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19th International Conference on Web Information Systems and Applications, WISA 2022

作者： Gao, Feng Yang, Zihang Gu, Jinguang Cheng, Junjun School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan430065 China Laboratory of Content Organization and Knowledge Service for Rich Media Digital Publishing Wuhan University of Science and Technology Wuhan430065 China China Information Security Evaluation Center Beijing100083 China

ISBN: (纸本)9783031203084

With the development of natural language processing technology, machine reading comprehension has been widely used in various fields such as QA systems and Intelligence engineering. However, despite numerous models are proposed in the general domain, there is still a lack of appropriate dataset and models for specific domains like anti-terrorism or homeland security to address this problem. Therefore, a Chinese reading comprehension data set in the field of anti-terrorism (ATCMRC) is constructed, and a generative machine reading comprehension model (AT-MT5) is proposed. ATCMRC was constructed in a semi-automated manner, and a domain-specific vocabulary is created based on the dataset to assist the AT-MT5. The model uses a hybrid attention layer is and a controlled answer generation layer to enhance text perception. Finally, the ATCMRC dataset and the AT-MT5 model is evaluated against existing approaches. The experimental results show that ATCMRC covers key issues in the domain and presents challenging MRC tasks for existent models, while AT-MT5 achieves better results in the domain specific dataset than the existing methods. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Terrorism

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Performance Evaluation of Rail Transit System Operation by Using Critic-Mtopsis Approach: Nigerian Rail Transit System Operation

SSRN

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SSRN 2022年

作者： Deng, Lianbo Bulus, Hosea Chen, Ming Abdulhakeem, Suleiman School of Traffic and Transportation Engineering Rail Data Research and Application Key Laboratory of Hunan Province Central South University Changsha410075 China Department of Civil Engineering Ahmadu Bello University Zaria Nigeria

The establishment of rail transit operation performance evaluation criteria is crucial for every rail transit operator, planner, passengers, managers, and the evaluation results are critical for sustainability of rail transit operation. We established the criteria for rail transit evaluation in Nigeria using a rail transit line that linked two major cities. In this study, we used a passenger survey, an operator interview, and a railway professional interview outcome to develop a rail transit operation evaluation performance. We then determine the objective weight of each of these criteria by adopting the method of Criteria Importance Through Inter-criteria Correlation (CRITIC). After that, the Modified Technique for Order Preference by Similarities to Ideal Solution (MTOPS-IS) was consider to select the best rail transit performance evaluation month to Ideal Solution (IS) of combined developed criterion. We began by establishing the evaluation criteria and sub-criteria, which included six criteria and 28 sub-criteria. A statistical data from the Abuja-Kaduna AKD rail transit operators was obtained, which was used as the approach's input. Finally, criteria evaluation approach was formulated, in which the product of the corresponding probability and the criteria weight will be used to formulate the performance of each indicator. The MTOPS-IS method will be employed to compute the comprehensive evaluation values and performance rankings for each month. The operation performance of AKD rail transit keeps improving with time. Terrorist incidents have little to no effect on the general performance of the entire system. The best four evaluation performances of the line fall between October 2021 and December 2021. © 2022, The Authors. All rights reserved.

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Towards Feature Distribution Alignment and Diversity Enhancement for data-Free Quantization

Towards Feature Distribution Alignment and Diversity Enhance...

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IEEE International Conference on data Mining (ICDM)

作者： Yangcheng Gao Zhao Zhang Richang Hong Haijun Zhang Jicong Fan Shuicheng Yan School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Department of Computer Science Harbin Institute of Technology (Shenzhen) Xili University Town Shenzhen China School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China National University of Singapore Singapore

To obtain lower inference latency and less memory footprint of deep neural networks, model quantization has been widely employed in deep model deployment, by converting the floating points to low-precision integers. However, previous methods (such as quantization aware training and post training quantization) require original data for the fine-tuning or calibration of quantized model, which makes them inapplicable to the cases that original data are not accessed due to privacy or security. This gives birth to the data-free quantization method with synthetic data generation. While current data-free quantization methods still suffer from severe performance degradation when quantizing a model into lower bit, caused by the low inter-class separability of semantic features. To this end, we propose a new and effective data-free quantization method termed ClusterQ, which utilizes the feature distribution alignment for synthetic data generation. To obtain high inter-class separability of semantic features, we cluster and align the feature distribution statistics to imitate the distribution of real data, so that the performance degradation is alleviated. Moreover, we incorporate the diversity enhancement to solve class-wise mode collapse. We also employ the exponential moving average to update the centroid of each cluster for further feature distribution improvement. Extensive experiments based on different deep models (e.g., ResNet-18 and MobileNet-V2) over the ImageNet dataset demonstrate that our proposed ClusterQ model obtains state-of-the-art performance.

