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IEEE Open Journal of Antennas and Propagation 2021年 2卷 676-678页

作者： Feng, Naixing Zhang, Yuxian Zeng, Qingsheng Tong, Mei Song Joines, William T. Wang, Guo Ping Institute of Microscale Optoelectronics Shenzhen University Shenzhen China College of Astronautics Nanjing University of Aeronautics and Astronautics Nanjing China Key Laboratory of Embedded System and Service Computing of the Ministry of Education College of Electronics and Information Engineering Tongji University Shanghai China Department of Electrical and Computer Engineering Duke University DurhamNC United States

In this response letter, we focus on addressing Yu Hang Yang's comments so that the correctness and validity of the proposed auxiliary-differential-equation-based (ADE) Crank-Nicolson finite-difference time-domain method with direct-splitting technique (CNDS-FDTD) are demonstrated. Moreover, related applications in modeling 2D material nanostructure problems are shown. © 2020 IEEE.

关键词： Finite difference time domain method

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Coupling Process between Droplet and Iron Investigated by Reactive Molecular Dynamics Simulations

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ACS omega 2024年第18期9卷 20410-20424页

作者： Hang Dong Can Zhao Na Kong Yu Zhou Jianping Zhou School of Mechanical Engineering Xinjiang University Urumqi 830046 China. Xinjiang Uygur Autonomous Region Science and Technology Project Service Center Urumqi 830011 China. State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University Shanghai 200240 China.

The droplet-to-iron electrochemical reaction is common in nature and industrial production, and it causes damage to the economy, safety, and the environment. The electrochemical reaction of droplet-to-iron is a coupling process of wetting and corrosion. Presently, investigations into electrochemical reactions mainly focus on the corrosions caused by a solution, and wetting is rarely considered. However, for the droplet-to-iron electrochemical reaction, the mechanism of charge transfer in the process is still unclear. In this paper, a reactive molecular dynamics simulation model for the droplet-to-iron electrochemical reaction is developed for the first time. The electrochemical reaction of droplet-to-iron is studied, and the interaction between droplet wetting and corrosion on iron is investigated. The effects of temperature, electric field strength, and salt concentration on the electrochemical reaction are explored. Results show that droplet wetting on the iron surface and the formation of a single-molecular-layer ordered structure are prerequisites for corrosion. The hydroxyl radicals that penetrate the ordered structure acquire electrons from iron atoms on the substrate surface under the action of Coulomb forces and form iron-containing oxides with these iron atoms. The corrosion products and craters lead to a reduced droplet height, which promotes droplet wetting on iron and further intensifies the droplet-to-iron electrochemical reaction.

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Bag2image: A Multi-Instance Network Traffic Representation for Network Security Event Prediction

SSRN

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SSRN 2024年

作者： Zhang, Jiachen Han, Daoqi Lv, Zhaoxuan Liu, Yang Lu, Yueming Duan, Junke Zhang, Xinyu School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education China School of Automation Beijing Institute of Technology Beijing China Beijing Institute of Astronautical Systems Engineering Beijing China

In practical scenarios, security events triggered by abnormal network traffic often result from the collective behavior of multiple data streams, embodying group security events with collective characteristics. Existing research methods, focusing on individual data streams, lack a macroscopic analysis and struggle with challenges of analyzing massive, imbalanced data sets. To address these challenges, this paper adopts a multi-instance learning approach, mapping multiple data streams into a bag with a coarse-grained approach, where each bag corresponds to a security event label and each data stream represents an instance. We propose a multi-instance network traffic conversion method, Bag2Image, which transforms temporal multi-instance network traffic data into image representations, preserving the spatio-temporal characteristics of instances within the bag through image channels and pixels. This strategy allows the network security event prediction task to be approached as an image classification problem, leveraging advanced image classification techniques for prediction. Our cross-experiments with six advanced multi-instance learning (MIL) algorithms and six different classification models demonstrate the superior performance of our method on both the UNSW-NB15 dataset and a private dataset. Specifically, our method achieved the highest F1 scores of 77.9% and 74.4% on these datasets, respectively, representing improvements of 4.1% and 13.5% over the second-best MIL algorithm. The recall rates also saw increases of 4.1% and 13.2%, respectively. © 2024, The Authors. All rights reserved.

关键词： Network security

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Training for stable explanation for free 24

Training for stable explanation for free

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Chao Chen Chenghua Guo Rufeng Chen Guixiang Ma Ming Zeng Xiangwen Liao Xi Zhang Sihong Xie School of Computer Science and Technology Harbin Institute of Technology (Shenzhen) China Key Laboratory of Trustworthy Distributed Computing and Service (MoE) Beijing University of Posts and Telecommunications China Artificial Intelligence Thrust The Hong Kong University of Science and Technology (Guangzhou) China Intel Carnegie Mellon University College of Computer and Data Science Fuzhou University China

ISBN: (纸本)9798331314385

To foster trust in machine learning models, explanations must be faithful and stable for consistent insights. Existing relevant works rely on the ℓp distance for stability assessment, which diverges from human perception. Besides, existing adversarial training (AT) associated with intensive computations may lead to an arms race. To address these challenges, we introduce a novel metric to assess the stability of top-k salient features. We introduce R2ET which trains for stable explanation by efficient and effective regularizer, and analyze R2ET by multi-objective optimization to prove numerical and statistical stability of explanations. Moreover, theoretical connections between R2ET and certified robustness justify R2ET's stability in all attacks. Extensive experiments across various data modalities and model architectures show that R2ET achieves superior stability against stealthy attacks, and generalizes effectively across different explanation methods. The code can be found at https://***/ccha005/R2ET.

