Search Results for author:
Found 131 papers, 54 papers with code
Event Extraction as Machine Reading Comprehension
ii) Our model is excelled in the data-scarce scenario, for example, obtaining 49. 8{\%} in F1 for event argument extraction with only 1{\%} data, compared with 2. 2{\%} of the previous method.
Saliency-based Multi-View Mixed Language Training for Zero-shot Cross-lingual Classification
Recent multilingual pre-trained models, like XLM-RoBERTa (XLM-R), have been demonstrated effective in many cross-lingual tasks.
Cross-Lingual Sentiment Classification
Dialogue State Tracking
+4
Machine Reading Comprehension as Data Augmentation: A Case Study on Implicit Event Argument Extraction
Implicit event argument extraction (EAE) is a crucial document-level information extraction task that aims to identify event arguments beyond the sentence level.
Iterative Constrained Back-Translation for Unsupervised Domain Adaptation of Machine Translation
In this paper, we propose an Iterative Constrained Back-Translation (ICBT) method to incorporate in-domain lexical knowledge on the basis of BT for unsupervised domain adaptation of NMT.
Saliency as Evidence: Event Detection with Trigger Saliency Attribution
Event detection (ED) is a critical subtask of event extraction that seeks to identify event triggers of certain types in texts. Despite significant advances in ED, existing methods typically follow a “one model fits all types” approach, which sees no differences between event types and often results in a quite skewed performance. Finding the causes of skewed performance is crucial for the robustness of an ED model, but to date there has been little exploration of this problem. This research examines the issue in depth and presents a new concept termed trigger salience attribution, which can explicitly quantify the underlying patterns of events.
基于多任务标签一致性机制的中文命名实体识别(Chinese Named Entity Recognition based on Multi-task Label Consistency Mechanism)
“实体边界预测对中文命名实体识别至关重要。现有研究为改善边界识别效果提出的多任务学习方法仅考虑与分词任务结合, 缺少多任务标签训练数据, 无法学到任务的标签一致性关系。本文提出一种新的基于多任务标签一致性机制的中文命名实体识别方法:将分词和词性信息融入命名实体识别模型, 使三种任务联合训练;建立基于标签一致性机制的多任务学习模式, 来捕获标签一致性关系及学习多任务表示。全样本和小样本实验表明了方法的有效性。”
Chinese Named Entity Recognition
named-entity-recognition
+1
Sparse Autoencoder as a Zero-Shot Classifier for Concept Erasing in Text-to-Image Diffusion Models
ItD first employs a sparse autoencoder (SAE) to interpret each concept as a combination of multiple features.
Patch-Wise Hypergraph Contrastive Learning with Dual Normal Distribution Weighting for Multi-Domain Stain Transfer
Experiments demonstrate that STNHCL achieves state-of-the-art performance in the two main categories of stain transfer tasks.
Novel Object 6D Pose Estimation with a Single Reference View
Using only a single reference view is more scalable, but challenging due to large pose discrepancies and limited geometric and spatial information.
Every FLOP Counts: Scaling a 300B Mixture-of-Experts LING LLM without Premium GPUs
Ultimately, our experimental findings demonstrate that a 300B MoE LLM can be effectively trained on lower-performance devices while achieving comparable performance to models of a similar scale, including dense and MoE models.
A Survey on Ordinal Regression: Applications, Advances and Prospects
To our best knowledge, this is the first systematic survey of ordinal regression, which lays a foundation for future research in this fundamental and generic domain.
Towards Collaborative Anti-Money Laundering Among Financial Institutions
The experimental results demonstrate that our methods can effectively identify cross-institution money laundering subgroups.
Bridging the Data Gap in AI Reliability Research and Establishing DR-AIR, a Comprehensive Data Repository for AI Reliability
To address this gap, this paper focuses on conducting a comprehensive review of available AI reliability data and establishing DR-AIR: a data repository for AI reliability.
Compressive Sensing Empirical Wavelet Transform for Frequency-Banded Power Measurement Considering Interharmonics
The empirical wavelet transform (EWT) has been demonstrated to improve measurement accuracy by effectively partitioning transition bands.
Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose Estimation
Our motivation is to leverage the latent generalization ability of the diffusion model to address the domain generalization challenge in object pose estimation.
