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MGKsite: Multi-Modal Knowledge-Driven Site Selection via Intra and Inter-Modal Graph Fusion

IEEE TRANSACTIONS ON MULTIMEDIA 2025年 27卷 1722-1735页

作者： Liang, Ke Meng, Lingyuan Li, Hao Liu, Meng Wang, Siwei Zhou, Sihang Liu, Xinwang He, Kunlun Natl Univ Def Technol Sch Comp Changsha 410073 Peoples R China Intelligent Game & Decis Lab Beijing 100853 Peoples R China Natl Univ Def Technol Coll Intelligence Sci & Technol Changsha 410073 Peoples R China Chinese Peoples Liberat Army Gen Hosp Med Big Data Res Ctr Beijing 100853 Peoples R China

Site selection aims to select optimal locations for new stores, which is crucial in business management and urban computing. The early data-driven models heavily relied on feature engineering, which could not effectively model the complex relationships and diverse influences among different data. To alleviate such issues, the knowledge-driven paradigm is proposed based on urban knowledge graphs (KGs). However, the research on them is at an early stage. They omit extra multi-modal information corresponding to brands and stores due to two main challenges, i.e., (1) building available datasets, and (2) designing effective models. It constrains the expressive ability and practical value of previous models. To this end, we first construct new multi-modal urban KGs for site selection with three extra modal (i.e., visual, textual, and acoustic) attributes. Then, we propose a novel multi-modal knowledge-driven model (MGKsite). Concretely, a graph neural network (GNN) based fusion network is designed to fuse the features based on the attribute K-Nearest Neighbor (KNN) graph, which models both intra and inter-modal correlations among the features. The fused embeddings are further injected into the knowledge-driven backbones for learning and inference. Experiments prove promising capacities of MGKsite from five aspects, i.e., superiority, effectiveness, sensitivity, transferability and complexity.

关键词： computational modeling Visualization Nearest neighbor methods Knowledge graphs Graph neural networks Data models Biological system modeling Acoustics Feature extraction Correlation Graph learning knowledge graph reasoning multi-view learning site selection urban computing

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Emerging Blockchain and Reputation Management in Federated Learning: Enhanced Security and Reliability for Internet of Vehicles (IoV)

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第2期74卷 1893-1908页

作者： Mun, Hyeran Han, Kyusuk Yeun, Hyun Ku Damiani, Ernesto Puthal, Deepak Kim, Tae-Yeon Yeun, Chan Yeob Khalifa Univ Ctr Cyber Phys Syst Dept Comp Sci Abu Dhabi U Arab Emirates Technol Innovat Inst TII Secure Syst Res Ctr SSRC Abu Dhabi 127788 U Arab Emirates Higher Coll Technol Sch Engn & Technol Math Abu Dhabi 127788 U Arab Emirates Indian Inst Management Bodh Gaya Turi Khurd Bihar India Khalifa Univ Dept Civil & Environm Engn Abu Dhabi 127788 U Arab Emirates

Artificial intelligence (AI) technologies have been applied to the Internet of Vehicles (IoV) to provide convenience services such as traffic flow prediction. However, concerns regarding privacy and security are on the rise as huge amounts of data are aggregated to form large models (LMs). Although federated learning (FL), which trains and updates a model without sharing the actual datasets, has been intensively researched to prevent privacy breaches, there are still potential security threats like a single point of failure and intentional tampering with malicious data. This is because of the vulnerability of a central curator and a lack of authentication. As participants, they (i.e., vehicles) may unintentionally update low-quality data caused by poor wireless connectivity, unstable availability, and insufficient training datasets. They may also intentionally update unreliable data to carry out poisoning attacks. The divergence among local models, trained on non-independent and identically distributed (non-IID) data, can slow convergence and diminish model accuracy when these models are aggregated. Therefore, it is important to carefully select trustworthy participants. In this paper, we propose a new reliable and secure federated learning for IoV based on decentralized blockchain and reputation management. To cope with a single point of failure, injection of malicious data, and lack of authentication while ensuring privacy and traceability, our scheme combines blockchain and a lightweight digital signature. Moreover, we employ the concept of the reputation of vehicles to select suitable participants with reliability, ultimately improving accuracy. Security analysis results, including comparisons with previous works, prove that the proposed scheme can address security concerns. The results of performance evaluations demonstrate the effectiveness of our proposed scheme.

