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Found 154 papers, 44 papers with code
DocEE: A Large-Scale and Fine-grained Benchmark for Document-level Event Extraction
Event extraction aims to identify an event and then extract the arguments participating in the event.
Automatic recognition of abdominal lymph nodes from clinical text
We then introduce an end-to-end approach based on the combination of rules and transformer-based methods to detect these abdominal lymph node mentions and classify their types from the MRI radiology reports.
Accurate Segmentation of Optic Disc And Cup from Multiple Pseudo-labels by Noise-Aware Learning
The training framework of the MPNN is constructed by a teacher-student architecture to learn segmentation from clean pixels and noisy pixels.
PMP-Swin: Multi-Scale Patch Message Passing Swin Transformer for Retinal Disease Classification
Specifically, we design a Patch Message Passing (PMP) module based on the Message Passing mechanism to establish global interaction for pathological semantic features and to exploit the subtle differences further between different diseases.
MSE-Nets: Multi-annotated Semi-supervised Ensemble Networks for Improving Segmentation of Medical Image with Ambiguous Boundaries
Medical image segmentation annotations exhibit variations among experts due to the ambiguous boundaries of segmented objects and backgrounds in medical images.
Unlock the Power: Competitive Distillation for Multi-Modal Large Language Models
The first stage pre-trains the student model on a large number of filtered multi-modal datasets.
CASTER: A Computer-Vision-Assisted Wireless Channel Simulator for Gesture Recognition
In the proposed CASTER simulator, however, the training dataset can be simulated via existing videos.
FDNet: Feature Decoupled Segmentation Network for Tooth CBCT Image
Precise Tooth Cone Beam Computed Tomography (CBCT) image segmentation is crucial for orthodontic treatment planning.
Evaluating Generative Ad Hoc Information Retrieval
Recent advances in large language models have enabled the development of viable generative information retrieval systems.
Privacy-preserving Federated Primal-dual Learning for Non-convex and Non-smooth Problems with Model Sparsification
Federated learning (FL) has been recognized as a rapidly growing research area, where the model is trained over massively distributed clients under the orchestration of a parameter server (PS) without sharing clients' data.
SparseByteNN: A Novel Mobile Inference Acceleration Framework Based on Fine-Grained Group Sparsity
Our framework consists of two parts: (a) A fine-grained kernel sparsity schema with a sparsity granularity between structured pruning and unstructured pruning.
PC-bzip2: a phase-space continuity enhanced lossless compression algorithm for light field microscopy data
Moreover, by introducing the temporal continuity, our method shows the superior compression ratio on time series data of zebrafish blood vessels.
Benchmarking and Explaining Large Language Model-based Code Generation: A Causality-Centric Approach
We illustrate the insights that our framework can provide by studying over 3 popular LLMs with over 12 prompt adjustment strategies.
Split and Merge: Aligning Position Biases in Large Language Model based Evaluators
Specifically, PORTIA splits the answers into multiple segments, aligns similar content across candidate answers, and then merges them back into a single prompt for evaluation by LLMs.
Biomedical Image Splicing Detection using Uncertainty-Guided Refinement
Besides, we construct a dataset for Biomedical image Splicing (BioSp) detection, which consists of 1, 290 spliced images.
DualVC 2: Dynamic Masked Convolution for Unified Streaming and Non-Streaming Voice Conversion
Third, the model is unable to effectively address the noise in the unvoiced segments, lowering the sound quality.
AutoPrep: An Automatic Preprocessing Framework for In-the-Wild Speech Data
To address this issue, we introduce an automatic in-the-wild speech data preprocessing framework (AutoPrep) in this paper, which is designed to enhance speech quality, generate speaker labels, and produce transcriptions automatically.
Prototype-Enhanced Hypergraph Learning for Heterogeneous Information Networks
The variety and complexity of relations in multimedia data lead to Heterogeneous Information Networks (HINs).
Multiple Satellites Collaboration for Joint Code-aided CFOs and CPOs Estimation
Low Earth Orbit (LEO) satellites are being extensively researched in the development of secure Internet of Remote Things (IoRT).
Leveraging In-the-Wild Data for Effective Self-Supervised Pretraining in Speaker Recognition
Current speaker recognition systems primarily rely on supervised approaches, constrained by the scale of labeled datasets.
