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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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Effective Training of PINNs by Combining CMA-ES with Gradient Descent

Effective Training of PINNs by Combining CMA-ES with Gradien...

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Congress on Evolutionary Computation

作者： Lin Liu Yuan Yuan School of Computer Science and Engineering Beihang University Beijing China State Key Laboratory of Software Development Environment Zhongguancun Laboratory School of Computer Science and Engineering Beihang University Beijing China

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Physics-Informed Neural Networks (PINNs) have recently received increasing attention, however, optimizing the loss function of PINNs is notoriously difficult, where the landscape of the loss function is often highly non-convex and rugged. Local optimization methods based on gradient information can converge quickly but are prone to being trapped in local minima for training PINNs. Evolutionary algorithms (EAs) are well known for the global search ability, which can help escape from local minima. It has been reported in the literature that EAs show some advantages over gradient-based methods in training PINNs. Inspired by the Memetic Algorithm, we combine global-search based EAs and local-search based batch gradient descent in order to make the best of both word. In addition, since the PINN loss function is composed of multiple terms, balancing these terms is also a challenging issue. Therefore, we also attempt to combine EAs with multiple-gradient descent algorithm for multi-objective optimization. Our experiments provide strong evidence for the superiority of the above algorithms.

关键词： Training Memetics Neural networks Evolutionary computation Pareto optimization Prediction algorithms Linear programming

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FL-GNN: Efficient Fusion of Fuzzy Neural Network and Graph Neural Network 27

FL-GNN: Efficient Fusion of Fuzzy Neural Network and Graph N...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Du, Boyu Zhou, Jingya Liu, Ling She, Xiaolong School of Computer Science and Technology Soochow University China Engineering Lab.of Big Data and Intelligence of Jiangsu Province Soochow University China State Key Lab.for Novel Software Technology Nanjing University China School of Computer Science Georgia Institute of Technology United States

ISBN: (纸本)9781643685489

Graph Neural Networks (GNNs) have become an important graph feature learning paradigm that is extensively applied to graph inference ***, GNNs still have limitations in some aspects such as representation capability, interpretability, *** this end, this paper introduces a novel hybrid Fuzzy-Logic Graph Neural Network (FL-GNN) that synergistically combines Fuzzy Neural Network (FNN) and GNN to effectively capture and aggregate local information flows within the ***-GNN exhibits three distinctive ***, we incorporate a specialized structure fuzzy rule to enhance FL-GNN's graph inference capability, surpassing representative GNN ***, we augment the interpretability of FL-GNN by integrating analytical exploration methods from two perspectives: Fuzzy Inference System and Message Passing Algorithm (MPA).Lastly, we refine the structure of FL-GNN based on MPA to optimize its calculation complexity and consequently improve learning *** results show that FL-GNN outperforms existing representative graph neural networks for graph inference tasks. © 2024 The Authors.

关键词： Graph neural networks

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Understanding heterophily for graph neural networks 24

Understanding heterophily for graph neural networks

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Proceedings of the 41st International Conference on Machine Learning

作者： Junfu Wang Yuanfang Guo Liang Yang Yunhong Wang State Key Laboratory of Software Development Environment and School of Computer Science and Engineering and Shen Yuan Honors College Beihang University Beijing China State Key Laboratory of Software Development Environment and School of Computer Science and Engineering Beihang University Beijing China School of Artificial Intelligence Hebei University of Technology Tianjin China School of Computer Science and Engineering Beihang University Beijing 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 $\sqrt{\mathbb{E}\left[\operatorname{deg}\right]}$, where $\mathbb{E}\left[\operatorname{deg}\right]$ is the averaged node degree. Secondly, we show that the neighborhood inconsistency has a detrimental impact on separability, which is similar to degrading $\mathbb{E}\left[\operatorname{deg}\right]$ 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 l-powered neighborhood distributions, indicating that nodes still possess separability in various regimes, even when over-smoothing occurs. Extensive experiments on both synthetic and realworld data verify the effectiveness of our theory.