关键词： Data models Servers Security Blockchains Authentication Reliability computational modeling Blockchain federated learning (FL) Internet of Vehicles (IoV) large models (LMs) privacy reputation security

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Intellectual Property Protection for Deep Models: Pioneering Cross-Domain Fingerprinting Solutions

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2025年 20卷 3587-3600页

作者： Xu, Tianhua Zhong, Sheng-Hua Zhang, Zhi Liu, Yan Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

The high cost of developing high-performance deep models highlights their value as intellectual property for creators. However, it is important to consider the potential risks of theft. Although various techniques have been developed to protect the intellectual property of deep models, there is still room for improvement in terms of efficiency, comprehensiveness, and generalization. Compared with the intrusiveness of watermarking methods, fingerprinting methods do not affect the training process of the source model. Consequently, this paper proposes a fingerprinting method to address the paucity of attempts in fingerprinting methods for model protection. Our method consists of two efficient algorithms for generating fingerprinting samples, where the first one possesses the advantage of efficiency, while the second one is better in terms of robustness. The first algorithm takes a comprehensive approach to modeling the fingerprint of the deep model. The generated samples are distributed within the stable region and near the decision boundary of the model, taking into account both the duality and the conviction factors. Then, a heuristic sample perturbation algorithm is introduced, which generates a fingerprint with solid stability and generalization across multiple domains. The two algorithms proposed in this paper have been shown to be capable of withstanding attacks on intellectual property removal, detection, and evasion. They also show some advantages in terms of efficiency. In addition, the proposed method is the first to apply fingerprinting techniques in a cross-domain context.

关键词： Fingerprint recognition Brain modeling Watermarking Protection IP networks computational modeling Context modeling Adaptation models Intellectual property Training Model protection intellectual property model fingerprint

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Toward Interactive Next Location Prediction Driven by Large Language Models

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IEEE TRANSACTIONS ON computational SOCIAL SYSTEMS 2025年

作者： Chen, Yong Chi, Ben Li, Chuanjia Zhang, Yuliang Liao, Chenlei Chen, Xiqun Xie, Na Zhejiang Univ Inst Intelligent Transportat Syst Hangzhou 310058 Peoples R China Zhejiang Univ Coll Civil Engn & Architecture Hangzhou 310058 Peoples R China Zhejiang Univ Polytech Inst Hangzhou 310058 Peoples R China Hangzhou City Univ Intelligent Transportat Syst Res Ctr Hangzhou 310015 Peoples R China Cent Univ Finance & Econ Sch Management Sci & Engn Beijing 100081 Peoples R China

Individual next location prediction plays a crucial role in location-based applications, such as route navigation and service recommendation. Although the existing research based on deep learning effectively captures users' spatiotemporal travel preferences, there are challenges in the interpretability of location prediction, heavily relying on large-scale historical travel data for model training. Drawing inspiration from the powerful reasoning capabilities of large language models (LLMs), this study proposes a novel multiround continuous dialogue mechanism and candidate set enhancement method, leveraging LLMs for next location prediction through step-by-step reasoning. In the first round of dialogue, we introduce activity prediction as an auxiliary task to narrow down the candidate locations. Subsequently, we establish an activity-aware prompt to enable LLM to achieve accurate location prediction and provide corresponding reasoning. Finally, we incorporate a third round of dialogue to prompt LLM to make necessary corrections by integrating the prediction results of deep learning models. To address the issues of LLMs being affected by element ranking within the candidate set, we propose a new candidate set enhancement method based on the entropy-weighted Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Our model can understand user travel preferences by fusing location, activity, and time information through natural language. Extensive experiments are conducted on two public datasets of user check-ins, and the results show that our model achieves prediction performance comparable to deep learning models in full-sample prediction and outperforms them in the few-shot settings. Our model provides logical and explainable reasoning, offering insightful guidance for downstream application tasks.

