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Adaptive Multi-strategy Rabbit Optimizer for Large-scale Optimization

Journal of Bionic engineering 2025年第1期22卷 398-416页

作者： Baowei Xiang Yixin Xiang Department of Computing School of Electronic and Information Engineering Taizhou UniversityTaizhou318000China Department of Software Engineering College of Software Nankai UniversityTianjin300350China

As optimization problems continue to grow in complexity,the need for effective metaheuristic algorithms becomes increasingly ***,the challenge lies in identifying the right parameters and strategies for these *** this paper,we introduce the adaptive multi-strategy Rabbit Algorithm(RA).RA is inspired by the social interactions of rabbits,incorporating elements such as exploration,exploitation,and adaptation to address optimization *** employs three distinct subgroups,comprising male,female,and child rabbits,to execute a multi-strategy *** parameters,including distance factor,balance factor,and learning factor,strike a balance between precision and computational *** offer practical recommendations for fine-tuning five essential RA parameters,making them versatile and *** is capable of autonomously selecting adaptive parameter settings and mutation strategies,enabling it to successfully tackle a range of 17 CEC05 benchmark functions with dimensions scaling up to *** results underscore RA’s superior performance in large-scale optimization tasks,surpassing other state-of-the-art metaheuristics in convergence speed,computational precision,and ***,RA has demonstrated its proficiency in solving complicated optimization problems in real-world engineering by completing 10 problems in CEC2020.

关键词： Adaptive parameter Large scale optimization Rabbit algorithm Swarm intelligence engineering optimization

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Bias analysis of AI models for undergraduate student admissions

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Neural computing and Applications 2025年第12期37卷 7785-7795页

作者： Van Busum, Kelly Fang, Shiaofen Department of Computer Science and Software Engineering Butler University IndianapolisIN United States Computing and Engineering Indiana University Luddy School of Informatics Indiana University Indianapolis IndianapolisIN United States

Bias detection and mitigation is an active area of research in machine learning. This work extends previous research done by the authors Van Busum and Fang (Proceedings of the 38th ACM/SIGAPP Symposium on Applied computing, 2023) to provide a rigorous and more complete analysis of the bias found in AI predictive models. Admissions data spanning six years was used to create an AI model to determine whether a given student would be directly admitted into the school of Science under various scenarios at a large urban research university. During this time, submission of standardized test scores as part of a student’s application became optional which led to interesting questions about the impact of standardized test scores on admission decisions. We developed and analyzed AI models to understand which variables are important in admissions decisions, and how the decision to exclude test scores affects the demographics of the students who are admitted. We then evaluated the predictive models to detect and analyze biases these models may carry with respect to three variables chosen to represent sensitive populations: gender, race, and whether a student was the first in his/her family to attend college. We also extended our analysis to show that the biases detected were persistent. Finally, we included several fairness metrics in our analysis and discussed the uses and limitations of these metrics. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Students

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Uncertainty-Calibrated Test-Time Model Adaptation without Forgetting

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年 PP卷 1-14页

作者： Tan, Mingkui Chen, Guohao Wu, Jiaxiang Zhang, Yifan Chen, Yaofo Zhao, Peilin Niu, Shuaicheng South China University of Technology School of Software Engineering China Pazhou Laboratory Guangzhou China XVERSE China National University of Singapore School of Computing Singapore Tencent AI Lab China Nanyang Technological University College of Computing and Data Science Singapore

