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IEEE International Conference on Web Services (ICWS)

作者： Renjie Zhou Zhenming Jin Jian Wan Jilin Zhang Xingyu Guo Qiang Liu Key Laboratory of Complex Systems Modeling and Simulation of the Ministry of Education School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Key Laboratory of Complex Systems Modeling and Simulation of the Ministry of Education ITMO Joint Institute Hangzhou Dianzi University Hangzhou China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou China Dept. of Computer and Information Technology Zhejiang Police College Hangzhou China

The rapid development of the Internet has revo-lutionized our lives, providing us with an array of convenient services. However, this revolution has also led to the problem of data overload. Personalized recommendation services have been widely recognized as an effective solution to this problem by both the industrial and academic communities. Accurate prediction of click-through rate (CTR) is a critical task in personalized recommendation systems since it can improve the user’s shopping experience, ultimately increasing revenue for the platform. To achieve accurate CTR prediction, it is crucial to capture user interests. While several methods for interest modeling exist, challenges related to user interest evolution and user behavior noise need further attention. This paper proposes a novel CTR prediction model named the Evolutionary Interest Representation Network (EIRN), which learns the evolving interest representation of users from their behaviors, profiles, and environmental attributes. In the model, we introduce an advertisement correlation graph decomposition layer and a noise filter layer to enhance and purify the user behavior representation. An interest extraction layer is designed to capture sequential correlations and common characteristics between user behaviors, representing their interests. Additionally, as user interests evolve over time and their importance changes, an attention-based interest evolution layer is designed to track the evolution of user interests. Our proposed method is validated using four public datasets and an industrial dataset, showing its superiority over existing CTR prediction methods.

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Quantitative distinction of the relative actions of climate change and human activities on vegetation evolution in the Yellow River Basin of China during 1981-2019

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Journal of Arid Land 2023年第1期15卷 91-108页

作者： LIU Yifeng GUO Bing LU Miao ZANG Wenqian YU Tao CHEN Donghua School of Civil Architectural Engineering Shandong University of TechnologyZibo 255000China Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural ResourcesShenzhen 518000China Key Laboratory of National Geographic Census and Monitoring Ministry of Natural ResourcesWuhan 430072China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100101China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China Key Laboratory of Agricultural Remote Sensing Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional PlanningChinese Academy of Agricultural SciencesBeijing 100081China Zhongke Langfang Institute of Spatial Information Applications Langfang 065000China College of Computer and Information Engineering Chuzhou UniversityChuzhou 239000China

Under the combined influence of climate change and human activities,vegetation ecosystem has undergone profound *** can be seen that there are obvious differences in the evolution patterns and driving mechanisms of vegetation ecosystem in different historical ***,it is urgent to identify and reveal the dominant factors and their contribution rates in the vegetation change *** on the data of climate elements(sunshine hours,precipitation and temperature),human activities(population intensity and GDP intensity)and other natural factors(altitude,slope and aspect),this study explored the spatial and temporal evolution patterns of vegetation NDVI in the Yellow River Basin of China from 1989 to 2019 through a residual method,a trend analysis,and a gravity center model,and quantitatively distinguished the relative actions of climate change and human activities on vegetation evolution based on Geodetector *** results showed that the spatial distribution of vegetation NDVI in the Yellow River Basin showed a decreasing trend from southeast to *** 1981-2019,the temporal variation of vegetation NDVI showed an overall increasing *** gravity centers of average vegetation NDVI during the study period was distributed in Zhenyuan County,Gansu Province,and the center moved northeastwards from 1981 to *** 1981-2000 and 2001-2019,the proportion of vegetation restoration areas promoted by the combined action of climate change and human activities was the *** the study period(1981-2019),the dominant factors influencing vegetation NDVI shifted from natural factors to human *** results could provide decision support for the protection and restoration of vegetation ecosystem in the Yellow River Basin.

