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Quasi-Developable B-Spline Surface Design with Control Rulings

Journal of Computer Science & technology 2022年第5期37卷 1221-1238页

作者： Zi-Xuan Hu Peng-Bo Bo Cai-Ming Zhang School of Computer Science and Technology Harbin Institute of TechnologyWeihai 264209China Shandong Co-Innovation Center of Future Intelligent Computing Yantai 264005China Shandong Province Key Laboratory of Digital Media Technology Shandong University of Finance and EconomicsJinan 250061China

We propose a method for generating a ruled B-spline surface fitting to a sequence of pre-defined ruling lines and the generated surface is required to be ***,the terminal ruling lines are treated as hard *** from existing methods that compute a quasi-developable surface from two boundary curves and cannot achieve explicit ruling control,our method controls ruling lines in an intuitive way and serves as an effective tool for computing quasi-developable surfaces from freely-designed *** treat this problem from the point of view of numerical optimization and solve for surfaces meeting the distance error tolerance allowed in *** performance and the efficacy of the proposed method are demonstrated by the experiments on a variety of models including an application of the method for path planning in 5-axis computer numerical control(CNC)flank milling.

关键词： developable surface surface modeling B-spline surface numerical optimization

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Accelerating Blake3 in RISC-V 2

Accelerating Blake3 in RISC-V

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2nd International Conference on computing, Communication, Perception and Quantum technology, CCPQT 2023

作者： Zou, Xiaofeng Peng, Yuanxi Li, Tuo Kong, Lingjun Pang, Zhaochun Zhang, Lu National University of Defense Technology Institute for Quantum Information State Key Laboratory of High Performance Computing Changsha China Cloud Computing Equipment Innovation Center Second Research Institute Jinan China

ISBN: (纸本)9798350342697

This paper proposes a novel hardware design to accelerate the Blake3 hash algorithm, a cryptographic hash function, on RISC-V architecture, using both vector extension (V1.0) and a specially designed Blake3 instruction. The design leverages the parallelism and simplicity of BLAKE3 to achieve high throughput and low latency, implementing the core compression function using a custom instruction set architecture that supports vector operations and constant computing. The Blake3 compression function is speed up in architecture based on hardware/software co-design. The design achieves a speed-up of 10 times for the compression function compare with a single core of 2.7GHz Xeon 8280 and 10% faster than an AVX512 implementation. © 2023 IEEE.

关键词： Hash functions

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Stable Learning via Triplex Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 5267-5276页

作者： Yang, Shuai Jiang, Tingting Dang, Qianlong Gu, Lichuan Wu, Xindong Anhui Agricultural University School of Information and Artificial Intelligence Hefei230036 China Anhui Provincial Engineering Research Center for Agricultural Information Perception and Intelligent Computing Hefei230036 China Northwest A & F University College of Science Yangling712100 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China Hefei230601 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China

Stable learning aims to learn a model that generalizes well to arbitrary unseen target domain by leveraging a single source domain. Recent advances in stable learning have focused on balancing the distribution of confounders for each feature to eliminate spurious correlations. However, previous studies treat all features equally without considering the difficulties of confounder balancing associated with different features, and regard irrelevant features as confounders, deteriorating generalization performance. To tackle these issues, this article proposes a novel triplex learning (TriL) based stable learning algorithm, which performs sample reweighting, causal feature selection, and representation learning to remove spurious correlations. Specifically, first, TriL adaptively assigns weights to the confounder balancing term of each feature in accordance with the difficulties of confounder balancing, and aligns the confounder distribution of each feature by learning a group of sample weights. Second, TriL integrates the sample weights into a weighted cross-entropy model to compute causal effects of features for excluding irrelevant features from the confounder set. Finally, TriL relearns a set of sample weights and uses them to guide a new supervised dual-autoencoder containing two classifiers to learn feature representations. TriL forces the results of two classifiers to remain consistent for removing spurious correlations by using a cross-classifier consistency regularization. Extensive experiments on synthetic and two real-world datasets show the superiority of TriL compared with seven methods. © 2024 IEEE.

关键词： Feature Selection

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SLDT: Sequential Latent Document Transformer for Multilingual Document-based Dialogue 3

SLDT: Sequential Latent Document Transformer for Multilingua...

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3rd Workshop on Document-grounded Dialogue and Conversational Question Answering, DialDoc 2023, co-located with ACL 2023

作者： Ma, Zhanyu Liu, Zeming Ye, Jian Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences China Research Center for Social Computing and Information Retrieval Harbin Institute of Technology Harbin China Beijing Key Laboratory of Mobile Computing and Pervasive Device China

ISBN: (纸本)9781959429982

Multilingual document-grounded dialogue, where the system is required to generate responses based on both the conversation multilingual context and external knowledge sources. Traditional pipeline methods for knowledge identification and response generation, while effective in certain scenarios, suffer from error propagation issues and fail to capture the interdependence between these two sub-tasks. To overcome these challenges, we propose the application of the SLDT method, which treats passage-knowledge selection as a sequential decision process rather than a single-step decision process. We achieved the winner 3rd in dialdoc 2023 and we also validated the effectiveness of our method on other datasets. The ablation experiment also shows that our method significantly improves the basic model compared to other methods. © 2023 Association for Computational Linguistics.

