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Journal of Computer Science & Technology 2024年第1期39卷 192-210页

作者：王罡李翔郭子义殷大伟马帅 State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Data Science and Engineering East China Normal UniversityShanghai 200062China *** Inc.Beijing 100176China Baidu Inc.Beijing 100085China

Scene-based recommendation has proven its usefulness in E-commerce,by recommending commodities based on a given ***,scenes are typically unknown in advance,which necessitates scene discovery for *** this article,we study scene discovery for E-commerce *** first formalize a scene as a set of commodity cate-gories that occur simultaneously and frequently in real-world situations,and model an E-commerce platform as a heteroge-neous information network(HIN),whose nodes and links represent different types of objects and different types of rela-tionships between objects,*** then formulate the scene mining problem for E-commerce as an unsupervised learning problem that finds the overlapping clusters of commodity categories in the *** solve the problem,we pro-pose a non-negative matrix factorization based method SMEC(Scene Mining for E-Commerce),and theoretically prove its *** six real-world E-commerce datasets,we finally conduct an extensive experimental study to evaluate SMEC against 13 other methods,and show that SMEC consistently outperforms its competitors with regard to various evaluation measures.

关键词： graph clustering E-commerce heterogeneous information network(HIN) scene mining

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Predicting accepted pull requests in GitHub

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Science China(Information Sciences) 2021年第7期64卷 241-243页

作者： Jing JIANG Jiateng ZHENG Yun YANG Li ZHANG Jie LUO State Key Laboratory of Software Development Environment Beihang University

Dear editor,Various open source software hosting sites, such as Github,provide support for pull-based development and allow developers to flexibly and efficiently make contributions [1].

关键词： None1 None2 None3 None4 None5

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Combining KNN with AutoEncoder for Outlier Detection

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Journal of Computer Science & Technology 2024年第5期39卷 1153-1166页

作者： Shu-Zheng Liu Shuai Ma Han-Qing Chen Li-Zhen Cui Jie Ding State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Software Shandong UniversityJinan 250100China Joint SDU-NTU Centre for Artificial Intelligence Research Shandong UniversityJinan 250100China School of Computer Science Jiangsu University of Science and TechnologyZhenjiang 212003China

K-nearest neighbor(KNN)is one of the most fundamental methods for unsupervised outlier detection because of its various advantages,e.g.,ease of use and relatively high ***,most data analytic tasks need to deal with high-dimensional data,and the KNN-based methods often fail due to“the curse of dimensionality”.AutoEncoder-based methods have recently been introduced to use reconstruction errors for outlier detection on high-dimensional data,but the direct use of AutoEncoder typically does not preserve the data proximity relationships well for outlier *** this study,we propose to combine KNN with AutoEncoder for outlier ***,we propose the Nearest Neighbor AutoEncoder(NNAE)by persevering the original data proximity in a much lower dimension that is more suitable for performing ***,we propose the K-nearest reconstruction neighbors(K NRNs)by incorporating the reconstruction errors of NNAE with the K-distances of KNN to detect ***,we develop a method to automatically choose better parameters for optimizing the structure of ***,using five real-world datasets,we experimentally show that our proposed approach NNAE+K NRN is much better than existing methods,i.e.,KNN,Isolation Forest,a traditional AutoEncoder using reconstruction errors(AutoEncoder-RE),and Robust AutoEncoder.

关键词： outlier detection AutoEncoder K-nearest neighbor(KNN) unsupervised learning

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Flow-Graph Based Optimization of Containerized Resource Scheduling: An Efficient Scheduling Algorithm for Kubernetes Clusters

Flow-Graph Based Optimization of Containerized Resource Sche...