关键词： Degradation Training Quantization (signal) Semantics Neural networks Memory management data models

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Spatio-temporal Dynamics and Drivers of Ecological Quality in Yulin City Using the MRSEI Model

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Journal of Resources and Ecology 2025年第2期16卷 340-355页

作者： MU Weichen HE Zhilin CHEN Yanglong GAO Dongkai YUE Tianming QIN Fen College of Geographical Sciences Faculty of Geographical Science and EngineeringHenan UniversityZhengzhou450046China Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions Ministry of EducationHenan UniversityKaifengHenan 475004China Henan Industrial Technology Academy of Spatio-Temporal Big Data Henan UniversityKaifengHenan 475004China Henan Technology Innovation Center of Spatial-Temporal Big Data Henan UniversityZhengzhou 450046China.

Urbanization has resulted in growing ecological pressures on cities,necessitating assessments of urban ecological ***-term characterization of regional dynamics and drivers is critical for environmental *** study proposes an enhanced ecological quality model(MRSEI)incorporating vegetation cover and EVI rather than just *** MRSEI model was applied to analyse ecological quality in Yulin City during 2000-2018 using Landsat TM/OLI data on Google Earth *** detectors also quantified anthropo-genic and environmental influences on the study *** results are summarized as follows:(1)MRSEI showed an average correlation coefficient of 0.840 with other indices,demonstrating higher representativeness than indi-vidual *** principal component analysis indicated a 12.88%increase in explained *** also exhibited significantly improved identification of roads,villages,and unused lands over RSEI,better matching ground conditions,and suitability for regional ecological assessment.(2)During 2000-2020,the average MRSEI in Yulin City was 0.481,peaking at 0.518 in 2018,indicating general ecological improvement over ***,conditions were better in the southeast than *** 38.81%of the area showed significant improvement,10.15%exhibited significant deterioration,concentrated in western Dingbian and Jingbian counties,highlighting areas requiring enhanced protection.(3)Ecological conditions in Yulin City remained stable over ***-high clusters were concentrated in eastern counties(Qingjian,Wubao,Jia,Fugu)and central lower-altitude areas near Yokoyama and ***-low clusters predominated in the northern Yuyang desert and high-altitude western Dingbian regions.(4)Enhanced vegetation cover had the greatest influence in improving Yulin’s ecological *** was the most impactful environmental driver,while precipitation and land use change interactions showed the strongest combined *** cont

关键词： Google Earth Engine improved remote sensing-based ecological indicator temporal and spatial variation geographical detector

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Prediction of turbulent channel flow using Fourier neural operator-based machine-learning strategy

arXiv

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arXiv 2024年

作者： Wang, Yunpeng Li, Zhijie Yuan, Zelong Peng, Wenhui Liu, Tianyuan Wang, Jianchun Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Turbulence Research and Applications Southern University of Science and Technology Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications Southern University of Science and Technology Shenzhen518055 China Harbin Engineering University Qingdao Innovation and Development Base Qingdao266000 China The Hong Kong Polytechnic University Department of Applied Mathematics 999077 Hong Kong Engineering College Peking University Beijing100091 China