关键词： Predictive models Accuracy Spatiotemporal phenomena Cognition Natural languages Feature extraction Data models computational modeling Deep learning Large language models Human mobility large language model (LLM) location prediction multiround continuous dialogue Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)

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The PVZEN Lab for Energy Communities: Monitoring System for Identification of Photovoltaic/Battery/Converter Energy Models

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Amato, Angela Carullo, Alessio Ciocia, Alessandro Corbellini, Simone Spertino, Filippo Vallan, Alberto Bertonasco, Marco Augusto Alfredo Politecn Torino Dept Energy G Ferraris I-10129 Turin Italy Politecn Torino Dept Elect & Telecommun I-10129 Turin Italy

The main topic of this article is the photovoltaic zero energy network (PVZEN) laboratory, which is an energy community for research purposes established at the Politecnico di Torino campus. The PVZEN laboratory has three users, each equipped with a photovoltaic (PV) generator, lithium batteries with proper electronic converters, and emulators of electric appliances, including heat pumps. These users are capable of exchanging energy based on specific strategies conceived for maximizing the use of renewable energy sources. This work is focused on the identification of energy models that can describe the main components of the community. With the aim of providing reliable models that can enable other researchers to simulate the microgrid in different operating conditions, a monitoring system has been installed close to the relevant commercial devices, to obtain traceable measurements of electrical and environmental quantities. The reference results provided by the monitoring system allowed the multimeter internal to the inverters to be characterized. Furthermore, the dc measurements provided by the monitoring system allowed reliable models to be identified for PV generators and storage systems. For the former, the identified models exhibited root-mean-square errors between measured and predicted energy lower than 1% during a time interval of about three months;for the latter, charge and discharge efficiencies and self-discharge were in agreement with the expected values for the lithium batteries.

关键词： Energy measurement Measurement uncertainty Integrated circuit modeling Temperature measurement Generators Photovoltaic systems Microgrids Mathematical models Battery charge measurement computational modeling Calibration data acquisition energy community photovoltaic (PV) power systems uncertainty

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Distilling Wisdom: A Review on Optimizing Learning From Massive Language Models

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IEEE ACCESS 2025年 13卷 56296-56325页

作者： Zhang, Dingzong Listiyani, Devi Singh, Priyanka Mohanty, Manoranjan Univ Queensland Sch Elect Engn & Comp Sci Brisbane Qld 4072 Australia Carnegie Mellon Univ Qatar Sch Elect Engn & Comp Sci Ar Rayyan Qatar

In the era of Large Language Models (LLMs), Knowledge Distillation (KD) enables the transfer of capabilities from proprietary LLMs to open-source models. This survey provides a detailed discussion of the basic principles, algorithms, and implementation methods of knowledge distillation. It explores KD's impact on LLMs, emphasizing its utility in model compression, performance enhancement, and self-improvement. Through the analysis of practical examples such as DistilBERT, TinyBERT, and MobileBERT, the paper demonstrates how knowledge distillation can markedly enhance the efficiency and applicability of large language models in real-world scenarios. The discussion encompasses the varied applications of KD across multiple domains, including industrial systems, embedded systems, Natural Language Processing (NLP), multi-modal processing, and vertical domains, such as medicine, law, science, finance, and materials science. This survey outlines current KD methodologies and future research directions, highlighting its role in advancing AI technologies and fostering innovation across different sectors.

