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IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE)

作者： Xuyang Jiang Yuanxin Ouyang Zhuang Liu Wenge Rong Zhang Xiong State Key Laboratory of Software Development Environment Beihang University Beijing China Engineering Research Center of Advanced Computer Application Technology Ministry of Education Beihang University Beijing China

As an effective and emerging component of intelligent education, Knowledge Tracing(KT) achieves the combination of artificial intelligence and individualized learning, whose aim is to assess students’ mastery of knowledge concepts and assist in developing learning plans. Several existing KT models either use concepts sequence as input and evaluate students’ knowledge state or treat exercise as input to predict students’ future performance. In this paper, we introduce a constraint factor to extract concepts’ and exercises’ relation matrix, design three methods in representation learning, and propose a Multichannel Attention Networks based KT model(MAKT). Specifically, we restrict the co-occurrence relationship within a time window to extract the relation matrix and then train their representations via graph generative learning, graph contrastive learning, and matrix decomposition, respectively. In MAKT, a sliding window is implemented by multichannel where input sequence is sequentially lagged in turn by one position and attention mechanism is applied. We conduct experiments on several benchmark datasets and demonstrate that MAKT with concepts’ and exercises’ representation trained by matrix decomposition outperforms state-of-the-art models.

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High-accurate Numerical Simulation for Vortex Flow Over a Slender Missile at High Angle of Attack 33

High-accurate Numerical Simulation for Vortex Flow Over a Sl...

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33rd Congress of the International Council of the Aeronautical Sciences, ICAS 2022

作者： Gao, Chang-Hao Song, Wen-Ping Han, Shao-Qiang Lu, Kuan Wang, Yue Han, Zhong-Hua Wu, Bin Institute of Aerodynamic and Multidisciplinary Design Optimization National Key Laboratory of Science and Technology on Aerodynamic Design and Research College of Aeronautics Northwestern Polytechnical University Xi’an710072 China Tianfu Engineering-oriented Numerical & Software Innovation Center College of Computer Science Sichuan University Chengdu710071 China Shanghai Electro-mechanical Engineering Institute Shanghai201109 China College of Astronautics Nanjing University of Aeronautics and Astronautics Nanjing211106 China

ISBN: (纸本)9781713871163

Accurate simulation of asymmetrical vortical flow over slender missiles at high angles of attack is crucial for predicting the aerodynamic characteristics of missiles. However, numerical solution for vortices is very sensitive to numerical dissipation. A novel high-order scheme (WENO-K) proposed by our research group can effectively reduce numerical dissipation and capture vortex structures more accurately compared to classical WENO schemes. In this paper, a new numerical method combining 5th WENO-K and IDDES is developed to accurately solve the supersonic unsteady viscous flow dominated by complex asymmetrical vortices over the Sparrow III type missile, and the mechanism of how asymmetrical vortices affect aerodynamic forces of the missile is revealed. © 2022 ICAS. All Rights Reserved.

关键词： Vortex flow

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Numerical Study of Water Entry of Vehicle Under Near Free Surface Caused by Underwater Explosion

SSRN

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SSRN 2023年

作者： Zhang, Zhifan Xie, Yujie Wang, Dapeng Yuan, Ziyi Zhang, Guiyong Zong, Zhi State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial Equipment School of Naval Architecture Engineering Dalian University of Technology Dalian116024 China Research & Development Center China Academy of Launch Vehicle Technology Beijing100076 China Collaborative Innovation Center for Advanced Ship and Deep-Sea Exploration Shanghai200240 China

Water entry of a high-speed vehicle with a small angle can easily cause such phenomena as bouncing and unstable trajectory. Underwater explosion near the free surface can provide better wave conditions for vehicle, which may significantly reduce slamming loads and improve trajectory stability. In this paper, The feasibility of water entry of vehicle with small angle in underwater explosion bubble is verified. First, in order to verify the effectively of CEL method for solving problems of water entry and underwater explosion, numerical models of a small sphere entering water and free-field underwater explosion are established. Then, a water entry model of a high-speed vehicle with a small angle under static water surface conditions is established;and its slamming loads and motion trajectory during entry process are analyzed. Finally, numerical model water entry of a vehicle in an underwater explosion bubble is developed. The obtained results for the trajectory and slamming load characteristics are compared with those under static water surface conditions. The results show that underwater explosion near free surface can provide a cavity before water entry can significantly reduce the overload of the vehicle and effectively improve its trajectory stability during entry process. © 2023, The Authors. All rights reserved.

