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arXiv 2025年

作者： Wu, Qiong Ji, Maoxin Fan, Pingyi Wang, Kezhi Cheng, Nan Chen, Wen Letaief, Khaled B. The School of Internet of Things Engineering Jiangnan University Wuxi214122 China The Department of Electronic Engineering State Key laboratory of Space Network and Communications Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China The Department of Computer Science Brunel University Middlesex LondonUB8 3PH United Kingdom The State Key Lab. of ISN School of Telecommunications Engineering Xidian University Xi’an710071 China The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China Hong Kong

On-ramp merging presents a critical challenge in autonomous driving, as vehicles from merging lanes need to dynamically adjust their positions and speeds while monitoring traffic on the main road to prevent collisions. To address this challenge, we propose a novel merging control scheme based on reinforcement learning, which integrates lateral control mechanisms. This approach ensures the smooth integration of vehicles from the merging lane onto the main road, optimizing both fuel efficiency and passenger comfort. Furthermore, we recognize the impact of vehicle-to-vehicle (V2V) communication on control strategies and introduce an enhanced protocol leveraging Cellular Vehicle-to-Everything (C-V2X) Mode 4. This protocol aims to reduce the Age of Information (AoI) and improve communication reliability. In our simulations, we employ two AoI-based metrics to rigorously assess the protocol's effectiveness in autonomous driving scenarios. By combining the NS3 network simulator with Python, we simulate V2V communication and vehicle control simultaneously. The results demonstrate that the enhanced C-V2X Mode 4 outperforms the standard version, while the proposed control scheme ensures safe and reliable vehicle operation during on-ramp merging. Copyright © 2025, The Authors. All rights reserved.

关键词： Problem oriented languages

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A malicious url detection model based on convolutional neural network 6th

A malicious url detection model based on convolutional neura...

引用

6th International Symposium on Security and Privacy in Social networks and Big Data, SocialSec 2020

作者： Wang, Zhiqiang Li, Shuhao Wang, Bingyan Ren, Xiaorui Yang, Tao State Information Center Beijing China Beijing Electronic Science and Technology Institute Beijing China Key Lab of Information Network Security Ministry of Public Security Shanghai China

ISBN: (纸本)9789811590306

With the development of Internet technology, network security is facing great challenges. Malicious URL detection can defend against attacks such as phishing, spams, and malware implantation. However there are some problems on current malicious URL detection, for example the methods used to extract features are inefficient and hard to adapt to the current complex network environment. To solve these problems, this paper uses the word embedding method based on character embedding as the way of vector embedding to improve the deep convolutional neural network, and designs a malicious URL detection system. Finally, we carry out experiments with the system, the results prove the effectiveness of our system. © Springer Nature Singapore Pte Ltd 2020.

关键词： network security

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Uplink Control Information Based Clustered Access and Discontinuous Reception Management for mMTC

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IEEE Communications Letters 2025年第6期29卷 1380-1384页

作者： Wang, Shao-Di Wang, Chang-Long Wang, Hui-Ming Zhou, Feng Leung, Victor C. M. Xidian University Key Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education Xi’an710071 China Xi’an Jiaotong University School of Information and Communication Engineering Ministry of Education Key Lab for Intelligent Networks and Network Security Xi’an710049 China Shenzhen MSU BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Uplink control information (UCI) and discontinuous reception (DRX) play important roles for massive machine type communication (mMTC). Despite their standalone significance, a conspicuous gap exists in comprehensively exploring the synergy between UCI and DRX concerning both energy conservation and latency reduction for mMTC. In this letter, we propose a UCI-based clustered access and DRX management (UCADM) scheme that strikes a balance between these two critical performance metrics, reducing energy usage while preserving low latency in uplink-downlink interaction for mMTC. Specifically, we present a strictly feasible solution for the proposed UCADM scheme by using the spectral radius and the constraint projection, which can effectively solve DRX switching operations as indicated by scheduling information from UCI. Simulation results confirm the effectiveness of our scheme. © 1997-2012 IEEE.

