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ARCosmetics:a real-time augmented reality cosmetics try-on system

Frontiers of Computer Science 2023年第4期17卷 15-28页

作者： Shan AN Jianye CHEN Zhaoqi ZHU Fangru ZHOU Yuxing YANG Yuqing MA Xianglong LIU Haogang ZHU Tech.&Data Center *** Inc.Beijing 100176China State Key Lab of Software Development Environment Beihang UniversityBeijing 100191China

A virtual cosmetics try-on system provides a realistic try-on experience for consumers and helps them efficiently choose suitable *** this article,we propose a real-time augmented reality virtual cosmetics try-on system for smartphones(ARCosmetics),taking speed,accuracy,and stability into consideration at each step to ensure a better user experience.A novel and very fast face tracking method utilizes the face detection box and the average position of facial landmarks to estimate the faces in continuous frames.A dynamic weight Wing loss is introduced to assign a dynamic weight to every landmark by the estimated error during *** balances the attention between small,medium,and large range error and thus increases the accuracy and *** also designed a weighted average method to utilize the information of the adjacent frame for landmark refinement,guaranteeing the stability of the generated *** experiments conducted on a large 106-point facial landmark dataset and the 300-VW dataset demonstrate the superior performance of the proposed method compared to other state-of-the-art *** also conducted user satisfaction studies further to verify the efficiency and effectiveness of our ARCosmetics system.

关键词： facial landmark localization face tracking stabilization augmented reality virtual try-on

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Research on performance optimization of virtual data space across WAN

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Frontiers of Computer Science 2024年第6期18卷 167-187页

作者： Jiantong HUO Zhisheng HUO Limin XIAO Zhenxue HE State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China High Performance Computing Center Beihang UniversityBeijing 100191China Hebei Key Laboratory of Agricultural Big Data Hebei Agricultural UniversityBaoding 071001China

For the high-performance computing in a WAN environment,the geographical locations of national supercomputing centers are scattered and the network topology is complex,so it is difficult to form a unified view of *** aggregate the widely dispersed storage resources of national supercomputing centers in China,we have previously proposed a global virtual data space named GVDS in the project of“High Performance Computing Virtual Data Space”,a part of the National key Research and development Program of *** GVDS enables large-scale applications of the high-performance computing to run efficiently across ***,the applications running on the GVDS are often data-intensive,requiring large amounts of data from multiple supercomputing centers across *** this regard,the GVDS suffers from performance bottlenecks in data migration and access across *** solve the above-mentioned problem,this paper proposes a performance optimization framework of GVDS including the multitask-oriented data migration method and the request access-aware IO proxy resource allocation *** a WAN environment,the framework proposed in this paper can make an efficient migration decision based on the amount of migrated data and the number of multiple data sources,guaranteeing lower average migration latency when multiple data migration tasks are running in *** addition,it can ensure that the thread resource of the IO proxy node is fairly allocated among different types of requests(the IO proxy is a module of GVDS),so as to improve the application’s performance across *** experimental results show that the framework can effectively reduce the average data access delay of GVDS while improving the performance of the application greatly.

关键词： storage aggregation across WANs large-scale applications GVDS data migration allocation of IO proxy resource

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Building a domain-specific compiler for emerging processors with a reusable approach

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Science China(Information Sciences) 2024年第1期67卷 20-38页

作者： Mingzhen LI Yi LIU Bangduo CHEN Hailong YANG Zhongzhi LUAN Depei QIAN State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University

High-performance computing and deep learning domains have been motivating the design of domain-specific *** these processors can provide promising computation capability,they are notorious for exotic programming *** improve programming productivity and fully exploit the performance potential of these processors,domain-specific compilers(DSCs) have been ***,building DSCs for emerging processors requires tremendous engineering efforts because the commonly used compilation stack is difficult to be *** to the advent of multilevel intermediate representation(MLIR),DSC developers can leverage reusable infrastructure to extend their customized functionalities without rebuilding the entire compilation *** this paper,we further demonstrate the effectiveness of MLIR by extending its reusable infrastructure to embrace a heterogeneous many-core processor(Sunway processor).In particular,we design a new Sunway dialect and corresponding backend for the Sunway processor,fully exploiting its architectural advantage and hiding its programming *** show the ease of building a DSC,we leverage the Sunway dialect and existing MLIR dialects to build a stencil compiler for the Sunway *** experimental results show that our stencil compiler,built with a reusable approach,can even perform better than state-of-the-art stencil compilers.

