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H2-MARL: Multi-agent reinforcement learning for Pareto optimality in hospital capacity strain and human mobility during epidemic

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Expert Systems with Applications 2025年 291卷

作者： Xueting Luo Hao Deng Jihong Yang Yao Shen Huanhuan Guo Zhiyuan Sun Mingqing Liu Jiming Wei Shengjie Zhao School of Computer Science and Technology Tongji University Shanghai 201804 China Engineering Research Center of Key Software Technologies for Smart City Perception and Planning Ministry of Education Shanghai 201804 China China Land Surveying and Planning Institute Beijing 100035 China College of Architecture and Urban Planning Tongji University Shanghai 200092 China Chongqing Institute of Planning and Natural Resources Investigation and Monitoring Chongqing 401121 China College of Electronic and Information Engineering Tongji University Shanghai 201804 China Guangdong Urban-rural Planning and Design Research Institute Technology Group Co. Ltd. Guangzhou 510290 China

Effectively balancing the losses from mobility restrictions and hospital capacity strain has drawn significant attention in the aftermath of COVID-19. Reinforcement learning (RL)-based strategies for human mobility management have recently advanced in addressing the dynamic evolution of cities and epidemics; however, they still face challenges in achieving coordinated control at the township level and adapting to cities of varying scales. To address the above issues, we propose a multi-agent RL approach that achieves Pareto optimality in managing hospital capacity and human mobility (H2-MARL), applicable across cities of different population scales. We first develop a township-level infection model with online-updatable parameters to simulate disease transmission and construct a city-wide dynamic spatiotemporal epidemic simulator. On this basis, H2-MARL is designed to treat each division as an agent, with a trade-off dual-objective reward function formulated and an experience replay buffer enriched with expert knowledge built. To evaluate the effectiveness of the model, we construct a township-level human mobility dataset containing over one billion records from four representative cities of varying scales. Extensive experiments demonstrate that H2-MARL has the optimal dual-objective trade-off capability, which can simultaneously minimize hospital capacity strain and human mobility restriction loss. Meanwhile, the applicability of the proposed model to epidemic control in cities of varying scales is verified, which showcases its feasibility and versatility in practical applications.

关键词： Reinforcement learning

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AI Enhanced Automatic Response System for Resisting Network Threats 4th

AI Enhanced Automatic Response System for Resisting Network ...

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4th International Conference on Smart Computing and Communications, SmartCom 2019

作者： Xia, Song Qiu, Meikang Liu, Meiqin Zhong, Ming Zhao, Hui College of Electronic and Information Wuhan University WuhanHubei China College of Computer Science Shenzhen University ShenzhenGuangdong China Department of Computer Science Harrisburg University of Science and Technology Harrisburg United States College of Electrical Engineering Zhejiang University Zhejiang China School of Software Henan University Kaifeng China

ISBN: (纸本)9783030341381

Network threats are malicious attacks that endanger network security. With terabits of information stored in the network and much of this information being confidential, cyber security turns to be very important. Most network protection mechanisms are based on firewall and Intrusion Detection System (IDS). However, with the diversification of cyber-attacks, traditional defense mechanisms cannot fully guarantee the security of the network. In this paper, we propose an automatic network threat response system based on machine learning and deep learning. It comprises three sub-modules: threat detection module, threat identification module and threat mitigation module. The experimental results show that the proposed system can handle 22 types of network threats in the KDD99 dataset and the rate of successful response is over 97%, which is much better than the traditional ways. © 2019, Springer Nature Switzerland AG.

关键词： Cybersecurity

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Privacy Computing: Concept, Computing Framework, and Future Development Trends

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engineering 2019年第6期5卷 1179-1192页

作者： Fenghua Li Hui Li Ben Niu Jinjun Chen Institute of Information Engineering Chinese Academy of SciencesBeijing 100093China School of Cyber Security University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory of Integrated Services Networks School of Cyber EngineeringXidian UniversityXi’an 710071China Department of Computer Science and Software Engineering Swinburne University of TechnologyHawthornVIC 3122Australia

With the rapid development of information technology and the continuous evolution of personalized ser- vices, huge amounts of data are accumulated by large internet companies in the process of serving users. Moreover, dynamic data interactions increase the intentional/unintentional persistence of private infor- mation in different information systems. However, problems such as the cask principle of preserving pri- vate information among different information systems and the dif culty of tracing the source of privacy violations are becoming increasingly serious. Therefore, existing privacy-preserving schemes cannot pro- vide systematic privacy preservation. In this paper, we examine the links of the information life-cycle, such as information collection, storage, processing, distribution, and destruction. We then propose a the- ory of privacy computing and a key technology system that includes a privacy computing framework, a formal de nition of privacy computing, four principles that should be followed in privacy computing, ffect algorithm design criteria, evaluation of the privacy-preserving effect, and a privacy computing language. Finally, we employ four application scenarios to describe the universal application of privacy computing, and discuss the prospect of future research trends. This work is expected to guide theoretical research on user privacy preservation within open environments.