Test-time adaptation (TTA) seeks to tackle potential distribution shifts between training and testing data by adapting a given model w.r.t. any testing sample. This task is particularly important when the test environment changes frequently. Although some recent attempts have been made to handle this task, we still face two key challenges: 1) prior methods have to perform backpropagation for each test sample, resulting in unbearable optimization costs to many applications;2) while existing TTA solutions can significantly improve the test performance on out-of-distribution data, they often suffer from severe performance degradation on in-distribution data after TTA (known as catastrophic forgetting). To this end, we have proposed an Efficient Anti-Forgetting Test-Time Adaptation (EATA) method which develops an active sample selection criterion to identify reliable and non-redundant samples for test-time entropy minimization. To alleviate forgetting, EATA introduces a Fisher regularizer estimated from test samples to constrain important model parameters from drastic changes. However, in EATA, the adopted entropy loss consistently assigns higher confidence to predictions even when the samples are underlying uncertain, leading to overconfident predictions that underestimate the data uncertainty. To tackle this, we further propose EATA with Calibration (EATA-C) to separately exploit the reducible model uncertainty and the inherent data uncertainty for calibrated TTA. Specifically, we compare the divergence between predictions from the full network and its sub-networks to measure the reducible model uncertainty, on which we propose a test-time uncertainty reduction strategy with divergence minimization loss to encourage consistent predictions instead of overconfident ones. To further re-calibrate predicting confidence on different samples, we utilize the disagreement among predicted labels as an indicator of the data uncertainty. Based on this, we devise a min-max entropy

关键词： Semantic Segmentation

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Blockchain-Enabled Secure, Fair and Scalable Data Sharing in Zero-Trust Edge-End Environment

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2056-2069页

作者： Xu, Xiaolong Meng, Ke Xiang, Haolong Cui, Guangming Xia, Xiaoyu Dou, Wanchun Nanjing210044 China Nanjing University of Information Science and Technology School of Software and the Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing210044 China RMIT University School of Computing Technologies MelbourneVIC3000 Australia Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China

In edge computing, the Zero-Trust Security Model (ZTSM), as a key enabling technology for next-generation networks, plays a crucial role in providing authentication for addressing data sharing concerns, such as frequent data breaches, data misuse, and cyberattacks. However, due to the complexity and diversity of edge environments, ZTSM struggles to meet the security requirements of data sharing frameworks solely through enhanced authentication. Consequently, such frameworks with ZTSM still face challenges in ensuring data integrity, evaluating various node behaviors, and coping with the increasing complexity of node attributes. To address these issues, we propose a blockchain-enabled secure, fair and scalable data sharing framework in a zero-trust edge-end environment in this paper. Specifically, we first propose a Merkle forest-based data storage model for the classified storage of loosely coupled data, consequently enhancing the scalability of the model. Then, we design a node behavior-based reputation assessment mechanism to ensure fairness during data sharing. Moreover, a data sharing protocol supervised by smart contract is proposed, working with the aforementioned storage and assessment schemes, to ensure the security of data sharing. Finally, comprehensive security analysis validates the security, fairness and scalability of the proposed framework. Extensive experimental results show that, as transaction volume grows, the time cost of data traversal in the storage model becomes progressively more efficient. Additionally, when the size of the smart contract is increased tenfold, the maximum time cost of the data sharing protocol rises by only 4.98 times. © 1983-2012 IEEE.

关键词： Data integrity

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Heterogeneous Parallel Key-Insulated Multi-Receiver Signcryption Scheme for IoV

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IEEE Transactions on Information Forensics and Security 2025年 20卷 5123-5137页

作者： Hou, Yingzhe Cao, Yue Xiong, Hu He, Debiao Chi, Chi-Hung Lam, Kwok-Yan Wuhan University School of Cyber Science and Engineering Wuhan430000 China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu610054 China Nanyang Technological University College of Computing and Data Science 639798 Singapore