Fast and accurate predictions of turbulent flows are of great importance in the science and engineering field. In this paper, we investigate the implicit U-Net enhanced Fourier neural operator (IUFNO) in the stable prediction of long-time dynamics of three-dimensional (3D) turbulent channel flows. The trained IUFNO models are tested in the large-eddy simulations (LES) at coarse grids for three friction Reynolds numbers: Reτ ≈ 180, 395 and 590. The adopted near-wall mesh grids are tangibly coarser than the general requirements for wall-resolved LES. Compared to the original Fourier neural operator (FNO), the implicit FNO (IFNO) and U-Net enhanced FNO (UFNO), the IUFNO model has a much better long-term predictive ability. The numerical experiments show that the IUFNO framework outperforms the traditional dynamic Smagorinsky model (DSM) and the wall-adapted local eddy-viscosity (WALE) model in the predictions of a variety of flow statistics and structures, including the mean and fluctuating velocities, the probability density functions (PDFs) and joint PDF of velocity fluctuations, the Reynolds stress profile, the kinetic energy spectrum, and the Q-criterion (vortex structures). Meanwhile, the trained IUFNO models are computationally much faster than the traditional LES models. Thus, the IUFNO model is a promising approach for the fast prediction of wall-bounded turbulent flow. Copyright © 2024, The Authors. All rights reserved.

关键词： Kinetic energy

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Lattice Boltzmann equation for convection-diffusion flows with Neumann boundary condition

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Physical Review E 2025年第3期111卷 035311-035311页

作者： Lin Zheng Song Zheng Qinglan Zhai MIIT Key Laboratory of Thermal Control of Electronic Equipment School of Energy and Power Engineering Nanjing University of Science and Technology Nanjing 210094 People's Republic of China School of Data Science Zhejiang University of Finance and Economic Hangzhou 310018 People's Republic of China School of Economics Management and Law Chaohu University Chaohu 238000 People's Republic of China

In this work, a lattice Boltzmann equation (LBE) is developed to convection-diffusion flows with Neumann boundary condition in complex geometry. The physical fluid domain together with the physical boundary is extended to a large domain similar to the fictitious domain method, and the original convection-diffusion equation (CDE) is reformulated for the large domain, where the Neumann boundary condition is naturally incorporated to CDE without separated explicit treatment. Based on this extended CDE for the large domain, the LBE solver is designed accordingly. Several classical simulations of pure thermal diffusion/natural convection between two concentrated cylinders, natural convection flow around a circular cylinder with constant heat flux in square cavity, and mixed convection flow in a square lid-driven cavity with a circular cylinder are carried out to validate the present method. Numerical results show that the predictions by present LBE agree well with theoretical or other results.

关键词： Circular cylinders

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Ppt4j: Patch Presence Test for Java Binaries

Ppt4j: Patch Presence Test for Java Binaries

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International Conference on Software engineering (ICSE)

作者： Zhiyuan Pan Xing Hu Xin Xia Xian Zhan David Lo Xiaohu Yang The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Software Engineering Application Technology Lab Huawei Hangzhou China The Hong Kong Polytechnic University Hong Kong China School of Computing and Information Systems Singapore Management University Singapore

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

The number of vulnerabilities reported in open source software has increased substantially in recent years. Security patches provide the necessary measures to protect software from attacks and vulnerabilities. In practice, it is difficult to identify whether patches have been integrated into software, especially if we only have binary files. Therefore, the ability to test whether a patch is applied to the target binary, a.k.a. patch presence test, is crucial for practitioners. However, it is challenging to obtain accurate semantic information from patches, which could lead to incorrect results. In this paper, we propose a new patch presence test framework named Ppt4j (Patch Presence Test forJava Binaries). Ppt4j is designed for open-source Java libraries. It takes Java binaries (i.e. bytecode files) as input, extracts semantic information from patches, and uses feature-based techniques to identify patch lines in the binaries. To evaluate the effectiveness of our proposed approach Ppt4j, we construct a dataset with binaries that include 110 vulnerabilities. The results show that Ppt4j achieves an F1 score of 98.5% with reasonable efficiency, improving the baseline by 14.2%. Furthermore, we conduct an in-the-wild evaluation of Ppt4j on JetBrains IntelliJ IDEA. The results suggest that a third-party library included in the software is not patched for two CVEs, and we have reported this potential security problem to the vendor.