Active Learning for Multilingual Fingerspelling Corpora
We apply active learning to help with data scarcity problems in sign languages.
Agents: An Open-source Framework for Autonomous Language Agents
Recent advances on large language models (LLMs) enable researchers and developers to build autonomous language agents that can automatically solve various tasks and interact with environments, humans, and other agents using natural language interfaces.
Unveiling Single-Bit-Flip Attacks on DNN Executables
Nevertheless, we find that DNN executables contain extensive, severe (e. g., single-bit flip), and transferrable attack surfaces that are not present in high-level DNN models and can be exploited to deplete full model intelligence and control output labels.
Generating Natural Language Queries for More Effective Systematic Review Screening Prioritisation
Our best approach is not only viable based on the information available at the time of screening, but also has similar effectiveness to the final title.
Enabling Runtime Verification of Causal Discovery Algorithms with Automated Conditional Independence Reasoning (Extended Version)
Based on the decision procedure to CIR, CICheck includes two variants: ED-CICheck and ED-CICheck, which detect erroneous CI tests (to enhance reliability) and prune excessive CI tests (to enhance privacy), respectively.
Integrated Robotics Networks with Co-optimization of Drone Placement and Air-Ground Communications
Terrestrial robots, i. e., unmanned ground vehicles (UGVs), and aerial robots, i. e., unmanned aerial vehicles (UAVs), operate in separate spaces.
Parsing is All You Need for Accurate Gait Recognition in the Wild
Furthermore, due to the lack of suitable datasets, we build the first parsing-based dataset for gait recognition in the wild, named Gait3D-Parsing, by extending the large-scale and challenging Gait3D dataset.
Least Squares Maximum and Weighted Generalization-Memorization Machines
Furthermore, we propose some different memory impact functions for the MIMM and WIMM.
Deep Equilibrium Object Detection
To be more specific to object decoding, we use a two-step unrolled equilibrium equation to explicitly capture the query vector refinement.
Compound Attention and Neighbor Matching Network for Multi-contrast MRI Super-resolution
Multi-contrast magnetic resonance imaging (MRI) reflects information about human tissue from different perspectives and has many clinical applications.
Communication Resources Constrained Hierarchical Federated Learning for End-to-End Autonomous Driving
While federated learning (FL) improves the generalization of end-to-end autonomous driving by model aggregation, the conventional single-hop FL (SFL) suffers from slow convergence rate due to long-range communications among vehicles and cloud server.
Wespeaker baselines for VoxSRC2023
This report showcases the results achieved using the wespeaker toolkit for the VoxSRC2023 Challenge.
Geometric Pooling: maintaining more useful information
However, by analyzing the statistical characteristic of activated units after pooling, we found that a large number of units dropped by sorting pooling are negative-value units that contain useful information and can contribute considerably to the final decision.
Towards Practical Federated Causal Structure Learning
Federated learning can solve this problem, but existing solutions for federated causal structure learning make unrealistic assumptions about data and lack convergence guarantees.
Precise and Generalized Robustness Certification for Neural Networks
We identify two key properties, independence and continuity, that convert the latent space into a precise and analysis-friendly input space representation for certification.
Enhancing Point Annotations with Superpixel and Confidence Learning Guided for Improving Semi-Supervised OCT Fluid Segmentation
Specifically, we use points to annotate fluid regions in unlabeled OCT images and the Superpixel-Guided Pseudo-Label Generation (SGPLG) module generates pseudo-labels and pixel-level label trust maps from the point annotations.
Causality-Aided Trade-off Analysis for Machine Learning Fairness
This paper uses causality analysis as a principled method for analyzing trade-offs between fairness parameters and other crucial metrics in ML pipelines.
Taxonomy Expansion for Named Entity Recognition
We experiment with 6 diverse datasets and show that PLM consistently performs better than most other approaches (0. 5 - 2. 5 F1), including in novel settings for taxonomy expansion not considered in prior work.
DualVC: Dual-mode Voice Conversion using Intra-model Knowledge Distillation and Hybrid Predictive Coding
Voice conversion is an increasingly popular technology, and the growing number of real-time applications requires models with streaming conversion capabilities.
A Weak Supervision Approach for Few-Shot Aspect Based Sentiment
We explore how weak supervision on abundant unlabeled data can be leveraged to improve few-shot performance in aspect-based sentiment analysis (ABSA) tasks.