The rapid growth of electric vehicle and autonomous vehicle populations has led to explosive expansion of IoV data being transmitted in the wireless communication infrastructure. Advances in IoV technologies also resulted in more complex and dynamic communication protocols/patterns, which are hard for the underlying wireless network to satisfy. Besides, security considerations of IoV communications require that key management must be stringently prohibit global failure mode of key management, meaning that, if a single IoV node compromises its private key, it will not lead to total security failure of the entire IoV network. To address these issues, in this paper, we propose a heterogeneous parallel key-insulated multi-receiver signcryption scheme for IoV (HPKI-MRSC). Firstly, the proposed scheme can realize one-to-many heterogeneous transmission, in which RSUs are deployed on certificateless cryptography (CLC) system, while vehicles are allocated in identity-based cryptography (IBC) system. In this manner, we observe that message transmission efficiency is improved greatly. Secondly, the parallel key-insulated mechanism can employ two helper keys to update private key periodically, and then solve key disclosure problem. Finally, when the number of receiver n is greater than or equal to 3, the proposed scheme has a lower signcryption overhead than other comparative schemes, and thus it is more suitable for IoV. © 2005-2012 IEEE.

关键词： Receivers Public key Protocols Encryption Polynomials Vehicle dynamics Electronic mail Training Symbols Real-time systems

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Redactable Blockchain From Decentralized Chameleon Hash Functions, Revisited

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IEEE Transactions on Computers 2025年第6期74卷 1911-1920页

作者： Li, Cong Shen, Qingni Wu, Zhonghai Peking University School of Software and Microelectronics Beijing100871 China Peking University National Engineering Research Center for Software Engineering Beijing100871 China Peking University Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing100871 China Peking University PKU-OCTA Laboratory for Blockchain and Privacy Computing Beijing100871 China

Recently, redactable blockchains have attracted attention owing to enabling the contents of blocks to be re-written. The existing redactable blockchain solutions can be classified as two categories, the centralized one and decentralized one. In centralized solutions, a single blockchain node possessing the trapdoor conducts redaction operations. However, they suffer from the issue of single point of failure. In decentralized solutions, redaction operations are performed by numerous blockchain nodes cooperatively. But there also exists the issue of inefficiency or requiring a trusted party in these solutions. Subsequently, Jia et al. proposed a redactable blockchain solution from a decentralized chameleon hash function (DCH) they designed, which supports the threshold redaction, traceability and consistency check. Nevertheless, after carefully analyzing their solution, we find that it fails to achieve the security they claimed by presenting a concrete attack. To resolve this security issue, we propose a novel chameleon hash function scheme that achieves strong collision-resistant security while maintaining simple and efficient as the building block. Based on it, we then present an improved DCH scheme with sufficient security, so that the redactable blockchain from it can resist the presented attack. Theoretical and experimental analyses demonstrate that improved DCH achieves efficiency comparable to DCH. © 1968-2012 IEEE.

关键词： Decentralized control

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Unpaired 3D Shape-to-Shape Translation via Gradient-Guided Triplane Diffusion

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IEEE Transactions on Visualization and Computer Graphics 2025年第5期31卷 3376-3386页

作者： Zhang, Wenxiao Rahmani, Hossein Liu, Jun Hohai University College of Computer Science and Software Engineering Nanjing211100 China Lancaster University School of Computing and Communications LancasterLA1 4YW United Kingdom

Unpaired shape-to-shape translation refers to the task of transforming the geometry and semantics of an input shape into a new shape domain without paired training data. Previous methods utilize GAN-based architectures to perform shape translation, employing adversarial training to transform the source shape encoding into the target domain in the low-dimensional latent feature space. However, these methods encounter difficulties in generating diverse and high-quality results, as they often suffer from issues such as "mode collapse". This leads to limited generation diversity and makes it challenging to find an accurate latent code that adequately represents the input shape. In this paper, we achieve unpaired shape-to-shape translation via a triplane diffusion model, in which we factorize 3D objects into triplane representations and conduct a diffusion process on these representations to accomplish shape domain transformation. We observe that by adding an appropriate amount of noise to an input object during the forward diffusion process, domain-specific shape structures are smoothed out while the overall structure is still preserved. Subsequently, we progressively remove the noise via an unconditional diffusion model trained on the target shape domain in the reverse diffusion process. This allows us to obtain a denoised output that retains the structural similarities of the source input while aligning with the distribution of the target shape domain. During this process, we propose two gradient-based guidance mechanisms to guide the translation process to guarantee more faithful results during the denoising process. We conduct extensive experiments on different shape domains, and the experimental results demonstrate that our method achieves superior shape fidelity with high quality compared to current state-of-the-art baselines. © 1995-2012 IEEE.

