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arXiv 2022年

作者： Gong, Shenjian Zhang, Shanshan Yang, Jian Dai, Dengxin Schiele, Bernt PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China MPI Informatics Germany

Recently, crowd density estimation has received increasing attention. The main challenge for this task is to achieve high-quality manual annotations on a large amount of training data. To avoid reliance on such annotations, previous works apply unsupervised domain adaptation (UDA) techniques by transferring knowledge learned from easily accessible synthetic data to real-world datasets. However, current state-of-the-art methods either rely on external data for training an auxiliary task or apply an expensive coarse-to-fine estimation. In this work, we aim to develop a new adversarial learning based method, which is simple and efficient to apply. To reduce the domain gap between the synthetic and real data, we design a bi-level alignment framework (BLA) consisting of (1) task-driven data alignment and (2) fine-grained feature alignment. In contrast to previous domain augmentation methods, we introduce AutoML to search for an optimal transform on source, which well serves for the downstream task. On the other hand, we do fine-grained alignment for foreground and background separately to alleviate the alignment difficulty. We evaluate our approach on five real-world crowd counting benchmarks, where we outperform existing approaches by a large margin. Also, our approach is simple, easy to implement and efficient to apply. The code is publicly available at https://***/Yankeegsj/BLA. © 2022, CC BY.

关键词： Alignment

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An Effective Hybrid Optimization Algorithm for Static Rebalance Problem of Bicycle-Sharing System

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IET Intelligent Transport Systems 2025年第1期19卷

作者： Liang, Shuhui Ye, Yutong Wu, Jingli Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University Guilin China School of Computer Science and Engineering Guangxi Normal University Guilin China Software Engineering Institute East China Normal University Shanghai China

Although the bicycle-sharing system plays an important role in easing urban traffic congestion and reducing carbon emissions, it still suffers from the problem of unbalanced bicycle distribution between different stations, which significantly limits the utilization of bicycles. To solve this problem, this paper proposes a novel hybrid optimization algorithm, MixPS, based on the combination of a partheno-genetic algorithm and a simulated annealing algorithm. This algorithm can effectively rebalance the distribution of bicycles. To further improve performance, we design a decimal code to represent a scheduled path and introduce six mutation operators to change the chromosome. The whole evolution is controlled with the simulated annealing process. Moreover, we apply the Metropolis acceptance criterion to effectively reduce the possibility that the population falls into the local optimum. Comprehensive experimental results obtained from both synthetic and real-world data sets show that our proposed MixPS algorithm can achieve better optimization and generate a shorter schedule path scheme in less time. These results confirm the efficiency and effectiveness of our method in solving the static bicycle rebalancing problem with single-vehicle and multiple-access. © 2025 The Author(s). IET Intelligent Transport Systems published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

关键词： bicycle rebalancing problem effective heuristic low-carbon optimization algorithm

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Time-Efficient EV Energy Management Through In-Motion V2V Charging

Time-Efficient EV Energy Management Through In-Motion V2V Ch...

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IEEE Conference on Vehicular Technology (VTC)

作者： Shuohan Liu Wei Zhang Yue Cao Qiang Ni Carsten Maple Hai Lin Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of Cyber Science and Engineering Wuhan University Wuhan China School of Computing and Communications Lancaster University Lancaster U.K. Warwick Manufacturing Group University of Warwick Coventry U.K.

