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Preparing a commercial quantum key distribution system for certification against implementation loopholes

Physical Review Applied 2024年第4期22卷 044076页

作者： Vadim Makarov Alexey Abrikosov Poompong Chaiwongkhot Aleksey K. Fedorov Anqi Huang Evgeny Kiktenko Mikhail Petrov Anastasiya Ponosova Daria Ruzhitskaya Andrey Tayduganov Daniil Trefilov Konstantin Zaitsev Skolkovo Moscow 121205 Russia Vigo Quantum Communication Center NTI Center for Quantum Communications Department of Physics Faculty of Science Institute for Quantum Computing Department of Physics and Astronomy Quantum technology foundation (Thailand) Bangkok 10110 Thailand QRate Skolkovo Moscow 143026 Russia Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer Science and Technology Moscow 119991 Russia atlanTTic Research Center School of Telecommunication Engineering Department of Signal Theory and Communications Moscow 101000 Russia

A commercial quantum key distribution (QKD) system needs to be formally certified to enable its wide deployment. The certification should include the system’s robustness against known implementation loopholes and attacks that exploit them. Here we ready a fiber-optic QKD system for this procedure. The system has a prepare-and-measure scheme with decoy-state BB84 protocol, polarization encoding, a qubit source rate of 312.5 MHz, and is manufactured by QRate. We detail its hardware and postprocessing. We analyze the hardware for known implementation loopholes, search for possible new loopholes, and discuss countermeasures. We then amend the system design to address the highest-risk loopholes identified. We also work out technical requirements on the certification lab and outline its possible structure.

关键词： Quantum communication Quantum cryptography Quantum protocols

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Verifying Safety of Neural Networks from Topological Perspectives

arXiv

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arXiv 2023年

作者： Liang, Zhen Ren, Dejin Xue, Bai Wang, Ji Yang, Wenjing Liu, Wanwei National University of Defense Technology Institute for Quantum Information State Key Laboratory of High Performance Computing Hunan Changsha410073 China Chinese Academy of Sciences State Key Lab. of Computer Science BeijingBAI XUE China

Neural networks (NNs) are increasingly applied in safety-critical systems such as autonomous vehicles. However, they are fragile and are often ill-behaved. Consequently, their behaviors should undergo rigorous guarantees before deployment in practice. In this paper, we propose a set-boundary reachability method to investigate the safety verification problem of NNs from a topological perspective. Given an NN with an input set and a safe set, the safety verification problem is to determine whether all outputs of the NN resulting from the input set fall within the safe set. In our method, the homeomorphism property and the open map property of NNs are mainly exploited, which establish rigorous guarantees between the boundaries of the input set and the boundaries of the output set. The exploitation of these two properties facilitates reachability computations via extracting subsets of the input set rather than the entire input set, thus controlling the wrapping effect in reachability analysis and facilitating the reduction of computation burdens for safety verification. The homeomorphism property exists in some widely used NNs such as invertible residual networks (i-ResNets) and Neural ordinary differential equations (Neural ODEs), and the open map is a less strict property and easier to satisfy compared with the homeomorphism property. For NNs establishing either of these properties, our set-boundary reachability method only needs to perform reachability analysis on the boundary of the input set. Moreover, for NNs that do not feature these properties with respect to the input set, we explore subsets of the input set for establishing the local homeomorphism property and then abandon these subsets for reachability computations. Finally, some examples demonstrate the performance of the proposed *** Codes 68Q60, 68T07 Copyright © 2023, The Authors. All rights reserved.

关键词： Topology

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DUNE: Improving Accuracy for Sketch-INT Network Measurement Systems

arXiv

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arXiv 2022年

作者： Wei, Zhongxiang Tian, Ye Chen, Wei Gu, Liyuan Zhang, Xinming The Anhui Key Laboratory of High Performance Computing School of Computer Science and Technology University of Science and Technology of China Anui Hefei230026 China

In-Band Network Telemetry (INT) and sketch algorithms are two representative methodologies for measuring network traffics in real time. To combine sketch with INT and to keep their advantages, the "reconstructing sketch at end-host" approach, which uses INT to send small pieces of switch sketch (i.e., sketchlet) to end-host for reconstructing an identical sketch, is a promising direction. However, we reveal that the naive column sketchlet is not efficient, and inaccuracies arise because of the invalid and stale measurement data in the end-host reconstructed sketch. In this paper, we present an innovative sketch-INT measurement system named DUNE. DUNE is lightweight by following the "reconstructing the sketch at end-host" approach, and to improve the measurement accuracy, we make two innovations: First, we design a novel sketchlet named scatter sketchlet that is more efficient in transferring measurement data by allowing a switch to select individual sketch buckets to add to sketchlet;Second, we develop data structures for tracing "freshness" of the sketch buckets, and present algorithms for smartly selecting buckets that contain valuable measurement data to send to end-host. We theoretically prove that our proposed methods are superior comparing with the existing solution, and implement a prototype on the commodity Barefoot Tofino switches. We conduct extensive experiments on DUNE, and the evaluation results show that the system considerably improves measurement accuracies at negligible costs. In particular, with less than 0.36% loss of the packet forwarding rate, DUNE avoids up to 60% errors in the end-host reconstructed sketch. Copyright © 2022, The Authors. All rights reserved.

