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Artificial Intelligence for Edge Service Optimization in Internet of Vehicles:A Survey

Tsinghua Science and Technology 2022年第2期27卷 270-287页

作者： Xiaolong Xu Haoyuan Li Weijie Xu Zhongjian Liu Liang Yao Fei Dai the School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 210044China the Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science and Technology and EngineeringNanjing 210044China the State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China the School of Big Data and Intelligence Engineering Southwest Forestry UniversityKunming 650233China

The Internet of Vehicles(IoV)plays a crucial role in providing diversified services because of its powerful capability of collecting real-time ***,collected information is transmitted to a centralized resourceintensive cloud platform for service *** Computing(EC)that deploys physical resources near road-side units is involved in IoV to support real-time services for vehicular ***,many measures are adopted to optimize the performance of EC-enabled IoV,but they hardly help make dynamic decisions according to real-time *** Intelligence(AI)is capable of enhancing the learning capacity of edge devices and thus assists in allocating resources *** extensive research has employed AI to optimize EC performance,summaries with relative concepts or prospects are quite *** address this gap,we conduct an exhaustive survey about utilizing AI in edge service optimization in ***,we establish the general condition and relative concepts about IoV,EC,and ***,we review the edge service frameworks for IoV and explore the use of AI in edge server placement and service ***,we discuss a number of open issues in optimizing edge services with AI.

关键词： edge service internet of vehicles artificial intel igence

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Spatial-Temporal Graph-Based AU Relationship Learning for Facial Action Unit Detection

Spatial-Temporal Graph-Based AU Relationship Learning for Fa...

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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Wang, Zihan Song, Siyang Luo, Cheng Zhou, Yuzhi Wu, Shiling Xie, Weicheng Shen, Linlin Shenzhen University Computer Vision Institute School of Computer Science & Software Engineering China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology China University of Leicester School of Computing and Mathematical Sciences United Kingdom

ISBN: (纸本)9798350302493

This paper presents our Facial Action Units (AUs) detection submission to the fifth Affective Behavior Analysis in-the-wild Competition (ABAW). Our approach consists of three main modules: (i) a pre-trained facial representation encoder which produce a strong facial representation from each input face image in the input sequence;(ii) an AU-specific feature generator that specifically learns a set of AU features from each facial representation;and (iii) a spatio-temporal graph learning module that constructs a spatio-temporal graph representation. This graph representation describes AUs contained in all frames and predicts the occurrence of each AU based on both the modeled spatial information within the corresponding face and the learned temporal dynamics among frames. The experimental results show that our approach outperformed the baseline and the spatio-temporal graph representation learning allows our model to generate the best results among all ablated systems. Our model ranks at the 4th place in the AU recognition track at the 5th ABAW Competition. Our code is publicly available at https://***/wzh125/ABAW-5. © 2023 IEEE.

关键词： Graphic methods

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OVERSAMPLING BY A CONSTRAINT-BASED CAUSAL NETWORK IN MEDICAL IMBALANCED data CLASSIFICATION

OVERSAMPLING BY A CONSTRAINT-BASED CAUSAL NETWORK IN MEDICAL...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Luo, Hao Liao, Jun Yan, Xuewen Liu, Li School of Big Data and Software Engineering Chongqing University Chongqing400044 China

ISBN: (纸本)9781665438643

A key challenge of oversampling in medical imbalanced data classification is that the generation of new minority samples often neglects rich causal dependencies among features, with each being responsible for disease diagnosis. This leads us to define a constraint-based approach that generates new samples by explicitly discovering and leveraging the inherent local causal variability of features under a global view. Our approach employs causal Markov property to construct a causal network that explicitly characterizes these unique causal configurations of a particular disease as a variable number of nodes and links. By perturbing those learned causal features from majority class, we synthesize new samples in the territory of minority space. An additional sample selection estimator is introduced to choose the most representative samples. Empirical evaluations on four medical datasets suggest our approach significantly outperforms the state-of-the-art methods. © 2021 IEEE

关键词： Classification (of information)

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Evaluating the Generalizability of LLMs in Automated Program Repair

arXiv

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arXiv 2025年

作者： Li, Fengjie Jiang, Jiajun Sun, Jiajun Zhang, Hongyu College of Intelligence and and Computing Tianjin University Tianjin China School of Big Data and Software Engineering Chongqing University Chongqing China