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

In recent years, Electric Vehicles (EVs) have emerged as a sustainable alternative to internal combustion vehicles, noted for better efficiency, lower operational costs, and reduced carbon emissions. However, with the growing adoption of EVs and limited charging infrastructure, challenges such as charging congestion arise. Traditional plug-in and in-Parking Vehicle-to-Vehicle (V2V) charging modes, constrained by fixed charging locations, lack flexibility and necessitate long charging times. Therefore, this paper introduces a novel in-Motion V2V charging mode, termed V2V (M) mode, allowing an EV as an energy Provider (EV-P) and an EV as an energy Consumer (EV-C) to form a V2V charging Pair (V2V-Pair). Then, the V2V-Pair can transfer energy via wireless V2V charging service while on-the-move. In this paper, the proposed V2V (M) management framework employs a Path Proximity-based V2V Pair matching algorithm and spatio-temporal cooperative path planning, to enhance charging efficiency and reduce charging trip duration. The urban environment simulation results demonstrate marked improvements of the proposed V2V (M) mode. It shorters the charging trip duration and enhances charging service efficiency, offering a viable solution to current EV charging constraints.

关键词： Wireless communication Vehicular and wireless technologies Renewable energy sources Simulation Urban areas Vehicular ad hoc networks Path planning

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Unsupervised Collaborative Metric Learning with Mixed-Scale Groups for General Object Retrieval

arXiv

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arXiv 2024年

作者： Kan, Shichao Deng, Yuhai Liang, Yixiong Cen, Lihui Qu, Zhe Cen, Yigang He, Zhihai School of Computer Science and Engineering Central South University Hunan Changsha410083 China School of Automation Central South University Hunan Changsha410083 China Institute of Information Science School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen China Pengcheng Lab Shenzhen518066 China

The task of searching for visual objects in a large image dataset is difficult because it requires efficient matching and accurate localization of objects that can vary in size. Although the segment anything model (SAM) offers a potential solution for extracting object spatial context, learning embeddings for local objects remains a challenging problem. This paper presents a novel unsupervised deep metric learning approach, termed unsupervised collaborative metric learning with mixed-scale groups (MS-UGCML), devised to learn embeddings for objects of varying scales. Following this, a benchmark of challenges is assembled by utilizing COCO 2017 and VOC 2007 datasets to facilitate the training and evaluation of general object retrieval models. Finally, we conduct comprehensive ablation studies and discuss the complexities faced within the domain of general object retrieval. Our object retrieval evaluations span a range of datasets, including BelgaLogos, Visual Genome, LVIS, in addition to a challenging evaluation set that we have individually assembled for open-vocabulary evaluation. These comprehensive evaluations effectively highlight the robustness of our unsupervised MS-UGCML approach, with an object level and image level mAPs improvement of up to 6.69% and 10.03%, respectively. The code is publicly available at https://***/dengyuhai/MSUGCML. Copyright © 2024, The Authors. All rights reserved.

关键词： Embeddings

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6G-Enabled Edge AI for Metaverse:Challenges, Methods,and Future Research Directions

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Journal of Communications and information networks 2022年第2期7卷 107-121页

作者： Luyi Chang Zhe Zhang Pei Li Shan Xi Wei Guo Yukang Shen Zehui Xiong Jiawen Kang Dusit Niyato Xiuquan Qiao Yi Wu School of Data Science and Technology Heilongjiang UniversityHarbin 150080China Institute for Cryptology and Network Security Heilongjiang UniversityHarbin 150080China SenseTime Group Limited Shenzhen 518000China Pillar of Information Systems Technology and Design Singapore University of Technology and Design.Singapore 487372Singapore School of Automation.Guangdong University of Technology and Center for Intelligenl Batch Manufacturing based on IoT TechnologyGuangzhou 510006China School of Computer Science and Engineering Nanyang Technological UniversitySingapore 639798.Singapore State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications.Beijing 100876China

Sixth generation(6G)enabled edge intelligence opens up a new era of Internet of everything and makes it possible to interconnect people-devices-cloud anytime,*** and more next-generation wireless network smart service applications are changing our way of life and improving our quality of *** the hottest new form of next-generation Internet applications,Metaverse is striving to connect billions of users and create a shared world where virtual and reality ***,limited by resources,computing power,and sensory devices,Metaverse is still far from realizing its full vision of immersion,materialization,and *** this end,this survey aims to realize this vision through the organic integration of 6G-enabled edge artificial intelligence(AI)and ***,we first introduce three new types of edge-Metaverse architectures that use 6G-enabled edge AI to solve resource and computing constraints in *** we summarize technical challenges that these architectures face in Metaverse and the existing ***,we explore how the edge-Metaverse architecture technology helps Metaverse to interact and share digital ***,we discuss future research directions to realize the true vision of Metaverse with 6G-enabled edge AI.

