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arXiv 2021年

作者： Wang, Shaoyang Lv, Tiejun Ni, Wei Beaulieu, Norman C. Guo, Y. Jay Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Sydney2122 Australia Beijing Key Laboratory for Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Global Big Data Technologies Centre University of Technology Sydney UltimoNSW2007 Australia

This paper presents a novel and effective deep reinforcement learning (DRL)-based approach to addressing joint resource management (JRM) in a practical multi-carrier non-orthogonal multiple access (MC-NOMA) system, where hardware sensitivity and imperfect successive interference cancellation (SIC) are considered. We first formulate the JRM problem to maximize the weighted-sum system throughput. Then, the JRM problem is decoupled into two iterative subtasks: subcarrier assignment (SA, including user grouping) and power allocation (PA). Each subtask is a sequential decision process. Invoking a deep deterministic policy gradient algorithm, our proposed DRL-based JRM (DRL-JRM) approach jointly performs the two subtasks, where the optimization objective and constraints of the subtasks are addressed by a new joint reward and internal reward mechanism. A multi-agent structure and a convolutional neural network are adopted to reduce the complexity of the PA subtask. We also tailor the neural network structure for the stability and convergence of DRL-JRM. Corroborated by extensive experiments, the proposed DRL-JRM scheme is superior to existing alternatives in terms of system throughput and resistance to interference, especially in the presence of many users and strong inter-cell interference. DRL-JRM can flexibly meet individual service requirements of users. © 2021, CC BY.

关键词： Resource allocation

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Uncertainty-Aware Deep Co-training for Semi-supervised Medical Image Segmentation

arXiv

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arXiv 2021年

作者： Zheng, Xu Fu, Chong Xie, Haoyu Chen, Jialei Wang, Xingwei Sham, Chiu-Wing School of Computer Science and Engineering Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China School of Computer Science The University of Auckland New Zealand

Semi-supervised learning has made significant strides in the medical domain since it alleviates the heavy burden of collecting abundant pixel-wise annotated data for semantic segmentation tasks. Existing semi-supervised approaches enhance the ability to extract features from unlabeled data with prior knowledge obtained from limited labeled data. However, due to the scarcity of labeled data, the features extracted by the models are limited in supervised learning, and the quality of predictions for unlabeled data also cannot be guaranteed. Both will impede consistency training. To this end, we proposed a novel uncertainty-aware scheme to make models learn regions purposefully. Specifically, we employ Monte Carlo Sampling as an estimation method to attain an uncertainty map, which can serve as a weight for losses to force the models to focus on the valuable region according to the characteristics of supervised learning and unsupervised learning. Simultaneously, in the backward process, we joint unsupervised and supervised losses to accelerate the convergence of the network via enhancing the gradient flow between different tasks. Quantitatively, we conduct extensive experiments on three challenging medical datasets. Experimental results show desirable improvements to state-of-the-art counterparts. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantics

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DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

arXiv

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arXiv 2025年

作者： Zeng, Jinzhe Zhang, Duo Peng, Anyang Zhang, Xiangyu He, Sensen Wang, Yan Liu, Xinzijian Bi, Hangrui Li, Yifan Cai, Chun Zhang, Chengqian Du, Yiming Zhu, Jia-Xin Mo, Pinghui Huang, Zhengtao Zeng, Qiyu Shi, Shaochen Qin, Xuejian Yu, Zhaoxi Luo, Chenxing Ding, Ye Liu, Yun-Pei Shi, Ruosong Wang, Zhenyu Bore, Sigbjørn Løland Chang, Junhan Deng, Zhe Ding, Zhaohan Han, Siyuan Jiang, Wanrun Ke, Guolin Liu, Zhaoqing Lu, Denghui Muraoka, Koki Oliaei, Hananeh Singh, Anurag Kumar Que, Haohui Xu, Weihong Xu, Zhangmancang Zhuang, Yong-Bin Dai, Jiayu Giese, Timothy J. Jia, Weile Xu, Ben York, Darrin M. Zhang, Linfeng Wang, Han School of Artificial Intelligence and Data Science Unversity of Science and Technology of China Hefei China AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China Department of Computer Science University of Toronto TorontoON Canada Department of Chemistry Princeton University PrincetonNJ08540 United States University of Chinese Academy of Sciences Beijing100871 China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China College of Integrated Circuits Hunan University Changsha410082 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Smart Materials and Device Integration School of Material Science and Engineering Wuhan University of Technology Wuhan430070 China College of Science National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Extreme Matter and Applications National University of Defense Technology Changsha410073 China ByteDance Research Beijing100098 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Geosciences Princeton University PrincetonNJ08544 United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States IKKEM Fujian Xiamen361005 China Graduate

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular dynamics

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Limited Times of Data Access Based on SGX in Cloud Storage*

Limited Times of Data Access Based on SGX in Cloud Storage*

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IEEE International Conference on systems, Man and Cybernetics

作者： Zhengwei Ren Xiaoshuang Chen Jinshan Tang Lina Wang Yan Tong Shiwei Xu School of Computer Science and Technology Wuhan University of Science and Technology and Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China College of Health and Human Services George Mason University Fairfax VA USA School of Cyber Science and Engineering Wuhan University Wuhan China Huazhong Agricultural University Wuhan China

