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12th International Conference on Identification, Information and Knowledge in the internet of Things, IIKI 2021

作者： Yang, Siming Shan, Zheng Cao, Jiang Gao, Yuan Guo, Yang Wang, Ping Wang, Xiaonan Wang, Jing Zhang, Tingting Guo, Jiayu State Key Laboratory of Mathematical Engineering and Advanced Computing Henan Zhengzhou450001 China Academy of Military Science of the PLA Beijing100091 China Network Technology Research Center of the PLA Beijing100090 China

UAV base station platform has become the current research hotspot of assisting ground base station for wireless *** present, the most important issue is how to make path planning to provide the stable communication guarantee for multiple mobile users. In this article, we model the air-to-ground channel to describe the path loss between the UAV platform and the user and build a simulation environment for training based on the OpenAI-GYM architecture. In addition, this paper proposes a reinforcement learning algorithm based on intrinsic rewards, which uses the mean square error of the state prediction results to quantify the novelty of the state. Algorithms enable agents to efficiently carry out strategy iterations. Experiments results showed that our algorithm has a higher score and takes less time. © 2022 The Authors. Published by Elsevier B.V.

关键词： Deep learning Deep reinforcement learning UAV path planning Wireless communication coverage

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A Cross-Platform Instant Messaging User Association Method Based on Supervised Learning 4th

A Cross-Platform Instant Messaging User Association Method B...

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4th International Conference on Big Data and Security, ICBDS 2022

作者： Zhou, Pei Luo, Xiangyang Du, Shaoyong Shi, Wenqi Guo, Jiashan School of Cyber Science and Engineering Zhengzhou University Zhengzhou450000 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450000 China

ISBN: (纸本)9789819932993

To solve the multi-platform user association problem of complex trajectory matching process and high time cost in cross-platform association positioning of instant messaging users, and at the same time make full use of the information in user trajectories, this paper proposes a supervised learning-based cross-platform instant messaging user association positioning method. The algorithm firstly places probes in the area where the target may appear to obtain user information;then gets user trajectories through the obtained user distance information and time information;selects user features through the classification algorithm of supervised learning, and designs a cross-platform instant messaging user association localization method based on supervised learning, so as to increase the association efficiency and accuracy of cross-platform instant messaging user association. The method conducts specific experiments for the most commonly used instant messaging tools in China, WeChat and Stranger users, and the results show that the method can achieve efficient and reliable association for these two types of instant messaging users. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Trajectories

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Distributed Deep Learning System for Efficient Face Recognition in Surveillance System 3

Distributed Deep Learning System for Efficient Face Recognit...

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3rd International Conference on advanced Information Science and System, AISS 2021

作者： Liu, Jinjin Chen, Zhifeng Li, Xiaonan Wei, Tongxin School of Computer Science Zhongyuan University of Technology China and State Key Laboratory of Mathematical Engineering and Advanced Computing China State Key Laboratory of Mathematical Engineering and Advanced Computing China School of Computer Science Zhongyuan University of Technology China School of Cyberspace Security Zhengzhou University China

ISBN: (纸本)9781450385862

In view of the bandwidth consumption caused by data stream transmission in video analysis system and the demand for accurate online real-time analysis of massive data, this paper proposes a deep learning model framework for face recognition employed in the embedded system. Through data collaboration, the cloud could build a more complex data set with a small amount of uploaded data gathered by the end devices. And the framework collaboration makes sure that the fully-trained cloud model directly download or distillate knowledge to the end devices. Experiments show that the deep model not only realizes the real-time response and the accurate response of the cloud system, but also greatly reduces the bandwidth consumption caused by sample data transmission in the model training process. © 2021 ACM.

关键词： Distillation

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A Review of Field Text Analysis

A Review of Field Text Analysis

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2021 IEEE Conference on Telecommunications, Optics and Computer Science, TOCS 2021

作者： Li, Zhuolun Kang, Fei Yu, Pu Shu, Hui Zhengzhou University School of Software Zhengzhou China Meac State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665424981

Field text analysis is the process of using natural language processing technology to understand the Text and extract information from unstructured text data. In recent years, due to the increasing maturity of machine learning technology, higher requirements have been put forward for field text analysis. This review first introduces the process of Text preprocessing and the development of text representation. Secondly, it summarizes the four typical techniques of text analysis, including named entity recognition, relation extraction, extractive text summarization, and text classification. Finally, for each typical technology, it introduces the commonly used methods and part of the researcher's exploration of this technology. © 2021 IEEE.

