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14th International Conference on Information Security and Cryptology, Inscrypt 2018

作者： Ma, Chuangui Ge, Aijun Zhang, Jie Army Aviation Institute Beijing China Key Laboratory of Cryptologic Technology and Information Security Ministry of Education Shandong University Jinan China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9783030142339

In this paper, we introduce a new multi-authority ciphertext policy attribute-based encryption (MA-CP-ABE) system. In our system, there are multiple central authorities (CAs) and attribute authorities (AAs). The CAs will not need to coordinate or even be aware of each other, and so do the AAs. In particular, we present two constructions that will be proved secure in the standard model. Our first scheme is fully secure under static assumptions in composite-order bilinear group, and can work for any monotone access structure. The second one achieves constant size ciphertexts for AND-gate policy in prime-order group. The security can be proved under the decisional linear (DLIN) assumption. © 2019, Springer Nature Switzerland AG.

关键词： Cryptography

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Recurrent Neural Network Based Binary Code Vulnerability Detection

Recurrent Neural Network Based Binary Code Vulnerability Det...

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2019第二届算法、计算和人工智能国际会议

作者： Jianyun Zheng Jianmin Pang Xiaochuan Zhang Xin Zhou MingLiang Li Jun Wang State Key Laboratory of Mathematical Engineering and Advanced Computing

Vulnerability detection, especially vulnerability automatic detection, have long been an area of active research. Considering that RNN can model dependencies automatically, we propose a recurrent neural network(RNN) based method for binary code vulnerability detection. We collected correspond to 4 types of vulnerabilities, including CWE-134, CWE-191, CWE-401 and *** find which RNN is better in vulnerability detection, we evaluated 6 different RNNs, including SRNN, BSRNN, LSTM, BLSTM, GRU and BGRU. The results demonstrated that BSRNN is better than others in vulnerability detection.

关键词： Vulnerability detection Deep Learning Recurrent Neural Network

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A Framework for Multi-Variant Execution Environment

A Framework for Multi-Variant Execution Environment

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作者： Wang Junchao Liu Zhenwu Liu Hao Pang Jianmin State key Laboratory of Mathematical Engineering and Advanced Computing

Software diversity has been proven to be an effective approach to enhance system *** make the best of the advantage brought by software diversity,a multi-variant execution environment is ***,although some MVEEs have been proposed,most of them are either too simple or only focusing one aspect which limits their widely adoption in *** this paper we propose a framework for multi-variant execution environment to enhance the security of software *** framework addresses different aspects when implementing a MVEE and can help to make the best of software diversity to enhance the system's security.

关键词： A Framework for Multi-Variant Execution Environment Framework

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Summary of Large Scale Graph computing Models

Summary of Large Scale Graph Computing Models

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2019 2nd International Conference on Mechanical,Electronic and engineering Technology(MEET 2019)

作者： Sheng Yang State Key Laboratory of Mathematical Engineering and Advanced Computing

Large-scale graph data is a research hotspot in the processing of big data and is generally used in traffic management, analysis of social network and semantic web, etc., resulting in the emergence of a variety of large-scale graph computing platforms. How to select a more suitable graph computing platform for different tasks has become one of the hot topics in the field of graph computing. The related knowledge of graph computing models is systematically reviewed in this paper. First of all, the concept of graph computing model is outlined, including the definition and characteristics of graph computing and the development of graph computing models. Then, three kinds of graph computing models are introduced. Finally, the development process of the graph computing models is summarized, and the future research direction is prospected.

关键词： Large-scale graph computing graph processing distributed computing

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Dynamic Nonreciprocity with a Kerr Nonlinear Resonator

arXiv

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arXiv 2022年

作者： Pan, Rui-Kai Tang, Lei Xia, keyu Nori, Franco College of Engineering and Applied Sciences National Laboratory of Solid State Microstructures Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing210093 China College of Physics and Electronic Engineering Sichuan Normal University Chengdu610101 China Jiangsu Key Laboratory of Artificial Functional Materials Nanjing University Nanjing210023 China Shishan Laboratory Suzhou Campus of Nanjing University Suzhou215000 China RIKEN Quantum Computing Center RIKEN Cluster for Pioneering Research Saitama Wako-shi351-0198 Japan Physics Department The University of Michigan Ann ArborMI48109-1040 United States

On-chip optical nonreciprocal devices are vital components for integrated photonic systems and scalable quantum information processing. Nonlinear optical isolators and circulators have attracted considerable attention because of their fundamental interest and their important advantages in integrated photonic circuits. However, optical nonreciprocal devices based on Kerr or Kerr-like nonlinearity are subject to dynamical reciprocity when the forward and backward signals coexist simultaneously in a nonlinear system. Here, we theoretically propose a method for realizing on-chip nonlinear isolators and circulators with dynamic nonreciprocity. Dynamic nonreciprocity is achieved via the chiral modulation on the resonance frequency due to coexisting self- and cross-Kerr nonlinearities in an optical ring resonator. This work showing dynamic nonreciprocity with a Kerr nonlinear resonator can be an essential step toward integrated optical isolation. © 2022, CC BY.