关键词： domain-specific compiler emerging processor reusable dialect performance optimization MLIR

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Rumor Detection on Social Media with Temporal Propagation Structure Optimization 31

Rumor Detection on Social Media with Temporal Propagation St...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Peng, Xingyu Wu, Junran Liu, Ruomei Xu, Ke State Key Laboratory of Complex & Critical Software Environment Beihang University Beijing China

ISBN: (纸本)9798891761964

Traditional methods for detecting rumors on social media primarily focus on analyzing textual content, often struggling to capture the complexity of online interactions. Recent research has shifted towards leveraging graph neural networks to model the hierarchical conversation structure that emerges during rumor propagation. However, these methods tend to overlook the temporal aspect of rumor propagation and may disregard potential noise within the propagation structure. In this paper, we propose a novel approach that incorporates temporal information by constructing a weighted propagation tree, where the weight of each edge represents the time interval between connected posts. Drawing upon the theory of structural entropy, we transform this tree into a coding tree. This transformation aims to preserve the essential structure of rumor propagation while reducing noise. Finally, we introduce a recursive neural network to learn from the coding tree for rumor veracity prediction. Experimental results on two common datasets demonstrate the superiority of our approach. © 2025 Association for Computational Linguistics.

关键词： Graph neural networks

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SeeMore: a spatiotemporal predictive model with bidirectional distillation and level-specific meta-adaptation

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Science China(Information Sciences) 2024年第8期67卷 147-171页

作者： Yuqing MA Wei LIU Yajun GAO Yang YUAN Shihao BAI Haotong QIN Xianglong LIU State Key Lab of Software Development Environment Beihang University

Predicting future frames using historical spatiotemporal data sequences is challenging and critical, and it is receiving a lot of attention these days from academic and industrial scholars. Most spatiotemporal predictive algorithms ignore the valuable backward reasoning ability and the disparate learning complexities among different layers and hence, cannot build good long-term dependencies and spatial correlations,resulting in suboptimal solutions. To address the aforementioned issues, we propose a two-stage coarse-to-fine spatiotemporal predictive model with bidirectional distillation and level-specific meta-adaptation(See More)in this paper, which includes a bidirectional distillation network(BDN) and a level-specific meta-adapter(LMA), to gain bidirectional multilevel reasoning. In the first stage, BDN concentrates on bidirectional dynamics modeling and coarsely constructs spatial correlations of different layers, while LMA is introduced in the second fine-tuning stage to refine the multilevel spatial correlations from a meta-learning *** particular, BDN mimics the forward and backward reasoning abilities of humans in a distillation manner,which aids in the development of long-term dependencies. The LMA views learning of different layers as disparate but related tasks and guides the transfer of learning experiences among these tasks through learning complexities. Thus, each layer could be closer to its solutions and could extract more informative spatial correlations. By capturing the enhanced short-term spatial correlations and long-term temporal dependencies,the proposed model could extract adequate knowledge from sequential historical observations and accurately predict future frames whose backtracking preconditions are consistent with the historical sequence. Our work is general and robust enough to be integrated into most spatiotemporal predictive models without requiring additional computation or memory cost during inference. Extensive experimen

关键词： spatiotemporal predictive learning knowledge transfer bidirectional distillation network level-specific meta-adapter coarse-to-fine training

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ValidGen: A Tool for Automatic Generation of Validation Scripts to Support Rapid Requirements Validation 24

ValidGen: A Tool for Automatic Generation of Validation Scri...

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46th International Conference on software Engineering: Companion, ICSE-Companion 2024

作者： Pan, Hongyue Yang, Yilong School of Software Beihang University State Key Laboratory of Complex & Critical Software Environment Beijing China

ISBN: (纸本)9798400705021

Rapid prototyping is an effective way for requirement validation in the earliest stages of software development. Our previous work, RM2PT, can automatically generate software prototypes from requirements models to support incremental and rapid requirements validation. This paper proposes a CASE tool named ValidGen based on RM2PT, which can automatically generate validation scripts to execute the prototype. Thanks to these validation scripts, the stakeholder only needs to monitor the execution process without selecting the system operation and typing the input parameters, which significantly reduces the time and effort needed for validating requirements. We adopted three case studies to evaluate the tool, and the results show that the tool requires only about 60% of the time for requirements validation compared to traditional methods. Overall, the results were satisfactory. The proposed tool can be further extended and applied for requirements validation in the software industry. The tool can be downloaded at https://***/advs/validgen/, and a demo video is at https://***/AP9Ymg1ewIA. © 2024 IEEE Computer Society. All rights reserved.

关键词： software prototyping

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Real-time prediction of mechanical behaviors of underwater shield tunnel structure using machine learning method based on structural health monitoring data

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Journal of Rock Mechanics and Geotechnical Engineering 2023年第4期15卷 886-895页

作者： Xuyan Tan Weizhong Chen Tao Zou Jianping Yang Bowen Du State Key Laboratory of Geomechanics and Geotechnical Engineering Institute of Rock and Soil MechanicsChinese Academy of SciencesWuhan430071China University of Chinese Academy of Sciences Beijing100049China State Key Laboratory of Software Development Environment Beihang UniversityBeijing100191China

Predicting the mechanical behaviors of structure and perceiving the anomalies in advance are essential to ensuring the safe operation of infrastructures in the long *** addition to the incomplete consideration of influencing factors,the prediction time scale of existing studies is ***,this study focuses on the development of a real-time prediction model by coupling the spatio-temporal correlation with external load through autoencoder network(ATENet)based on structural health monitoring(SHM)*** autoencoder mechanism is performed to acquire the high-level representation of raw monitoring data at different spatial positions,and the recurrent neural network is applied to understanding the temporal correlation from the time ***,the obtained temporal-spatial information is coupled with dynamic loads through a fully connected layer to predict structural performance in next 12 *** a case study,the proposed model is formulated on the SHM data collected from a representative underwater shield *** robustness study is carried out to verify the reliability and the prediction capability of the proposed ***,the ATENet model is compared with some typical models,and the results indicate that it has the best *** model is of great value to predict the realtime evolution trend of tunnel structure.

