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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3703-3713页

作者： Zhang, Shengdong Zhang, Xiaoqin Wan, Shaohua Ren, Wenqi Zhao, Liping Zhao, Li Shen, Linlin Shaoxing University Department of Computer Science and Engineering Shaoxing312000 China Shenzhen United Aircraft Technology Company Ltd. Guangdong Shenzhen518129 China Wenzhou University College of Computer and Artificial Intelligence Wenzhou325035 China University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen518110 China Sun Yat-sen University School of Cyber Science and Technology Shenzhen518110 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

Convolutional neural network (CNN)-based dehazing methods have achieved great success in single image dehazing. However, the absence of real-world haze image datasets hinders the deep development of single image dehazing. To address this issue, we propose a diverse hazy image synthesis method based on generative adversarial network (GAN) and matting. Specially, we train a GAN-based model that can transform a gray image into a hazy image. To boost the diversity of hazy images, we propose to simulate hazy images via image matting, which can fuse a real haze image with another image containing diverse objects. To evaluate the performance of dehazing methods, we propose two novel metrics: part-based peak signal-to-noise ratio (PSNR) and structure similarity index measure (SSIM). Extensive experiments are conducted to show the effectiveness of the proposed model, dataset, and criteria. © 2020 IEEE.

关键词： Three dimensional displays

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Profinder: Towards Professionals Recognition on Mobile Devices for Users with Cognitive Decline 18th

Profinder: Towards Professionals Recognition on Mobile Devi...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Wang, Yong Xiong, Yi Yang, Tianyu Shen, Yiran College of Computer Science and Technology Harbin Engineering University Harbin China Modeling and Emulation in E-Government National Engineering Laboratory Harbin China School of Software Shandong University Jinan China

ISBN: (纸本)9783031714696

As the global population continues to age, there is a concurrent rise in the number of individuals experiencing cognitive impairment and dementia, underscoring the critical necessity to address their hospice needs and quality of life concerns. Numerous studies have underscored the positive impact of exposure to outdoor environments on their overall condition and well-being. However, cognitive challenges often hinder their capacity to perform basic tasks independently, necessitating frequent assistance from onsite professionals. Complicating matters further, the complexity of outdoor settings often results in difficulties in identifying and recognizing personnel. This paper introduces Profinder, a system designed to assist individuals with cognitive impairment in identifying occupational personnel at specific outdoor locations and accessing essential assistance. Utilizing advanced deep learning techniques, Profinder seamlessly integrates foreground and background information, incorporates attention mechanisms, and harnesses siamese networks for efficient information decoupling, thereby achieving occupational recognition on smart mobile devices with an accuracy rate exceeding 90%. Additionally, a meticulously curated dataset dedicated to occupation recognition was devel0oped to facilitate and validate this research endeavor. By providing a robust auxiliary tool for individuals with cognitive impairment, this research endeavors to enhance their autonomy and overall quality of life, while also laying a solid foundation for further exploration and innovation in this burgeoning field, both within academia and industry. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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REORDER++: Enhanced Randomized Real-Time Scheduling Strategy Against Side-Channel Attacks

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IEEE Transactions on Network science and engineering 2023年第6期10卷 3253-3266页

作者： Ren, Jiankang Wang, Zheng Lin, Chi Obaidat, Mohammad S. Xie, Hongrui Zhu, Haihui Liu, Chunxiao Wang, Kaiwen Tan, Guozhen School of Computer Science and Technology Dalian116024 China Dalian University of Technology School of Software Dalian116024 China University of Jordan King Abdullah II School of Information Technology Amman11942 Jordan University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Amity University Honorary Distinguished Professor Noida201301 India

Embedded real-time systems are widely adopted in safety-critical domains such as aircrafts, automobiles and space vehicles. Unfortunately, with the sharp rise in the use of common-off-the-shelf components in systems and the drive towards remote communication through untrusted networks, such as WiFi, radio or cellular, the security is increasingly becoming the key consideration in real-time system design. In particular, the real-time system is vulnerable to side-channel attacks from the external networks, which attempt to infer the timing of task execution by exploiting the system deterministic execution patterns. In this article, we present an enhanced online randomized scheduling strategy (named REORDER++), which breaks the deterministic task execution pattern of systems by random priority inversions at run-time to counteract the timing side-channel attacks in dynamic-priority real-time systems. In order to realize the feasible priority inversions under real-time constraint, we propose an online priority inversion test to increase the opportunity of tasks' priority inversions by judging the feasibility of tasks' priority inversions at run-time. Owing to such online priority inversion test, REORDER++ can generate highly randomized schedule of real-time tasks to mitigate the side-channel attack vulnerability. Experiments with synthesized task sets show that REORDER++ significantly outperforms the existing approaches in terms of schedule randomness. © 2013 IEEE.