关键词： Information management

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Temporal knowledge graph reasoning based on evolutional representation learning

arXiv

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arXiv 2021年

作者： Li, Zixuan Jin, Xiaolong Li, Wei Guan, Saiping Guo, Jiafeng Shen, Huawei Wang, Yuanzhuo Cheng, Xueqi School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China CAS Key Lab of Network Data Science and Technology ICT CAS Beijing China Baidu Inc. Beijing China

Knowledge Graph (KG) reasoning that predicts missing facts for incomplete KGs has been widely explored. However, reasoning over Temporal KG (TKG) that predicts facts in the future is still far from resolved. The key to predict future facts is to thoroughly understand the historical facts. A TKG is actually a sequence of KGs corresponding to different timestamps, where all concurrent facts in each KG exhibit structural dependencies and temporally adjacent facts carry informative sequential patterns. To capture these properties effectively and efficiently, we propose a novel Recurrent Evolution network based on Graph Convolution network (GCN), called RE-GCN, which learns the evolutional representations of entities and relations at each timestamp by modeling the KG sequence recurrently. Specifically, for the evolution unit, a relation-aware GCN is leveraged to capture the structural dependencies within the KG at each timestamp. In order to capture the sequential patterns of all facts in parallel, the historical KG sequence is modeled auto-regressively by the gate recurrent components. Moreover, the static properties of entities such as entity types, are also incorporated via a static graph constraint component to obtain better entity representations. Fact prediction at future timestamps can then be realized based on the evolutional entity and relation representations. Extensive experiments demonstrate that the RE-GCN model obtains substantial performance and efficiency improvement for the temporal reasoning tasks on six benchmark datasets. Especially, it achieves up to 11.46% improvement in MRR for entity prediction with up to 82 times speedup comparing to the state-of-the-art baseline. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolution

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NIR-Prompt: A Multi-task Generalized Neural Information Retrieval Training Framework

arXiv

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arXiv 2022年

作者： Xu, Shicheng Pang, Liang Shen, Huawei Cheng, Xueqi Data Intelligence System Research Center Institute of Computing Technology CAS University of Chinese Academy of Sciences Beijing China Data Intelligence System Research Center Institute of Computing Technology CAS Beijing China CAS Key Lab of Network Data Science and Technology Institute of Computing Technology CAS University of Chinese Academy of Sciences Beijing China

Information retrieval aims to find information that meets users' needs from the corpus. Different needs correspond to different IR tasks such as document retrieval, open-domain question answering, retrieval-based dialogue, etc., while they share the same schema to estimate the relationship between texts. It indicates that a good IR model can generalize to different tasks and domains. However, previous studies indicate that state-of-the-art neural information retrieval (NIR) models, e.g., pre-trained language models (PLMs) are hard to generalize. It is mainly because the end-to-end fine-tuning paradigm makes the model overemphasize task-specific signals and domain biases but loses the ability to capture generalized essential signals. To address this problem, we propose a novel NIR training framework named NIR-Prompt for retrieval and reranking stages based on the idea of decoupling signal capturing and combination. NIR-Prompt exploits Essential Matching Module (EMM) to capture the essential matching signals and gets the description of tasks by Matching Description Module (MDM). The description is used as task-adaptation information to combine the essential matching signals to adapt to different tasks. Experiments under in-domain multi-task, out-of-domain multitask, and new task adaptation settings show that NIR-Prompt can improve the generalization of PLMs in NIR for both retrieval and reranking stages compared with baselines. Copyright © 2022, The Authors. All rights reserved.

关键词： Information retrieval

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Hybrid Dirac semimetal-based photodetector with efficient low-energy photon harvesting

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Light(science & Applications) 2022年第3期11卷 107-116页

作者： Lin Wang Li Han Wanlong Guo Libo Zhang Chenyu Yao Zhiqingzi Chen Yulu Chen Cheng Guo Kaixuan Zhang Chia-Nung Kuo Chin Shan Lue Antonio Politano Huaizhong Xing Mengjie Jiang Xianbin Yu Xiaoshuang Chen Wei Lu State Key Laboratory for Infrared Physics Shanghai Institute of Technical PhysicsChinese Academy of Sciences500 Yu-tian RoadShanghai 200083China Department of Optoelectronic Science and Engineering Donghua UniversityShanghai 201620China College of Physics and Optoelectronic Engineering Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesNo.1Sub-Lane XiangshanXihu DistrictHangzhou 310024China School of Physical Science and Technology ShanghaiTech UniversityShanghai 201210China The 50th Research Institute of China Electronics Technology Group Shanghai 200331China Research Center for Intelligent Network Zhejiang LabHangzhou 311121China Department of Physics Cheng Kung University1 Ta-Hsueh Road70101 TainanTaiwanChina INSTM and Department of Physical and Chemical Sciences University of L’Aquilavia Vetoio67100 L’Aquila(AQ)Italy CNR-IMM Istituto per la Microelettronica e Microsistemi VIII strada 5I-95121 CataniaItaly

