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Found 264 papers, 62 papers with code
Recurrent Convolutional Neural Networks for Text Classification
The experimental results show that the proposed method outperforms the state-of-the-art methods on several datasets, particularly on document-level datasets.
Ranked #4 on
Emotion Recognition in Conversation
on CPED
Short Text Hashing Improved by Integrating Multi-Granularity Topics and Tags
However, topics of certain granularity are not adequate to represent the intrinsic semantic information.
Knowledge Graph Embedding via Dynamic Mapping Matrix
Ranked #9 on
Link Prediction
on FB15k
(MR metric)
How to Generate a Good Word Embedding?
We analyze three critical components of word embedding training: the model, the corpus, and the training parameters.
Convolutional Neural Networks for Text Hashing
Meanwhile word features and position features are together fed into a convolutional network to learn the implicit features which are further incorporated with the explicit features to fit the pretrained binary code.
Question Answering over Knowledge Base with Neural Attention Combining Global Knowledge Information
With the rapid growth of knowledge bases (KBs) on the web, how to take full advantage of them becomes increasingly important.
Removal of Batch Effects using Distribution-Matching Residual Networks
We apply our method to mass cytometry and single-cell RNA-seq datasets, and demonstrate that it effectively attenuates batch effects.
Self-Taught Convolutional Neural Networks for Short Text Clustering
Short text clustering is a challenging problem due to its sparseness of text representation.
Ranked #2 on
Short Text Clustering
on Stackoverflow
Which is the Effective Way for Gaokao: Information Retrieval or Neural Networks?
As one of the most important test of China, Gaokao is designed to be difficult enough to distinguish the excellent high school students.
Exploiting Argument Information to Improve Event Detection via Supervised Attention Mechanisms
This paper tackles the task of event detection (ED), which involves identifying and categorizing events.
Automatically Labeled Data Generation for Large Scale Event Extraction
Modern models of event extraction for tasks like ACE are based on supervised learning of events from small hand-labeled data.
Handling Cold-Start Problem in Review Spam Detection by Jointly Embedding Texts and Behaviors
Solving cold-start problem in review spam detection is an urgent and significant task.
Generating Natural Answers by Incorporating Copying and Retrieving Mechanisms in Sequence-to-Sequence Learning
Generating answer with natural language sentence is very important in real-world question answering systems, which needs to obtain a right answer as well as a coherent natural response.
An End-to-End Model for Question Answering over Knowledge Base with Cross-Attention Combining Global Knowledge
This simple representation strategy is not easy to express the proper information in the question.
IJCNLP-2017 Task 5: Multi-choice Question Answering in Examinations
The IJCNLP-2017 Multi-choice Question Answering(MCQA) task aims at exploring the performance of current Question Answering(QA) techniques via the realworld complex questions collected from Chinese Senior High School Entrance Examination papers and CK12 website1.
Deep Reinforcement Learning for Sponsored Search Real-time Bidding
In this paper, we consider the RTB problem in sponsored search auction, named SS-RTB.
Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors
LRM is a general method for real-time detectors, as it utilizes the final feature map which exists in all real-time detectors to mine hard examples.
Towards Practical Visual Search Engine within Elasticsearch
In this paper, we describe our end-to-end content-based image retrieval system built upon Elasticsearch, a well-known and popular textual search engine.
Extracting Relational Facts by an End-to-End Neural Model with Copy Mechanism
The relational facts in sentences are often complicated.
Ranked #12 on
Relation Extraction
on NYT11-HRL
DCFEE: A Document-level Chinese Financial Event Extraction System based on Automatically Labeled Training Data
We present an event extraction framework to detect event mentions and extract events from the document-level financial news.
Pattern-revising Enhanced Simple Question Answering over Knowledge Bases
Question Answering over Knowledge Bases (KB-QA), which automatically answer natural language questions based on the facts contained by a knowledge base, is one of the most important natural language processing (NLP) tasks.
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.
Collective Event Detection via a Hierarchical and Bias Tagging Networks with Gated Multi-level Attention Mechanisms
Traditional approaches to the task of ACE event detection primarily regard multiple events in one sentence as independent ones and recognize them separately by using sentence-level information.
Adversarial Transfer Learning for Chinese Named Entity Recognition with Self-Attention Mechanism
However, existing methods for Chinese NER either do not exploit word boundary information from CWS or cannot filter the specific information of CWS.
Ranked #1 on
Chinese Named Entity Recognition
on SighanNER
Chinese Named Entity Recognition
Chinese Word Segmentation
+4
Fast and Exact Nearest Neighbor Search in Hamming Space on Full-Text Search Engines
A growing interest has been witnessed recently from both academia and industry in building nearest neighbor search (NNS) solutions on top of full-text search engines.
