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arXiv 2023年

作者： Li, Shaofei Dong, Feng Xiao, Xusheng Wang, Haoyu Shao, Fei Chen, Jiedong Guo, Yao Chen, Xiangqun Li, Ding School of Computer Science Peking University China Arizona State University United States Case Western Reserve University United States Sangfor Technologies Inc. China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology China

Advanced Persistent Threats (APT) attacks have plagued modern enterprises, causing significant financial losses. To counter these attacks, researchers propose techniques that capture the complex and stealthy scenarios of APT attacks by using provenance graphs to model system entities and their dependencies. Particularly, to accelerate attack detection and reduce financial losses, online provenance-based detection systems that detect and investigate APT attacks under the constraints of timeliness and limited resources are in dire need. Unfortunately, existing online systems usually sacrifice detection granularity to reduce computational complexity and produce provenance graphs with more than 100,000 nodes, posing challenges for security admins to interpret the detection results. In this paper, we design and implement NODLINK, the first online detection system that maintains high detection accuracy without sacrificing detection granularity. Our insight is that the APT attack detection process in online provenance-based detection systems can be modeled as a Steiner Tree Problem (STP), which has efficient online approximation algorithms that recover concise attack-related provenance graphs with a theoretically bounded error. To utilize the frameworks of the STP approximation algorithm for APT attack detection, we propose a novel design of in-memory cache, an efficient attack screening method, and a new STP approximation algorithm that is more efficient than the conventional one in APT attack detection while maintaining the same complexity. We evaluate NODLINK in a production environment. The open-world experiment shows that NODLINK outperforms two state-of-the-art (SOTA) online provenance analysis systems by achieving magnitudes higher detection and investigation accuracy while having the same or higher throughput. Copyright © 2023, The Authors. All rights reserved.

关键词： Approximation algorithms

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A dual-polarized zhe-shaped conformal patch antenna for 5G millimeter-wave applications 4

A dual-polarized zhe-shaped conformal patch antenna for 5G m...

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4th IEEE International Conference on Electronic Information and Communication technology, ICEICT 2021

作者： Yang, Baifa Huang, Haixuan Feng, Botao Deng, Li College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9781665432030

We propose a dual-polarized conformal Zhe-shaped patch antenna that is suitable for future millimeter wave (mmW) applications. The proposed antenna is composed of 3 different layers. A Zhe-shaped (喆) patch is printed on the upper surface of the top dielectric layer. In order to excite the Zhe-shaped radiating patch, two feeding networks are designed in the other two layers. The simulation result shows that a frequency bandwidth of 10.5% (24.75-27.5 GHz) and a good isolation performance ( © 2021 IEEE

关键词： Microstrip antennas

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Supporting Vision-Language Model Inference with Confounder-pruning Knowledge Prompt

arXiv

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arXiv 2022年

作者： Li, Jiangmeng Mo, Wenyi Qiang, Wenwen Su, Bing Zheng, Changwen Xiong, Hui Wen, Ji-Rong The University of Chinese Academy of Sciences Beijing China The Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China The Beijing Key Laboratory of Big Data Management and Analysis Methods Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China Hong Kong

Vision-language models are pre-trained by aligning image-text pairs in a common space to deal with open-set visual concepts. To boost the transferability of the pre-trained models, recent works adopt fixed or learnable prompts, i.e., classification weights are synthesized from natural language describing task-relevant categories, to reduce the gap between tasks in the training and test phases. However, how and what prompts can improve inference performance remains unclear. In this paper, we explicitly clarify the importance of including semantic information in prompts, while existing prompting methods generate prompts without exploring the semantic information of textual labels. Manually constructing prompts with rich semantics requires domain expertise and is extremely time-consuming. To cope with this issue, we propose a semantic-aware prompt learning method, namely CPKP, which retrieves an ontological knowledge graph by treating the textual label as a query to extract task-relevant semantic information. CPKP further introduces a double-tier confounder-pruning procedure to refine the derived semantic information. The graph-tier confounders are gradually identified and phased out, inspired by the principle of Granger causality. The feature-tier confounders are demolished by following the maximum entropy principle in information theory. Empirically, the evaluations demonstrate the effectiveness of CPKP, e.g., with two shots, CPKP outperforms the manual-prompt method by 4.64% and the learnable-prompt method by 1.09% on average, and the superiority of CPKP in domain generalization compared to benchmark approaches. Our implementation is available at https://***/Mowenyii/CPKP. Copyright © 2022, The Authors. All rights reserved.

