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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Ma, Xiaomin Ding, Shengli Zhang, Tingyu Oral Roberts University School of Engineering TulsaOK74171 United States Purdue University Elmore Family School of Electrical and Computer Engineering United States Dalian University of Science and Technology College of Software Engineering China

ISBN: (纸本)9798331517786

It is anticipated that wireless vehicular ad hoc networks (VANETs) can further enhance the safety of autonomous vehicles. Recently, two major standards for the next generation of VANET technologies have been suggested and tested: IEEE 802.11p/bd and 3GPP NR-V2X. Compared with human driving VANET, autonomous driving VANET poses big challenges for more stringent requirements on reliability and delay of message transmissions. Up to date, the quality of service (QoS) requirements of VANETs for the safety services of autonomous driving vehicles have not been investigated systematically. Whether or not the current proposed communication systems can meet such high QoS requirements is unclear. This paper reviews the communication requirements for safety use cases, and the QoS requirements for level 5 autonomous driving VANETs are deduced. Then, the QoS of IEEE 802.11-based VANETs, including 802.11p, 802.11bd, and other 802.11 cutting-edge versions, are evaluated by simulations and analyses on a few selected safety use cases and benchmarked against the corresponding QoS requirements. The numerical results reveal that the current IEEE 802.11p/bd cannot meet the QoS requirements for some selected safety applications in autonomous driving vehicles. However, IEEE 802.11 systems with new enhancements can potentially support safety services with assured QoS. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Business Scenario Driven Reinforcement Learning Testing Method 26

Business Scenario Driven Reinforcement Learning Testing Meth...

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26th ACIS International Winter Conference on software engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD-Winter 2023

作者： Cai, Lizhi Wang, Jin School of Information Science and Engineering East China University of Science and Technology Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai China

ISBN: (纸本)9798350345865

Reinforcement learning has been successfully applied in software testing, but the existing testing methods cannot perform effective testing according to the characteristics of applications, and using outdated interactive experience during training, resulting in inefficient testing. In this paper, we propose BSDRTesting. Firstly, the demonstration experience of human users is collected according to the functional scenarios and business logic of each application, and combining reinforcement learning and imitation learning to maximize rewards while imitating user behavior, experience replay aims to sample experiences from the agent's self-exploration and expert demonstrations to improve sampling efficiency. At the same time, according to the input rules, the black-box testing method is used to fully test the input events, and finally an experience filtering mechanism is proposed, and the reward value and TD-Error are used as the basis for priority sampling. The experimental results on 10 open source applications show BSDRTesting has achieved significant improvements in code coverage and branch coverage compared with existing methods. © 2023 IEEE.

关键词： Reinforcement learning

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Fast Verification of Network Configuration Updates

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computers, Materials & Continua 2023年第1期74卷 293-311页

作者： Jiangyuan Yao Zheng Jiang Kaiwen Zou Shuhua Weng Yaxin Li Deshun Li Yahui Li Xingcan Cao Scool of Computer Science and Technology Hainan UniversityHaikou570228China School of Cyberspace Security Hainan UniversityHaikou570228China School of Software Engineering Jilin UniversityChangchun130012China School of Software Beijing Jiaotong UniversityBeijing100091China University of British Columbia VancouverV5K1K5Canada

With the expansion of network services,large-scale networks have progressively become *** network status changes rapidly in response to customer needs and configuration changes,so network configuration changes are also very ***,no matter what changes,the network must ensure the correct conditions,such as isolating tenants from each other or guaranteeing essential *** changes occur,it is necessary to verify the after-changed ***,for the verification of large-scale network configuration changes,many current verifiers show poor *** order to solve the problem ofmultiple global verifications caused by frequent updates of local configurations in large networks,we present a fast configuration updates verification tool,FastCUV,for distributed control *** aims to enhance the efficiency of distributed control plane verification for medium and large networks while ensuring *** paper presents a method to determine the network range affected by the configuration *** present a flow model and graph structure to facilitate the design of verification algorithms and speed up *** scheme verifies the network area affected by obtaining the change of the Forwarding Information Base(FIB)before and *** supports rich network attributes,meanwhile,has high efficiency and correctness *** experimental verification and result analysis,our method outperforms the state-of-the-art method to a certain extent.

关键词： Network verification configuration updates network control plane forwarding information base

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Chinese Speech Emotion Recognition Based on Pre-Trained Model 5

Chinese Speech Emotion Recognition Based on Pre-Trained Mode...

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5th International Conference on Big Data and Artificial Intelligence and software engineering, ICBASE 2024

作者： Xu, Sukui Jiang, Nan Lian, Shun Pan, Jingmin Anhui Institute of Information Technology College of Computer and Software Engineering Wuhu China Fujian Polytechnic of Information Technology School of General Education Fuzhou China Anhui University School of Computer Science and Technology Hefei China

ISBN: (纸本)9798331506612

This paper proposes a method based on large-scale speech pre-trained models for the task of Chinese speech emotion recognition. Similar to the transfer learning approach in image classification task, speech pre-trained model is used as a fixed feature extractor to extract features from speech data, followed by a fully connected network for classification. Experimental results show that, on the seven-class speech emotion recognition task of the Chinese M3ED dataset, the proposed method achieves classification accuracies of 0.506 and 0.560 using base and large sized pre-trained models respectively, surpassing the baseline accuracy of 0.454 for the ECAPA-TDNN model. © 2024 IEEE.