Despite the considerable effort,fast and highly sensitive photodetection is not widely available at the low-photon-energy range(~meV)of the electromagnetic spectrum,owing to the challenging light funneling into small active areas with efficient conversion into an electrical ***,we provide an alternative strategy by efficiently integrating and manipulating at the nanoscale the optoelectronic properties of topological Dirac semimetal PtSe_(2)and its van der Waals ***,we realize strong plasmonic antenna coupling to semimetal states near the skin-depth regime(λ/10^(4)),featuring colossal photoresponse by in-plane symmetry *** observed spontaneous and polarization-sensitive photocurrent are correlated to strong coupling with the nonequilibrium states in PtSe_(2)Dirac semimetal,yielding efficient light absorption in the photon range below 1.24 meV with responsivity exceeding∼0.2 A/W and noise-equivalent power(NEP)less than~38 pW/Hz^(0.5),as well as superb ambient *** results pave the way to efficient engineering of a topological semimetal for high-speed and low-energy photon harvesting in areas such as biomedical imaging,remote sensing or security applications.

关键词： stability Dirac topological

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Mask Approximation Net: A Novel Diffusion Model Approach for Remote Sensing Change Captioning

arXiv

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arXiv 2024年

作者： Sun, Dongwei Yao, Jing Zhou, Changsheng Cao, Xiangyong Ghamisi, Pedram School of Computer Science and Technology the Ministry of Education Key Lab for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an710049 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Mathematics and Statistics Guangdong University of Technology Guangzhou China Helmholtz-Zentrum Dresden-Rossendorf Freiberg09599 Germany Lancaster Environment Centre Lancaster University LancasterLA1 4YR United Kingdom

Remote sensing image change description represents an innovative multimodal task within the realm of remote sensing processing. This task not only facilitates the detection of alterations in surface conditions, but also provides comprehensive descriptions of these changes, thereby improving human interpretability and interactivity. Generally, existing deep-learning-based methods predominantly utilized a three-stage framework that successively perform feature extraction, feature fusion, and localization from bitemporal images before text generation. However, this reliance often leads to an excessive focus on the design of specific network architectures and restricts the feature distributions to the dataset at hand, which in turn results in limited generalizability and robustness during application. To address these limitations, this paper proposes a novel approach for remote sensing image change detection and description that incorporates diffusion models, aiming to transition the emphasis of modeling paradigms from conventional feature learning to data distribution learning. The proposed method primarily includes a simple multi-scale change detection module, whose output features are subsequently refined by an well-designed diffusion model. Furthermore, we introduce a frequency-guided complex filter module to boost the model performance by managing high-frequency noise throughout the diffusion process. We validate the effectiveness of our proposed method across several datasets for remote sensing change detection and description, showcasing its superior performance compared to existing techniques. The code will be available at MaskApproxNet after a possible publication. Copyright © 2024, The Authors. All rights reserved.

关键词： Change detection

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Carrier Injection and Manipulation of Charge-Density Wave in Kagome Superconductor CsV3Sb5

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Physical Review X 2022年第1期12卷 011001-011001页

作者： Kosuke Nakayama Yongkai Li Takemi Kato Min Liu Zhiwei Wang Takashi Takahashi Yugui Yao Takafumi Sato Department of Physics Tohoku University Sendai 980-8578 Japan Precursory Research for Embryonic Science and Technology (PRESTO) Japan Science and Technology Agency (JST) Tokyo 102-0076 Japan Centre for Quantum Physics Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE) School of Physics Beijing Institute of Technology Beijing 100081 China Beijing Key Lab of Nanophotonics and Ultrafine Optoelectronic Systems Beijing Institute of Technology Beijing 100081 China Center for Spintronics Research Network Tohoku University Sendai 980-8577 Japan WPI Research Center Advanced Institute for Materials Research Tohoku University Sendai 980-8577 Japan

