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arXiv 2022年

作者： Zhang, Rong Yang, Ran Guo, Jian Sun, Chang-Wei Duan, Jia-Chen Zhou, Heng Xie, Zhenda Xu, Ping Gong, Yan-Xiao Zhu, Shi-Ning National Laboratory of Solid State Microstructure School of Physics School of Electronic Science and Engineering Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing210093 China College of Electronic and Optical Engineering Nanjing University of Posts and Telecommunication Nanjing210023 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computing National University of Defense Technology Changsha410073 China

We report the position-inhomogeneous quantum walk (IQW) can be utilized to produce the maximal high dimensional entanglement while maintaining the quadratic speedup spread of the wave-function. Our calculations show that the maximal coin-walker entanglement can be generated in any odd steps or asymptotically in even steps, and the nearly maximal entanglement can be obtained in even steps after 2. We implement the IQW by a stable resource-saving time-bin optical network, in which a polarization Sagnac loop is employed to realize the precisely tunable phase shift. Our approach opens up an efficient way for high-dimensional entanglement engineering as well as promotes investigations on the role of coin-walker interactions in QW based applications. Copyright © 2022, The Authors. All rights reserved.

关键词： Quantum entanglement

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SmartGift: Learning to Generate Practical Inputs for Testing Smart Contracts

SmartGift: Learning to Generate Practical Inputs for Testing...

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International Conference on Software Maintenance (ICSM)

作者： Teng Zhou Kui Liu Li Li Zhe Liu Jacques Klein Tegawendé F. Bissyandé Nanjing University of Aeronautics and Astronautics Nanjing China Key Laboratory of Safety-Critical Software Nanjing University of Aeronautics and Astronautics Nanjing China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China Monash University Melbourne Australia Interdisciplinary Centre for Security Reliability and Trust (SnT) University of Luxembourg Luxembourg

With the boom of Initial Coin Offerings (ICO) in the financial markets, smart contracts have gained rapid popularity among consumers. Smart contract vulnerabilities however made them a prime target to malicious attacks that are leading to huge losses. The research community is thus applying various software engineering technologies to smart contracts to address them. In general, to detect vulnerabilities in smart contracts, mutation and fuzz based testing approaches have been widely studied and indeed achieved promising performance on benchmark datasets. Generating test inputs with mutation approaches essentially relies on the available test cases in a smart contract program. In our preliminary study, however, we observed that 56.4% of 218 identified open-source smart contract project repositories do not provide any test case for validation. Fuzzing test inputs leads to random values and lacks practical usefulness. Our work addresses this problem: we propose an approach, Smartgift, which generates practical inputs for testing smart contracts by learning from the transaction records of real-world smart contracts. Leveraging a collected set of over 60 thousand transaction records, Smartgift is able to generate relevant test inputs for ~77% smart contract functions, largely outperforming the traditional fuzzing approach (successful for only 60% functions). We further demonstrate the practicality of the test inputs by using them to replace the test inputs of the ContractFuzzer state of the art smart contract vulnerability detector: with inputs by Smartgift, ContractFuzzer can now detect 131 of the 154 vulnerabilities in its benchmark.

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Binary semantic Similarity Comparison based on software gene

Binary semantic Similarity Comparison based on software gene

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2019 International Conference on Artificial Intelligence Technologies and Applications, ICAITA 2019

作者： Sun, Wenjie Chen, Yihang Shan, Zheng Liu, Fudong Wang, Qiuna State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Materials Branch of State Grid Henan Electric Power Company Zhengzhou China

The similarity of binary code is widely used in code copyright protection, vulnerability mining, malicious code analysis and etc. In this paper, we proposed a method for measuring/evaluating the similarity of two binary files based on software genes. Some of Natural language processing methods were introduced into program semantics analysis, including word2vec and doc2vec models to generate assembly instruction embeddings and gene semantic embeddings. Then the longest common subsequence method was applied to evaluate the software similarity. Experiments show that our method can effectively evaluate the similarity of binary files. © 2019 IOP Publishing Ltd. All rights reserved.

