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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Le Yu Leilei Sun Bowen Du Weifeng Lv State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University

We propose DyGFormer, a new Transformer-based architecture for dynamic graph learning. DyGFormer is conceptually simple and only needs to learn from nodes' historical first-hop interactions by: (i) a neighbor co-occurrence encoding scheme that explores the correlations of the source node and destination node based on their historical sequences; (ii) a patching technique that divides each sequence into multiple patches and feeds them to Transformer, allowing the model to effectively and efficiently benefit from longer histories. We also introduce DyGLib, a unified library with standard training pipelines, extensible coding interfaces, and comprehensive evaluating protocols to promote reproducible, scalable, and credible dynamic graph learning research. By performing exhaustive experiments on thirteen datasets for dynamic link prediction and dynamic node classification tasks, we find that DyGFormer achieves state-of-the-art performance on most of the datasets, demonstrating its effectiveness in capturing nodes' correlations and long-term temporal dependencies. Moreover, some results of baselines are inconsistent with previous reports, which may be caused by their diverse but less rigorous implementations, showing the importance of DyGLib. All the used resources are publicly available at https://***/yule-BUAA/DyGLib.

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Personalized Federated Learning System Based on Permissioned Blockchain

Personalized Federated Learning System Based on Permissioned...

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2021 International Conference on Intelligent Computing, Automation and Systems, ICICAS 2021

作者： Yuan, Bo Qiu, Wangjie Beihang University School of Computer Science and Engineering State Key Laboratory of Software Development Environment Beijing100191 China Beihang University State Key Laboratory of Software Development Environment Beijing100191 China

ISBN: (纸本)9781665428101

Federated learning ensures the privacy of data generated by large-scale IoT devices. Existing federated learning frameworks, based on centralized model coordinators, still face serious security challenges such as single point of failure and lack of privacy. In this paper, we propose a personalized federated learning system based on permissioned blockchain, which is divided into four layers of architecture are IoT device layer, network layer, edge computing layer, blockchain layer, and application layer, using permissioned blockchain as a federated learning server. And a permission blockchain-based personalized federation learning algorithm is proposed, which can achieve privacy protection and resistance to poisoning attacks with high accuracy. The experimental results revealed that the system has high privacy protection and anti-poisoning attack capability and can be deployed in edge computing situations. © 2021 IEEE.

关键词： Internet of things

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Differentially Private Generative Model with Ratio-Based Gradient Clipping 4th

Differentially Private Generative Model with Ratio-Based Gr...

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Lin, Jianchen Yao, Yanqing State Key Laboratory of Software Development Environment Beihang University Beijing100191 China State Key Laboratory of Cryptology Beijing100878 China Key Laboratory of Aerospace Network Security Ministry of Industry and Information Technology School of Cyber Science and Technology Beihang University Beijing100191 China State Grid Fujian Fuzhou350100 China

ISBN: (纸本)9783031200953

As a branch of deep learning, conditional Generative Adversarial Net (cGAN) has achieved remarkable results in target-oriented image generation, but the process of training the model lacks privacy guarantee. Differential Privacy (DP) is a privacy-preserving mechanism that could be utilized in the Stochastic Gradient Descent (SGD) of deep learning to maintain the confidentiality of training data. However, the quality and diversity of differentially private conditional image synthesis remain large room for improvement because traditional mechanisms with thick granularities and rigid clipping bounds in Differentially Private SGD (DPSGD) could lead to huge performance loss. To tackle this, we propose three algorithms with different clipping granularities and clipping bound distributions, and investigate the effects on enhancing conditional image generation under Differentially Private Projection cGAN (DP-PcGAN). We compare and analyze the performance of our strategies with methods concerned in terms of seven metrics, finding ratio-based gradient clipping highly effective and robust in training DP-PcGAN for generating visually promising images. Specifically, the Per-layer RGC surpasses previous methods under all evaluation metrics utilizing the second least privacy expense for MNIST dataset, while Per-unit RGC attains encouraging outcomes with a moderate converging speed under FashionMNIST dataset. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Stochastic systems

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Using Query Expansion in Manifold Ranking for Query-Oriented Multi-document Summarization 20

Using Query Expansion in Manifold Ranking for Query-Oriented...

