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data-driven Optimization of Complex Systems (DOCS), International Conference on

作者： Yang Liu Guo Yu Jian Cheng Chao Jiang Xinzhe Wang Lianbo Ma Software College Northeastern University Shenyang China The Institute of Intelligent Manufacturing Nanjing Tech University Nanjing China Research Institute of Mine Big Data Chinese Institute of Coal Science Beijing China

Hydrocracking is a complex and time-consuming chemical process converting heavy oil fractions into various valuable products with low billing points, which plays a pivotal role in improving the quality of products in the oil refining process. Therefore, designing efficient surrogate models to simulate the hydrocracking process and finding appropriate solutions for multi-objective oil refining process become a research topic. To this end, we propose a novel transferable preference learning driven evolutionary algorithm for the multi-objective decision analysis in the oil refining process. Specifically, we firstly take account of the user preference to partition the objective space into a region of interest (ROI) and other subspaces, and then adopt Kriging models to approximate the sub-problems in the ROI. After that, we transfer the mutual information between the sub-problems in the ROI to enhance the robustness and generalization ability of the Kriging models during the evolutionary process. The results of the experiments on the the oil refining process have showcased the efficiency and effectiveness of our proposed approach in the multi-objective optimization and decision analysis.

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The Impact of Medical Resources and Oral Antiviral Drugs on SARS-CoV-2 Mortality—Hong Kong SAR,China,2022

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China CDC weekly 2024年第21期6卷 469-477,I0001-I0005页

作者： Zhiqi Zeng Jingyi Liang Zhijie Lin Wenda Guan Wei He Qianying Li Lixi Liang Weiqi Pan Zige Liu Guibin Lu Xiaoyan Deng Eric HY Lau Chitin Hon Zifeng Yang Guangzhou key laboratory for clinical rapid diagnosis and early warning of infectious diseases KingMed School of Laboratory MedicineGuangzhou Medical UniversityGuangzhou CityGuangdong ProvinceChina Macao Institute of Systems Engineering Macao University of Science and TechnologyMacao SARChina Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications Faculty of Innovative EngineeringMacao University of Science and TechnologyMacao SARChina State Key Laboratory of Respiratory Disease National Clinical Research Center for Respiratory DiseaseGuangzhou Institute of Respiratory Healththe First Affiliated Hospital of Guangzhou Medical UniversityGuangzhou CityGuangdong ProvinceChina School of Public Health The University of Hong KongHong Kong SARChina Laboratory of Data Discovery for Health Hong Kong SARChina Guangzhou Laboratory Guangzhou CityGuangdong ProvinceChina

Introduction:The Omicron variant of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)demonstrates increased transmissibility compared to earlier strains,contributing to a significant number of fatalities in Hong Kong Special Administrative Region(HKSAR),*** medical resources and medications are essential in mitigating these *** study evaluates the effects of supplementary resources from the Chinese mainland during the fifth wave of the pandemic in ***:Vector autoregression(VAR)was employed to analyze data from the Oxford coronavirus disease 2019(COVID-19)Government Response Tracker to assess the effectiveness of control measures during five waves of the pandemic in ***,a transmission dynamics model was created to investigate the influence of supplementary medical resources from the Chinese mainland and oral medications on ***:In the initial four waves,workplace closures,restrictions on public events,international travel bans,and shielding the elderly significantly influenced pandemic ***,during the fifth wave,these measures showed no notable *** comparing a situation without extra medical resources or COVID-19 oral medication,there was a 17.7%decrease in COVID-19 fatalities with mainland medical resources and an additional 10.2%reduction with oral ***,they contributed to a 26.6%decline in ***:With the rapid spread of the virus,regional reallocation of medical resources may reduce mortality even when the local healthcare system is overstretched.