关键词： Text processing

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An accurate identification method for network devices based on spatial attention mechanism

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Security and Safety 2023年第2期2卷 13-29页

作者： Xiuting Wang Ruixiang Li Shaoyong Du Xiangyang Luo Henan Polytechnic Institute Nanyang 473000China Henan Province Key Laboratory of Cyberspace Situation Awareness Zhengzhou 450001China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou 450001China Institute of Information Engineering State Key Laboratory of Information SecurityBeijing 100093China

With the metaverse being the development direction of the next generation Internet,the popularity of intelligent devices,and the maturity of various emerging technologies,more and more intelligent devices try to connect to the Internet,which poses a major threat to the management and security protection of network *** present,the mainstream method of network equipment identification in the metaverse is to obtain the network traffic data generated in the process of device communication,extract the device features through analysis and processing,and identify the device based on a variety of learning *** methods often require manual participation,and it is difficult to capture the small differences between similar devices,leading to identification ***,we propose a deep learning device recognition method based on a spatial attention ***,we extract the required feature fields from the acquired network traffic ***,we normalize the data and convert it into grayscale *** that,we add a spatial attention mechanism to CNN and MLP respectively to increase the difference between similar network devices and further improve the recognition ***,we identify devices based on the deep learning model.A large number of experiments were carried out on 31 types of network devices such as web cameras,wireless routers,and *** results show that the accuracy of the proposed recognition method based on the spatial attention mechanism is increased by 0.8%and 2.0%,respectively,compared with the recognition method based only on the deep learning model under the CNN and MLP *** method proposed in this paper is significantly superior to the existing method of device-type recognition based only on a deep learning model.

关键词： Metaverse Device identification Deep learning Spatial attention

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CroPrompt: Cross-task Interactive Prompting for Zero-shot Spoken Language Understanding

CroPrompt: Cross-task Interactive Prompting for Zero-shot Sp...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Qin, Libo Wei, Fuxuan Chen, Qiguang Zhou, Jingxuan Huang, Shijue Si, Jiasheng Lu, Wenpeng Che, Wanxiang School of Computer Science and Engineering Central South University China Key Laboratory of Data Intelligence and Advanced Computing in Provincial Universities Soochow University China Research Center for Social Computing and Information Retrieval Harbin Institute of Technology China Harbin Institute of Technology Shenzhen China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences China

ISBN: (纸本)9798350368741

Slot filling and intent detection are two highly correlated tasks in spoken language understanding (SLU). Recent SLU research attempts to explore zero-shot prompting techniques in large language models to alleviate the data scarcity problem. Nevertheless, the existing prompting work ignores the cross-task interaction information for SLU, which leads to sub-optimal performance. To solve this problem, we present the pioneering work of Cross-task Interactive Prompting (CroPrompt) for SLU, which enables the model to interactively leverage the information exchange across the correlated tasks in SLU. Additionally, we further introduce a multi-task self-consistency mechanism to mitigate the error propagation caused by the intent information injection. We conduct extensive experiments on the standard SLU benchmark and the results reveal that CroPrompt consistently outperforms the existing prompting approaches. In addition, the multi-task self-consistency mechanism can effectively ease the error propagation issue, thereby enhancing the performance. We hope this work can inspire more research on cross-task prompting for SLU. © 2025 IEEE.

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The use of an oxidized carbon nanotube film to control Zn deposition and eliminate dendrite formation in a Zn ion battery

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新型炭材料(中英文) 2025年第1期40卷 154-166页

作者： LI Pin-xiang YI Zhe-han WANG Ye-xing HE Chang LIANG Ji HOU Feng Key Laboratory of Advanced Ceramics and Machining Technology of Ministry of Education School of Materials Science and Engineering Tianjin UniversityTianjin 300072China Interdisciplinary Graduate Programme-Collaborative Initiative Graduate CollegeNanyang Technological UniversitySingapore 637371Singapore School of Physical and Mathematical Sciences Nanyang Technological UniversitySingapore 637371Singapore Tianjin Key Laboratory of Low-dimensional Electronic Materials and Advanced Instrumentation Tianjin 300072China

Aqueous zinc ion batteries are regarded as one of the most promising candidates for large-scale energy stor-age due to their high safety,cost-effectiveness,and environ-mental ***,uncontrolled zinc dendrite growth and side reactions of the zinc anode decrease the sta-bility of Zn *** report the synthesis of an air-oxid-ized carbon nanotube(O-CNT)film by chemical vapor de-position followed by heat treatment in air which is used as a protective layer on the Zn foil to suppress zinc dendrite *** increase in the hydrophilicity of the O-CNT film caused by air oxidation facilitates zinc deposition between the film and the anode instead of deposition on the film *** porous structure of the O-CNT film homogenizes the Zn^(2+)ion flux and the electric field on the surface of the Zn foil,leading to the uniform deposition of *** a result,a O-CNT@Zn symmetric cell has a much better cycling stability with a life of more than 3000 h at 1 mA cm^(−2) with a capacity of 1 mAh cm^(−2),and values of more than 2000 h and 1 mAh cm^(−2) at 5 mA cm^(−2).In addition,a O-CNT@Zn||Mn^(2+)inserted hydrated vanadium pentoxide(MnVOH)full cell has a better rate per-formance than a Zn||MnVOH cell,achieving a high discharge capacity of 194 mAh g^(−1) at a high current density of 8 A g^(−1).In a long-term cycling test,the O-CNT@Zn||MnVOH full cell has a capacity retention of 58.8%after 2000 cycles at a current density of 5 A·g^(−1).