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Prompting Continual Person Search 24

Prompting Continual Person Search

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhang, Pengcheng Yu, Xiaohan Bai, Xiao Zheng, Jin Ning, Xin School of Computer Science and Engineering State Key Laboratory of Complex & Critical Software Environment Jiangxi Research Institute Beihang University Beijing China School of Computing Macquarie University Sydney Australia Institute of Semiconductors Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400706868

The development of person search techniques has been greatly promoted in recent years for its superior practicality and challenging goals. Despite their significant progress, existing person search models still lack the ability to continually learn from increasing real-world data and adaptively process input from different domains. To this end, this work introduces the continual person search task that sequentially learns on multiple domains and then performs person search on all seen domains. This requires balancing the stability and plasticity of the model to continually learn new knowledge without catastrophic forgetting. For this, we propose a Prompt-based Continual Person Search (PoPS) model in this paper. First, we design a compositional person search transformer to construct an effective pre-trained transformer without exhaustive pre-training from scratch on large-scale person search data. This serves as the fundamental for prompt-based continual learning. On top of that, we design a domain incremental prompt pool with a diverse attribute matching module. For each domain, we independently learn a set of prompts to encode the domain-oriented knowledge. Meanwhile, we jointly learn a group of diverse attribute projections and prototype embeddings to capture discriminative domain attributes. By matching an input image with the learned attributes across domains, the learned prompts can be properly selected for model inference. Extensive experiments are conducted to validate the proposed method for continual person search. The source code is available at https://***/PatrickZad/PoPS. © 2024 ACM.

关键词： Contrastive Learning

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A Framework to Analyze the Impact of Maps on VANET Simulation

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Chinese Journal of Electronics 2023年第3期20卷 401-406页

作者： Xiaoming LI Jingxuan WANG Xin JIN Tong ZHAO Wei YAN State Key Laboratory of Software Development Environment Beihang University Beijing China School of Electronic Engineering and Computer Science Peking University Beijing China

Mobility models play an important role on VANET simulation, which has been discussed in many studies. However, few studies have analyzed the impact of maps. We find that maps can greatly influence simulation results. We present a framework to help researchers analyze the impact of maps. We regard intersections in the map as virtual nodes and propose Virtual node coverage (VNC) and Virtual connected components (VCC) as two metrics to estimate the impact of maps. Through the-oretical analysis and extensive simulations, we show that our approach can be applied to various scenarios and researchers can use our approach to analyze the impact of maps on VANET simulation.

关键词： Measurement Analytical models Electric potential Mobility models Simulation Vehicular ad hoc networks Research and development

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Pooling Under the Sun: A Mining Pool Centralized Revisit and Solution

Pooling Under the Sun: A Mining Pool Centralized Revisit and...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Kundu Chen Jie Luo State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Mining pools are fundamental infrastructure in most public blockchain networks that utilize Proof-of-Work consensus, ensuring that miners receive stable rewards and contribute to the computational power and storage of the network. However, mining pools also introduce centralization to the network, which increases the risk of security issues such as bribe attacks and censorship. To address the issue, this paper proposes a decentralized mining pool schema SSMP. Firstly, we distinguish between two aspects of centralization in mining pools, which are data determination, and block ownership, and suggest solutions to mitigate these issues. The proposed method utilizes anonymous broadcasting for a random leader selection schema to ensure each participating miner has an equal possibility to generate the block data. Additionally, a special threshold signature design prevents the leader miner from controlling the valid block solely of the mining pool through bribery. To validate our approach, we theoretically verify the security of the proposed method and compare it to traditional centralized mining pools. In summary, SSMP withholds the essential properties of the decentralized mining pools that maintain efficiency.

关键词： Privacy Voting Proof of Work Censorship Blockchains Safety Encryption

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Investigating the Impact of Bug Dependencies on Bug-Fixing Time Prediction 17

Investigating the Impact of Bug Dependencies on Bug-Fixing T...

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17th ACM/IEEE International Symposium on Empirical software engineering and Measurement, ESEM 2023

作者： Li, Chenglin Zhao, Yangyang Yang, Yibiao Zhou, Yuming Nie, Liming Ding, Zuohua School of Computer Science and Technology Zhejiang Sci-Tech University Hangzhou China Nanjing University State Key Lab for Novel Software Technology Nanjing China School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (纸本)9781665452236