Privacy-preserving Crowd-guided AI Decision-making in Ethical Dilemmas
To investigate such ethical dilemmas, recent studies have adopted preference aggregation, in which each voter expresses her/his preferences over decisions for the possible ethical dilemma scenarios, and a centralized system aggregates these preferences to obtain the winning decision.
Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices
To help manufacturers develop a smart home system, we design a federated learning (FL) system leveraging the reputation mechanism to assist home appliance manufacturers to train a machine learning model based on customers' data.
Collecting and Analyzing Multidimensional Data with Local Differential Privacy
Motivated by this, we first propose novel LDP mechanisms for collecting a numeric attribute, whose accuracy is at least no worse (and usually better) than existing solutions in terms of worst-case noise variance.
Vocabulary Pyramid Network: Multi-Pass Encoding and Decoding with Multi-Level Vocabularies for Response Generation
To tackle the above two problems, we present a Vocabulary Pyramid Network (VPN) which is able to incorporate multi-pass encoding and decoding with multi-level vocabularies into response generation.
AdaNSP: Uncertainty-driven Adaptive Decoding in Neural Semantic Parsing
To keep the model aware of the underlying grammar in target sequences, many constrained decoders were devised in a multi-stage paradigm, which decode to the sketches or abstract syntax trees first, and then decode to target semantic tokens.
IntentGC: a Scalable Graph Convolution Framework Fusing Heterogeneous Information for Recommendation
In this work, we collect abundant relationships from common user behaviors and item information, and propose a novel framework named IntentGC to leverage both explicit preferences and heterogeneous relationships by graph convolutional networks.
Copy-Enhanced Heterogeneous Information Learning for Dialogue State Tracking
How to integrate the semantic information of pre-defined ontology and dialogue text (heterogeneous texts) to generate unknown values and improve performance becomes a severe challenge.
A Survey of Simultaneous Localization and Mapping
The paper makes an overview in SLAM including Lidar SLAM, visual SLAM, and their fusion.
Robotics Simultaneous Localization and Mapping
Resource Allocation in Mobility-Aware Federated Learning Networks: A Deep Reinforcement Learning Approach
However, federated learning faces the energy constraints of the workers and the high network resource cost due to the fact that a number of global model transmissions may be required to achieve the target accuracy.
Generating Questions for Knowledge Bases via Incorporating Diversified Contexts and Answer-Aware Loss
Meanwhile, such generated question can express the given predicate and correspond to a definitive answer.
Incorporating Interlocutor-Aware Context into Response Generation on Multi-Party Chatbots
Facing this challenge, we present a response generation model which incorporates Interlocutor-aware Contexts into Recurrent Encoder-Decoder frameworks (ICRED) for RGMPC.
Leverage Lexical Knowledge for Chinese Named Entity Recognition via Collaborative Graph Network
The lack of word boundaries information has been seen as one of the main obstacles to develop a high performance Chinese named entity recognition (NER) system.
Ranked #11 on
Chinese Named Entity Recognition
on Weibo NER
Chinese Named Entity Recognition
named-entity-recognition
+2
Machine Reading Comprehension Using Structural Knowledge Graph-aware Network
Our method dynamically updates the representation of the knowledge according to the structural information of the constructed sub-graph.
Learning the Extraction Order of Multiple Relational Facts in a Sentence with Reinforcement Learning
Existing works didn{'}t consider the extraction order of relational facts in a sentence.
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.
Adaptive Statistical Learning with Bayesian Differential Privacy
Our results in adaptive statistical learning generalize the results of Dwork et al. for i. i. d.
Blockchain for Future Smart Grid: A Comprehensive Survey
In this paper, we aim to provide a comprehensive survey on application of blockchain in smart grid.
Cryptography and Security Distributed, Parallel, and Cluster Computing Networking and Internet Architecture Social and Information Networks Systems and Control Systems and Control
Reviewing and Improving the Gaussian Mechanism for Differential Privacy
Although both classical Gaussian mechanisms [1, 2] assume $0 < \epsilon \leq 1$, our review finds that many studies in the literature have used the classical Gaussian mechanisms under values of $\epsilon$ and $\delta$ where the added noise amounts of [1, 2] do not achieve $(\epsilon,\delta)$-DP.
Reconfigurable Intelligent Surface Aided Power Control for Physical-Layer Broadcasting
Our goalis to minimize the transmit power at the BS by jointly designing the transmit beamforming at the BSand the phase shifts of the passive elements at the RIS.
An Adaptive and Fast Convergent Approach to Differentially Private Deep Learning
ADADP significantly reduces the privacy cost by improving the convergence speed with an adaptive learning rate and mitigates the negative effect of differential privacy upon the model accuracy by introducing adaptive noise.