关键词： Privacy computing Private information description Privacy metric Evaluation of the privacy-preserving effect Privacy computing language

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A Dynamic Network Resource Demand Predicting Algorithm Based on Incremental Design of RBF

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Procedia computer Science 2019年 147卷 29-35页

作者： Xiancui Xiao Xiangwei Zheng School of Information Science and Engineering Shandong Normal University Jinan 250014 China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan 250014 China

Nowadays, the research on network mapping is mostly limited to static resource allocation. In fact, the user’s demand of network resources changes dynamically over time. Therefore, how to predict the time-varying demand of users and allocate appropriate resources becomes an important way to improve resource utilization. As a fully connected artificial neural network (ANN), the RBFN (Radial Basis Function Network) has diagnostic, predictive and classification functions. However, due to the excessive use of hidden RBF units during training process, it suffers from expensive core inner product calculations and long training time. This paper proposes a dynamic network resource demand predicting algorithm based on RBF incremental design (GSO-INC-RBFDM). In the network mapping, the group search optimizer (GSO) is used to optimize the mapping scheme, and then the radial basis function (RBF) of the incremental construction is used to predict the time-varying demand of the user. GSO-INC-RBFDM based on incremental design of RBF can construct a compact neural network structure, which not only accelerates the training speed, but also improves the predictive accuracy. Simulation experiments show, compared with traditional algorithms and the original RBF, GSO-INC-RBFDM have lower cost, higher acceptance rate and network revenue.

关键词： Static resource allocation Dynamically Time-varying RBFN GSO-INC-RBFDM

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Initial ASEAN digital security and interoperability standard proposal

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AIP Conference Proceedings 2023年第1期2739卷

作者： Sangsuree Vasupongayya Gunawan Hutagalung Obrina Candra Chalee Vorakulpipat Doni Purnomo Parinda Wattanasri Eko Supriyanto Shukor Abd Razak Othman Ibrahim Anazida Zainal Sri Devi Ravana Hiep Cong Pham Tharith Sriv Daw Khin Myo Su Mora Hertanto Agus Winarno Prabaswari Raden Muhammad Taufik Yuniantoro Badrus Zaman Fatmawati Retna Apsari Yutika Amelia Effendi Norjihan Abdul Ghani Zarina Shukur Hamidah Ibrahim Nur Izura Udzir Marnel Peradilla Jude Michael Teves Nestor Michael Tiglao Cenon Alfonso Pg Emeroylariffion Abas Mueen Uddin Agustin Kusumayati Brian Sri Prahastuti Department of Computer Engineering Faculty of Engineering Prince of Songkla University Hatyai Songkhla 90110 THAILAND Directorate General of Post and Information Technology Ministry of Communication and Information Technology Jakarta INDONESIA Politeknik Siber dan Sandi Negara National Cyber & Crypto Agency (BSSN) Depok West Java INDONESIA Information Security Research Team at National Electronics and Computer Technology Center Bangkok THAILAND National Standardization Agency of Indonesia Jakarta INDONESIA Department of Disease Control Ministry of Public Health Mueang Nonthaburi Nonthaburi THAILAND School of Biomedical Engineering and Health Sciences Faculty of Engineering Universiti Teknologi Malaysia Johor Bahru Johor MALAYSIA IJN-UTM Cardiovascular Engineering Center Institute of Human Centered Engineering Universiti Teknologi Malaysia Johor Bahru Johor MALAYSIA School of Computing Faculty of Engineering Universiti Teknologi Malaysia Johor Bahru Johor MALAYSIA Faculty of Computer Science & Information Technology Universiti Malaya Kuala Lumpur MALAYSIA RMIT University Vietnam Ho Chi Minh City VIETNAM Royal University of Phnom Penh Phnom Penh CAMBODIA Yangon University of Economics Yangon MYANMAR Agency for the Assessment and Application of Technology (BPPT) Jakarta INDONESIA Faculty of Science and Technology Universitas Airlangga Surabaya East Java INDONESIA Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Surabaya East Java INDONESIA Centre for Cyber Security Universiti Kebangsaan Malaysia Bangi Selangor MALAYSIA Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang Selangor MALAYSIA Computer Technology Department College of Computer Studies De La Salle University Manila PHILIPPINES Software Technology Department College of Computer Studies De La Salle University Manila PHILIPPINES Electrical and Electronics Engineering Institute University of the Philippines Diliman Quezon City P

