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

作者： Xiaotao Wu Zhaoxin Fan Huiguang He Dinggang Shen School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai China NeuBCI Group State Key Laboratory of Brain Cognition and Brain-Inspired Intelligence Technology Institute of Automation Chinese Academy of Sciences Beijing China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Institute of Artificial Intelligence Beihang University Beijing China Beijing Academy of Blockchain and Edge Computing China Shanghai United Imaging Intelligence Co. Ltd. Shanghai China Shanghai Clinical Research and Trial Center Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Sandwich-like structures have shown remarkable efficacy in clothed human reconstruction. However, these approaches often generate unrealistic side geometries due to inadequate handling of lateral regions. This paper addresses this limitation by incorporating the side geometry of clothed humans as a prior. We propose ThicknessVAE, a novel two-stage method that makes two key contributions: (1) We learn a prototype from point clouds for the lateral regions of clothed humans to extract common and detailed geometric features. (2) We utilize this prototype as a prior to transform geometric features into a thickness map associated with clothed human images, enabling refined normal integration for sandwich-like reconstruction methods. By seamlessly integrating our model into the sandwich-like reconstruction pipeline, we achieve highly realistic side views. Both qualitative and quantitative experiments demonstrate that our approach is comparable to state-of-the-art methods in terms of side-view realism.

关键词： Geometry Surface reconstruction Three-dimensional displays Pipelines Prototypes Transforms Feature extraction Speech processing Image reconstruction Surface treatment

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Stealthy Malware Detection Based on Deep Neural Network 2

Stealthy Malware Detection Based on Deep Neural Network

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2019 2nd International Symposium on Big Data and Applied Statistics, ISBDAS 2019

作者： Lu, Shoupu Li, Qingbao Zhu, Xinbing State Key Laboratory of Mathematical Engineering and Advanced Computing Henan Province Zhengzhou450001 China Henan University of Economics and Law Henan Province Zhengzhou450000 China

Network attacks using advanced local hiding technology have not only increased, but also become a serious threat. However, attacks using these technologies can not be detected through traffic detection, and some attacks imitate benign traffic to avoid detection. To solve these problems, a malware process detection method based on process behavior in possibly infected terminals is proposed. In this method, a deep neural network is introduced to classify malware processes. Firstly, the recurrent neural network is trained to extract the characteristics of process behavior. Secondly, training convolutional neural network is used to classify feature images generated by trained RNN features. The experiments results show that this method can effectively extract the features of malicious processes, and the AUC of ROC curve is 0.97 in the best case. © Published under licence by IOP Publishing Ltd.

关键词： Malware

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Parallelizing Neural Network Models Effectively on GPU by Implementing Reductions Atomically 22

Parallelizing Neural Network Models Effectively on GPU by Im...

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Proceedings of the International Conference on Parallel Architectures and Compilation Techniques

作者： Jie Zhao Cédric Bastoul Yanzhi Yi Jiahui Hu Wang Nie Renwei Zhang Zhen Geng Chong Li Thibaut Tachon Zhiliang Gan State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Huawei Technologies France SASU Paris France Huawei Technologies Co. Ltd. Beijing China Huawei Technologies Co. Ltd. Hangzhou China Huawei Technologies Co. Ltd. Shenzhen China

ISBN: (纸本)9781450398688

Due to the missing of a good orchestration of loop transformations, existing optimizing compilers for deploying neural networks on GPU either parallelize reductions ineffectively or miss the fusion opportunities with other operators. Neural network models thus exhibit sub-optimal performance on GPU. We present a practical approach called Panamera for the effective parallelization of reductions in neural networks on GPU. Panamera first leverages loop coalescing to flatten the loop dimensions of reductions, converting all reduction operators into canonical forms eligible for the polyhedral model. Next, Panamera uses polyhedral transformations to reduce the data movements caused by unfused reductions and perform multi-block hardware binding not considered by many compilers. Finally, Panamera embeds a highly optimized routine implemented using GPU atomic instructions, further improving the performance of neural network models while guaranteeing the correctness of parallel reductions. The experimental results demonstrate the effectiveness of our approach: for single operators our code obtains a mean speedup of 33.7×, 3.5×, 5.4× and 9.6× over cuDNN, CUB, TVM and Ansor, for sub-graphs our approach outperforms cuDNN, TVM and Ansor by 9.5×, 2.6× and 2.7×, and for end-to-end workloads, a tensor compiler integrated with our approach outperforms them by 122.5%, 19.3% and 15.2%.