SecPE: Secure Prompt Ensembling for Private and Robust Large Language Models
With the growing popularity of LLMs among the general public users, privacy-preserving and adversarial robustness have become two pressing demands for LLM-based services, which have largely been pursued separately but rarely jointly.
Activation Approximations Can Incur Safety Vulnerabilities Even in Aligned LLMs: Comprehensive Analysis and Defense
Large Language Models (LLMs) have showcased remarkable capabilities across various domains.
Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation
This system includes two foundation components: a large-scale shape generation model -- Hunyuan3D-DiT, and a large-scale texture synthesis model -- Hunyuan3D-Paint.
BiDepth Multimodal Neural Network: Bidirectional Depth Deep Learning Architecture for Spatial-Temporal Prediction
Accurate prediction of spatial-temporal (ST) information in dynamic systems, such as urban mobility and weather patterns, is a crucial yet challenging problem.
TextSleuth: Towards Explainable Tampered Text Detection
To fill the data gap for this task, we propose a large-scale, comprehensive dataset, ETTD, which contains both pixel-level annotations for tampered text region and natural language annotations describing the anomaly of the tampered text.
Domain Generalization
Optical Character Recognition (OCR)
+1
PhysAug: A Physical-guided and Frequency-based Data Augmentation for Single-Domain Generalized Object Detection
Single-Domain Generalized Object Detection~(S-DGOD) aims to train on a single source domain for robust performance across a variety of unseen target domains by taking advantage of an object detector.
Ranked #1 on
Robust Object Detection
on Cityscapes
CtrlNeRF: The Generative Neural Radiation Fields for the Controllable Synthesis of High-fidelity 3D-Aware Images
In this paper, we introduce a controllable generative model (i. e. \textbf{CtrlNeRF}) that uses a single MLP network to represent multiple scenes with shared weights.
Attributed Graph Clustering in Collaborative Settings
Graph clustering is an unsupervised machine learning method that partitions the nodes in a graph into different groups.
Scalable Autoregressive Monocular Depth Estimation
Our idea is simple: We tackle the monocular depth estimation (MDE) task with an autoregressive prediction paradigm, based on two core designs.
Scaling Mesh Generation via Compressive Tokenization
We propose a compressive yet effective mesh representation, Blocked and Patchified Tokenization (BPT), facilitating the generation of meshes exceeding 8k faces.
MME-Finance: A Multimodal Finance Benchmark for Expert-level Understanding and Reasoning
In recent years, multimodal benchmarks for general domains have guided the rapid development of multimodal models on general tasks.
Reducing the Cost of Dropout in Flash-Attention by Hiding RNG with GEMM
Further, we extend our theoretical model to different RNG implementations and hardware architectures, and discuss the widely applicable benefits for overlapping RNG with GEMM layers.
EAB-FL: Exacerbating Algorithmic Bias through Model Poisoning Attacks in Federated Learning
In this paper, we propose a new type of model poisoning attack, EAB-FL, with a focus on exacerbating group unfairness while maintaining a good level of model utility.
PVContext: Hybrid Context Model for Point Cloud Compression
Efficient storage of large-scale point cloud data has become increasingly challenging due to advancements in scanning technology.
Object Gaussian for Monocular 6D Pose Estimation from Sparse Views
Monocular object pose estimation, as a pivotal task in computer vision and robotics, heavily depends on accurate 2D-3D correspondences, which often demand costly CAD models that may not be readily available.
Diffusion-Occ: 3D Point Cloud Completion via Occupancy Diffusion
In this paper, we introduce \textbf{Diffusion-Occ}, a novel framework for Diffusion Point Cloud Completion.
Mayer-homology learning prediction of protein-ligand binding affinities
Our recent work has extended this concept by employing Mayer homology with a generalized differential that satisfies \(d^N = 0\) for \(N \geq 2\), leading to the development of persistent Mayer homology (PMH) theory and richer topological information across various scales.
WTU-EVAL: A Whether-or-Not Tool Usage Evaluation Benchmark for Large Language Models
We then introduce the Whether-or-not tool usage Evaluation benchmark (WTU-Eval) to assess LLMs with eleven datasets, where six of them are tool-usage datasets, and five are general datasets.
TokenPacker: Efficient Visual Projector for Multimodal LLM
However, the visual tokens are redundant and can be considerably increased when dealing with high-resolution images, impairing the efficiency of MLLMs significantly.
