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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Wang, Jiaxin Zhang, Lingling Liu, Jun Liang, Xi Zhong, Yujie Wu, Yaqiang Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering School of Computer Science and Technology Xi'an Jiaotong University China National Engineering Lab for Big Data Analytics Xi'an Jiaotong University China Lenovo Research Beijing China

Relation clustering is a general approach for open relation extraction (OpenRE). Current methods have two major problems. One is that their good performance relies on large amounts of labeled and pre-defined relational instances for pre-training, which are costly to acquire in reality. The other is that they only focus on learning a high-dimensional metric space to measure the similarity of novel relations and ignore the specific relational representations of clusters. In this work, we propose a new prompt-based framework named MatchPrompt, which can realize OpenRE with efficient knowledge transfer from only a few predefined relational instances as well as mine the specific meanings for cluster interpretability. To our best knowledge, we are the first to introduce a prompt-based framework for unlabeled clustering. Experimental results on different datasets show that MatchPrompt achieves the new SOTA results for OpenRE. © 2022 Association for Computational Linguistics.

关键词： Extraction

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HMvisor: dynamic hybrid memory management for virtual machines

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Science China(Information Sciences) 2021年第9期64卷 74-89页

作者： Dang YANG Haikun LIU Hai JIN Yu ZHANG National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing LabSchool of Computer Science and Technology Huazhong University of Science and Technology

Emerging non-volatile memory(NVM) technologies promise high density, low cost and dynamic random access memory(DRAM)-like performance, at the expense of limited write endurance and high write energy consumption. It is more practical to use NVM combining with the traditional DRAM. However, the hybrid memory management such as page migration becomes more challenging in a virtualization environment because virtual machines(VMs) are unaware of the memory heterogeneity. In this paper, we propose HMvisor, a hypervisor and VM coordinated hybrid memory management mechanism to better utilize DRAM and NVM resources. HMvisor exposes the memory heterogeneity to VMs by mapping virtual NUMA nodes to different physical NUMA nodes. We propose a lightweight and efficient page migration mechanism by decoupling page hotness tracking from page migration. HMvisor performs those operations in the hypervisor and VMs separately, without disrupting the execution of VMs. We also propose a memory resource trading policy to adjust the capacity of DRAM and NVM for each VM, with the monetary cost unchanged. We implement our prototype system based on QEMU/KVM and evaluate it with several benchmarks. Experimental results show that HMvisor can reduce 50% of write traffic to NVM with less than 5% performance overhead. Moreover, the hybrid memory adjustment scheme in HMvisor can significantly improve application performance by up to 30×.

关键词： hypervisor virtual machine non-volatile memory hybrid memory management

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Towards trustworthy blockchain systems in the era of “Internet of value”: development, challenges, and future trends

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Science China(Information Sciences) 2022年第5期65卷 250-260页

作者： Hai JIN Jiang XIAO National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and Technology

Since the advent of cryptocurrencies such as Bitcoin, blockchain, as their underlying technologies,has drawn a massive amount of attention from both academia and the industry. This ever-evolving technology inherits the “genes” of distributed systems, offering significant advantages of immutability, transparency,auditability, and tamper-resistance. These benefits help blockchain re-establish public confidence, and hold the significant promise of reliable information sharing and value transfer. Therefore, blockchain has become the foundation of crucial strategic deployments in countries across the world, and the fundamental basis for building the next generation Web 3.0 — “Internet of value”. In this article, we will start with unraveling the essential ingredients of blockchain technology, and showing the characteristics of each of these ingredients in the context of distributed systems. We will then present the core technical challenges that need to be addressed prior to unleashing its full potential, including its performance, scalability, and cross-chain interoperability. Finally, we will introduce the recent developments of blockchain systems, and discuss the future trends of the blockchain ecosystem.

关键词： blockchain distributed systems internet of value challenges development

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Tracking the carbon footprint of global generative artificial intelligence

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Innovation 2025年第5期6卷

作者： Ding, Zhaohao Wang, Jianxiao Song, Yiyang Zheng, Xiaokang He, Guannan Chen, Xiupeng Zhang, Tiance Lee, Wei-Jen Song, Jie State Key Lab of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing102206 China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing100871 China Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong999077 China Department of Industrial Engineering and Management College of Engineering Peking University Beijing100871 China Jan C. Willems Center for Systems and Control University of Groningen Groningen9700AB Netherlands Energy Systems Research Center University of Texas at Arlington ArlingtonTX76019 United States

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egoStellar: Visual Analysis of Anomalous Communication Behaviors from Egocentric Perspective 23rd

egoStellar: Visual Analysis of Anomalous Communication Behav...

