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Pairing-Free Blockchain-Assisted Certificateless Aggregation Signcryption Scheme for VANETs

IEEE Internet of Things Journal 2025年第11期12卷 15545-15557页

作者： Wang, Yahui Peng, Cong Jia, Xiaoying Wen, Jiaming Zhang, Yuanyuan Xinyang Normal University School of Computer and Information Technology School of Software Xinyang464000 China Wuhan University School of Cyber Science and Engineering Wuhan430072 China South-Central Minzu University School of Mathematics and Statistics Wuhan430074 China Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China University of Wollongong Institute of Cybersecurity and Cryptology School of Computing and Information Technology WollongongNSW2522 Australia Hubei University of Technology School of computer science Wuhan430072 China

Smart vehicle applications play a crucial role in intelligent transportation systems, enabling sensor-equipped vehicles to establish dynamic networks for efficient collection, sharing, and aggregation. This significantly enhances road security and efficiency by transmitting crucial information to traffic authorities. However, partial research on certificateless aggregation signcryption (CLAS) scheme reveals an intriguing phenomenon where each proposed scheme consistently exhibits numerous security vulnerabilities, particularly susceptible to public key replacement attacks. To address these challenges, this paper proposes a blockchain-assisted certificateless aggregation sign-cryption scheme (BACLAS), leveraging blockchain technology to securely store users' public key on a distributed ledger and prevent public key replacement attacks effectively. Furthermore, it is a provably secure communication scheme for real-world Vehicle-to-Infrastructure (V2I) communication while addressing practical security concerns. The BACLAS scheme ensures security in terms of existential unforgeability against adaptive chosen message attacks (EUF-CMA) and indistinguishability against adaptive chosen ciphertext attacks (IND-CCA2) based on the hardness assumption of the elliptic curve discrete logarithm problem and computational Diffie-Hellman problem in the random oracle model. The proposed scheme effectively reduces computational costs and time consumption, resulting in a significant reduction of the computation burden ranging from 50.02% to 88.28% compared to other competitive schemes. Moreover, it successfully addresses the key-escrow problem, thereby achieving enhanced security properties. © 2014 IEEE.

关键词： Vehicular ad hoc networks

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YOLO-RSOD: Improved YOLO Remote Sensing Object Detection 27th

YOLO-RSOD: Improved YOLO Remote Sensing Object Detection

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Xu, Yang Lu, Jun College of Computer Science and Technology Heilongjiang University Harbin150080 China Jiaxiang Industrial Technology Research Institute of Heilongjiang University Shandong Jining272400 China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Heilongjiang University Harbin150080 China

ISBN: (纸本)9783031784460

Remote sensing object detection has important application value in fields such as environmental monitoring and resource detection and analysis. However, the current universal object detectors are not very effective in detecting remote sensing objects. To this end, this paper proposes an efficient, low-complexity and anchor-free remote sensing object detection framework YOLO-RSOD based on YOLOv7. First, an additional Tiny Object Head is proposed for better detection of micro-remote sensing objects. The original Head is then replaced with Decoupled Head (DH) to explore the detection potential of the decoupled detection head structure. Then the Explicit Vision Center (EVC) in the Centralized Feature Pyramid Network (CFP) is added to further improve the detection ability of remote sensing objects. Finally, this article also integrates a global attention module (GAM) to find attention areas in dense object scenes. Ablation experiments on the general remote sensing target detection dataset VisDrone2021 demonstrate the effectiveness of several modules introduced in this paper in remote sensing target detection. On the VisDrone2021 data set, YOLO-RSOD can achieve accuracy rates of 30.7% AP50:95 and 51.7% AP50, which are 3.1% and 3.2% higher than the baseline model respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Object tracking

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False Positive Detection for Text-Based Person Retrieval 21st

False Positive Detection for Text-Based Person Retrieval

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Cao, Yan Lu, Jun College of Computer Science and Technology Heilongjiang University Harbin150080 China Jiaxiang Industrial Technology Research Institute of Heilongjiang University Shandong Jining272400 China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Heilongjiang University Harbin150080 China