Explain Any Concept: Segment Anything Meets Concept-Based Explanation
For computer vision tasks, mainstream pixel-based XAI methods explain DNN decisions by identifying important pixels, and emerging concept-based XAI explore forming explanations with concepts (e. g., a head in an image).
Adversarial Speaker Disentanglement Using Unannotated External Data for Self-supervised Representation Based Voice Conversion
Experimental results show that our proposed method achieves comparable similarity and higher naturalness than the supervised method, which needs a huge amount of annotated corpora for training and is applicable to improve similarity for VC methods with other SSL representations as input.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Towards Generalizable Medical Image Segmentation with Pixel-wise Uncertainty Estimation
In contrast, the IID hypothesis is not universally guaranteed in numerous real-world applications, especially in medical image analysis.
Black-box Source-free Domain Adaptation via Two-stage Knowledge Distillation
In Stage \uppercase\expandafter{\romannumeral1}, we train the target model from scratch with soft pseudo-labels generated by the source model in a knowledge distillation manner.
"Oops, Did I Just Say That?" Testing and Repairing Unethical Suggestions of Large Language Models with Suggest-Critique-Reflect Process
Moreover, we propose a novel on-the-fly (OTF) repairing scheme that repairs unethical suggestions made by LLMs in real-time.
Meta-Reinforcement Learning Based on Self-Supervised Task Representation Learning
On MuJoCo and Meta-World benchmarks, MoSS outperforms prior works in terms of asymptotic performance, sample efficiency (3-50x faster), adaptation efficiency, and generalization robustness on broad and diverse task distributions.
DiffuseExpand: Expanding dataset for 2D medical image segmentation using diffusion models
Based on this insight, we propose an approach called DiffuseExpand for expanding datasets for 2D medical image segmentation using DPM, which first samples a variety of masks from Gaussian noise to ensure the diversity, and then synthesizes images to ensure the alignment of images and masks.
Towards Open-Vocabulary Video Instance Segmentation
Video Instance Segmentation (VIS) aims at segmenting and categorizing objects in videos from a closed set of training categories, lacking the generalization ability to handle novel categories in real-world videos.
Demonstration of InsightPilot: An LLM-Empowered Automated Data Exploration System
In brief, an IQuery is an abstraction and automation of data analysis operations, which mimics the approach of data analysts and simplifies the exploration process for users.
Simple Yet Effective Synthetic Dataset Construction for Unsupervised Opinion Summarization
Opinion summarization provides an important solution for summarizing opinions expressed among a large number of reviews.
Natural Language Inference
Unsupervised Opinion Summarization
Feature Alignment and Uniformity for Test Time Adaptation
Test time adaptation (TTA) aims to adapt deep neural networks when receiving out of distribution test domain samples.
Prototype Knowledge Distillation for Medical Segmentation with Missing Modality
Specifically, our ProtoKD can not only distillate the pixel-wise knowledge of multi-modality data to single-modality data but also transfer intra-class and inter-class feature variations, such that the student model could learn more robust feature representation from the teacher model and inference with only one single modality data.
Bootstrap The Original Latent: Learning a Private Model from a Black-box Model
In this paper, considering the balance of data/model privacy of model owners and user needs, we propose a new setting called Back-Propagated Black-Box Adaptation (BPBA) for users to better train their private models via the guidance of the back-propagated results of a Black-box foundation/source model.
Dynamic Benchmarking of Masked Language Models on Temporal Concept Drift with Multiple Views
Temporal concept drift refers to the problem of data changing over time.
Can ChatGPT Write a Good Boolean Query for Systematic Review Literature Search?
The ability of ChatGPT to follow complex instructions and generate queries with high precision makes it a valuable tool for researchers conducting systematic reviews, particularly for rapid reviews where time is a constraint and often trading-off higher precision for lower recall is acceptable.
Predictions of photophysical properties of phosphorescent platinum(II) complexes based on ensemble machine learning approach
Here, we develop a general protocol for accurate predictions of emission wavelength, radiative decay rate constant, and PL quantum yield for phosphorescent Pt(II) emitters based on the combination of first-principles quantum mechanical method, machine learning (ML) and experimental calibration.