LLM-based automated program repair methods have attracted significant attention for their state-of-the-art performance. However, they were primarily evaluated on a few well-known datasets like Defects4J, raising questions about their effectiveness on new datasets. In this study, we evaluate 11 top-performing LLMs on DEFECTS4J-TRANS, a new dataset derived from transforming Defects4J while maintaining the original semantics. Results from experiments on both Defects4J and DEFECTS4J-TRANS show that all studied LLMs have limited generalizability in APR tasks, with the average number of correct and plausible patches decreasing by 49.48% and 42.90%, respectively, on DEFECTS4J-TRANS. Further investigation into incorporating additional repair-relevant information in repair prompts reveals that, although this information significantly enhances the LLMs’ capabilities (increasing the number of correct and plausible patches by up to 136.67% and 121.82%, respectively), performance still falls short of their original results. This indicates that prompt engineering alone is insufficient to substantially enhance LLMs’ repair capabilities. Based on our study, we also offer several recommendations for future research. © 2025, CC BY-NC-SA.

关键词： Repair

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Monkeypox Detection based on MIRNET-v2 and Transfer Learning 23

Monkeypox Detection based on MIRNET-v2 and Transfer Learning

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Proceedings of the 2023 6th International Conference on Artificial Intelligence and Pattern Recognition

作者： Xiaoyin Wang Wenao Li Department of Software Engineering Xi'an University of Posts and Telecommunications China Big Data Technology and Engineering Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9798400707674

At present, the infectious disease caused by monkeypox virus is spreading rapidly all over the world. In order to diagnose it quickly and accurately and then block the transmission chain of the epidemic, this paper proposes an improved transfer learning method based on MIRNet-v2 image enhancement model. Firstly, the network structure of five different deep learning models, ResNet50, ResNet101, AlexNet, VGG19 and VGG16, was adjusted, and the transfer learning method was used to pre-train the models. Secondly, the MIRNet-v2 image enhancement model was combined with the transfer learning model to improve the model performance without reducing the training speed of the transfer learning model. In addition, LIME model is used to explain the internal principle of training for the black box of deep learning model. The “Monkeypox2022” monkeypox dataset was used as the experimental dataset. The optimal performance model of this method was ResNet101, whose accuracy, precision, recall rate and F1 value reached 84.14%, 81.75%, 81.86% and 81.31%, respectively. These data are superior to standard deep learning training methods, so the method proposed in this paper provides a feasible protocol for health personnel to conduct mass screening for monkeypox virus.

关键词： Deep learning MIRNet-v2 model image processing monkeypox recognition and detection transfer learning

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CONTEXT DRIVEN NETWORK WITH BAYES FOR WEAKLY SUPERVISED TEMPORAL ACTION LOCALIZATION

CONTEXT DRIVEN NETWORK WITH BAYES FOR WEAKLY SUPERVISED TEMP...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Yu, Jiaruo Ge, Yongxin Li, Ziqiang Chen, Zhongming Qin, Xiaolei School of Big Data and Software Engineering Chongqing University Chongqing 400030 China

ISBN: (纸本)9781665438643

Weakly supervised temporal action localization (WTAL) aims to detect action instances from untrimmed videos. It may cause two problems, namely action incompleteness and background disturbance, due to only video-level class labels given. In this paper, we propose a context driven network with Bayes to alleviate the two problems, in which an attention module is used to predict coarse probability for each snippet, and then a Bayesian refinement module is designed to refine the coarse results by capturing the relationship between context snippets. Finally, the coarse and refined probabilities are combined as the inputs of the classifier for training. Quantitative and qualitative studies on two benchmark datasets, i.e., THUMOS'14 and ActivityNet 1.2, demonstrate that the proposed approach exceeds state-of-the-art methods. © 2021 IEEE

关键词： Computer vision

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Early Fault Detection in Rolling Element Bearings Using Signal Processing Techniques 21

Early Fault Detection in Rolling Element Bearings Using Sign...

