code implementation29 Mar 2022 Peng Yang, Yuning Jiang, Ting Wang, Yong Zhou, Yuanming Shi, Colin N. Jones

To address this issue, in this paper, we instead propose a novel over-the-air second-order federated optimization algorithm to simultaneously reduce the communication rounds and enable low-latency global model aggregation.

Federated Learning

The Variable Volatility Elasticity Model from Commodity Markets

no code implementations17 Mar 2022 Fuzhou Gong, Ting Wang

In this paper, we propose and study a novel continuous-time model, based on the well-known constant elasticity of variance (CEV) model, to describe the asset price process.

Machine Learning Empowered Intelligent Data Center Networking: A Survey

no code implementations28 Feb 2022 Bo Li, Ting Wang, Peng Yang, Mingsong Chen, Shui Yu, Mounir Hamdi

To support the needs of ever-growing cloud-based services, the number of servers and network devices in data centers is increasing exponentially, which in turn results in high complexities and difficulties in network optimization.

BIG-bench Machine Learning Management

Seeing is Living? Rethinking the Security of Facial Liveness Verification in the Deepfake Era

no code implementations22 Feb 2022 Changjiang Li, Li Wang, Shouling Ji, Xuhong Zhang, Zhaohan Xi, Shanqing Guo, Ting Wang

Facial Liveness Verification (FLV) is widely used for identity authentication in many security-sensitive domains and offered as Platform-as-a-Service (PaaS) by leading cloud vendors.

DeepFake Detection Face Swapping

Towards Fast and Accurate Federated Learning with non-IID Data for Cloud-Based IoT Applications

no code implementations29 Jan 2022 Tian Liu, Jiahao Ding, Ting Wang, Miao Pan, Mingsong Chen

However, since our grouping method is based on the similarity of extracted feature maps from IoT devices, it may incur additional risks of privacy exposure.

Federated Learning

CatchBackdoor: Backdoor Testing by Critical Trojan Neural Path Identification via Differential Fuzzing

no code implementations24 Dec 2021 Haibo Jin, Ruoxi Chen, Jinyin Chen, Yao Cheng, Chong Fu, Ting Wang, Yue Yu, Zhaoyan Ming

Existing DNN testing methods are mainly designed to find incorrect corner case behaviors in adversarial settings but fail to discover the backdoors crafted by strong trojan attacks.

DNN Testing

MedAttacker: Exploring Black-Box Adversarial Attacks on Risk Prediction Models in Healthcare

no code implementations11 Dec 2021 Muchao Ye, Junyu Luo, Guanjie Zheng, Cao Xiao, Ting Wang, Fenglong Ma

Deep neural networks (DNNs) have been broadly adopted in health risk prediction to provide healthcare diagnoses and treatments.

Adversarial Attack Position +1

Auto robust relative radiometric normalization via latent change noise modelling

no code implementations24 Nov 2021 Shiqi Liu, Lu Wang, Jie Lian, Ting Chen, Cong Liu, Xuchen Zhan, Jintao Lu, Jie Liu, Ting Wang, Dong Geng, Hongwei Duan, Yuze Tian

Relative radiometric normalization(RRN) of different satellite images of the same terrain is necessary for change detection, object classification/segmentation, and map-making tasks.

Change Detection

Backdoor Attack through Frequency Domain

1 code implementation22 Nov 2021 Tong Wang, Yuan YAO, Feng Xu, Shengwei An, Hanghang Tong, Ting Wang

We also evaluate FTROJAN against state-of-the-art defenses as well as several adaptive defenses that are designed on the frequency domain.

Autonomous Driving Backdoor Attack

On the Security Risks of AutoML

1 code implementation12 Oct 2021 Ren Pang, Zhaohan Xi, Shouling Ji, Xiapu Luo, Ting Wang

Neural Architecture Search (NAS) represents an emerging machine learning (ML) paradigm that automatically searches for models tailored to given tasks, which greatly simplifies the development of ML systems and propels the trend of ML democratization.

Model Poisoning Neural Architecture Search

UAV-Assisted Over-the-Air Computation

no code implementations25 Jan 2021 Min Fu, Yong Zhou, Yuanming Shi, Ting Wang, Wei Chen

Over-the-air computation (AirComp) provides a promising way to support ultrafast aggregation of distributed data.

Optimize the trajectory of UAV which plays a BS in communication system

i-Algebra: Towards Interactive Interpretability of Deep Neural Networks

no code implementations22 Jan 2021 Xinyang Zhang, Ren Pang, Shouling Ji, Fenglong Ma, Ting Wang

Providing explanations for deep neural networks (DNNs) is essential for their use in domains wherein the interpretability of decisions is a critical prerequisite.

Composite Adversarial Training for Multiple Adversarial Perturbations and Beyond

no code implementations1 Jan 2021 Xinyang Zhang, Zheng Zhang, Ting Wang

One intriguing property of deep neural networks (DNNs) is their vulnerability to adversarial perturbations.