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2024 International Conference on Intelligent Computing and Robotics, ICICR 2024

作者： Zhang, Le Ji, Xin Peng, Fang Li, Junting Chen, Yiting Zhang, Kui Big Data Center State Grid Corporation of China Beijing China State Key Laboratory of Software Development Environment Beihang University Beijing China

ISBN: (纸本)9798350372274

With the widespread adoption of containerization technology and the evolution of cloud computing, efficient scheduling of containerized applications in cluster environments has become an increasingly important area of research. Kubernetes, as a mainstream container orchestration tool, faces several challenges with its default queue-based and greedy scheduling algorithm, especially in large-scale deployments, such as high latency and low scheduling robustness. This paper introduces a flow-graph-based optimization method for containerized resource scheduling, modeling the scheduling problem as a Minimum Cost Maximum Flow problem to achieve global optimization. Comparative experiments with traditional queue-based and random scheduling algorithms demonstrate the superiority of the proposed method in terms of balanced resource utilization, robustness of scheduling outcomes, and reduced algorithmic latency. This study provides a more efficient resource scheduling strategy for container orchestration tools like Kubernetes and offers a new perspective on resource management in large-scale containerized application environments. ©2024 IEEE.

关键词： Scheduling algorithms

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ICCG:low-cost and efficient consistency with adaptive synchronization for metadata replication

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Frontiers of Computer Science 2025年第1期19卷 53-70页

作者： Chenhao ZHANG Liang WANG Jing SHANG Zhiwen XIAO Limin XIAO Meng HAN Bing WEI Runnan SHEN Jinquan WANG State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China School of Cyberspace Security Hainan UniversityHaikou 570228China China Mobile Information Technology Center Beijing 100033China

The rapid growth in the storage scale of wide-area distributed file systems (DFS) calls for fast and scalable metadata management. Metadata replication is the widely used technique for improving the performance and scalability of metadata management. Because of the POSIX requirement of file systems, many existing metadata management techniques utilize a costly design for the sake of metadata consistency, leading to unacceptable performance overhead. We propose a new metadata consistency maintenance method (ICCG), which includes an incremental consistency guaranteed directory tree synchronization (ICGDT) and a causal consistency guaranteed replica index synchronization (CCGRI), to ensure system performance without sacrificing metadata consistency. ICGDT uses a flexible consistency scheme based on the state of files and directories maintained through the conflict state tree to provide an incremental consistency for metadata, which satisfies both metadata consistency and performance requirements. CCGRI ensures low latency and consistent access to data by establishing a causal consistency for replica indexes through multi-version extent trees and logical time. Experimental results demonstrate the effectiveness of our methods. Compared with the strong consistency policies widely used in modern DFSes, our methods significantly improve the system performance. For example, in file creation, ICCG can improve the performance of directory tree operations by at least 36.4 times.

关键词： metadata management metadata replication consistency directory tree replica index

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A survey for light field super-resolution

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High-Confidence Computing 2024年第1期4卷 118-129页

作者： Mingyuan Zhao Hao Sheng Da Yang Sizhe Wang Ruixuan Cong Zhenglong Cui Rongshan Chen Tun Wang Shuai Wang Yang Huang Jiahao Shen State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Key Laboratory of Data Science and Intelligent Computing International Innovation InstituteBeihang UniversityHangzhou 311115China State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Compared to 2D imaging data,the 4D light field(LF)data retains richer scene’s structure information,which can significantly improve the computer’s perception capability,including depth estimation,semantic segmentation,and LF ***,there is a contradiction between spatial and angular resolution during the LF image acquisition *** overcome the above problem,researchers have gradually focused on the light field super-resolution(LFSR).In the traditional solutions,researchers achieved the LFSR based on various optimization frameworks,such as Bayesian and Gaussian *** learning-based methods are more popular than conventional methods because they have better performance and more robust generalization *** this paper,the present approach can mainly divided into conventional methods and deep learning-based *** discuss these two branches in light field spatial super-resolution(LFSSR),light field angular super-resolution(LFASR),and light field spatial and angular super-resolution(LFSASR),***,this paper also introduces the primary public datasets and analyzes the performance of the prevalent approaches on these ***,we discuss the potential innovations of the LFSR to propose the progress of our research field.

关键词： Light field super-resolution Convolutional neural network Transformer Sub-aperture image Epipolar-plane image

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Effective Training of PINNs by Combining CMA-ES with Gradient Descent 13

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

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Liu, Lin Yuan, Yuan School of Computer Science and Engineering Beihang University Beijing100191 China State Key Laboratory of Software Development Environment Zhongguancun Laboratory Beijing100191 China

ISBN: (纸本)9798350308365

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. © 2024 IEEE.

关键词： Optimization algorithms

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A Virtual Hand Based Manipulation Learning Framework for Dexterous Hand 14

A Virtual Hand Based Manipulation Learning Framework for Dex...