AI-GAN: Attack-Inspired Generation of Adversarial Examples
Deep neural networks (DNNs) are vulnerable to adversarial examples, which are crafted by adding imperceptible perturbations to inputs.
Poisson Kernel Avoiding Self-Smoothing in Graph Convolutional Networks
Graph convolutional network (GCN) is now an effective tool to deal with non-Euclidean data, such as social networks in social behavior analysis, molecular structure analysis in the field of chemistry, and skeleton-based action recognition.
Deep Robust Multilevel Semantic Cross-Modal Hashing
Hashing based cross-modal retrieval has recently made significant progress.
Residual-Sparse Fuzzy $C$-Means Clustering Incorporating Morphological Reconstruction and Wavelet frames
To further enhance the segmentation effects of the improved FCM algorithm, we also employ the morphological reconstruction to smoothen the labels generated by clustering.
Deep Reinforcement Learning Based Intelligent Reflecting Surface for Secure Wireless Communications
As the system is highly dynamic and complex, and it is challenging to address the non-convex optimization problem, a novel deep reinforcement learning (DRL)-based secure beamforming approach is firstly proposed to achieve the optimal beamforming policy against eavesdroppers in dynamic environments.
Overview of the CCKS 2019 Knowledge Graph Evaluation Track: Entity, Relation, Event and QA
Knowledge graph models world knowledge as concepts, entities, and the relationships between them, which has been widely used in many real-world tasks.
Refinements in Motion and Appearance for Online Multi-Object Tracking
Modern multi-object tracking (MOT) system usually involves separated modules, such as motion model for location and appearance model for data association.
Knowledge Guided Metric Learning for Few-Shot Text Classification
This is mainly due to the fact that human beings can leverage knowledge obtained from relevant tasks.
Local Differential Privacy based Federated Learning for Internet of Things
To avoid the privacy threat and reduce the communication cost, in this paper, we propose to integrate federated learning and local differential privacy (LDP) to facilitate the crowdsourcing applications to achieve the machine learning model.
Observed mobility behavior data reveal social distancing inertia
The study revealed that statistics related to social distancing, namely trip rate, miles traveled per person, and percentage of population staying at home have all showed an unexpected trend, which we named social distancing inertia.
Computers and Society
Attacks to Federated Learning: Responsive Web User Interface to Recover Training Data from User Gradients
In a case where the aggregator is untrusted and LDP is not applied to each user gradient, the aggregator can recover sensitive user data from these gradients.
Secure Beamforming for Multiple Intelligent Reflecting Surfaces Aided mmWave Systems
In this system, the secrecy rate is maximized by controlling the on-off status of each IRS as well as optimizing the phase shift matrix of the IRSs.
HyperCore: Hyperbolic and Co-graph Representation for Automatic ICD Coding
Specifically, we propose a hyperbolic representation method to leverage the code hierarchy.
MIE: A Medical Information Extractor towards Medical Dialogues
Electronic Medical Records (EMRs) have become key components of modern medical care systems.
Clinical-Coder: Assigning Interpretable ICD-10 Codes to Chinese Clinical Notes
In this paper, we introduce Clinical-Coder, an online system aiming to assign ICD codes to Chinese clinical notes.
Intelligent Reflecting Surface Aided MISO Uplink Communication Network: Feasibility and Power Minimization for Perfect and Imperfect CSI
In this paper, we consider the weighted sum-power minimization under quality-of-service (QoS) constraints in the multi-user multi-input-single-output (MISO) uplink wireless network assisted by intelligent reflecting surface (IRS).
Reconfigurable Intelligent Surfaces Aided mmWave NOMA: Joint Power Allocation,Phase Shifts, and Hybrid Beamforming Optimization
Then, we solve the power allocation problem under fixed phase shifts of the RIS and hybrid beamforming.
A novel random access scheme for M2M communication in crowded asynchronous massive MIMO systems
Simulation results show that, compared to the exiting random access scheme for the crowded asynchronous massive MIMO systems, the proposed scheme can improve the uplink throughput and estimate the effective timing offsets accurately at the same time.
Secrecy Rate Maximization for Intelligent Reflecting Surface Aided SWIPT Systems
Simultaneous wireless information and power transfer (SWIPT) and intelligent reflecting surface (IRS) are two promising techniques for providing enhanced wireless communication capability and sustainable energy supply to energy-constrained wireless devices.
Local Differential Privacy and Its Applications: A Comprehensive Survey
Local differential privacy (LDP), as a strong privacy tool, has been widely deployed in the real world in recent years.
Cryptography and Security
Distantly Supervised Relation Extraction in Federated Settings
In this paper, we propose a federated denoising framework to suppress label noise in federated settings.
MAT: Motion-Aware Multi-Object Tracking
Modern multi-object tracking (MOT) systems usually model the trajectories by associating per-frame detections.