关键词： Shied tunnel Machine learning Monitoring Real-time prediction Data analysis

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CAPS: An Efficient Whole-Program Critical Paths Search Framework for Large-Scale software 35

CAPS: An Efficient Whole-Program Critical Paths Search Frame...

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35th International Conference on software Engineering and Knowledge Engineering, SEKE 2023

作者： Li, Peiyang Liu, Zixin Su, Yuening Wang, Hao Jiang, Bo State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University China

Tracking the flow of external inputs in a program with taint-analysis techniques can help developers better identify potential security vulnerabilities in the software. However, directly using the static taint analysis provided by Clang Static Analyzer is inefficient for large-scale software due to the huge but redundant ExplodedGraph generated. Therefore, we propose an efficient Whole-Program Critical Paths Search (CAPS) framework. It first performs a set of optimizations to reduce the ExplodedGraph of each function. Then, it constructs a global exploded graph by inserting call edges among the reduced ExplodedGraphs for each function within the Neo4j graph database. Finally, it proposes loop removal and graph segmentation to optimize the search process for critical paths on the global exploded graph. Our experiments on 3 large-scale software show that CAPS can significantly improve the efficiency of critical path search for large-scale software. © 2023 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Static analysis

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SMEC:Scene Mining for E-Commerce

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Journal of Computer Science & Technology 2024年第1期39卷 192-210页

作者：王罡李翔郭子义殷大伟马帅 State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Data Science and Engineering East China Normal UniversityShanghai 200062China *** Inc.Beijing 100176China Baidu Inc.Beijing 100085China

Scene-based recommendation has proven its usefulness in E-commerce,by recommending commodities based on a given ***,scenes are typically unknown in advance,which necessitates scene discovery for *** this article,we study scene discovery for E-commerce *** first formalize a scene as a set of commodity cate-gories that occur simultaneously and frequently in real-world situations,and model an E-commerce platform as a heteroge-neous information network(HIN),whose nodes and links represent different types of objects and different types of rela-tionships between objects,*** then formulate the scene mining problem for E-commerce as an unsupervised learning problem that finds the overlapping clusters of commodity categories in the *** solve the problem,we pro-pose a non-negative matrix factorization based method SMEC(Scene Mining for E-Commerce),and theoretically prove its *** six real-world E-commerce datasets,we finally conduct an extensive experimental study to evaluate SMEC against 13 other methods,and show that SMEC consistently outperforms its competitors with regard to various evaluation measures.

关键词： graph clustering E-commerce heterogeneous information network(HIN) scene mining

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Combining KNN with AutoEncoder for Outlier Detection

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Journal of Computer Science & Technology 2024年第5期39卷 1153-1166页

作者： Shu-Zheng Liu Shuai Ma Han-Qing Chen Li-Zhen Cui Jie Ding State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Software Shandong UniversityJinan 250100China Joint SDU-NTU Centre for Artificial Intelligence Research Shandong UniversityJinan 250100China School of Computer Science Jiangsu University of Science and TechnologyZhenjiang 212003China

K-nearest neighbor(KNN)is one of the most fundamental methods for unsupervised outlier detection because of its various advantages,e.g.,ease of use and relatively high ***,most data analytic tasks need to deal with high-dimensional data,and the KNN-based methods often fail due to“the curse of dimensionality”.AutoEncoder-based methods have recently been introduced to use reconstruction errors for outlier detection on high-dimensional data,but the direct use of AutoEncoder typically does not preserve the data proximity relationships well for outlier *** this study,we propose to combine KNN with AutoEncoder for outlier ***,we propose the Nearest Neighbor AutoEncoder(NNAE)by persevering the original data proximity in a much lower dimension that is more suitable for performing ***,we propose the K-nearest reconstruction neighbors(K NRNs)by incorporating the reconstruction errors of NNAE with the K-distances of KNN to detect ***,we develop a method to automatically choose better parameters for optimizing the structure of ***,using five real-world datasets,we experimentally show that our proposed approach NNAE+K NRN is much better than existing methods,i.e.,KNN,Isolation Forest,a traditional AutoEncoder using reconstruction errors(AutoEncoder-RE),and Robust AutoEncoder.

关键词： outlier detection AutoEncoder K-nearest neighbor(KNN) unsupervised learning

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