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Multi-Grained Feature Pruning for Video-Based Human Pose Estimation

Multi-Grained Feature Pruning for Video-Based Human Pose Est...

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

作者： Zhigang Wang Shaojing Fan Zhenguang Liu Zheqi Wu Sifan Wu Yingying Jiao College of Computer Science and Technology Zhejiang Gongshang University Hangzhou China School of Computing National University of Singapore Singapore The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security Hangzhou China College of Computer Science and Technology Jilin University Changchun China College of Computer Science and Technology Zhejiang University of Technology Hangzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Human pose estimation, with its broad applications in action recognition and motion capture, has experienced significant advancements. However, current Transformer-based methods for video pose estimation often face challenges in managing redundant temporal information and achieving fine-grained perception because they only focus on processing low-resolution features. To address these challenges, we propose a novel multi-scale resolution framework that encodes spatiotemporal representations at varying granularities and executes fine-grained perception compensation. Furthermore, we employ a density peaks clustering method to dynamically identify and prioritize tokens that offer important semantic information. This strategy effectively prunes redundant feature tokens, especially those arising from multi-frame features, thereby optimizing computational efficiency without sacrificing semantic richness. Empirically, it sets new benchmarks for both performance and efficiency on three large-scale datasets. Our method achieves a 93.8% improvement in inference speed compared to the baseline, while also enhancing pose estimation accuracy, reaching 87.4 mAP on the PoseTrack2017 dataset.

关键词： Accuracy Pose estimation Semantics Signal processing algorithms Benchmark testing Signal processing Motion capture Computational efficiency Spatiotemporal phenomena Speech processing

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Optimal Refinement-based Array Constraint Solving for Symbolic Execution

Optimal Refinement-based Array Constraint Solving for Symbol...

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Asia-Pacific Conference on Software Engineering

作者： Meixi Liu Ziqi Shuai Luyao Liu Kelin Ma Ke Ma College of Computer National University of Defense Technology Changsha China State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China

ISBN: (纸本)9781665455381

Array constraint solving is widely adopted by the existing symbolic execution engines for encoding programs precisely. The counterexample-guided abstraction refinement (CEGAR) based method is state-of-the-art for array constraint solving. However, we observed that the CEGAR-based method may need many refinements to solve the array constraints produced by the symbolic executor, which decreases the performance of constraint solving. Based on the observation, we propose a machine learning-based method to improve the efficiency of the CEGAR-based array constraint solving. Our method adaptively turns on or off the CEGAR loop according to different solving problems. We have implemented our method on the symbolic executor KLEE and its underlying CEGAR-based constraint solver STP. We have conducted an extensive experiment on 55 real-world programs. On average, our method increases the number of explored paths by 21%. The results of the extensive experiments on real-world C programs show the effectiveness of our method.

关键词： Training Machine learning algorithms Switches Machine learning Predictive models Benchmark testing Prediction algorithms

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A novel vision-based multi-task robotic grasp detection method for multi-object scenes

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science China(Information sciences) 2022年第12期65卷 157-169页

作者： Yanan SONG Liang GAO Xinyu LI Weiming SHEN Kunkun PENG College of Computer Science and Technology Zhejiang University Institute of Computing Innovation Zhejiang University State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology School of Management Wuhan University of Science and Technology

Grasping a specified object from multi-object scenes is an essential ability for intelligent *** ability depends on the affiliation between the grasp position and the object category. Most existing multi-object grasp detection methods considering the affiliation rely on object detection results, thus limiting the improvement of robotic grasp detection accuracy. This paper proposes a decoupled single-stage multitask robotic grasp detection method based on the Faster R-CNN framework for multi-object scenes. The designed network independently detects the category of an object and its possible grasp positions by using one loss function. A new grasp matching strategy is designed to determine the relationship between object categories and predicted grasp positions. The VMRD grasp dataset is used to test the performance of the proposed method. Compared with other grasp detection methods, the proposed method achieves higher object detection accuracy and grasp detection accuracy.

关键词： robotic grasp detection multi-object scene object detection grasp matching strategy deep learning

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Symbolic Execution of Floating-point Programs: How far are we?

Symbolic Execution of Floating-point Programs: How far are w...