Background: Bug dependencies refer to the link relationships between bugs and related issues, which are commonly observed in software evolution. It has been found that bugs with bug dependencies often take longer time to be resolved than other bugs without any dependencies. Despite the potential impact of bug dependencies on bug-fixing time, previous studies use traditional metrics without considering bug dependencies to build bug-fixing time prediction models. As a result, there is currently little empirical evidence to support the use of bug dependencies in improving prediction accuracy. Aims: We aim to conduct a comprehensive empirical study to investigate the value of considering bug dependencies for bug-fixing time prediction. Method: We define a set of bug dependency metrics based on bug dependencies. We first investigate the correlation between bug dependency metrics and bug-fixing time to investigate whether bugs with more complex dependencies are more time-consuming to be fixed. Next, we employ principal component analysis to study whether bug dependency metrics capture additional dimensions of a bug compared to traditional metrics. Finally, we build multivariate prediction models to explore whether considering bug dependencies can improve the effectiveness of bug-fixing time prediction. Results: The experimental results suggest that: (1) bugs with more complex dependencies require more time to be fixed;(2) bug dependency metrics are complementary to traditional metrics;(3) considering bug dependencies can improve the effectiveness of bug-fixing time prediction. Conclusions: These findings highlight the importance of considering bug dependencies in bug-fixing time prediction, and provide valuable insights into the potential impact of bug dependencies on software development processes. © 2023 IEEE.

关键词： Principal component analysis

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TRANSFORMER-PATCHER: ONE MISTAKE WORTH ONE NEURON 11

TRANSFORMER-PATCHER: ONE MISTAKE WORTH ONE NEURON

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11th International Conference on Learning Representations, ICLR 2023

作者： Huang, Zeyu Shen, Yikang Zhang, Xiaofeng Zhou, Jie Rong, Wenge Xiong, Zhang State Key Laboratory of Software Development Environment Beihang University China Sino-French Engineer School Beihang University China School of Computer Science and Engineering Beihang University China Mila University of Montreal Canada WeChat AI Tencent Inc. China

Large Transformer-based Pretrained Language Models (PLMs) dominate almost all Natural Language Processing (NLP) tasks. Nevertheless, they still make mistakes from time to time. For a model deployed in an industrial environment, fixing these mistakes quickly and robustly is vital to improve user experiences. Previous works formalize such problems as Model Editing (ME) and mostly focus on fixing one mistake. However, the one-mistake-fixing scenario is not an accurate abstraction of the real-world challenge. In the deployment of AI services, there are ever-emerging mistakes, and the same mistake may recur if not corrected in time. Thus a preferable solution is to rectify the mistakes as soon as they appear nonstop. Therefore, we extend the existing ME into Sequential Model Editing (SME) to help develop more practical editing methods. Our study shows that most current ME methods could yield unsatisfying results in this scenario. We then introduce Transformer-Patcher, a novel model editor that can shift the behavior of transformer-based models by simply adding and training a few neurons in the last Feed-Forward Network layer. Experimental results on both classification and generation tasks show that Transformer-Patcher can successively correct up to thousands of errors (Reliability) and generalize to their equivalent inputs (Generality) while retaining the model's accuracy on irrelevant inputs (Locality). Our method outperforms previous fine-tuning and HyperNetwork-based methods and achieves state-of-the-art performance for Sequential Model Editing (SME). The code is available at https://***/ZeroYuHuang/Transform er-Patcher. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Network layers

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Towards Fully Decoupled End-to-End Person Search

Towards Fully Decoupled End-to-End Person Search

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2023 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2023

作者： Zhang, Pengcheng Bai, Xiao Zheng, Jin Ning, Xin Jiangxi Research Institute Beihang University School of Computer Science and Engineering State Key Laboratory of Software Development Environment Beijing China Institute of Semiconductors Chinese Academy of Sciences Cognitive Computing Technology Joint Laboratory Wave Group Beijing China

ISBN: (纸本)9798350382204

End-to-end person search aims to jointly detect and re-identify a target person in raw scene images with a unified model. The detection task unifies all persons while the re-id task discriminates different identities, resulting in conflict optimal objectives. Existing works proposed to decouple end-to-end person search to alleviate such conflict. Yet these methods are still sub-optimal on one or two of the sub-tasks due to their partially decoupled models, which limits the overall person search performance. In this paper, we propose to fully decouple person search towards optimal person search. A task-incremental person search network is proposed to incrementally construct an end-to-end model for the detection and re-id sub-task, which decouples the model architecture for the two sub-tasks. The proposed task-incremental network allows task-incremental training for the two conflicting tasks. This enables independent learning for different objectives thus fully decoupled the model for personsearch. Comprehensive experimental evaluations demonstrate the effectiveness of the proposed fully decoupled models for end-to-end person search. © 2023 IEEE.

关键词： computer vision

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