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23rd International Computer Symposium, ICS 2018

作者： Han, Mei Wang, Qing Wei, Lirui Zhang, Yuwei Cao, Yunbo Pu, Jiansu Visual Analytics of Big Data Lab UESTC Chengdu China School of Information and Software Engineering UESTC Chengdu China

ISBN: (纸本)9789811391897

Detection and analysis of anomalous communication behaviors in cellar networks are extremely important in identifying potential advertising agency or fraud users. Visual analytics benefits domain experts in this problem for its intuitiveness and friendly interactive interface in presenting and exploring large volumes of data. In this paper, we propose a visual analytics system, egoStellar, to interactively explore the communication behaviors of mobile users from an ego network perspective. Ego network is composed of a centered individual and the relationships between the ego and his/her direct contacts (alters). Based on the graph model, egoStellar presents an overall statistical view to explore the distribution of mobile users for behavior inspection, a group view to classify the users and extract features for anomalous detection and comparison, and a ego-centric view to show the interactions between an ego and the alters in details. Our system can help analysts to interactively explore the communication patterns of mobile users from egocentric perspectives. Thus, this system makes it easier for the government or operators to visually inspect the massive communication behaviors in a intuitive way to detect and analyze anomalous users. Furthermore, our design can provide the researchers a good opportunity to observe the personal communication patterns to uncover new knowledge about human social interactions. Our proposed design can be applied to other fields where network structure exists. We evaluated egoStellar with real datasets containing the anomalous users with extremely large contacts in a short time period. The results show our system is effective in identifying anomalous communication behaviors, and its front-end interactive visualizations are intuitive and useful for analysts to discover insights in data. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Large dataset

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Rethinking Node-wise Propagation for Large-scale Graph Learning 24

Rethinking Node-wise Propagation for Large-scale Graph Learn...

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33rd ACM Web Conference, WWW 2024

作者： Li, Xunkai Ma, Jingyuan Wu, Zhengyu Su, Daohan Zhang, Wentao Li, Rong-Hua Wang, Guoren Beijing Institute of Technology Beijing China Peking University National Engineering Laboratory for Big Data Analytics and Applications Beijing China

ISBN: (纸本)9798400701719

Scalable graph neural networks (GNNs) have emerged as a promising technique, which exhibits superior predictive performance and high running efficiency across numerous large-scale graph-based web applications. However, (i) Most scalable GNNs tend to treat all nodes with the same propagation rules, neglecting their topological uniqueness;(ii) Existing node-wise propagation optimization strategies are insufficient on web-scale graphs with intricate topology, where a full portrayal of nodes' local properties is required. Intuitively, different nodes in web-scale graphs possess distinct topological roles, and therefore propagating them indiscriminately or neglecting local contexts may compromise the quality of node representations. To address the above issues, we propose Adaptive Topology-aware Propagation (ATP), which reduces potential high-bias propagation and extracts structural patterns of each node in a scalable manner to improve running efficiency and predictive performance. Remarkably, ATP is crafted to be a plug-and-play node-wise propagation optimization strategy, allowing for offline execution independent of the graph learning process in a new perspective. Therefore, this approach can be seamlessly integrated into most scalable GNNs while remaining orthogonal to existing node-wise propagation optimization strategies. Extensive experiments on 12 datasets have demonstrated the effectiveness of ATP. © 2024 ACM.

关键词： Graph neural networks

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Text-augmented long-term relation dependency learning for knowledge graph representation

High-Confidence Computing

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High-Confidence Computing 2025年

作者： Quntao Zhu Mengfan Li Yuanjun Gao Yao Wan Xuanhua Shi Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

Knowledge graph (KG) representation learning aims to map entities and relations into a low-dimensional representation space, showing significant potential in many tasks. Existing approaches follow two categories: (1) Graph-based approaches encode KG elements into vectors using structural score functions. (2) Text-based approaches embed text descriptions of entities and relations via pre-trained language models (PLMs), further fine-tuned with triples. We argue that graph-based approaches struggle with sparse data, while text-based approaches face challenges with complex relations. To address these limitations, we propose a unified Text-Augmented Attention-based Recurrent Network, bridging the gap between graph and natural language. Specifically, we employ a graph attention network based on local influence weights to model local structural information and utilize a PLM based prompt learning to learn textual information, enhanced by a mask-reconstruction strategy based on global influence weights and textual contrastive learning for improved robustness and generalizability. Besides, to effectively model multi-hop relations, we propose a novel semantic-depth guided path extraction algorithm and integrate cross-attention layers into recurrent neural networks to facilitate learning the long-term relation dependency and offer an adaptive attention mechanism for varied-length information. Extensive experiments demonstrate that our model exhibits superiority over existing models across KG completion and question-answering tasks.

关键词： Knowledge graph representation Graph attention network Pre-trained language model Attention-based recurrent network Masked autoencoder Contrastive learning

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CDC: Classification driven compression for bandwidth efficient edge-cloud collaborative deep learning 29

CDC: Classification driven compression for bandwidth efficie...