Feature Distillation With Guided Adversarial Contrastive Learning
In this paper, we propose a novel approach called Guided Adversarial Contrastive Distillation (GACD), to effectively transfer adversarial robustness from teacher to student with features.
Generating Adversarial yet Inconspicuous Patches with a Single Image
Through extensive experiments, our ap-proach shows strong attacking ability in both the white-box and black-box setting.
Event Coreference Resolution via a Multi-loss Neural Network without Using Argument Information
Event coreference resolution(ECR) is an important task in Natural Language Processing (NLP) and nearly all the existing approaches to this task rely on event argument information.
Towards Causal Explanation Detection with Pyramid Salient-Aware Network
PSAN can assist in causal explanation detection via capturing the salient semantics of discourses contained in their keywords with a bottom graph-based word-level salient network.
FTN: Foreground-Guided Texture-Focused Person Re-Identification
Person re-identification (Re-ID) is a challenging task as persons are often in different backgrounds.
Secrecy Rate Maximization for Reconfigurable Intelligent Surface Aided Millimeter Wave System with Low-resolution DAC
In this letter, we investigate the secrecy rate of an reconfigurable intelligent surface (RIS)-aided millimeter-wave (mmWave) system with hardware limitations.
Pre-trained Language Model Based Active Learning for Sentence Matching
Active learning is able to significantly reduce the annotation cost for data-driven techniques.
Secure Weighted Aggregation for Federated Learning
The situation is exacerbated by the cloud-based implementation of digital services when user data are captured and stored in distributed locations, hence aggregation of the user data for ML could be a serious breach of privacy regulations.
Cryptography and Security Distributed, Parallel, and Cluster Computing
KnowDis: Knowledge Enhanced Data Augmentation for Event Causality Detection via Distant Supervision
Modern models of event causality detection (ECD) are mainly based on supervised learning from small hand-labeled corpora.
Joint Entity and Relation Extraction with Set Prediction Networks
Compared with cross-entropy loss that highly penalizes small shifts in triple order, the proposed bipartite matching loss is invariant to any permutation of predictions; thus, it can provide the proposed networks with a more accurate training signal by ignoring triple order and focusing on relation types and entities.
Ranked #1 on
Joint Entity and Relation Extraction
on NYT
Recent Advances in Understanding Adversarial Robustness of Deep Neural Networks
Adversarial examples are inevitable on the road of pervasive applications of deep neural networks (DNN).
Field-Tuned Quantum Effects in a Triangular-Lattice Ising Magnet
We report thermodynamic and neutron scattering measurements of the triangular-lattice quantum Ising magnet TmMgGaO 4 in longitudinal magnetic fields.
Strongly Correlated Electrons Materials Science
Intelligent Reflecting Surface Aided MISO Uplink Communication Network: Feasibility and Power Minimization for Perfect and Imperfect CSI
In this paper, we consider the weighted sum-power minimization under quality-of-service (QoS) constraints in the multi-user multi-input-single-output (MISO) uplink wireless network assisted by intelligent reflecting surface (IRS).
Uplink Achievable Rate Maximization for Reconfigurable Intelligent Surface Aided Millimeter Wave Systems with Resolution-Adaptive ADCs
In this letter, we investigate the uplink of a reconfigurable intelligent surface (RIS)-aided millimeter-wave (mmWave) multi-user system.
Privacy-Preserving Federated Learning for UAV-Enabled Networks: Learning-Based Joint Scheduling and Resource Management
However, due to the privacy concerns of devices and limited computation or communication resource of UAVs, it is impractical to send raw data of devices to UAV servers for model training.
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
Reconstructing Event Regions for Event Extraction via Graph Attention Networks
Event information is usually scattered across multiple sentences within a document.
Privacy and Robustness in Federated Learning: Attacks and Defenses
Besides training powerful global models, it is of paramount importance to design FL systems that have privacy guarantees and are resistant to different types of adversaries.
Resource Allocation for Intelligent Reflecting Surface Aided Cooperative Communications
This paper investigates an intelligent reflecting surface (IRS) aided cooperative communication network, where the IRS exploits large reflecting elements to proactively steer the incident radio-frequency wave towards destination terminals (DTs).
A Comprehensive Survey of 6G Wireless Communications
First, we give an overview of 6G from perspectives of technologies, security and privacy, and applications.
Intelligent Reflecting Surface Assisted Anti-Jamming Communications Based on Reinforcement Learning
Aiming to enhance the communication performance against smart jammer, an optimization problem for jointly optimizing power allocation at the base station (BS) and reflecting beamforming at the IRS is formulated.
An LSTM-Aided Hybrid Random Access Scheme for 6G Machine Type Communication Networks
In this paper, an LSTM-aided hybrid random access scheme (LSTMH-RA) is proposed to support diverse quality of service (QoS) requirements in 6G machine-type communication (MTC) networks, where massive MTC (mMTC) devices and ultra-reliable low latency communications (URLLC) devices coexist.
