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Constructing self-supporting surfaces with planar quadrilateral elements

Computational Visual Media 2022年第4期8卷 571-583页

作者： Long Ma Sidan Yao Jianmin Zheng Yang Liu Yuanfeng Zhou Shi-Qing Xin Ying He School of Software Shandong UniversityJinan 250101China School of Computer Science and Engineering Nanyang Technological UniversitySingapore 639798Singapore Internet Graphics Group Microsoft Research AsiaBeijing 100080China School of Computer Science and Technology Shandong UniversityQingdao 266237China

We present a simple yet effective method for constructing 3D self-supporting surfaces with planar quadrilateral(PQ)*** with a triangular discretization of a self-supporting surface,we firstcompute the principal curvatures and directions of each triangular face using a new discrete differential geometryapproach,yielding more accurate results than existing ***,we smooth the principal direction field to reduce the number of ***,we partition all faces into two groups in terms of principalcurvature *** each face with small curvature difference,we compute a stretch matrix that turns the principal directions into a pair of conjugate *** the remaining triangular faces,we simply keep their smoothed principal ***,applying a mixed-integer programming solver to the mixed principal and conjugate direction field,we obtain a planar quadrilateral *** results show that our method is computationally efficient and can yield high-quality PQ meshes that well approximate the geometry of the input surfaces and maintain their self-supporting properties.

关键词： self-supporting surfaces planar quadri-lateral(PQ)meshes conjugate direction fields(CDFs)

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General Optimal Trajectory Planning:Enabling Autonomous Vehicles with the Principle of Least Action

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Engineering 2024年第2期33卷 63-76页

作者： Heye Huang Yicong Liu Jinxin Liu Qisong Yang Jianqiang Wang David Abbink Arkady Zgonnikov School of Vehicle and Mobility Tsinghua UniversityBeijing 100084China Department of Cognitive Robotics Faculty of MechanicalMaritime and Materials Engineering(3mE)Delft University of TechnologyDelft 2628Netherlands Department of Software Technology Faculty of Electrical EngineeringMathematics and Computer ScienceDelft University of TechnologyDelft 2628Netherlands Xi’an Institute of High-Technology Xi’an 710051China

This study presents a general optimal trajectory planning(GOTP)framework for autonomous vehicles(AVs)that can effectively avoid obstacles and guide AVs to complete driving tasks safely and ***,we employ the fifth-order Bezier curve to generate and smooth the reference path along the road *** coordinates are then transformed to achieve the curvature continuity of the generated *** the road constraints and vehicle dynamics,limited polynomial candidate trajectories are generated and smoothed in a curvilinear coordinate ***,in selecting the optimal trajectory,we develop a unified and auto-tune objective function based on the principle of least action by employing AVs to simulate drivers’behavior and summarizing their manipulation characteristics of“seeking benefits and avoiding losses.”Finally,by integrating the idea of receding-horizon optimization,the proposed framework is achieved by considering dynamic multi-performance objectives and selecting trajectories that satisfy feasibility,optimality,and *** simulations and experiments are performed,and the results demonstrate the framework’s feasibility and effectiveness,which avoids both dynamic and static obstacles and applies to various scenarios with multi-source interactive traffic ***,we prove that the proposed method can guarantee real-time planning and safety requirements compared to drivers’manipulation.

关键词： Autonomous vehicle Trajectory planning Multi-performance objectives Principle of least action

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Efficient Privacy Preserving Graph Neural Network for Node Classification 48

Efficient Privacy Preserving Graph Neural Network for Node C...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Pei, Xinjun Deng, Xiaoheng Tian, Shengwei Xue, Kaiping Central South University School of Computer Science and Engineering Changsha China Xinjiang University School of Software Wulumuqi China University of Science and Technology of China School of Cyber Science and Technology Hefei China

ISBN: (纸本)9781728163277

Graph Neural Networks (GNNs) as an emerging technique have shown excellent performance in a variety of fields, such as social networks and recommendation systems. However, GNNs may have to overcome privacy concerns as large amounts of information about their training datasets may be compromised. In this paper, we develop a privacy-preserving GNN to enforce privacy preservation, which utilizes a private Functional Mechanism (FM) to train the learning model. This mechanism perturbs the polynomial approximation of the objective function to enforce Differential Privacy (DP) in the GNN model. We show that our method can maximize the accuracy of the results with comparable prediction power to the unperturbed results while satisfying the privacy guarantees. © 2023 IEEE.

关键词： Polynomial approximation

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RENN: A Rule Embedding Enhanced Neural Network Framework for Temporal Knowledge Graph Completion 30

RENN: A Rule Embedding Enhanced Neural Network Framework for...