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Dong, Yuanrui Zhao, Peng Yu, Hanqiao Zhao, Cong Yang, Shusen National Engineering Laboratory for Big Data Analytics Xi'an Jiaotong University China Department of Computing Imperial College London United Kingdom

ISBN: (纸本)9780999241165

The emerging edge-cloud collaborative Deep Learning (DL) paradigm aims at improving the performance of practical DL implementations in terms of cloud bandwidth consumption, response latency, and data privacy preservation. Focusing on bandwidth efficient edge-cloud collaborative training of DNN-based classifiers, we present CDC, a Classification Driven Compression framework that reduces bandwidth consumption while preserving classification accuracy of edge-cloud collaborative DL. Specifically, to reduce bandwidth consumption, for resource-limited edge servers, we develop a lightweight autoencoder with a classification guidance for compression with classification driven feature preservation, which allows edges to only upload the latent code of raw data for accurate global training on the Cloud. Additionally, we design an adjustable quantization scheme adaptively pursuing the tradeoff between bandwidth consumption and classification accuracy under different network conditions, where only fine-tuning is required for rapid compression ratio adjustment. Results of extensive experiments demonstrate that, compared with DNN training with raw data, CDC consumes 14.9× less bandwidth with an accuracy loss no more than 1.06%, and compared with DNN training with data compressed by AE without guidance, CDC introduces at least 100% lower accuracy loss. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Classification (of information)

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AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity 40

AdaFGL: A New Paradigm for Federated Node Classification wit...

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40th IEEE International Conference on data engineering, ICDE 2024

作者： Li, Xunkai Wu, Zhengyu Zhang, Wentao Sun, Henan Li, Rong-Hua Wang, Guoren Beijing Institute of Technology Beijing China Peking University China National Engineering Labratory for Big Data Analytics and Applications Beijing China

ISBN: (纸本)9798350317152

Recently, Federated Graph Learning (FGL) has attracted significant attention as a distributed framework based on graph neural networks, primarily due to its capability to break data silos. Existing FGL studies employ community split on the homophilous global graph by default to simulate federated semisupervised node classification settings. Such a strategy assumes the consistency of topology between the multi-client subgraphs and the global graph, where connected nodes are highly likely to possess similar feature distributions and the same label. However, in real-world implementations, the varying perspectives of local data engineering result in various subgraph topologies, posing unique heterogeneity challenges in FGL. Unlike the well-known label Non-independent identical distribution (Non-iid) problems in federated learning, FGL heterogeneity essentially reveals the topological divergence among multiple clients, namely homophily or heterophily. To simulate and handle this unique challenge, we introduce the concept of structure Non-iid split and then present a new paradigm called Adaptive Federated Graph Learning (AdaFGL), a decoupled two-step personalized approach. To begin with, AdaFGL employs standard multi-client federated collaborative training to acquire the federated knowledge extractor by aggregating uploaded models in the final round at the server. Then, each client conducts personalized training based on the local subgraph and the federated knowledge extractor. Extensive experiments on the 12 graph benchmark datasets validate the superior performance of AdaFGL over state-of-the-art baselines. Specifically, in terms of test accuracy, our proposed AdaFGL outperforms baselines by significant margins of 3.24 % and 5.57 % on community split and structure Non-iid split, respectively. © 2024 IEEE.

关键词： Graph neural networks

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Neural Machine Translation Models with Attention-Based Dropout Layer

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Computers, Materials & Continua 2023年第5期75卷 2981-3009页

作者： Huma Israr Safdar Abbas Khan Muhammad Ali Tahir Muhammad Khuram Shahzad Muneer Ahmad Jasni Mohamad Zain School of Electrical Engineering and Computer Science(SEECS) National University of Science and TechnologyIslamabadPakistan Institute for Big Data Analytics and Artificial Intelligence(IBDAAI) Kompleks Al-KhawarizmiUniversiti Teknologi MARA40450Shah AlamSelangorMalaysia

In bilingual translation,attention-based Neural Machine Translation(NMT)models are used to achieve synchrony between input and output sequences and the notion of *** model has obtained state-of-the-art performance for several language ***,there has been little work exploring useful architectures for Urdu-to-English machine *** conducted extensive Urdu-to-English translation experiments using Long short-term memory(LSTM)/Bidirectional recurrent neural networks(Bi-RNN)/Statistical recurrent unit(SRU)/Gated recurrent unit(GRU)/Convolutional neural network(CNN)and *** results show that Bi-RNN and LSTM with attention mechanism trained iteratively,with a scalable data set,make precise predictions on unseen *** trained models yielded competitive results by achieving 62.6%and 61%accuracy and 49.67 and 47.14 BLEU scores,*** a qualitative perspective,the translation of the test sets was examined manually,and it was observed that trained models tend to produce repetitive output more *** attention score produced by Bi-RNN and LSTM produced clear alignment,while GRU showed incorrect translation for words,poor alignment and lack of a clear ***,we considered refining the attention-based models by defining an additional attention-based dropout *** dropout fixes alignment errors and minimizes translation errors at the word *** empirical demonstration and comparison with their counterparts,we found improvement in the quality of the resulting translation system and a decrease in the perplexity and over-translation *** ability of the proposed model was evaluated using Arabic-English and Persian-English datasets as *** empirically concluded that adding an attention-based dropout layer helps improve GRU,SRU,and Transformer translation and is considerably more efficient in translation quality and speed.

关键词： Natural language processing neural machine translation word embedding attention perplexity selective dropout regularization Urdu Persian Arabic BLEU

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