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Semantic-enhanced relation modeling for fine-grained aspect-based sentiment analysis

International Journal of Machine Learning and Cybernetics 2025年 1-16页

作者： Zheng, Yanxi Tang, Mingwei Yang, Zhendong Hu, Jie Zhao, Mingfeng School of Computer and Software Engineering Xihua University Chengdu 610039 China School of Computing and Artificial Intelligence Southwest Jiaotong University Sichuan Province Chengdu 611756 China China Mobile Group Design Institute Co. Ltd. Sichuan Branch Sichuan Province Chengdu 610045 China

Aspect-based sentiment analysis (ABSA) is a fine-grained task that aims to identify the sentiment polarity associated with specific aspects in a sentence. To address ABSA tasks, state-of-the-art methods incorporate attention mechanisms with Abstract Meaning Representation (AMR) to model the relationships between specific aspects and their contextual information. However, existing ABSA datasets are limited in scale and difficult to acquire. Furthermore, extracting semantic information from AMR remains challenging due to its inherent structural complexity, and the performance of ABSA is highly dependent on the quality of AMR parsing. To overcome these challenges, we propose a novel Semantic Enhanced Relation Aggregation Network (SERAN), which encodes AMR to extract semantic information and integrates global features to enrich semantic representation. First, the Word Replacement (WR) module generates adversarial samples by randomly replacing words in sentences, thereby augmenting the dataset and enhancing its lexical diversity. Furthermore, the semantic iteration module employs a self-attention mechanism to compute attention score matrix of the sentence, which is subsequently encoded by using graph convolution to enhance the semantic representation in AMR. Additionally, a relation aggregation module is designed to further enhance global sentence features by leveraging semantic information derived from AMR. Finally, extensive experiments demonstrate that the proposed model outperforms several state-of-the-art methods on four benchmark datasets. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Abstract meaning representation Aspect-based sentiment analysis Relational aggregation Semantic enhancement

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Zero Trust Networks: Evolution and Application from Concept to Practice

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Computers, Materials & Continua 2025年第2期82卷 1593-1613页

作者： Yongjun Ren Zhiming Wang Pradip Kumar Sharma Fayez Alqahtani Amr Tolba Jin Wang School of Computer Science School of Cyber Science and EngineeringEngineering Research Center of Digital ForensicsMinistry of EducationNanjing University of Information Science and TechnologyNanjing210044China Department of Computing Science University of AberdeenAberdeenAB243FXUK Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Sanya Institute of Hunan University of Science and Technology Sanya572024China

In the context of an increasingly severe cybersecurity landscape and the growing complexity of offensive and defen-sive techniques,Zero Trust Networks(ZTN)have emerged as a widely recognized *** Trust not only addresses the shortcomings of traditional perimeter security models but also consistently follows the fundamental principle of“never trust,always verify.”Initially proposed by John Cortez in 2010 and subsequently promoted by Google,the Zero Trust model has become a key approach to addressing the ever-growing security threats in complex network *** paper systematically compares the current mainstream cybersecurity models,thoroughly explores the advantages and limitations of the Zero Trust model,and provides an in-depth review of its components and key ***,it analyzes the latest research achievements in the application of Zero Trust technology across various fields,including network security,6G networks,the Internet of Things(IoT),and cloud computing,in the context of specific use *** paper also discusses the innovative contributions of the Zero Trust model in these fields,the challenges it faces,and proposes corresponding solutions and future research directions.

关键词： Zero trust cybersecurity software-defined perimeter micro-segmentation internet of things

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An efficient and balanced feature pyramid network: shared-FPN

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Optoelectronics Letters 2025年

作者： Tian Sulong Qin Longfei Pang Wenchao Gao Ying Zhao Dexin School of Computer Science and Engineering， Tianjin University of Technology Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology， Tianjin University of Technology

