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arXiv 2024年

作者： Ying, Mingsheng Zhou, Li Barthe, Gilles Centre for Quantum Software and Information University of Technology Sydney Ultimo Australia Key Laboratory of System Software Chinese Academy of Sciences State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Max Plamck Institute for Security and Privacy Bochum Germany IMDEA Software Institute Madrid Spain

In this paper, we investigate the fundamental laws of quantum programming. We extend a comprehensive set of Hoare et al.’s basic laws of classical programming to the quantum setting. These laws characterise the algebraic properties of quantum programs, such as the distributivity of sequential composition over (quantum) if-statements and the unfolding of nested (quantum) if-statements. At the same time, we clarify some subtle differences between certain laws of classical programming and their quantum counterparts. Additionally, we derive a fixpoint characterization of quantum while-loops and a loop-based realisation of tail recursion in quantum programming. Furthermore, we establish two normal form theorems: one for quantum circuits and one for finite quantum programs. The theory in which these laws are established is formalised in the Coq proof assistant, and all of these laws are mechanically verified. As an application case of our laws, we present a formal derivation of the principle of deferred measurements in dynamic quantum circuits. We expect that these laws can be utilized in correctness-preserving transformation, compilation, and automatic code optimization in quantum programming. In particular, because these laws are formally verified in Coq, they can be confidently applied in quantum program development. © 2024, CC BY.

关键词： Algebra

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AutoQNN: An End-to-End Framework for Automatically Quantizing Neural Networks

arXiv

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arXiv 2023年

作者： Gong, Cheng Lu, Ye Dai, Surong Qian, Deng Du, Chenkun Li, Tao College of Software Nankai University Tianjin300350 China College of Computer Science Nankai University Tianjin300350 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

Exploring the expected quantizing scheme with suitable mixed-precision policy is the key point to compress deep neural networks (DNNs) in high efficiency and accuracy. This exploration implies heavy workloads for domain experts, and an automatic compression method is needed. However, the huge search space of the automatic method introduces plenty of computing budgets that make the automatic process challenging to be applied in real scenarios. In this paper, we propose an end-to-end framework named AutoQNN, for automatically quantizing different layers utilizing different schemes and bitwidths without any human labor. AutoQNN can seek desirable quantizing schemes and mixed-precision policies for mainstream DNN models efficiently by involving three techniques: quantizing scheme search (QSS), quantizing precision learning (QPL), and quantized architecture generation (QAG). QSS introduces five quantizing schemes and defines three new schemes as a candidate set for scheme search, and then uses the differentiable neural architecture search (DNAS) algorithm to seek the layer- or model-desired scheme from the set. QPL is the first method to learn mixed-precision policies by reparameterizing the bitwidths of quantizing schemes, to the best of our knowledge. QPL optimizes both classification loss and precision loss of DNNs efficiently and obtains the relatively optimal mixed-precision model within limited model size and memory footprint. QAG is designed to convert arbitrary architectures into corresponding quantized ones without manual intervention, to facilitate end-to-end neural network quantization. We have implemented AutoQNN and integrated it into Keras. Extensive experiments demonstrate that AutoQNN can consistently outperform state-of-the-art quantization. For 2-bit weight and activation of AlexNet and ResNet18, AutoQNN can achieve the accuracy results of 59.75% and 68.86%, respectively, and obtain accuracy improvements by up to 1.65% and 1.74%, respectively, compared

关键词： Deep neural networks

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Fix the Tests: Augmenting LLMs to Repair Test Cases with Static Collector and Neural Reranker

Fix the Tests: Augmenting LLMs to Repair Test Cases with Sta...