ISBN: (纸本)9789819601189

Text-based person retrieval (TBPR) is a challenging topic in cross-modal retrieval tasks, aiming to query corresponding person images based on textual descriptions. This task is complicated by noisy correspondences between images and text due to incorrect text annotations and low-quality images. Although many robust noise learning methods have been proposed with satisfactory performance, they tend to directly filter out a portion of the noise in the dataset, which decreases the ability to distinguish the noise as the number of training epochs increases. This paper proposes a noise-robust learning strategy based on false positive detection (FPD). Precisely, FPD consists of two main components: 1) The key-value library (KVL) module utilizes a set of clean samples, assigns confidence weights to each training sample during the training process, and adaptively adjusts the contribution of each sample to reduce the accumulation of noise. 2) To improve the performance of TBPR, this paper designs a local token-based selection method, which utilizes the contribution of each local token to the weights in the self-attention matrix and selects the local tokens with vital information as global representations. Extensive experiments are conducted on three datasets (CUHK-PEDES, ICFG-PEDES, and RSTPReID) to evaluate the performance and robustness of FPD under noisy conditions. The experimental results show that the FPD has better results at high noise ratios, and all the metrics outperform the state-of-the-art method under the RSTPReID dataset with a noise ratio of 80%, where Rank-1 outperforms the best method by 2.28%, 1.64% for mAP, and 1.92% for mINP. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image annotation

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DCI-Net: Remote Sensing Image-Based Object Detector 27th

DCI-Net: Remote Sensing Image-Based Object Detector

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Cui, Quanyue Lu, Jun College of Computer Science and Technology Heilongjiang University Harbin150080 China Jiaxiang Industrial Technology Research Institute of Heilongjiang University Shandong Jining272400 China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Heilongjiang University Harbin150080 China

ISBN: (纸本)9783031781124

Recently the analysis of remotely sensed images has played a vital role in various aspects of research. The current researches ignore the unique prior knowledge in remote sensing images and do not consider exploring the contextual information of the object, while the existence of multi-scale and high image resolution of objects in remote sensing images also affects the accuracy of the object detection task. Based on the above problems, this paper proposes a object detector DCI-Net (Dynamic Context-Aware IoU Network) based on remote sensing images, in which the proposed CASK (Context-Aware Selective Kernel) module can explicitly model the interdependence between the convolutional feature channels. A loss function Pi_IoU is proposed, which adaptively adjusts the penalty factor in combination with the size of the detected object. A DySample module is introduced, which is able to effectively extract and utilize the spatial structure features. The model in this paper improves the detection accuracy of complex objects in remote sensing images. On the DIOR dataset, compared with the baseline model YOLOV9, the accuracy is improved by 0.6%, the number of parameters is decreased by 4%, and the floating point operation speed is improved by 27.8%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Optical remote sensing

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An effective meta-heuristics for trajectory planning problem in UAV-assisted vessel emission detection system

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Peer-to-Peer Networking and Applications 2025年第4期18卷 1-21页

作者： Zhu, Jie Cui, Weizhi Huang, Haiping Zhu, Tao Sun, Yuzhong School of Computer Science Nanjing University of Posts and Telecommunications Nanjing 210003 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing University of Posts and Telecommunications Nanjing 210003 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100864 China

With the development of the shipping industry, the pollution problem caused by vessel fuels is becoming increasingly severe. Due to the great mobility and on-demand service capability of Unmanned Aerial Vehicles (UAVs), UAVs have become effective tools for detecting pollution in Emission Control Areas (ECAs). How to obtain the effective trajectories of UAVs within the energy limitation and the time limitation is a difficult problem to solve. The paper investigates the trajectory planning problem in UAV-assisted vessel emission detection system. In the problem, the sailing vessels in a given area are taken as the detection tasks and multiple UAVs are ready to detect these vessels. Each vessel is assigned a different detecting weight and sails to the port at a different speed. The objective is to complete as many detection tasks as possible before the deadline and maximize the total weight of these completed tasks. A Genetic Algorithm (GA)-based trajectory planning algorithm framework is proposed for the problem under study. The framework consists of key components, such as the chromosome generation method to generate chromosomes of each generation, the feasible solution generation method to compute the feasible solution according to the sequence defined in a chromosome, and the chromosome reconstruction method to improve the quality of chromosomes based on history information. The experimental results show that the proposed algorithm outperforms the baseline algorithms in terms of effectiveness and robustness. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Emission control areas Genetic algorithm Trajectory planning UAV scheduling