关键词： GPU polyhedral compilation deep learning reduction

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PS-HOMO: A Protection Scheme for iOS SMS 4

PS-HOMO: A Protection Scheme for iOS SMS

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2019 4th International Seminar on Computer Technology, Mechanical and Electrical engineering, ISCME 2019

作者： Zhou, Guomiao Wu, Qianqiong Duan, Ming Xi, Qi State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Henan450000 China Institute of Electrical Engineering and Automation Luoyang Institute of Science and Technology Luoyang Henan471000 China

iOS Short Message Service (SMS) transmits messages in plaintext, which would lead to potential security threats of data. It is meaningful to design a safe and effective SMS. In this paper, all iOS Security Guides are referred to and all the cryptographic algorithms in iOS data protection are analyzed. Because there is no homomorphic encryption, a library of homomorphic encryption algorithms for iOS based on RSA and Paillier is developed. Using this library, a protection scheme PS-HOMO for protecting iOS SMS data is proposed and implemented. Under the premise of ensuring the data confidentiality, PS-HOMO also has the function of ciphertext operation due to the multiplicative homomorphism of RSA and additive homomorphism of Paillier. A comprehensive performance test was performed on PS-HOMO. The results show that the performance is in line with our expectations and will not have a major impact on the original system. The security of PS-HOMO is theoretically analyzed from man-in-the-middle attack, replay attack, and traffic analysis attack. Two application scenarios of the PS-HOMO, remote anonymous voting and privacy telemedicine service are envisaged. We hope PS-HOMO would play a significant role in privacy protection. © 2020 IOP Publishing Ltd. All rights reserved.

关键词： Cryptography

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Improved Impossible Polytopic Attacks on Round-reduced des 4

Improved Impossible Polytopic Attacks on Round-reduced des

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2019 4th International Seminar on Computer Technology, Mechanical and Electrical engineering, ISCME 2019

作者： Wang, Jing Wu, Qianqiong Fu, Chaohui Zhou, Guomiao Duan, Ming State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Henan450000 China College of Electrical Engineering and Automation Luoyang Institute of Science and Technology Luoyang Henan471000 China

In Eurocrypt 2016, Tyge Tiessen introduced the d-difference which considering the differences between multiple plaintexts, and proposed the impossible polytopic attack on block cipher which effectively reducing the data complexity. In this paper, we improve the impossible polytopic attacks of round-reduced DES by some ideas like truncated differentials. Given the input 3-difference of each S-box in the third round, the number of the output 3-difference is actually smaller than the theoretical upper bound, which helps us reduce the memory complexity of the attack on 5-round DES from 212 bytes to 26.9 bytes and increase the success rate of the attack. Using the idea of truncated differentials, the time complexity of the attack on 6-round DES is reduced from 232.2 encryptions to 225.8 encryptions by selecting the output 3-differences of 6 S-boxes for key recovery. We also improve the attack on 7-round DES by using more plaintexts based on our improved attack on 6-round DES. © 2020 IOP Publishing Ltd. All rights reserved.

关键词： Gears

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Design and implementation of high performance cryptographic service framework based on trustzone

Design and implementation of high performance cryptographic ...

