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arXiv 2021年

作者： Feng, Chenghao Shen, Wenqian Gao, Xinyu An, Jianping School of Information and Electronics Beijing Institute of Technology Beijing100081 China Shaanxi Key Laboratory of Information Communication Network and Security Xian University of Posts and Telecommunications Shaanxi Xian710121 China Huawei Technologies Co. Ltd. Beijing100085 China

Intelligent reflecting surfaces (IRSs) constitute passive devices, which are capable of adjusting the phase shifts of their reflected signals, and hence they are suitable for passive beamforming. In this paper, we conceive their design with the active beamforming action of multiple-input multiple-output (MIMO) systems used at the access points (APs) for improving the beamforming gain, where both the APs and users are equipped with multiple antennas. Firstly, we decouple the optimization problem and design the active beamforming for a given IRS configuration. Then we transform the optimization problem of the IRS-based passive beamforming design into a tractable non-convex quadratically constrained quadratic program (QCQP). For solving the transformed problem, we give an approximate solution based on the technique of widely used semidefinite relaxation (SDR). We also propose a low-complexity iterative solution. We further prove that it can converge to a locally optimal value. Finally, considering the practical scenario of discrete phase shifts at the IRS, we give the quantization design for IRS elements on basis of the two solutions. Our simulation results demonstrate the superiority of the proposed solutions over the relevant benchmarks. © 2021, CC BY.

关键词： MIMO systems

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Velocity-Driven Particle Swarm Optimization 19

Velocity-Driven Particle Swarm Optimization

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Proceedings of the 2019 8th International Conference on computing and Pattern Recognition

作者： Wei Li Yaochi Fan Qiaoyong Jiang Qingzheng Xu School of Computer Science and Engineering Xi'an University of Technology and Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China School of Computer Science and Engineering Xi'an University of Technology Xi'an China College of Information and Communication National University of Defense Technology Xi'an China

ISBN: (纸本)9781450376570

Particle swarm optimization (PSO) is an efficient nature-inspired optimization algorithm, which has been widely applied in many engineering fields. The performance of particle swarm optimization (PSO) has been significantly influenced by velocity update strategy. Traditionally, each particle updates its velocity based on its historical best experience and the global best experience, which may make the swarm lose its diversity and lead to premature convergence. To strengthen the performance of PSO, this paper proposes an improved PSO with the velocity-driven strategy (VD-PSO). In VD-PSO algorithm, the particles whose velocities are driven by better velocities focus on exploitation. The historical velocities, such as better velocities, may effectively characterize the landscape information of the optimization problems. In addition, the particles focus on exploration through information exchange amongst velocities. To verify the effectiveness of the proposed algorithm, experiments are conducted with CEC2014 test problems. The experimental results demonstrate the effectiveness of the proposed algorithm for solving the global optimization problems.

关键词： Evolutionary algorithms Velocity-driven strategy Particle swarm optimization Optimization problems

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Research on machine learning prediction of air quality index based on SPSS 3

Research on machine learning prediction of air quality index...

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3rd International Conference on Computer network, Electronic and Automation, ICCNEA 2020

作者： Ma, Limei Gao, Yijun Zhao, Chen College of Information Technology Hebei Normal University Shijiazhuang China School of Information Studies Dominican University River Forest United States Key Laboratory of Network and Information Security in Hebei Province Shijiazhuang China School of Information Studies Dominican University River Forest River ForestIN United States

ISBN: (纸本)9781728170831

Air From autumn to winter, air pollution in North China is very serious. If we can make effective prediction, we can achieve effective prevention. In this paper, SPSS is used to associate a phenomenon with multiple factors through the optimal combination of multiple independent variables. It is more effective and realistic to predict or estimate the dependent variables than to predict or estimate only one independent variable. Regression with two or more independent variables is multivariate linear regression, and multivariate linear regression has more practical significance than univariate linear regression °Through the establishment of multiple linear regression model, the factors affecting air quality were screened and analyzed. The factors influencing air quality index were PM2.5, PM10, SO2, NO2, CO and O3. Through regression analysis of one year's data samples, the prediction model is obtained. It has been proved that the prediction method is worth popularizing. © 2020 IEEE.