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20th China National Conference on Computational Linguistics, CCL 2021

作者： Jia, Quanye Liu, Rui Lin, Jianying State Key Laboratory of Software Development Environment Beihang University Beijing100191 China

ISBN: (纸本)9783030841850

Manifold ranking has been successfully applied in query-oriented multi-document summarization. It not only makes use of the relationships among the sentences, but also the relationships between the given query and the sentences. However, the information of original query is often insufficient. So we present a query expansion method, which is combined in the manifold ranking to resolve this problem. Our method not only utilizes the information of the query term itself and the knowledge base WordNet to expand it by synonyms, but also uses the information of the document set itself to expand the query in various ways (mean expansion, variance expansion and TextRank expansion). Compared with the previous query expansion methods, our method combines multiple query expansion methods to better represent query information, and at the same time, it makes a useful attempt on manifold ranking. In addition, we use the degree of word overlap and the proximity between words to calculate the similarity between sentences. We performed experiments on the datasets of DUC 2006 and DUC2007, and the evaluation results show that the proposed query expansion method can significantly improve the system performance and make our system comparable to the state-of-the-art systems. © 2021, Springer Nature Switzerland AG.

关键词： Knowledge based systems

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Joint Visual Perception and Linguistic Commonsense for Daily Events Causality Reasoning

Joint Visual Perception and Linguistic Commonsense for Daily...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Ma, Bole Tong, Chao School of Computer Science and Engineering Beihang University China Beihang University State Key Laboratory of Software Development Environment China Yunnan Key Laboratory of Blockchain Application Technology China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Multimodal networks that juxtapose visual and linguistic modalities are currently widely adopted for solving vision-and-language tasks. They perform well in simple and intu-itive tasks, but are prone to mistakes in tasks involving latent or implicit details, due to the difficulty of capturing crucial but imperceptible visual signals in the real world. Perception errors lead to nonsensical results, but can be corrected by commonsense knowledge. To this end, we combine visual perception and linguistic commonsense to solve the challenging daily events causality reasoning task. We propose a novel Object-Aware Reasoning Network to focus on object inter-action while ignoring distracting information to refine visual perception. Further, a language branch with an independent prediction head is supervised to learn causality commonsense to help correct obvious perception errors, resulting in more plausible conclusions. Extensive experiments demonstrate that our method achieves new state-of-the-art results on Vis-Causal dataset. © 2022 IEEE.

关键词： Linguistics

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SWin: A Set-Window Division Mechanism to Alleviate I/O Jitters of NVMe SSD Drives 24

SWin: A Set-Window Division Mechanism to Alleviate I/O Jitte...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Liu, Yuting Wang, Liang Zhu, Jinbin Shen, Runnan Jiang, Shixuan Xiao, Limin Beihang University State Key Laboratory of Software Development Environment Beijing100191 China School of Computer Science and Engineering Beihang University Beijing100191 China

ISBN: (纸本)9798350319934

NVMe SSDs increase the performance of I/O system significantly, but they still suffer from the problem of performance jitters. Although existing studies adopt optimized GC or schedulers to make up for the vulnerabilities, they cannot provide a near 'GC-free' performance under various scenarios. In this paper, we propose SWin, a Set-Window division mechanism to alleviate I/O jitters of NVMe SSDs, in which we build an optimized scheduler with cache management on sets. First, we first divide NVMe SSD into set level and determine time windows based on sets. Then, an efficient I/O scheduler is designed to process requests of applications. Finally, we develop a host cache management strategy to ensure a stable response time for I/O requests. The experimental results indicate that the OCSSD system integrated with SWin can gain performance improvement by 32%, 34% and 14% in completion time, IOPS and latency performance, respectively. © 2022 IEEE.

关键词： Jitter

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Understanding Heterophily for Graph Neural Networks 41

Understanding Heterophily for Graph Neural Networks

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41st International Conference on Machine Learning, ICML 2024

作者： Wang, Junfu Guo, Yuanfang Yang, Liang Wang, Yunhong State Key Laboratory of Software Development Environment Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China Shen Yuan Honors College Beihang University Beijing China School of Artificial Intelligence Hebei University of Technology Tianjin China

Graphs with heterophily have been regarded as challenging scenarios for Graph Neural Networks (GNNs), where nodes are connected with dissimilar neighbors through various patterns. In this paper, we present theoretical understandings of heterophily for GNNs by incorporating the graph convolution (GC) operations into fully connected networks via the proposed Heterophilous Stochastic Block Models (HSBM), a general random graph model that can accommodate diverse heterophily patterns. Our theoretical investigation comprehensively analyze the impact of heterophily from three critical aspects. Firstly, for the impact of different heterophily patterns, we show that the separability gains are determined by two factors, i.e., the Euclidean distance of the neighborhood distributions and pE [deg], where E [deg] is the averaged node degree. Secondly, we show that the neighborhood inconsistency has a detrimental impact on separability, which is similar to degrading E [deg] by a specific factor. Finally, for the impact of stacking multiple layers, we show that the separability gains are determined by the normalized distance of the lpowered neighborhood distributions, indicating that nodes still possess separability in various regimes, even when over-smoothing occurs. Extensive experiments on both synthetic and real-world data verify the effectiveness of our theory. Copyright 2024 by the author(s)

