Search Results for author: Jun Zhao

Found 263 papers, 61 papers with code

Incremental Event Detection via Knowledge Consolidation Networks

no code implementations EMNLP 2020 Pengfei Cao, Yubo Chen, Jun Zhao, Taifeng Wang

However, existing incremental learning methods cannot handle semantic ambiguity and training data imbalance problems between old and new classes in the task of incremental event detection.

Event Detection Incremental Learning

FedED: Federated Learning via Ensemble Distillation for Medical Relation Extraction

no code implementations EMNLP 2020 Dianbo Sui, Yubo Chen, Jun Zhao, Yantao Jia, Yuantao Xie, Weijian Sun

In this paper, we propose a privacy-preserving medical relation extraction model based on federated learning, which enables training a central model with no single piece of private local data being shared or exchanged.

Federated Learning Knowledge Distillation +4

Scene Restoring for Narrative Machine Reading Comprehension

no code implementations EMNLP 2020 Zhixing Tian, Yuanzhe Zhang, Kang Liu, Jun Zhao, Yantao Jia, Zhicheng Sheng

Inspired by this behavior of humans, we propose a method to let the machine imagine a scene during reading narrative for better comprehension.

Cloze Test Machine Reading Comprehension +1

Leveraging Explicit Lexico-logical Alignments in Text-to-SQL Parsing

no code implementations ACL 2022 Runxin Sun, Shizhu He, Chong Zhu, Yaohan He, Jinlong Li, Jun Zhao, Kang Liu

Text-to-SQL aims to parse natural language questions into SQL queries, which is valuable in providing an easy interface to access large databases.

SQL Parsing Text-To-SQL

CroAno : A Crowd Annotation Platform for Improving Label Consistency of Chinese NER Dataset

no code implementations EMNLP (ACL) 2021 Baoli Zhang, Zhucong Li, Zhen Gan, Yubo Chen, Jing Wan, Kang Liu, Jun Zhao, Shengping Liu, Yafei Shi

2) Inconsistency Detector: CroAno employs a detector to locate corpus-level label inconsistency and provides users an interface to correct inconsistent entities in batches.

Chinese Named Entity Recognition Management +3

CASIA at SemEval-2022 Task 11: Chinese Named Entity Recognition for Complex and Ambiguous Entities

no code implementations SemEval (NAACL) 2022 Jia Fu, Zhen Gan, Zhucong Li, Sirui Li, Dianbo Sui, Yubo Chen, Kang Liu, Jun Zhao

This paper describes our approach to develop a complex named entity recognition system in SemEval 2022 Task 11: MultiCoNER Multilingual Complex Named Entity Recognition, Track 9 - Chinese.

Chinese Named Entity Recognition Data Augmentation +3

Uncertain Local-to-Global Networks for Document-Level Event Factuality Identification

1 code implementation EMNLP 2021 Pengfei Cao, Yubo Chen, Yuqing Yang, Kang Liu, Jun Zhao

Moreover, we propose an Uncertain Information Aggregation module to leverage the global structure for integrating the local information.

Sentence

Biomedical Concept Normalization by Leveraging Hypernyms

1 code implementation EMNLP 2021 Cheng Yan, Yuanzhe Zhang, Kang Liu, Jun Zhao, Yafei Shi, Shengping Liu

Biomedical Concept Normalization (BCN) is widely used in biomedical text processing as a fundamental module.

Knowledge Transfer with Visual Prompt in multi-modal Dialogue Understanding and Generation

no code implementations TU (COLING) 2022 Minjun Zhu, Yixuan Weng, Bin Li, Shizhu He, Kang Liu, Jun Zhao

In this work, we propose a knowledge transfer method with visual prompt (VPTG) fusing multi-modal data, which is a flexible module that can utilize the text-only seq2seq model to handle visual dialogue tasks.

Dialogue Understanding Knowledge Distillation +2

Read Extensively, Focus Smartly: A Cross-document Semantic Enhancement Method for Visual Documents NER

no code implementations COLING 2022 Jun Zhao, Xin Zhao, WenYu Zhan, Tao Gui, Qi Zhang, Liang Qiao, Zhanzhan Cheng, ShiLiang Pu

To deal with this problem, this work proposes a cross-document semantic enhancement method, which consists of two modules: 1) To prevent distractions from irrelevant regions in the current document, we design a learnable attention mask mechanism, which is used to adaptively filter redundant information in the current document.

NER

Generating Temporally-ordered Event Sequences via Event Optimal Transport

no code implementations COLING 2022 Bo Zhou, Yubo Chen, Kang Liu, Jun Zhao, Jiexin Xu, XiaoJian Jiang, Qiuxia Li

The other issue is that the model adopts a word-level objective to model events in texts, failing to evaluate the predicted results of the model from the perspective of event sequence.

Augmentation, Retrieval, Generation: Event Sequence Prediction with a Three-Stage Sequence-to-Sequence Approach

no code implementations COLING 2022 Bo Zhou, Chenhao Wang, Yubo Chen, Kang Liu, Jun Zhao, Jiexin Xu, XiaoJian Jiang, Qiuxia Li

Currently existing approach models this task as a statistical induction problem, to predict a sequence of events by exploring the similarity between the given goal and the known sequences of events.

Retrieval

CMQA: A Dataset of Conditional Question Answering with Multiple-Span Answers

1 code implementation COLING 2022 Yiming Ju, Weikang Wang, Yuanzhe Zhang, Suncong Zheng, Kang Liu, Jun Zhao

To bridge the gap, we propose a new task: conditional question answering with hierarchical multi-span answers, where both the hierarchical relations and the conditions need to be extracted.

Question Answering

Self-Demos: Eliciting Out-of-Demonstration Generalizability in Large Language Models

1 code implementation1 Apr 2024 wei he, Shichun Liu, Jun Zhao, Yiwen Ding, Yi Lu, Zhiheng Xi, Tao Gui, Qi Zhang, Xuanjing Huang

The generated demos strategically interpolate between existing demos and the given query, transforming the query from OOD to ID.