关键词： Information theory

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A Spectral Efficient Hybrid Precoding Algorithm for MmWave MIMO systems 8

A Spectral Efficient Hybrid Precoding Algorithm for MmWave M...

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8th International Conference on Identification, Information and Knowledge in the Internet of Things, IIKI 2019

作者： Li, Tingting Zhao, Feng Key Laboratory of Cognitive Radio and Information Processing Guilin University of Electronic Technology Guilin541004 China Guangxi Colleges Universities Key Laboratory of Complex System Optimization and Big Data Processing Yulin Normal University Yulin China

Millimeter-wave MIMO will use hybrid precoding to improve the spectral efficiency. In this paper, we propose a spectral efficient hybrid precoding algorithm. In order to maximize the projection of hybrid precoding matrix on the optimal precoding matrix, we design the analog precoding matrix by selecting an optimal combination from the candidate matrix. Then, we regard the channel matrix and the obtained analog precoding matrix as an equivalent channel matrix, and use regularized zero-forcing to acquire the digital precoding matrix. Finally, we present simulation results of the proposed hybrid precoding algorithm and show that the algorithm approaches the optimal precoding. © 2020 The Authors. Published by Elsevier B.V.

关键词： Spectrum efficiency

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Active legibility in multiagent reinforcement learning

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Artificial Intelligence 2025年 346卷

作者： Liu, Yanyu Pan, Yinghui Zeng, Yifeng Ma, Biyang Doshi, Prashant School of Automation Central South University No. 605 South Lushan Road Hunan Changsha410083 China School of Artificial Intelligence & National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Shenzhen China Department of Computer and Information Sciences Northumbria University Newcastle United Kingdom School of Computer Science Minnan Normal University Fujian Zhangzhou China Department of Computer Science University of Georgia AthensGA United States

A multiagent sequential decision problem has been seen in many critical applications including urban transportation, autonomous driving cars, military operations, etc. Its widely known solution, namely multiagent reinforcement learning, has evolved tremendously in recent years. Among them, the solution paradigm of modeling other agents attracts our interest, which is different from traditional value decomposition or communication mechanisms. It enables agents to understand and anticipate others' behaviors and facilitates their collaboration. Inspired by recent research on the legibility that allows agents to reveal their intentions through their behavior, we propose a multiagent active legibility framework to improve their performance. The legibility-oriented framework drives agents to conduct legible actions so as to help others optimize their behaviors. In addition, we design a series of problem domains that emulate a common legibility-needed scenario and effectively characterize the legibility in multiagent reinforcement learning. The experimental results demonstrate that the new framework is more efficient and requires less training time compared to several multiagent reinforcement learning algorithms. © 2025 The Author(s)

关键词： Urban transportation

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Neuromodulated Meta-Learning

arXiv

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arXiv 2024年

作者： Wang, Jingyao Guo, Huijie Qiang, Wenwen Li, Jiangmeng Zheng, Changwen Xiong, Hui Hua, Gang University of Chinese Academy of Sciences Beijing China National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Thrust of Artificial Intelligence Hong Kong University of Science and Technology Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Wormpex AI Research LLC BellevueWA98004 United States