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Binary-Encoding-Based Quantized Kalman Filter: An Approximate MMSE Approach

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IEEE Transactions on Automatic Control 2025年第5期70卷 3181-3196页

作者： Liu, Qinyuan Nie, Yao Wang, Zidong Dong, Hongli Jiang, Changjun Tongji University School of Computer Science and Technology Shanghai201804 China Tongji University Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai201804 China Tongji University Shanghai Artificial Intelligence Laboratory Shanghai201804 China Brunel University London Department of Computer Science UxbridgeUB8 3PH United Kingdom Northeast Petroleum University Artificial Intelligence Energy Research Institute Daqing163318 China Northeast Petroleum University Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Daqing163318 China

In this article, the Kalman filter design problem is investigated for linear discrete-time systems under binary encoding schemes. Under such a scheme, the local information is quantized into a bit string by the remote sensor based on a probabilistic quantizer, and then the bit string is transmitted via memoryless binary symmetric channels (BSCs). Due to the communication link noises, the bit flipping occurs in a random manner, and thus, the transmission of the bit string would suffer from specific bit-error rates. With the received bits, a recursive binary-encoding-based quantized Kalman filter is established in the approximate minimum mean-square error (MMSE) sense, which relies on the Gaussian approximation of the conditional probability density function at each iteration. Furthermore, the proposed estimator is shown to be of a Kalman-like type through performance analysis, which exhibits computational complexity comparable to the conventional Kalman filter. Subsequently, a posterior Cramér-Rao lower bound is derived for the proposed binary-encoding-based quantized Kalman filter. The effectiveness of the proposed estimator is demonstrated through numerical results. © 1963-2012 IEEE.

关键词： Mean square error

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A survey on deep learning for polyp segmentation: techniques, challenges and future trends

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Visual Intelligence 2025年第1期3卷 1-20页

作者： Mei, Jiaxin Zhou, Tao Huang, Kaiwen Zhang, Yizhe Zhou, Yi Wu, Ye Fu, Huazhu PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Computer Science and Engineering Southeast University Nanjing China Institute of High Performance Computing A*STAR Singapore Singapore

Early detection and assessment of polyps play a crucial role in the prevention and treatment of colorectal cancer (CRC). Polyp segmentation provides an effective solution to assist clinicians in accurately locating and segmenting polyp regions. In the past, people often relied on manually extracted lower-level features such as color, texture, and shape, which often had problems capturing global context and lacked robustness to complex scenarios. With the advent of deep learning, more and more medical image segmentation algorithms based on deep learning networks have emerged, making significant progress in the field. This paper provides a comprehensive review of polyp segmentation algorithms. We first review some traditional algorithms based on manually extracted features and deep segmentation algorithms, and then describe benchmark datasets related to the topic. Specifically, we carry out a comprehensive evaluation of recent deep learning models and results based on polyp size, taking into account the focus of research topics and differences in network structures. Finally, we discuss the challenges of polyp segmentation and future trends in the field. © The Author(s) 2025.

关键词： Comprehensive evaluation Deep learning Medical imaging Polyp segmentation

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Coupled-mode phase matching on a silicon chip

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Physical Review A 2025年第3期111卷 033504-033504页

作者： Chao Wu Pingyu Zhu Miaomiao Yu Weihong Luo Qilin Zheng Kaikai Zhang Yingwen Liu Ping Xu Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha 410073 China Hefei National Laboratory Hefei 230088 China