TrojanZoo: Towards Unified, Holistic, and Practical Evaluation of Neural Backdoors

1 code implementation16 Dec 2020 Ren Pang, Zheng Zhang, Xiangshan Gao, Zhaohan Xi, Shouling Ji, Peng Cheng, Xiapu Luo, Ting Wang

To bridge this gap, we design and implement TROJANZOO, the first open-source platform for evaluating neural backdoor attacks/defenses in a unified, holistic, and practical manner.

Visual Perception Generalization for Vision-and-Language Navigation via Meta-Learning

no code implementations10 Dec 2020 Ting Wang, Zongkai Wu, Donglin Wang

In the training phase, we first locate the generalization problem to the visual perception module, and then compare two meta-learning algorithms for better generalization in seen and unseen environments.

Meta-Learning Navigate +1

UNIFUZZ: A Holistic and Pragmatic Metrics-Driven Platform for Evaluating Fuzzers

1 code implementation5 Oct 2020 Yuwei Li, Shouling Ji, Yuan Chen, Sizhuang Liang, Wei-Han Lee, Yueyao Chen, Chenyang Lyu, Chunming Wu, Raheem Beyah, Peng Cheng, Kangjie Lu, Ting Wang

We hope that our findings can shed light on reliable fuzzing evaluation, so that we can discover promising fuzzing primitives to effectively facilitate fuzzer designs in the future.

Cryptography and Security

Trojaning Language Models for Fun and Profit

1 code implementation1 Aug 2020 Xinyang Zhang, Zheng Zhang, Shouling Ji, Ting Wang

Recent years have witnessed the emergence of a new paradigm of building natural language processing (NLP) systems: general-purpose, pre-trained language models (LMs) are composed with simple downstream models and fine-tuned for a variety of NLP tasks.

Question Answering Specificity +1

Graph Backdoor

2 code implementations21 Jun 2020 Zhaohan Xi, Ren Pang, Shouling Ji, Ting Wang

One intriguing property of deep neural networks (DNNs) is their inherent vulnerability to backdoor attacks -- a trojan model responds to trigger-embedded inputs in a highly predictable manner while functioning normally otherwise.

Backdoor Attack Descriptive +3

AdvMind: Inferring Adversary Intent of Black-Box Attacks

1 code implementation16 Jun 2020 Ren Pang, Xinyang Zhang, Shouling Ji, Xiapu Luo, Ting Wang

Deep neural networks (DNNs) are inherently susceptible to adversarial attacks even under black-box settings, in which the adversary only has query access to the target models.

PoisHygiene: Detecting and Mitigating Poisoning Attacks in Neural Networks

no code implementations24 Mar 2020 Junfeng Guo, Ting Wang, Cong Liu

Being able to detect and mitigate poisoning attacks, typically categorized into backdoor and adversarial poisoning (AP), is critical in enabling safe adoption of DNNs in many application domains.

Data Poisoning

Portably parallel construction of a CI wave function from a matrix-product state using the Charm++ framework

1 code implementation24 Mar 2020 Ting Wang, Yingjin Ma, Lian Zhao, Jinrong Jiang

In this work, we present an efficient procedure for constructing CI expansions from MPS using the Charm++ parallel programming framework, upon which automatic load balancing and object migration facilities can be employed.

Computational Physics Strongly Correlated Electrons

A Tale of Evil Twins: Adversarial Inputs versus Poisoned Models

1 code implementation5 Nov 2019 Ren Pang, Hua Shen, Xinyang Zhang, Shouling Ji, Yevgeniy Vorobeychik, Xiapu Luo, Alex Liu, Ting Wang

Specifically, (i) we develop a new attack model that jointly optimizes adversarial inputs and poisoned models; (ii) with both analytical and empirical evidence, we reveal that there exist intriguing "mutual reinforcement" effects between the two attack vectors -- leveraging one vector significantly amplifies the effectiveness of the other; (iii) we demonstrate that such effects enable a large design spectrum for the adversary to enhance the existing attacks that exploit both vectors (e. g., backdoor attacks), such as maximizing the attack evasiveness with respect to various detection methods; (iv) finally, we discuss potential countermeasures against such optimized attacks and their technical challenges, pointing to several promising research directions.

Provable Defenses against Spatially Transformed Adversarial Inputs: Impossibility and Possibility Results

no code implementations ICLR 2019 Xinyang Zhang, Yifan Huang, Chanh Nguyen, Shouling Ji, Ting Wang

On the possibility side, we show that it is still feasible to construct adversarial training methods to significantly improve the resilience of networks against adversarial inputs over empirical datasets.

SirenAttack: Generating Adversarial Audio for End-to-End Acoustic Systems

no code implementations23 Jan 2019 Tianyu Du, Shouling Ji, Jinfeng Li, Qinchen Gu, Ting Wang, Raheem Beyah

Despite their immense popularity, deep learning-based acoustic systems are inherently vulnerable to adversarial attacks, wherein maliciously crafted audios trigger target systems to misbehave.