SrTR: Self-reasoning Transformer with Visual-linguistic Knowledge for Scene Graph Generation
The execution efficiency of the one-stage scene graph generation approaches are quite high, which infer the effective relation between entity pairs using sparse proposal sets and a few queries.
MeSH Suggester: A Library and System for MeSH Term Suggestion for Systematic Review Boolean Query Construction
One challenge to creating an effective systematic review Boolean query is the selection of effective MeSH Terms to include in the query.
Neural Rankers for Effective Screening Prioritisation in Medical Systematic Review Literature Search
An empirical analysis compares how effective neural methods compare to traditional methods for this task.
Teaching What You Should Teach: A Data-Based Distillation Method
To be specific, we design a neural network-based data augmentation module with priori bias, which assists in finding what meets the teacher's strengths but the student's weaknesses, by learning magnitudes and probabilities to generate suitable data samples.
Beyond ADMM: A Unified Client-variance-reduced Adaptive Federated Learning Framework
In this paper, we firstly reveal the fact that the federated ADMM is essentially a client-variance-reduced algorithm.
Knowledge-Guided Exploration in Deep Reinforcement Learning
This paper proposes a new method to drastically speed up deep reinforcement learning (deep RL) training for problems that have the property of state-action permissibility (SAP).
Large-Scale Bandwidth and Power Optimization for Multi-Modal Edge Intelligence Autonomous Driving
However, wireless channels between the edge server and the autonomous vehicles are time-varying due to the high-mobility of vehicles.
Instruction Tuning for Few-Shot Aspect-Based Sentiment Analysis
Aspect-based Sentiment Analysis (ABSA) is a fine-grained sentiment analysis task which involves four elements from user-generated texts: aspect term, aspect category, opinion term, and sentiment polarity.
Contrastive Training Improves Zero-Shot Classification of Semi-structured Documents
The standard classification setting where categories are fixed during both training and testing falls short in dynamic environments where new document categories could potentially emerge.
Decompiling x86 Deep Neural Network Executables
BTD takes DNN executables and outputs full model specifications, including types of DNN operators, network topology, dimensions, and parameters that are (nearly) identical to those of the input models.
Accelerated partial separable model using dimension-reduced optimization technique for ultra-fast cardiac MRI
The main objective of this study is to accelerate the PS model, shorten the time required for acquisition and reconstruction, and maintain good image quality simultaneously.
Automated MeSH Term Suggestion for Effective Query Formulation in Systematic Reviews Literature Search
However, identifying the correct MeSH terms to include in a query is difficult: information experts are often unfamiliar with the MeSH database and unsure about the appropriateness of MeSH terms for a query.
Language-aware Domain Generalization Network for Cross-Scene Hyperspectral Image Classification
Text information including extensive prior knowledge about land cover classes has been ignored in hyperspectral image classification (HSI) tasks.
Multi-Point Integrated Sensing and Communication: Fusion Model and Functionality Selection
Integrated sensing and communication (ISAC) represents a paradigm shift, where previously competing wireless transmissions are jointly designed to operate in harmony via the shared use of the hardware platform for improving the spectral and energy efficiencies.
Longitudinal Prediction of Postnatal Brain Magnetic Resonance Images via a Metamorphic Generative Adversarial Network
In this paper, we propose a deep learning framework to predict missing scans from acquired scans, catering to longitudinal infant studies.
XInsight: eXplainable Data Analysis Through The Lens of Causality
XInsight is a three-module, end-to-end pipeline designed to extract causal graphs, translate causal primitives into XDA semantics, and quantify the quantitative contribution of each explanation to a data fact.
Cross Vision-RF Gait Re-identification with Low-cost RGB-D Cameras and mmWave Radars
Human identification is a key requirement for many applications in everyday life, such as personalized services, automatic surveillance, continuous authentication, and contact tracing during pandemics, etc.
A Framework Based on Generational and Environmental Response Strategies for Dynamic Multi-objective Optimization
In this paper, a novel framework based on generational and environmental response strategies (FGERS) is proposed, in which response strategies are run both in the environmental change stage and the environmental static stage to obtain population evolution information of those both stages.
VIP-SLAM: An Efficient Tightly-Coupled RGB-D Visual Inertial Planar SLAM
Even if the plane parameters are involved in the optimization, we effectively simplify the back-end map by using planar structures.