关键词： Java Semantics Feature extraction Libraries Security Software measurement data mining

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Fil-Cad: A Fault Detection Method for Iiot Based on Federated Incremental Learning with Class Accuracy Distillation

SSRN

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SSRN 2024年

作者： Liu, Yanhua Fang, Wenyu Huang, Wei Wang, Xiaofeng Zhao, Baokang Liu, Ximeng College of Computer and Data Science Fuzhou University Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China College of Computer National University of Defense Technology Changsha410073 China

Fault detection is a research topic attracting much attention in IIoT. However, the continuous accumulation and growth of IIoT data leads to the catastrophic forgetting problem in the fault detection model and data heterogeneity, which creates a bottleneck for the performance and generalization of federated learning in fault detection. To improve the fault detection performance of federated learning models in data incremental scenarios, we propose a federatedincremental-learning-based fault detection method for IIoT, namely FIL-CAD. Firstly, comparing the knowledge distillation, the accuracies of different data classes in the global and local models are chosen as the model lightweight learning features, and the loss function is weighted. Secondly, we introduce the concept of data learning depth. It computes different model weights in the aggregation based on their degrees of data sample learning and model training participation and obtains the global model through weighted aggregation. Additionally, FIL-CAD supports the combination of differential privacy to protect data privacy. The proposed method is compared with four benchmark methods on four datasets, which include CWRU and MFPT bearing datasets. Then, the experimental results show that FIL-CAD performs optimally in accuracy and F1-Score, and the average accuracy is improved by 1.32%-18.52%, demonstrating the superiority of FIL-CAD. © 2024, The Authors. All rights reserved.

关键词： Fault detection

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Timing-Driven High-Level Synthesis for Continuous-Flow Microfluidic Biochips

Timing-Driven High-Level Synthesis for Continuous-Flow Micro...

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IEEE International Symposium on Quality Electronic Design

作者： Zhengyang Ye Zhisheng Chen Youlin Pan Genggeng Liu Wenzhong Guo Tsung-Yi Ho Xing Huang College of Computer and Data Science Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China School of Informatics Xiamen University Xiamen China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China School of Computer Science Northwestern Polytechnical University Xi’an China

ISBN: (数字)9798350309270

ISBN: (纸本)9798350309287

As continuous-flow microfluidic biochips technology rapidly advances, its automated synthesis methods require adapting to increasingly complex and stringent biochemical applications. In particular, with the introduction of time-sensitive bioassays, in this work, we present a timing-driven high-level synthesis method to optimize the resource efficiency of bioassays considering the time constraints. Specifically, we investigate the existing forms of time constraints and propose a corresponding flexible modeling approach based on reaction-control which can achieve downward compatibility for physical design. Additionally, we also analyze the impact of storage and wash on time constraints. The experimental results show that our method achieves efficient execution and low devices cost while satisfying time constraints.

关键词： Costs Biochips Time factors Microfluidics Physical design

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Improving Zero-Shot Coordination with Diversely Rewarded Partner Agents

Improving Zero-Shot Coordination with Diversely Rewarded Par...

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International Joint Conference on Neural Networks (IJCNN)

作者： Peilin Wu Zhenhua Yang Peng Yang Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen China Department of Statistics and Data Science Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Zero-shot coordination studies the training of well-generalizing human-AI coordination agents in the scenario where human data is unavailable. To obtain a coordination agent generalize to unseen humans, prevailing methods generate a population of partner agents as proxy models of human partners and then train a coordination agent with these partner agents. Constructed partner agents are expected to be as diverse as possible to cover a wide range of human behaviors, preventing a distribution shift between training and testing stages. Recent works concentrate on studying effective methods of creating a group of high-reward while diverse partner agents to model unseen human partners. However, the resulting high-reward partner agents do not accurately reflect real-world situations, considering that human decisions are not always optimal and may sometimes even hinder the progression of coordination. Therefore, these studies still struggle to capture the potential characteristics of human partners. In this work, reinforcement learning (RL) and supervised learning (SL) are integrated to train a reward-conditioned policy. By conditioned on different desired rewards, a reward-conditioned policy simulates both low-reward and high-reward partners. Additionally, a reward-bucketed replay buffer and curriculum learning are applied to enhance reward diversity and boost the training of coordination agents. Experiments demonstrate that the proposed reward-conditioned policy is capable of generating agents with different rewards. Moreover, the zero-shot coordination performance of agents trained with these partners surpasses previous methods in the majority of scenarios within the Overcooked human-AI coordination benchmark.

关键词： Training Impedance matching Supervised learning Neural networks Reinforcement learning Benchmark testing Trajectory

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