关键词： Nonlinear optics

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Fair Efficiency Comparisons of Decoy-state Quantum key Distribution Protocols

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Chinese Journal of Electronics 2018年第2期27卷 241-249页

作者： LI Hongxin GAO Ming YAN Xueping YAN Bao HAN Yu SHAN Ling MA Zhi Information Engineering University State Key Laboratory of Mathematical Engineering and Advanced Computing College of Animal Science and Technology Henan University of Science and Technology

Taking the schemes based on different formulas of secure key rate as examples, we give a fair comparison between typical protocols under Universal composable(UC) security frame. Through analyzing the relationship of security parameters in post-processing stage and final secure key rate, we achieve the unified quantification between protocols based on Gottesman-Lo-L¨utkenhausPreskill(GLLP) formula and the ones under UC *** on the above research, the impact of different sending lengths and secure parameters on secure key rate is investigated. Besides, we analyze the conditions of fair comparison, and for the first time we give a fair comparison between the protocols based on GLLP formula and smooth entropy. By typical efficiency comparisons, we summarize the way for improving secure key rate in the light intensity choice and the single bit error rate estimation method.

关键词： Quantum key distribution(QKD) Finite-key Universal composable(UC) Fair comparison

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Upper Security Bounds for Coherent-One-Way Quantum key Distribution

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Physical Review Letters 2020年第26期125卷 260510-260510页

作者： Javier González-Payo Róbert Trényi Weilong Wang Marcos Curty Escuela de Ingeniería de Telecomunicación Department of Signal Theory and Communications University of Vigo Vigo E-36310 Spain State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Henan 450001 China Henan Key Laboratory of Network Cryptography Technology Zhengzhou Henan 450001 China

The performance of quantum key distribution (QKD) is severely limited by multiphoton pulses emitted by laser sources due to the photon-number splitting attack. Coherent-one-way (COW) QKD has been introduced as a promising solution to overcome this limitation, and thus extend the achievable distance of practical QKD. Indeed, thanks to its experimental simplicity, the COW protocol is already used in commercial applications. Here, we derive simple upper security bounds on its secret key rate, which demonstrate that it scales at most quadratically with the system’s transmittance, thus solving a long-standing problem. That is, in contrast to what has been claimed, this approach is inappropriate for long-distance QKD transmission. Remarkably, our findings imply that all implementations of the COW protocol performed so far are insecure.

关键词： Quantum communication Quantum cryptography

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Model-Based Reinforcement Learning for Quantized Federated Learning Performance Optimization

Model-Based Reinforcement Learning for Quantized Federated L...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Nuocheng Yang Sihua Wang Mingzhe Chen Christopher G. Brinton Changchuan Yin Walid Saad Shuguang Cui Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China State Key Laboratory Of Networking And Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA School of Electrical and Computer Engineering Purdue University West Lafayette IN USA Bradley Department of Electrical and Computer Engineering Virginia Tech Arlington VA USA Shenzhen Research Institute of Big Data (SRIBD) and the Future Network of Intelligence Institute (FNii) Chinese University of Hong Kong Shenzhen China

ISBN: (纸本)9781665435413

This paper considers improving wireless communication and computation efficiency in federated learning (FL) via model quantization. In the proposed bitwidth FL scheme, edge devices train and transmit quantized versions of their local FL model parameters to a coordinating server, which, in turn, aggregates them into a quantized global model and synchronizes the devices. With the goal of jointly determining the set of participating devices in each training iteration and the bitwidths employed at the devices, we pose an optimization problem for minimizing the training loss of quantized FL under a device sampling budget and delay requirement. Our analytical results show that the improvement of FL training loss between two consecutive iterations depends on not only the device selection and quantization scheme, but also on several parameters inherent to the model being learned. As a result, we propose, a model-based reinforcement learning (RL) method to optimize action selection over iterations. Compared to model-free RL, the proposed approach leverages the derived mathematical characterization of the FL training process to discover an effective device selection and quantization scheme without imposing additional device communication overhead. Numerical evaluations show that the proposed FL framework can achieve the same classification performance while reducing the number of training iterations needed for convergence by 20% compared to model-free RL-based FL.