关键词： Genes

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An OFDM based interference reducing scheme with trajectory and resource optimization for UAV-powered IoT Networks

An OFDM based interference reducing scheme with trajectory a...

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General Assembly and Scientific Symposium, URSI

作者： Yuan Gao Jiang Cao Yang Guo Ping Wang Siming Yang Jing Wang Weidang Lu Peiyuan Si Ming Zhao Xiao Wu Academy of Military Science of the PLA China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Henan China College of Information Engineering Zhejiang University of Technology Hangzhou China Beijing National Research Center for Information Science and Technology (BNRist) Tsinghua University Beijing China

The massive and pervasive deployment of sensors for information collecting in IoT networks result to the scarcity of energy. Unmanned aerial vehicle (UAV) enabled wireless power transfer (WPT) is a feasible solution to provide energy for pervasively deployed sensors. In this paper, we propose an orthogonal frequency division multiplexing (OFDM) based interference reducing scheme with trajectory and resource optimization for UAV-powered IoT networks. The simulation results show that our proposed scheme outperforms two benchmark schemes in sum transmission rate.

关键词： OFDM Simulation Interference Wireless power transfer Benchmark testing Unmanned aerial vehicles Trajectory

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Asynchronous Functional Brain Network Construction with Spatiotemporal Transformer for MCI Classification

TechRxiv

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TechRxiv 2023年

作者： Tang, Xiang Zhang, Jianjia Wu, Xiaotong Zhou, Luping Wang, Lei Wu, Weiwen Shen, Dinggang The School of Biomedical Engineering Sun Yat-sen University China School of Electrical and Information Engineering University of Sydney Australia School of Computing and Information Technology University of Wollongong Australia The School of Biomedical Engineering State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai201210 China Shanghai United Imaging Intelligence Company Ltd. Shanghai200230 China Shanghai Clinical Research and Trial Center Shanghai201210 China

Construction and analysis of functional brain networks (FBNs) with rs-fMRI is a promising method to diagnose functional brain diseases. Nevertheless, the existing methods suffer from several limitations. First, the functional connectivities (FCs) of the FBN are usually measured by the temporal co-activation level between rs-fMRI time series from regions of interest (ROIs). While enjoying simplicity, the existing approach implicitly assumes simultaneous co-activation of all the ROIs, and models only their synchronous dependencies. However, the functional coactivation is not necessarily always synchronous due to the time lag of information flow and cross-time interactions between ROIs. Therefore, it is desirable to model the asynchronous functional interactions. Second, the traditional methods usually construct FBNs at individual level for feature extraction and classification, leading to large variability and degraded diagnosis accuracy when modeling asynchronous FBN. Third, the FBN construction and analysis are conducted in two independent steps without joint alignment for the target diagnosis task. To address the first limitation, this paper proposes an effective sliding-windowbased method to model spatiotemporal FCs in Transformer. Regarding the second limitation, we propose to learn common and individual FBNs adaptively with the common FBN as prior knowledge, thus alleviating the variability and enabling the network to focus on the individual diseasespecific asynchronous FCs. To address the third limitation, the common and individual asynchronous FBNs are built and analyzed by an integrated network, enabling end-to-end training and improving the flexibility and discriminativity. The effectiveness of the proposed method is consistently demonstrated on three data sets for MCI diagnosis. © 2023, CC BY-NC-SA.

