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IEEE Transactions on Green Communications and Networking 2025年

作者： Ergu, Yared Abera Nguyen, Van-Linh Department of Computer Science and Information Engineering Chiayi Taiwan Advanced Institute of Manufacturing with High-tech Innovations CCU Taiwan

The advent of open radio access networks (O-RAN) has introduced intelligent, flexible, and multi-vendor network ecosystems. While O-RAN's open interfaces and artificial intelligence (AI)-driven solutions offer improved performance, energy efficiency, and resource minimization for green networking, they also expose the system to new security vulnerabilities, particularly adversarial attacks. This paper presents a robust defense approach, termed RADAR, designed to secure deep reinforcement learning (DRL)-powered resource allocation mechanisms in O-RAN. RADAR is a multi-faceted defense framework that integrates adversarial input sanitization, proactive adversarial training, and adapted defensive distillation to counter policy infiltration attacks, gradient-based deceptive loss maximization, and signal perturbation injections into the O-CU via the O-DU in O-RAN. This study evaluates the effectiveness of RADAR not only against a novel attack variant - policy infiltration attack (PIA), which manipulates environmental parameters to disrupt allocation decisions, but also against well-known adversarial techniques such as the fast gradient sign method (FGSM) and projected gradient descent (PGD). Experimental results demonstrate that RADAR achieves significant recovery in user data rates across three network slices: 73.33% for eMBB, 64.71% for mMTC and 52.94% for uRLLC, outperforming the existing standalone approach. The findings highlight RADAR's effectiveness in mitigating adversarial attack techniques, underscoring its potential to secure AI-driven core functions in intelligent O-RAN. © 2025 IEEE.

关键词： Energy efficiency

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Toward a 2D Local Implementation of Quantum Low-Density Parity-Check Codes

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PRX Quantum 2025年第1期6卷 010306-010306页

作者： Noah Berthusen Dhruv Devulapalli Eddie Schoute Andrew M. Childs Michael J. Gullans Alexey V. Gorshkov Daniel Gottesman Joint Center for Quantum Information and Computer Science National Institute of Standards and Technology (NIST)–University of Maryland College Park Maryland 20742 USA Computer Computational and Statistical Sciences Division Los Alamos National Laboratory Los Alamos New Mexico 87545 USA Department of Computer Science and Institute for Advanced Computer Studies University of Maryland College Park Maryland 20742 USA Joint Quantum Institute National Institute of Standards and Technology (NIST)–University of Maryland College Park Maryland 20742 USA

Geometric locality is an important theoretical and practical factor for quantum low-density parity-check (qLDPC) codes that affects code performance and ease of physical realization. For device architectures restricted to two-dimensional (2D) local gates, naively implementing the high-rate codes suitable for low-overhead fault-tolerant quantum computing incurs prohibitive overhead. In this work, we present an error-correction protocol built on a bilayer architecture that aims to reduce operational overheads when restricted to 2D local gates by measuring some generators less frequently than others. We investigate the family of bivariate-bicycle qLDPC codes and show that they are well suited for a parallel syndrome-measurement scheme using fast routing with local operations and classical communication (LOCC). Through circuit-level simulations, we find that in some parameter regimes, bivariate-bicycle codes implemented with this protocol have logical error rates comparable to the surface code while using fewer physical qubits.

关键词： Quantum computers

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Oral Cancer Classification Using a Hybrid Attention-Aided Deep Learning Model 5th

Oral Cancer Classification Using a Hybrid Attention-Aided D...

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5th International Conference on Medical Imaging and computer Aided Diagnosis, MICAD 2024

作者： Dutta, Srinjan Roy, Jibesh Mandal, Diptarka Kaplun, Dmitrii Shpakovskaya, Irina Sarkar, Ram Department of Computer Science and Engineering Jalpaiguri Government Engineering College Jalpaiguri India School of Computer Engineering and Mathematical Sciences Defence Institute of Advanced Technology Pune India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Process Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata India I.P. Pavlov Saint Petersburg First State Medical University St. Petersburg Russia