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International Symposium on Software Reliability Engineering (ISSRE)

作者： Jun Liu Jiwei Yan Yuanyuan Xie Jun Yan Jian Zhang Key Laboratory of System Software (Chinese Academy of Sciences) and State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences (UCAS) Technology Center of Software Engineering Institute of Software Chinese Academy of Sciences School of Intelligent Science and Technology Hangzhou Institute for Advanced Study UCAS

ISBN: (数字)9798350353884

ISBN: (纸本)9798350353891

During software evolution, it is advocated that test code should co-evolve with production code. In real development scenarios, test updating may lag behind production code changing, which may cause compilation failure or bring other troubles. Existing techniques based on pre-trained language models can be directly adopted to repair obsolete tests caused by such unsynchronized code changes, especially syntactic-related ones. However, the lack of task-oriented contextual information affects the repair accuracy on large-scale projects. Starting from an obsolete test, the key challenging task is precisely identifying and constructing Test-Repair-Oriented Contexts (TROCtxs) from the whole repository within a limited token *** this paper, we propose Synter (SYNtactic-breaking- changes-induced TEst Repair), a novel approach based on LLMs to automatically repair obsolete test cases via precise and concise TROCtxs construction. Inspired by developers’ programming practices, we design three types of TROCtx: class context, usage context, and environment context. Given an obsolete test case to repair, Synter firstly collects the related code information for each type of TROCtx through static analysis techniques automatically. Then, it generates reranking queries to identify the most relevant TROCtxs, which will be taken as the repair-required key contexts and be input to the large language model for the final test *** evaluate the effectiveness of Synter, we construct a benchmark dataset that contains a set of obsolete tests caused by syntactic breaking changes. The experimental results show that Synter outperforms baseline approaches both on textual- and intent-matching metrics. With the augmentation of constructed TROCtxs, hallucinations are reduced by 57.1%.

关键词： Measurement Codes Large language models Static analysis Production Maintenance engineering Syntactics Programming Software Software reliability

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A wolf in sheep's clothing: practical black-box adversarial attacks for evading learning-based windows malware detection in the wild 24

A wolf in sheep's clothing: practical black-box adversarial ...

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Proceedings of the 33rd USENIX Conference on Security Symposium

作者： Xiang Ling Zhiyu Wu Bin Wang Wei Deng Jingzheng Wu Shouling Ji Tianyue Luo Yanjun Wu Intelligent Software Research Center Institute of Software Chinese Academy of Sciences and Key Laboratory of System Software (Chinese Academy of Sciences) and State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Zhejiang University Zhejiang Key Laboratory of Artificial Intelligence of Things (AIoT) Network and Data Security and Hangzhou Research Institute Xidian University Intelligent Software Research Center Institute of Software Chinese Academy of Sciences

ISBN: (纸本)9781939133441

Given the remarkable achievements of existing learning-based malware detection in both academia and industry, this paper presents MalGuise, a practical black-box adversarial attack framework that evaluates the security risks of existing learning-based Windows malware detection systems under the black-box setting. MalGuise first employs a novel semantics-preserving transformation of call-based redividing to concurrently manipulate both nodes and edges of malware's control-flow graph, making it less noticeable. By employing a Monte-Carlo-tree-search-based optimization, MalGuise then searches for an optimized sequence of call-based redividing transformations to apply to the input Windows malware for evasions. Finally, it reconstructs the adversarial malware file based on the optimized transformation sequence while adhering to Windows executable format constraints, thereby maintaining the same semantics as the original. MalGuise is systematically evaluated against three state-of-the-art learning-based Windows malware detection systems under the black-box setting. Evaluation results demonstrate that MalGuise achieves a remarkably high attack success rate, mostly exceeding 95%, with over 91% of the generated adversarial malware files maintaining the same semantics. Furthermore, MalGuise achieves up to a 74.97% attack success rate against five anti-virus products, highlighting potential tangible security concerns to real-world users.