Cryptography and Security

TextBugger: Generating Adversarial Text Against Real-world Applications

1 code implementation13 Dec 2018 Jinfeng Li, Shouling Ji, Tianyu Du, Bo Li, Ting Wang

Deep Learning-based Text Understanding (DLTU) is the backbone technique behind various applications, including question answering, machine translation, and text classification.

Adversarial Text Machine Translation +6

Interpretable Deep Learning under Fire

no code implementations3 Dec 2018 Xinyang Zhang, Ningfei Wang, Hua Shen, Shouling Ji, Xiapu Luo, Ting Wang

The improved interpretability is believed to offer a sense of security by involving human in the decision-making process.

Decision Making

Model-Reuse Attacks on Deep Learning Systems

no code implementations2 Dec 2018 Yujie Ji, Xinyang Zhang, Shouling Ji, Xiapu Luo, Ting Wang

By empirically studying four deep learning systems (including both individual and ensemble systems) used in skin cancer screening, speech recognition, face verification, and autonomous steering, we show that such attacks are (i) effective - the host systems misbehave on the targeted inputs as desired by the adversary with high probability, (ii) evasive - the malicious models function indistinguishably from their benign counterparts on non-targeted inputs, (iii) elastic - the malicious models remain effective regardless of various system design choices and tuning strategies, and (iv) easy - the adversary needs little prior knowledge about the data used for system tuning or inference.

Cryptography and Security

EagleEye: Attack-Agnostic Defense against Adversarial Inputs (Technical Report)

no code implementations1 Aug 2018 Yujie Ji, Xinyang Zhang, Ting Wang

Deep neural networks (DNNs) are inherently vulnerable to adversarial inputs: such maliciously crafted samples trigger DNNs to misbehave, leading to detrimental consequences for DNN-powered systems.

Differentially Private Releasing via Deep Generative Model (Technical Report)

2 code implementations5 Jan 2018 Xinyang Zhang, Shouling Ji, Ting Wang

Privacy-preserving releasing of complex data (e. g., image, text, audio) represents a long-standing challenge for the data mining research community.

Privacy Preserving

Where Classification Fails, Interpretation Rises

no code implementations2 Dec 2017 Chanh Nguyen, Georgi Georgiev, Yujie Ji, Ting Wang

We believe that this work opens a new direction for designing adversarial input detection methods.

Classification General Classification

Modular Learning Component Attacks: Today's Reality, Tomorrow's Challenge

no code implementations25 Aug 2017 Xinyang Zhang, Yujie Ji, Ting Wang

Many of today's machine learning (ML) systems are not built from scratch, but are compositions of an array of {\em modular learning components} (MLCs).

Dense 3D Facial Reconstruction from a Single Depth Image in Unconstrained Environment

no code implementations24 Apr 2017 Shu Zhang, Hui Yu, Ting Wang, Junyu Dong, Honghai Liu

With the increasing demands of applications in virtual reality such as 3D films, virtual Human-Machine Interactions and virtual agents, the analysis of 3D human face analysis is considered to be more and more important as a fundamental step for those virtual reality tasks.

DIMM-SC: A Dirichlet mixture model for clustering droplet-based single cell transcriptomic data

no code implementations6 Apr 2017 Zhe Sun, Ting Wang, Ke Deng, Xiao-Feng Wang, Robert Lafyatis, Ying Ding, Ming Hu, Wei Chen

More importantly, as a model-based approach, DIMM-SC is able to quantify the clustering uncertainty for each single cell, facilitating rigorous statistical inference and biological interpretations, which are typically unavailable from existing clustering methods.

Clustering

Context-Aware Online Learning for Course Recommendation of MOOC Big Data

no code implementations11 Oct 2016 Yifan Hou, Pan Zhou, Ting Wang, Li Yu, Yuchong Hu, Dapeng Wu

In this respect, the key challenge is how to realize personalized course recommendation as well as to reduce the computing and storage costs for the tremendous course data.

Recommendation Systems

Neural Mechanism of Language

no code implementations22 Aug 2014 Peilei Liu, Ting Wang

Firstly, we briefly introduce this model in this paper, and then we explain the neural mechanism of language and reasoning with it.

Position

Motor Learning Mechanism on the Neuron Scale

no code implementations18 Jul 2014 Peilei Liu, Ting Wang

Finally, we compare motor system with sensory system.

A Quantitative Neural Coding Model of Sensory Memory

no code implementations25 Jun 2014 Peilei Liu, Ting Wang

The coding mechanism of sensory memory on the neuron scale is one of the most important questions in neuroscience.

A Unified Quantitative Model of Vision and Audition

no code implementations23 Jun 2014 Peilei Liu, Ting Wang

This is complementary to existing theories and has provided better explanations for sound localization.

Automatic Extraction of Protein Interaction in Literature

no code implementations8 Jun 2014 Peilei Liu, Ting Wang

Protein-protein interaction extraction is the key precondition of the construction of protein knowledge network, and it is very important for the research in the biomedicine.

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