关键词： Trajectories

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OpenLS-DGF: An Adaptive Open-Source Dataset Generation Framework for Machine Learning Tasks in Logic Synthesis

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Ni, Liwei Wang, Rui Liu, Miao Meng, Xingyu Lin, Xiaoze Liu, Junfeng Luo, Guojie Chu, Zhufei Qian, Weikang Yang, Xiaoyan Xie, Biwei Li, Xingquan Li, Huawei Chinese Academy of Sciences State Key Lab of Processors Institute of Computing Technology Beijing100190 China Pengcheng Laboratory Shenzhen518055 China University of Chinese Academy of Sciences Beijing101408 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China University of Chinese Academy of Sciences School of Computer Science and Technology Beijing100049 China Peking University School of Computer Science Center for Energy-Efficient Computing and Applications Beijing100871 China Ninbo University Faculty of Electrical Engineering and Computer Science Ninbo315211 China Shanghai Jiao Tong University University of Michigan-Shanghai Jiao Tong University Joint Institute MoE Key Laboratory of Artificial Intelligence Shanghai200240 China Hangzhou Dianzi University School of Electronics and Information Engineering Hangzhou311121 China

This paper introduces OpenLS-DGF, an adaptive logic synthesis dataset generation framework, to enhance machine learning (ML) applications within the logic synthesis process. Previous dataset generation flows were tailored for specific tasks or lacked integrated ML capabilities. While OpenLS-DGF supports various machine learning tasks by encapsulating the three fundamental steps of logic synthesis: Boolean representation, logic optimization, and technology mapping. It preserves the original information in both Verilog and ML-friendly GraphML formats. The Verilog files offer semi-customizable capabilities, enabling researchers to insert additional steps and incrementally refine the generated dataset. Furthermore, OpenLS-DGF includes an adaptive circuit engine that facilitates the final dataset management and downstream tasks. The generated OpenLS-D-v1 dataset comprises 46 combinational designs from established benchmarks, totaling over 966,000 Boolean circuits. OpenLS-D-v1 supports integrating new data features, making it more versatile for new tasks. This paper demonstrates the versatility of OpenLS-D-v1 through four distinct downstream tasks: circuit classification, circuit ranking, quality of results (QoR) prediction, and probability prediction. Each task is chosen to represent essential steps of logic synthesis, and the experimental results show the generated dataset from OpenLSDGF achieves prominent diversity and applicability. The source code and datasets are available at https://***/Logic-Factory/ACE/blob/master/OpenLS-DGF. © 1982-2012 IEEE.

关键词： Information management

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FedDADP: A Privacy-Risk-Adaptive Differential Privacy Protection Method for Federated Android Malware Classifier

FedDADP: A Privacy-Risk-Adaptive Differential Privacy Protec...

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International Joint Conference on Neural Networks (IJCNN)

作者： Changnan Jiang Chunhe Xia Mengyao Liu Rui Fang Pengfei Li Huacheng Li Tianbo Wang Beijing Key Lab. of Network Technology Beihang University Beijing China Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing Guangxi Normal University Guilin China School of Cyber Science and Technology Beihang University Beijing China SNationa Innovation Center of Intelligent and Connected Vehicles Beijing China Shanghai Key Laboratory of Computer Software Evaluating and Testing China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The federated Android malware classifier has attracted much attention owing to its advantages of privacy protection and multi-party joint modeling. However, the research indicates that the gradient transmitted within the federated classifier still encodes the user's sensitive information, exposing it to indirect privacy inference threats from curious servers. Differential privacy is a recognized and effective way to address this privacy breach threat by adding noise to the user's model parameters to limit the attacker's inference of sensitive information. However, the protection effect of existing differential privacy methods is at the cost of significantly reducing the model's classification accuracy, and it cannot be reasonably balanced. To address this challenge, we propose a privacy protection method, FedDADP. FedDADP performs adaptive, lightweight privacy configuration in its training time dimension and model space dimension according to the privacy risk distribution law in the federated Android malware classifier to protect users' privacy while maintaining the model's utility. Numerous experiments on the Androzoo dataset and multiple baseline classifiers show that FedDADP protects users' sensitive information better (7% more effectiveness against adversaries' inference) than baseline differential privacy methods and achieves better model utility (classification accuracy improves by about 8%) with the same privacy budget.