关键词： vegetation evolution driving mechanisms climate change human activities relative actions Geodetector Yellow River Basin

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Multi2human: Controllable Human Image Generation with Multimodal Controls

SSRN

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SSRN 2023年

作者： Gu, Xiaoling Xu, Shengwenzhuo Wong, Yongkang Wu, Zizhao Yu, Jun Fan, Jianping Kankanhalli, Mohan S. Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University China National University of Singapore Singapore School of Digital Media Technology Hangzhou Dianzi University China

Generating high-quality and diverse human images presents a substantial difficulty within the field of computer vision, especially in developing controllable generative models that can utilize input from various modalities. Such models could enable innovative applications like digital human, fashion design, and content creation. In this study, we introduce Multi2Human, a two-stage image synthesis framework for controllable human image generation with multimodal controls. In the first stage, a novel WAvelet-vqVaE (WAVE) architecture is designed to embed human images using a learnable codebook. The WAVE model enhances the conventional Vector Quantized Variational Autoencoder (VQVAE) by integrating wavelets throughout the encoder, thereby enhancing the quality of image reconstruction and synthesis. In the second stage, a new Multimodal Conditioned Diffusion Model (MCDM) is de- signed to estimate the underlying prior distribution within the discrete latent space using a discrete diffusion process, thus allowing for human image generation condi- tioned on multimodal controls. Quantitative and qualitative analysis demonstrates that the proposed method has the ability to create high-quality, lifelike full-body human images while satisfying the specified multimodal controls. Our code is available at https://***/gxl-groups/Multi2Human. © 2023, The Authors. All rights reserved.

关键词： Image reconstruction

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Key Techniques on Unified Identity Authentication in OpenMBEE Integration 4

Key Techniques on Unified Identity Authentication in OpenMBE...

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4th IEEE International Conference on Electronics and Communication Engineering, ICECE 2021

作者： Xue, Junjie Zhou, Junhua Shi, Guoqiang Feng, Chaoqun Liu, Penghua Quan, Hongyan Beijing Complex Product Advanced Manufacturing Engineering Research Center Beijing Simulation Center Beijing China State Key Laboratory of Intelligent Manufacturing System Technology Beijing China Institute of Electronic System Engineering Beijing China Beijing Complex Product Advanced Manufacturing Engineering Research Center Beijing Simulation Center Beijing China Science and Technology on Space System Simulation Laboratory Beijing Simulation Center Beijing China School of Computer Science and Technology East China Normal University Shanghai China

ISBN: (纸本)9781728194226

In order to make full use of the concept of model-based systems engineering (MBSE) and the collaborative design capability of the open model-based engineering environment (OpenMBEE) in establishing complex product development platform, we propose an effective method to solve the problem of unified identity authentication in OpenMBEE integration. The proposed method is based on the Model View Controller(MVC) architecture in OpenMBEE, which has the characteristics strong plasticity and can be benefit for expanding the function in the controller layer. In our research, we make full use of the Software Development Kit (SDK) of OpenMBEE to expand the existing functions in the controller layer, and increase the function of communicating with the application development platform, then realize the function of sharing user authentication information between the development platform and OpenMBEE. After expansion, the system includes three modules, including the Front-end Service Module(FSM), MQ based Information Receiving Service Module(IFRSM), and the OpenMBEE Back-end Server Module(BSM). The effectiveness of the proposed strategies is verified by some practical instances, which verifies that our study can provide an effective design idea for the identity authentication in OpenMBEE integration. ©2021 IEEE

关键词： Integration

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Integrating NLP and Ontology Matching into a Unified System for Automated Information Extraction from Geological Hazard Reports

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Journal of Earth Science 2023年第5期34卷 1433-1446页

作者： Qinjun Qiu Zhen Huang Dexin Xu Kai Ma Liufeng Tao Run Wang Jianguo Chen Zhong Xie Yongsheng Pan Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural ResourcesShenzhen 518034China Key Laboratory of Geological Survey and Evaluation of Ministry of Education China University of GeosciencesWuhan 430074China Beijing Key Laboratory of Urban Spatial Information Engineering Beijing 100045China School of Geography and Information Engineering China University of GeosciencesWuhan 430074China Wuhan Geomatics Institute Wuhan 430074China College of Computer and Information Technology China Three Gorges UniversityYichang 443002China Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang 443002China Geological Environmental Center of Hubei Province Wuhan 430034China Faculty of Earth Resources China University of GeosciencesWuhan 430074China State Key Laboratory of Geological Processes and Mineral Resources China University of GeosciencesWuhan 430078China Wuhan Zondy Cyber Science&Technology Co. Ltd.Wuhan 430074China

Many detailed data on past geological hazard events are buried in geological hazard reports and have not been fully utilized. The growing developments in geographic information retrieval and temporal information retrieval offer opportunities to analyse this wealth of data to mine the spatiotemporal evolution of geological disaster occurrence and enhance risk decision making. This study presents a combined NLP and ontology matching information extraction framework for automatically recognizing semantic and spatiotemporal information from geological hazard reports. This framework mainly extracts unstructured information from geological disaster reports through named entity recognition, ontology matching and gazetteer matching to identify and annotate elements, thus enabling users to quickly obtain key information and understand the general content of disaster reports. In addition, we present the final results obtained from the experiments through a reasonable visualization and analyse the visual results. The extraction and retrieval of semantic information related to the dynamics of geohazard events are performed from both natural and human perspectives to provide information on the progress of events.