关键词： mainland Impact travel

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Denoising and Reconstructed Hypergraph-Based Collaborative Filtering for Recommendation

SSRN

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SSRN 2024年

作者： Yi, Jun Han, Xiaoqi Zhou, Wei Xiao, Shan Mu, Chaoxu College of Intelligent Technology and Engineering Chongqing University of Science and Technology Chongqing401331 China Institute of Big Data and Optimization Chongqing College of Electronic Engineering Chongqing401331 China School of Electrical and Information Engineering Tianjin University Tianjin300072 China

The collaborative filtering (CF) techniques based on graph neural networks (GNNs) have become a hot topic in recommender systems because they can better model the diffusion process of interactive information in bipartite graphs, and thus capture higher-order user-item interaction features more efficiently. However, two key challenges that would most likely cause degradation of recommendation performance have not been sufficiently explored in existing solutions: 1) The inevitable noise generated in user-item interactions may mask real preference information of users. 2) The sparsity of interactive supervisory signals limits the ability of CF paradigm to represent the implicit relationship between users and items. To tackle these challenges, a two-branch denoising and reconstructed hypergraph-based collaborative filtering (DRHCF) framework is proposed for recommendation. Specifically, the graph denoising module (GDM) is designed to provide interaction graphs (IGs) by soft-hard denoising strategy that evaluates the reliability of direct interactions based on the spatial structural similarity for the graph-based learning branch, and it also output IGs by hard denoising strategy where interactive edges with low credibility are directly cropped for the hypergraph-based learning branch to improve the representation of the CF paradigm. Next, a hypergraph reconstruction module (HRM) is developed to construct higher-order associations for each user and item to explore more reliable global collaboration signals. Additionally, a global soft-hard denoising module (GSHDM) is employed to further improve the noise immunity of the hypergraph collaborative relation encoding. Thanks to that foundation, we unify the modeling of local collaborative relation encoder with the global hypergraph relation encoder to obtain high-quality embeddings of users and items in the cross-view contrastive learning module. Extensive experiments on four benchmark datasets demonstrate the superiority of o

关键词： Graph neural networks

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Micromotion Gesture Recognition Technology Based on Millimeter-wave Radar

Micromotion Gesture Recognition Technology Based on Millimet...

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IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Zhaoqi Li Fei Gao Yingchun Li Panpan Tang Dayu Cai Zheyi Li Cong Wang Jingchang Nan Liaoning Key Laboratory of Radio Frequency and Big Data for Intelligent Applications Liaoning Technical University Huludao China School of Information Science and Engineering Harbin Institute of Technology Weihai China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

Different from the traditional FMCW radar signal processing method, this paper mainly explores the application of the time-frequency analysis method of the reflected intermediate frequency of millimeter wave radar in fretting detection. The radar echo data are analyzed by FFT, wavelet transform and Hilbert yellow transform. This topic mainly uses human gesture targets as the micromotion feature to detect. Under the three designed gestures, the time-frequency analysis method proposed in this topic combined with a convolutional neural network achieves a high recognition success rate. The motion detection technology proposed in this paper can be applied to millimeter wave radar under THE FMCW system. It can detect and classify tiny motions under complex backgrounds and show good recognition ability under strong interference. It can be used in medical treatment, automobile and other fields to play the role of accurate identification.

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Multi-IRS Enhanced Wireless Coverage: Deployment Optimization Based on Large-Scale Channel Knowledge

arXiv

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arXiv 2024年

作者： Fu, Min Zhu, Lipeng Zhang, Rui Department of Electrical and Computer Engineering National University of Singapore 117583 Singapore School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen Guangdong518172 China

In this paper, we study the intelligent reflecting surface (IRS) deployment problem where a number of IRSs are optimally placed in a target area to improve its signal coverage with the serving base station (BS). To achieve this, we assume that there is a given set of candidate sites in the target area for deploying IRSs and divide the area into multiple grids of identical size. Then, we derive the average channel power gains from the BS to IRS in each candidate site and from this IRS to any grid in the target area in terms of IRS deployment parameters, including its size, position, height, and orientation. Thus, we are able to approximate the average cascaded channel power gain from the BS to each grid via any IRS, assuming an effective IRS reflection gain based on the large-scale channel knowledge only. Next, we formulate a multi-IRS deployment optimization problem to minimize the total deployment cost by selecting a subset of candidate sites for deploying IRSs and jointly optimizing their heights, orientations, and numbers of reflecting elements while satisfying a given coverage rate performance requirement over all grids in the target area. To solve this challenging combinatorial optimization problem, we first reformulate it as an integer linear programming problem and solve it optimally using the branchand- bound (BB) algorithm. In addition, we propose an efficient successive refinement algorithm to further reduce computational complexity. Simulation results demonstrate that the proposed lower-complexity successive refinement algorithm achieves nearoptimal performance but with significantly reduced running time compared to the proposed optimal BB algorithm, as well as superior performance-cost trade-off than other baseline IRS deployment strategies. © 2024, CC BY.