In this work, the result of the initial attempt to propose an Association of Southeast Asian Nations (ASEAN) digital security and interoperability standard to support the mobility among ASEAN countries is presented. Literature reviews on existing coronavirus disease 2019 (COVID-19) related digital technologies of six ASEAN member countries are conducted. Several concerns related to creating/implementing the ASEAN digital security and interoperability standard are discussed including the technology accessing concern, the data security and privacy concern, and the data interoperability and availability concern. The proposed solution toward creating an ASEAN digital security and interoperability standard is then presented. To evaluate the proposed solution, a survey has been conducted on representatives of nine ASEAN countries on four main issues including the security standards and requirements, the interoperability standard and requirements, the certifications, and the implementation challenges. According to the survey and the discussion, seven recommendations were proposed toward creating an ASEAN digital security and interoperability standard. First, each country collects and maintains the data of its own citizens and also provides a method to authenticate and access the data by authorized requests. Second, QR code standard or ASEAN QR code standard must be agreed upon so that it can be authenticated and verified by ASEAN countries. Third, health risk assessment must be agreed upon to be used. Forth, the security standard, the data, the data format and the exchange protocol must be agreed upon at the ASEAN policy level while adoption of international standards as an implementation guideline will ease of the process and neutralize different requirements from various countries. Fifth, any accountability of incidents must be agreed upon at the ASEAN policy level. Sixth, certification might be extended to certification of risk assessment for organizations, products or

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Bitwise Division Searching Algorithm for VANETs

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Journal of Physics: Conference Series 2021年第1期1856卷

作者： Xin Ye Gencheng Xu Zhiguang School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu Sichuan Province 610054 P.R. China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu Sichuan Province 610054 P.R. China

Vehicular ad-hoc networks (VANETs) are the key technology supporting the intelligent transportation system (ITS), which is composed of vehicle nodes with wireless communication capability and roadside infrastructures. One of the obstacles is how to use batch verification to verify signatures simultaneously. To solve the problem of low verification efficiency in VANETs. This paper defines the problem and proposes an early-stopping factorial bitwise divisions (EFBD) algorithm. The proposed parallel-friendly algorithm achieves better performance in both theory and practice at low invalid signatures rate. Especially, in the parallel condition, the proposed algorithm costs only one aggregation-verification delay when the number of invalid signatures is 1.

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Vehicle Classification Based on Deep Convolutional Neural Networks Model for Traffic Surveillance Systems 15

Vehicle Classification Based on Deep Convolutional Neural Ne...

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15th International computer Conference on Wavelet Active Media technology and information Processing, ICCWAMTIP 2018

作者： Zakria Cai, Jingye Deng, Jianhua Khokhar, Muhammad Saddam Umar Aftab, Muhammad School of Information and Software Engineering University of Electronic Science and Technology of China China School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China

ISBN: (纸本)9781728115351

Due to growth of vehicles in urban cities, such problems are increases traffic control systems, security and crime investigation, intelligent parking and electronic toll collection. Moreover, Logistics access management system (LMS)is also a big problem in urban cities and all the issues are challenging and demands to develop effective and efficient approach for vehicle classification. Mostly, traditional vehicle classification uses hand craft feature extraction method like SIFT, Surf, HoG etc. However, these approaches are not efficient in results. This paper presents the efficient framework for vehicle classification by using Inception-v3 model for image vector features extraction that is most advance deep learning neural network, and this model is not used before for vehicle classification. After that, different classification algorithms are implemented on the feature vector. The classification conducted on three vehicle datasets and results demonstration is helpful for researchers to choose dataset with algorithm performance. © 2018 IEEE.