Graph Information Aggregation Cross-Domain Few-Shot Learning for Hyperspectral Image Classification
The IDE-block is used to characterize and aggregate the intradomain nonlocal relationships and the interdomain feature and distribution similarities are captured in the CSA-block.
Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verification
However, it is challenging to determine the network resources and road sensor placements for multi-stage training with multi-modal datasets in multi-variant scenarios.
Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy Guarantees
In contrast to prior work, our proposed protocols improve the dimension dependence and achieve a tight statistical rate in terms of all the parameters for strongly convex losses.
To Interpolate or not to Interpolate: PRF, Dense and Sparse Retrievers
In this paper we consider the problem of combining the relevance signals from sparse and dense retrievers in the context of Pseudo Relevance Feedback (PRF).
Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methods
Reconfigurable intelligent surfaces (RISs) have a revolutionary capability to customize the radio propagation environment for wireless networks.
Federated Stochastic Primal-dual Learning with Differential Privacy
Specifically, we show that, by guaranteeing $(\epsilon, \delta)$-DP for each client per communication round, the proposed algorithm guarantees $(\mathcal{O}(q\epsilon \sqrt{p T}), \delta)$-DP after $T$ communication rounds while maintaining an $\mathcal{O}(1/\sqrt{pTQ})$ convergence rate for a convex and non-smooth learning problem, where $Q$ is the number of local SGD steps, $p$ is the client sampling probability, $q=\max_{i} q_i/\sqrt{1-q_i}$ and $q_i$ is the data sampling probability of each client under PCP.
From Little Things Big Things Grow: A Collection with Seed Studies for Medical Systematic Review Literature Search
However, we show pseudo seed studies are not representative of real seed studies used by information specialists.
Accelerating Federated Edge Learning via Topology Optimization
In this paper, a novel topology-optimized federated edge learning (TOFEL) scheme is proposed to tackle the heterogeneity issue in federated learning and to improve the communication-and-computation efficiency.
Semantic-guided Disentangled Representation for Unsupervised Cross-modality Medical Image Segmentation
Disentangled representation is a powerful technique to tackle domain shift problem in medical image analysis in unsupervised domain adaptation setting. However, previous methods only focus on exacting domain-invariant feature and ignore whether exacted feature is meaningful for downstream tasks. We propose a novel framework, called semantic-guided disentangled representation (SGDR), an effective method to exact semantically meaningful feature for segmentation task to improve performance of cross modality medical image segmentation in unsupervised domain adaptation setting.
Plug-and-play Shape Refinement Framework for Multi-site and Lifespan Brain Skull Stripping
For instance, the model trained on a dataset with specific imaging parameters cannot be well applied to other datasets with different imaging parameters.
Low Earth Orbit Satellite Security and Reliability: Issues, Solutions, and the Road Ahead
Low Earth Orbit (LEO) satellites undergo a period of rapid development driven by ever-increasing user demands, reduced costs, and technological progress.
ADI: Adversarial Dominating Inputs in Vertical Federated Learning Systems
Vertical federated learning (VFL) system has recently become prominent as a concept to process data distributed across many individual sources without the need to centralize it.
Automated Side Channel Analysis of Media Software with Manifold Learning
Recent advances in representation learning and perceptual learning inspired us to consider the reconstruction of media inputs from side channel traces as a cross-modality manifold learning task that can be addressed in a unified manner with an autoencoder framework trained to learn the mapping between media inputs and side channel observations.
Seed-driven Document Ranking for Systematic Reviews: A Reproducibility Study
Our results also indicate that our reproduced screening prioritisation method, (1) is generalisable across datasets of similar and different topicality compared to the original implementation, (2) that when using multiple seed studies, the effectiveness of the method increases using our techniques to enable this, (3) and that the use of multiple seed studies produces more stable rankings compared to single seed studies.
MDPFuzz: Testing Models Solving Markov Decision Processes
During fuzzing, MDPFuzz decides which mutated state to retain by measuring if it can reduce cumulative rewards or form a new state sequence.
Revisiting Neuron Coverage for DNN Testing: A Layer-Wise and Distribution-Aware Criterion
We demonstrate that NLC is significantly correlated with the diversity of a test suite across a number of tasks (classification and generation) and data formats (image and text).
Provably Valid and Diverse Mutations of Real-World Media Data for DNN Testing
In contrast, we discuss the feasibility of mutating real-world media data with provably high DIV and VAL based on manifold.