关键词： Integer linear programming

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Two-Stage OD Flow Prediction for Emergency in Urban Rail Transit

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IEEE Transactions on intelligent Transportation Systems 2024年第1期25卷 920-928页

作者： Zhu, Guangyu Ding, Jiacun Wei, Yun Yi, Yang Xu, Sendren Sheng-Dong Wu, Edmond Q. Beijing Jiaotong University Key Lab. of Transport Industry of Big Data Application Technologies for Comprehensive Transport The Beijing Research Center of Urban Traffic Information Sensing and Service Technologies Beijing100044 China Beijing Mass Transit Railway Operation Corporation Ltd. Beijing100014 China Yangzhou University College of Information Engineering Yangzhou225127 China National Taiwan University of Science and Technology Automation and Control Center The Graduate Institute of Automation and Control Taipei106335 Taiwan Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China The Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China

Urban rail transit (URT) is vulnerable to natural disasters and social emergencies including fire, storm and epidemic (such as COVID-19), and real-time origin-destination (OD) flow prediction provides URT operators with important information to ensure the safety of URT system. However, hindered by the high dimensionality of OD flow and the lack of supportive information reflecting the real-time passenger flow changes, study in this area is at the beginning stage. A novel model consisting of two stages is proposed for OD flow prediction. The first stage predicts the inflows of all stations by Long Short-Term Memory (LSTM) in real time, where the dimension is reduced compared with predicting OD flows directly. In the second stage, the notion of separation rate, namely, the proportion of inbound passengers bounding for another station, is estimated. Finally, The OD flow is predicted by multiplying the inflow and separation rate. Experiments based on Hangzhou Metro dataset show the proposed model outperforms the contrast model in weighted mean average error (WMAE) and weighted mean square error (WMSE). Results also suggest that the proposed prediction model performs better on weekdays than on weekends, and with greater accuracy on larger OD flows. © 2000-2011 IEEE.

关键词： Forecasting

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Self-Supervised Acoustic Word Embedding Learning via Correspondence Transformer Encoder

arXiv

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arXiv 2023年

作者： Lin, Jingru Yue, Xianghu Ao, Junyi Li, Haizhou Department of Electrical and Computer Engineering National University of Singapore Singapore Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong Shenzhen China

Acoustic word embeddings (AWEs) aims to map a variable-length speech segment into a fixed-dimensional representation. High-quality AWEs should be invariant to variations, such as duration, pitch and speaker. In this paper, we introduce a novel self-supervised method to learn robust AWEs from a large-scale unlabelled speech corpus. Our model, named Correspondence Transformer Encoder (CTE), employs a teacher-student learning framework. We train the model based on the idea that different realisations of the same word should be close in the underlying embedding space. Specifically, we feed the teacher and student encoder with different acoustic instances of the same word and pre-train the model with a word-level loss. Our experiments show that the embeddings extracted from the proposed CTE model are robust to speech variations, e.g. speakers and domains. Additionally, when evaluated on Xitsonga, a low-resource cross-lingual setting, the CTE model achieves new state-of-the-art performance. © 2023, CC BY.

关键词： Embeddings

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data Poisoning Attack to X-armed Bandits 21

Data Poisoning Attack to X-armed Bandits

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21st IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2022

作者： Luo, Zhi Li, Youqi Chen, Lixing Xu, Zichuan Zhou, Pan Huazhong University of Science and Technology School of Cyber Science and Engineering China Beijing Institute of Technology School of Cyberspace Science and Technology School of Computer Science China Shanghai Jiao Tong University Institute of Cyber Science and Technology China Dalian University of Technology School of Software China Huazhong University of Science and Technology Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China

ISBN: (纸本)9781665494250

X-armed bandits have achieved the state-of-the-art performance in optimizing unknown stochastic continuous functions, which can model many machine learning tasks, specially in big data-driven personalized recommendation. However, bandit algorithms are vulnerable to adversarial attacks. Existing works mainly focus on attacking multi-armed bandits in discrete setting;nevertheless, the attacks against X-armed bandits in continuous setting have not been well explored. In this paper, we aim to bridge this gap and investigate the robustness problem for the X-armed bandits. Specifically, we consider data poisoning attack and propose an attack algorithm named Confidence Poisoning Attack algorithm, which could hijack the clean tree-based X-armed bandits algorithm, i.e., high confidence tree (HCT) and make it choose the nodes including the arm targeted by the attacker very frequently with a sub-linear attack cost, i.e., O(Tα)(0 © 2022 IEEE.