In-Context Learning Math

STBA: Towards Evaluating the Robustness of DNNs for Query-Limited Black-box Scenario

no code implementations30 Mar 2024 Renyang Liu, Kwok-Yan Lam, Wei Zhou, Sixing Wu, Jun Zhao, Dongting Hu, Mingming Gong

Many attack techniques have been proposed to explore the vulnerability of DNNs and further help to improve their robustness.

Continual Few-shot Event Detection via Hierarchical Augmentation Networks

1 code implementation26 Mar 2024 Chenlong Zhang, Pengfei Cao, Yubo Chen, Kang Liu, Zhiqiang Zhang, Mengshu Sun, Jun Zhao

The CFED task is challenging as it involves memorizing previous event types and learning new event types with few-shot samples.

Event Detection

RSTAR: Rotational Streak Artifact Reduction in 4D CBCT using Separable and Circular Convolutions

no code implementations25 Mar 2024 Ziheng Deng, Hua Chen, Haibo Hu, Zhiyong Xu, Tianling Lyu, Yan Xi, Yang Chen, Jun Zhao

In this paper, we first explore the origin and appearance of streak artifacts in 4D CBCT images. Specifically, we find that streak artifacts exhibit a periodic rotational motion along with the patient's respiration.

Image Reconstruction

Imagination Augmented Generation: Learning to Imagine Richer Context for Question Answering over Large Language Models

1 code implementation22 Mar 2024 Huanxuan Liao, Shizhu He, Yao Xu, Yuanzhe Zhang, Kang Liu, Shengping Liu, Jun Zhao

Retrieval-Augmented-Generation and Gener-ation-Augmented-Generation have been proposed to enhance the knowledge required for question answering over Large Language Models (LLMs).

Open-Domain Question Answering

Perennial Semantic Data Terms of Use for Decentralized Web

no code implementations12 Mar 2024 Rui Zhao, Jun Zhao

We believe this work demonstrates a practicality of a perennial DToU language and the potential of a paradigm shift to how users interact with data and applications in a decentralized Web, offering both improved privacy and usability.

Navigate

ItD: Large Language Models Can Teach Themselves Induction through Deduction

no code implementations9 Mar 2024 Wangtao Sun, Haotian Xu, Xuanqing Yu, Pei Chen, Shizhu He, Jun Zhao, Kang Liu

Although Large Language Models (LLMs) are showing impressive performance on a wide range of Natural Language Processing tasks, researchers have found that they still have limited ability to conduct induction.

From Chain to Tree: Refining Chain-like Rules into Tree-like Rules on Knowledge Graphs

no code implementations8 Mar 2024 Wangtao Sun, Shizhu He, Jun Zhao, Kang Liu

With good explanatory power and controllability, rule-based methods play an important role in many tasks such as knowledge reasoning and decision support.

Knowledge Graphs Link Prediction

SimuCourt: Building Judicial Decision-Making Agents with Real-world Judgement Documents

1 code implementation5 Mar 2024 Zhitao He, Pengfei Cao, Chenhao Wang, Zhuoran Jin, Yubo Chen, Jiexin Xu, Huaijun Li, XiaoJian Jiang, Kang Liu, Jun Zhao

In this paper, (1) we introduce SimuCourt, a judicial benchmark that encompasses 420 judgment documents from real-world, spanning the three most common types of judicial cases, and a novel task Judicial Decision-Making to evaluate the judicial analysis and decision-making power of agents.

Decision Making Information Retrieval

Focus on Your Question! Interpreting and Mitigating Toxic CoT Problems in Commonsense Reasoning

no code implementations28 Feb 2024 Jiachun Li, Pengfei Cao, Chenhao Wang, Zhuoran Jin, Yubo Chen, Daojian Zeng, Kang Liu, Jun Zhao

Large language models exhibit high-level commonsense reasoning abilities, especially with enhancement methods like Chain-of-Thought (CoT).

Position

Cutting Off the Head Ends the Conflict: A Mechanism for Interpreting and Mitigating Knowledge Conflicts in Language Models

no code implementations28 Feb 2024 Zhuoran Jin, Pengfei Cao, Hongbang Yuan, Yubo Chen, Jiexin Xu, Huaijun Li, XiaoJian Jiang, Kang Liu, Jun Zhao

Moreover, we reveal that the pivotal point at which knowledge conflicts emerge in LMs is the integration of inconsistent information flows by memory heads and context heads.

Unveiling Linguistic Regions in Large Language Models

no code implementations22 Feb 2024 Zhihao Zhang, Jun Zhao, Qi Zhang, Tao Gui, Xuanjing Huang

Furthermore, this core region exhibits significant dimensional dependency, perturbations to even a single parameter on specific dimensions leading to a loss of linguistic competence.

Tug-of-War Between Knowledge: Exploring and Resolving Knowledge Conflicts in Retrieval-Augmented Language Models

no code implementations22 Feb 2024 Zhuoran Jin, Pengfei Cao, Yubo Chen, Kang Liu, XiaoJian Jiang, Jiexin Xu, Qiuxia Li, Jun Zhao

Then, we investigate the behavior and preference of RALMs from the following two perspectives: (1) Conflicts between internal memory and external sources: We find that stronger RALMs emerge with the Dunning-Kruger effect, persistently favoring their faulty internal memory even when correct evidence is provided.

Retrieval

The Da Vinci Code of Large Pre-trained Language Models: Deciphering Degenerate Knowledge Neurons

no code implementations21 Feb 2024 YuHeng Chen, Pengfei Cao, Yubo Chen, Yining Wang, Shengping Liu, Kang Liu, Jun Zhao

This paper provides a comprehensive definition of DKNs that covers both structural and functional aspects, pioneering the study of structures in PLMs' factual knowledge storage units.