Kagome metals AV3Sb5 (A=K, Rb, and Cs) exhibit a unique superconducting ground state coexisting with charge-density wave (CDW), whereas how these characteristics are affected by carrier doping remains unexplored because of the lack of an efficient carrier-doping method. Here we report successful electron doping to CsV3Sb5 by Cs dosing, as visualized by angle-resolved photoemission spectroscopy. We found that the electron doping with Cs dosing proceeds in an orbital-selective way, as characterized by a marked increase in electron filling of the Sb 5pz and V 3dxz/yz bands as opposed to the relatively insensitive nature of the V 3dxy/x2−y2 bands. By monitoring the temperature evolution of the CDW gap around the M¯ point, we found that the CDW can be completely killed by Cs dosing while keeping the saddle point with the V 3dxy/x2−y2 character almost pinned at the Fermi level. The present result suggests a crucial role of multiorbital effect to the occurrence of CDW and provides an important step toward manipulating the CDW and superconductivity in AV3Sb5.

关键词： Electronic structure Peierls transition Superconductivity Kagome lattice Angle-resolved photoemission spectroscopy

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Toward Enhanced Robustness in Unsupervised Graph Representation Learning: A Graph Information Bottleneck Perspective

arXiv

引用

arXiv 2022年

作者： Wang, Jihong Luo, Minnan Li, Jundong Liu, Ziqi Zhou, Jun Zheng, Qinghua The Ministry of Education Key Lab for Intelligent Networks and Network Security School of Computer Science and Technology Xi’an Jiaotong University Xi’an710049 China The Department of Electrical and Computer Engineering Department of Computer Science School of Data Science University of Virginia United States The Ant Financial Services Group Zhejiang Hangzhou310000 China

Recent studies have revealed that GNNs are vulnerable to adversarial attacks. Most existing robust graph learning methods measure model robustness based on label information, rendering them infeasible when label information is not available. A straightforward direction is to employ the widely used Infomax technique from typical Unsupervised Graph Representation Learning (UGRL) to learn robust unsupervised representations. Nonetheless, directly transplanting the Infomax technique from typical UGRL to robust UGRL may involve a biased assumption. In light of the limitation of Infomax, we propose a novel unbiased robust UGRL method called Robust Graph Information Bottleneck (RGIB), which is grounded in the Information Bottleneck (IB) principle. Our RGIB attempts to learn robust node representations against adversarial perturbations by preserving the original information in the benign graph while eliminating the adversarial information in the adversarial graph. There are mainly two challenges to optimize RGIB: 1) high complexity of adversarial attack to perturb node features and graph structure jointly in the training procedure;2) mutual information estimation upon adversarially attacked graphs. To tackle these problems, we further propose an efficient adversarial training strategy with only feature perturbations and an effective mutual information estimator with subgraph-level summary. Moreover, we theoretically establish a connection between our proposed RGIB and the robustness of downstream classifiers, revealing that RGIB can provide a lower bound on the adversarial risk of downstream classifiers. Extensive experiments over several benchmarks and downstream tasks demonstrate the effectiveness and superiority of our proposed method. Copyright © 2022, The Authors. All rights reserved.

关键词： Learning systems

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Outage Probability and Power Allocation Analysis of AmBC-Enabled NOMA for Future IoT

Outage Probability and Power Allocation Analysis of AmBC-Ena...

引用

International Conference on Communication technology (ICCT)

作者： Chongye Wang Yang Liu Cheng-Xiang Wang School of Internet of Things Engineering Jiangnan University Wuxi China Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Ministry of Education Nanjing China National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University Nanjing China Purple Mountain Laboratories Nanjing China

Ambient backscatter communication (AmBC) combined with non-orthogonal multiple access (NOMA) presents superior energy reusability and higher sum rate. However, existing studies failed to utilize the information of backscatter links. Therefore, to explore the impact of backscatter link, this paper employs three-user NOMA to improve the spectral efficiency. We propose a simple distance power allocation method to accomplish successive interference cancellation. Based on the Monte Carlo method, the outage probability of the proposal is analyzed. Our work provides a scheme for coexistence between AmBC and NOMA systems in the future Internet of Things.

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