关键词： Computer aided diagnosis

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High?Index?Faceted Ni_3S_2 Branch Arrays as Bifunctional Electrocatalysts for Efficient Water Splitting

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Nano-Micro Letters 2019年第1期11卷 205-216页

作者： Shengjue Deng Kaili Zhang Dong Xie Yan Zhang Yongqi Zhang Yadong Wang Jianbo Wu Xiuli Wang Hong Jin Fan Xinhui Xia Jiangping Tu State Key Laboratory of Silicon Materials Key Laboratory of Advanced Materials and Applications for Batteries of Zhejiang Province and Department of Materials Science and Engineering Zhejiang University Guangdong Engineering and Technology Research Center for Advanced Nanomaterials School of Environment and Civil Engineering Dongguan University of Technology School of Physical and Mathematical Sciences Nanyang Technological University School of Engineering Nanyang Polytechnic Zhejiang Provincial Key Laboratory for Cutting Tools Taizhou University

For efficient electrolysis of water for hydrogen generation or other valueadded chemicals, it is highly relevant to develop low-temperature synthesis of low-cost and high-e ciency metal sulfide electrocatalysts on a large scale. Herein, we construct a new core–branch array and binder-free electrode by growing Ni_3S_2 nanoflake branches on an atomic-layer-deposited(ALD) TiO_2 skeleton. Through induced growth on the ALD-TiO_2 backbone, cross-linked Ni_3S_2 nanoflake branches with exposed { 210} highindex facets are uniformly anchored to the preformed TiO_2 core forming an integrated electrocatalyst. Such a core–branch array structure possesses large active surface area, uniform porous structure, and rich active sites of the exposed { 210 } high-index facet in the Ni_3S_2 nanoflake. Accordingly, the TiO_2@Ni_3S_2 core/branch arrays exhibit remarkable electrocatalytic activities in an alkaline medium, with lower overpotentials for both oxygen evolution reaction(220 mV at 10 mA cm^(-2)) and hydrogen evolution reaction(112 m V at 10 mA cm^(-2)), which are better than those of other Ni_3S_2 counterparts. Stable overall water splitting based on this bifunctional electrolyzer is also demonstrated.

关键词： Nickel sulfide Core/branch arrays Porous film Bifunctional electrocatalysts Electrochemical water splitting Oxygen evolution reaction(OER) Hydrogen evolution reaction(HER)

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Semantic-based malware behavior description: Past and future 9

Semantic-based malware behavior description: Past and future

引用

9th International Conference on Communication and Network Security, ICCNS 2019

作者： Yang, Ping Shu, Hui Xiong, Xiaobing Kang, Fei State Key Laboratory of Mathematical Engineering Advanced University of Information and Engineering Zhengzhou450001 China

ISBN: (纸本)9781450376624

Traditional malware detection is faced with problems of low efficiency and high cost. In recent years, in order to improve the efficiency and accuracy of malware detection, a method based on semantic malware behavior description has been widely studied. In this paper, we firstly classified the current methods of semantic-based malware behavior description by their evolution and characteristics. Secondly, we performed a statistical analysis for the typical description in different aspects. Finally, through statistical analysis, we revealed the challenges faced by semantic-based malware behavior description and its future development prospects. © 2019 Association for computing Machinery.

关键词： Semantics

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Joint Beamforming Design for Intelligent Omni Surface Assisted Wireless Communication Systems

arXiv

引用

arXiv 2022年

作者： Cai, Wenhao Li, Ming Liu, Yang Wu, Qingqing Liu, Qian The School of Information and Communication Engineering Dalian University of Technology Dalian116024 China The State Key Laboratory of Internet of Things for Smart City University of Macau 999078 China Guangdong-Macau Joint Laboratory for Advanced and Intelligent Computing China The School of Computer Science and Technology Dalian University of Technology Dalian116024 China