Ranked #167 on
Visual Question Answering
on MM-Vet
Strengthening Layer Interaction via Dynamic Layer Attention
In this paper, we delve into the distinction between layer attention and the general attention mechanism, noting that existing layer attention methods achieve layer interaction on fixed feature maps in a static manner.
Efficient Multi-View Fusion and Flexible Adaptation to View Missing in Cardiovascular System Signals
The progression of deep learning and the widespread adoption of sensors have facilitated automatic multi-view fusion (MVF) about the cardiovascular system (CVS) signals.
DINO-SD: Champion Solution for ICRA 2024 RoboDepth Challenge
However, given that most of the data in the autonomous driving dataset is collected in daytime scenarios, this leads to poor depth model performance in the face of out-of-distribution(OoD) data.
Text-guided 3D Human Motion Generation with Keyframe-based Parallel Skip Transformer
Text-driven human motion generation is an emerging task in animation and humanoid robot design.
Deep Learning for Protein-Ligand Docking: Are We There Yet?
The effects of ligand binding on protein structures and their in vivo functions carry numerous implications for modern biomedical research and biotechnology development efforts such as drug discovery.
Towards Better Question Generation in QA-based Event Extraction
Event Extraction (EE) is an essential information extraction task that aims to extract event-related information from unstructured texts.
The RoboDrive Challenge: Drive Anytime Anywhere in Any Condition
In the realm of autonomous driving, robust perception under out-of-distribution conditions is paramount for the safe deployment of vehicles.
Deep Learning-Based Object Pose Estimation: A Comprehensive Survey
Object pose estimation is a fundamental computer vision problem with broad applications in augmented reality and robotics.
The Third Monocular Depth Estimation Challenge
This paper discusses the results of the third edition of the Monocular Depth Estimation Challenge (MDEC).
Non-Uniform Exposure Imaging via Neuromorphic Shutter Control
To address this challenge, we propose a novel Neuromorphic Shutter Control (NSC) system to avoid motion blurs and alleviate instant noises, where the extremely low latency of events is leveraged to monitor the real-time motion and facilitate the scene-adaptive exposure.
Amplitude-Phase Fusion for Enhanced Electrocardiogram Morphological Analysis
Considering the variability of amplitude and phase patterns in electrocardiogram (ECG) signals due to cardiac activity and individual differences, existing entropy-based studies have not fully utilized these two patterns and lack integration.
Scalable second-order consensus of hierarchical groups
Motivated by widespread dominance hierarchy, growth of group sizes, and feedback mechanisms in social species, we are devoted to exploring the scalable second-order consensus of hierarchical groups.
Stealing Stable Diffusion Prior for Robust Monocular Depth Estimation
This paper introduces a novel approach named Stealing Stable Diffusion (SSD) prior for robust monocular depth estimation.
REPS: Reconstruction-based Point Cloud Sampling
Our method outperforms previous approaches in preserving the structural features of the sampled point clouds.
OccTransformer: Improving BEVFormer for 3D camera-only occupancy prediction
This technical report presents our solution, "occTransformer" for the 3D occupancy prediction track in the autonomous driving challenge at CVPR 2023.
A Self-Healing Magnetic-Array-Type Current Sensor with Data-Driven Identification of Abnormal Magnetic Measurement Units
This analysis is conducted by scrutinizing the measured data obtained from the MMUs.
A Comprehensive Survey on 3D Content Generation
Recent years have witnessed remarkable advances in artificial intelligence generated content(AIGC), with diverse input modalities, e. g., text, image, video, audio and 3D.
TrustLLM: Trustworthiness in Large Language Models
This paper introduces TrustLLM, a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions.
GLOCALFAIR: Jointly Improving Global and Local Group Fairness in Federated Learning
Specifically, we utilize constrained optimization to enforce local fairness on the client side and adopt a fairness-aware clustering-based aggregation on the server to further ensure the global model fairness across different sensitive groups while maintaining high utility.
SyCoCa: Symmetrizing Contrastive Captioners with Attentive Masking for Multimodal Alignment
To achieve multimodal alignment from both global and local perspectives, this paper proposes Symmetrizing Contrastive Captioners (SyCoCa), which introduces bidirectional interactions on images and texts across the global and local representation levels.