关键词： Training Privacy Adaptation models Differential privacy Accuracy Privacy breach Malware

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A Novel Scene Coupling Semantic Mask Network for Remote Sensing Image Segmentation

arXiv

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arXiv 2025年

作者： Ma, Xiaowen Lian, Rongrong Wu, Zhenkai Guan, Renxiang Hong, Tingfeng Zhao, Mengjiao Ma, Mengting Nie, Jiangtao Du, Zhenhong Song, Siyang Zhang, Wei School of Software Technology Zhejiang University Hangzhou310027 China Noah's Ark Lab Huawei Shanghai201206 China School of Computer Science and Technology Zhejiang University Hangzhou310027 China School of Computer Science and Technology Northwestern Polytechnical University Xian710072 China School of Earth Sciences Zhejiang University Hangzhou310027 China School of Computer Science and Technology National University of Defense Technology Changsha410073 China School of Computing and Mathematical Sciences University of Leicester United Kingdom Innovation Center of Yangtze River Delta Zhejiang University Jiaxing314103 China

As a common method in the field of computer vision, spatial attention mechanism has been widely used in semantic segmentation of remote sensing images due to its outstanding long-range dependency modeling capability. However, remote sensing images are usually characterized by complex backgrounds and large intra-class variance that would degrade their analysis performance. While vanilla spatial attention mechanisms are based on dense affine operations, they tend to introduce a large amount of background contextual information and lack of consideration for intrinsic spatial correlation. To deal with such limitations, this paper proposes a novel scene-Coupling semantic mask network, which reconstructs the vanilla attention with scene coupling and local global semantic masks strategies. Specifically, scene coupling module decomposes scene information into global representations and object distributions, which are then embedded in the attention affinity processes. This Strategy effectively utilizes the intrinsic spatial correlation between features so that improve the process of attention modeling. Meanwhile, local global semantic masks module indirectly correlate pixels with the global semantic masks by using the local semantic mask as an intermediate sensory element, which reduces the background contextual interference and mitigates the effect of intra-class variance. By combining the above two strategies, we propose the model SCSM, which not only can efficiently segment various geospatial objects in complex scenarios, but also possesses inter-clean and elegant mathematical representations. Experimental results on four benchmark datasets demonstrate the the effectiveness of the above two strategies for improving the attention modeling of remote sensing images. For example, compared to the recent advanced method LOGCAN++, the proposed SCSM has 1.2%, 0.8%, 0.2%, and 1.9% improvements on the LoveDA, Vaihingen, Potsdam, and iSAID datasets, respectively. The dataset and cod

关键词： Semantic Segmentation

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Heuristic Elastic Scaling for Kubernetes Heterogeneous Microservices

Heuristic Elastic Scaling for Kubernetes Heterogeneous Micro...

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IEEE International Conference on Networking, Sensing and Control

作者： Song Wang Sheng He Yishuang Ning Xiang Gao Zhijun Ding Department of Computer Science and Technology Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Tongji University Shanghai China Kingdee Research Kingdee International Software Group Co. Ltd Shenzhen China Intelligent Computing Facility Innovation Center ZhejiangLab Hangzhou China

ISBN: (数字)9798350365221

ISBN: (纸本)9798350365238

Kubernetes has become the basic platform for building cloud native applications. However, existing horizontal scaling methods based on Kubernetes have problems with resource redundancy. Furthermore, the combined horizontal and vertical scaling based on Kubernetes takes a long time. Regarding the issue above, first, we describe the execution processes of three scaling techniques and compare them in different dimensions; second, we construct a formal elastic scaling model for expansion scenarios, with the goal of minimizing the total cost; and third, we introduce a heuristic expansion algorithm after balancing resource redundancy and execution time as the heuristic information. The experiment results show that the elastic scaling algorithm proposed in this paper achieves better performance than other algorithms.

关键词： Costs Heuristic algorithms Redundancy Buildings Microservice architectures Sensors

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A Stackelberg Game-Based Electric Carbon Market Trading Mechanism for Shared Energy Storage Considering Carbon Capture, Utilization, and Storage

A Stackelberg Game-Based Electric Carbon Market Trading Mech...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Yunyang Liang Yi-Chang Li Mengmeng Yu Yike He Department of Computer Technology China University of Petroleum (Beijing) Xinjiang P. R. China Research & Development Center Nestfield Co. Ltd Ansan Gyeonggi-do South Korea Department of Software Engineering China University of Petroleum (Beijing) Xinjiang P. R. China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

This paper proposes a Stackelberg game trading model for shared energy storage and carbon market combined with carbon capture, utilization and storage (CCUS) technology, aimed at optimizing energy storage scheduling and promoting low-carbon electricity production and consumption. By applying Stackelberg game theory, the interactions between energy storage operators (leaders) and market participants (followers) are analyzed to enhance the economic and environmental benefits. The innovation of this model lies in the integration of energy storage trading and CCUS technology to provide economic incentives for renewable energy generation, and encourage emission reduction through carbon trading to promote green transformation of energy systems. Finally, the case study of Xinjiang energy industry CCUS Innovation development Alliance Industrial Park verifies the effectiveness of the model in improving energy storage utilization and economic returns while significantly reducing carbon emissions, which aligns with the global carbon reduction goal.