ISBN: (纸本)9789819638628

Oral cancer remains a critical global health challenge, characterized by high morbidity and mortality due to late-stage diagnosis. This paper addresses the need for improved diagnostic accuracy by introducing a novel hybrid-attention aided deep learning model for oral cancer classification. Our approach integrates the EfficientNetB0 model for feature extraction with a customized hybrid attention module, combining self, spatial, and soft attention mechanisms to emphasize crucial features. The model has been trained and evaluated on both microscopic and macroscopic oral cancer datasets, demonstrating its generalizability. Our proposed model achieves an accuracy of 96.97% on a macroscopic dataset and 92% on a microscopic dataset, with respective F1-scores of 0.97 and 0.89, outperforming baseline models. This hybrid attention-aided deep learning model shows significant potential in improving early detection and treatment planning of oral cancer. Source code and additional results of this work can be found at the GitHub repository. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Chaotic Map based Compression Approach to Classification

arXiv

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arXiv 2025年

作者： Harikrishnan, N.B. Vats, Anuja Nagaraj, Nithin Pedersen, Marius Department of Computer Science and Information Systems BITS Pilani K K Birla Goa Campus Goa 403726 India Adjunct Faculty Consciousness Studies Programme National Institute of Advanced Studies Indian Institute of Science Campus Bengaluru560012 India Department of Computer Science Norwegian Institute of Science and Technology Gjøvik Norway Complex Systems Programme National Institute of Advanced Studies Indian Institute of Science Campus Karnataka Bengaluru560012 India

Modern machine learning approaches often prioritize performance at the cost of increased complexity, computational demands, and reduced interpretability. This paper introduces a novel framework that challenges this trend by reinterpreting learning from an information-theoretic perspective, viewing it as a search for encoding schemes that capture intrinsic data structures through compact representations. Rather than following the conventional approach of fitting data to complex models, we propose a fundamentally different method that maps data to intervals of initial conditions in a dynamical system. Our GLS (Generalized Lüroth Series) coding compression classifier employs skew tent maps - a class of chaotic maps - both for encoding data into initial conditions and for subsequent recovery. The effectiveness of this simple framework is noteworthy, with performance closely approaching that of well-established machine learning methods. On the breast cancer dataset, our approach achieves 92.98% accuracy, comparable to Naive Bayes at 94.74%. While these results do not exceed state-of-the-art performance, the significance of our contribution lies not in outperforming existing methods but in demonstrating that a fundamentally simpler, more interpretable approach can achieve competitive results. Copyright © 2025, The Authors. All rights reserved.

关键词： Encoding (symbols)

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IoT-Driven Agricultural Monitoring Architecture with Novel Light Machine Learning for Enhanced Precision Crop Health Analysis 3rd

IoT-Driven Agricultural Monitoring Architecture with Novel L...

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3rd International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Andi, Hari Krishnan Ramasamy, Lokeshkumar Nramban Kannan, Senthil Kumar Murugesan, Sasikumar Benarji, Bejjam S. N. Manogaran, Umamaheswari Sengan, Sudhakar Centre for Postgraduate Studies Asia Metropolitan University Johor81750 Malaysia School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computer Science and Engineering Deemed to Be University Vel Tech Rangarajan Dr Sagunthala R&H12D Institute of Science and Technology Tamil Nadu Chennai600062 India C. Abdul Hakeem College of Engineering & Technology Tamil Nadu Melvisharam Ranipet632509 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627451 India

ISBN: (纸本)9789819783540

In the age of digital transformation, agriculture is harnessing the potential of advanced technologies to address global challenges related to food security and environmental sustainability. While Smart Agriculture and Precision Agriculture lay the foundation, there remains a pressing need for real-time, granular data processing to realize their full potential. Edge computing, coupled with Machine Intelligence (MI), promises a solution, yet existing models often lack the adaptability and real-time efficiency demanded by diverse agricultural scenarios. Addressing these challenges, this paper proposes the Precision Intensive Crop Health Observer (PICHO). PICHO is an innovative three-tier architecture comprising the Field Data Interface Unit (FDIU) for seamless sensor data collection, the Edge Analytics Processor (EAP) for real-time edge-based analytics, and the Cloud Coordination Hub (CCH) for data storage and in-depth analysis. Central to the EAP's efficacy is the lightweight learning model LENet, which is designed with dual pipelines for processing sensor and image data. Specifically crafted to be trained on resource-constrained edge devices, LENet ensures that PICHO remains adaptable, efficient, and poised to redefine the future of precision agriculture. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart agriculture