Phase matching lies at the heart of the classical nonlinear process for frequency conversion and all-optical processing, as well as the spontaneous parametric process for the quantum light sources generation. In this work, we experimentally demonstrate the coupled-mode phase-matching (CMPM) engineered by the two-paralleled-coupled waveguides on a silicon-on-insulator (SOI) chip, extending the converted beam and entangled photons to the S band and L band. We measure the spectrum and efficiency of wavelength conversion, which successfully validates the designed CMPM scheme. We further measure the pairs generation rate of the biphoton based on the spontaneous four-wave mixing, which features a narrow bandwidth consistent well with the theoretical simulations. Multiple CMPM conditions can be achieved by exploiting the combinations of even and odd supermodes of the coupled system and tuned by designing a proper coupling gap between the two waveguides. The CMPM can extend the FWM nonlinear process ranging from the O band to the U band and even the midinfrared, enriching the working wavelength to a large extent. In addition, the CMPM features a discrete-band narrow bandwidth, which will greatly enhance the encoding density of frequency and the spectral purity of generated photon. In addition, our methods can be easily extended to a wide range of materials such as silicon nitride, dispensing with the dispersion engineering for phase matching.

关键词： Silicon nitride

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Multitask-Oriented Efficient Computational Offloading Orchestrator for IoT Applications in Mobile-Edge computing

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IEEE Internet of Things Journal 2025年第10期12卷 14096-14108页

作者： Wang, Leilei Deng, Xiaoheng Zhang, Honggang Wan, Shaohua Min, Geyong Xi’an University of Technology School of Computer Science and Engineering Shanxi Key Laboratory for Network Computing and Security Technology Xi’an710048 China Central South University School of Computer Science and Engineering Changsha410075 China Central South University Shenzhen Research Institute Shenzhen518057 China University of Massachusetts at Boston Engineering Department BostonMA02125 United States University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen518110 China University of Exeter Department of Computer Science ExeterEX4 4QF United Kingdom

Mobile-edge computing (MEC) can accelerate computation-intensive applications and emerge as a promising technology for enabling Internet of Things (IoT). MEC improves the processing performance of tasks by assigning them to the edge nodes. However, with massive terminals contending for computation and communication resources simultaneously, how to develop a flexible computational offloading mechanism becomes the fundamental issue of MEC-enabled IoT systems. This article aims to develop an effective computational offloading decision scheme by jointly considering the computational resource and diverse user demands with two goals, i.e., minimizing both the latency and the energy consumption. Specifically, we develop a two-stage computational offloading mechanism, where the computational resources and offloading decisions can be allocated and coordinated with the variation of computation requirements. To achieve the two goals, this work introduces an edge node recommendation model within the cloud-edge–end architecture to reduce the offloading optimization search space. Furthermore, we propose a new computational offloading (CROCA) algorithm based on chemical reaction optimization (CRO) for optimizing offloading utility, which thoroughly considers the competition between mobile device requests and computational resources. Extensive evaluation results demonstrate that the proposed CROCA scheme can effectively improve the computational offloading performance. © 2014 IEEE.

关键词： Computation offloading

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In-situ NAS: A Plug-and-Search Neural architecture Search framework across hardware platforms

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IEEE Transactions on computers 2025年

作者： Lv, Hao Zhang, Lei Wang, Ying Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China State Key Laboratory of Computer Architecture China

Hardware-aware Neural architecture Search (HW-NAS) has garnered significant research interest due to its ability to automate the design of neural networks for various hardware platforms. Prevalent HW-NAS frameworks often use fast predictors to estimate network performance, bypassing the time-consuming actual profiling step. However, the resource-intensive nature of building these predictors and their accuracy limitations hinder their practical use in diverse deployment scenarios. In response, we emphasize the indispensable role of actual profiling in HW-NAS and explore efficiency optimization possibilities within the HW-NAS framework. We provide a systematic analysis of profiling overhead in HW-NAS and identify many redundant and unnecessary operations during the search phase. We then optimize the workflow and present Insitu NAS, which leverages similarity features and exploration history to eliminate redundancy and improve runtime efficiency. In-situ NAS also offers simplified interfaces to ease the user’s effort in managing the complex device-dependent profiling flow, enabling plug-and-search functionality across diverse hardware platforms. Experimental results show that In-situ NAS achieves an average 10x speedup across different hardware platforms while reducing the search overhead by 8x compared to predictor-based approaches in various deployment scenarios. Additionally, In-situ NAS consistently discovers networks with better accuracy (about 1.5%) across diverse hardware platforms compared to predictor-based NAS. © 1968-2012 IEEE.

关键词： AutoML Deep Learning Hardware-Aware Neural architecture Search Optimization Performance evaluation

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