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21st IEEE International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT, HONET 2024

作者： Saeed, Ali Akram, M. Usman Khan, Muazzam A. Afsar, M. Belal Quaid-i-Azam University ICESCO Big Data Analytics and Edge Computing Department of Computer Science Islamabad Pakistan National University of Sciences and Technology Department of Computer and Software Engineering Islamabad Pakistan Otto-von-Guericke University Faculty of Electrical Engineering and Information Technology Maddeburg Germany

ISBN: (纸本)9798350378078

Many studies show that bearings are the most vulnerable components in low-voltage motors. While advanced bearing diagnostic systems exist, their cost can be a barrier for non-critical machinery due to the potential wait time to see a return on investment. This research explores the feasibility of using basic signal processing techniques on vibration data for bearing condition monitoring. The primary goal is to analyze benchmark data from the Case Western Reserve University (CWRU) dataset and establish a baseline performance for this data-driven approach. The results suggest that, with appropriate signal processing, it's possible to achieve early detection of bearing faults, leading to more efficient time-To-fault identification with higher detection accuracies ranging from 83 % to 100 % for each method. © 2024 IEEE.

关键词： Signal modulation

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Evaluating the Generalizability of LLMs in Automated Program Repair

Evaluating the Generalizability of LLMs in Automated Program...

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IEEE/ACM International Conference on software engineering: New Ideas and Emerging Technologies Results Track (ICSE-NIER)

作者： Fengjie Li Jiajun Jiang Jiajun Sun Hongyu Zhang College of Intelligence and and Computing Tianjin University Tianjin China School of Big Data and Software Engineering Chongqing University Chongqing China

ISBN: (数字)9798331537111

ISBN: (纸本)9798331537128

关键词： Semantics Maintenance engineering Prompt engineering software engineering

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Fine-Grained Discrepancy Contrastive Learning for Robust Fake News Detection

Fine-Grained Discrepancy Contrastive Learning for Robust Fak...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Junwei Yin Min Gao Kai Shu Jia Wang Yinqiu Huang Wei Zhou School of Big Data and Software Engineering Chongqing University Chongqing China Illinois Institute of Technology Chicago IL USA

In recent years, fake news on social media has become a significant threat to societal security, elevating fake news detection to a research priority. Among various strategies, fact-checking detection methods stand out for their accuracy, leveraging evidence from dedicated fact databases. However, these methods often retrieve raw truth, including vast amounts of irrelevant data, based on semantic similarity. This approach results in information redundancy and risks missing the nuanced differences between fake news and the truth. As a result, subtle changes in fake news can greatly increase the risk of misclassification, compromising the methods’ robustness. To this end, we propose a robust fake news detection framework with Fine-grained Discrepancy Contrastive Learning (FinDCL). By simulating subtle discrepancies between fake news and event-related truth, our method enhances the capture and identification of nuanced falsehoods. Specifically, we construct an adversarial dataset to pre-train a fine-grained discrepancy calculation module with contrastive learning. Moreover, the truth extraction module is devised to alleviate information redundancy by extracting event-related truth. At last, FinDCL jointly utilizes the aforementioned modules to detect fake news in event truth-known and truth-unknown scenarios. Extensive experiments on six real-world datasets demonstrate the effectiveness of FinDCL.

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AI-Enhanced Secure data Aggregation for Smart Grids with Privacy Preservation

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Computers, Materials & Continua 2025年第1期82卷 799-816页

作者： Congcong Wang Chen Wang Wenying Zheng Wei Gu School of Software Nanjing University of Information Science and TechnologyNanjing210044China School of Information Science and Engineering Zhejiang Sci-Tech UniversityHangzhou310018China State Key Laboratory of Public Big Data Guizhou UniversityGuiyang550025China School of Computer Science and Technology(School of Artificial Intelligence) Zhejiang Sci-Tech UniversityHangzhou310018China School of Computer Science(School of Cyber Science and Engineering) Nanjing University of Information Science and TechnologyNanjing210044China

As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy *** research emphasizes data security and user privacy concerns within smart ***,existing methods struggle with efficiency and security when processing large-scale *** efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent *** paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data *** approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user *** also explores the application of Boneh Lynn Shacham(BLS)signatures for user *** proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.

关键词： Smart grid data security privacy protection artificial intelligence data aggregation

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