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Listwise ranking predictor for evolutionary neural architecture search

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Swarm and Evolutionary Computation 2025年 96卷

作者： Li, Nan Ma, Lianbo Wang, Rui Cheng, Shi Sun, Yanan Xue, Bing Zhang, Mengjie College of Software Northeastern University Liaoning Shenyang110819 China Centre for Data Science and Artificial Intelligence & School of Engineering and Computer Science Victoria University of Wellington Wellington 6140 New Zealand Foshan Graduate School of Innovation Guangdong Foshan528000 China College of System Engineering National University of Defense Technology Hunan Changsha410073 China School of Computer Science Shaanxi Normal University Shaanxi Xian710119 China School of Computer Science Sichuan University Sichuan Chengdu610000 China

In evolutionary neural architecture search (ENAS), the accuracy predictors (i.e., regression models) have been successfully applied to save computational costs for the evaluation of network architectures. However, the accuracy of these predictors is largely limited by the small amount of evaluated architectures that may be difficult to obtain. Such accuracy predictors with prediction bias often lead to an inaccurate ranking, misleading the selection of ENAS. To alleviate the above limitations, we design an efficient and novel listwise ranking predictor (LRP) for ENAS to directly predict the ranking of each architecture instead of the numerical accuracy value of each architecture. Specifically, the training data is constructed by the proposed random encoding-combination (REC) strategy, which can generate substantial training data using the small number of evaluated architectures (data level). These specially constructed training data are used to train LRP, which can convert the complex regression task into a ranking task to reduce ranking bias (model level). The proposed NAS method is compared with state-of-the-art NAS methods on widely-used benchmark datasets and practical application. Experimental results demonstrate that LRP can alleviate the ranking disorder problem and outperform others in terms of both effectiveness and efficiency. © 2025 Elsevier B.V.

关键词： Linear regression

Blockchain-Driven Decentralization for data Sharing in the Metaverse

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Blockchain-Driven Decentralization for Data Sharing in the M...

Computing and Information Technology (ICCIT-1441), 2020 International Conference on

作者： Saeed Hamood Alsamhi Ammar Hawbani Abdu Saif Edward Curry Insight Centre for Data Analytics University of Galway Galway Ireland School of Computer Science Shenyang Aerospace University Shenyang China Faculty of Engineering and IT Taiz University Taiz Yemen

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

This paper presents a novel decentralized architecture that aims to transform the metaverse by smoothly incorporating blockchain technology. The proposed framework promotes decentralization principles to address centralization issues and restrict user empowerment in existing metaverse systems. It prioritizes putting people at the centre of their digital experiences. The decentralized framework goes beyond traditional tokenization methods by adding extra layers, like non-fungible tokens (NFTs) that change who owns an asset, and decentralized autonomous organizations (DAOs) that give people in communities more power, thereby fostering creativity and inclusion in virtual environments. Built upon the fundamental concepts of decentralization and user-centric design, the framework tackles the existing limits of the metaverse and envisions a future in which people actively define their virtual destiny. In addition to addressing issues, the framework provides possibilities for cooperative advancement, decentralized exchange of information, incorporation of future technology, and market growth, acting as a catalyst for innovation within the developing metaverse ecosystem. The framework embodies not only a framework but also a vision for a decentralized and user-centric metaverse, fostering creativity, inclusion, and the realization of virtual aspirations.

关键词： Technological innovation Metaverse Catalysts Ecosystems computer architecture Transforms Tokenization Nonfungible tokens Information technology Creativity

Neurodynamic Mapping for Cybersickness Prediction in 3D Game Immersion

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Neurodynamic Mapping for Cybersickness Prediction in 3D Game...

Emerging Systems and Intelligent Computing (ESIC), International Conference on

作者： C. Balaji Shruti Mishra Sandeep Kumar Satapathy School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamil Nadu India Centre for Advanced Data Science Vellore Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

The rapid expansion of virtual reality (VR) applications in gaming and training has highlighted cybersickness, a condition marked by symptoms such as nausea, dizziness, and disorientation. Traditional assessment methods often rely on subjective reporting, which hampers timely intervention. This study aims to develop a novel approach for predicting the onset of cybersickness through neurodynamic mapping of electroencephalogram (EEG) data and heart rate variability (HRV) in immersive 3D VR environments. Spiking neural network (SNN) architecture to analyze integrated EEG and HRV data collected from participants engaged in 3D VR games. Key EEG channels, particularly F7, Cz, and O2, were identified as critical biomarkers for susceptibility to cybersickness. Real-time monitoring of brainwave frequencies (delta, theta, alpha, and beta) enabled tracking of changes associated with symptom onset. SNN model achieved a prediction accuracy of 85.9% at resting baseline conditions and detected cybersickness events with an accuracy of 76.6%. The dynamic characteristics of the identified biomarkers suggest potential for enhancing prediction models over time. This research introduces a groundbreaking method for predicting cybersickness in VR gaming by leveraging neurodynamic mapping and SNNs. Our findings provide insights into the neural mechanisms underlying VR-induced motion sickness and have significant implications for improving user experience across various applications, including gaming and virtual therapy. The integration of neurodynamic feedback systems could foster more immersive and user-friendly VR environments by facilitating timely interventions to mitigate cybersickness.