ANL: Anti-Noise Learning for Cross-Domain Person Re-Identification
Usually, attributed to the domain gaps, the pre-trained source domain model cannot extract appropriate target domain features, which will dramatically affect the clustering performance and the accuracy of pseudo-labels.
EXPLORING VULNERABILITIES OF BERT-BASED APIS
We then demonstrate how the extracted model can be exploited to develop effective attribute inference attack to expose sensitive information of the training data.
Sum-Rate Maximization for UAV-assisted Visible Light Communications using NOMA: Swarm Intelligence meets Machine Learning
As the integration of unmanned aerial vehicles (UAVs) into visible light communications (VLC) can offer many benefits for massive-connectivity applications and services in 5G and beyond, this work considers a UAV-assisted VLC using non-orthogonal multiple-access.
Smart City Enabled by 5G/6G Networks: An Intelligent Hybrid Random Access Scheme
mMTC and URLLC will co-exist in MTC networks for 5G 6G-enabled smart city.
Spectrum Sharing for 6G Integrated Satellite-Terrestrial Communication Networks Based on NOMA and Cognitive Radio
ISTCN aims to provide high speed and pervasive network services by integrating broadband terrestrial mobile networks with satellite communication networks.
Joint Transmit Precoding and Reflect Beamforming Design for IRS-Assisted MIMO Cognitive Radio Systems
Cognitive radio (CR) is an effective solution to improve the spectral efficiency (SE) of wireless communications by allowing the secondary users (SUs) to share spectrum with primary users (PUs).
Information Theory Signal Processing Information Theory
Recent Advances in Adversarial Training for Adversarial Robustness
Adversarial training is one of the most effective approaches defending against adversarial examples for deep learning models.
CogNet: Bridging Linguistic Knowledge, World Knowledge and Commonsense Knowledge
In this paper, we present CogNet, a knowledge base (KB) dedicated to integrating three types of knowledge: (1) linguistic knowledge from FrameNet, which schematically describes situations, objects and events.
TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing
To guarantee user acceptability, all the text transformations are linguistically based, and we provide a human evaluation for each one.
Probing into the Root: A Dataset for Reason Extraction of Structural Events from Financial Documents
This paper proposes a new task regarding event reason extraction from document-level texts.
Path-based knowledge reasoning with textual semantic information for medical knowledge graph completion
To address the above issues, this paper proposes two novel path-based reasoning methods to solve the sparsity issues of entity and path respectively, which adopts the textual semantic information of entities and paths for MedKGC.
Improving Event Causality Identification via Self-Supervised Representation Learning on External Causal Statement
Current models for event causality identification (ECI) mainly adopt a supervised framework, which heavily rely on labeled data for training.
LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification
On the other hand, our approach employs a dual mechanism, which is a learnable augmentation framework and can interactively adjust the generation process to generate task-related sentences.
MG-DVD: A Real-time Framework for Malware Variant Detection Based on Dynamic Heterogeneous Graph Learning
In this paper, we propose MG-DVD, a novel detection framework based on dynamic heterogeneous graph learning, to detect malware variants in real time.
Inconspicuous Adversarial Patches for Fooling Image Recognition Systems on Mobile Devices
The patches are encouraged to be consistent with the background images with adversarial training while preserving strong attack abilities.
Economic Dispatch of an Integrated Microgrid Based on the Dynamic Process of CCGT Plant
Intra-day economic dispatch of an integrated microgrid is a fundamental requirement to integrate distributed generators.
Alignment Rationale for Natural Language Inference
Deep learning models have achieved great success on the task of Natural Language Inference (NLI), though only a few attempts try to explain their behaviors.
Automatic ICD Coding via Interactive Shared Representation Networks with Self-distillation Mechanism
The ICD coding task aims at assigning codes of the International Classification of Diseases in clinical notes.
CogIE: An Information Extraction Toolkit for Bridging Texts and CogNet
CogNet is a knowledge base that integrates three types of knowledge: linguistic knowledge, world knowledge and commonsense knowledge.
Document-level Event Extraction via Parallel Prediction Networks
We argue that sentence-level extractors are ill-suited to the DEE task where event arguments always scatter across sentences and multiple events may co-exist in a document.
Knowledge-Enriched Event Causality Identification via Latent Structure Induction Networks
Specifically, to make use of the descriptive knowledge, we devise a Descriptive Graph Induction module to obtain and encode the graph-structured descriptive knowledge.
A Large-Scale Chinese Multimodal NER Dataset with Speech Clues
In this paper, we aim to explore an uncharted territory, which is Chinese multimodal named entity recognition (NER) with both textual and acoustic contents.