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2020 International Conference on engineering Management of Communication and Technology, EMCTECH 2020

作者： Yuan, Lu Huang, Chenlin Wang, Lulu Ding, Yan Li, Yun Cheng, Hua National University of Defense Technology Academy of Computer Science Changsha China Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China

ISBN: (纸本)9781665404488

Due to its favorable isolation and security features, TrustZone technology has been widely used in mobile payment, cloud computing, Internet of Things, etc. However, the cryptographic algorithm provided by the trusted execution environment such as Open-TEE and OP-TEE is inadequate, which brings inconvenience to the development of trusted applications. In addition, the system mainly uses the software algorithms to provide cryptographic services and does not make full use of hardware cryptographic resources, which results in the low efficiency of cryptographic service. To Figure out these two issues, aiming at features of domestic Phytium processors, this paper establishes a unified cryptographic service framework in the TrustZone platform to build a more rich and complete cryptographic algorithm library. Meanwhile, it makes full use of the hardware cryptographic resources of the processor platform to improve the performance of cryptographic service. Test result shows that the proposed framework can bring significant convenience to the development of trusted applications. Meanwhile, compared with the original cryptographic service performance, the cryptographic service provided by the new framework can achieve significant performance improvement. © 2020 IEEE.

关键词： Cryptography

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MAST-Pro: Dynamic Mixture-of-Experts for Adaptive Segmentation of Pan-Tumors with Knowledge-Driven Prompts

arXiv

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arXiv 2025年

作者： Meng, Runqi Song, Sifan Jin, Pengfei Oh, Yujin Teng, Lin Wang, Yulin Sun, Yiqun Chen, Ling Li, Xiang Li, Quanzheng Guo, Ning Shen, Dinggang School of Biomedical Engineering State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University China Center of Advanced Medical Computing and Analysis Massachusetts General Hospital Harvard Medical School Harvard University United States Shanghai United Imaging Intelligence Co. Ltd China Shanghai Clinical Research and Trial Center China

Accurate tumor segmentation is crucial for cancer diagnosis and treatment. While foundation models have advanced general-purpose segmentation, existing methods still struggle with: (1) limited incorporation of medical priors, (2) imbalance between generic and tumor-specific features, and (3) high computational costs for clinical adaptation. To address these challenges, we propose MAST-Pro (Mixture-of-experts for Adaptive Segmentation of pan-Tumors with knowledge-driven Prompts), a novel framework that integrates dynamic Mixture-of-Experts (D-MoE) and knowledge-driven prompts for pan-tumor segmentation. Specifically, text and anatomical prompts provide domain-specific priors, guiding tumor representation learning, while D-MoE dynamically selects experts to balance generic and tumor-specific feature learning, improving segmentation accuracy across diverse tumor types. To enhance efficiency, we employ Parameter-Efficient Fine-Tuning (PEFT), optimizing MAST-Pro with significantly reduced computational overhead. Experiments on multi-anatomical tumor datasets demonstrate that MAST-Pro outperforms state-of-the-art approaches, achieving up to a 5.20% improvement in average DSC while reducing trainable parameters by 91.04%, without compromising accuracy. © 2025, CC BY-NC-ND.

关键词： Image segmentation

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PediaBench: A Comprehensive Chinese Pediatric Dataset for Benchmarking Large Language Models

arXiv

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arXiv 2024年

作者： Zhang, Qian Chen, Panfeng Li, Jiali Feng, Linkun Liu, Shuyu Zhao, Heng Chen, Mei Li, Hui Wang, Yanhao State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China Guizhou Engineering Laboratory for Advanced Computing and Medical Information Services Guiyang China College of Big Data and Internet Shenzhen Technology University Shenzhen China School of Data Science and Engineering East China Normal University Shanghai China