Query-Based Knowledge Sharing for Open-Vocabulary Multi-Label Classification
Identifying labels that did not appear during training, known as multi-label zero-shot learning, is a non-trivial task in computer vision.
Osprey: Pixel Understanding with Visual Instruction Tuning
In this paper, we propose Osprey, a mask-text instruction tuning approach, to extend MLLMs by incorporating fine-grained mask regions into language instruction, aiming at achieving pixel-wise visual understanding.
Text as Image: Learning Transferable Adapter for Multi-Label Classification
Pre-trained vision-language models have notably accelerated progress of open-world concept recognition.
Planning Reliability Assurance Tests for Autonomous Vehicles
This paper aims to fill in this gap by developing statistical methods for planning AV reliability assurance tests based on recurrent events data.
A Quality-based Syntactic Template Retriever for Syntactically-controlled Paraphrase Generation
Furthermore, for situations requiring multiple paraphrases for each source sentence, we design a Diverse Templates Search (DTS) algorithm, which can enhance the diversity between paraphrases without sacrificing quality.
Label-efficient Segmentation via Affinity Propagation
In this work, we formulate the affinity modeling as an affinity propagation process, and propose a local and a global pairwise affinity terms to generate accurate soft pseudo labels.
GLoRE: Evaluating Logical Reasoning of Large Language Models
Recently, large language models (LLMs), including notable models such as GPT-4 and burgeoning community models, have showcased significant general language understanding abilities.
Equirectangular image construction method for standard CNNs for Semantic Segmentation
In addition, there are few publicly dataset of equirectangular images with labels, which presents a challenge for standard CNNs models to process equirectangular images effectively.
ChatGPT for Computational Topology
This work serves as an initial step towards effectively transforming pure mathematical theories into practical computational tools, with the ultimate goal of enabling real applications across diverse fields.
AffordPose: A Large-scale Dataset of Hand-Object Interactions with Affordance-driven Hand Pose
We collect a total of 26. 7K hand-object interactions, each including the 3D object shape, the part-level affordance label, and the manually adjusted hand poses.
Point2Mask: Point-supervised Panoptic Segmentation via Optimal Transport
Weakly-supervised image segmentation has recently attracted increasing research attentions, aiming to avoid the expensive pixel-wise labeling.
Towards Understanding and Improving Knowledge Distillation for Neural Machine Translation
In this work, we first unravel this mystery from an empirical perspective and show that the knowledge comes from the top-1 predictions of teachers, which also helps us build a potential connection between word- and sequence-level KD.
Full Scaling Automation for Sustainable Development of Green Data Centers
The rapid rise in cloud computing has resulted in an alarming increase in data centers' carbon emissions, which now accounts for >3% of global greenhouse gas emissions, necessitating immediate steps to combat their mounting strain on the global climate.
Exploring the Mutual Influence between Self-Supervised Single-Frame and Multi-Frame Depth Estimation
Although both self-supervised single-frame and multi-frame depth estimation methods only require unlabeled monocular videos for training, the information they leverage varies because single-frame methods mainly rely on appearance-based features while multi-frame methods focus on geometric cues.
False Claims against Model Ownership Resolution
Our core idea is that a malicious accuser can deviate (without detection) from the specified MOR process by finding (transferable) adversarial examples that successfully serve as evidence against independent suspect models.
RecUP-FL: Reconciling Utility and Privacy in Federated Learning via User-configurable Privacy Defense
In this paper, we seek to reconcile utility and privacy in FL by proposing a user-configurable privacy defense, RecUP-FL, that can better focus on the user-specified sensitive attributes while obtaining significant improvements in utility over traditional defenses.
Multi-Sample Consensus Driven Unsupervised Normal Estimation for 3D Point Clouds
Comprehensive experiments demonstrate that the two proposed unsupervised methods are noticeably superior to some supervised deep normal estimators on the most common synthetic dataset.
Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4
With the release of Generative Pretrained Transformer 4 (GPT-4), highlighted as "advanced" at reasoning tasks, we are eager to learn the GPT-4 performance on various logical reasoning tasks.
Speech Privacy Leakage from Shared Gradients in Distributed Learning
Distributed machine learning paradigms, such as federated learning, have been recently adopted in many privacy-critical applications for speech analysis.