关键词： Vehicles

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Tips and Tricks for Webly-Supervised Fine-Grained Recognition: Learning from the WebFG 2020 Challenge

arXiv

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arXiv 2020年

作者： Wei, Xiu-Shen Xu, Yu-Yan Yao, Yazhou Wei, Jia Xi, Si Xu, Wenyuan Zhang, Weidong Lv, Xiaoxin Fu, Dengpan Li, Qing Chen, Baoying Guo, Haojie Xue, Taolue Jing, Haipeng Wang, Zhiheng Zhang, Tianming Zhang, Mingwen PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China Jiangsu Key Lab of Image Video Understanding for Social Security China Netease Games AI Lab Netease Yidun AI Lab

WebFG 2020 is an international challenge hosted by Nanjing University of Science and technology, University of Edinburgh, Nanjing University, The University of Adelaide, Waseda University, etc. This challenge mainly pays attention to the webly-supervised fine-grained recognition problem. In the literature, existing deep learning methods highly rely on large-scale and high-quality labeled training data, which poses a limitation to their practicability and scalability in real world applications. In particular, for fine-grained recognition, a visual task that requires professional knowledge for labeling, the cost of acquiring labeled training data is quite high. It causes extreme difficulties to obtain a large amount of high-quality training data. Therefore, utilizing free web data to train fine-grained recognition models has attracted increasing attentions from researchers in the fine-grained community. This challenge expects participants to develop webly-supervised fine-grained recognition methods, which leverages web images in training fine-grained recognition models to ease the extreme dependence of deep learning methods on large-scale manually labeled datasets and to enhance their practicability and scalability. In this technical report, we have pulled together the top WebFG 2020 solutions of total 54 competing teams, and discuss what methods worked best across the set of winning teams, and what surprisingly did not help. © 2020, CC BY.

关键词： Scalability

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Analysis and experimental demonstration of orthant-symmetric four-dimensional 7 bit/4D-sym modulation for optical fiber communication

arXiv

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arXiv 2020年

作者： Chen, Bin Alvarado, Alex van der Heide, Sjoerd van den Hout, Menno Hafermann, Hartmut Okonkwo, Chigo The School of Computer Science and Information Engineering Hefei University of Technology Hefei China Eindhoven University of Technology Eindhoven5600 MB Netherlands The Information and Communication Theory Lab Signal Processing Systems Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven5600 MB Netherlands The High Capacity Optical Transmission Laboratory Eindhoven University of Technology Eindhoven5600 MB Netherlands The Optical Communication Technology Lab Paris Research Center Huawei Technologies France SASU Boulogne-Billancourt92100 France

We propose a new four-dimensional orthant-symmetric 128-ary modulation format (4D-OS128) with a spectral efficiency of 7 bit/4D-sym. The proposed format fills the gap between polarization-multiplexed 8- and 16-ary quadrature-amplitude modulation (PM-8QAM and PM-16QAM). Numerical simulations show that 4D-OS128 outperforms two well-studied 4D geometrically-shaped modulation formats: 128SP-16QAM and 7b4D-2A8PSK by up to 0.65 dB for bit-interleaved coded modulation at the same spectral efficiency. These gains are experimentally demonstrated in a 11×233 Gbit/s wavelength division multiplexing (WDM) transmission system operating at 5.95 bit/4D-sym over 6000 km and 9000 km for both EDFA-only and hybrid amplification scenarios, respectively. A reach increase of 15% is achieved with respect to 128-ary set-partitioning 16QAM. Furthermore, the proposed 4D-OS128 is also compared to D4 lattice-based constellation, 16QAM and probabilistically-shaped 16QAM with finite blocklength via simulation. © 2020, CC BY.

关键词： Quadrature amplitude modulation

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Quantum wasserstein GANs

arXiv

引用

arXiv 2019年

作者： Chakrabarti, Shouvanik Huang, Yiming Li, Tongyang Feizi, Soheil Wu, Xiaodi Joint Center for Quantum Information and Computer Science University of Maryland Department of Computer Science University of Maryland School of Information and Software Engineering University of Electronic Science and Technology of China

The study of quantum generative models is well motivated, not only because of its importance in quantum machine learning and quantum chemistry but also because of the perspective of its implementation on near-term quantum machines. Inspired by previous studies on the adversarial training of classical and quantum generative models, we propose the first design of quantum Wasserstein Generative Adversarial Networks (WGANs), which has been shown to improve the robustness and the scalability of the adversarial training of quantum generative models even on noisy quantum hardware. Specifically, we propose a definition of the Wasserstein semimetric between quantum data, which inherits a few key theoretical merits of its classical counterpart. We also demonstrate how to turn the quantum Wasserstein semimetric into a concrete design of quantum WGANs that can be efficiently implemented on quantum machines. Our numerical study, via classical simulation of quantum systems, shows the more robust and scalable numerical performance of our quantum WGANs over other quantum GAN proposals. As a surprising application, our quantum WGAN has been used to generate a 3-qubit quantum circuit of ∼50 gates that well approximates a 3-qubit 1-d Hamiltonian simulation circuit that requires over 10k gates using standard techniques. Copyright © 2019, The Authors. All rights reserved.

关键词： Quantum optics

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