Lifelong Intent Detection via Multi-Strategy Rebalancing
In this paper, we propose Lifelong Intent Detection (LID), which continually trains an ID model on new data to learn newly emerging intents while avoiding catastrophically forgetting old data.
Logic Traps in Evaluating Attribution Scores
Meanwhile, since the reasoning process of deep models is inaccessible, researchers design various evaluation methods to demonstrate their arguments.
A Relation-Oriented Clustering Method for Open Relation Extraction
The clustering-based unsupervised relation discovery method has gradually become one of the important methods of open relation extraction (OpenRE).
Ranked #1 on
Relation Extraction
on FewRel
Adversarial Purification through Representation Disentanglement
Deep learning models are vulnerable to adversarial examples and make incomprehensible mistakes, which puts a threat on their real-world deployment.
Optimizing the Age of Information in RIS-aided SWIPT Networks
In this letter, a reconfigurable intelligent surface (RIS)-assisted simultaneous wireless information and power transfer (SWIPT) network is investigated.
Towards Efficiently Evaluating the Robustness of Deep Neural Networks in IoT Systems: A GAN-based Method
Through extensive experiments, AI-GAN achieves high attack success rates, outperforming existing methods, and reduces generation time significantly.
Robust PCA Unrolling Network for Super-resolution Vessel Extraction in X-ray Coronary Angiography
Although robust PCA has been increasingly adopted to extract vessels from X-ray coronary angiography (XCA) images, challenging problems such as inefficient vessel-sparsity modelling, noisy and dynamic background artefacts, and high computational cost still remain unsolved.
LingYi: Medical Conversational Question Answering System based on Multi-modal Knowledge Graphs
The medical conversational system can relieve the burden of doctors and improve the efficiency of healthcare, especially during the pandemic.
P2ANet: A Dataset and Benchmark for Dense Action Detection from Table Tennis Match Broadcasting Videos
While deep learning has been widely used for video analytics, such as video classification and action detection, dense action detection with fast-moving subjects from sports videos is still challenging.
Answering Numerical Reasoning Questions in Table-Text Hybrid Contents with Graph-based Encoder and Tree-based Decoder
In the real-world question answering scenarios, hybrid form combining both tabular and textual contents has attracted more and more attention, among which numerical reasoning problem is one of the most typical and challenging problems.
Resource Allocation for Mobile Metaverse with the Internet of Vehicles over 6G Wireless Communications: A Deep Reinforcement Learning Approach
Being able to access scenes and information associated with the physical world, in the Metaverse in real-time and under mobility, is essential in developing a highly accessible, interactive and interconnective experience for all users.
Mobile Edge Computing, Metaverse, 6G Wireless Communications, Artificial Intelligence, and Blockchain: Survey and Their Convergence
Compared to cloud computing, as the distributed and closer infrastructure, the convergence of MEC with other emerging technologies, including the Metaverse, 6G wireless communications, artificial intelligence (AI), and blockchain, also solves the problems of network resource allocation, more network load as well as latency requirements.
Resource Allocation and Resolution Control in the Metaverse with Mobile Augmented Reality
With the development of blockchain and communication techniques, the Metaverse is considered as a promising next-generation Internet paradigm, which enables the connection between reality and the virtual world.
Joint Optimization of Energy Consumption and Completion Time in Federated Learning
Federated Learning (FL) is an intriguing distributed machine learning approach due to its privacy-preserving characteristics.
Facial Landmark Predictions with Applications to Metaverse
This research aims to make metaverse characters more realistic by adding lip animations learnt from videos in the wild.
Time Minimization in Hierarchical Federated Learning
Therefore, we formulate a joint learning and communication optimization problem to minimize total model parameter communication and computation delay, by optimizing local iteration counts and edge iteration counts.
Edge-Cloud Cooperation for DNN Inference via Reinforcement Learning and Supervised Learning
A reinforcement learning (RL)-based DNN compression approach is used to generate the lightweight model suitable for the edge from the heavyweight model.
ReasonChainQA: Text-based Complex Question Answering with Explainable Evidence Chains
Recently, natural language database (NLDB) conducts complex QA in knowledge base with textual evidences rather than structured representations, this task attracts a lot of attention because of the flexibility and richness of textual evidence.
Generating Hierarchical Explanations on Text Classification Without Connecting Rules
The opaqueness of deep NLP models has motivated the development of methods for interpreting how deep models predict.
Resource Allocation of Federated Learning for the Metaverse with Mobile Augmented Reality
The Metaverse has received much attention recently.
Mobile Augmented Reality with Federated Learning in the Metaverse
The Metaverse is deemed the next evolution of the Internet and has received much attention recently.