Diffusion Models in Bioinformatics: A New Wave of Deep Learning Revolution in Action
This review aims to provide a rather thorough overview of the applications of diffusion models in bioinformatics to aid their further development in bioinformatics and computational biology.
Artificial Intelligence System for Detection and Screening of Cardiac Abnormalities using Electrocardiogram Images
In this study, we present an AI system developed to detect and screen cardiac abnormalities (CAs) from real-world ECG images.
Scene-Aware Label Graph Learning for Multi-Label Image Classification
Multi-label image classification refers to assigning a set of labels for an image.
Protocol selection for second-order consensus against disturbance
Noticing that both the absolute and relative velocity protocols can solve the second-order consensus of multi-agent systems, this paper aims to investigate which of the above two protocols has better anti-disturbance capability, in which the anti-disturbance capability is measured by the L2 gain from the disturbance to the consensus error.
On the Robustness of Dataset Inference
However, in a subspace of the same setting, we prove that DI suffers from high false positives (FPs) -- it can incorrectly identify an independent model trained with non-overlapping data from the same distribution as stolen.
OST: Efficient One-stream Network for 3D Single Object Tracking in Point Clouds
Although recent Siamese network-based trackers have achieved impressive perceptual accuracy for single object tracking in LiDAR point clouds, they usually utilized heavy correlation operations to capture category-level characteristics only, and overlook the inherent merit of arbitrariness in contrast to multiple object tracking.
Improved Data Augmentation for Translation Suggestion
This paper introduces the system used in our submission to the WMT'22 Translation Suggestion shared task.
RIBAC: Towards Robust and Imperceptible Backdoor Attack against Compact DNN
Recently backdoor attack has become an emerging threat to the security of deep neural network (DNN) models.
Multi-Forgery Detection Challenge 2022: Push the Frontier of Unconstrained and Diverse Forgery Detection
In this paper, we present the solutions from the Top 3 teams, in order to boost the research work in the field of image forgery detection.
Multi-parametric Analysis for Mixed Integer Linear Programming: An Application to Transmission Planning and Congestion Control
Finally, the amount of capacity upgrade will be chosen by balancing the cost-reduction rate of the objective function on parameters and the cost of the line upgrade.
Watermark Vaccine: Adversarial Attacks to Prevent Watermark Removal
Inspired by the vulnerability of DNNs on adversarial perturbations, we propose a novel defence mechanism by adversarial machine learning for good.
DProQ: A Gated-Graph Transformer for Protein Complex Structure Assessment
We challenge this significant task with DProQ, which introduces a gated neighborhood-modulating Graph Transformer (GGT) designed to predict the quality of 3D protein complex structures.
EGR: Equivariant Graph Refinement and Assessment of 3D Protein Complex Structures
Protein complexes are macromolecules essential to the functioning and well-being of all living organisms.
Visual Attention-based Self-supervised Absolute Depth Estimation using Geometric Priors in Autonomous Driving
Although existing monocular depth estimation methods have made great progress, predicting an accurate absolute depth map from a single image is still challenging due to the limited modeling capacity of networks and the scale ambiguity issue.
VPNets: Volume-preserving neural networks for learning source-free dynamics
We propose volume-preserving networks (VPNets) for learning unknown source-free dynamical systems using trajectory data.
Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakage
Federated Learning (FL) framework brings privacy benefits to distributed learning systems by allowing multiple clients to participate in a learning task under the coordination of a central server without exchanging their private data.
Conditional Bilingual Mutual Information Based Adaptive Training for Neural Machine Translation
Token-level adaptive training approaches can alleviate the token imbalance problem and thus improve neural machine translation, through re-weighting the losses of different target tokens based on specific statistical metrics (e. g., token frequency or mutual information).
Label Relation Graphs Enhanced Hierarchical Residual Network for Hierarchical Multi-Granularity Classification
Hierarchical multi-granularity classification (HMC) assigns hierarchical multi-granularity labels to each object and focuses on encoding the label hierarchy, e. g., ["Albatross", "Laysan Albatross"] from coarse-to-fine levels.
Solving Aspect Category Sentiment Analysis as a Text Generation Task
Aspect category sentiment analysis has attracted increasing research attention.
Spatio-temporal-spectral-angular observation model that integrates observations from UAV and mobile mapping vehicle for better urban mapping
The integrated results combined both the characteristic of UAV and mobile mapping vehicle point cloud, confirming the practicability of the proposed joint data acquisition platform and the effectiveness of spatio-temporal-spectral-angular observation model.