关键词： Forecasting

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A Semantic-based Replacement for Event Image Privacy

A Semantic-based Replacement for Event Image Privacy

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IEEE International Conference on Computational Science and Engineering, CSE

作者： Zhenfei Chen Zhu Tianqing Bing Tian Yu Wang Wei Ren School of Computer Science China University of Geosciences Wuhan P.R. China Institute of Artificial Intelligence and Blockchain Guangzhou University Guangzhou P.R. China School of Computer Science China University of Geosciences Guangxi Key Laboratory of Cryptography and Information Security Key Laboratory of Network Assessment Technology CAS Beijing P.R. China

ISBN: (纸本)9781665416610

Thanks to the continuous development of deep learning and the updating of deep neural networks, the accuracy of various computer vision tasks continue improving. On the one hand, the accuracy of image recognition is significantly improved. On the other hand, it also poses a higher challenge of image-based privacy preservation. Although traditional privacy protection methods such as cryptography methods can provide a good privacy protection, they are extremely inconvenient to use and cannot provide good image utility. In order to obtain a balance between image privacy and utility, we propose a privacy-preserving model based on image semantic replacement. We perform semantic replacement or obfuscation to multiple information. Taking the human figures as an example, the information includes faces, scenes, and dressing style. As that information contributes the most to the recognition, we define those items as the privacy of the original image. We replace the event information of the original image, so that the figures in the image can no longer be recognized. With this strategy, the image can still be detected by various detection networks, such as scene detection, which ensures utility. The framework consists of three parts: detection network, scene replacement network, and clothing replacement network. A comprehensive and quantitative experiment set proves the effectiveness of the proposed model.

关键词： Deep learning Privacy Image recognition Scientific computing Semantics Clothing Neural networks

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Two-phases detection scheme: Detecting android malware in android markets

Two-phases detection scheme: Detecting android malware in an...

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International Conference on Applications and Techniques in Cyber Intelligence, ATCI 2018

作者： Su, Xin Lin, Jiuchuan Shen, Fuhui Zheng, Yi Hunan Provincial Key Laboratory of Network Investigational Technology Hunan Police Academy Changsha China Department of Information Technology Hunan Police Academy Changsha China Key Lab of Information Network Security of Ministry of Public Security The Third Research Institute of Ministry of Public Security Shanghai China Zhejiang Economic Information Center Hangzhou China

ISBN: (纸本)9783319987750

Recently, Android application becomes popular and important in human’s daily work, life, entertainment. However, because of open source of Android application, more and more malware aim to this platform and launch various malicious attacks to threaten Android users’ security. Previous research works focus on using static behavioral analysis to detect Android malware, which cannot capture dynamic behaviors and in-efficiency to detect Android malware. In this paper, we present a Android application two-stage detection scheme that using two kinds of dynamic behavioral characteristics to detect Android malware. This framework first uses system call statistics to identify potential malicious apps. After verification, if the software is clean, the application will then be released to the relevant markets. To mitigate against false negative cases, users who run new apps can invoke our network traffic monitoring (NTM) tool which triggers network traffic capture upon detecting some suspicious behaviors e.g. detecting sensitive data being sent to output stream of an open socket. The network traffic will be analyzed to see if it matches network characteristics observed from malware apps. If suspicious network traffic is observed, the relevant Android markets will be notified to remove the application from the repository. We trained our system call and network traffic classifiers using 32 families of known Android malware families and some typical normal apps. Later, we evaluated our framework using other malware and normal apps that used in the training set. Our experimental results using 120 test apps (which consist of 50 malware and 70 normal apps) indicate that we can achieve a 94.2% and 99.2% accuracy with J.48 and Random forest classifier respectively using our framework. © Springer Nature Switzerland AG 2019.