Unified, User and Task (UUT) Centered Artificial Intelligence for Metaverse Edge Computing
The Metaverse can be considered the extension of the present-day web, which integrates the physical and virtual worlds, delivering hyper-realistic user experiences.
Large Language Models are Better Reasoners with Self-Verification
By performing a backward verification of the answers that LLM deduced for itself, we can obtain interpretable answer validation scores to select the candidate answer with the highest score.
Asynchronous Hybrid Reinforcement Learning for Latency and Reliability Optimization in the Metaverse over Wireless Communications
In the DL stage, the larger-size 3D virtual objects need to be transmitted back to the XUs.
UAV aided Metaverse over Wireless Communications: A Reinforcement Learning Approach
A huge amount of data in physical world needs to be synchronized to the virtual world to provide immersive experience for users, and there will be higher requirements on coverage to include more users into Metaverse.
Knowledge Reasoning via Jointly Modeling Knowledge Graphs and Soft Rules
The former has high accuracy and good interpretability, but a major challenge is to obtain effective rules on large-scale KGs.
User-centric Heterogeneous-action Deep Reinforcement Learning for Virtual Reality in the Metaverse over Wireless Networks
In this paper, for a system consisting of a Metaverse server and multiple VR users, we consider two cases of (i) the server generating frames and transmitting them to users, and (ii) users generating frames locally and thus consuming device energy.
Virtual Reality in Metaverse over Wireless Networks with User-centered Deep Reinforcement Learning
Virtual reality (VR) technologies are the backbone for the virtual universe within the Metaverse as they enable a hyper-realistic and immersive experience, and especially so in the context of socialization.
Detection of Uncertainty in Exceedance of Threshold (DUET): An Adversarial Patch Localizer
We then conduct further analyses on our choice of model priors and the adoption of Bayesian Neural Networks in different layers within our model architecture.
Mobile Edge Adversarial Detection for Digital Twinning to the Metaverse with Deep Reinforcement Learning
Nevertheless, as real-time, accurate detection of adversarial patches is compute-intensive, these physical world scenes have to be offloaded to the Metaverse Map Base Stations (MMBS) for computation.
Towards Adversarially Robust Continual Learning
Recent studies show that models trained by continual learning can achieve the comparable performances as the standard supervised learning and the learning flexibility of continual learning models enables their wide applications in the real world.
Mastering Symbolic Operations: Augmenting Language Models with Compiled Neural Networks
Our work highlights the potential of seamlessly unifying explicit rule learning via CoNNs and implicit pattern learning in LMs, paving the way for true symbolic comprehension capabilities.
Multi-View Graph Representation Learning for Answering Hybrid Numerical Reasoning Question
Hybrid question answering (HybridQA) over the financial report contains both textual and tabular data, and requires the model to select the appropriate evidence for the numerical reasoning task.
Graph Representation Learning
Machine Reading Comprehension
+1
Large Language Models Need Holistically Thought in Medical Conversational QA
The medical conversational question answering (CQA) system aims at providing a series of professional medical services to improve the efficiency of medical care.
S$^3$HQA: A Three-Stage Approach for Multi-hop Text-Table Hybrid Question Answering
In this paper, we propose a three-stage TextTableQA framework S3HQA, which comprises of retriever, selector, and reasoner.
Towards Graph-hop Retrieval and Reasoning in Complex Question Answering over Textual Database
In Textual question answering (TQA) systems, complex questions often require retrieving multiple textual fact chains with multiple reasoning steps.
A Hybrid Framework of Reinforcement Learning and Convex Optimization for UAV-Based Autonomous Metaverse Data Collection
Unmanned aerial vehicles (UAVs) are promising for providing communication services due to their advantages in cost and mobility, especially in the context of the emerging Metaverse and Internet of Things (IoT).
RE-Matching: A Fine-Grained Semantic Matching Method for Zero-Shot Relation Extraction
However, general matching methods lack explicit modeling of the above matching pattern.
Actively Supervised Clustering for Open Relation Extraction
The key to the setting is selecting which instances to label.
Open Set Relation Extraction via Unknown-Aware Training
Inspired by text adversarial attacks, we adaptively apply small but critical perturbations to original training instances and thus synthesizing negative instances that are more likely to be mistaken by the model as known relations.
Heterogeneous 360 Degree Videos in Metaverse: Differentiated Reinforcement Learning Approaches
Advanced video technologies are driving the development of the futuristic Metaverse, which aims to connect users from anywhere and anytime.
UAV-assisted Semantic Communication with Hybrid Action Reinforcement Learning
In this paper, we aim to explore the use of uplink semantic communications with the assistance of UAV in order to improve data collection effiicency for metaverse users in remote areas.
SHARK: A Lightweight Model Compression Approach for Large-scale Recommender Systems
Increasing the size of embedding layers has shown to be effective in improving the performance of recommendation models, yet gradually causing their sizes to exceed terabytes in industrial recommender systems, and hence the increase of computing and storage costs.
