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Found 68 papers, 22 papers with code
Continual Learning for Smart City: A Survey
We believe this survey can help relevant researchers quickly familiarize themselves with the current state of continual learning research used in smart city development and direct them to future research trends.
GNeRP: Gaussian-guided Neural Reconstruction of Reflective Objects with Noisy Polarization Priors
Supervised by polarization priors, this representation guides the learning of geometry behind the specular reflection and captures more details than existing methods.
CDSE-UNet: Enhancing COVID-19 CT Image Segmentation with Canny Edge Detection and Dual-Path SENet Feature Fusion
Accurate segmentation of COVID-19 CT images is crucial for reducing the severity and mortality rates associated with COVID-19 infections.
A Survey of Route Recommendations: Methods, Applications, and Opportunities
Our survey offers a comprehensive review of route recommendation work based on urban computing.
NFT1000: A Visual Text Dataset For Non-Fungible Token Retrieval
With the rise of 'Metaverse' and 'Web3. 0', NFT ( Non-Fungible Token ) has emerged as a kind of pivotal digital asset, garnering significant attention.
Point Cloud in the Air
Acquisition and processing of point clouds (PCs) is a crucial enabler for many emerging applications reliant on 3D spatial data, such as robot navigation, autonomous vehicles, and augmented reality.
Zero-Touch Networks: Towards Next-Generation Network Automation
The Zero-touch network and Service Management (ZSM) framework represents an emerging paradigm in the management of the fifth-generation (5G) and Beyond (5G+) networks, offering automated self-management and self-healing capabilities to address the escalating complexity and the growing data volume of modern networks.
Mind the map! Accounting for existing map information when estimating online HDMaps from sensor
While HDMaps are a crucial component of autonomous driving, they are expensive to acquire and maintain.
In-Context Learning for Knowledge Base Question Answering for Unmanned Systems based on Large Language Models
However, generating the most appropriate knowledge base query code based on Natural Language Questions (NLQ) poses a significant challenge in KBQA.
ChatRadio-Valuer: A Chat Large Language Model for Generalizable Radiology Report Generation Based on Multi-institution and Multi-system Data
Radiology report generation, as a key step in medical image analysis, is critical to the quantitative analysis of clinically informed decision-making levels.
LLaMA-Reviewer: Advancing Code Review Automation with Large Language Models through Parameter-Efficient Fine-Tuning
In response to this research gap, we present LLaMA-Reviewer, an innovative framework that leverages the capabilities of LLaMA, a popular LLM, in the realm of code review.
Forensic Data Analytics for Anomaly Detection in Evolving Networks
In the prevailing convergence of traditional infrastructure-based deployment (i. e., Telco and industry operational networks) towards evolving deployments enabled by 5G and virtualization, there is a keen interest in elaborating effective security controls to protect these deployments in-depth.
SMC-UDA: Structure-Modal Constraint for Unsupervised Cross-Domain Renal Segmentation
Medical image segmentation based on deep learning often fails when deployed on images from a different domain.
MF-NeRF: Memory Efficient NeRF with Mixed-Feature Hash Table
Neural radiance field (NeRF) has shown remarkable performance in generating photo-realistic novel views.
ChatABL: Abductive Learning via Natural Language Interaction with ChatGPT
The proposed method uses the strengths of LLMs' understanding and logical reasoning to correct the incomplete logical facts for optimizing the performance of perceptual module, by summarizing and reorganizing reasoning rules represented in natural language format.
Efficient Self-supervised Continual Learning with Progressive Task-correlated Layer Freezing
Inspired by the success of Self-supervised learning (SSL) in learning visual representations from unlabeled data, a few recent works have studied SSL in the context of continual learning (CL), where multiple tasks are learned sequentially, giving rise to a new paradigm, namely self-supervised continual learning (SSCL).
Model Extraction Attacks on Split Federated Learning
We show that under practical cases, the proposed ME attacks work exceptionally well for SFL.