关键词： Transfer learning

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Research on Image Style Transfer Algorithm Based on Structural Consistency 8

Research on Image Style Transfer Algorithm Based on Structur...

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8th International Conference on Intelligent Computing and Signal Processing, ICSP 2023

作者： Yang, Chuxiong Chen, Zhuang Chongqing University of Posts and Telecommunications School of Software Engineering Chongqing China Chongqing University of Technology College of Computer Science and Engineering Chongqing China

ISBN: (纸本)9798350302455

The algorithm proposed in this paper aims to address the issue of structural content distortions in images that occur after applying image style transfer. It introduces a structural consistency-based approach called the PoolNet algorithm. This algorithm incorporates saliency regions to perform style transfer at the block level. By employing a saliency detection network, a saliency map is generated for both the synthetic and content images. During the training process, two losses are computed to ensure that the salient regions in the synthetic image remain consistent with those in the content image. Additionally, local affine constraints in the color space are employed to enhance the stylized images by constraining the image transformation. Experimental results demonstrate that the stylized images produced by this style transfer model exhibit improved visual effects and preserve the semantic information of the content images. This improvement is particularly noticeable when dealing with distorted migration images. © 2023 IEEE.

关键词： Convolutional neural networks

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Deep visual-linguistic fusion network considering cross-modal inconsistency for rumor detection

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science China(Information sciences) 2023年第12期66卷 16-32页

作者： Yang YANG Ran BAO Weili GUO De-Chuan ZHAN Yilong YIN Jian YANG School of Computer Science and Engineering Nanjing University of Science and Technology National Key Laboratory for Novel Software Technology Nanjing University School of Software Shandong University

With the development of the Internet, users can freely publish posts on various social media platforms, which offers great convenience for keeping abreast of the world. However, posts usually carry many rumors, which require plenty of manpower for monitoring. Owing to the success of modern machine learning techniques, especially deep learning models, we tried to detect rumors as a classification problem automatically. Early attempts have always focused on building classifiers relying on image or text information, i.e., single modality in posts. Thereafter, several multimodal detection approaches employ an early or late fusion operator for aggregating multiple source information. Nevertheless, they only take advantage of multimodal embeddings for fusion and ignore another important detection factor, i.e., the intermodal inconsistency between modalities. To solve this problem, we develop a novel deep visual-linguistic fusion network(DVLFN) considering cross-modal inconsistency, which detects rumors by comprehensively considering modal aggregation and contrast information. Specifically, the DVLFN first utilizes visual and textual deep encoders, i.e., Faster R-CNN and bidirectional encoder representations from transformers, to extract global and regional embeddings for image and text modalities. Then, it predicts posts' authenticity from two aspects:(1) intermodal inconsistency, which employs the Wasserstein distance to efficiently measure the similarity between regional embeddings of different modalities, and(2) modal aggregation, which experimentally employs the early fusion to aggregate two modal embeddings for prediction. Consequently, the DVLFN can compose the final prediction based on the modal fusion and inconsistency measure. Experiments are conducted on three real-world multimedia rumor detection datasets collected from Reddit, Good News, and Weibo. The results validate the superior performance of the proposed DVLFN.

关键词： multimodal learning Wasserstein distance rumor detection

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GRETO: REMEDYING DYNAMIC GRAPH TOPOLOGY-TASK DISCORDANCE VIA TARGET HOMOPHILY 11

GRETO: REMEDYING DYNAMIC GRAPH TOPOLOGY-TASK DISCORDANCE VIA...

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11th International Conference on Learning Representations, ICLR 2023

作者： Zhou, Zhengyang Huang, Qihe Lin, Gengyu Yang, Kuo Bai, Lei Wang, Yang Hefei China School of Software Engineering USTC China School of Computer Science and Technology USTC China Shanghai AI Laboratory Shanghai China

Dynamic graphs are ubiquitous across disciplines where observations usually change over time. Regressions on dynamic graphs often contribute to diverse critical tasks, such as climate early-warning and traffic controlling. Existing homophily Graph Neural Networks (GNNs) adopt physical connections or feature similarity as adjacent matrix to perform node-level aggregations. However, on dynamic graphs with diverse node-wise relations, exploiting a pre-defined fixed topology for message passing inevitably leads to the aggregations of target-deviated neighbors. We designate such phenomenon as the topology-task discordance, which naturally challenges the homophily assumption. In this work, we revisit node-wise relationships and explore novel homophily measurements on dynamic graphs with both signs and distances, capturing multiple node-level spatial relations and temporal evolutions. We discover that advancing homophily aggregations to signed target-oriented message passing can effectively resolve the discordance and promote aggregation capacity. Therefore, a GReTo is proposed, which performs signed message passing in immediate neighborhood, and exploits both local environments and target awareness to realize high-order message propagation. Empirically, our solution achieves significant improvements against best baselines, notably improving 24.79% on KnowAir and 3.60% on Metr-LA. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Message passing

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EEiF: Efficient Isolated Forest with e Branches for Anomaly Detection 24

EEiF: Efficient Isolated Forest with e Branches for Anomaly ...