关键词： Stochastic systems

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Movable Antenna Enhanced Wireless Sensing Via Antenna Position Optimization

arXiv

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arXiv 2024年

作者： Ma, Wenyan Zhu, Lipeng Zhang, Rui The Department of Electrical and Computer Engineering National University of Singapore 117583 Singapore School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China

In this paper, we propose a new wireless sensing system equipped with the movable-antenna (MA) array, which can flexibly adjust the positions of antenna elements for improving the sensing performance over conventional antenna arrays with fixed-position antennas (FPAs). First, we show that the angle estimation performance in wireless sensing is fundamentally determined by the array geometry, where the Cramer-Rao bound (CRB) of the mean square error (MSE) for angle of arrival (AoA) estimation is derived as a function of the antennas’ positions for both one-dimensional (1D) and two-dimensional (2D) MA arrays. Then, for the case of 1D MA array, we obtain a globally optimal solution for the MAs’ positions in closed form to minimize the CRB of AoA estimation MSE. While in the case of 2D MA array, we aim to achieve the minimum of maximum (min-max) CRBs of estimation MSE for the two AoAs with respect to the horizontal and vertical axes, respectively. In particular, for the special case of circular antenna movement region, an optimal solution for the MAs’ positions is derived under certain numbers of MAs and circle radii. Thereby, both the lower- and upper-bounds of the min-max CRB are obtained for the antenna movement region with arbitrary shapes. Moreover, we develop an efficient alternating optimization algorithm to obtain a locally optimal solution for MAs’ positions by iteratively optimizing one between their horizontal and vertical coordinates with the other being fixed. Numerical results demonstrate that our proposed 1D/2D MA arrays can significantly decrease the CRB of AoA estimation MSE as well as the actual MSE compared to conventional uniform linear arrays (ULAs)/uniform planar arrays (UPAs) with different values of uniform inter-antenna spacing. Furthermore, it is revealed that the steering vectors of our designed 1D/2D MA arrays exhibit low correlation in the angular domain, thus effectively reducing the ambiguity of angle estimation. © 2024, CC BY.

关键词： Antenna arrays

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Finding Simplex Items in data Streams 39

Finding Simplex Items in Data Streams

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39th IEEE International Conference on data engineering, ICDE 2023

作者： Fan, Zhuochen Guo, Jiarui Li, Xiaodong Yang, Tong Zhao, Yikai Wu, Yuhan Cui, Bin Xu, Yanwei Uhlig, Steve Zhang, Gong Peking University School of Computer Science National Engineering Laboratory for Big Data Analysis Technology and Application Beijing China Peng Cheng Laboratory Shenzhen China Central Research Institute 2012 Labs Huawei Technologies Co. Ltd. Theory Lab Hong Kong Queen Mary University of London School of Electronic Engineering and Computer Science London United Kingdom

ISBN: (纸本)9798350322279

In this paper, we propose a new type of item in data streams, called simplex items. Simplex items have frequencies in consecutive p windows that can be approximated by a polynomial of degree at most k, where k = 0, 1, 2. These low-order representable simplex items have a wide range of potential applications. For example, when k = 1, we can leverage these items whose frequency has obvious linear increase or decrease to speed up the running time of a class of machine learning models and detect network attacks such as distributed denial-of-service (DDoS), etc. To find k-degree simplex items in real time, we propose a novel sketch, namely X-Sketch, to accurately record simplex items in a compact space. The key idea of X-Sketch is to effectively filter out non-simplex items with less memory overhead, and then monitor the remaining potential simplex items and keep those items with more consecutive windows. We conduct extensive experiments, and the experimental results show that the F1 Score of X-Sketch is on average 68.6%, 57.9%, and 42.2% higher than the baseline solution for k = 0, 1, 2, respectively. Finally, we also provide a case study that applies X-Sketch to "accelerate"the two machine learning models through end-to-end experiments. We have released our source code at GitHub. © 2023 IEEE.

关键词： Denial-of-service attack

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