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14th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2023

作者： Zhou, Luyue Hu, Yumeng Lin, Mengxiang State Key Laboratory of Software Development Environment China Beihang University School of Mechanical Engineering and Automation China

ISBN: (纸本)9798350324228

Dexterous hand programming is considered to be generally difficult suffering from high degree of freedom. Vision-based manipulation learning provides an efficient way to support automatic programming by human demonstration. However, complex visual perception is tightly coupled with the kinematics structure of dexterous hand in existing solutions, which makes it hard to migrate the developed algorithm from one kind of dexterous hand to another due to their kinematic difference. To address the issue, we introduce an intermediary called virtual hand and a new paradigm of motion mapping, serving as the link between human hand and dexterous hand. The virtual hand is generated in real time by hand pose estimation to track the motion of human hand. Then the joint configurations of dexterous hand and robotic arm can be calculated using geometry and optimization method based on the motion mapping paradigm we proposed. The method we introduced decouples the visual perception and kinematics calculation, which brings flexibility and low cost. The results of our preliminary experiments show the effectiveness of our method. © 2023 IEEE.

关键词： Kinematics

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Self-Supervised Exploration via Temporal Inconsistency in Reinforcement Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5530-5539页

作者： Gao, Zijian Xu, Kele Zhai, Yuanzhao Ding, Bo Feng, Dawei Mao, Xinjun Wang, Huaimin National University of Defense Technology School of Computer Changsha410000 China State Key Laboratory of Complex and Critical Software Environment Beijing100191 China

In sparse extrinsic reward settings, reinforcement learning remains a challenge despite increasing interest in this field. Existing approaches suggest that intrinsic rewards can alleviate issues caused by reward sparsity. However, many studies overlook the critical role of temporal information, essential for human curiosity. This article introduces a novel intrinsic reward mechanism inspired by human learning processes, where curiosity is evaluated by comparing current observations with historical knowledge. Our method involves training a self-supervised prediction model, periodically saving snapshots of the model parameters, and employing the nuclear norm to assess the temporal inconsistency between predictions from different snapshots as intrinsic rewards. Additionally, we propose a variational weighting mechanism to adaptively assign weights to the snapshots, enhancing the model's robustness and performance. Experimental results across various benchmark environments demonstrate the efficacy of our approach, which outperforms other state-of-the-art methods without incurring additional training costs and exhibits higher noise tolerance. Our findings indicate that leveraging temporal information in intrinsic rewards can significantly improve exploration performance, motivating future research to develop more robust and accurate reward systems for reinforcement learning. © 2024 IEEE.

关键词： Reinforcement learning

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Hyperbolic Graph Neural Network for Temporal Knowledge Graph Completion 30

Hyperbolic Graph Neural Network for Temporal Knowledge Graph...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Li, Yancong Zhang, Xiaoming Cui, Ying Ma, Shuai School of Cyber Science and Technology Beihang University Beijing China State Key Laboratory of Software Development Environment Beihang University Beijing China

ISBN: (纸本)9782493814104

Temporal Knowledge Graphs (TKGs) represent a crucial source of structured temporal information and exhibit significant utility in various real-world applications. However, TKGs are susceptible to incompleteness, necessitating Temporal Knowledge Graph Completion (TKGC) to predict missing facts. Existing models have encountered limitations in effectively capturing the intricate temporal dynamics and hierarchical relations within TKGs. To address these challenges, HyGNet is proposed, leveraging hyperbolic geometry to effectively model temporal knowledge graphs. The model comprises two components: the Hyperbolic Gated Graph Neural Network (HGGNN) and the Hyperbolic Convolutional Neural Network (HCNN). HGGNN aggregates neighborhood information in hyperbolic space, effectively capturing the contextual information and dependencies between entities. HCNN interacts with embeddings in hyperbolic space, effectively modeling the complex interactions between entities, relations, and timestamps. Additionally, a consistency loss is introduced to ensure smooth transitions in temporal embeddings. The extensive experimental results conducted on four benchmark datasets for TKGC highlight the effectiveness of HyGNet. It achieves state-of-the-art performance in comparison to previous models, showcasing its potential for real-world applications that involve temporal reasoning and knowledge prediction. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Geometry

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