LongAgent: Scaling Language Models to 128k Context through Multi-Agent Collaboration

1 code implementation18 Feb 2024 Jun Zhao, Can Zu, Hao Xu, Yi Lu, wei he, Yiwen Ding, Tao Gui, Qi Zhang, Xuanjing Huang

Large language models (LLMs) have demonstrated impressive performance in understanding language and executing complex reasoning tasks.

Multi-hop Question Answering Question Answering +1

Advancing Translation Preference Modeling with RLHF: A Step Towards Cost-Effective Solution

no code implementations18 Feb 2024 Nuo Xu, Jun Zhao, Can Zu, Sixian Li, Lu Chen, Zhihao Zhang, Rui Zheng, Shihan Dou, Wenjuan Qin, Tao Gui, Qi Zhang, Xuanjing Huang

To address this issue, we propose a cost-effective preference learning strategy, optimizing reward models by distinguishing between human and machine translations.

Machine Translation Translation

LongHeads: Multi-Head Attention is Secretly a Long Context Processor

1 code implementation16 Feb 2024 Yi Lu, Xin Zhou, wei he, Jun Zhao, Tao Ji, Tao Gui, Qi Zhang, Xuanjing Huang

Instead of allowing each head to attend to the full sentence, which struggles with generalizing to longer sequences due to out-of-distribution (OOD) issues, we allow each head to process in-distribution length by selecting and attending to important context chunks.

Sentence

WilKE: Wise-Layer Knowledge Editor for Lifelong Knowledge Editing

no code implementations16 Feb 2024 Chenhui Hu, Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao

Knowledge editing aims to rectify inaccuracies in large language models (LLMs) without costly retraining for outdated or erroneous knowledge.

knowledge editing

Enhancing Large Language Models with Pseudo- and Multisource- Knowledge Graphs for Open-ended Question Answering

no code implementations15 Feb 2024 Jiaxiang Liu, Tong Zhou, Yubo Chen, Kang Liu, Jun Zhao

In summary, our results pave the way for enhancing LLMs by incorporating Pseudo- and Multisource-KGs, particularly in the context of open-ended questions.

Graph Generation Knowledge Graphs +1

HyCubE: Efficient Knowledge Hypergraph 3D Circular Convolutional Embedding

no code implementations14 Feb 2024 Zhao Li, Xin Wang, JianXin Li, Wenbin Guo, Jun Zhao

Existing knowledge hypergraph embedding methods mainly focused on improving model performance, but their model structures are becoming more complex and redundant.

hypergraph embedding

Device Scheduling and Assignment in Hierarchical Federated Learning for Internet of Things

no code implementations4 Feb 2024 Tinghao Zhang, Kwok-Yan Lam, Jun Zhao

For scalability, practical HFL schemes select a subset of IoT devices to participate in the training, hence the notion of device scheduling.

Federated Learning Scheduling

DE$^3$-BERT: Distance-Enhanced Early Exiting for BERT based on Prototypical Networks

no code implementations3 Feb 2024 Jianing He, Qi Zhang, Weiping Ding, Duoqian Miao, Jun Zhao, Liang Hu, Longbing Cao

DE$^3$-BERT implements a hybrid exiting strategy that supplements classic entropy-based local information with distance-based global information to enhance the estimation of prediction correctness for more reliable early exiting decisions.

A Survey on the Applications of Frontier AI, Foundation Models, and Large Language Models to Intelligent Transportation Systems

no code implementations12 Jan 2024 Mohamed R. Shoaib, Heba M. Emara, Jun Zhao

This survey paper explores the transformative influence of frontier AI, foundation models, and Large Language Models (LLMs) in the realm of Intelligent Transportation Systems (ITS), emphasizing their integral role in advancing transportation intelligence, optimizing traffic management, and contributing to the realization of smart cities.

Autonomous Vehicles Management +1

LLaMA Beyond English: An Empirical Study on Language Capability Transfer

no code implementations2 Jan 2024 Jun Zhao, Zhihao Zhang, Luhui Gao, Qi Zhang, Tao Gui, Xuanjing Huang

In recent times, substantial advancements have been witnessed in large language models (LLMs), exemplified by ChatGPT, showcasing remarkable proficiency across a range of complex tasks.

Informativeness Text Generation

LoRAMoE: Alleviate World Knowledge Forgetting in Large Language Models via MoE-Style Plugin

1 code implementation15 Dec 2023 Shihan Dou, Enyu Zhou, Yan Liu, Songyang Gao, Jun Zhao, Wei Shen, Yuhao Zhou, Zhiheng Xi, Xiao Wang, Xiaoran Fan, ShiLiang Pu, Jiang Zhu, Rui Zheng, Tao Gui, Qi Zhang, Xuanjing Huang

Supervised fine-tuning (SFT) is a crucial step for large language models (LLMs), enabling them to align with human instructions and enhance their capabilities in downstream tasks.

Language Modelling Multi-Task Learning +1

SSTA: Salient Spatially Transformed Attack

no code implementations12 Dec 2023 Renyang Liu, Wei Zhou, Sixin Wu, Jun Zhao, Kwok-Yan Lam

Extensive studies have demonstrated that deep neural networks (DNNs) are vulnerable to adversarial attacks, which brings a huge security risk to the further application of DNNs, especially for the AI models developed in the real world.

Offloading and Quality Control for AI Generated Content Services in 6G Mobile Edge Computing Networks

no code implementations11 Dec 2023 Yitong Wang, Chang Liu, Jun Zhao

In pursuit of enhancing the accessibility of AIGC services, the deployment of AIGC models (e. g., diffusion models) to edge servers and local devices has become a prevailing trend.

Edge-computing

Mobile Edge Computing and AI Enabled Web3 Metaverse over 6G Wireless Communications: A Deep Reinforcement Learning Approach

no code implementations11 Dec 2023 Wenhan Yu, Terence Jie Chua, Jun Zhao

In spite of the rapid advancements in current technologies, the computation required for a smooth, seamless and immersive socialization experience in the Metaverse is overbearing, and the accumulated user experience is essential to be considered.