关键词： Signal encoding

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Multi-Modal Incomplete Brain Tumor Segmentation with Mamba 24

Multi-Modal Incomplete Brain Tumor Segmentation with Mamba

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5th International Symposium on Artificial Intelligence for Medicine Science, ISAIMS 2024

作者： Li, Yudan Zhang, Xiaozhi Xiong, Dongping Ouyang, Lijun School of Electrical Engineering University of South China Hunan Hengyang China School of Computing/Software University of South China Hunan Hengyang China

ISBN: (纸本)9798400717826

Accurately segmenting brain tumors from multimodal MRI sequences is a key prerequisite for brain tumor diagnosis, prognosis assessment, and surgical treatment. However, in practical applications, one or more modal data is often missing due to image corruption, different acquisition protocols, artifacts, contrast agent allergies, or cost considerations. To address the challenges of brain tumor segmentation under modality loss, this paper proposes an innovative tumor feature perception strategy. The core of this strategy is to introduce a Mamba-based Encoder (MBE) architecture, which effectively improves the feature expression ability of each modality encoder under limited data conditions. In view of the irregularity of tumor morphology, a Modulation and Demodulation Fusion Block (MDFB) is designed to accurately capture the semantic features of the tumor from the missing multimodal image data, providing strong guidance for the network to locate the tumor area. Experimental results on the widely used BraTS2020 dataset demonstrate the effectiveness of MMITS, and the brain tumor segmentation effect under various incomplete modalities is better than the state-of-the-art methods. © 2024 Copyright held by the owner/author(s).

关键词： Multicarrier modulation

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Quantum search algorithm for continuous domains

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Science China(Physics,Mechanics & Astronomy) 2025年第6期Mechanics & Astronomy) .卷 215页

作者： Lvzhou Li Institute of Quantum Computing and Software School of Computer Science and Engineering Sun Yat-sen University

The paper “Fixed-point quantum continuous search algorithm with optimal query complexity’’[1] presents another interesting application of quantum search algorithms by addressing one of the long-standing challenges in quantum computing:how to efficiently perform search over continuous domains. While Grover’s algorithm has been a cornerstone in discrete quantum search with its well-known quadratic speedup [2], many real-world problems—ranging from high-dimensional optimization to spectral analysis of infinite dimensional operators—require searching over continuous, uncountably infinite solution spaces.

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Multi-Sensor Fusion for Fall Detection and Early Warning: Overcoming Camera Blind Spots 24

Multi-Sensor Fusion for Fall Detection and Early Warning: Ov...

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8th International Conference on Electronic Information Technology and Computer engineering, EITCE 2024

作者： Zhao, Yehong Deng, Junwen Zhang, Mingyue Liu, Huan Liu, Mingtao Li, Jinmei College of Electrical and Information Engineering Hunan Institute of Traffic Engineering Hunan Hengyang China School of Computing / Software University of South China Hunan Hengyang China

ISBN: (纸本)9798400710094

The aging population has led to an increased need for reliable fall detection systems that can provide timely assistance to individuals who have fallen. This paper presents a multi-sensor fusion system designed to detect falls and issue early warnings, overcoming the limitations of camera-based systems, particularly in areas where camera surveillance is not feasible. We utilize a combination of an MPU6050 six-axis pose sensor and a MAX30102 heart rate and blood oxygen sensor to achieve high accuracy in fall detection. The system is designed to differentiate between falling and normal physical activities, reducing false positives. When a fall is detected, the system triggers an alert and sends real-time health data to a TCP server for monitoring on a mobile terminal. Our approach addresses camera blind spots and provides a comprehensive solution for fall detection in non-visual environments. © 2024 Copyright held by the owner/author(s).

关键词： Fall detection

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