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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： Zong, Linlin Xie, Zhenrong Ma, Chi Liu, Xinyue Zhang, Xianchao Xu, Bo Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology China School of Computer Science and Technology Dalian University of Technology China Key Laboratory of Social Computing and Cognitive Intelligence Dalian University of Technology Ministry of Education China

ISBN: (纸本)9782493814104

Existing approaches encompass deep neural network-based methods for temporal knowledge graph embedding and rule-based logical symbolic reasoning. However, the former may not adequately account for structural dependencies between ***, the latter methods relies heavily on strict logical rule reasoning and lacks robustness in the face of fuzzy or noisy data. In response to these challenges, we present RENN, a groundbreaking framework that enhances temporal knowledge graph completion through rule embedding. RENN employs a three-step approach. First, it utilizes temporary random walk to extract temporal logic rules. Then, it pre-trains by learning embeddings for each logical rule and its associated relations, thereby enhancing the likelihood of existing quadruples and logical rules. Finally, it incorporates the embeddings of logical rules into the deep neural network. Our methodology has been validated through experiments conducted on various temporal knowledge graph models and datasets, consistently demonstrating its effectiveness and potential in improving temporal knowledge graph completion. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Knowledge graph

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CoExploreDS: Framing and Advancing Collaborative Design Space Exploration Between Human and AI 25

CoExploreDS: Framing and Advancing Collaborative Design Spac...

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2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Chen, Pei Yao, Jiayi Cheng, Zhuoyi Cai, Yichen Li, Jiayang You, Weitao Sun, Lingyun College of Computer Science and Technology Zhejiang University Hangzhou China School of Software Technology Zhejiang University Hangzhou China

ISBN: (纸本)9798400713941

In product design, effective design space exploration (DSE) is crucial for generating high-quality design ideas, requiring designers to possess broad knowledge and balance various constraints. As large-scale models thrive, AI has become an indispensable design collaborator by providing cross-domain knowledge and assistance with complex reasoning. To facilitate collaborative DSE between designers and AI, we frame and advance the design process through the problem-solution co-evolution model and design reasoning methods. A formative study was conducted to identify key strategies for the implementation. Then we developed CoExploreDS, a system that formalizes problems and solutions emerging in the human-AI collaborative design space into nodes. Using four reasoning methods, this system dynamically generates suggestions based on the ongoing design process. User studies confirmed that CoExploreDS significantly improves design quality and the human-AI collaboration experience. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Design space exploration Generative AI Human-AI collaboration

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Energy-Efficient Shop Scheduling Using Space-Cooperation Multi-Objective Optimization

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2024年第3期10卷 601-615页

作者： Ding, Jiepin Xia, Jun Yang, Yaning Zhou, Junlong Chen, Mingsong Li, Keqin East China Normal University MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application Shanghai200062 China Ningxia Normal University School of Physics and Electronic Information Engineering Guyuan756500 China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China State University of New York Department of Computer Science New PaltzNY12561 United States

Since Industry 5.0 emphasizes that manufacturing enterprises should raise awareness of social contribution to achieve sustainable development, more and more meta-heuristic algorithms are investigated to save energy in manufacturing systems. Although non-dominated sorting-based meta-heuristics have been recognized as promising multi-objective optimization methods for solving the energy-efficient flexible job shop scheduling problem (EFJSP), it is hard to guarantee the quality of the Pareto front (e.g., total energy consumption, makespan) due to the lack of population diversity. This is mainly because an improper individual comparison inevitably reduces population diversity, thus limiting exploration and exploitation abilities during population updates. To achieve efficient population evolution, this paper introduces a novel space-cooperation multi-objective optimization (SCMO) method that can effectively solve EFJSP to obtain scheduling schemes with better trade-offs. By cooperatively evaluating the similarity among individuals in both the decision space and objective space, we propose a space-cooperation population update method based on a three-vector representation that can accurately eliminate repetitive individuals to derive higher-quality Pareto solutions. To further improve search efficiency, we propose a difference-driven local search, which selectively changes the positions of operations with higher differences to search for neighbors effectively. Based on the Taguchi method, we conduct experiments to obtain a suitable parameter combination of SCMO. Comprehensive experimental results show that, compared to state-of-the-art methods, our SCMO method achieves the highest HV and NR and the lowest IGD, with an average of 0.990, 0.952, and 0.001, respectively. Meanwhile, compared to traditional local search approaches, our difference-driven local search obtains twice the HV on instance Mk12 and reduces the solving time from 1521s to 475s. © 2016 IEEE.

关键词： Job shop scheduling

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Biomedical Document-level Relation Extraction with Coreference and Anaphor Graphs

Biomedical Document-level Relation Extraction with Coreferen...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Li, Jiru Sun, Yuanyuan Yang, Zhihao Luo, Ling Wang, Lei Liu, Wei Zhang, Yin Xu, Bo Wang, Jian Yang, Yumeng Zhao, Zhehuan Lin, Hongfei Dalian University of Technology College of Computer Science and Technology Dalian China Academy of Military Medical Sciences Beijing China Dalian University of Technology School of Software Dalian China