Edge-computing

Resource Allocation for Semantic Communication under Physical-layer Security

no code implementations7 Dec 2023 Yang Li, Xinyu Zhou, Jun Zhao

The secrecy rate is the communication rate at which no information is disclosed to an eavesdropper.

Oasis: Data Curation and Assessment System for Pretraining of Large Language Models

1 code implementation21 Nov 2023 Tong Zhou, Yubo Chen, Pengfei Cao, Kang Liu, Jun Zhao, Shengping Liu

To this end, we present a pretraining corpus curation and assessment platform called Oasis -- a one-stop system for data quality improvement and quantification with user-friendly interactive interfaces.

Language Modelling Large Language Model

ExpNote: Black-box Large Language Models are Better Task Solvers with Experience Notebook

1 code implementation13 Nov 2023 Wangtao Sun, Xuanqing Yu, Shizhu He, Jun Zhao, Kang Liu

Black-box Large Language Models (LLMs) have shown great power in solving various tasks and are considered general problem solvers.

S3Eval: A Synthetic, Scalable, Systematic Evaluation Suite for Large Language Models

2 code implementations23 Oct 2023 Fangyu Lei, Qian Liu, Yiming Huang, Shizhu He, Jun Zhao, Kang Liu

The rapid development of Large Language Models (LLMs) has led to great strides in model capabilities like long-context understanding and reasoning.

Long-Context Understanding

TableQAKit: A Comprehensive and Practical Toolkit for Table-based Question Answering

no code implementations23 Oct 2023 Fangyu Lei, Tongxu Luo, Pengqi Yang, Weihao Liu, Hanwen Liu, Jiahe Lei, Yiming Huang, Yifan Wei, Shizhu He, Jun Zhao, Kang Liu

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.

Question Answering

Unveiling A Core Linguistic Region in Large Language Models

no code implementations23 Oct 2023 Jun Zhao, Zhihao Zhang, Yide Ma, Qi Zhang, Tao Gui, Luhui Gao, Xuanjing Huang

We have discovered a core region in LLMs that corresponds to linguistic competence, accounting for approximately 1% of the total model parameters.

Query2Triple: Unified Query Encoding for Answering Diverse Complex Queries over Knowledge Graphs

1 code implementation17 Oct 2023 Yao Xu, Shizhu He, Cunguang Wang, Li Cai, Kang Liu, Jun Zhao

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.

Complex Query Answering

Generative Calibration for In-context Learning

1 code implementation16 Oct 2023 Zhongtao Jiang, Yuanzhe Zhang, Cao Liu, Jun Zhao, Kang Liu

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 $p(x|y)$.

In-Context Learning text-classification +1

SCME: A Self-Contrastive Method for Data-free and Query-Limited Model Extraction Attack

no code implementations15 Oct 2023 Renyang Liu, Jinhong Zhang, Kwok-Yan Lam, Jun Zhao, Wei Zhou

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.

Model extraction

Can LSH (Locality-Sensitive Hashing) Be Replaced by Neural Network?

no code implementations15 Oct 2023 Renyang Liu, Jun Zhao, Xing Chu, Yu Liang, Wei Zhou, Jing He

With the rapid development of GPU (Graphics Processing Unit) technologies and neural networks, we can explore more appropriate data structures and algorithms.

Boosting Black-box Attack to Deep Neural Networks with Conditional Diffusion Models

no code implementations11 Oct 2023 Renyang Liu, Wei Zhou, Tianwei Zhang, Kangjie Chen, Jun Zhao, Kwok-Yan Lam

Existing black-box attacks have demonstrated promising potential in creating adversarial examples (AE) to deceive deep learning models.

Denoising

Loose lips sink ships: Mitigating Length Bias in Reinforcement Learning from Human Feedback

no code implementations8 Oct 2023 Wei Shen, Rui Zheng, WenYu Zhan, Jun Zhao, Shihan Dou, Tao Gui, Qi Zhang, Xuanjing Huang

Reinforcement learning from human feedback serves as a crucial bridge, aligning large language models with human and societal values.

Language Modelling

MenatQA: A New Dataset for Testing the Temporal Comprehension and Reasoning Abilities of Large Language Models

1 code implementation8 Oct 2023 Yifan Wei, Yisong Su, Huanhuan Ma, Xiaoyan Yu, Fangyu Lei, Yuanzhe Zhang, Jun Zhao, Kang Liu

As a result, it is natural for people to believe that LLMs have also mastered abilities such as time understanding and reasoning.

counterfactual

MMHQA-ICL: Multimodal In-context Learning for Hybrid Question Answering over Text, Tables and Images

no code implementations9 Sep 2023 Weihao Liu, Fangyu Lei, Tongxu Luo, Jiahe Lei, Shizhu He, Jun Zhao, Kang Liu

Most importantly, we propose a Type-specific In-context Learning Strategy for MMHQA, enabling LLMs to leverage their powerful performance in this task.

In-Context Learning Question Answering +1

Interpreting Sentiment Composition with Latent Semantic Tree

1 code implementation31 Aug 2023 Zhongtao Jiang, Yuanzhe Zhang, Cao Liu, Jiansong Chen, Jun Zhao, Kang Liu

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.

Classification Domain Adaptation +1

ZhuJiu: A Multi-dimensional, Multi-faceted Chinese Benchmark for Large Language Models

no code implementations28 Aug 2023 Baoli Zhang, Haining Xie, Pengfan Du, JunHao Chen, Pengfei Cao, Yubo Chen, Shengping Liu, Kang Liu, Jun Zhao

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.

Journey to the Center of the Knowledge Neurons: Discoveries of Language-Independent Knowledge Neurons and Degenerate Knowledge Neurons

1 code implementation25 Aug 2023 YuHeng Chen, Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao

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.