ISBN: (纸本)9781665442084

It is straightforward to encrypt the outsourced data to protect its confidentiality using symmetric cryptography in cloud storage. How to control and restrict the use of the encryption key in data users’ devices becomes one of the critical issues. In most of existing time-based and policy-based schemes, the key cannot be stored locally in data users’ devices, making the traffic cost linear with the data access times as the key should be retrieved in each data access. In this paper, we propose a times-based scheme with Intel SGX to restrict the times the key can be used in the data user’s device to restrict the times of data access. The basic idea is to compare the current used times with the specified maximal times to determine whether the key can be used or not. To this end, we use a monotonic counter to count the times the key has been used. When the use condition is not satisfied, we destroy the key securely and generate public proof so that (i) the encrypted data cannot be accessed anymore, i.e., the data is deleted assuredly, (ii) the deletion can be verified. In addition, a hash-based integrity check approach is utilized to detect and prevent replay attacks. The experimental results on the implemented prototype show our scheme is feasible in practice.

关键词： Cloud computing Costs Conferences Prototypes Encryption Cybernetics

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The Performance Evaluation and Resilience Analysis of Supply Chain Based on Logistics Network

The Performance Evaluation and Resilience Analysis of Supply...

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第三十九届中国控制会议

作者： Weiqi Sun Yuanlong Li Liangren Shi Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information Processing of the Ministry of Education Shanghai Key Laboratory of Navigation and Location-based Services and Academy of Information Technology and Electrical Engineering Shanghai Jiao Tong University

With the development of globalization, more and more enterprises are involved in the supply chain network with increasingly complex structure. In this paper, enterprises and relations in the logistics network are abstracted as nodes and edges of the complex network. A graph model for a supply chain network to specified industry is constructed, and the Neo4 j graph database is employed to store the graph data. This paper uses the theoretical research tool of complex network to model and analyze the supply chain, and designs a supply chain network evaluation system which include static and dynamic measurement indexes according to the statistical characteristics of complex network. In this paper both the static and dynamic resilience characteristics of the the constructed supply chain network are evaluated from the perspective of complex network. The numeric experimental simulations are conducted for validation. This research has practical and theoretical significance for enterprises to make strategies to improve the anti-risk capability of supply chain network based on logistics network information.

关键词： Complex network Supply chain network Graph database Neo4j Resilience evaluation

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A Distributed Dynamic Adaptive and Fast Balancing SDN Controller Management 6

A Distributed Dynamic Adaptive and Fast Balancing SDN Contro...

引用

6th Annual 2018 International Conference on Geo-Spatial Knowledge and Intelligence, GSKI 2018

作者： Liu, Yang He, Qian Li, Xiongying Zhou, Shuiming Guangxi Transportation Research and Consulting Co. Ltd Nanning530007 China Guangxi Key Laboratory of Cryptograph and Information Security Guangxi University Key Laboratory of Cloud Computing and Complex System Guilin University of Electronic Technology Guilin541004 China

Tradition Software Defined Network (SDN) suffers from single point failure and uneven load distribution problems. Multiple SDN controllers work together to construct a distributed dynamic adaptive cluster with dynamic load balancing for large scale SDN switch stream requesting. Introducing such features as CPU, memory and network load, a Dynamic Adaptive and Fast algorithm (DAF) can select source, target and destination SDN controllers to balance load dynamically. The results of experiments show that DAF works well and improve the cloud service system throughput. © Published under licence by IOP Publishing Ltd.

关键词： Controllers

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A Blockchain-based Revocable Certificateless Signature Scheme for IoT Device

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International Journal of Network Security 2021年第6期23卷 1012-1027页

作者： Chen, Yushuang Zheng, Dong Guo, Rui Zhang, Yinghui Tao, Xiaoling School of Cyberspace Security Xi’an University of Posts and Telecommunications Xi’an710121 China State Key Laboratory of Integrated Services Networks Xidian University Xi’an710071 China National Engineering Laboratory for Wireless Security Xi’an University of Posts and Telecommunications Guilin University of Electronic Technology Guilin541004 China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic Technology Guilin541004 China

With the rapid development of Internet of Things (IoT) technology, completing a transaction with intelligent devices has been an essential communication mode in our daily life. Generally, lightweight smart devices conduct transactions relying on the third-party payment platform, which takes several inherent challenges of mutual distrust, data authentication and low efficiency in the IoT environment. To mitigate these issues, in this paper, we design a paring-free revocable certificateless signature (CLS) scheme with high efficiency based on the blockchain technique. Our scheme is a viable approach to enhance the performance without bilinear parings operation in the IoT lightweight equipment. And there are two different address lists stored in the blockchain to record the secret and public transaction data, respectively. According to this method, not only can the key management of the user be implemented in an efficient solution, but also enables the public verification to be performed transparently between the two parties. Furthermore, with the help of cloud server, it generates a time key for the user to accomplish revocation with low computation cost. We also make an analysis on the security to demonstrate that our proposal is existential unforgeability against adaptive chosen message attacks (EUF-CMA) under the elliptic curve discrete logarithm (ECDL) problem. Finally, the performance comparison indicates that our protocol is better than other related schemes. © 2021. International Journal of Network Security. All Rights Reserved.