关键词： Neurodynamics Training Solid modeling Three-dimensional displays Accuracy Cybersickness Spiking neural networks Biomarkers Electroencephalography Heart rate variability

Fed-GAN: Federated Generative Adversarial Network with Privacy-Preserving for Cross-Device Scenarios

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Han, Song Ding, Hongxin Zhao, Shuai Ren, Siqi Zeng, Shengke Xue, Mengqi Wang, Ruili Hangzhou City University School of Computer and Computing Science Hangzhou China Zhejiang Gongshang University School of Computer Science and Technology China Zhejiang Key Laboratory of Big Data and Future E-Commerce Technology Hangzhou China Xihua University School of Computer and Software Engineering Chengdu China Massey University School of Mathematical and Computational Science Auckland New Zealand

Huge amounts of data from various sources are substantial to dependable distributed machine learning, especially for trustworthy federated learning (FL). However, existing FL methods are difficult to collect enough data for training the global model more accurately, especially in cross-device scenarios. In this paper, we propose a new federated generative adversarial network empowered by differential privacy and knowledge transfer named Fed-GAN, which can be used to address the problem of data shortage and prevent generator leakage from resource-constrained devices, as well as generate high-quality synthetic data while ensuring strict DP guarantees. Different from other generative model methods, our Fed-GAN framework can achieve efficient and secure generative model training and limited permission for resource-constrained devices to prevent them from leaking or misusing the generator. In addition, we propose a pHash-KT method for our Fed-GAN framework, which selects potentially high-quality data through the knowledge of each client for improving the utility of synthetic data. Our FedGAN framework satisfies (Formula presented)-DP, and also has high resistance when number of adversaries is 10%-70% of the total number of clients. Extensive experiments demonstrate that our Fed-GAN framework not only generates high-quality synthetic data, but also provides strict DP guarantees, compared with other generative model methods. © 2004-2012 IEEE.

关键词： Generative adversarial networks

Movable Antenna Enhanced Integrated Sensing and Communication Via Antenna Position Optimization

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arXiv 2025年

作者： Ma, Wenyan Zhu, Lipeng Zhang, Rui Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China

In this paper, we propose an integrated sensing and communication (ISAC) system aided by the movable-antenna (MA) array, which can improve the communication and sensing performance via flexible antenna movement over conventional fixed-position antenna (FPA) array. First, we consider the downlink multiuser communication, where each user is randomly distributed within a given three-dimensional zone with local movement. To reduce the overhead of frequent antenna movement, the antenna position vector (APV) is designed based on users’ statistical channel state information (CSI), so that the antennas only need to be moved in a large timescale. Then, for target sensing, the Cramer-Rao bounds (CRBs) of the estimation mean square error for different spatial angles of arrival (AoAs) are derived as functions of MAs’ positions. Based on the above, we formulate an optimization problem to maximize the expected minimum achievable rate among all communication users, with given constraints on the maximum acceptable CRB thresholds for target sensing. An alternating optimization algorithm is proposed to iteratively optimize one of the horizontal and vertical APVs of the MA array with the other being fixed. Numerical results demonstrate that our proposed MA arrays can significantly enlarge the trade-off region between communication and sensing performance compared to conventional FPA arrays with different inter-antenna spacing. It is also revealed that the steering vectors of the designed MA arrays exhibit low correlation in the angular domain, thus effectively reducing channel correlation among communication users to enhance their achievable rates, while alleviating ambiguity in target angle estimation to achieve improved sensing accuracy. Copyright © 2025, The Authors. All rights reserved.

关键词： Mean square error

BRAINGUARD: Privacy-Preserving Multisubject Image Reconstructions from Brain Activities

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arXiv 2025年

作者： Tian, Zhibo Quan, Ruijie Ma, Fan Zhan, Kun Yang, Yi School of Information Science and Engineering Lanzhou University China College of Computing and Data Science Nanyang Technological University Singapore College of Computer Science and Technology Zhejiang University China

Reconstructing perceived images from human brain activity forms a crucial link between human and machine learning through Brain-computer Interfaces. Early methods primarily focused on training separate models for each individual to account for individual variability in brain activity, overlooking valuable cross-subject commonalities. Recent advancements have explored multisubject methods, but these approaches face significant challenges, particularly in data privacy and effectively managing individual variability. To overcome these challenges, we introduce BRAINGUARD, a privacy-preserving collaborative training framework designed to enhance image reconstruction from multisubject fMRI data while safeguarding individual privacy. BRAINGUARD employs a collaborative global-local architecture where individual models are trained on each subject’s local data and operate in conjunction with a shared global model that captures and leverages cross-subject patterns. This architecture eliminates the need to aggregate fMRI data across subjects, thereby ensuring privacy preservation. To tackle the complexity of fMRI data, BRAINGUARD integrates a hybrid synchronization strategy, enabling individual models to dynamically incorporate parameters from the global model. By establishing a secure and collaborative training environment, BRAINGUARD not only protects sensitive brain data but also improves the image reconstructions accuracy. Extensive experiments demonstrate that BRAINGUARD sets a new benchmark in both high-level and low-level metrics, advancing the state-of-the-art in brain decoding through its innovative design. © 2025, CC BY.