Fact Checking knowledge editing

LMTuner: An user-friendly and highly-integrable Training Framework for fine-tuning Large Language Models

1 code implementation20 Aug 2023 Yixuan Weng, Zhiqi Wang, Huanxuan Liao, Shizhu He, Shengping Liu, Kang Liu, Jun Zhao

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.

SHARK: A Lightweight Model Compression Approach for Large-scale Recommender Systems

no code implementations18 Aug 2023 Beichuan Zhang, Chenggen Sun, Jianchao Tan, Xinjun Cai, Jun Zhao, Mengqi Miao, Kang Yin, Chengru Song, Na Mou, Yang song

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.

Model Compression Quantization +1

UAV-assisted Semantic Communication with Hybrid Action Reinforcement Learning

no code implementations18 Aug 2023 Peiyuan Si, Jun Zhao, Kwok-Yan Lam, Qing Yang

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.

reinforcement-learning Reinforcement Learning (RL)

Heterogeneous 360 Degree Videos in Metaverse: Differentiated Reinforcement Learning Approaches

no code implementations8 Aug 2023 Wenhan Yu, Jun Zhao

Advanced video technologies are driving the development of the futuristic Metaverse, which aims to connect users from anywhere and anytime.

reinforcement-learning

Open Set Relation Extraction via Unknown-Aware Training

1 code implementation8 Jun 2023 Jun Zhao, Xin Zhao, WenYu Zhan, Qi Zhang, Tao Gui, Zhongyu Wei, Yunwen Chen, Xiang Gao, Xuanjing Huang

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.

Relation Relation Extraction

A Hybrid Framework of Reinforcement Learning and Convex Optimization for UAV-Based Autonomous Metaverse Data Collection

no code implementations29 May 2023 Peiyuan Si, Liangxin Qian, Jun Zhao, Kwok-Yan Lam

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).

Towards Graph-hop Retrieval and Reasoning in Complex Question Answering over Textual Database

no code implementations23 May 2023 Minjun Zhu, Yixuan Weng, Shizhu He, Kang Liu, Jun Zhao

In Textual question answering (TQA) systems, complex questions often require retrieving multiple textual fact chains with multiple reasoning steps.

Question Answering Retrieval

S$^3$HQA: A Three-Stage Approach for Multi-hop Text-Table Hybrid Question Answering

1 code implementation19 May 2023 Fangyu Lei, Xiang Li, Yifan Wei, Shizhu He, Yiming Huang, Jun Zhao, Kang Liu

In this paper, we propose a three-stage TextTableQA framework S3HQA, which comprises of retriever, selector, and reasoner.

Question Answering Reading Comprehension

Large Language Models Need Holistically Thought in Medical Conversational QA

1 code implementation9 May 2023 Yixuan Weng, Bin Li, Fei Xia, Minjun Zhu, Bin Sun, Shizhu He, Kang Liu, Jun Zhao

The medical conversational question answering (CQA) system aims at providing a series of professional medical services to improve the efficiency of medical care.

Conversational Question Answering

Multi-View Graph Representation Learning for Answering Hybrid Numerical Reasoning Question

1 code implementation5 May 2023 Yifan Wei, Fangyu Lei, Yuanzhe Zhang, Jun Zhao, Kang Liu

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

Mastering Symbolic Operations: Augmenting Language Models with Compiled Neural Networks

3 code implementations4 Apr 2023 Yixuan Weng, Minjun Zhu, Fei Xia, Bin Li, Shizhu He, Kang Liu, Jun Zhao

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.

Arithmetic Reasoning Language Modelling

Towards Adversarially Robust Continual Learning

no code implementations31 Mar 2023 Tao Bai, Chen Chen, Lingjuan Lyu, Jun Zhao, Bihan Wen

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.

Adversarial Robustness Continual Learning

Mobile Edge Adversarial Detection for Digital Twinning to the Metaverse with Deep Reinforcement Learning

no code implementations18 Mar 2023 Terence Jie Chua, Wenhan Yu, Jun Zhao

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.

Detection of Uncertainty in Exceedance of Threshold (DUET): An Adversarial Patch Localizer

no code implementations18 Mar 2023 Terence Jie Chua, Wenhan Yu, Jun Zhao

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.

Self-Driving Cars

Virtual Reality in Metaverse over Wireless Networks with User-centered Deep Reinforcement Learning

no code implementations8 Mar 2023 Wenhan Yu, Terence Jie Chua, Jun Zhao

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.

User-centric Heterogeneous-action Deep Reinforcement Learning for Virtual Reality in the Metaverse over Wireless Networks

no code implementations3 Feb 2023 Wenhan Yu, Terence Jie Chua, Jun Zhao

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.

Knowledge Reasoning via Jointly Modeling Knowledge Graphs and Soft Rules

no code implementations7 Jan 2023 Yinyu Lan, Shizhu He, Kang Liu, Jun Zhao

The former has high accuracy and good interpretability, but a major challenge is to obtain effective rules on large-scale KGs.

Knowledge Graph Embeddings Question Answering

UAV aided Metaverse over Wireless Communications: A Reinforcement Learning Approach

no code implementations4 Jan 2023 Peiyuan Si, Wenhan Yu, Jun Zhao, Kwok-Yan Lam, Qing Yang

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.

reinforcement-learning Reinforcement Learning (RL)

Large Language Models are Better Reasoners with Self-Verification

1 code implementation19 Dec 2022 Yixuan Weng, Minjun Zhu, Fei Xia, Bin Li, Shizhu He, Shengping Liu, Bin Sun, Kang Liu, Jun Zhao

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.

Arithmetic Reasoning Common Sense Reasoning +3

Unified, User and Task (UUT) Centered Artificial Intelligence for Metaverse Edge Computing

no code implementations19 Dec 2022 Terence Jie Chua, Wenhan Yu, Jun Zhao

The Metaverse can be considered the extension of the present-day web, which integrates the physical and virtual worlds, delivering hyper-realistic user experiences.