"Adversarial Examples" for Proof-of-Learning
Jia et al. claimed that an adversary merely knowing the final model and training dataset cannot efficiently find a set of intermediate models with correct data points.
The Effect of Dust and Sand on the 5G Terrestrial Links
A recent study investigates the effect of rain and snow on the 5G communication channel to reduce the challenge of using high millimeter-wave frequencies.
Byzantine-robust Federated Learning through Spatial-temporal Analysis of Local Model Updates
Federated Learning (FL) enables multiple distributed clients (e. g., mobile devices) to collaboratively train a centralized model while keeping the training data locally on the client.
Template-Based Named Entity Recognition Using BART
To address the issue, we propose a template-based method for NER, treating NER as a language model ranking problem in a sequence-to-sequence framework, where original sentences and statement templates filled by candidate named entity span are regarded as the source sequence and the target sequence, respectively.
OFEI: A Semi-black-box Android Adversarial Sample Attack Framework Against DLaaS
With the growing popularity of Android devices, Android malware is seriously threatening the safety of users.
Unsupervised Sentiment Analysis by Transferring Multi-source Knowledge
The first mechanism is a selective domain adaptation (SDA) method, which transfers knowledge from the closest source domain.
CelebA-Spoof Challenge 2020 on Face Anti-Spoofing: Methods and Results
It is the largest face anti-spoofing dataset in terms of the numbers of the data and the subjects.
A hybrid deep-learning approach for complex biochemical named entity recognition
In this approach, the MHATT mechanism aims to improve the recognition accuracy of abbreviations to efficiently deal with the problem of inconsistency in full-text labels.
Graph-Based Knowledge Integration for Question Answering over Dialogue
Despite many advances, existing approaches for this task did not consider dialogue structure and background knowledge (e. g., relationships between speakers).
Ranked #6 on
Question Answering
on FriendsQA
Natural Language Inference in Context -- Investigating Contextual Reasoning over Long Texts
Natural language inference (NLI) is a fundamental NLP task, investigating the entailment relationship between two texts.
How Does Context Matter? On the Robustness of Event Detection with Context-Selective Mask Generalization
Event detection (ED) aims to identify and classify event triggers in texts, which is a crucial subtask of event extraction (EE).
Singular equivalences induced by bimodules and quadratic monomial algebras
We investigate the problem when the tensor functor by a bimodule yields a singular equivalence.
Representation Theory Rings and Algebras 18G80, 16E45, 16D20, 16G20
LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning
Machine reading is a fundamental task for testing the capability of natural language understanding, which is closely related to human cognition in many aspects.
Adversarial Training Based Multi-Source Unsupervised Domain Adaptation for Sentiment Analysis
Existing algorithms of MS-UDA either only exploit the shared features, i. e., the domain-invariant information, or based on some weak assumption in NLP, e. g., smoothness assumption.
Multi-Source Unsupervised Domain Adaptation
Sentiment Analysis
+2
Enabling Fast and Universal Audio Adversarial Attack Using Generative Model
These idealized assumptions, however, makes the existing audio adversarial attacks mostly impossible to be launched in a timely fashion in practice (e. g., playing unnoticeable adversarial perturbations along with user's streaming input).
Learn to Forget: Machine Unlearning via Neuron Masking
To this end, machine unlearning becomes a popular research topic, which allows users to eliminate memorization of their private data from a trained machine learning model. In this paper, we propose the first uniform metric called for-getting rate to measure the effectiveness of a machine unlearning method.
Real-time, Universal, and Robust Adversarial Attacks Against Speaker Recognition Systems
As the popularity of voice user interface (VUI) exploded in recent years, speaker recognition system has emerged as an important medium of identifying a speaker in many security-required applications and services.
Review and Examination of Input Feature Preparation Methods and Machine Learning Models for Turbulence Modeling
Model extrapolation to unseen flow is one of the biggest challenges facing data-driven turbulence modeling, especially for models with high dimensional inputs that involve many flow features.
Fluid Dynamics Computational Physics
Free-riders in Federated Learning: Attacks and Defenses
Although the incentive model for federated learning has not been fully developed, it is supposed that participants are able to get rewards or the privilege to use the final global model, as a compensation for taking efforts to train the model.