关键词： Real time systems

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A Comprehensive Review on Deep Learning System Testing 24th

A Comprehensive Review on Deep Learning System Testing

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Li, Ying Shan, Chun Liu, Zhen Liao, Shuyan Beijing Key Laboratory of Software Security Engineering Technology Beijing Institute of Technology Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China Computer School Beijing Information Science and Technology University Beijing China School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9789819615476

Deep learning(DL) systems exhibit multiple behavioral characteristics such as correctness, robustness, and fairness. Ensuring that these behavioral characteristics function properly is crucial for maintaining the accuracy of DL systems’ outputs. As a specialized form of software, DL systems’ security testing techniques have increasingly become a focus of research in quality assurance. We analyze and organize the testing techniques for DL systems based on an investigation of the current state of the art both domestically and internationally. This paper categorizes existing approaches as component-oriented and attribute-oriented methods, providing a detailed review based on this classification. Additionally, we forecast the future development of testing techniques for DL systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Sparse is Enough in Fine-tuning Pre-trained Large Language Models 41

Sparse is Enough in Fine-tuning Pre-trained Large Language M...

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41st International Conference on Machine Learning, ICML 2024

作者： Song, Weixi Li, Zuchao Zhang, Lefei Zhao, Hai Du, Bo National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China Hubei Luojia Laboratory Wuhan430072 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China

With the prevalence of pre-training-fine-tuning paradigm, how to efficiently adapt the pre-trained model to the downstream tasks has been an intriguing issue. Parameter-Efficient Fine-Tuning (PEFT) methods have been proposed for low-cost adaptation. Although PEFT has demonstrated effectiveness and been widely applied, the underlying principles are still unclear. In this paper, we adopt the PAC-Bayesian generalization error bound, viewing pre-training as a shift of prior distribution which leads to a tighter bound for generalization error. We validate this shift from the perspectives of oscillations in the loss landscape and the quasi-sparsity in gradient distribution. Based on this, we propose a gradient-based sparse finetuning algorithm, named Sparse Increment Fine-Tuning (SIFT), and validate its effectiveness on a range of tasks including the GLUE Benchmark and Instruction-tuning. The code is accessible at https://***/song-wx/SIFT. Copyright 2024 by the author(s)

关键词： Benchmarking

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Hypergraph-based Self-supervised Multi-channel Network for Drug-target Interaction Prediction

Hypergraph-based Self-supervised Multi-channel Network for D...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zeng, Xiaoting Liang, Yu Yang, Peng Chen, Weilin Lei, Baiying Shenzhen University School of Computer Science and Software Engineering Shenzhen China Shenzhen University School of Biomedical Engineering Shenzhen China Shenzhen University Medical School Shenzhen University Institute of Biological Therapy Shenzhen China Shenzhen University School of Biomedical Engineering Health Science Center Shenzhen China

ISBN: (纸本)9798350386226

Drug-target interaction (DTI) prediction is vital for drug discovery and repurposing. Hypergraph is utilized in DTI prediction for modeling higher-order relationships in biomedical networks. Although the strategies of modeling hypergraph-based drug-related interactions with multi-channel and utilizing self-supervised learning task to improve DTI prediction performance have been proven promising, current researches fail to effectively model feature interaction across different channels and fully exploit cross-channel information for self-supervised task. In this study, we propose a hypergraph-based self-supervised multi-channel interaction framework HSMI-DTI for DTI prediction. HSMI-DTI aims to extract hypergraph features effectively and model cross-channel correlations, leveraging hierarchical self-supervised learning to uncover the discover correlations between different channels. We compare HSMI-DTI with advanced baselines, and experiment results show our model outperforms existing methods, thereby optimizing DTI prediction performance. © 2024 IEEE.

关键词： Self-supervised learning

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IEEE 802.11 VANETs for Safety of Autonomous Driving: QoS Requirements & Benchmarking 100

IEEE 802.11 VANETs for Safety of Autonomous Driving: QoS Req...

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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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