Active Learning in Brain Tumor Segmentation with Uncertainty Sampling, Annotation Redundancy Restriction, and Data Initialization
We investigated uncertainty sampling, annotation redundancy restriction, and initial dataset selection techniques.
LostNet: A smart way for lost and find
Due to the enormous population growth of cities in recent years, objects are frequently lost and unclaimed on public transportation, in restaurants, or any other public areas.
Beyond Not-Forgetting: Continual Learning with Backward Knowledge Transfer
By learning a sequence of tasks continually, an agent in continual learning (CL) can improve the learning performance of both a new task and `old' tasks by leveraging the forward knowledge transfer and the backward knowledge transfer, respectively.
Get More at Once: Alternating Sparse Training with Gradient Correction
In this work, for the first time, we propose a novel alternating sparse training (AST) scheme to train multiple sparse sub-nets for dynamic inference without extra training cost compared to the case of training a single sparse model from scratch.
A Multi-Stage Automated Online Network Data Stream Analytics Framework for IIoT Systems
In this paper, we propose a novel Multi-Stage Automated Network Analytics (MSANA) framework for concept drift adaptation in IIoT systems, consisting of dynamic data pre-processing, the proposed Drift-based Dynamic Feature Selection (DD-FS) method, dynamic model learning & selection, and the proposed Window-based Performance Weighted Probability Averaging Ensemble (W-PWPAE) model.
IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspective
Machine Learning (ML) approaches have shown their capacity for IoT data analytics.
LCCDE: A Decision-Based Ensemble Framework for Intrusion Detection in The Internet of Vehicles
Modern vehicles, including autonomous vehicles and connected vehicles, have adopted an increasing variety of functionalities through connections and communications with other vehicles, smart devices, and infrastructures.
Brain Cortical Functional Gradients Predict Cortical Folding Patterns via Attention Mesh Convolution
The convolution on mesh considers the spatial organization of functional gradients and folding patterns on a cortical sheet and the newly designed channel attention block enhances the interpretability of the contribution of different functional gradients to cortical folding prediction.
Improving Visual Grounding with Visual-Linguistic Verification and Iterative Reasoning
They base the visual grounding on the features from pre-generated proposals or anchors, and fuse these features with the text embeddings to locate the target mentioned by the text.
Cointegration of SARS-CoV-2 Transmission with Weather Conditions and Mobility during the First Year of the COVID-19 Pandemic in the United States
We examined the cointegration of the effective reproductive rate, Rt, of the virus with the dewpoint at two meters, the temperature at two meters, Apple driving mobility, and Google workplace mobility measurements.
Shifting Trends of COVID-19 Tweet Sentiment with Respect to Voting Preferences in the 2020 Election Year of the United States
COVID-19 related policies were extensively politicized during the 2020 election year of the United States, resulting in polarizing viewpoints.
TRGP: Trust Region Gradient Projection for Continual Learning
To tackle this challenge, we propose Trust Region Gradient Projection (TRGP) for continual learning to facilitate the forward knowledge transfer based on an efficient characterization of task correlation.
A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehicles
Modern vehicles, including autonomous vehicles and connected vehicles, are increasingly connected to the external world, which enables various functionalities and services.
Event-based EV Charging Scheduling in A Microgrid of Buildings
Considering the mobility of EVs from building to building and their uncertain charging demand, it is of great practical interest to control the EV charging process in a microgrid of buildings to optimize the total operation cost while ensuring the transmission safety between the microgrid and the main grid.
Rep-Net: Efficient On-Device Learning via Feature Reprogramming
To develop memory-efficient on-device transfer learning, in this work, we are the first to approach the concept of transfer learning from a new perspective of intermediate feature reprogramming of a pre-trained model (i. e., backbone).
Contrastive Dual Gating: Learning Sparse Features With Contrastive Learning
Contrastive learning (or its variants) has recently become a promising direction in the self-supervised learning domain, achieving similar performance as supervised learning with minimum fine-tuning.