关键词： Carbon nanotubes Zn metal anodes Dendrite-free Hydrophilic Surface functionalization

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Preparation and Simulation of Quantum States of Matrices 6

Preparation and Simulation of Quantum States of Matrices

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6th International Conference on Electronic Information Technology and Computer engineering, EITCE 2022

作者： Liu, Zhengyu Liu, Xiaonan Xie, Haoshan Zhao, Chenyan School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450000 China National Supercomputing Center in Zhengzhou Zhengzhou450000 China State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University Zhengzhou450000 China

ISBN: (纸本)9781450397148

A basic problem of quantum computing is how to effectively express classical data in quantum systems. This problem is called state preparation problem, and the process of preparing quantum states is called coding. In this paper, the classical data of matrix is transformed into quantum states in the form of amplitude coding and simulated. This paper first introduces the detailed process of matrix transformation into the linear combination of quantum ground state, then designs a quantum circuit model to realize amplitude coding according to the idea of binary tree model and quantum random walk algorithm, and finally simulates the three matrices on IBM quantum cloud platform, and analyzes and introduces many situations. The experimental results verify the feasibility of converting the matrix into quantum states by amplitude coding, analyze the causes of errors in the experimental process, and put forward the corresponding solutions. This paper introduces in detail the whole process of matrix conversion to quantum states and the construction of quantum circuits for simulation. Compared with the existing experiments, the quantum circuits constructed in this paper are shorter in length, less in number of quantum gates, and more versatile. © 2022 Association for computing Machinery.

关键词： Matrix algebra

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BContext2Name: Naming Functions in Stripped Binaries with Multi-Label Learning and Neural Networks

BContext2Name: Naming Functions in Stripped Binaries with Mu...

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IEEE International Conference on Cyber Security and Cloud computing (CSCloud)

作者： Bing Xia Yunxiang Ge Ruinan Yang Jiabin Yin Jianmin Pang Chongjun Tang Key Laboratory of Mathematical Engineering and Advanced Computing dept.ZhongYuan University of Technology Zhengzhou China Henan Key Laboratory on Public Opinion Intelligent Analysis dept.ZhongYuan University of Technology Zhengzhou China dept.ZhongYuan University of Technology Zhengzhou China dept.Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

Conducting binary function naming helps reverse engineers understand the internal workings of the code and perform malicious code analysis without accessing the source code. However, the loss of debugging information poses the challenge of insufficient high-level semantic information description for stripping binary code function naming. Meanwhile, the existing binary function naming scheme has one function label for only one sample. The long-tail effect of function labels for a single sample makes the machine learning-based prediction models face the challenge. To obtain a function correlation label and improve the propensity score of uncommon tail labels, we propose a multi-label learning-based binary function naming model BContext2Name. This model automatically generates relevant labels for binary function naming by function context information with the help of PfastreXML model. The experimental results show that BContext2Name can enrich function labels and alleviate the long-tail effect that exists for a single sample class. To obtain high-level semantics of binary functions, we align pseudocode and basic blocks based on disassembly and decompilation, identify concrete or abstract values of API parameters by variable tracking, and construct API-enhanced control flow graphs. Finally, a seq2seq neural network translation model with attention mechanism is constructed between function multi-label learning and enhanced control flow graphs. Experiments on the dataset reveal that the F1 values of the BContext2Name model improve by 3.55% and 15.23% over the state-of-the-art XFL and Nero, respectively. This indicates that function multi-label learning can provide accurate labels for binary functions and can help reverse analysts understand the inner working mechanism of binary code. Code and data for this evaluation are available at https://***/CSecurityZhongYuan/BContext2Name.

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Multi-image encryption combining four-dimensional chaotic systems and multi-layer embedding

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The European Physical Journal Special Topics 2024年 1-18页

作者： Zhou, Kai Zhang, Jiaxin Xiang, Jianhong Zhong, Yu College of Information and Communication Engineering Harbin Engineering University Harbin China Key Laboratory of Advanced Ship Communication and Information Technology Harbin Engineering University Harbin China Sichuan Key Laboratory of Agile Intelligent Computing Chengdu China

A multi-image encryption technique combining a four-dimensional chaotic system and multi-layer embedding is proposed because images are easily attacked from the outside during network transmission and that traditional image encryption algorithms have limitations such as long encryption time, low ciphertext image security, and poor information diffusion when a single image is encrypted multiple times. algorithm first creates a four-dimensional chaotic system to produce chaotic sequences; the system’s positive Lyapunov exponent indicates that it has chaotic properties in four directions; then, multiple images are processed using multi-layer embedding and chaotic sequences to create a composite image. Next, block scrambling and variable parameter Arnold’s scrambling are combined to perform block sorting and pixel disorder on the composite image. Lastly, the composite image is processed in separate blocks to execute various encryption techniques. To obtain the ciphertext image, several blocks are finally treated using either same-or-different-or diffusion processing. Simulation experiments show that this algorithm has a faster encryption time, better security and information diffusion in the encrypted image, a maximum correlation value of $$-$$ 0.00244 between adjacent pixels in the ciphertext image, and UACI of 33.4663% and 99.6086%, respectively (the theoretical values are 0, 99.61% and 33.46%)

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