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Boosting Kernel Search Optimizer with Slime Mould Foraging Behavior for Combined Economic Emission Dispatch Problems

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Journal of Bionic Engineering 2023年第6期20卷 2863-2895页

作者： Ruyi Dong Lixun Sun Long Ma Ali Asghar Heidari Xinsen Zhou Huiling Chen College of Information and Control Engineering Jilin Institute of Chemical TechnologyJilin132000China School of Surveying and Geospatial Engineering College of EngineeringUniversity of TehranTehran***Iran Key Laboratory of Intelligent Informatics for Safety&Emergency of Zhejiang Province Wenzhou UniversityWenzhou325000China

Reducing pollutant emissions from electricity production in the power system positively impacts the control of greenhouse gas *** kernel search optimizer(BKSO)is introduced in this research to solve the combined economic emission dispatch(CEED)*** by the foraging behavior in the slime mould algorithm(SMA),the kernel matrix of the kernel search optimizer(KSO)is *** proposed BKSO is superior to the standard KSO in terms of exploitation ability,robustness,and convergence *** CEC2013 test function is used to assess the improved KSO's performance and compared to 11 well-known optimization *** performs better in statistical results and convergence *** the same time,BKSO achieves better fuel costs and fewer pollution emissions by testing with four real CEED cases,and the Pareto solution obtained is also better than other *** on the experimental results,BKSO has better performance than other comparable MAs and can provide more economical,robust,and cleaner solutions to CEED problems.

关键词： Combined economic emission dispatch Kernel search optimization Slime mould algorithm Valve point effect Greenhouse gases

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Congestion Control Method for Campus Opportunity Network Based on Ant Colony Algorithm

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Chinese Journal of Electronics 2025年第2期34卷 576-585页

作者： Li Peng Cao Yumei Jia Huan Wang Xiaoming Wu Xiaojun School of Computer Science Shaanxi Normal University Xi’an 710119 China Key Laboratory of Intelligent Computing and Service Technology for Folk Song Ministry of Culture and Tourism Xi’an 710119 China Key Laboratory of Modern Teaching Technology Ministry of Education Xi’an 710062 China Engineering Laboratory of Teaching Information Technology of Shaanxi Province Xi’an 710119 China Xi’an Key Laboratory of Cultural Tourism Resources Development and Utilization Xi’an 710062 China

Due to the limited storage resources of portable devices, congestion control has become a hot direction in opportunity networks. To address the issue of heavy loads on certain nodes, which can impact routing efficiency and overall network performance, this paper proposes a load-balancing opportunity routing algorithm based on ant colony optimization (LBOR) in a campus environment. The congestion status is represented by the ratio of message drop receptions within a certain period and the occupancy of the cache. Path selection is based on the concentration of pheromones and the pheromones on the path are updated when a data transmission is completed. In the event of congestion, the algorithm prevents a large amount of data from entering the node and unloads the data to other nodes, even if they are not the optimal relay nodes. Experimental results demonstrate that the proposed algorithm effectively improves data transmission success rates, reduces network loads, and decreases the number of packet losses, especially under low latency conditions.

关键词： Opportunity network Routing protocol Congestion control Cache management

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Long Video Micro-expression Spotting Based On OCC Theory

Long Video Micro-expression Spotting Based On OCC Theory

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Liu, BingTong Zhang, ZheYuan Zhou, Ju Wang, HanPu Chen, Tong Southwest University Institute of Legal Psychology and Intelligent Computing Chongqing400715 China Chongqing Key Laboratory of Non-Linear Circuit and Intelligent Information Processing Chongqing400715 China Southwest University School of Electronic and Information Engineering Chongqing400715 China

ISBN: (纸本)9798350337488

The generation of micro-expression (ME) is unconscious and unaffected by human subjective consciousness. It can reveal hidden emotions in humans, which has important applications in the domains of psychological diagnosis, lie detection, and so on. ME detection, which aims to locate ME apex frames in long videos, is the foundation of ME recognition. In this paper, we propose a ME detection method based on One-Class Classification (OCC) theory for long videos. The method is the first to use neutral expression frames to detect MEs, which addresses the problem of insufficient training samples in ME detection. We design a geometric motion feature based on the prior knowledge from the FACS and utilize it in conjunction with the features extracted by CNN to train separate one-class classifiers for ME detection. Finally, we evaluate the proposed method on publicly available datasets CASME I, CASME II, SAMM, and a mixed database comprising data from the three datasets, to verify the feasibility of the proposed method in both single-database and cross-database ME detection. The experimental results show that our method outperforms three other ME detection methods, demonstrating its ability to more accurately locate ME apex frames in long videos. © 2023 IEEE.

关键词： Micro-expression Detection OCC One-Class Classifier

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Learn over Past, Evolve for Future: Forecasting Temporal Trends for Fake News Detection 61

Learn over Past, Evolve for Future: Forecasting Temporal Tre...