MAVE: A Product Dataset for Multi-source Attribute Value Extraction
Attribute value extraction refers to the task of identifying values of an attribute of interest from product information.
On the Dilution of Precision for Time Difference of Arrival with Station Deployment
The paper aims to reveal the relationship between the performance of moving object tracking algorithms and the tracking anchors (station) deployment.
Design and Implementation of Real-Time Localization System (RTLS) based on UWB and TDoA Algorithm
The challenges of indoor localization include inadequate localization accuracy, unreasonable anchor deployment in complex scenarios, lack of stability, and high cost.
Blind VQA on 360° Video via Progressively Learning from Pixels, Frames and Video
When assessing the quality of 360{\textdegree} video, human tends to perceive its quality degradation from the viewport-based spatial distortion of each spherical frame to motion artifact across adjacent frames, ending with the video-level quality score, i. e., a progressive quality assessment paradigm.
GROWN: GRow Only When Necessary for Continual Learning
To address this issue, continual learning has been developed to learn new tasks sequentially and perform knowledge transfer from the old tasks to the new ones without forgetting.
Fast and Efficient Once-For-All Networks for Diverse Hardware Deployment
In this work, we continue the effort to reduce the training cost of OFA methods.
The Report on China-Spain Joint Clinical Testing for Rapid COVID-19 Risk Screening by Eye-region Manifestations
The image-level performance of COVID-19 prescreening model in the China-Spain multicenter study achieved an AUC of 0. 913 (95% CI, 0. 898-0. 927), with a sensitivity of 0. 695 (95% CI, 0. 643-0. 748), a specificity of 0. 904 (95% CI, 0. 891 -0. 919), an accuracy of 0. 875(0. 861-0. 889), and a F1 of 0. 611(0. 568-0. 655).
Concept Drift Detection in Federated Networked Systems
As next-generation networks materialize, increasing levels of intelligence are required.
PWPAE: An Ensemble Framework for Concept Drift Adaptation in IoT Data Streams
As the number of Internet of Things (IoT) devices and systems have surged, IoT data analytics techniques have been developed to detect malicious cyber-attacks and secure IoT systems; however, concept drift issues often occur in IoT data analytics, as IoT data is often dynamic data streams that change over time, causing model degradation and attack detection failure.
Multi-Perspective Content Delivery Networks Security Framework Using Optimized Unsupervised Anomaly Detection
Content delivery networks (CDNs) provide efficient content distribution over the Internet.
LAU-Net: Latitude Adaptive Upscaling Network for Omnidirectional Image Super-Resolution
In addition, we propose a deep reinforcement learning scheme with a latitude adaptive reward, in order to automatically select optimal upscaling factors for different latitude bands.
Rapid COVID-19 Risk Screening by Eye-region Manifestations
It is still nontrivial to develop a new fast COVID-19 screening method with the easier access and lower cost, due to the technical and cost limitations of the current testing methods in the medical resource-poor districts.
MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of Vehicles
In this paper, the vulnerabilities of intra-vehicle and external networks are discussed, and a multi-tiered hybrid IDS that incorporates a signature-based IDS and an anomaly-based IDS is proposed to detect both known and unknown attacks on vehicular networks.
PDNet: Toward Better One-Stage Object Detection With Prediction Decoupling
However, the most suitable positions for inferring different targets, i. e., the object category and boundaries, are generally different.
A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streams
In recent years, with the increasing popularity of "Smart Technology", the number of Internet of Things (IoT) devices and systems have surged significantly.
Adaptive Degradation Process with Deep Learning-Driven Trajectory
Remaining useful life (RUL) estimation is a crucial component in the implementation of intelligent predictive maintenance and health management.
RA-BNN: Constructing Robust & Accurate Binary Neural Network to Simultaneously Defend Adversarial Bit-Flip Attack and Improve Accuracy
Apart from recovering the inference accuracy, our RA-BNN after growing also shows significantly higher resistance to BFA.