Dispensed Transformer Network for Unsupervised Domain Adaptation
To mitigate this problem, a novel unsupervised domain adaptation (UDA) method named dispensed Transformer network (DTNet) is introduced in this paper.
Dual Shape Guided Segmentation Network for Organs-at-Risk in Head and Neck CT Images
To deal with the large shape variation and unclear boundary of OARs in CT images, we represent the organ shape using an organ-specific unilateral inverse-distance map (UIDM) and guide the segmentation task from two different perspectives: direct shape guidance by following the segmentation prediction and across shape guidance by sharing the segmentation feature.
GT U-Net: A U-Net Like Group Transformer Network for Tooth Root Segmentation
In this paper, we propose a novel end-to-end U-Net like Group Transformer Network (GT U-Net) for the tooth root segmentation.
FED-$\chi^2$: Secure Federated Correlation Test
In this paper, we propose the first secure federated $\chi^2$-test protocol, FED-$\chi^2$.
Secure Byzantine-Robust Federated Learning with Dimension-free Error
In the present work, we propose a federated learning protocol with bi-directional security guarantees.
Paint4Poem: A Dataset for Artistic Visualization of Classical Chinese Poems
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.
Unit-Modulus Wireless Federated Learning Via Penalty Alternating Minimization
Wireless federated learning (FL) is an emerging machine learning paradigm that trains a global parametric model from distributed datasets via wireless communications.
Accelerating Federated Edge Learning via Optimized Probabilistic Device Scheduling
Building on the analytical result, an optimized probabilistic scheduling policy is derived in closed-form by solving the approximate communication time minimization problem.
Accelerating Edge Intelligence via Integrated Sensing and Communication
Conventionally, the sensing and communication stages are executed sequentially, which results in excessive amount of dataset generation and uploading time.
Integrated Sensing and Communication from Learning Perspective: An SDP3 Approach
Characterizing the sensing and communication performance tradeoff in integrated sensing and communication (ISAC) systems is challenging in the applications of learning-based human motion recognition.
Learning from Miscellaneous Other-Class Words for Few-shot Named Entity Recognition
Few-shot Named Entity Recognition (NER) exploits only a handful of annotations to identify and classify named entity mentions.
Perception Matters: Detecting Perception Failures of VQA Models Using Metamorphic Testing
MetaVQA checks whether the answer to (i, q) satisfies metamorphic relationships (MRs), denoting perception consistency, with the composed answers of transformed questions and images.
Multi-Task Learning and Adapted Knowledge Models for Emotion-Cause Extraction
Detecting what emotions are expressed in text is a well-studied problem in natural language processing.
Privileged Graph Distillation for Cold Start Recommendation
The teacher model is composed of a heterogeneous graph structure for warm users and items with privileged CF links.
Federated Meta Learning Enhanced Acoustic Radio Cooperative Framework for Ocean of Things Underwater Acoustic Communications
In summary, the proposed ARC/FML for OoT is a promising scheme for information exchange across water and air.
AGMB-Transformer: Anatomy-Guided Multi-Branch Transformer Network for Automated Evaluation of Root Canal Therapy
Accurate evaluation of the treatment result on X-ray images is a significant and challenging step in root canal therapy since the incorrect interpretation of the therapy results will hamper timely follow-up which is crucial to the patients' treatment outcome.
A Knowledge Enhanced Learning and Semantic Composition Model for Multi-Claim Fact Checking
Our method consists of two modules, KG-based learning enhancement and multi-claim semantic composition.
Wireless Sensing With Deep Spectrogram Network and Primitive Based Autoregressive Hybrid Channel Model
Although wireless channel models can be adopted for dataset generation, current channel models are mostly designed for communication rather than sensing.
Sequential Cross-Document Coreference Resolution
Relating entities and events in text is a key component of natural language understanding.
coreference-resolution
Cross Document Coreference Resolution
+1
High-Resolution Segmentation of Tooth Root Fuzzy Edge Based on Polynomial Curve Fitting with Landmark Detection
As the most economical and routine auxiliary examination in the diagnosis of root canal treatment, oral X-ray has been widely used by stomatologists.
Distributed Dynamic Map Fusion via Federated Learning for Intelligent Networked Vehicles
The technology of dynamic map fusion among networked vehicles has been developed to enlarge sensing ranges and improve sensing accuracies for individual vehicles.