Edge-computing

Mobile Augmented Reality with Federated Learning in the Metaverse

no code implementations16 Dec 2022 Xinyu Zhou, Jun Zhao

The Metaverse is deemed the next evolution of the Internet and has received much attention recently.

Federated Learning object-detection +2

Generating Hierarchical Explanations on Text Classification Without Connecting Rules

no code implementations24 Oct 2022 Yiming Ju, Yuanzhe Zhang, Kang Liu, Jun Zhao

The opaqueness of deep NLP models has motivated the development of methods for interpreting how deep models predict.

Clustering text-classification +1

ReasonChainQA: Text-based Complex Question Answering with Explainable Evidence Chains

no code implementations17 Oct 2022 Minjun Zhu, Yixuan Weng, Shizhu He, Kang Liu, Jun Zhao

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.

Answer Generation Question Answering +1

Edge-Cloud Cooperation for DNN Inference via Reinforcement Learning and Supervised Learning

no code implementations11 Oct 2022 Tinghao Zhang, Zhijun Li, Yongrui Chen, Kwok-Yan Lam, Jun Zhao

A reinforcement learning (RL)-based DNN compression approach is used to generate the lightweight model suitable for the edge from the heavyweight model.

Image Classification object-detection +3

Time Minimization in Hierarchical Federated Learning

no code implementations7 Oct 2022 Chang Liu, Terence Jie Chua, Jun Zhao

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.

Federated Learning

Joint Optimization of Energy Consumption and Completion Time in Federated Learning

no code implementations29 Sep 2022 Xinyu Zhou, Jun Zhao, Huimei Han, Claude Guet

Federated Learning (FL) is an intriguing distributed machine learning approach due to its privacy-preserving characteristics.

Federated Learning Privacy Preserving +1

Facial Landmark Predictions with Applications to Metaverse

1 code implementation29 Sep 2022 Qiao Han, Jun Zhao, Kwok-Yan Lam

This research aims to make metaverse characters more realistic by adding lip animations learnt from videos in the wild.

Transfer Learning

Mobile Edge Computing, Metaverse, 6G Wireless Communications, Artificial Intelligence, and Blockchain: Survey and Their Convergence

no code implementations28 Sep 2022 Yitong Wang, Jun Zhao

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.

Cloud Computing Edge-computing

Resource Allocation and Resolution Control in the Metaverse with Mobile Augmented Reality

no code implementations28 Sep 2022 Peiyuan Si, Jun Zhao, Huimei Han, Kwok-Yan Lam, Yang Liu

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.

Resource Allocation for Mobile Metaverse with the Internet of Vehicles over 6G Wireless Communications: A Deep Reinforcement Learning Approach

no code implementations27 Sep 2022 Terence Jie Chua, Wenhan Yu, Jun Zhao

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.

Answering Numerical Reasoning Questions in Table-Text Hybrid Contents with Graph-based Encoder and Tree-based Decoder

1 code implementation COLING 2022 Fangyu Lei, Shizhu He, Xiang Li, Jun Zhao, Kang Liu

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.

Models Alignment Question Answering

P2ANet: A Dataset and Benchmark for Dense Action Detection from Table Tennis Match Broadcasting Videos

no code implementations26 Jul 2022 Jiang Bian, Xuhong LI, Tao Wang, Qingzhong Wang, Jun Huang, Chen Liu, Jun Zhao, Feixiang Lu, Dejing Dou, Haoyi Xiong

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.

Action Detection Action Localization +2

LingYi: Medical Conversational Question Answering System based on Multi-modal Knowledge Graphs

1 code implementation20 Apr 2022 Fei Xia, Bin Li, Yixuan Weng, Shizhu He, Kang Liu, Bin Sun, Shutao Li, Jun Zhao

The medical conversational system can relieve the burden of doctors and improve the efficiency of healthcare, especially during the pandemic.

Conversational Question Answering Dialogue Generation +3

Robust PCA Unrolling Network for Super-resolution Vessel Extraction in X-ray Coronary Angiography

no code implementations16 Apr 2022 Binjie Qin, Haohao Mao, Yiming Liu, Jun Zhao, Yisong Lv, Yueqi Zhu, Song Ding, Xu Chen

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.

feature selection Rolling Shutter Correction +1

Towards Efficiently Evaluating the Robustness of Deep Neural Networks in IoT Systems: A GAN-based Method

no code implementations19 Nov 2021 Tao Bai, Jun Zhao, Jinlin Zhu, Shoudong Han, Jiefeng Chen, Bo Li, Alex Kot

Through extensive experiments, AI-GAN achieves high attack success rates, outperforming existing methods, and reduces generation time significantly.

Optimizing the Age of Information in RIS-aided SWIPT Networks

no code implementations14 Nov 2021 Wanting Lyu, Yue Xiu, Jun Zhao, Zhongpei Zhang

In this letter, a reconfigurable intelligent surface (RIS)-assisted simultaneous wireless information and power transfer (SWIPT) network is investigated.

Scheduling

Adversarial Purification through Representation Disentanglement

no code implementations15 Oct 2021 Tao Bai, Jun Zhao, Lanqing Guo, Bihan Wen

Deep learning models are vulnerable to adversarial examples and make incomprehensible mistakes, which puts a threat on their real-world deployment.

Disentanglement

A Relation-Oriented Clustering Method for Open Relation Extraction

1 code implementation EMNLP 2021 Jun Zhao, Tao Gui, Qi Zhang, Yaqian Zhou

The clustering-based unsupervised relation discovery method has gradually become one of the important methods of open relation extraction (OpenRE).