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Numerical Study of the Effects of Ventilation Rates on Supercavity Dynamics and Acoustic Characteristics

SSRN

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SSRN 2023年

作者： Xie, Bohan Ding, Yanyi Zhao, Chenggong Zhao, Changqing Sun, Tiezhi School of Naval Architecture State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial Equipment Dalian University of Technology Dalian116024 China Beijing Institute of Electronic System Engineering Beijing100854 China

This paper presents a numerical simulation investigation of the hydrodynamic and acoustic characteristics of ventilated supercavities that operate at various ventilation rates. The large eddy simulation (LES) and Ffowcs Williams–Hawkings (FW-H) methods are used to solve the unsteady flow field and noise. The results show that the ventilation rate plays an important role in cavity closure patterns and can improve the hydrodynamic performance. There is a transition from re-entrant flow closure to a stable double-vortex tube closure form at the supercavity tail as the ventilation rate increases. Meanwhile, under the condition of high ventilation rate, the re-entrant flow region in the cavity gradually shrinks and hairpin vortices appear, the body length of cavity increases and the unsteadiness at the supercavity tail is weakened. However, the turbulence intensity and integral scale both increase with the ventilation rate. Moreover, it is found that the noise is closely related to supercavity closure form. Ventilated supercavities with closure forms of double-vortex tubes have better noise performance. Interestingly, as the ventilation rate increases, the total sound pressure levels are improved. The wavenumber-frequency spectra of supercavity turbulent pulsating pressures under studied ventilation rates are similar, but there are some differences in some local areas. © 2023, The Authors. All rights reserved.

关键词： Vortex flow

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Network-on-Interposer Design for Agile Neural-Network Processor Chip Customization 21

Network-on-Interposer Design for Agile Neural-Network Proces...

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Proceedings of the 58th Annual ACM/IEEE Design Automation Conference

作者： Mengdi Wang Ying Wang Cheng Liu Lei Zhang Institute of Computing Technology Chinese Academy of Sciences Institute of Computing Technology Chinese Academy of Sciences and State Key Laboratory of Computer Architecture

ISBN: (纸本)9781665432740

Chiplet based multi-die integration has been thought as a key enabler of the agile chip development flow. For 2.5D based multi-die system, Network on Interposer plays an essential role in the performance and the development cost of the chips. This work proposed a reusable NoI design for agile AI chip customization. The proposed NoI design can self-adapt to the inter-die communication patterns of various neural network applications, so the produced interposers can be reused across different AI chip specifications. Experimental results show the proposed NoI design brings 42.7%~79.5% of total data communication latency reduction in different scenarios, and it also decreased the area overhead by 26.4%.

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LACC:a hardware and software co-design accelerator for deep neural networks

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High Technology Letters 2021年第1期27卷 62-67页

作者： Yu Yong Zhi Tian Zhou Shengyuan State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190P.R.China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100049P.R.China Cambricon Technologies Ltd Beijing 100190P.R.China

With the increasing of data size and model size,deep neural networks(DNNs)show outstanding performance in many artificial intelligence(AI)*** the big model size makes it a challenge for high-performance and low-power running DNN on processors,such as central processing unit(CPU),graphics processing unit(GPU),and tensor processing unit(TPU).This paper proposes a LOGNN data representation of 8 bits and a hardware and software co-design deep neural network accelerator LACC to meet the *** data representation replaces multiply operations to add and shift operations in running *** accelerator achieves higher efficiency than the state-of-the-art DNN accelerators by domain specific arithmetic computing ***,LACC speeds up the performance per watt by 1.5 times,compared to the state-of-the-art DNN accelerators on average.

关键词： deep neural network(DNN) domain specific accelerator domain specific data type

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KOP-Fuzzer: A key-Operation-based Fuzzer for Type Confusion Bugs in JavaScript Engines

KOP-Fuzzer: A Key-Operation-based Fuzzer for Type Confusion ...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Lili Sun Chenggang Wu Zhe Wang Yan Kang Bowen Tang State Key Laboratory of Computer Architecture Chinese Academy of Sciences Institute of Computing Technology University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