Intelligent reflecting surface (IRS) has been widely considered as one of the key enabling techniques for future wireless communication networks owing to its ability of dynamically controlling the phase shift of reflected electromagnetic (EM) waves to construct a favorable propagation environment. While IRS only focuses on signal reflection, the recently emerged innovative concept of intelligent omni-surface (IOS) can provide the dual functionality of manipulating reflecting and transmitting signals. Thus, IOS is a new paradigm for achieving ubiquitous wireless communications. In this paper, we consider an IOS-assisted multi-user multi-input single-output (MU-MISO) system where the IOS utilizes its reflective and transmissive properties to enhance the MU-MISO transmission. Both power minimization and sum-rate maximization problems are solved by exploiting the second-order cone programming (SOCP), Riemannian manifold, weighted minimum mean square error (WMMSE), and block coordinate descent (BCD) methods. Simulation results verify the advancements of the IOS for wireless systems and illustrate the significant performance improvement of our proposed joint transmit beamforming, reflecting and transmitting phase-shift, and IOS energy division design algorithms. Compared with conventional IRS, IOS can significantly extend the communication coverage, enhance the strength of received signals, and improve the quality of communication links. © 2022, CC BY.

关键词： Mean square error

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Structure-Preserving Operator Learning: Modeling the Collision Operator of Kinetic Equations

arXiv

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arXiv 2024年

作者： Lee, Jae Yong Schotthöfer, Steffen Xiao, Tianbai Krumscheid, Sebastian Frank, Martin Center for Artificial Intelligence and Natural Sciences Korea Institute for Advanced Study Seoul Korea Republic of Oak Ridge National Laboratory Computer Science and Mathematics Division Oak RidgeTN United States State Key Laboratory of High Temperature Gas Dynamics Centre for Interdisciplinary Research in Fluids Institute of Mechanics Chinese Academy of Sciences Beijing China School of Engineering Science University of Chinese Academy of Sciences Beijing China Karlsruhe Institute of Technology Scientific Computing Center Karlsruhe76131 Germany

This work explores the application of deep operator learning principles to a problem in statistical physics. Specifically, we consider the linear kinetic equation, consisting of a differential advection operator and an integral collision operator, which is a powerful yet expensive mathematical model for interacting particle systems with ample applications, e.g., in radiation transport. We investigate the capabilities of the Deep Operator network (DeepONet) approach to modelling the high dimensional collision operator of the linear kinetic equation. This integral operator has crucial analytical structures that a surrogate model, e.g., a DeepONet, needs to preserve to enable meaningful physical simulation. We propose several DeepONet modifications to encapsulate essential structural properties of this integral operator in a DeepONet model. To be precise, we adapt the architecture of the trunk-net so the DeepONet has the same collision invariants as the theoretical kinetic collision operator, thus preserving conserved quantities, e.g., mass, of the modeled many-particle system. Further, we propose an entropy-inspired data-sampling method tailored to train the modified DeepONet surrogates without requiring an excessive expensive simulation-based data generation. © 2024, CC BY-NC-SA.

关键词： Kinetic energy

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Machine learning prediction of network dynamics with privacy protection

arXiv

引用

arXiv 2022年

作者： Xia, Xin Su, Yansen Lü, Linyuan Zhang, Xingyi Lai, Ying-Cheng Zhang, Hai-Feng The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Mathematical Science Anhui University Hefei230601 China The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Anhui University Hefei230601 China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu610054 China School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States

Predicting network dynamics based on data, a problem with broad applications, has been studied extensively in the past, but most existing approaches assume that the complete set of historical data from the whole network is available. This requirement presents a great challenge in applications, especially for large, distributed networks in the real world, where data collection is accomplished by many clients in a parallel fashion. Often, each client only has the time series data from a partial set of nodes and the client has access to only partial timestamps of the whole time series data and partial structure of the network. Due to privacy concerns or license related issues, the data collected by different clients cannot be shared. To accurately predict the network dynamics while protecting the privacy of different parties is a critical problem in the modern time. Here, we propose a solution based on federated graph neural networks (FGNNs) that enables the training of a global dynamic model for all parties without data sharing. We validate the working of our FGNN framework through two types of simulations to predict a variety of network dynamics (four discrete and three continuous dynamics). As a significant real-world application, we demonstrate successful prediction of state-wise influenza spreading in the USA. Our FGNN scheme represents a general framework to predict diverse network dynamics through collaborative fusing of the data from different parties without disclosing their privacy. © 2022, CC BY.

关键词： Forecasting

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