关键词： Application programs

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OPEN: orthogonal propagation with ego-network modeling 22

OPEN: orthogonal propagation with ego-network modeling

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Liang Yang Lina Kang Qiuliang Zhang Mengzhe Li Bingxin Niu Dongxiao He Zhen Wang Chuan Wang Xiaochun Cao Yuanfang Guo School of Artificial Intelligence Hebei University of Technology Tianjin China College of Intelligence and Computing Tianjin University Tianjin China School of Artificial Intelligence OPtics and ElectroNics (iOPEN) Northwestern Polytechnical University Xi'an China and School of Cybersecurity Northwestern Polytechnical University Xi'an China State Key Laboratory of Information Security IIE CAS Beijing China School of Cyber Science and Technology Sun Yat-sen University Shenzhen China School of Computer Science and Engineering Beihang University Beijing China and Zhongguancun Laboratory Beijing China

ISBN: (纸本)9781713871088

To alleviate the unfavorable effect of noisy topology in Graph Neural networks (GNNs), some efforts perform the local topology refinement through the pairwise propagation weight learning and the multi-channel extension. Unfortunately, most of them suffer a common and fatal drawback: irrelevant propagation to one node and in multi-channels. These two kinds of irrelevances make propagation weights in multi-channels free to be determined by the labeled data, and thus the GNNs are exposed to overfitting. To tackle this issue, a novel Orthogonal Propagation with Ego-network modeling (OPEN) is proposed by modeling relevances between propagations. Specifically, the relevance between propagations to one node is modeled by whole ego-network modeling, while the relevance between propagations in multi-channels is modeled via diversity requirement. By interpreting the propagations to one node from the perspective of dimension reduction, propagation weights are inferred from principal components of the ego-network, which are orthogonal to each other. Theoretical analysis and experimental evaluations reveal four attractive characteristics of OPEN as modeling high-order relationships beyond pairwise one, preventing overfitting, robustness, and high efficiency.

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On layer normalization in the transformer architecture 37

On layer normalization in the transformer architecture

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37th International Conference on Machine Learning, ICML 2020

作者： Xiong, Ruibin Yang, Yunchang He, Di Zheng, Kai Zheng, Shuxin Xing, Chen Zhang, Huishuai Lan, Yanyan Wang, Liwei Liu, Tie-Yan CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technolog Chinese Academy of Sciences China University of Chinese Academy of Sciences China Center for Data Science Peking University Beijing Institute of Big Data Research China Key Laboratory of Machine Perception MOE School of EECS Peking University China Microsoft Research College of Computer Science Nankai University China

ISBN: (纸本)9781713821120

The Transformer is widely used in natural language processing tasks. To train a Transformer however, one usually needs a carefully designed learning rate warm-up stage, which is shown to be crucial to the final performance but will slow down the optimization and bring more hyperparameter tunings. In this paper, we first study theoretically why the learning rate warm-up stage is essential and show that the location of layer normalization matters. Specifically, we prove with mean field theory that at initialization, for the original-designed Post-LN Transformer, which places the layer normalization between the residual blocks, the expected gradients of the parameters near the output layer are large. Therefore, using a large learning rate on those gradients makes the training unstable. The warm-up stage is practically helpful for avoiding this problem. On the other hand, our theory also shows that if the layer normalization is put inside the residual blocks (recently proposed as Pre-LN Transformer), the gradients are well-behaved at initialization. This motivates us to remove the warm-up stage for the training of Pre-LN Transformers. We show in our experiments that Pre-LN Transformers without the warm-up stage can reach comparable results with baselines while requiring significantly less training time and hyper-parameter tuning on a wide range of applications. Copyright 2020 by the author(s).

关键词： Mean field theory

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Exact distributed quantum algorithm for generalized Simon’s problem

arXiv

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arXiv 2023年

作者： Li, Hao Qiu, Daowen Luo, Le Mateus, Paulo Institute of Quantum Computing and Computer Theory School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China The Guangdong Key Laboratory of Information Security Technology Sun Yat-sen University 510006 China QUDOOR Technologies Inc. Zhuhai China School of Physics and Astronomy Sun Yat-sen University Zhuhai519082 China Instituto de Telecomunicações Departamento de Matemática Instituto Superior Técnico Av. Rovisco Pais Lisbon1049-001 Portugal