JavaScript (JS) engines are a core component of a lot of software, such as web browsers, PDF readers and flash players. There has been much research on finding JS engine vulnerabilities. However, due to the fact that a JS engine's input space is infinite and the vulnerability triggering conditions are extremely strict, it is difficult to generate test cases that are able to trigger deep logic errors in fuzzing. This paper aims to explore an approach which incorporates the human experience into fuzzing. We propose a key-Operation-based Fuzzer (KOP-Fuzzer), to explore the type confusion vulnerabilities in JS engines. Based on human knowledge, we summarize a trigger model and extract key operations for type confusion vulnerabilities in JS engines. We use clustering to extract the key-operation methods from the engine's source code and develop a fuzzing system for key -operation mutation. Our experimental results demonstrate that the KOP-Fuzzer generates valid test cases with 1.5x fewer runtime errors, while also improving the edge coverage (2.082 %) and key-operation coverage (6.452 %), when compared with the state-of-the-art JS engine fuzzers. The KOP-Fuzzer discovered a total of 21 new bugs in ChakraCore and JavaScriptCore, where 16 of them are caused by the engine's incorrect handling of key operations and 5 of them are caused by type confusions.

关键词： Codes Runtime Conferences computer bugs Fuzzing Software Browsers

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Virtually coupled train set control subject to space-time separation:A distributed economic MPC approach with emergency braking configuration

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High-Speed Railway 2024年第3期2卷 143-152页

作者： Xiaolin Luo Tao Tang Le Wang Hongjie Liu State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong UniversityBeijing 100044China Department of Electrical and Computer Engineering University of CaliforniaRiversideCA 92521USA Beijing Zongheng Electro-Mechanical Technology Development Co. Ltd.Beijing 100081China School of Automation and Intelligence Beijing Jiaotong UniversityBeijing 100044China National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong UniversityBeijing 100044China

The emerging virtual coupling technology aims to operate multiple train units in a Virtually Coupled Train Set(VCTS)at a minimal but safe *** guarantee collision avoidance,the safety distance should be calculated using the state-of-the-art space-time separation principle that separates the Emergency Braking(EB)trajectories of two successive units during the whole EB *** this case,the minimal safety distance is usually numerically calculated without an analytic ***,the constrained VCTS control problem is hard to address with space-time separation,which is still a gap in the existing *** solve this problem,we propose a Distributed Economic Model Predictive Control(DEMPC)approach with computation efficiency and theoretical ***,to alleviate the computation burden,we transform implicit safety constraints into explicitly linear ones,such that the optimal control problem in DEMPC is a quadratic programming problem that can be solved *** theoretical analysis,sufficient conditions are derived to guarantee the recursive feasibility and stability of DEMPC,employing compatibility constraints,tube techniques and terminal ingredient ***,we extend our approach with globally optimal and distributed online EB configuration methods to shorten the minimal distance among ***,experimental results demonstrate the performance and advantages of the proposed approaches.

关键词： Virtually coupled train set Space-time separation Economic model predictive control Distributed model predictive control Emergency braking configuration

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Matching Based Intelligent Edge Resource Allocation 3rd

Matching Based Intelligent Edge Resource Allocation

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3rd International Conference on Artificial Intelligence, 2022

作者： Qin, Peng Wang, Miao Zhao, Xiongwen Liu, Jiayan Wang, Shuo He, Haoting Xie, Yuanbo State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources School of Electrical and Electronic Engineering North China Electric Power University Beijing102206 China Hebei Key Laboratory of Power Internet of Things Technology North China Electric Power University Baoding071003 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China

ISBN: (纸本)9789811694226

With the rapid development of the Internet of Things, multi-access edge computing (MEC) has emerged as a key technique and intelligent edge resource allocation has become one of the important research issues in MEC. In this paper, we utilize advantages of matching game to integrate users’ needs and the position of edge computing nodes in IoT networks. The proposed GS-based two-sided matching approach simulates the competition and negotiation relationship between different user sets in IoT edge nodes, and provides feasible options based on the preference between different entities. Simulations show that it not only solves the problem of edge resource allocation in a semi-distributed manner, but also effectively improves SP’s revenue. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Edge computing

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