In the field of object detection， it is challenging to achieve a balance between neck complexity and accuracy. To address this issue， we propose an efficient and decoupled neck module called Shared-FPN(Shared Feature Pyramid Network). Our design aims to effectively address the trade-off between these two factors. Neck modules are crucial for effectively fusing multi-scale information in neural network models. However， traditional neck designs， with their single structure， have struggled to significantly improve detection performance. In recent years， the incorporation of neural architecture search techniques in neck design has led to an increase in model complexity. This trade-off between model complexity and detection performance highlights the need for more efficient neck design approaches. Not only does Shared-FPN not increase the number of model parameters and FLOPs， but it also can be easily ported to any of the detection models. It improves on PAFPN by using transposed convolution with a large convolution kernel as the upsampling module， designing spatial pyramidal pooling-fast downsampling(SPPFD) based on shared pooling， and designing shared convolution as the right part module. To evaluate the performance of Shared-FPN in object detection tasks， we conducted experiments on object detection datasets. The results show that Shared-FPN achieved excellent performance across all sizes. In particular， on the VOC 2012 dataset， which contains mostly large objects， Shared-FPN's mAP was improved by 11.2% compared to FPN with YOLOX-s as the detector. On the COCO dataset， which contains mostly small objects， Shared-FPN's mAP was improved by 7.8% compared to FPN and 7.1% compared to PAFPN with Faster RCNN as the detector. Shared-FPN can be easily inserted into any of the detectors for better performance in various scenarios， such as small， medium or large objects.

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Deep Learning and IoT-Enabled Accident Detection and Reporting for Smart Cities Domain

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KSII Transactions on Internet and Information Systems 2025年第4期19卷 1140-1166页

作者： Javed, Rawal Mazhar, Tehseen Aoun, Muhammad Shahzad, Tariq Al-AlShaikh, Halah A. Ghadi, Yazeed Yasin Saudagar, Abdul Khader Jilani Khan, Muhammad Amir School of Automation Central South University Changsha China School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Science and Information Technology School Education Department Government of Punjab Layyah31200 Pakistan Department of CS IT Ghazi University Punjab Dera Ghazi Khan Pakistan School of Electrical Engineering Dept. of Electrical and Electronic Eng. Science University of Johannesburg Johannesburg2006 South Africa Riyadh11432 Saudi Arabia Department of Computer Science and Software Engineering Al Ain University Abu Dhabi12555 United Arab Emirates School of Computing Sciences College of Computing Informatics and Mathematics University Technology MARA Selangor Shah Alam40450 Malaysia

Even though numerous ICT-based solutions have been put forth for the detection of accidents and rescue missions, they often suffer from compatibility issues with different vehicles and are accompanied by high costs. This paper introduces a cutting-edge intelligent system designed for accident detection and rescue, mirroring human cognitive functions through integrating the Internet of Things (IoT) and artificial intelligence. A specialized kit for IoT has been created to identify accidents and gather comprehensive incident-related data, such as pressure, position, gravitational force, and speed. This information is then transmitted to the cloud for processing. A deep learning model is implemented in the cloud environment to verify the IoT module's output and activate the rescue mechanism when an accident is detected. The DL module, employing ensemble transfer learning with dynamic weights, aims to minimize false detection rates. Because there isn't an appropriate dataset available, a personalized dataset is generated from diverse online videos. To validate the proposed method, a comparative analysis is conducted on Yolo V4. Experimental results indicate that Yolo V4 outperforms, achieving training, validation, and test accuracies of 98.2% each. To assess the system's real-world applicability, it undergoes validation using a robot car model. Copyright © 2025 KSII.

关键词： Deep learning

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Towards Integrated Spatial Crowdsourcing: Online Privacy-Preserving Selection

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IEEE Transactions on Dependable and Secure computing 2025年

作者： Wang, Hengzhi Ma, Minghe Cui, Laizhong Liu, Jiangchuan Shenzhen University College of Computer Science and Software Engineering Shenzhen China Simon Fraser University School of Computing Science Burnaby Canada

We study an intriguing and practical scenario of online Spatial Crowdsourcing (SC), in which workers have the flexibility to perform tasks using various methods, such as walking, driving, or utilizing Unmanned Aerial Vehicles (UAVs). This results in workers having heterogeneous, arbitrary, and non-stationary utilities over time. We refer to this scenario as integrated SC. Unfortunately, existing studies are limited in addressing integrated SC settings due to two aspects: (1) these studies are based on the assumption that workers' utilities are independently and identically distributed and follow a stationary distribution like Gaussian, which does not hold in integrated SC;(2) their approaches fail to provide personalized privacy preservation for different workers. Motivated by these limitations, we closely investigate the heterogeneous utility and personalized privacy requirement in integrated SC and propose an Online Personalized Privacy-preserving Selection framework (OPPS). In this framework, we present an online selection policy that balances the exploration-exploitation trade-off given heterogeneous utilities and develop a built-in privacy policy that ensures differential privacy guarantee. We then demonstrate that our framework effectively addresses the trade-off by deriving a sublinear, privacy-related upper bound on regret that scales as O(√T). Extensive numerical simulations based on real-world drone datasets are conducted to validate the effectiveness of our framework compared with state-of-the-art approaches. © 2004-2012 IEEE.