Humans excel at adapting perceptions and actions to diverse environments, enabling efficient interaction with the external world. This adaptive capability relies on the biological nervous system (BNS), which activates different brain regions for distinct tasks. Meta-learning similarly trains machines to handle multiple tasks but relies on a fixed network structure, not as flexible as BNS. To investigate the role of flexible network structure (FNS) in meta-learning, we conduct extensive empirical and theoretical analyses, finding that model performance is tied to structure, with no universally optimal pattern across tasks. This reveals the crucial role of FNS in meta-learning, ensuring meta-learning to generate the optimal structure for each task, thereby maximizing the performance and learning efficiency of meta-learning. Motivated by this insight, we propose to define, measure, and model FNS in meta-learning. First, we define that an effective FNS should possess frugality, plasticity, and sensitivity. Then, to quantify FNS in practice, we present three measurements for these properties, collectively forming the structure constraint with theoretical supports. Building on this, we finally propose Neuromodulated Meta-Learning (NeuronML) to model FNS in meta-learning. It utilizes bi-level optimization to update both weights and structure with the structure constraint. Extensive theoretical and empirical evaluations demonstrate the effectiveness of NeuronML on various tasks. Code is publicly available at https://***/WangJingyao07/NeuronML. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Stvanet: A Spatio-Temporal Visual Attention Framework with Large Kernel Attention Mechanism for Citywide Traffic Dynamics Prediction

SSRN

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SSRN 2023年

作者： Yang, Hongtai Jiang, Junbo Zhao, Zhan Pan, Renbin School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology National United Engineering Laboratory of Integrated and Intelligent Transportation Institute of System Science and Engineering Southwest Jiaotong University Chengdu611756 China Department of Urban Planning and Design The University of Hong Kong Hong Kong Special Administrative Region Hong Kong School of Business Administration Southwestern University of Finance and Economics Chengdu611130 China

Enhancing the efficiency and safety of the Intelligent Transportation system requires effective modeling and prediction of citywide traffic dynamics. Most studies employ convolutional neural networks (CNNs) with a 3D convolutional structure or spatio-temporal models with self-attention mechanisms to capture the spatio-temporal information of traffic distribution. Although 3D CNNs excel at capturing local contextual information, they are computationally complex due to the large number of parameters and cannot capture long-range dependence. By contrast, although self-attention mechanisms originally designed to address challenges in natural language processing can capture long-range dependence, their application to 2D image structures requires breaking down the inherent 2D context into a 1D sequence, increasing the computational complexity and neglecting the adaptability between local contextual information and channels. Accordingly, we propose a spatio-temporal visual attention neural network (STVANet), a novel spatio-temporal visual attention 2D CNN, which integrates a unique visual attention module with a large kernel attention (LKA) mechanism and a feedforward component to capture long-range dependence and channel information in urban traffic data while preserving the 2D image structure. LKA-based spatio-temporal attention networks extract spatial and temporal features from weekly, daily, and recent hourly periods, and aggregate them with weighted consideration of external features to make predictions. Evaluation of real-world datasets demonstrates STVANet’s superiority over baseline models, showcasing its potential in citywide traffic prediction. © 2023, The Authors. All rights reserved.

关键词： Convolution

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A Unified Runoff Generation Scheme for Applicability Across Different Hydrometeorological Zones

SSRN

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SSRN 2024年

作者： Zhang, Qinuo Zhang, Ke Chao, Lijun Chen, Xinyu Wu, Nan The National Key Laboratory of Water Disaster Prevention Hohai University Jiangsu Nanjing210024 China Yangtze Institute for Conservation and Development Hohai University Jiangsu Nanjing210024 China College of Hydrology and Water Resources Hohai University Jiangsu Nanjing210024 China China Meteorological Administration Hydro-Meteorology Key Laboratory Hohai University Jiangsu Nanjing210024 China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Jiangsu Nanjing210024 China Key Laboratory of Hydrologic-Cycle and Hydrodynamic-System of Ministry of Water Resources Hohai University jiangsu Nanjing210024 China