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The effects of beam drifts on elastic scattering measured by the large solid-angle covered detector array

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Nuclear Science and Techniques 2021年第2期32卷 24-31页

作者： Yong-Jin Yao Cheng-Jian Lin Lei Yang Nan-Ru Ma Dong-Xi Wang Gao-Long Zhang Guang-Xin Zhang Hui-Ming Jia Feng Yang State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Physics Beihang UniversityBeijing 100191China China Institute of Atomic Energy Beijing 102413China

A new detector array characterized by compact structure and large solid-angle coverage was designed for radioactive ion beam(RIB)experiments and measuring multi-particle correlations.A Monte Carlo simulation was performed to explore the effects of beam drifts in different directions and distances on the angular distribution of the Rutherford scattering,as measured by the detector *** results indicate that when the beam drift distance is less than 2.0 mm,the symmetry of the detector array can maintain a count error of less than 5%.This confirms the property of the detector array for RIB ***,the simulation was validated through the elastic scattering angular distributions of 6;7 Li measured by the detector array in 6;7Li t209 Bi experiments at different energies.

关键词： Large solid-angle detector array Beam drift Monte Carlo simulation Elastic scattering

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Ppmgs: An Efficient and Effective Solution for Distributed Privacy-Preserving Semi-Supervised Learning

SSRN

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SSRN 2023年

作者： Li, Zhi Li, Chaozhuo Li, Zhoujun Weng, Jian Huang, Feiran Zhou, Zhibo College of Cyber Security Jinan University Guangzhou China State Key Lab of Software Development Environment Beihang University Beijing China

Recently, distributed semi-supervised learning has attracted increasing research attention considering its tremendous practical values. A promising distributed semi-supervised learning should be able to not only achieve desirable classification performance, but also protect the data privacy in the distributed scenario. Existing approaches usually capture the similarities between data instances through the privacy-preserving calculations, which bring extra computation and heuristic changes to the algorithm, leading to sub-optimal solutions with huge *** this paper, we propose a Privacy-preserving Mixture-distribution based Graph Smoothing (PPMGS) model for distributed privacy-preserving semi-supervised learning. Our motivation lies in constructing a graph based on a Gaussian mixture distribution instead of individual data instances, which contributes to better capturing the underlying data distribution and improving model efficiency. PPMGS contains a privacy-preserving expectation-maximization phase to estimate the Gaussian mixture distribution depicting the input data, and a mixture-distribution-based graph smoothing algorithm to learn a distribution-based classifier by fitting a few labeled *** results on both synthetic and real-world datasets demonstrate that our proposal outperforms state-of-the-art baselines in terms of both efficiency and effectiveness. © 2023, The Authors. All rights reserved.

关键词： Graphic methods

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Federated k-Dominant Skyline: An Efficient Approach Under Differential Privacy 5

Federated k-Dominant Skyline: An Efficient Approach Under Di...

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5th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2023

作者： Cai, Guoqing Shi, Yexuan Zhou, Hao Zhou, Nan Guo, Manxue Jia, Zimeng China Mobile Research Institute Beijing China Beihang University State Key Laboratory of Software Development Environment Beijing China Zhongguancun Pan Connected Mobile Commun. Technology Innovation and Application Research Institute Beijing China

ISBN: (纸本)9798350393521

k-dominant skyline queries have been widely used in many applications, such as multi-criteria decision making and recommendation. In recent years, a new computing paradigm, called data federation, has emerged to provide collaborative data analytics among several data owners who would like to share their data under privacy protections. This paradigm brings a new challenge to the k-dominant skyline query, where data is distributed among these owners and privacy leakages should be prevented during the query processing. Accordingly, we define the problem of federated k-dominant skyline query and show that existing solutions cannot be extended to answer this query with satisfactory efficiency. To improve the efficiency, we present an observation of a vector aggregation based re-formulation and design an efficient solution under differential privacy. Finally, extensive experiments are conducted to demonstrate that our solution is notably faster than the state-of-the-arts. © 2023 IEEE.

关键词： Big data

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