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Routing Recovery for UAV Networks with Deliberate Attacks: A Reinforcement Learning based Approach

Routing Recovery for UAV Networks with Deliberate Attacks: A...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Sijie He Ziye Jia Chao Dong Wei Wang Yilu Cao Yang Yang Qihui Wu The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics Nanjing Jiangsu State Key Laboratory of Integrated Services Networks (Xidian University) Xi'an Institute of Computing Technologies China Academy of Railway Sciences Corporation Limited Beijing

The unmanned aerial vehicle (UAV) network is popular these years due to its various applications. In the UAV network, routing is significantly affected by the distributed network topology, leading to the issue that UAVs are vulnerable to deliberate damage. Hence, this paper focuses on the routing plan and recovery for UAV networks with attacks. In detail, a deliberate attack model based on the importance of nodes is designed to represent enemy attacks. Then, a node importance ranking mechanism is presented, considering the degree of nodes and link importance. However, it is intractable to handle the routing problem by traditional methods for UAV networks, since link connections change with the UAV availability. Hence, an intelligent algorithm based on reinforcement learning is proposed to recover the routing path when UAVs are attacked. Simulations are conducted and numerical results verify the proposed mechanism performs better than other referred methods.

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Robin: A Novel Method to Produce Robust Interpreters for Deep Learning-Based Code Classifiers

Robin: A Novel Method to Produce Robust Interpreters for Dee...

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IEEE International Conference on Automated Software Engineering (ASE)

作者： Zhen Li Ruqian Zhang Deqing Zou Ning Wang Yating Li Shouhuai Xu Chen Chen Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Cluster and Grid Computing Lab National Engineering Research Center for Big Data Technology and System Hubei Engineering Research Center on Big Data Security Department of Computer Science University of Colorado Colorado Springs USA Center for Research in Computer Vision University of Central Florida USA School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Deep learning has been widely used in source code classification tasks, such as code classification according to their functionalities, code authorship attribution, and vulnerability detection. Unfortunately, the black-box nature of deep learning makes it hard to interpret and understand why a classifier (i.e., classification model) makes a particular prediction on a given example. This lack of interpretability (or explainability) might have hindered their adoption by practitioners because it is not clear when they should or should not trust a classifier's prediction. The lack of interpretability has motivated a number of studies in recent years. However, existing methods are neither robust nor able to cope with out-of-distribution examples. In this paper, we propose a novel method to produce Robust interpreters for a given deep learning-based code classifier; the method is dubbed Robin. The key idea behind Robin is a novel hybrid structure combining an interpreter and two approximators, while leveraging the ideas of adversarial training and data augmentation. Experimental results show that on average the interpreter produced by Robin achieves a 6.11% higher fidelity (evaluated on the classifier), 67.22% higher fidelity (evaluated on the approximator), and 15.87x higher robustness than that of the three existing interpreters we evaluated. Moreover, the interpreter is 47.31% less affected by out-of-distribution examples than that of LEMNA.

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Research on Generating Xi'an Drum Music Based on Generative Adversarial Network

Research on Generating Xi'an Drum Music Based on Generative ...

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Sensors, Electronics and Computer Engineering (ICSECE), IEEE International Conference on

作者： Tianmian Liang Peng Li Yumei Cao Xiaojun Wu Huifang Zhao Chenye Qi Ministry of Culture and Tourism Key Laboratory of Intelligent Computing and Service Technology for Folk Song Xi’an China School of Computer Science Shaanxi Normal University Xi'an China Key Laboratory of Modern Teaching Technology Ministry of Education Xi’an China Engineering Laboratory of Teaching Information Technology of Shaanxi Province Xi’an China Xi'an Key Laboratory of Cultural Tourism Resources Development and Utilization Xi’an China School of Music Shaanxi Normal University Xi'an China

Currently most AI music generator platforms mainly focus on creating modern music, without focusing on the creation of folk music. And most music generation neural network models use recursive neural networks and their variants, resulting in excessively repetitive music information, greatly reducing the fun of music. When using only generative adversarial networks (GANs) to generate music, it is easy to experience unstable training, vanishing gradients, and mode collapse. Failure to consider time dependence can reduce the authenticity of generated music. In order to solve the problem that most of the conventional deep learning GAN music generation network models do not consider music theory rules in music generation and Xi'an drum music faces the scarcity of tracks and difficulties in composition, we proposed a GAN network music generative model based on music theory rules and characteristics of Chinese Folk Songs. When generating music, the generator CNN and the regulator CNN train together, and generate music melodies under the constraints of the voice column CNN module. The generated results are sent to the discriminator CNN, which submits feedback to the generator CNN for competition, to compensate for the shortcomings of previous technology and ultimately generate better melodies, thereby improving the authenticity, sweetness, and rhythm of the generated music.