Edge Federated Learning Via Unit-Modulus Over-The-Air Computation
Simulation results show that the proposed UMAirComp framework with PAM algorithm achieves a smaller mean square error of model parameters' estimation, training loss, and test error compared with other benchmark schemes.
On Secure Degrees of Freedom of the MIMO Interference Channel with Local Output Feedback
This paper studies the problem of sum-secure degrees of freedom (SDoF) of the (M, M, N, N) multiple-input multiple-output (MIMO) interference channel with local output feedback, so as to build an information-theoretic foundation and provide practical transmission schemes for 6G-enabled vehicles-to-vehicles (V2V).
Information Theory Information Theory
Private Image Reconstruction from System Side Channels Using Generative Models
Given the ever-growing adoption of machine learning as a service (MLaaS), image analysis software on cloud platforms has been exploited by reconstructing private user images from system side channels.
F^2ed-Learning: Good Fences Make Good Neighbors
In this paper, we present F^2ed-Learning, the first federated learning protocol simultaneously defending against both semi-honest server and Byzantine malicious clients.
A General Recurrent Tracking Framework Without Real Data
Based on this framework, a Recurrent Tracking Unit (RTU) is designed to score potential tracks through long-term information.
Reconfigurable Intelligent Surface Assisted Mobile Edge Computing with Heterogeneous Learning Tasks
The ever-growing popularity and rapid improving of artificial intelligence (AI) have raised rethinking on the evolution of wireless networks.
MT-Teql: Evaluating and Augmenting Consistency of Text-to-SQL Models with Metamorphic Testing
Envisioning the general difficulty for text-to-SQL models to preserve prediction consistency against linguistic and schema variations, we propose MT-Teql, a Metamorphic Testing-based framework for systematically evaluating and augmenting the consistency of TExt-to-SQL models.
Multi-Domain Multi-Task Rehearsal for Lifelong Learning
Rehearsal, seeking to remind the model by storing old knowledge in lifelong learning, is one of the most effective ways to mitigate catastrophic forgetting, i. e., biased forgetting of previous knowledge when moving to new tasks.
Bayes-enhanced Lifelong Attention Networks for Sentiment Classification
After each sentiment classification task is learned, its knowledge is retained to help future task learning.
Multiscale Attention Guided Network for COVID-19 Diagnosis Using Chest X-ray Images
Moreover, massive data collection is impractical for a newly emerged disease, which limited the performance of data thirsty deep learning models.
Learning Centric Wireless Resource Allocation for Edge Computing: Algorithm and Experiment
Edge intelligence is an emerging network architecture that integrates sensing, communication, computing components, and supports various machine learning applications, where a fundamental communication question is: how to allocate the limited wireless resources (such as time, energy) to the simultaneous model training of heterogeneous learning tasks?
A Knowledge-Driven Approach to Classifying Object and Attribute Coreferences in Opinion Mining
Classifying and resolving coreferences of objects (e. g., product names) and attributes (e. g., product aspects) in opinionated reviews is crucial for improving the opinion mining performance.
Resource-Enhanced Neural Model for Event Argument Extraction
(2) Capturing the long-range dependency, specifically, the connection between an event trigger and a distant event argument.
Towards Bidirectional Protection in Federated Learning
At one end of the spectrum, some work uses secure aggregation techniques to hide the individual client's updates and only reveal the aggregated global update to a malicious server that strives to infer the clients' privacy from their updates.
Edge Learning with Unmanned Ground Vehicle: Joint Path, Energy and Sample Size Planning
However, different devices may transmit different data for different machine learning jobs and a fundamental question is how to jointly plan the UGV path, the devices' energy consumption, and the number of samples for different jobs?
Learning Centric Power Allocation for Edge Intelligence
While machine-type communication (MTC) devices generate massive data, they often cannot process this data due to limited energy and computation power.
Improving Event Detection via Open-domain Trigger Knowledge
Event Detection (ED) is a fundamental task in automatically structuring texts.
Detecting Domain Polarity-Changes of Words in a Sentiment Lexicon
We refer to this problem as domain polarity-changes of words.
Improving Positive Unlabeled Learning: Practical AUL Estimation and New Training Method for Extremely Imbalanced Data Sets
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.