Spatial Attention-based Non-reference Perceptual Quality Prediction Network for Omnidirectional Images
To alleviate this issue, we propose a spatial attention-based perceptual quality prediction network for non-reference quality assessment on ODIs (SAP-net).
Diffusion approximation for multi-scale stochastic reaction-diffusion equations
In this paper, we study the diffusion approximation for singularly perturbed stochastic reaction-diffusion equation with a fast oscillating term.
Probability
$DA^3$:Dynamic Additive Attention Adaption for Memory-EfficientOn-Device Multi-Domain Learning
We observe that large memory used for activation storage is the bottleneck that largely limits the training time and cost on edge devices.
MetaGater: Fast Learning of Conditional Channel Gated Networks via Federated Meta-Learning
In this work, we advocate a holistic approach to jointly train the backbone network and the channel gating which enables dynamical selection of a subset of filters for more efficient local computation given the data input.
KSM: Fast Multiple Task Adaption via Kernel-wise Soft Mask Learning
Thus motivated, we propose a new training method called \textit{kernel-wise Soft Mask} (KSM), which learns a kernel-wise hybrid binary and real-value soft mask for each task, while using the same backbone model.
A Progressive Sub-Network Searching Framework for Dynamic Inference
Many techniques have been developed, such as model compression, to make Deep Neural Networks (DNNs) inference more efficiently.
On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice
In this paper, optimizing the hyper-parameters of common machine learning models is studied.
Big Bird: Transformers for Longer Sequences
To remedy this, we propose, BigBird, a sparse attention mechanism that reduces this quadratic dependency to linear.
Ranked #1 on
Text Classification
on Arxiv HEP-TH citation graph
Improving Multimodal Named Entity Recognition via Entity Span Detection with Unified Multimodal Transformer
To tackle the first issue, we propose a multimodal interaction module to obtain both image-aware word representations and word-aware visual representations.
Multi-modal Named Entity Recognition
named-entity-recognition
+1
ETC: Encoding Long and Structured Inputs in Transformers
Transformer models have advanced the state of the art in many Natural Language Processing (NLP) tasks.
Ranked #3 on
Question Answering
on ConditionalQA
Tree-based Intelligent Intrusion Detection System in Internet of Vehicles
The results from the implementation of the proposed intrusion detection system on standard data sets indicate that the system has the ability to identify various cyber-attacks in the AV networks.
Isoflat: Flat Provider Network Multiplexing and Firewalling in OpenStack Cloud
Our evaluation results show that a provider network with Isoflat can achieve similar network performance as a flat or VLAN provider network.
Naive Bayes and BiLSTM Ensemble for Discriminating between Mainland and Taiwan Variation of Mandarin Chinese
Automatic dialect identification is a more challengingctask than language identification, as it requires the ability to discriminate between varieties of one language.
Robust Sparse Regularization: Simultaneously Optimizing Neural Network Robustness and Compactness
In this work, we show that shrinking the model size through proper weight pruning can even be helpful to improve the DNN robustness under adversarial attack.
Computer-aided Detection of Squamous Carcinoma of the Cervix in Whole Slide Images
Traditionally, the most indispensable diagnosis of cervix squamous carcinoma is histopathological assessment which is achieved under microscope by pathologist.
Deep Learning-Enhanced Variational Monte Carlo Method for Quantum Many-Body Physics
Artificial neural networks have been successfully incorporated into variational Monte Carlo method (VMC) to study quantum many-body systems.
Strongly Correlated Electrons Disordered Systems and Neural Networks
Saliency Prediction on Omnidirectional Images with Generative Adversarial Imitation Learning
According to these findings, our SalGAIL approach applies deep reinforcement learning (DRL) to predict the head fixations of one subject, in which GAIL learns the reward of DRL, rather than the traditional human-designed reward.
Optimized Extreme Learning Machine for Power System Transient Stability Prediction Using Synchrophasors
The novelty of the proposal is in the fact that it improves the prediction performance of the ELM-based TSP model by using IPSO to optimize the parameters of the model with synchrophasors.