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Asia-Pacific Conference on Software Engineering

作者： Guofeng Zhang Zhenbang Chen Ziqi Shuai College of Computer National University of Defense Technology ChangSha China State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China

ISBN: (纸本)9781665455381

Floating-point programs are challenging for symbolic execution due to the constraint solving problem. To investigate the effectiveness and limitations of the existing methods, we conduct the first empirical study in this paper on five existing symbolic execution methods for floating-point programs. We have implemented the existing methods on the state-of-the-art symbolic execution KLEE and use the real-world representative floatingpoint programs as the benchmarks, which are used to evaluate the existing methods with respect to code coverage and bug finding. The results indicate that the existing methods complement each other in bug finding. Based on the findings of the experimental results, we propose synergizing the existing methods to improve symbolic execution‘s effectiveness. The experimental results demonstrate that our synergic method can detect more bugs.

关键词： Codes computer bugs Fuzzing Benchmark testing Software engineering Arithmetic

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HRPDF: A Software-Based Heterogeneous Redundant Proactive Defense Framework for Programmable Logic Controller

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Journal of computer science & technology 2021年第6期36卷 1307-1324页

作者： Ke Liu Jing-Yi Wang Qiang Wei Zhen-Yong Zhang Jun Sun Rong-Kuan Ma Rui-Long Deng State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou 450001China College of Control Science and Engineering Zhejiang UniversityHangzhou 310027China College of Computer Science and Technology Guizhou UniversityGuiyang 550025China School of Information Systems Singapore Management UniversitySingapore 689867Singapore

Programmable logic controllers(PLCs)play a critical role in many industrial control systems,yet face increasingly serious cyber *** this paper,we propose a novel PLC-compatible software-based defense mechanism,called Heterogeneous Redundant Proactive Defense Framework(HRPDF).We propose a heterogeneous PLC architecture in HRPDF,including multiple heterogeneous,equivalent,and synchronous runtimes,which can thwart multiple types of attacks against PLC without the need of external *** ensure the availability of PLC,we also design an inter-process communication algorithm that minimizes the overhead of *** implement a prototype system of HRPDF and test it in a real-world PLC and an OpenPLC-based device,*** results show that HRPDF can defend against multiple types of attacks with 10.22%additional CPU and 5.56%additional memory overhead,and about 0.6 ms additional time overhead.

关键词： industrial control system programmable logic controller proactive defense heterogeneous redundant architecture

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VNet: a versatile network to train real-time semantic segmentation models on a single GPU

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science China(Information sciences) 2022年第3期65卷 272-273页

作者： Wenxing LI Ning LIN Mingzhe ZHANG Hang LU Xiaoming CHEN Xiaowei LI College of Computer Science and Technology Guizhou University State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences

Dear editor,Modern semantic segmentation, which has important applications such as medical image analysis, image editing, and video surveillance, has made remarkable progress using deep convolution neural network models. Recently, an efficient real-time semantic segmentation method has received considerable attention, as intelligent edge devices not only have faster inference speed requirements for semantic segmentation models but also cannot rely on the cloud services of data centers. There are two feasible approaches to develop an efficient semantic segmentation model.

关键词： Efficiency Machine learning Real-time systems Segmentation Light-weight

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Association Pattern-enhanced Molecular Representation Learning 39

Association Pattern-enhanced Molecular Representation Learni...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Jia, Lingxiang Ying, Yuchen Qiu, Tian Yao, Shaolun Xue, Liang Lei, Jie Song, Jie Song, Mingli Feng, Zunlei State Key Laboratory of Blockchain and Data Security Zhejiang University China Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security China Computing Science and Artificial Intelligence College Suzhou City University China College of Computer Science Zhejiang University of Technology China

ISBN: (纸本)157735897X

The applicability of drug molecules in various clinical scenarios is significantly influenced by a diverse range of molecular properties. By leveraging self-supervised conditions such as atom attributes and interatomic bonds, existing advanced molecular foundation models can generate expressive representations of these molecules. However, such models often overlook the fixed association patterns within molecules that influence physiological or chemical properties. In this paper, we introduce a novel association pattern-aware message passing method, which can serve as an effective yet general plug- and-play plugin, thereby enhancing the atom representations generated by molecular foundation models without requiring additional pretraining. Additionally, molecular property-specific pattern libraries are constructed to collect the generated interpretable common patterns that bind to these properties. Extensive experiments conducted on 11 benchmark molecular property prediction tasks across 8 advanced molecular foundation models demonstrate significant superiority of the proposed method, with performance improvements of up to approximately 20%. Furthermore, a property-specific pattern library is tailored for blood-brain barrier penetration, which has undergone corresponding mechanistic validation. Copyright © 2025, Association for the Advancement of Artificial Intelligence.

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