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Base Station Network Traffic Prediction Approach Based on LMA -DeepAR 6

Base Station Network Traffic Prediction Approach Based on LM...

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6th IEEE International Conference on Computer and Communication systems, ICCCS 2021

作者： Zhang, Jiachen Zuo, Xingquan Xu, Mingying Han, Jing Zhang, Baisheng Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing Uinversity of Posts and Telecommunications Beijing China Key Laboratory of Intelligent Telecommunication Software and Multimedia Beijing Uinversity of Posts and Telecommunications Beijing China Network Interface Virtualization Shanghai System Design Deptment Zhongxing Telecommunication Equipment Corporation Shanghai China

ISBN: (纸本)9780738126043

Accurate network traffic prediction of base station cell is very vital for the expansion and reduction of wireless devices in base station cell. The burst and uncertainty of base station cell network traffic makes the network traffic nonlinear and nonstationary, which brings challenges to the long-term prediction of network traffic. In this paper, the traffic model LMA-DeepAR for base station network is established based on DeepAR. Acordding to the distribution characteristics of network traffic, this paper proposes an artificial feature sequence calculation method based on local moving average (LMA). The feature sequence is input into DeepAR as covariant, which makes the statistical characteristics of network traffic near a period of time in the past be considered when updating parameters, and the interference of non-stationary network traffic on model training will be reduced. Experimental results show that the proposed prediction approach (LMA-DeepAR) outperforms other methods in the overall long-term prediction performance and stability of multi cell network traffic. © 2021 IEEE.

关键词： Base stations

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Synergistic Inhibition of Azoles Compounds on Chloride-Induced Atmospheric Corrosion of Copper: Experimental and Theoretical Characterization

SSRN

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SSRN 2023年

作者： Ma, Xiao-Ze Cai, Guang-Yi Cao, Xiang-Kang Zhang, Xin-Xin Meng, Ling-Dong Dong, Ze-Hua Hubei Key Laboratory of Material Chemistry and Service Failure School of Chemistry and Chemical Engineering Huazhong University of Science and Technology Wuhan430074 China School of Materials Academy of Armed Forces Beijing100086 China National Key Laboratory of Science and Technology on Vessel Integrated Power System Naval University of Engineering Wuhan430033 China

In this work, the adsorption dynamics and inhibition behaviour of 2-phenyl imidazoline (2-PI) and benzotriazole (BTA) are investigated in atmospheric and aqueous environments by electrochemical and quartz crystal microbalance sensors. Moreover, their synergistic mechanisms are illustrated from molecule scales by theoretical calculations. Electrochemical tests show that 2-PI and BTA exhibit promising synergistic effects on the corrosion of copper with inhibition efficiency up to 99.1% in the 0.1 mol/L NaCl solution and 94.1% in the atmosphere, respectively. In addition, the adsorption kinetics of inhibitors on copper obey a pseudo-first-order kinetic model with exponential damping. © 2023, The Authors. All rights reserved.

关键词： Quartz crystal microbalances

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Evaluating the Performance to Detecting Topic Shift of Large Language Models: A Study

Evaluating the Performance to Detecting Topic Shift of Large...

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6th International Conference on Intelligent computing and Signal Processing (ICSP)

作者： Pingfang Tian Deting Liu Department of Computer Science and Technology Wuhan University of Science and Technology Wuhan China The Key Laboratory of the Rich Media Knowledge Organisation and Service of Digital Publishing Content Institute of Scientific and Technical Information of China Beijing China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Topic shift detection aims to identify whether there is a change in the current topic of conversation or if a change is needed. The study found previous work did not evaluate the performance of large language models like ChatGPT on the task of topic shift. Therefore, this paper's main task and innovation lie in analyzing ChatGPT's performance on topic shift detection. To provide a more comprehensive evaluation, we conducted topic shift detection tasks on ChatGPT from three aspects: single utterances, adjacent utterances, and contextual levels. Additionally, to gauge the performance of large language models, we conducted experiments on multiple small-scale models and compared the results of the two models. Experimental results on the publicly available English TIAGE dataset showed that small-scale models exhibited lower recall in all three aspects, while ChatGPT performed better in the recall. This suggests that compared to small-scale models, large models are more capable of accurately detecting topic shifts. However, large models also exhibited lower precision, indicating that while ChatGPT can recognize content differences in utterances, its judgment on whether these different contents belong to the same topic is poor.

关键词： Technological innovation Large language models Oral communication Signal processing Chatbots Context modeling

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