关键词： edge artificial intelligence artificial intelli-gence 6G metaverse federated learning

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LESSON: Multi-label Adversarial False Data Injection Attack for Deep Learning Locational Detection

arXiv

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arXiv 2024年

作者： Tian, Jiwei Shen, Chao Wang, Buhong Xia, Xiaofang Zhang, Meng Lin, Chenhao Li, Qian Ministry of Education Key Laboratory for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi’an Jiaotong University Xi’an China Air Traffic Control and Navigation College Air Force Engineering University Xi’an China Information and Navigation College Air Force Engineering University Xi’an China School of Computer Science and Technology Xidian University Xi’an China

—Deep learning methods can not only detect false data injection attacks (FDIA) but also locate attacks of FDIA. Although adversarial false data injection attacks (AFDIA) based on deep learning vulnerabilities have been studied in the field of single-label FDIA detection, the adversarial attack and defense against multi-label FDIA locational detection are still not involved. To bridge this gap, this paper first explores the multi-label adversarial example attacks against multi-label FDIA locational detectors and proposes a general multi-label adversarial attack framework, namely muLti-labEl adverSarial falSe data injectiON attack (LESSON). The proposed LESSON attack framework includes three key designs, namely Perturbing State Variables, Tailored Loss Function Design, and Change of Variables, which can help find suitable multi-label adversarial perturbations within the physical constraints to circumvent both Bad Data Detection (BDD) and Neural Attack Location (NAL). Four typical LESSON attacks based on the proposed framework and two dimensions of attack objectives are examined, and the experimental results demonstrate the effectiveness of the proposed attack framework, posing serious and pressing security concerns in smart grids. © 2024, CC BY.

关键词： Location

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Few-Shot Legal Knowledge Question Answering System for COVID-19 Epidemic 3

Few-Shot Legal Knowledge Question Answering System for COVID...

引用

3rd International Conference on Algorithms, Computing and Artificial Intelligence, ACAI 2020

作者： Wu, Jiaye Liu, Jie Luo, Xudong Guangxi Key Lab of Multi-Source Information Mining and Security College of Computer Science and Information Engineering Guangxi Normal University Guilin Guangxi China

ISBN: (纸本)9781450388115

Recently, the pandemic caused by COVID-19 is severe in the entire world. The prevention and control of crimes associated with COVID-19 are critical for controlling the pandemic. Therefore, to provide efficient and convenient intelligent legal knowledge services during the pandemic, in this paper, we develop an intelligent system for answering legal questions on the WeChat platform. The data source we used for training our system is "The typical cases of national procuratorial authorities handling crimes against the prevention and control of the new coronary pneumonia pandemic following the law", which is published online by the Supreme People's Procuratorate of the People's Republic of China. We base our system on BERT (a well-known pre-trained language model) and use the shared attention mechanism to capture the text information further. Then we train a model to minimise the contrastive loss. Finally, the system uses the trained model to identify the information entered by a user, and accordingly responds to the user with a reference case similar to the query case and give the reference legal gist applicable to the query case. © 2020 ACM.