关键词： Training Performance evaluation Wireless communication Quantization (signal) Computational modeling Reinforcement learning mathematical models

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Automated identification and quantification of myocardial inflammatory infiltration in digital histological images to diagnose myocarditis

arXiv

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arXiv 2023年

作者： Liu, Yanyun Hua, Xiumeng Zhu, Shouping Wang, Congrui Chen, Xiao Shi, Yu Song, Jiangping Zhou, Weihua School of Life Science and Technology Xidian University Engineering Research Center of Molecular and Neuro Imaging Ministry of Education Shaanxi Xi'An China Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment School of Life Science and Technology Xidian University Shaanxi Xi'An China Innovation Center for Advanced Medical Imaging and Intelligent Medicine Guangzhou Institute of Technology Xidian University Guangdong Guangzhou China Beijing Key Laboratory of Preclinical Research and Evaluation for Cardiovascular Implant Materials Animal Experimental Centre Fuwai Hospital National Centre for Cardiovascular Disease Chinese Academy of Medical Sciences Peking Union Medical College Beijing China State Key Laboratory of Cardiovascular Disease Fuwai Hospital National Center for Cardiovascular Diseases Chinese Academy of Medical Sciences Peking Union Medical College 167A Beilishi Road Xi Cheng District Beijing China Department of Cardiovascular Surgery Fuwai Hospital National Center for Cardiovascular Diseases National Clinical Research Center of Cardiovascular Diseases State Key Laboratory of Cardiovascular Disease Chinese Academy of Medical Sciences Peking Union Medical College Beijing China The Cardiomyopathy Research Group Fuwai Hospital Beijing China Department of Applied Computing Michigan Technological University HoughtonMI United States Center for Biocomputing and Digital Health Institute of Computing and Cybersystems Health Research Institute Michigan Technological University HoughtonMI United States

Background Given the dimensionality of WSIs and the increase in the number of potential cases, the manual histological diagnosis of myocarditis based on Dallas Criteria is time-consuming, experience-dependent. Objectives To overcome the limitations of manual diagnosis methods, this study aims to develop a new computational pathology approach that automates the identification and quantification of myocardial inflammatory infiltration in digital hematoxylin and eosin (HE)-stained images to assist pathologists in diagnosing myocarditis. Methods 898 HE-stained whole slide images (WSIs) of myocardium from 154 heart transplant patients diagnosed with myocarditis or dilated cardiomyopathy (DCM) were included in this study. An automated deep-learning (DL)-based computational pathology approach was developed to identify nuclei and detect myocardial inflammatory infiltration, enabling the quantification of the lymphocyte nuclear density (LND) on myocardial WSIs. A cutoff value based on the quantification of LND was proposed to determine if the myocardial inflammatory infiltration was present. The performance of our approach was evaluated with a five-fold cross-validation experiment, tested with an internal test set from the myocarditis group, and confirmed by an external test from a double-blind trial group. Results An LND of 1.02/mm2 could distinguish WSIs with myocarditis from those without. The accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) in the five-fold cross-validation experiment were 0.899 ± 0.035, 0.971 ± 0.017, 0.728 ± 0.073 and 0.849 ± 0.044, respectively. For the internal test set, the accuracy, sensitivity, specificity, and AUC were 0.887, 0.971, 0.737, and 0.854, respectively. The accuracy, sensitivity, specificity, and AUC for the external test set reached 0.853, 0.846, 0.858, and 0.852, respectively. Conclusion Our new approach provides accurate and reliable quantification of the LND of myocardial WSIs bas

关键词： Pathology

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Multi-sensor Fusion Detection Method for Vehicle Target Based on Kalman Filter and Data Association Filter 7th

Multi-sensor Fusion Detection Method for Vehicle Target Base...

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7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Duan, Xuting Sun, Chengming Tian, Daxin Zheng, Kunxian Zhou, Gang E, Wenjuan Zhang, Yundong Beijing Advanced Innovation Center for Big Data and Brain Computing School of Transpotation Science and Engineering Beihang University Beijing100191 China Research Institute of Highway Ministry of Transport of the People’s Republic of China Beijing100088 China School of Rail Transportation Soochow University Suzhou215000 China Suzhou Automotive Research Institute Tsinghua University Suzhou215000 China State Key Laboratory of Digital Multimedia Chip Technology Chongqing Vimicro AI Chip Technology Co. Ltd. Chongqing400722 China

ISBN: (纸本)9783030786175

Multi-sensor data fusion is an emerging technology, which has been widely used in medical diagnosis, remote sensing, inertial navigation and many other fields. What’s more, the implementation and application of automatic driving system rely heavily on target detection technology. Due to the high mobility and unpredictability of vehicle-mounted equipment, for automatic vehicles, it is arduous to achieve real-time and accurate vehicle target detection by a single sensor means, thus it is difficult to reliably guarantee the safety and stability. This paper proposes a novel object detection method based on a multi-sensor fusion mechanism, which considers the real-time sensing data from two types of sensors including radar and camera. It collects multi-vehicle speed and position information efficiently and reliably. Then, it filters and integrates data according to Extended Kalman Filter, Data Association Filter and some other methods. Furthermore, vehicle-borne equipment makes intelligent decision based on the data. In addition to theoretical support, the designed simulation results also show that the multi-sensor fusion mechanism can detect target vehicles efficiently and accurately, and it has superiority in the stability and accuracy of perception than single sensor sensing method. © 2021, Springer Nature Switzerland AG.

关键词： Extended Kalman filters

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