Runoff generation in humid and arid regions are usually saturation-excess-driven and infiltration-excess-driven respectively, whereas semi-humid regions is confronted with complex nonlinear rainfall-runoff process influenced by both of them. In this study, we proposed a unified runoff generation scheme to delineate the mixed runoff generation processes by integrating a modified Horton infiltration module with the saturation excess runoff generation module of the Xin'anjiang model at the grid scale, and a new distributed hydrological model, grid-Xin'anjiang-infiltration-excess (GXAJ-IE) model was developed. The model was tested in four watersheds and two models with single runoff generation mode were set as benchmarks for comparison. The results indicate that GXAJ-IE model has higher flexibility and robustness in reproducing flood hydrographs, especially flood peaks in semi-humid watersheds. It achieves comparable performances with the benchmark models in humid, arid and semi-arid regions. Overall, GXAJ-IE model shows potential for universal application in flood simulation in different hydrometeorological regions. © 2024, The Authors. All rights reserved.

关键词： Flood control

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ExpoNAS: Using Exposure-based Candidate Exclusion to Reduce NAS Space for Heterogeneous Pipeline of CNNs

ExpoNAS: Using Exposure-based Candidate Exclusion to Reduce ...

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International Conference on Parallel and Distributed systems (ICPADS)

作者： Huijuan Duan Danghui Wang Xingting Zhao Kuizhi Mei Jihe Wang School of Computer Science Northwestern Polytechnical University Xi'an China Engineering Research Center of Embedded System Integration Ministry of Education Northwestern Polytechnical University Xi'an China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Northwestern Polytechnical University Xi'an China

Efficiently deploying neural network models on heterogeneous edge systems faces unique challenges. Two major issues are underutilized pipelines caused by hardware heterogeneity and explosive growth of architecture search space. To address these challenges, we propose ExpoNAS, a framework enabling fast deployment-oriented neural architecture search on heterogeneous platforms. ExpoNAS evaluates model partitioning and deployability before search to meet accuracy and latency constraints. However, repeated partitioning leads to exponential growth in architecture search space. We first observe a strong correlation between operational exposure and accuracy improvement, and then we propose an exposure-based reduction technique that excludes the operations of low exposure. By removing these non-critical operations, the exposure-based pruning successfully reduces architecture search time. Our experiments on popular datasets show that the ExpoNAS pipelined design improves execution efficiency by a factor of 2 compared to traditional batch processing. Without compromising accuracy, ExpoNAS is able to reduce the architecture search time by 47.5% for discovering high-accuracy networks, compared to the SOTA NAS strategies.

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GradSA: Gradient Sparsification and Accumulation for Communication-Efficient Distributed Deep Learning 15th

GradSA: Gradient Sparsification and Accumulation for Communi...

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15th International Conference on Green, Pervasive, and Cloud Computing, GPC 2020

作者： Liu, Bo Jiang, Wenbin Zhao, Shaofeng Jin, Hai He, Bingsheng National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan China Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Huazhong University of Science and Technology Wuhan China Library Henan University of Economics and Law Zhengzhou China Department of Computer Science School of Computing National University of Singapore Singapore Singapore

ISBN: (纸本)9783030642426

Large-scale distributed deep learning is of great importance in various applications. For distributed training, the inter-node gradient communication often becomes the performance bottleneck. Gradient sparsification has been proposed to reduce the communication overhead. However, the sparsification only arbitrarily selects a small fraction of gradients, while the efficiency of selection dimension has been overlooked. Furthermore, gradient staleness is inevitable after applying the sparsification which will ultimately lead to model divergence. In this paper, we propose a staleness-compensated sparse stochastic gradient descent algorithm, GradSA, to improve the training efficiency. Layer-level gradients are sparsified to reduce the communication overhead, which conform to the characteristics of the network structure, and historical accumulation of the approximated gradients is utilized to speed up convergence. We demonstrate the model convergence acceleration and the efficiency of our layer-level selection over existing state-of-the-art works, such as DGC, TernGrad, and 8-Bit quantization. © 2020, Springer Nature Switzerland AG.

关键词： Efficiency

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