ISBN: (纸本)9798350386226

Biomedical document-level relation extraction is a crucial technology for mining the biomedical relationships necessary for clinical diagnosis, treatment, and medical discovery. Although existing intrasentential relation extraction methods have achieved significant results, the complexity and scattered nature of information in biomedical literature require relation extraction techniques to effectively handle cross-sentence information. For example, existing methods have not been able to explicitly model the phenomena of coreference and anaphor in documents, thus affecting the model's understanding of complex semantics within the document. To address this issue, we propose a new document-level relation extraction model with coreference and anaphor graphs. By abstracting the document into an undirected graph that includes coreference and anaphor information, the framework effectively models the interactions between entities and leverages graph convolutional network in conjunction with pretrained language model to dynamically understand graph structures. Additionally, the shift from fine-grained entity-pair level to coarse-grained document-level training and inference significantly enhances the model's efficiency while maintaining high extraction performance. Extensive experiments demonstrate that our model achieves a 5.3% increase in F1-score over baseline models on the BioRED dataset with higher efficiency, confirming its effectiveness in handling relation extraction tasks in complex biomedical literature. © 2024 IEEE.

关键词： Semantics

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S2M: Converting Single-Turn to Multi-Turn Datasets for Conversational Question Answering 26

S2M: Converting Single-Turn to Multi-Turn Datasets for Conve...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Li, Baokui Zhang, Sen Zhang, Wangshu Chen, Yicheng Yang, Changlin Hu, Sen Xu, Teng Liu, Siye Li, Jiwei School of Software Technology Zhejiang University China Ant Group China College of Computer Science and Technology Zhejiang University China

ISBN: (纸本)9781643684369

Supplying data augmentation to conversational question answering (CQA) can effectively improve model performance. However, there is less improvement from single-turn datasets in CQA due to the distribution gap between single-turn and multi-turn datasets. On the other hand, while numerous single-turn datasets are available, we have not utilized them effectively. To solve this problem, we propose a novel method to convert single-turn datasets to multi-turn datasets. The proposed method consists of three parts, namely, a QA pair Generator, a QA pair Reassembler, and a question Rewriter. Given a sample consisting of context and single-turn QA pairs, the Generator obtains candidate QA pairs and a knowledge graph based on the context. The Reassembler utilizes the knowledge graph to get sequential QA pairs, and the Rewriter rewrites questions from a conversational perspective to obtain a multi-turn dataset S2M. Our experiments show that our method can synthesize effective training resources for CQA. Notably, S2M ranks 1st place on the QuAC leaderboard (https://***/) at the time of submission (Aug 24th, 2022). © 2023 The Authors.

关键词： Knowledge graph

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FedQMIX:Communication-efficient federated learning via multi-agent reinforcement learning

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High-Confidence Computing 2024年第2期4卷 96-104页

作者： Shaohua Cao Hanqing Zhang Tian Wen Hongwei Zhao Quancheng Zheng Weishan Zhang Danyang Zheng Qingdao Institute of Software College of Computer Science and TechnologyChina University of Petroleum(East China)Qingdao 266580China School of Computing and Artificial Intelligence Xipu CampusSouthwest Jiaotong UniversityChengdu 611756China

Since the data samples on client devices are usually non-independent and non-identically distributed(non-IID),this will challenge the convergence of federated learning(FL)and reduce communication *** paper proposes FedQMIX,a node selection algorithm based on multi-agent reinforcement learning(MARL),to address these ***,we observe a connection between model weights and data distribution,and a clustering algorithm can group clients with similar data distribution into the same ***,we propose a QMIX-based mechanism that learns to select devices from clustering results in each communication round to maximize the reward,penalizing the use of more communication rounds and thereby improving the communication efficiency of ***,experiments show that FedQMIX can reduce the number of communication rounds by 11%and 30%on the MNIST and CIFAR-10 datasets,respectively,compared to the baseline algorithm(Favor).

关键词： Communication efficient Federated learning MARL

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A Code Search Method Incorporating Code Annotations 19th

A Code Search Method Incorporating Code Annotations

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19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Li, Qi Liu, Jianxun Zhang, Xiangping School of Computer Science and Engineering Hunan University of Science and Technology Hunan Xiangtan411201 China Hunan Key Lab for Services Computing and Novel Software Technology Hunan University of Science and Technology Hunan Xiangtan411201 China

ISBN: (纸本)9783031545207

Code search is a technique for users to retrieve code snippets from the Code base using natural language, which is dedicated to retrieve the target code accurately and quickly to improve the efficiency of software development. The deep learning based code search technique greatly improves the accuracy of search by learning the relationship between code and query statements. Since it relies on the extracted code features, acquiring more code features is the key to quickly improve the search performance. However, most of the previous works have not taken code annotations into consideration. In this paper, we take code annotations as code features and apply them to code search, which is named ICA-CS (Code Search that Incorporates Code Annotations). In the method, firstly, the code features are embedded to get the corresponding vector representation. It is then processed by bidirectional LSTM (Long Short-Term Memory) network or multi-head attention respectively, followed by features fusion. And finally, the model is trained by joint embedding and using the minimised ranking loss function. As the experimental results show, on the evaluation metric MRR (mean reciprocal rank) compared to the state-of-the-art models DeepCS, SAN-CS, CARLCS-CNN and SelfAtt, the proposed model improves 48.96%, 17.11%, 41.01% and 13.07%, respectively. © ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Long short-term memory

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