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Decentralized Authentication Scheme Incorporating Reputation and Attribute Signature for Cross-Web3 Applications

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IEEE Internet of Things Journal 2025年第10期12卷 14526-14537页

作者： Song, Xiaoling Xu, Guangxia Huang, Yongfei Liu, Yong Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing400065 China Guangzhou University Cyberspace Institute of Advanced Technology Guangzhou510006 China Peng Cheng Laboratory Department of New Networks Shenzhen518000 China Chongqing University of Posts and Telecommunications School of Software Engineering Chongqing400065 China

Driven by Web 3.0, the number of decentralized applications (DAPPs) continues to increase, and the need for decentralized authentication across applications becomes more and more significant. In addition, multi-dimensional reputation assessment becomes especially critical in a decentralized environment. However, there is still a lack of trusted and flexible self-sovereign identity (SSI) authentication mechanisms in Web 3.0 decentralized zero-trust cross-applications access scenarios. In response to these challenges, we propose a decentralized authentication scheme that fuses reputation assessment and attribute signature for the open and self-organized Web3 application. Firstly, we design an attribute signature method based on the attributes in a user's verifiable credentials (VCs). The signature can be used for authentication across Web3 applications, and the authenticator can flexibly adjust the attributes to be verified and their number according to specific needs. Meanwhile, in the attribute signature, we construct a multi-dimensional reputation assessment mechanism to comprehensively assess the user's reputation from multiple aspects to determine his/her security risk level, so as to reduce the possibility of identity fraud. Ultimately, through comparative analysis, we verify the advantages of the scheme in terms of privacy, scalability and attack resistance. Moreover, we analyze the performance of the key algorithms in the scheme through simulation experiments, and the experimental results show that our scheme is feasible. © 2014 IEEE.

关键词： Authentication Protocol

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Comparative Analysis of Improved K-Means Clustering for Human Freedom Index

Comparative Analysis of Improved K-Means Clustering for Huma...

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3rd International Conference on Advancements in Smart Computing and Information Security, ASCIS 2024

作者： Ilyas, F. Mohamed Priscila, S. Silvia Sheela, K. Vimala Roselin, J. Sona, K.V. Prema, R. Department of Computer Applications Bharath Institute of Higher Education and Research Tamil Nadu Chennai India Department of Computer Science Bharath Institute of Higher Education and Research Tamil Nadu Chennai India Department of Computer Science Vels Institute of Science Technology and Advanced Studies Tamil Nadu Chennai India Department of Computer Science (PG) Kristu Jayanti College Autonomous Bangalore India Department of Computer Science KristuJayanti College Bangalore India New Horizon College Kasturinagar Bangalore India

ISBN: (纸本)9783031863011

The Human Freedom Index (HFI) assesses the extent of human freedom in different nations by considering both personal and economic freedoms. It incorporates measures such as the rule of law, freedom of expression, and freedom to trade, giving a holistic picture of the freedoms people have in different countries. In order to improve the HFI analysis, this research examines three advanced clustering strategies. The first technique optimises clustering performance and minimises iterations by combining the Hamely algorithm with Mini Batch k-means (MBK-means). The second method uses chromosomes to construct cluster centroids, therefore leveraging an Improved Genetic Algorithm (GA) based K-means (IGBKM) to address concerns with initial cluster centre sensitivity and local optimisation. The third and most promising method combines Ant Colony Optimisation (ACO) and Improved K-means (ACO-IKM) to solve the shortcomings of K-means clustering by selecting cluster centroids optimally and maximising ability based on the disparity statistics. This comparative research study shows that in terms of clustering robustness and accuracy, the ACO-IKM method performs much better than the other approaches. The results of this study show that the ACO-IKM approach is a better way to analyse the HFI dataset, providing dependable and effective clustering performance using Silhouette score. This assists in identifying trends in global freedom and pinpoints areas that require improvement in order to advance human dignity and autonomy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Ant Colony Optimisation with Integrated K-means fitness function genetic algorithm Human freedom Index K-Means cluster Mini Batch k-means Silhouette score

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A Hybrid Approach for Stock Market Price Forecasting Using Long Short-Term Memory and Seahorse Optimization Algorithm