Clustering Relation +1

Logic Traps in Evaluating Attribution Scores

no code implementations ACL 2022 Yiming Ju, Yuanzhe Zhang, Zhao Yang, Zhongtao Jiang, Kang Liu, Jun Zhao

Meanwhile, since the reasoning process of deep models is inaccessible, researchers design various evaluation methods to demonstrate their arguments.

Lifelong Intent Detection via Multi-Strategy Rebalancing

no code implementations10 Aug 2021 Qingbin Liu, Xiaoyan Yu, Shizhu He, Kang Liu, Jun Zhao

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.

Intent Detection Knowledge Distillation

Alignment Rationale for Natural Language Inference

no code implementations ACL 2021 Zhongtao Jiang, Yuanzhe Zhang, Zhao Yang, Jun Zhao, Kang Liu

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.

feature selection Natural Language Inference

Knowledge-Enriched Event Causality Identification via Latent Structure Induction Networks

no code implementations ACL 2021 Pengfei Cao, Xinyu Zuo, Yubo Chen, Kang Liu, Jun Zhao, Yuguang Chen, Weihua Peng

Specifically, to make use of the descriptive knowledge, we devise a Descriptive Graph Induction module to obtain and encode the graph-structured descriptive knowledge.

Descriptive Event Causality Identification

Document-level Event Extraction via Parallel Prediction Networks

2 code implementations ACL 2021 Hang Yang, Dianbo Sui, Yubo Chen, Kang Liu, Jun Zhao, Taifeng Wang

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.

Document-level Event Extraction Event Extraction +1

CogIE: An Information Extraction Toolkit for Bridging Texts and CogNet

1 code implementation ACL 2021 Zhuoran Jin, Yubo Chen, Dianbo Sui, Chenhao Wang, Zhipeng Xue, Jun Zhao

CogNet is a knowledge base that integrates three types of knowledge: linguistic knowledge, world knowledge and commonsense knowledge.

Entity Linking Entity Typing +7

A Large-Scale Chinese Multimodal NER Dataset with Speech Clues

1 code implementation ACL 2021 Dianbo Sui, Zhengkun Tian, Yubo Chen, Kang Liu, Jun Zhao

In this paper, we aim to explore an uncharted territory, which is Chinese multimodal named entity recognition (NER) with both textual and acoustic contents.

named-entity-recognition Named Entity Recognition +1

Economic Dispatch of an Integrated Microgrid Based on the Dynamic Process of CCGT Plant

no code implementations5 Jul 2021 Zhiyi Lin, Chunyue Song, Jun Zhao, Chao Yang, Huan Yin

Intra-day economic dispatch of an integrated microgrid is a fundamental requirement to integrate distributed generators.

energy management Management

Inconspicuous Adversarial Patches for Fooling Image Recognition Systems on Mobile Devices

no code implementations29 Jun 2021 Tao Bai, Jinqi Luo, Jun Zhao

The patches are encouraged to be consistent with the background images with adversarial training while preserving strong attack abilities.

MG-DVD: A Real-time Framework for Malware Variant Detection Based on Dynamic Heterogeneous Graph Learning

no code implementations23 Jun 2021 Chen Liu, Bo Li, Jun Zhao, Ming Su, Xu-Dong Liu

In this paper, we propose MG-DVD, a novel detection framework based on dynamic heterogeneous graph learning, to detect malware variants in real time.

Blocking Graph Learning

LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification

no code implementations ACL 2021 Xinyu Zuo, Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao, Weihua Peng, Yuguang Chen

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.

Data Augmentation Event Causality Identification

Path-based knowledge reasoning with textual semantic information for medical knowledge graph completion

no code implementations27 May 2021 Yinyu Lan, Shizhu He, Xiangrong Zeng, Shengping Liu, Kang Liu, Jun Zhao

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.

CogNet: Bridging Linguistic Knowledge, World Knowledge and Commonsense Knowledge

no code implementations3 Mar 2021 Chenhao Wang, Yubo Chen, Zhipeng Xue, Yang Zhou, Jun Zhao

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.

World Knowledge

Joint Transmit Precoding and Reflect Beamforming Design for IRS-Assisted MIMO Cognitive Radio Systems

no code implementations2 Feb 2021 Weiheng Jiang, Yu Zhang, Jun Zhao, Zehui Xiong, Zhiguo Ding

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

no code implementations2 Feb 2021 Tao Bai, Jinqi Luo, Jun Zhao, Bihan Wen, Qian Wang

Adversarial training is one of the most effective approaches defending against adversarial examples for deep learning models.

Adversarial Robustness

Spectrum Sharing for 6G Integrated Satellite-Terrestrial Communication Networks Based on NOMA and Cognitive Radio

no code implementations27 Jan 2021 Xin Liu, Kwok-Yan Lam, Feng Li, Jun Zhao, Li Wang

ISTCN aims to provide high speed and pervasive network services by integrating broadband terrestrial mobile networks with satellite communication networks.

Management

Smart City Enabled by 5G/6G Networks: An Intelligent Hybrid Random Access Scheme

no code implementations16 Jan 2021 Huimei Han, Wenchao Zhai, Jun Zhao

mMTC and URLLC will co-exist in MTC networks for 5G 6G-enabled smart city.

Sum-Rate Maximization for UAV-assisted Visible Light Communications using NOMA: Swarm Intelligence meets Machine Learning

no code implementations10 Jan 2021 Quoc-Viet Pham, Thien Huynh-The, Mamoun Alazab, Jun Zhao, Won-Joo Hwang

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.

BIG-bench Machine Learning

EXPLORING VULNERABILITIES OF BERT-BASED APIS

no code implementations1 Jan 2021 Xuanli He, Lingjuan Lyu, Lichao Sun, Xiaojun Chang, Jun Zhao

We then demonstrate how the extracted model can be exploited to develop effective attribute inference attack to expose sensitive information of the training data.

Attribute Inference Attack +4

ANL: Anti-Noise Learning for Cross-Domain Person Re-Identification

no code implementations27 Dec 2020 Hongliang Zhang, Shoudong Han, Xiaofeng Pan, Jun Zhao

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.