关键词： COVID-19

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A Survey of Sentiment Analysis Based on Machine Learning 1

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9th CCF International Conference on Natural Language Processing and Chinese Computing, NLPCC 2020

作者： Lin, Pingping Luo, Xudong Guangxi Key Lab of Multi-source Information Mining and Security College of Computer Science and Information Engineering Guangxi Normal University Guilin541004 China

ISBN: (数字)9783030604509

ISBN: (纸本)9783030604493

Every day, Facebook, Twitter, Weibo and other social network sites and major e-commerce sites generate a large number of online reviews with emotions. The analysing peoples opinions from these reviews can assist a variety of decision-making processes in organisations, products, and administrations. Therefore, it is practically and theoretically important to study how to analyse online reviews with emotions. To help researchers study sentiment analysis, in this paper, we survey the machine learning based method for sentiment analysis of online reviews. These methods are main based on Support Vector Machine, Neural networks, Naïve Bayes, Bayesian network, Maximum entropy, and some hybrid methods. In particular, we point out the main problems in the machine learning based methods for sentiment analysis and the problems to be solved in the future. © 2020, Springer Nature Switzerland AG.

关键词： Sentiment analysis

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A Survey of Sentiment Analysis Based on Pretrained Language Models

A Survey of Sentiment Analysis Based on Pretrained Language ...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Kaili Sun Xudong Luo Michael Y. Luo Guangxi Key Lab of Multi-source Information Mining and Security School of Computer Science and Engineering Guangxi Normal University Guilin China Emmanuel College Cambridge University Cambridge UK

Pretrained Language Models (PLMs) can be applied to downstream tasks with only fine-tuning, without learning the model from scratch. In particular, PLMs have been applied to Sentiment Analysis (SA), which detects, analyses, and extracts the polarity of the sentiment expressed in texts. To help researchers quickly grasp the state-of-art PLM-based SA, we survey PLM-based methods for mono-lingual and cross-lingual SA in this paper. Specifically, we brief these methods, compare their per-formance and point out the challenges for future research.

关键词： Sentiment analysis Analytical models Task analysis Artificial intelligence

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AIDE: Attack Inference Based on Heterogeneous Dependency Graphs with MITRE ATT&CK 23

AIDE: Attack Inference Based on Heterogeneous Dependency Gra...

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23rd IEEE International Conference on Trust, security and Privacy in Computing and Communications, TrustCom 2024

作者： Zhou, Weidong Xia, Chunhe Feng, Nan Pan, Xinyi Wang, Tianbo Li, Xiaojian Beihang University Beijing Key Lab. of Network Technology Beijing China Guangxi Normal University Guangxi Collab. Innovation Center of Multi-Source Information Integration and Intelligent Processing Guilin China Peking University School of Software & Microelectronics Beijing China Beihang University School of Cyber Science and Technology Beijing China Guangxi Normal University School of Computer Science and Engineering Guilin China

The MITRE ATT&CK Framework provides a rich and actionable repository of adversarial tactics, techniques, and procedures (TTPs). By leveraging the associations among these techniques, we can infer unobserved techniques based on those have been observed. Current machine learning inference methods based on small-scale data face the following problems: "logical dependencies missing"and "complex associations missing."The former means that current association methods rely solely on the statistical dependencies between attack techniques while ignoring the logical dependencies involved in executing those techniques. The latter indicates that current inference methods simplify the complex associations between technologies based on statistical assumptions, failing to adequately capture the long-range and intricate real-world relationships between techniques. To address these problems, we propose an Attack Inference model based on heterogeneous DEpendency graphs (AIDE). First, we extract three types of logical dependencies: CVE, Pattern, and Conditional, which allows us to construct a heterogeneous dependency graph among the techniques, thus addressing the "logical dependencies missing"problem. Second, we employ Relation Graph Convolution Neural networks (RGCN) to aggregate heterogeneous dependencies into representation vectors of the techniques. Finally, we utilize the sequence inference model Decoder, which excels at capturing implicit contextual associations, to infer unknown attacks, thus addressing the "complex associations missing"problem. Experiments conducted on real datasets demonstrate that our model significantly outperforms state-of-the-art baseline models in both hits@k and map@k metrics. © 2024 IEEE.

关键词： attack inference heterogeneous dependency graph MITRE ATT&CK RGCN

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