Simon’s problem is one of the most important problems demonstrating the power of quantum algorithms, as it greatly inspired the proposal of Shor’s algorithm. The generalized Simon’s problem is a natural extension of Simon’s problem, and also a special hidden subgroup problem: Given a function f : {0, 1}n → {0, 1}m, with the property that for any x, y ∈ {0, 1}n, there is some unknown hidden subgroup S ≤ 2n such that f(x) = f(y) iff x ⊕ y ∈ S, where |S| = 2k for some 0 ≤ k ≤ n (m ≥ n − k). The goal of generalized Simon’s problem is to find the hidden subgroup S. In this paper, we present two key contributions. Firstly, we characterize the structure of the generalized Simon’s problem in distributed scenario and introduce a corresponding distributed quantum algorithm. Secondly, we refine the algorithm to ensure exactness due to the application of quantum amplitude amplification technique. Our algorithm offers exponential acceleration compared to the distributed classical algorithm. When contrasted with the centralized quantum algorithm for the generalized Simon’s problem, our algorithm’s oracle requires fewer qubits, thus making it easier to be physically implemented. Particularly, the exact distributed quantum algorithm we develop for the generalized Simon’s problem outperforms the best previously proposed distributed quantum algorithm for Simon’s problem in terms of generalizability and exactness. Copyright © 2023, The Authors. All rights reserved.

关键词： Qubits

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Global-and-Local Collaborative Learning for Co-Salient Object Detection

arXiv

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arXiv 2022年

作者： Cong, Runmin Yang, Ning Li, Chongyi Fu, Huazhu Zhao, Yao Huang, Qingming Kwong, Sam The Institute of Information Science Beijing Jiaotong University Beijing100044 China The Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China The Department of Computer Science City University of Hong Kong Hong Kong The School of Computer Science and Engineering Nanyang Technological University Singapore A*STAR Singapore The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China University of Chinese Academy of Sciences Beijing101408 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518055 China The City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

The goal of co-salient object detection (CoSOD) is to discover salient objects that commonly appear in a query group containing two or more relevant images. Therefore, how to effectively extract inter-image correspondence is crucial for the CoSOD task. In this paper, we propose a global-and-local collaborative learning architecture, which includes a global correspondence modeling (GCM) and a local correspondence modeling (LCM) to capture comprehensive inter-image corresponding relationship among different images from the global and local perspectives. Firstly, we treat different images as different time slices and use 3D convolution to integrate all intra features intuitively, which can more fully extract the global group semantics. Secondly, we design a pairwise correlation transformation (PCT) to explore similarity correspondence between pairwise images and combine the multiple local pairwise correspondences to generate the local inter-image relationship. Thirdly, the inter-image relationships of the GCM and LCM are integrated through a global-and-local correspondence aggregation (GLA) module to explore more comprehensive inter-image collaboration cues. Finally, the intra- and inter-features are adaptively integrated by an intra-and-inter weighting fusion (AEWF) module to learn co-saliency features and predict the co-saliency map. The proposed GLNet is evaluated on three prevailing CoSOD benchmark datasets, demonstrating that our model trained on a small dataset (about 3k images) still outperforms eleven state-of-the-art competitors trained on some large datasets (about 8k̴200k images). © 2022, CC BY-NC-SA.

关键词： Convolution

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An Imbalanced Learning Based Method for Esophageal Squamous Cell Carcinoma (ESCC) Distant Metastasis Predicting 3rd

An Imbalanced Learning Based Method for Esophageal Squamous ...

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3rd International Conference on Machine Learning for Cyber security, ML4CS 2020

作者： Li, Xuemei Yang, Bo Peng, Lizhi Li, Baosheng Zhu, Jian Educational Technology Center Shandong College of Traditional Chinese Medicine Yantai264199 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China Department of Radiation Oncology Shandong Cancer Hospital affiliated to Shandong University Jinan250117 China

ISBN: (纸本)9783030624620

Accurately predicting distant metastasis is very important for esophageal squamous cell carcinoma (ESCC) patients, as it is a key evidence to determine the degree of this disease. Traditional metastasis predictions are made by doctors manually, which is time and labor consuming. In recent years, some researchers try to predict ESCC distant metastasis using machine learning techniques. However, they ignored the factor that the problem of predicting ESCC distant metastasis is a highly imbalanced task. In this study, we try to build a new imbalanced learning based ESCC distant metastasis predicting scheme using blood cell analysis testing results. We first extract effective features from blood cell analysis testing data. And then we use over-sampling method to generate new positive instances (instances with ESCC distant metastasis). Finally, we apply k-nearest neighbor (k-NN) algorithms to predict unknown instances. Experimental results show that our method is effective in cases with few positive instances. © 2020, Springer Nature Switzerland AG.

关键词： Machine learning

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