The emergence of Large Language Models (LLMs) in the medical domain has stressed a compelling need for standard datasets to evaluate their question-answering (QA) performance. Although there have been several benchmark datasets for medical QA, they either cover common knowledge across different departments or are specific to another department rather than pediatrics. Moreover, some of them are limited to objective questions and do not measure the generation capacity of LLMs. Therefore, they cannot comprehensively assess the QA ability of LLMs in pediatrics. To fill this gap, we construct PediaBench, the first Chinese pediatric dataset for LLM evaluation. Specifically, it contains 4,117 objective questions and 1,632 subjective questions spanning 12 pediatric disease groups. It adopts an integrated scoring criterion based on different difficulty levels to thoroughly assess the proficiency of an LLM in instruction following, knowledge understanding, clinical case analysis, etc. Finally, we validate the effectiveness of PediaBench with extensive experiments on 20 open-source and commercial LLMs. Through an in-depth analysis of experimental results, we offer insights into the ability of LLMs to answer pediatric questions in the Chinese context, highlighting their limitations for further improvements. Our code and data are published anonymously at https://***/ACMISLab/PediaBench. © 2024, CC BY-SA.

关键词： Question answering

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Universal conservation laws of the wave-particleentanglement triad:theory and experiment

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Light(Science & Applications) 2025年第3期14卷 861-868页

作者： Ziheng Ding Yaohao Deng Shao-Ming Fei Si-Qi Zhou Xiaojiong Chen Ziwen Rui Zhihao Ma Yunlong Xiao Jianwei Wang School of Mathematical Sciences MOE-LSCShanghai Jiao Tong UniversityShanghai 200240China State Key Laboratory for Mesoscopic Physics School of PhysicsPeking UniversityBejing 100871China Bejing Academy of Quantum Information Sciences Beijing 100871China School of Mathematical Sciences Capital Normal UniversityBeijing 100048China Max Planck Institute for Mathematics in the Sciences Leipzig 04103Germany Department of Physics and Astronomy Stony Brook UniversityNew YorkNY11794-3800USA Shanghai Seres Information Technology Co. LtdShanghai 200040China shenzhen Institute for Quantum Science and Engineering Southern University of Science and TechnologyShenzhen 518055China A*STAR Quantum Innovation Centre(Q.InC) Agency for ScienceTechnology and Research(A*STAR)2Fusionopolis WayInnovis\#08-03Singapore138634Republic of Singapore institute of High Performance Computing(IHPC) Agency for ScienceTechnology and Research(A*STAR)1 Fusionopolis WayConnexis\#16-16Singapore 138632Republic of Singapore

When observed,a quantum system exhibits either wave-like or particle-like properties,depending on how it is ***,this duality is affected by the entanglement of the system with its quantum memory,raising a fundamental question:how are wave-particle duality and entanglement related?Here,we broaden the scope of wave-particle duality to include entanglement,introduce universal conservation laws for the wave-particle-entanglement triad,and perform demonstrations on silicon-integrated nanophotonic quantum *** experiments not only mark the first confirmation of universal conservation laws but also highlight the potential of integrated photonics for exploring complex quantum phenomena in high-dimensional systems.

关键词： high dimensional systems universal conservation laws entanglement nanophotonic quantum chips quantum systems wave particle duality quantum system

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引用

IEEE International Conference on Software engineering and Service Sciences (ICSESS)

作者： Duanyi Wang Hui Shu Fei Kang Wenjuan Bu State Key Laboratory of Mathematical Engineering and Advanced Computing Henan Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Henan China

ISBN: (数字)9781728165790

ISBN: (纸本)9781728165806

Recently, graph-based malware similarity analysis has been widely used in the field of malware detection. However, the wide application of code obfuscation, polymorphism, and deformation changes the structure of malicious code, which brings great challenges to the malware similarity analysis. To solve these problems, in this paper, we present a new approach to malware similarity analysis based on the network control structure graph (NCSG). This method analyzed the behavior of malware by application program interface (API) association and constructed NCSG. The graph could reflect the command-and-control(C&C) logic of malware. Therefore, it can resist the interference of code obfuscation technology. The structural features extracted from NCSG will be used as the basis of similarity analysis for training the detection model. Finally, we tested the dataset constructed from five known malware family samples, and the experimental results showed that the accuracy of this method for malware variation analysis reached 92.75%. In conclusion, the malware similarity analysis based on NCSG has a strong application value for identifying the same family of malware.

关键词： Training Software algorithms Resists Feature extraction Malware Strain Software engineering

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