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Annals of Data science 2025年 1-27页

作者： Gülmez, Burak Leiden Institute of Advanced Computer Science Leiden University Leiden Netherlands Department of Industrial Engineering Mudanya University Bursa Türkiye

Stock market price forecasting is a challenging task due to the complexity and volatility of market dynamics. This paper proposes a novel approach that combines the strengths of Long Short-Term Memory (LSTM) networks and the Seahorse Optimization (SHO) algorithm for stock market price forecasting. The LSTM-SHO model is compared with other LSTM models optimized using Genetic Algorithm (GA) and different dimensionalities (1D, 2D, 3D), as well as an Artificial Neural Network (ANN) model. The results show that the LSTM-SHO model outperforms the other models in terms of mean squared error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (R2). The hyperparameter tuning process using SHO significantly improves the forecasting accuracy of the LSTM model. The proposed approach demonstrates its potential in developing more accurate and robust stock market forecasting models, which can aid investors and analysts in making informed decisions. The findings of this study have important implications for investors, analysts, and policymakers, and contribute to the existing body of literature on stock market forecasting.

关键词： Genetic algorithms

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Adaptive Fine-Grained Feature Mining and RoI Feature Interaction Network for Small Object Detection in Aerial Images

Adaptive Fine-Grained Feature Mining and RoI Feature Interac...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Fangmin Xie Zhan Xiong Zhuoyue Wang Kun Yang Guoqiang Xiao College of Computer and Information Science Southwest University China Institute of Advanced Computer Science Leiden University Netherlands Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Object detection in drone view images remains challenging due to small-scale objects distributed non-uniformly and exhibiting weak semantic features. Additionally, external factors such as lighting conditions, viewing angles, and background clutter further complicate the detection of small objects. To address these issues, we propose a small object detector called Adaptive Fine-grained Feature Mining Network (AFMNet) based on an anchor-free framework. Firstly, an Adaptive Fine-grained Feature Mining (AFFM) module is designed to generate Regions of Interest (RoI) on high-resolution feature maps and mine fine-grained information that benefits the representation of small objects. Secondly, an Inter-RoI Feature Interaction (IRFI) block is developed to efficiently integrate complementary cues— namely, deep layer semantic information and shallow layer spatial details—and enhance RoI features. Experiments on the VisDrone dataset demonstrate that our method achieves state-of-the-art performance. The source code of our AFMNet is available at https://***/xiefangmin/AFMNet.

关键词： Adaptive systems Source coding Semantics Lighting Object detection Detectors Signal processing Feature extraction Speech processing Drones

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Cubature Kalman Fusion Filtering Under Amplify-and-Forward Relays With Randomly Varying Channel Parameters

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 356-368页

作者： Jiaxing Li Zidong Wang Jun Hu Hongli Dong Hongjian Liu the Department of Applied Mathematics Harbin University of Science and Technology IEEE the Department of Computer Science Brunel University London the School of Automation Harbin University of Science and Technology the Artificial Intelligence Energy Research Institute Northeast Petroleum University the Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum University the National Key Laboratory of Continental Shale Oil of China Northeast Petroleum University the Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment Ministry of Education Anhui Polytechnic University the School of Mathematics and Physics Anhui Polytechnic University

In this paper, the problem of cubature Kalman fusion filtering(CKFF) is addressed for multi-sensor systems under amplify-and-forward(AaF) relays. For the purpose of facilitating data transmission, AaF relays are utilized to regulate signal communication between sensors and filters. Here, the randomly varying channel parameters are represented by a set of stochastic variables whose occurring probabilities are permitted to exhibit bounded uncertainty. Employing the spherical-radial cubature principle, a local filter under AaF relays is initially constructed. This construction ensures and minimizes an upper bound of the filtering error covariance by designing an appropriate filter gain. Subsequently, the local filters are fused through the application of the covariance intersection fusion rule. Furthermore, the uniform boundedness of the filtering error covariance's upper bound is investigated through establishing certain sufficient conditions. The effectiveness of the proposed CKFF scheme is ultimately validated via a simulation experiment concentrating on a three-phase induction machine.

关键词： Amplify-and-forward (AaF) relays covariance intersection fusion cubature Kalman filtering multi-sensor systems uniform boundedness

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