Clustering Contrastive Learning +1

An LSTM-Aided Hybrid Random Access Scheme for 6G Machine Type Communication Networks

no code implementations25 Dec 2020 Wenchao Zhai, Huimei Han, Lei Liu, Jun Zhao

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.

Intelligent Reflecting Surface Assisted Anti-Jamming Communications Based on Reinforcement Learning

no code implementations23 Dec 2020 Helin Yang, Zehui Xiong, Jun Zhao, Dusit Niyato, Qingqing Wu, Massimo Tornatore, Stefano Secci

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.

reinforcement-learning Reinforcement Learning (RL)

A Comprehensive Survey of 6G Wireless Communications

no code implementations21 Dec 2020 Yang Zhao, Wenchao Zhai, Jun Zhao, Tinghao Zhang, Sumei Sun, Dusit Niyato, Kwok-Yan Lam

First, we give an overview of 6G from perspectives of technologies, security and privacy, and applications.

Resource Allocation for Intelligent Reflecting Surface Aided Cooperative Communications

no code implementations18 Dec 2020 Yulan Gao, Chao Yong, Zehui Xiong, Dusit Niyato, Yue Xiao, Jun Zhao

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).

Privacy and Robustness in Federated Learning: Attacks and Defenses

no code implementations7 Dec 2020 Lingjuan Lyu, Han Yu, Xingjun Ma, Chen Chen, Lichao Sun, Jun Zhao, Qiang Yang, Philip S. Yu

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.

Federated Learning Privacy Preserving

Graph-Based Knowledge Integration for Question Answering over Dialogue

no code implementations COLING 2020 Jian Liu, Dianbo Sui, Kang Liu, Jun Zhao

Despite many advances, existing approaches for this task did not consider dialogue structure and background knowledge (e. g., relationships between speakers).

Machine Reading Comprehension Question Answering +1

Privacy-Preserving Federated Learning for UAV-Enabled Networks: Learning-Based Joint Scheduling and Resource Management

no code implementations28 Nov 2020 Helin Yang, Jun Zhao, Zehui Xiong, Kwok-Yan Lam, Sumei Sun, Liang Xiao

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.

Distributed Computing Federated Learning +3

Uplink Achievable Rate Maximization for Reconfigurable Intelligent Surface Aided Millimeter Wave Systems with Resolution-Adaptive ADCs

no code implementations27 Nov 2020 Yue Xiu, Jun Zhao, Ertugrul Basar, Marco Di Renzo, Wei Sun, Guan Gui, Ning Wei

In this letter, we investigate the uplink of a reconfigurable intelligent surface (RIS)-aided millimeter-wave (mmWave) multi-user system.

Quantization

Intelligent Reflecting Surface Aided MISO Uplink Communication Network: Feasibility and Power Minimization for Perfect and Imperfect CSI

no code implementations22 Nov 2020 Yang Liu, Jun Zhao, Ming Li, Qingqing Wu

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).

Field-Tuned Quantum Effects in a Triangular-Lattice Ising Magnet

no code implementations18 Nov 2020 Yayuan Qin, Yao Shen, ChangLe Liu, Hongliang Wo, Yonghao Gao, Yu Feng, Xiaowen Zhang, Gaofeng Ding, Yiqing Gu, Qisi Wang, Shoudong Shen, Helen C. Walker, Robert Bewley, Jianhui Xu, Martin Boehm, Paul Steffens, Seiko Ohira-Kawamura, Naoki Murai, Astrid Schneidewind, Xin Tong, Gang Chen, Jun Zhao

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

Joint Entity and Relation Extraction with Set Prediction Networks

1 code implementation3 Nov 2020 Dianbo Sui, Yubo Chen, Kang Liu, Jun Zhao, Xiangrong Zeng, Shengping Liu

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.

Joint Entity and Relation Extraction Relation +1

Recent Advances in Understanding Adversarial Robustness of Deep Neural Networks

no code implementations3 Nov 2020 Tao Bai, Jinqi Luo, Jun Zhao

Adversarial examples are inevitable on the road of pervasive applications of deep neural networks (DNN).

Adversarial Robustness

KnowDis: Knowledge Enhanced Data Augmentation for Event Causality Detection via Distant Supervision

no code implementations COLING 2020 Xinyu Zuo, Yubo Chen, Kang Liu, Jun Zhao

Modern models of event causality detection (ECD) are mainly based on supervised learning from small hand-labeled corpora.

Data Augmentation

Secure Weighted Aggregation for Federated Learning

no code implementations17 Oct 2020 Jiale Guo, Ziyao Liu, Kwok-Yan Lam, Jun Zhao, Yiqiang Chen, Chaoping Xing

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

Secrecy Rate Maximization for Reconfigurable Intelligent Surface Aided Millimeter Wave System with Low-resolution DAC

no code implementations9 Oct 2020 Yue Xiu, Jun Zhao, Zhongpei Zhang

In this letter, we investigate the secrecy rate of an reconfigurable intelligent surface (RIS)-aided millimeter-wave (mmWave) system with hardware limitations.

FTN: Foreground-Guided Texture-Focused Person Re-Identification

no code implementations24 Sep 2020 Donghaisheng Liu, Shoudong Han, Yang Chen, Chenfei Xia, Jun Zhao

Person re-identification (Re-ID) is a challenging task as persons are often in different backgrounds.

Person Re-Identification

Towards Causal Explanation Detection with Pyramid Salient-Aware Network

no code implementations CCL 2020 Xinyu Zuo, Yubo Chen, Kang Liu, Jun Zhao

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.

Event Coreference Resolution via a Multi-loss Neural Network without Using Argument Information

no code implementations22 Sep 2020 Xinyu Zuo, Yubo Chen, Kang Liu, Jun Zhao

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.

coreference-resolution Event Argument Extraction +1