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7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Xue, Junmin Rao, Yunbo Wu, Jiangang Huang, Mei School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China Operation Data Center of Postal Savings Bank of China Beijing Beijing610000 China Chengdu Aerospace Science and Industry Big Data Research Institute Corporation Limited Chengdu610299 China

ISBN: (纸本)9798350350890

The common approach for small object detection does not work well in complex scenes with objects of multiple sizes. This is because the detection of small objects and objects of other sizes often difficult to take into account at the same time. To overcome these limitations, we introduce an efficient Partial Convolution in Cross Stage Partial (PC-CSP) network for detecting small targets. First, the introduction of the DCNv3 module improves the feature extraction for detection, slimneck is utilized to enhance the attention mechanism suitable for objects of varying sizes. Then, PC-CSP is employed to streamline the detection backbone network. Last, the loss function is refined by incorporating the concept of Inner. Through extensive experiments, our method demonstrates a significant increase in accuracy of 6.8%, and mAP@0.5 by 8.97% on the standard dataset compared to the original model. Overall, the proposed method exhibits excellent performance for small objects, such as: mobile phones, food, and faces. © 2024 IEEE.

关键词： Feature extraction

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Labelrepair: Sequence Labelling for Compilation Errors Repair 31

Labelrepair: Sequence Labelling for Compilation Errors Repai...

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31st IEEE International Conference on software Analysis, Evolution and Reengineering, SANER 2024

作者： Wu, Zhenyu Yang, Deheng Lei, Yan Xie, Huan Tang, Minghua Li, Maojin Academy of Military Sciences Beijing China School of Big Data & Software Engineering Chongqing University Chongqing China Peng Cheng Laboratory Shenzhen China Kunming University of Science and Technology Kunming China

ISBN: (纸本)9798350330663

Manual fixing of compilation errors could be a tedious and time-consuming task for novice programmers, and even for experienced ones. In recent years, an increasing number of automated repair techniques have been proposed to guide novice programmers and improve the efficiency of software development. Among them, learning-based automated repair techniques have achieved promising results in terms of repair accuracy. However, existing approaches neglect the time efficiency of patch generation, and often treat the compilation errors repair as a neural machine translation task. The end-to-end repair model decoding cannot be parallelized during the inference stage and suffers from redundant decoding search space. Furthermore, the large search space brought by the model poses a potential risk of semantic tampering. To this end, we propose Labelrepair, a novel repair technique that treats the repair of compilation errors as a sequence labelling task. Labelrepair discards the decoding model and converts the search for patches to a search for the error mapping actions between broken code and patch code pairs. In this way, the search space for patch tokens is plum-meted from the entire vocabulary to the size of edit action labels, and the logical semantics of the original program are preserved to some extent. The time complexity of inference is reduced from O(n) to O (1) owing to the parallel generation of edit action labels. Through a comprehensive evaluation of Labelrepair on two datasets, we demonstrate that Labelrepair is able to generate patches instantly (0.71ms on average), which is 28 times faster than existing end-to-end repair models. Compared with existing edit-based repair models, Labelrepair achieves the state-of-art repair accuracy. © 2024 IEEE.

关键词： Errors

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Forces of fully nonlinear interfacial periodic waves on a cylindrical pile in a two-layer fluid with free-surface boundary conditions

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Journal of Ocean engineering and science 2023年第6期8卷 662-674页

作者： Jiyang Li Zeng Liu Jie Cui School of Naval Architecture and Ocean Engineering Jiangsu University of Science and TechnologyZhenjiangJiangsu 212000PR China School of Naval Architecture and Ocean Engineering Huazhong University of Science and TechnologyWuhan 430074PR China Hubei Key Laboratory of Naval Architecture and Ocean Engineering Hydrodynamics(HUST) Wuhan 430074China Hubei Provincial Engineering Research Center of Data Techniques and Supporting Software for Ships Wuhan 430074PR China

In the frame of fully nonlinear potential flow theory,series solutions of interfacial periodic gravity waves in a two-layer fluid with free surface are obtained by the homotopy analysis method(HAM),and the related wave forces on a vertical cylinder are *** solution procedure of the HAM for the interfacial wave model with rigid upper surface is further developed to consider the free surface *** forces of nonlinear interfacial periodic waves are estimated by both the classical and modified Mori-son *** is found that the estimated wave forces by the classical Morison equation are more conservative than those by the modified Morison’s formula,and the relative error between the total inertial forces calculated by these two kinds of Morison’s formulae remains over 25%for most cases unless the upper and lower layer depths are both large *** demonstrates that the convective acceleration neglected in the classical Morison equation is rather important for inertial force exerted by not only internal solitary waves but also interfacial periodic *** of these should further deepen our understanding of internal periodic wave forces on a vertical marine riser.