关键词： Differential privacy

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Automated Fabrication of Magnetic Soft Microrobots

arXiv

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arXiv 2025年

作者： Clancy, Kaitlyn Xie, Siwen Smith, Griffin Onaizah, Onaizah School of Biomedical Engineering McMaster University Hamilton Canada Department of Computing and Software McMaster University Hamilton Canada

The advent of 3D printing has revolutionized many industries and has had similar improvements for soft robots. However, many challenges persist for these functional devices. Magnetic soft robots require the addition of magnetic particles that must be correctly oriented. There is a significant gap in the automated fabrication of 3D geometric structures with 3D magnetization direction. A fully automated 3D printer was designed to improve accuracy, speed, and reproducibility. This design was able to achieve a circular spot size (voxels) of 1.6mm in diameter. An updated optical system can improve the resolution to a square spot size of 50µm by 50µm. The new system achieves higher resolution designs as shown through magneto-mechanical simulations. Various microrobots including’worm’,’gripper’ and’zipper’ designs are evaluated with the new spot size. Copyright © 2025, The Authors. All rights reserved.

关键词： Microfabrication

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VEGAS: Towards Visually Explainable and Grounded Artificial Social Intelligence

arXiv

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arXiv 2025年

作者： Li, Hao Fei, Hao Hu, Zechao Yang, Zhengwei Wang, Zheng National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China School of Computing National University Singapore

Social Intelligence Queries (Social-IQ) serve as the primary multimodal benchmark for evaluating a model’s social intelligence level. While impressive multiple-choice question (MCQ) accuracy is achieved by current solutions, increasing evidence shows that they are largely, and in some cases entirely, dependent on language modality, overlooking visual context. Additionally, the closed-set nature further prevents the exploration of whether and to what extent the reasoning path behind selection is correct. To address these limitations, we propose the Visually Explainable and Grounded Artificial Social Intelligence (VEGAS) model. As a generative multimodal model, VEGAS leverages open-ended answering to provide explainable responses, which enhances the clarity and evaluation of reasoning paths. To enable visually grounded answering, we propose a novel sampling strategy to provide the model with more relevant visual frames. We then enhance the model’s interpretation of these frames through Generalist Instruction Fine-Tuning (GIFT), which aims to: i) learn multimodal-language transformations for fundamental emotional social traits, and ii) establish multimodal joint reasoning capabilities. Extensive experiments, comprising modality ablation, open-ended assessments, and supervised MCQ evaluations, consistently show that VEGAS effectively utilizes visual information in reasoning to produce correct and also credible answers. We expect this work to offer a new perspective on Social-IQ and advance the development of human-like social AI. © 2025, CC BY.

关键词： Emotional intelligence

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RPPFL: Robust and Privacy-Preserving Federated Learning via Trusted Execution Environments

RPPFL: Robust and Privacy-Preserving Federated Learning via ...

引用

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

作者： Xiaolei Zhang Zhaoyu Chen Guangpu Chen Xinyu Feng Qingni Shen Zhonghai Wu School of Computer Science Peking University National Engineering Research Center for Software Engineering Peking University PKU-OCTA Laboratory for Blockchain and Privacy Computing Peking University School of Software and Microelectronics Peking University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Federated Learning (FL) is a distributed framework that enables multi-participant collaborative model training without the need for data sharing. Despite its advantages, FL is vulnerable to poisoning and inference attacks, which compromise model accuracy and data privacy. Trusted execution environments (TEEs) offer a potential solution by providing a secure and isolated execution space to address these security and privacy concerns in FL. However, existing TEE-based FL schemes often suffer from reduced training speed and compromised model accuracy. To mitigate these issues, we propose a robust and privacy-preserving framework for federated learning (RPPFL) that leverages TEE and pseudorandom masking. In our approach, a trusted local model is trained on a secure subset of local data within the client-side TEE, which is then used for anomaly detection to resist poisoning attacks. Additionally, we employ pseudorandom masking to obfuscate local updates and global parameters. Experimental results indicate that RPPFL effectively counters both poisoning and inference attacks, with only a minimal decrease in training speed and no adverse impact on model accuracy. Compared to full-TEE approaches, our method improved local training efficiency by 10× , with less than a 9% loss in model performance under poisoning attacks.

