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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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Legal Contract Analysis and Risk Assessment Using Pre-Trained Legal-T5 and Law-GPT 3

Legal Contract Analysis and Risk Assessment Using Pre-Traine...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Poornima, A. Nagaraja, K.V. Venugopalan, Manju Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Mathematics Bengaluru India

ISBN: (纸本)9798331508456

The need for effective solutions in reviewing, risk identification, and compliance management of complex legal contracts has remained a critical challenge. This research explores how modern machine learning models like Legal T5, and Law GPT can automate contract analysis and improve review accuracy. Legal T5 summarizes the complex legal documents while Law GPT captures the critical risks. K-Means clustering was used to improve the contract segmentation by grouping similar clauses for a structured risk assessment. Tested on the CUAD dataset, the system's text summarizing findings showed coherence and a strong semantic similarity of 0.78, ensuring that the final summaries retained significant legal importance. The model achieved an average precision of 0.85, recall of 0.77, and F1-score of 0.80 across various risk categories. The findings prove that Legal T5, along with clustering, improves contract analysis with the ability to provide a context and structure-aware summary with better identification of risks. © 2025 IEEE.

关键词： Risk management

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A Grading Model for Jasmine Flowers Using Deep Learning and Quantum Machine Learning 4

A Grading Model for Jasmine Flowers Using Deep Learning and ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Pratiksha Cauvery, K.P. Thrisha, R. Harshitha, T. Panigrahi, Suman Thangavel, Senthil Kumar Somasundaram, K. Boopathy, C.P. Amrita School of Physical Sciences Department of Mathematics Tamil Nadu Coimbatore India Amrita School of Computing Department of Computer Science and Engineering Tamil Nadu Coimbatore India Amrita School of Engineering Department of Elelctical and Electronics Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798331523923

The flowers of Jasmine hold immense economic importance in the agricultural sector and the perfume industry, as the value and satisfaction of the market, are heavily influenced by the quality of these flowers. The already very labor-intensive, subjective, and inefficient manual method of grading plants based on their physical and aromatic characteristics is unsuitable on a large scale. This paper handles these limitations by introducing automation in the grading process using Deep Learning and Quantum Machine Learning. The conceptual methodology mandates automatic flower placement and high-resolution imagery with a conveyor belt. The deep learning model, exactly a type of Convolutional Neural Network (CNN), would explain freshness in terms of visual features like color, texture, structural strength, and vice versa. Additionally, quantum-inspired algorithms have been employed to enhance the computation and performance of the model. From the analysis, a decision-making module classifies entrant flowers into fresh and otherwise, thereby sending them to their respective places, where all packing or disposal could be achieved. This DL-QML hybrid approach points out the exemplary high accuracy, scalability, and efficiency in jasmine grading and hence minimizes manual intervention in quality control assembly lines. The findings provide the card of the feasibility of joining QML in addressing computational challenges of agro-product quality assessment, opening avenues in application far broader than floriculture. © 2025 IEEE.

关键词： Decision making

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Efficiency of a cylindrical wave energy converter with different bottom shapes in a chambered breakwater

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Marine Systems and Ocean Technology 2025年第2期20卷 1-15页

作者： Aiswaria, K. Balaji, Ramakrishnan Krishnendu, P. Jodha, Shweta Department of Civil Engineering Indian Institute of Technology Bombay Mumbai400076 India School of Engineering Computing and Mathematics University of Plymouth Drake Circus PlymouthPL48AA United Kingdom Naval Architecture and Ocean Engineering Indian Maritime University Visakhapatnam Andhra Pradesh Visakhapatnam531035 India

Ocean waves are one of the high-density renewable energy resources and can work as a generous, sustainable, and clean energy source. The energy capturing of a floating point absorber system is designed to be maximum when it is close to resonance. Integrating coastal protection structures with a wave energy converter can be a promising solution to extract wave energy economically. This paper investigates the influence of bottom shapes on the hydrodynamic performance of a cylindrical heaving floating point absorber wave energy converter stand-alone and integrated with breakwater conditions. The study is carried out for constant mass and constant draft model cases, providing identical conditions for comparison. It is seen that a cylinder with a 90° conical bottom has better performance for the same waterline diameter than all the other models. After the integration with a chambered breakwater, the power performance of the wave energy device was found to increase by a maximum of 3.5 times more than that of the stand-alone case for all the models at quasi-resonance conditions, especially when the WEC is positioned at locations of amplified wave elevations inside the chamber. © The Author(s), under exclusive licence to Sociedade Brasileira de Engenharia Naval 2025.