关键词： Interfacial periodic waves Wave force Cylindrical pile Homotopy analysis method Morison equation

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IIB-DDI: Invariant Information Bottleneck Theory for Out-of-Distribution Drug-Drug Interaction Prediction

IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and Bioinformatics 2025年第3期22卷 1009-1022页

作者： Jiahui Zhang Shuai Zhang Xuqiang Li Di Wu Sihan Wang Limin Li Wenjie Du Yang Wang School of Software Engineering University of Science and Technology of China Hefei China School of Computer Science and Technology University of Science and Technology of China Hefei China School of Data Science University of Science and Technology of China Hefei China

Rapid and accurate identification of drug-drug interactions (DDIs) among multiple medications is crucial for various medical treatments, and clinical therapies. Currently, the increasing significance of molecular substructure interactions in DDI prediction has become a consensus. However, due to the uneven distribution of substructures in molecules and most methods do not consider the differences in substructure distributions, models may rely on spurious substructure relationships, thereby weakening their out-of-distribution (OOD) generalization ability. To address this challenge, we propose an OOD-DDI framework called IIB-DDI. Specifically, information bottleneck theory is initially utilized to extract core subgraphs of drug pairs. Then, considering the diversity and unknown nature of environments, we introduce the vector quantization to design an environment codebook, where the potential environments in the dataset are clustered into a specified number of categories. Subsequently, we position the extracted core subgraphs under various latent environmental factors to attain invariant core substructures (rationales). Additionally, the learned environmental distribution also could be acted as noise injection for optimizing mutual information, achieving a smoother and more stable training curve, thereby leading to lower loss. Extensive experiments conducted on real-world DDI datasets demonstrate the superiority of our model over state-of-the-art baselines.

关键词： Drugs Noise Training Predictive models data mining Biological system modeling Vector quantization Mutual information Deep learning Accuracy

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A Cost-Efficient Anonymous Authenticated and Key Agreement Scheme for V2I-Based Vehicular Ad-Hoc Networks

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IEEE Transactions on Intelligent Transportation Systems 2024年第9期25卷 12621-12630页

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Shamshad, Salman Alenazi, Mohammed J. F. Das, Ashok Kumar University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518000 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500032 India

The rise of smart cities is directly connected to the increasing use of vehicles. The growing vehicle utilization has driven the emergence of Vehicular Ad-hoc Networks (VANETs), facilitating instant information exchange among vehicles. The system provides essential information regarding road conditions, traffic patterns, and more relevant data. VANETs encompass two fundamental categories of communication exchanges, namely Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I). V2I technology facilitates the integration of cars and transportation infrastructure, enabling effective communication between vehicles and infrastructure. Nevertheless, the potential of V2I communication has various security concerns arising from prevalent security threats. Current authentication techniques encounter challenges regarding complexity, security, and privacy considerations. We designed a hash-based lightweight and anonymous authentication scheme to address the aforementioned restrictions and enhance the effectiveness of authentication in V2I architecture. This scheme effectively combines identity, password, and bio-metric to enhance resistance against impersonation, denial of service, and privileged insider attacks. The devised scheme distinguishes itself by a comparative analysis and security proofs, highlighting its superior capability in guaranteeing secure authentication in V2I communication. The comprehensive security analysis conducted formally and informally showcases the robustness of the proposed solution against several threats. The performance evaluation results show that our scheme demonstrates a decrease in the computational cost of 51.40% approximately and a reduction in communication overhead of around 22.57%. These results establish the efficiency and scalability of the proposed scheme as a viable solution for V2I architecture. © 2000-2011 IEEE.