关键词： Blockchain

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Network Risk Assessment Based on Improved MulVAL Framework and HMM 2nd

Network Risk Assessment Based on Improved MulVAL Framework a...

引用

2nd EAI International Conference on Security and Privacy in New computing Environments, SPNCE 2019

作者： Wang, Chundong Li, Kongbo Tian, Yunkun He, Xiaonan Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Ministry of Education Tianjin University of Technology Tianjin300384 China Tianjin E-Hualu Information Technology Co. Ltd. Tianjin China

ISBN: (纸本)9783030213725

With the increasingly extensive applications of the network, the security of internal network of enterprises is facing more and more threats from the outside world, which implies the importance to master the network risk assessment skills. In the big data era, there are various security protection techniques and different types of group data. Meanwhile, Online Social Networks (OSNs) and Social Internet of Things (SIoT) are becoming popular patterns of meeting people and keeping in touch with friends [2, 5]. However, risk assessment, as a bridge between security experts and network administrators, to some extent, whose accuracy can influence the judgment of administrators to the entire network state. In order to solve this problem, this essay proposes the improved MulVAL framework to optimize the risk assessment process by establishing the HMM model and the Bayesian model, which can improve the accuracy of the evaluation value. Firstly, behavior of the attacker is described in-depth by the attack graph generated through MulVAL. Then, with the quantitative evaluation conducted by the Common Vulnerability Scoring system, the nodes on the attack path can will be evaluated and the value will be further evaluated by the Bayesian model. Finally, by establishing the hidden Markov model, the corresponding parameters can be defined and the most likely probabilistic state transition sequence can be calculated by using the Viterbi algorithm to deduce the attack intent with the highest possibility. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Network security

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Two-Level Feature Selection Method for Low Detection Rate Attacks in Intrusion Detection 2nd

Two-Level Feature Selection Method for Low Detection Rate At...

引用

2nd EAI International Conference on Security and Privacy in New computing Environments, SPNCE 2019

作者： Wang, Chundong Ye, Xin He, Xiaonan Tian, Yunkun Gong, Liangyi Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Ministry of Education Tianjin University of Technology Tianjin300384 China Tianjin E-Hualu Information Technology Co. Ltd. Tianjin300350 China

ISBN: (纸本)9783030213725

In view of the fact that some attacks have low detection rates in intrusion detection dataset, a two-level feature selection method based on minimal-redundancy-maximal-relevance (mRMR) and information gain (IG) was proposed. In this method, irrelevant and redundant features were filtered preliminarily to reduce data dimension by using mRMR algorithm, and highly correlated features to low detection rate attacks were obtained based on the calculation of information gain, and finally these features were integrated together to get final feature subset. The experimental results showed that the classification result of the feature subset filtered by this method had a better classification performance than the current filtering methods and improved the testing results of some attacks with low detection rates effectively. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Intrusion detection

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The roles of urban buildings and vegetation in adjusting seasonal and daily air temperature 4

The roles of urban buildings and vegetation in adjusting sea...

引用

4th ISPRS Geospatial Week 2019

作者： Lan, Y. Huang, Z. Guo, R. Zhan, Q. Research Institute for Smart Cities School of Architecture and Urban Planning Shenzhen University Shenzhen China Shenzhen Key Laboratory of Spatial Information Smart Sensing and Services Shenzhen University China Guangdong Key Laboratory of Urban Informatics Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China School of Urban Design Wuhan University Wuhan China

Exploring the spatiotemporal patterns of the relationships between urban indicators and urban temperature is essential to improve the mitigation effectiveness when we intend to adjust built environment for moderating urban thermal environment. In this study, RS, GIS technology and statistical methods were involved to investigate the spatiotemporal patterns of the impacts of urban buildings and vegetation on Air Temperature (AT). Building Density (BD) and Normalized Difference Vegetation Index (NDVI) are the indicators for urban buildings and vegetation respectively. The objectives of this study are: 1) to determine an appropriate scale for examining the building-AT relationships and vegetation-AT relationships;2) to explore the seasonal and daily characteristics of these relationships;and 3) to compare the effects of urban buildings and vegetation. The results show that, for both summer and winter, a scale of 200–250 m is optimal for examining building-AT relationships, and 960–1020 m is the desirable scale for studying vegetation-AT relationships. Based on the optimal scales, we find that for both buildings and vegetation, they only significantly impact night-time temperature in both summer and winter. For seasonal comparison, the building-AT relationships and vegetation-AT relationships are relatively stronger in summer than in winter, which are indicated by R-square of the regression results. When comparing the effects of urban building and vegetation, we find that increasing vegetation is more effective than reduce buildings to achieve the same air temperature reduction. Our findings are conducive to generating space-time targeted Urban Heat Island (UHI) mitigation strategies. © Authors 2019.

关键词： Vegetation

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