Severing the Edge Between Before and After: Neural Architectures for Temporal Ordering of Events
In this paper, we propose a neural architecture and a set of training methods for ordering events by predicting temporal relations.
MUTATT: Visual-Textual Mutual Guidance for Referring Expression Comprehension
In this paper, we argue that for REC the referring expression and the target region are semantically correlated and subject, location and relationship consistency exist between vision and language. On top of this, we propose a novel approach called MutAtt to construct mutual guidance between vision and language, which treat vision and language equally thus yield compact information matching.
Intelligent Home 3D: Automatic 3D-House Design from Linguistic Descriptions Only
To this end, we propose a House Plan Generative Model (HPGM) that first translates the language input to a structural graph representation and then predicts the layout of rooms with a Graph Conditioned Layout Prediction Network (GC LPN) and generates the interior texture with a Language Conditioned Texture GAN (LCT-GAN).
Federated Matrix Factorization: Algorithm Design and Application to Data Clustering
Recent demands on data privacy have called for federated learning (FL) as a new distributed learning paradigm in massive and heterogeneous networks.
Metamorphic Testing for Object Detection Systems
To fill this critical gap, we introduce the design and realization of MetaOD, the first metamorphic testing system for object detectors to effectively reveal erroneous detection results by commercial object detectors.
Machine Intelligence at the Edge with Learning Centric Power Allocation
However, power allocation in this paradigm requires maximizing the learning performance instead of the communication throughput, for which the celebrated water-filling and max-min fairness algorithms become inefficient.
Building an Application Independent Natural Language Interface
Traditional approaches to building natural language (NL) interfaces typically use a semantic parser to parse the user command and convert it to a logical form, which is then translated to an executable action in an application.
BUT System Description for DIHARD Speech Diarization Challenge 2019
This paper describes the systems developed by the BUT team for the four tracks of the second DIHARD speech diarization challenge.
BUT System Description to VoxCeleb Speaker Recognition Challenge 2019
The last two networks are one-dimensional CNN and are based on the x-vector extraction topology.
Lifelong and Interactive Learning of Factual Knowledge in Dialogues
Dialogue systems are increasingly using knowledge bases (KBs) storing real-world facts to help generate quality responses.
Margin Matters: Towards More Discriminative Deep Neural Network Embeddings for Speaker Recognition
The proposed approach can achieve the state-of-the-art performance, with 25% ~ 30% equal error rate (EER) reduction on both tasks when compared to strong baselines using cross entropy loss with softmax, obtaining 2. 238% EER on VoxCeleb1 test set and 2. 761% EER on SITW core-core test set, respectively.
Forward and Backward Knowledge Transfer for Sentiment Classification
That is, it is possible to improve the NB classifier for a task by improving its model parameters directly by using the retained knowledge from other tasks.
Clustering by Orthogonal NMF Model and Non-Convex Penalty Optimization
We then apply a non-convex penalty (NCP) approach to add them to the objective as penalty terms, leading to a problem that is efficiently solvable.
BML: A High-performance, Low-cost Gradient Synchronization Algorithm for DML Training
In distributed machine learning (DML), the network performance between machines significantly impacts the speed of iterative training.
Guided Exploration in Deep Reinforcement Learning
This paper proposes a new method to drastically speed up deep reinforcement learning (deep RL) training for problems that have the property of \textit{state-action permissibility} (SAP).
Target-Sensitive Memory Networks for Aspect Sentiment Classification
In MNs, attention mechanism plays a crucial role in detecting the sentiment context for the given target.
Disentangling Aspect and Opinion Words in Target-based Sentiment Analysis using Lifelong Learning
Stage one extracts/groups the target-related words (call t-words) for a given target.
Deep Learning for Sentiment Analysis : A Survey
Deep learning has emerged as a powerful machine learning technique that learns multiple layers of representations or features of the data and produces state-of-the-art prediction results.
Contextual and Position-Aware Factorization Machines for Sentiment Classification
While existing machine learning models have achieved great success for sentiment classification, they typically do not explicitly capture sentiment-oriented word interaction, which can lead to poor results for fine-grained analysis at the snippet level (a phrase or sentence).
Translingual Obfuscation
We introduce translingual obfuscation, a new software obfuscation scheme which makes programs obscure by "misusing" the unique features of certain programming languages.
Cryptography and Security Software Engineering