关键词： Vehicular ad hoc networks

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Cancer classification with data augmentation based on generative adversarial networks

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Frontiers of Computer science 2022年第2期16卷 69-79页

作者： Kaimin WEI Tianqi LI Feiran HUANG Jinpeng CHEN Zefan HE College of Information Science and Technology Jinan UniversityGuangzhou 510632China Guangdong Key Laboratory of Data Security and Privacy Protection Guangzhou 510632China School of Software Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China

Accurate diagnosis is a significant step in cancer *** learning can support doctors in prognosis decision-making,and its performance is always weakened by the high dimension and small quantity of genetic ***,deep learning can effectively process the high dimensional data with ***,the problem of inadequate data remains unsolved and has lowered the performance of deep *** end it,we propose a generative adversarial model that uses non target cancer data to help target generator *** use the reconstruction loss to further stabilize model training and improve the quality of generated *** also present a cancer classification model to optimize classification *** results prove that mean absolute error of cancer gene made by our model is 19.3%lower than DC-GAN,and the classification accuracy rate of our produced data is higher than the data created by *** for the classification model,the classification accuracy of our model reaches 92.6%,which is 7.6%higher than the model without any generated data.

关键词： data mining cancer data analysis deep learning generative adversarial networks

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A Security Enhanced Chaotic-Map-Based Authentication Protocol for Internet of Drones

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IEEE Internet of Things Journal 2024年第12期11卷 22301-22309页

作者： Mahmood, Khalid Ghaffar, Zahid Farooq, Muhammad Yahya, Khalid Das, Ashok Kumar Chaudhry, Shehzad Ashraf National Yunlin University Of Science And Technology Graduate School Of Intelligent Data Science Douliu64002 Taiwan National Yunlin University Of Science And Technology Graduate School Of Engineering Science And Technology Douliu64002 Taiwan Abu Dhabi University Department Of Mathematics And Statistics College Of Arts And Sciences Abu Dhabi United Arab Emirates Istanbul Gelisim University Department Of Electrical And Electronics Engineering Istanbul34310 Turkey Sabratah University Department Of Electrical Engineering Faculty Of Engineering Regdalen Libya Center For Security Theory And Algorithmic Research Hyderabad500 032 India Abu Dhabi University Department Of Computer Science And Information Technology College Of Engineering Abu Dhabi United Arab Emirates Nisantasi University Faculty Of Engineering And Architecture Department Of Software Engineering Istanbul34398 Turkey

The Internet of Drones (IoD) extends the capabilities of unmanned aerial vehicles, enabling them to participate in a connected network. In IoD infrastructure, drones communicate not only among themselves but also with users and a control center. This interconnected communication framework holds promise for various applications, from collaborative decision-making to real-time data exchange. However, the expansion of communication in IoD also introduces new challenges, particularly in terms of security, privacy and authentication. Unfortunately, the current authentication protocols are inadequate in offering robust security features against various attacks in the IoD environment. To address these security issues and limitations, we proposed a secure protocol for the IoD environment using chaotic maps and hash functions. In addition, we also employed a physically unclonable function in the development of the proposed protocol. We assess the security of the protocol through both informal and formal security analysis. The formal security analysis is conducted through a widely used random or real (RoR) model. The informal analysis shows the rigorous security features against various attacks, such as masquerading, anonymity violation, and physical cloning attacks. Moreover, we compare the performance of the devised protocol with similar existing protocols across important performance parameters, such as communication overhead, computation overhead, and security features. The devised protocol provides a 67.86% and 17.80% reduction in computation and communication overheads, respectively, as compared to related protocols. The analysis demonstrates the proposed protocol's capacity to support secure communication in the IoD environment and satisfy desirable security attributes. © 2014 IEEE.

关键词： Drones

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Category-Prompt Refined Feature Learning for Long-Tailed Multi-Label Image Classification 24

Category-Prompt Refined Feature Learning for Long-Tailed Mul...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Yan, Jiexuan Huang, Sheng Mu, Nankun Huangfu, Luwen Liu, Bo School of Big Data & Software Engineering Chongqing University Chongqing China College of Computer Science Chongqing University Chongqing China Fowler College of Business San Diego State University San DiegoCA United States School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9798400706868

Real-world data consistently exhibits a long-tailed distribution, often spanning multiple categories. This complexity underscores the challenge of content comprehension, particularly in scenarios requiring Long-Tailed Multi-Label image Classification (LTMLC). In such contexts, imbalanced data distribution and multi-object recognition pose significant hurdles. To address this issue, we propose a novel and effective approach for LTMLC, termed Category-Prompt Refined Feature Learning (CPRFL), utilizing semantic correlations between different categories and decoupling category-specific visual representations for each category. Specifically, CPRFL initializes category-prompts from the pretrained CLIP's embeddings and decouples category-specific visual representations through interaction with visual features, thereby facilitating the establishment of semantic correlations between the head and tail classes. To mitigate the visual-semantic domain bias, we design a progressive Dual-Path Back-Propagation mechanism to refine the prompts by progressively incorporating context-related visual information into prompts. Simultaneously, the refinement process facilitates the progressive purification of the category-specific visual representations under the guidance of the refined prompts. Furthermore, taking into account the negative-positive sample imbalance, we adopt the Asymmetric Loss as our optimization objective to suppress negative samples across all classes and potentially enhance the head-to-tail recognition performance. We validate the effectiveness of our method on two LTMLC benchmarks and extensive experiments demonstrate the superiority of our work over *** code is available at https://***/jiexuanyan/CPRFL. © 2024 ACM.