关键词： Training Accuracy Federated learning Resists Signal processing Data models Robustness Security Speech processing Anomaly detection

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Development and evaluation of an early childhood caries prediction model: a deep learning-based hybrid statistical modelling approach

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European Archives of Paediatric Dentistry 2025年 1-12页

作者： Eusufzai, S.Z. Jamayet, N.B. Ahmed, S. Islam, M.B. Ahmad, W.M.A.W. Alam, M.K. Department of Biostatistics Universiti Sains Malaysia Kota Bharu Malaysia School of Dentistry IMU University Kuala Lumpur Malaysia Department of Electrical and Computer Engineering North South University Dhaka Bangladesh Department of Computing and Software Engineering Florida Gulf Coast University Florida United States College of Dentistry Jouf University Sakaka Saudi Arabia

Purpose: An effective Deep learning (DL) based Early Childhood Caries (ECC) prediction model is crucial for early detection of ECC. This study aims to develop and evaluate a deep learning (DL) based hybrid statistical model for ECC prediction. Methods: The study employed a computational cross-sectional design, conducted over a three-year period from March 2021 to March 2024. Data analysis was carried out using a hybrid statistical approach that integrated bootstrap methods, Logistic Regression Modelling (LRM), and Multilayer Feed-Forward Neural Networks (MLFFNN). The sample comprised 157 parent–child pairs, providing a robust dataset for examining the research questions. Results: In the current study, the predictors named, “mother’s education” (β₁: 0.423;p < 0.25), “parent’s knowledge of bottle-feeding habit during sleep can cause tooth decay” (β₂: −1.264;p < 0.25), “attitude towards the importance of oral health as general health” (β₄: −1.052;p < 0.25) and “parent’s self-reported oral pain among their children” (β₅: −2.107;p < 0.25) showed significant association with ECC. For this model, the Mean Absolute Deviation (MAD) was 0.02211, Predictive Mean Squared Error (PMSE) was 0.07909, and the accuracy level was 99.98%. No significant difference was observed from the t-test between the actual values and the predicted values of the model (p > 0.05). Conclusion: It has been shown that this unique deep learning-based ECC prediction model appears an effective tool with high accuracy and interpretability for ECC prediction. After implementing the oral health intervention program, focusing on the potential predictors of ECC obtained from this innovative model, policymakers could be able to evaluate their prediction models comparing their results with the findings of the current study. This comparison will guide them in understanding, designing, and implementing a more effective intervention program for ECC prevention. © The Autho

关键词： Artificial intelligence Caries prediction model Early childhood caries Machine learning Neural networks

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Quantum phase discrimination with applications to quantum search on graphs

arXiv

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arXiv 2025年

作者： Li, Guanzhong Li, Lvzhou Luo, Jingquan Institute of Quantum Computing and Software School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China

We study the phase discrimination problem, in which we want to decide whether the eigenphase θ ∈ (−π,π] of a given eigenstate |ψ〉 with eigenvalue eiθ is zero or not, using applications of the unitary U provided as a black box oracle. We propose a quantum algorithm named quantum phase discrimination(QPD) for this task, with optimal query complexity Θ(1/λ log 1 /δ) to the oracle U, where λ is the gap between zero and nonzero eigenphases and δ the allowed one-sided error. The quantum circuit is simple, consisting of only one ancillary qubit and a sequence of controlled-U interleaved with single qubit Y rotations, whose angles are given by a simple analytical formula. Quantum phase discrimination could become a fundamental subroutine in other quantum algorithms, as we present two applications to quantum search on graphs: 1. Spatial search on graphs. Inspired by the structure of QPD, we propose a new quantum walk model, and based on them we tackle the spatial search problem, obtaining a novel quantum search algorithm. For any graph with any number of marked vertices, the quantum algorithm that can find a marked vertex with probability Ω(1) in total evolution time (formula presented) and query complexity (formula presented), where λ is the gap between the zero and non-zero eigenvalues of the graph Laplacian and Ε is a lower bound on the proportion of marked vertices. 2. Path-finding on graphs. By using QPD, we reduce the query complexity of a path-finding algorithm proposed by Li and Zur [arxiv: 2311.07372] from O̴(n11) to O̴(n8), in a welded-tree circuit graph with Θ(n2n) vertices. Besides these two applications, we argue that more quantum algorithms might benefit from QPD. © 2025, CC BY.

关键词： Quantum electronics

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