Neural Cross-Lingual Event Detection with Minimal Parallel Resources
In this paper, we propose a new method for cross-lingual ED, demonstrating a minimal dependency on parallel resources.
Adversarial Attack on Skeleton-based Human Action Recognition
We also explore the possibility of semantically imperceptible localized attacks with CIASA, and succeed in fooling the state-of-the-art skeleton action recognition models with high confidence.
Spearphone: A Speech Privacy Exploit via Accelerometer-Sensed Reverberations from Smartphone Loudspeakers
In this paper, we build a speech privacy attack that exploits speech reverberations generated from a smartphone's inbuilt loudspeaker captured via a zero-permission motion sensor (accelerometer).
Cryptography and Security
Temporally Coherent Full 3D Mesh Human Pose Recovery from Monocular Video
A major challenge in this regard is the lack of appropriately annotated video data for learning the desired deep models.
Deep Cross Networks with Aesthetic Preference for Cross-domain Recommendation
Then, we integrate the aesthetic features into a cross-domain network to transfer users' domain independent aesthetic preferences.
Biophysics at the coffee shop: lessons learned working with George Oster
Over the past 50 years, the use of mathematical models, derived from physical reasoning, to describe molecular and cellular systems has evolved from an art of the few to a cornerstone of biological inquiry.
Latent Dirichlet Allocation in Generative Adversarial Networks
We study the problem of multimodal generative modelling of images based on generative adversarial networks (GANs).
iQIYI-VID: A Large Dataset for Multi-modal Person Identification
Multi-modal person identification is a more promising way that we can jointly utilize face, head, body, audio features, and so on.
Faster super-resolution imaging with auto-correlation two-step deconvolution
Despite super-resolution fluorescence blinking microscopes break the diffraction limit, the intense phototoxic illumination and long-term image sequences thus far still pose to major challenges in visualizing live-organisms.
Optics
Event Detection via Gated Multilingual Attention Mechanism
In specific, to alleviate data scarcity problem, we exploit the consistent information in multilingual data via context attention mechanism.
Hierarchical Attention-Based Recurrent Highway Networks for Time Series Prediction
Time series prediction has been studied in a variety of domains.
Understanding Android Obfuscation Techniques: A Large-Scale Investigation in the Wild
In this paper, we seek to better understand Android obfuscation and depict a holistic view of the usage of obfuscation through a large-scale investigation in the wild.
Cryptography and Security
Defense against Universal Adversarial Perturbations
A rigorous evaluation shows that our framework can defend the network classifiers against unseen adversarial perturbations in the real-world scenarios with up to 97. 5% success rate.
Skepxels: Spatio-temporal Image Representation of Human Skeleton Joints for Action Recognition
The proposed action recognition exploits the representation in a hierarchical manner by first capturing the micro-temporal relations between the skeleton joints with the Skepxels and then exploiting their macro-temporal relations by computing the Fourier Temporal Pyramids over the CNN features of the skeletal images.
Viewpoint Invariant Action Recognition using RGB-D Videos
The proposed technique capitalizes on the spatio-temporal information available in the two data streams to the extract action features that are largely insensitive to the viewpoint variations.
Learning Human Pose Models from Synthesized Data for Robust RGB-D Action Recognition
We propose Human Pose Models that represent RGB and depth images of human poses independent of clothing textures, backgrounds, lighting conditions, body shapes and camera viewpoints.
Ranked #108 on
Skeleton Based Action Recognition
on NTU RGB+D
Improvement of training set structure in fusion data cleaning using Time-Domain Global Similarity method
Traditional data cleaning identifies dirty data by classifying original data sequences, which is a class$-$imbalanced problem since the proportion of incorrect data is much less than the proportion of correct ones for most diagnostic systems in Magnetic Confinement Fusion (MCF) devices.
Preference-based performance measures for Time-Domain Global Similarity method
To obtain the general expressions of performance measures based on the preferences of tasks, the mapping relations between performance of TDGS method about physical similarity and correctness of data sequences are investigated by probability theory in this paper.
Context-aware System Service Call-oriented Symbolic Execution of Android Framework with Application to Exploit Generation
We present novel ideas and techniques to resolve the challenges, and have built the first system for symbolic execution of Android Framework.
Cryptography and Security Software Engineering