关键词： Technological innovation Energy consumption Renewable energy sources Simulation Collaboration Games Production Carbon capture and storage Optimization Energy storage

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Patch Target Guided Dual-Branch Deep Multiple Instance Learning for 3D MRI Analysis

Patch Target Guided Dual-Branch Deep Multiple Instance Learn...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Mingzhe Dai Xiaoshuang Shi Xiaofeng Zhu Tingrui Pan Kang Li School of Software Engineering University of Science and Technology of China Hefei China West China Biomedical Big Data Center West China Hospital of Sichuan University Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Sichuan Artificial Intelligence Research Institute Yibin China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Shanghai Artificial Intelligence Laboratory Shanghai China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Deep multiple instance learning (MIL) has attracted considerable attention in medical image analysis, since it only requires image-level labels for model training without using fine-grained (or patch) annotations. Unfortunately, MIL-based methods might lose some significant patch features. Although pseudo-label-based methods, which assign a pre-defined label to each patch, can explore more patch-level features, they might bring label noise and make the patch-level features lose diversity, thereby possibly restricting the model performance. To overcome this issue, we propose a novel gradient-based patch target generation (PTG) module to dynamically produce a feature vector for each patch as its target. Additionally, based on the PTG module, we propose a patch-target guided dual-branch deep MIL framework for 3D MRI data analysis, where both the two branches consist of a CNN model to extract patch-level features, an attention module to interpret the significance of patches, and a bag-level classifier, while the second branch also contains the PTG module to generate patch targets of patches. Moreover, the two branches are alternatively updated in our framework, resulting in a bi-level optimization problem, and thus we design a bi-level optimization algorithm to solve our proposed objective function. Extensive experiments demonstrate the superior classification and interpretation performance of the proposed framework over recent state-of-the-art methods. Codes are available at https://***/daimz1213/mil2024.

关键词： Training Analytical models Solid modeling Three-dimensional displays Annotations Magnetic resonance imaging Feature extraction Vectors Optimization Biomedical imaging

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Enhanced Practical Byzantine Fault Tolerance for Service Function Chain Deployment: Advancing Big Data Intelligence in Control Systems

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computers, Materials and Continua 2025年第3期83卷 4393-4409页

作者： Peiying Zhang Yihong Yu Jing Liu Chong Lv Lizhuang Tan Yulin Zhang Qingdao Institute of Software College of Computer Science and Technology China University of Petroleum (East China) Qingdao 266580 China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software Qingdao 266580 China Library of Shanghai Lixin University of Accounting and Finance Shanghai 201209 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan 250014 China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan 250014 China Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE Minzu University of China Beijing 100081 China Key Laboratory of Intelligent Game Yangtze River Delta Research Institute of NPU Taicang 215400 China Key Laboratory of Education Informatization for Nationalities (Yunnan Normal University) Ministry of Education Kunming 650092 China

As Internet of Things (IoT) technologies continue to evolve at an unprecedented pace, intelligent big data control and information systems have become critical enablers for organizational digital transformation, facilitating data-driven decision making, fostering innovation ecosystems, and maintaining operational stability. In this study, we propose an advanced deployment algorithm for Service Function Chaining (SFC) that leverages an enhanced Practical Byzantine Fault Tolerance (PBFT) mechanism. The main goal is to tackle the issues of security and resource efficiency in SFC implementation across diverse network settings. By integrating blockchain technology and Deep Reinforcement Learning (DRL), our algorithm not only optimizes resource utilization and quality of service but also ensures robust security during SFC deployment. Specifically, the enhanced PBFT consensus mechanism (VRPBFT) significantly reduces consensus latency and improves Byzantine node detection through the introduction of a Verifiable Random Function (VRF) and a node reputation grading model. Experimental results demonstrate that compared to traditional PBFT, the proposed VRPBFT algorithm reduces consensus latency by approximately 30% and decreases the proportion of Byzantine nodes by 40% after 100 rounds of consensus. Furthermore, the DRL-based SFC deployment algorithm (SDRL) exhibits rapid convergence during training, with improvements in long-term average revenue, request acceptance rate, and revenue/cost ratio of 17%, 14.49%, and 20.35%, respectively, over existing algorithms. Additionally, the CPU resource utilization of the SDRL algorithm reaches up to 42%, which is 27.96% higher than other algorithms. These findings indicate that the proposed algorithm substantially enhances resource utilization efficiency, service quality, and security in SFC deployment.

关键词： Big data intelligent transformation heterogeneous networks service function chain blockchain deep reinforcement learning trusted deployment

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