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24th IEEE International Conference on Data Mining, ICDM 2024

作者： Zhang, Yifan Xiang, Haolong Zhang, Xuyun Xu, Xiaolong Fan, Wei Zhang, Qin Qi, Lianyong School of Software Nanjing University of Information Science and Technology China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services China School of Computing Macquarie University Australia University of Oxford Uffield Department of Women's & Reproductive Health United Kingdom College of Computer Science and Software Engineering Shenzhen University China Faculty of Science and Engineering China

ISBN: (纸本)9798331506681

Anomaly detection is a popular research topic in Artificial Intelligence and has been widely applied in network security, financial fraud detection, and industrial equipment failure detection. Isolation forest based methods are the base algorithms to detect anomalies in these scenarios for their simplicity and efficiency, which has been further exploited with multi-folk trees and learning mechanisms to realize the optimal isolation forest for high detection accuracy. However, the optimal isolation forest is time-consuming with the learning mechanisms, resulting in the task failing of time-constrained applications. Moreover, the original optimal isolation forest fails to construct the optimal tree structure restricted by the time complexity. To address the above challenges, we propose an efficient anomaly detection method called EEIF, which realizes the real e-folk structure of the optimal isolation forest in our practical algorithm design. Specifically, we design a distribution that perfectly matches the e-branch theory to construct the optimal isolation forest. Then, we design an FR clustering scheme to achieve fast training of the isolation forest with learning to hash and provide related proofs of accuracy and efficiency. Besides, a parallel algorithm is integrated into our method to reduce prediction time. Finally, extensive experiments are conducted on a large amount of real-world datasets and the results demonstrate that our method significantly improves efficiency while ensuring effectiveness, compared with the state-of-the-art methods. © 2024 IEEE.

关键词： Anomaly Detection Efficient FR clustering Parallel Algorithm

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RPPFL: Robust and Privacy-Preserving Federated Learning via Trusted Execution Environments

RPPFL: Robust and Privacy-Preserving Federated Learning via ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Xiaolei Chen, Zhaoyu Chen, Guangpu Feng, Xinyu Shen, Qingni Wu, Zhonghai School of Computer Science Peking University China School of Software and Microelectronics Peking University China National Engineering Research Center for Software Engineering Peking University China PKU-OCTA Laboratory for Blockchain and Privacy Computing Peking University China

ISBN: (纸本)9798350368741

Federated Learning (FL) is a distributed framework that enables multi-participant collaborative model training without the need for data sharing. Despite its advantages, FL is vulnerable to poisoning and inference attacks, which compromise model accuracy and data privacy. Trusted execution environments (TEEs) offer a potential solution by providing a secure and isolated execution space to address these security and privacy concerns in FL. However, existing TEE-based FL schemes often suffer from reduced training speed and compromised model accuracy. To mitigate these issues, we propose a robust and privacy-preserving framework for federated learning (RPPFL) that leverages TEE and pseudorandom masking. In our approach, a trusted local model is trained on a secure subset of local data within the client-side TEE, which is then used for anomaly detection to resist poisoning attacks. Additionally, we employ pseudorandom masking to obfuscate local updates and global parameters. Experimental results indicate that RPPFL effectively counters both poisoning and inference attacks, with only a minimal decrease in training speed and no adverse impact on model accuracy. Compared to full-TEE approaches, our method improved local training efficiency by 10×, with less than a 9% loss in model performance under poisoning attacks. © 2025 IEEE.

关键词： Data Obfuscation Federated Learning Privacy Preservation Robustness Trusted Execution Environments

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FAS-reid: Fair Architecture Search for Person Re-IDentification 3

FAS-reid: Fair Architecture Search for Person Re-IDentificat...

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3rd IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2023

作者： Chen, Shengbo Chen, Shijie Lei, Zhou Shanghai University School of Computer Engineering and Science Shanghai200444 China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai201112 China

ISBN: (纸本)9781665490788

The development of deep learning has driven the development of ReID, and more and more excellent methods have been proposed, but most of these are artificially designed network backbones. Automation is a trend in the development of deep learning. This paper propose FAS-ReID(Fair Architecture Search for Person Re-IDentification), which can automatically design and generate a neural network backbone for ReID. This paper compare operation selection to competition, and inject noise to make the competition fairer, while making the architecture of automated search More adaptable to ReID We use TriHard loss to improve the feature extraction ability. Our experiments show that the backbone searched by FAS-ReID is better and reduces the search time. Meanwhile, as for the problem of performance collapse caused by skip-connection enrichment in the search process, FAS-Reid does not use the early stop strategy to avoid performance loss like other solutions, which is also the reason why our method is more reliable and robust. © 2023 IEEE.

关键词： Automation

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