关键词： Semantics

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Research on key technologies of edge cache in virtual data space across WAN

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Frontiers of Computer science 2023年第1期17卷 15-34页

作者： Jiantong HUO Yaowen XU Zhisheng HUO Limin XIAO Zhenxue HE State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China High Performance Computing Center Beihang UniversityBeijing 100191China College of Software Beihang UniversityBeijing 100191China Hebei Key Laboratory of Agricultural Big Data Hebei Agricultural UniversityBaoding 071001China College of Computer Science and Technology Zhejiang UniversityHangZhou 310013China

The authors of this paper have previously proposed the global virtual data space system (GVDS) to aggregate the scattered and autonomous storage resources in China’s national supercomputer grid (National Supercomputing Center in Guangzhou, National Supercomputing Center in Jinan, National Supercomputing Center in Changsha, Shanghai Supercomputing Center, and Computer Network Information Center in Chinese Academy of sciences) into a storage system that spans the wide area network (WAN), which realizes the unified management of global storage resources in China. At present, the GVDS has been successfully deployed in the China National Grid environment. However, when accessing and sharing remote data in the WAN, the GVDS will cause redundant transmission of data and waste a lot of network bandwidth resources. In this paper, we propose an edge cache system as a supplementary system of the GVDS to improve the performance of upper-level applications accessing and sharing remote data. Specifically, we first designs the architecture of the edge cache system, and then study the key technologies of this architecture: the edge cache index mechanism based on double-layer hashing, the edge cache replacement strategy based on the GDSF algorithm, the request routing based on consistent hashing method, and the cluster member maintenance method based on the SWIM protocol. The experimental results show that the edge cache system has successfully implemented the relevant operation functions (read, write, deletion, modification, etc.) and is compatible with the POSIX interface in terms of function. Further, it can greatly reduce the amount of data transmission and increase the data access bandwidth when the accessed file is located at the edge cache system in terms of performance, i.e., its performance is close to the performance of the network file system in the local area network (LAN).

关键词： virtual data space system wide area network edge cache redundant data transmission

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Design of PMCW Millimeter Wave Radar Algorithms on an Embedded DSP 16

Design of PMCW Millimeter Wave Radar Algorithms on an Embedd...

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16th International Conference on Signal Processing Systems, ICSPS 2024

作者： Zhao, Jinghao Liu, Qiuming Tan, Bin School of Software engineering Jiangxi University of Science and Technology Nanchang China Nanchang Key laboratory of Virtual Digital Factory and Cultural Communications Nanchang China Jiangxi Provincial Key Laboratory of Electronic Data Control and Forensics China School of Electronics and Information Engineering Jinggangshan University Ji’an China

ISBN: (纸本)9781510689251

The rapid advancements in autonomous driving technology necessitate the extensive deployment of automotive radars operating within the 77-81 GHz millimeter-wave band in the forthcoming years. In contrast to earlier Frequency Modulated Continuous Wave (FMCW) radars, Phase-Modulated Continuous Wave (PMCW) radars exhibit notable improvements in processing speed and flexibility, offering superior range and velocity resolution. These enhancements are critical for the precise detection and interpretation of dynamic and complex traffic scenarios. This paper initially presents the simulation and testing of algorithms for range and speed measurement using single-input single-output (SISO) PMCW millimeter-wave radar. Building upon these results, further simulations incorporating multi-input multi-output (MIMO) systems utilizing Hadamard codes are conducted to augment PMCW radar performance. The final phase of this study involves implementing the relevant signal processing algorithms on a custom-developed digital signal processor (DSP) named SWIFT. Experimental findings demonstrate that PMCW radar significantly mitigates multipath effects and clutter, maintaining high performance in complex environments. Furthermore, the algorithms executed on the DSP meet the anticipated performance standards. The proposed methodology not only validates the theoretical framework but also establishes a foundation for future hardware implementation. © 2025 SPIE.

关键词： Automotive radar

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