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61st Annual Meeting of the Association for Computational Linguistics, ACL 2023

作者： Hu, Beizhe Sheng, Qiang Cao, Juan Zhu, Yongchun Wang, Danding Wang, Zhengjia Jin, Zhiwei Key Lab of Intelligent Information Processing Chinese Academy of Sciences Institute of Computing Technology China University of Chinese Academy of Sciences China ZhongKeRuijian Technology Co. Ltd. China

ISBN: (纸本)9781959429685

Fake news detection has been a critical task for maintaining the health of the online news ecosystem. However, very few existing works consider the temporal shift issue caused by the rapidly-evolving nature of news data in practice, resulting in significant performance degradation when training on past data and testing on future data. In this paper, we observe that the appearances of news events on the same topic may display discernible patterns over time, and posit that such patterns can assist in selecting training instances that could make the model adapt better to future data. Specifically, we design an effective framework FTT (Forecasting Temporal Trends), which could forecast the temporal distribution patterns of news data and then guide the detector to fast adapt to future distribution. Experiments on the real-world temporally split dataset demonstrate the superiority of our proposed framework. The code is available at https://***/ICTMCG/FTT-ACL23. © ACL *** rights reserved.

关键词： Forecasting

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Choose Your Simulator Wisely: A Review on Open-source Simulators for Autonomous Driving

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on intelligent Vehicles 2024年第5期9卷 1-19页

作者： Li, Yueyuan Yuan, Wei Zhang, Songan Yan, Weihao Shen, Qiyuan Wang, Chunxiang Yang, Ming Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Innovation Center of Intelligent Connected Vehicle Global Institute of Future Technology Shanghai Jiao Tong University Shanghai China Global Institute of Future Technology Shanghai Jiao Tong University Shanghai China

Simulators play a crucial role in autonomous driving, offering significant time, cost, and labor savings. Over the past few years, the number of simulators for autonomous driving has grown substantially. However, there is a growing concern about the validity of algorithms developed and evaluated in simulators, indicating a need for a thorough analysis of the development status of the simulators. To address existing gaps in research, this paper undertakes a comprehensive review of the history of simulators, proposes a utility-based taxonomy, and investigates the critical issues within open-source simulators. Analysis of the past thirty years' development trajectory reveals a trend characterized by an increase in open-source simulators and an expansion of their functionality scope. The categorization of simulators based on feature functionalities delineates five primary classes: traffic flow, sensory data, driving policy, vehicle dynamics, and comprehensive simulators. Furthermore, the paper identifies critical unresolved issues in open-source simulators, including concerns regarding the fidelity of sensory data, representation of traffic scenarios, and accuracy in vehicle dynamics simulation, all of which have the potential to undermine experimental confidence. Additionally, challenges in data format inconsistency, labor-intensive map construction processes, sluggish step updating, and insufficient support for Hardware-In-the-Loop testing are discussed as hindrances to experimental efficiency. In light of these findings, the survey furnishes task-oriented recommendations to aid in the selection of simulators, taking into account factors such as accessibility, maintenance status, and quality, while highlighting the inherent limitations of existing open-source simulators in validating algorithms and facilitating real-world experimentation. IEEE

关键词： Autonomous vehicles

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AutoSketch: Automatic Sketch-Oriented Compiler for Query-driven Network Telemetry 21

AutoSketch: Automatic Sketch-Oriented Compiler for Query-dri...

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21st USENIX Symposium on Networked Systems Design and Implementation, NSDI 2024

作者： Sun, Haifeng Huang, Qun Sun, Jinbo Wang, Wei Li, Jiaheng Li, Fuliang Bao, Yungang Yao, Xin Zhang, Gong National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China Institute of Computing Technology CAS China Northeastern University China Huawei Theory Department

ISBN: (纸本)9781939133397

Recent network telemetry witnesses tremendous progress in two directions: query-driven telemetry that targets expressiveness as the primary goal, and sketch-based algorithms that address resource-accuracy trade-offs. In this paper, we propose AutoSketch that aims to integrate the advantages of both classes. In a nutshell, AutoSketch automatically compiles high-level operators into sketch instances that can be readily deployed with low resource usage and incur limited accuracy drop. However, there remains a gap between the expressiveness of high-level operators and the underlying realization of sketch algorithms. AutoSketch bridges this gap in three aspects. First, AutoSketch extends its interface derived from existing query-driven telemetry such that users can specify the desired telemetry accuracy. The specified accuracy intent will be utilized to guide the compiling procedure. Second, AutoSketch leverages various techniques, such as syntax analysis and performance estimation, to construct efficient sketch instances. Finally, AutoSketch automatically searches for the most suitable parameter configurations that fulfill the accuracy intent with minimum resource usage. Our experiments demonstrate that AutoSketch can achieve high expressiveness, high accuracy, and low resource usage compared to state-of-the-art telemetry solutions. © 2024 Proceedings of the 21st USENIX Symposium on Networked Systems Design and Implementation, NSDI 2024. All rights reserved.

关键词： Economic and social effects

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