关键词： Wave power

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Modification of Maxwell model for conductivity prediction of carbon nanotubes-filled polymer composites with tunneling effect

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Applied mathematics and Mechanics(English Edition) 2025年第1期46卷 25-36页

作者： Jue ZHU Longyuan LI Ningtao ZHU Zhejiang Provincial Engineering Research Center for the Safety of Pressure Vessel and Pipeline Ningbo 315211Zhejiang ProvinceChina Key Laboratory of Impact and Safety Engineering(Ningbo University) Ministry of EducationNingbo 315211Zhejiang ProvinceChina School of Engineering Computing and MathematicsUniversity of PlymouthPlymouth PL48AAU.K. Xi'an Zhanshi Testing&Engineering Co.Ltd. Xi'an 710018China

Carbon nanotubes(CNTs)have garnered great attention in recent years due to their outstanding electrical,thermal,and mechanical *** incorporation of small amounts of CNTs in polymers can substantially improve the sensitivity of the polymer's electrical *** paper presents a modified Maxwell model to evaluate the electrical conductivity of CNTs-filled polymer composites by introducing a transition zone to account for the tunneling *** this modified Maxwell model,the CNTs-filled polymer composite is modeled as a three-phase composite,consisting of a matrix(polymer),inclusions(CNTs),and a transition zone(tunneling zone).The effective electrical conductivity(EEC)of the composite is calculated based on the volume fractions and electrical conductivities of the matrix,inclusions,and transition *** model's validity is confirmed through the use of available test data,which demonstrates its capability to accurately capture the nonlinear conductivity behavior observed in CNTs-polymer *** study offers valuable insights into the design of high-performance conductive polymer nanocomposites,and enhances the understanding of electrical conduction mechanisms in CNT-dispersed polymer composites.

关键词： carbon nanotube(CNT) polymer composite electrical conductivity tunneling Maxwell model

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A DECOUPLED,LINEARLY IMPLICIT AND UNCONDITIONALLY ENERGY STABLE SCHEME FOR THE COUPLED CAHN-HILLIARD SYSTEMS

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Journal of Computational mathematics 2025年第3期43卷 708-730页

作者： Dan Zhao Dongfang Li Yanbin Tang Jinming Wen School of Mathematics and Statistics Huazhong University of Science and TechnologyWuhan 430074China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and TechnologyWuhan 430074China College of Information Science and Technology Jinan UniversityGuangzhou 510632China

We present a decoupled,linearly implicit numerical scheme with energy stability and mass conservation for solving the coupled Cahn-Hilliard *** time-discretization is done by leap-frog method with the scalar auxiliary variable(SAV)*** only needs to solve three linear equations at each time step,where each unknown variable can be solved *** is shown that the semi-discrete scheme has second-order accuracy in the temporal *** convergence results are proved by a rigorous analysis of the boundedness of the numerical solution and the error estimates at different *** examples are presented to further confirm the validity of the methods.

关键词： Coupled Cahn-Hilliard system Leap-frog method Scalar auxiliary variable Error estimate

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Person Re-Identification Using Siamese Triplet Network 4

Person Re-Identification Using Siamese Triplet Network

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Menon, Aparna Yashwanthika, R. Shreya, J.V. Somasundaram, K. Thangavel, Senthil Kumar Asawa, Shreeya Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Coimbatore India Amrita School of Physical Sciences Department of Mathematics Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9798331523923

Due to the increasing demand for public safety and the rapid development of intelligent surveillance networks, the re-identification of individuals (Re-ID) has become one of the most important research topics in computer vision. Person re-identification (Re-ID) has a wide range of applications, which increases its popularity in recent studies. The main goal of person re-identification (Re-ID) is to retrieve people with the same identity from different cameras. In the open world, there are still some challenges. First, existing re-ID research usually assumes that only one-factor variable (view, clothing, pedestrian pose, pedestrian occlusion, image resolution, RGB/IR modality) changes, and ignores the complexity of multi-factor variables in the open world. Second, existing ID methods are overly dependent on clothing color and other obvious pedestrian features that can be easily obscured or changed. In addition, the lack of benchmark datasets that include multifactor variables hinders the practical application of ID in the open world. The proposed model uses a Deep learning model -Siamese triplet network to extract the features, and a similarity measure to re-identify the individual. The evaluation metrics used are cumulative matching features (CMC) and mean average precision (mAP). Preliminary results show that the model performs well in comparison to existing methods, and future work will focus on improving robustness under varying environmental conditions. © 2025 IEEE.

关键词： Reamers

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ACRL-10K: A Dataset for Air Conditioner Refrigerant Leak Smoke Detection

ACRL-10K: A Dataset for Air Conditioner Refrigerant Leak Smo...