关键词： geological hazard report spatiotemporal information geological hazard ontology natural language processing gazetteers onlology machine learning

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3S-TSE: Efficient Three-Stage Target Speaker Extraction for Real-Time and Low-Resource Applications

3S-TSE: Efficient Three-Stage Target Speaker Extraction for ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shulin He Jinjiang Liu Hao Li Yang Yang Fei Chen Xueliang Zhang College of Computer Science Inner Mongolia University China Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen China Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural Resources China

Target speaker extraction (TSE) aims to isolate a specific voice from multiple mixed speakers relying on a registerd sample. Since voiceprint features usually vary greatly, current end-to-end neural networks require large model parameters which are computational intensive and impractical for real-time applications, espetially on resource-constrained platforms. In this paper, we address the TSE task using microphone array and introduce a novel three-stage solution that systematically decouples the process: First, a neural network is trained to estimate the direction of the target speaker. Second, with the direction determined, the Generalized Sidelobe Canceller (GSC) is used to extract the target speech. Third, an Inplace Convolutional Recurrent Neural Network (ICRN) acts as a denoising post-processor, refining the GSC output to yield the final separated speech. Our approach delivers superior performance while drastically reducing computational load, setting a new standard for efficient real-time target speaker extraction.

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Cross-Attention-Guided Wavenet for Mel Spectrogram Reconstruction in The ICASSP 2024 Auditory EEG Challenge

Cross-Attention-Guided Wavenet for Mel Spectrogram Reconstru...

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Acoustics, Speech, and Signal Processing Workshops (ICASSPW), IEEE International Conference on

作者： Yuan Fang Hao Li Xueliang Zhang Fei Chen Guanglai Gao College of Computer Science Inner Mongolia University China Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen China Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural Resources China

ISBN: (数字)9798350374513

ISBN: (纸本)9798350374520

This paper provides an overview of our submission to Task 2 of the Auditory EEG Challenge at ICASSP 2024 Signal Processing Grand Challenge (SPGC). We introduce a novel approach, employing a cross-attention-guided WaveNet with a coarse-to-fine generation strategy, aimed at enhancing the detailed reconstruction of Mel spectrograms from time-domain EEG. Specifically, the model utilizes WaveNet to sequentially reconstruct the envelope, 10-band Mel, 80-band Mel, and magnitude from coarse to fine granular levels. To bridge the gap between different modalities, we introduce a cross-attention mechanism, exploring correlations across modalities. A combined loss function is employed to refine the reconstruction performance. Notably, we achieved Pearson correlation values of 0.0651 ± 0.0153 for the validation set and 0.0413 ± 0.0169 for the heldout-subjects test set, securing the second position in the competition. We release the training code for our model online 1 .

关键词： Training Analytical models Correlation Brain modeling Electroencephalography Decoding Time-domain analysis

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A Post-Processing Module Scheme of Finite Element Software 2021

A Post-Processing Module Scheme of Finite Element Software

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2021 5th International Conference on Management Engineering, Software Engineering and Service Sciences, ICMSS 2021

作者： Yang, Deyun Zhao, Yingfeng Jin, Xiaolin Li, Bin Academy of Computer Simulation Technology National Key Laboratory of Science and Technology on Vacuum Electronics University of Electronic Science and Technology of China and NO.4 Section 2 North Jian she Road Chengdu610054 China