LMTuner: An user-friendly and highly-integrable Training Framework for fine-tuning Large Language Models
With the burgeoning development in the realm of large language models (LLMs), the demand for efficient incremental training tailored to specific industries and domains continues to increase.
Journey to the Center of the Knowledge Neurons: Discoveries of Language-Independent Knowledge Neurons and Degenerate Knowledge Neurons
We design cross-lingual knowledge editing experiments, demonstrating that the PLMs can accomplish this task based on language-independent neurons; (2) The discovery of Degenerate Knowledge Neurons, a novel type of neuron showing that different knowledge neurons can store the same fact.
ZhuJiu: A Multi-dimensional, Multi-faceted Chinese Benchmark for Large Language Models
To this end, we propose the ZhuJiu benchmark, which has the following strengths: (1) Multi-dimensional ability coverage: We comprehensively evaluate LLMs across 7 ability dimensions covering 51 tasks.
Interpreting Sentiment Composition with Latent Semantic Tree
As the key to sentiment analysis, sentiment composition considers the classification of a constituent via classifications of its contained sub-constituents and rules operated on them.
MMHQA-ICL: Multimodal In-context Learning for Hybrid Question Answering over Text, Tables and Images
Most importantly, we propose a Type-specific In-context Learning Strategy for MMHQA, enabling LLMs to leverage their powerful performance in this task.
User Assignment and Resource Allocation for Hierarchical Federated Learning over Wireless Networks
The large population of wireless users is a key driver of data-crowdsourced Machine Learning (ML).
HRoT: Hybrid prompt strategy and Retrieval of Thought for Table-Text Hybrid Question Answering
Answering numerical questions over hybrid contents from the given tables and text(TextTableQA) is a challenging task.
MenatQA: A New Dataset for Testing the Temporal Comprehension and Reasoning Abilities of Large Language Models
As a result, it is natural for people to believe that LLMs have also mastered abilities such as time understanding and reasoning.
Loose lips sink ships: Mitigating Length Bias in Reinforcement Learning from Human Feedback
Reinforcement learning from human feedback serves as a crucial bridge, aligning large language models with human and societal values.
Boosting Black-box Attack to Deep Neural Networks with Conditional Diffusion Models
Existing black-box attacks have demonstrated promising potential in creating adversarial examples (AE) to deceive deep learning models.
Can LSH (Locality-Sensitive Hashing) Be Replaced by Neural Network?
With the rapid development of GPU (Graphics Processing Unit) technologies and neural networks, we can explore more appropriate data structures and algorithms.
SCME: A Self-Contrastive Method for Data-free and Query-Limited Model Extraction Attack
However, the distribution of these fake data lacks diversity and cannot detect the decision boundary of the target model well, resulting in the dissatisfactory simulation effect.
Generative Calibration for In-context Learning
In this paper, we for the first time theoretically and empirically identify that such a paradox is mainly due to the label shift of the in-context model to the data distribution, in which LLMs shift the label marginal $p(y)$ while having a good label conditional $p(x|y)$.
Query2Triple: Unified Query Encoding for Answering Diverse Complex Queries over Knowledge Graphs
However, these methods train KG embeddings and neural set operators concurrently on both simple (one-hop) and complex (multi-hop and logical) queries, which causes performance degradation on simple queries and low training efficiency.
Unveiling A Core Linguistic Region in Large Language Models
We have discovered a core region in LLMs that corresponds to linguistic competence, accounting for approximately 1% of the total model parameters.
S3Eval: A Synthetic, Scalable, Systematic Evaluation Suite for Large Language Models
The rapid development of Large Language Models (LLMs) has led to great strides in model capabilities like long-context understanding and reasoning.
TableQAKit: A Comprehensive and Practical Toolkit for Table-based Question Answering
Table-based question answering (TableQA) is an important task in natural language processing, which requires comprehending tables and employing various reasoning ways to answer the questions.
FedPEAT: Convergence of Federated Learning, Parameter-Efficient Fine Tuning, and Emulator Assisted Tuning for Artificial Intelligence Foundation Models with Mobile Edge Computing
FedPEAT uses adapters, emulators, and PEFT for federated model tuning, enhancing model privacy and memory efficiency.
Orchestration of Emulator Assisted Mobile Edge Tuning for AI Foundation Models: A Multi-Agent Deep Reinforcement Learning Approach
The efficient deployment and fine-tuning of foundation models are pivotal in contemporary artificial intelligence.
Revolutionizing Global Food Security: Empowering Resilience through Integrated AI Foundation Models and Data-Driven Solutions
Food security, a global concern, necessitates precise and diverse data-driven solutions to address its multifaceted challenges.