关键词： Differential privacy

SR-FoT: A Syllogistic-Reasoning Framework of Thought for Large Language Models Tackling Knowledge-based Reasoning Tasks

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arXiv 2025年

作者： Wan, Wentao Yang, Zhuojie Chen, Yongcan Luo, Chenglin Wang, Ruilin Cai, Kehao Kang, Nan Lin, Liang Wang, Keze School of Computer Science and Engineering Sun Yat-sen University China Guangdong Key Laboratory of Big Data Analysis and Processing China South China Normal University China

Deductive reasoning is a crucial logical capability that assists us in solving complex problems based on existing knowledge. Although augmented by Chain-of-Thought prompts, Large Language Models (LLMs) might not follow the correct reasoning paths. Enhancing the deductive reasoning abilities of LLMs, and leveraging their extensive built-in knowledge for various reasoning tasks, remains an open question. Attempting to mimic the human deductive reasoning paradigm, we propose a multi-stage Syllogistic-Reasoning Framework of Thought (SR-FoT) that enables LLMs to perform syllogistic deductive reasoning to handle complex knowledge-based reasoning tasks. Our SR-FoT begins by interpreting the question and then uses the interpretation and the original question to propose a suitable major premise. It proceeds by generating and answering minor premise questions in two stages to match the minor premises. Finally, it guides LLMs to use the previously generated major and minor premises to perform syllogistic deductive reasoning to derive the answer to the original question. Extensive and thorough experiments on knowledge-based reasoning tasks have demonstrated the effectiveness and advantages of our SR-FoT. Copyright © 2025, The Authors. All rights reserved.

关键词： Knowledge based systems

An agent-based model of citation behavior

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arXiv 2025年

作者： Chacko, George Park, Minhyuk Ramavarapu, Vikram Grama, Ananth Robles Granda, Pablo Warnow, Tandy Siebel School of Computing and Data Science Grainger College of Engineering University of Illinois Urbana-ChampaignIL United States Department of Computer Science Purdue University United States

Whether citations can be objectively and reliably used to measure productivity and scientific quality of articles and researchers can, and should, be vigorously questioned. However, citations are widely used to estimate the productivity of researchers and institutions, effectively creating a ‘grubby’ motivation to be well-cited. We model citation growth, and this grubby interest using an agent-based model (ABM) of network growth. In this model, each new node (article) in a citation network is an autonomous agent that cites other nodes based on a ‘citation personality’ consisting of a composite bias for locality, preferential attachment, recency, and fitness. We ask whether strategic citation behavior (reference selection) by the author of a scientific article can boost subsequent citations to it. Our study suggests that fitness and, to a lesser extent, out degree and locality effects are influential in capturing citations, which raises questions about similar effects in the real world. © 2025, CC BY.

关键词： Autonomous agents

Optimizing Software Demands Using Fuzzy-Based Evaluation Techniques 1st

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Optimizing Software Demands Using Fuzzy-Based Evaluation Tec...

1st International Conference on Innovation and Emerging Trends in Computing and Information Technologies, IETCIT 2024

作者： Rubi Singh, Jagendra Sahu, Dinesh Prasad Tiwari, Mohit Shaik, Nazeer Shrivastav, A.K. School of Education Lovely Professional University Jalandhar India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi India Department of Artificial Intelligence and Data Science Rajalakshmi Institute of Technology Tamil Nadu Chennai India Ambala Mullana India

ISBN: (纸本)9783031808418

The purpose of this study was to show the benefits of fuzzy logic in software scaling. We made use of fuzzy logic systems and logic, including sets rules and inference, to cope with uncertainty in making decisions;by the way all three of those have been used. We found different degrees of set membership during ten trials, which would change to reflect how much the software today was needed in real life. Decisions produced by fuzzy inference systems exhibited both adaptability and context-dependency, illustrating possible pathways toward better software demand optimization strategies. Further, we took measurements like response time, throughput, and resource utilization both before & after fuzzy optimization. In all experiments we put the system under realistic working conditions, looking for changes in speed, through better resource usage. The response time was reduced, throughput increased, and resource utilizations improved. These results suggest that fuzzy-based methods not only facilitate a better user experience but enhance software performance. In a word, this study represents an advance in software engineering optimization methods. It draws attention to software demand’s inherent uncertainties and emphasizes the importance of fuzzy logic. With fuzzy evaluation techniques, this research’s achievement was thus to open up practical means of improving decision-making processes and optimizing the vital performance indicator, enabling people to create computer systems that are more reliable and practical. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： computer circuits