ISBN: (纸本)9781450389709

Scientific visualization is widely used in engineering design, finite element analysis visualization as an important application area is a significant tool to understand and analyze the result data of finite element. However, large quantity of calculation results causes it hardly meet the real time requirement of finite element application, meanwhile, due to the complexity and irregularity of 3D solid models and meshes, observing the fully data sets becomes difficult. This paper designs a data structure suitable for handling finite element complex calculation results, which improves the efficiency of data execution, furthermore, designs and develops a finite element post-processing module for data visualization. The module is developed based on the theory of Object-Oriented Programming and Microsoft Foundation Classes (MFC). The paper demonstrates the key technology regarding post-processing, including finite element meshes generation, data interpolation processing, nephogram rendering, contour line generation, vector data display and model-user interaction. Advanced scene rendering realization based on HOOPS 3D Graphics system, including lighting, rendering mode, interacting with the scene. The paper evaluates the module performance through simulating the high frequency electric field and magnetic field distribution in the rectangular micro-strip antenna. As the module is designed to be lean, fast, easy-To-use, and good visual quality, it satisfies the requirement of visualizing the result of the finite element simulation well. © 2021 ACM.

关键词： Data visualization

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D4M: Dataset Distillation via Disentangled Diffusion Model

D4M: Dataset Distillation via Disentangled Diffusion Model

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Duo Su Junjie Hou Weizhi Gao Yingjie Tian Bowen Tang UCAS School of Computer Science and Technology Research Center on Fictitious Economy and Data Science CAS Key Laboratory of Big Data Mining and Knowledge Management CAS UCAS Sino-Danish College Department of Computer Science NCSU UCAS School of Economics and Management UCAS MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation CAS Institute of Computing Technology

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Dataset distillation offers a lightweight synthetic dataset for fast network training with promising test accuracy. To imitate the performance of the original dataset, most approaches employ bi-level optimization and the distillation space relies on the matching architecture. Nevertheless, these approaches either suffer significant computational costs on large-scale datasets or experience performance decline on cross-architectures. We advocate for designing an economical dataset distillation framework that is independent of the matching architectures. With empirical observations, we argue that constraining the consistency of the real and synthetic image spaces will enhance the cross-architecture generalization. Motivated by this, we introduce Dataset Distillation via Disentangled Diffusion Model (D 4 M), an efficient framework for dataset distillation. Compared to architecture-dependent methods, D 4 M employs latent diffusion model to guarantee consistency and incorporates label information into category prototypes. The distilled datasets are versatile, eliminating the need for repeated generation of distinct datasets for various architectures. Through comprehensive experiments, D4M demonstrates superior performance and robust generalization, surpassing the SOTA methods across most aspects.

关键词： Training computer vision Computational modeling Prototypes computer architecture Diffusion models Pattern recognition

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Million-scale data integrated deep neural network for phonon properties of heuslers spanning the periodic table

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npj Computational Materials 2023年第1期9卷 2155-2166页

作者： Alejandro Rodriguez Changpeng Lin Hongao Yang Mohammed Al-Fahdi Chen Shen Kamal Choudhary Yong Zhao Jianjun Hu Bingyang Cao Hongbin Zhang Ming Hu Department of Mechanical Engineering University of South CarolinaColumbiaSC 29208USA Theory and Simulation of Materials(THEOS) École Polytechnique Fédérale de LausanneCH-1015 LausanneSwitzerland Key Laboratory for Thermal Science and Power Engineering of Ministry of Education Department of Engineering MechanicsTsinghua UniversityBeijing 100084China Institute of Materials Science Technical University of DarmstadtDarmstadt 64287Germany Materials Science and Engineering Division National Institute of Standards and TechnologyGaithersburgMD 20899USA Department of Computer Science and Engineering University of South CarolinaColumbiaSC 29208USA

Existing machine learning potentials for predicting phonon properties of crystals are typically limited on a material-to-materialbasis, primarily due to the exponential scaling of model complexity with the number of atomic species. We address this bottleneckwith the developed Elemental Spatial Density Neural Network Force Field, namely Elemental-SDNNFF. The effectiveness andprecision of our Elemental-SDNNFF approach are demonstrated on 11,866 full, half, and quaternary Heusler structures spanning 55elements in the periodic table by prediction of complete phonon properties. Self-improvement schemes including active learningand data augmentation techniques provide an abundant 9.4 million atomic data for training. Deep insight into predicted ultralowlattice thermal conductivity (<1 Wm^(−1) K^(−1)) of 774 Heusler structures is gained by p–d orbital hybridization analysis. Additionally, aclass of two-band charge-2 Weyl points, referred to as “double Weyl points”, are found in 68% and 87% of 1662 half and 1550quaternary Heuslers, respectively.

关键词： properties phonon spanning

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