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

作者： Jiang, Yuhan Wang, Zhongyuan Hua, Jie Liang, Jinbi Xu, Zengmin National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798350368741

In this paper, we introduce a new dataset for air conditioner refrigerant leak smoke detection, called ACRL-10K. The dataset is designed to develop algorithms for detecting refrigerant leak smoke faults during air conditioner recycling. It contains a total of 10, 724 images covering three common scenarios of air conditioner refrigerant leak: loading port, refrigerant extraction, and disassembly. All images are sourced from 656 video segments of real air conditioner recycling scenarios, captured by surveillance cameras deployed on an environmental company's recycling production line. The annotations for the refrigerant leak smoke in the images are performed by industry experts to ensure high accuracy and consistency. To the best of our knowledge, ACRL-10K is the first dataset specifically designed for refrigerant leak smoke detection. Based on the ACRL-10K dataset, we present the performance of mainstream object detectors, including the YOLO series, as a baseline to conduct the following work: 1) preliminarily summarize the challenges of using the dataset for refrigerant leak smoke detection;2) show the detection results of benchmark methods;and 3) make a comparison to identify the strengths and weaknesses of the baseline algorithms. In practice, the ACRL-10K dataset would hopefully advance research and applications in refrigerant leak early warning during air conditioner dismantling and recycling processes. © 2025 IEEE.

关键词： Air Conditioner Dataset Object Detection Refrigerant Leak Smoke Detection

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Multi-View Few-Shot Reasoning for Emerging Entities in Knowledge Graphs

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IEEE Transactions on Big Data 2025年第3期11卷 1321-1333页

作者： Yan, Cheng Zhao, Feng Tao, Xiaohui Zhu, Xiaofeng Huazhong University of Science and Technology Natural Language Processing and Knowledge Graph Lab School of Computer Science and Technology Wuhan430074 China University of Southern Queensland School of Mathematics Physics & Computing Toowoomba4350 Australia University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu610054 China

A knowledge graph (KG) is a form of representing knowledge of the objective world. With the expansion of knowledge, KGs frequently incorporate new entities, which often possess limited associated data, known as few-shot features. Addressing the missing knowledge for these emerging entities is crucial practically, but there are significant challenges due to data scarcity. Previously developed methods based on knowledge graph embedding (KGE) and graph neural networks (GNNs) focusing on instance-level KGs are confronted with challenges of data scarcity and model simplicity, rendering them inapplicable to reasoning tasks in few-shot scenarios. To tackle these issues, we propose a multi-view few-shot KG reasoning method for emerging entities. The primary focus of our method lies in resolving the problem of link prediction for emerging entities with limited associated triples from multiple perspectives. Distinct from previous methods, our approach initially abstracts a concept-view KG from the conventional instance-view KG, enabling the formulation of commonsense rules. Additionally, we employ the aggregation of multi-hop subgraph features to enhance the representation of emerging entities. Furthermore, we introduce a more efficient cross-domain negative sampling strategy and a multi-view triple scoring function based on commonsense rules. Our experimental evaluations highlight the effectiveness of our method in few-shot contexts, demonstrating its robustness and adaptability in both cross-shot and zero-shot scenarios, significantly outperforming existing models in these challenging settings. © 2015 IEEE.

关键词： Knowledge graph

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A survey on lattice‑based digital signature

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Cybersecurity 2025年第2期8卷 42-59页

作者： Fengxia Liu Zhiyong Zheng Zixian Gong Kun Tian Yi Zhang Zhe Hu Jia Li Qun Xu Institute of Artificial Intelligence Beihang UniversityBeijing 100191People’s Republic of China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing BeijingPeople’s Republic of China Engineering Research Center of Ministry of Education for Financial Computing and Digital Engineering Renmin University of ChinaBeijing 100872People’s Republic of China School of Mathematics Renmin University of ChinaBeijing 100872People’s Republic of China

Lattice-based digital signature has become one of the widely recognized post-quantum algorithms because of its simple algebraic operation,rich mathematical foundation and worst-case security,and also an important tool for constructing *** survey explores lattice-based digital signatures,a promising post-quantum resistant alternative to traditional schemes relying on factoring or discrete logarithm problems,which face increasing risks from quantum *** study covers conventional paradigms like Hash-and-Sign and Fiat-Shamir,as well as specialized applications including group,ring,blind,and proxy *** analyzes the versatility and security strengths of lattice-based schemes,providing practical *** chapter summarizes advancements in schemes,identifying emerging *** also pinpoint future directions to deploy lattice-based digital signatures including quantum cryptography.

关键词： Post-quantum cryptography Lattice-based cryptography Lattice-based digital signatures

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