关键词： Transformers computational modeling Surveys Natural language processing Predictive models Artificial intelligence Technological innovation Encoding Context modeling Artificial intelligence (AI) large language model (LLM) knowledge distillation (KD) optimization optimization

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Evading Detection Actively: Toward Anti-Forensics Against Forgery Localization

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IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING 2025年第2期22卷 852-869页

作者： Zhuo, Long Luo, Shenghai Tan, Shunquan Chen, Han Li, Bin Huang, Jiwu Shenzhen MSU BIT Univ Guangdong Lab Machine Percept & Intelligent Comp Fac Engn Shenzhen 518116 Peoples R China Shenzhen Key Lab Media Secur Guangdong Key Lab Intelligent Informat Proc Shenzhen 518060 Guangdong Peoples R China

Anti-forensics seeks to eliminate or conceal traces of tampering artifacts. Typically, anti-forensic methods are designed to deceive binary detectors and persuade them to misjudge the authenticity of an image. However, to the best of our knowledge, no attempts have been made to deceive forgery detectors at the pixel level and mis-locate forged regions. Traditional adversarial attack methods cannot be directly used against forgery localization due to the following defects: 1) they tend to just naively induce the target forensic models to flip their pixel-level pristine or forged decisions;2) their anti-forensics performance tends to be severely degraded when faced with the unseen forensic models;3) they lose validity once the target forensic models are retrained with the anti-forensics images generated by them. To tackle the three defects, we propose SEAR (Self-supErvised Anti-foRensics), a novel self-supervised and adversarial training algorithm that effectively trains deep-learning anti-forensic models against forgery localization. SEAR sets a pretext task to reconstruct perturbation for self-supervised learning. In adversarial training, SEAR employs a forgery localization model as a supervisor to explore tampering features and constructs a deep-learning concealer to erase corresponding traces. We have conducted large-scale experiments across diverse datasets. The experimental results demonstrate that, through the combination of self-supervised learning and adversarial learning, SEAR successfully deceives the state-of-the-art forgery localization methods, as well as tackle the three defects regarding traditional adversarial attack methods mentioned above.

关键词： Location awareness Training Forensics Perturbation methods computational modeling Self-supervised learning Detectors Forgery Adversarial machine learning Robustness Anti-forensics concealer supervisor self-supervised training adversarial learning

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Digital Twin-Aided Vehicular Edge Network: A Large-Scale Model Optimization by Quantum-DRL

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第2期74卷 2156-2173页

作者： Paul, Anal Singh, Keshav Li, Chih-Peng Dobre, Octavia A. Duong, Trung Q. Natl Sun Yat Sen Univ Inst Commun Engn Kaohsiung 80424 Taiwan Mem Univ Fac Engn & Appl Sci St John NF A1C 5S7 Canada Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast BT7 1NN North Ireland Kyung Hee Univ Dept Elect Engn Yongin 17104 South Korea

This paper presents an innovative large model framework for optimizing the task offloading efficiency in vehicular edge networks, with a focus on ultra-reliable low-latency communication. We introduce a comprehensive model that integrates quantum computing with a deep reinforcement learning (DRL) model, supported by long short-term memory (LSTM) networks and a digital twin framework. This integration is designed to address the complexities of distributed vehicular edge computing networks, targeting efficient latency, energy, and quality-of-service management. Our model utilizes the parallel processing capabilities of quantum computing to enhance the DRL algorithm, effectively handling high-dimensional decision spaces. LSTM networks provide predictive insights into future network states in a digital twin framework and ensure real-time synchronization and adaptive strategy optimization. We employ a multi-agent framework, encompassing vehicles, unmanned aerial vehicles, and base stations, each utilizing a Nash equilibrium-based strategy for optimal decision-making, supplemented by incentive and penalty functions for reward optimization. Simulation results demonstrate notable improvements in task offloading efficiency, highlighting the model's efficacy over conventional DRL models.

关键词： Task analysis computational modeling Quantum computing Optimization Ultra reliable low latency communication Edge computing Data models Vehicular edge computing task offloading ultra-reliable low-latency communication quantum deep reinforcement learning large model framework long short-term memory networks digital twin and multi-agent systems

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Detection of Potential Breeding Sites Based on AAV Remote Sensing Imagery and MultiDCCSP-YOLO Network

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IEEE ACCESS 2025年 13卷 52972-52982页

作者： Gao, Zichen Liu, Qinmei Zhou, Yibin Liu, Qing Ma, Zu Ding, Huaiyue Ren, Bin Xiang, Deliang Zhang, Hengduan State Key Lab Pathogen & Biosecur Beijing 100071 Peoples R China Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China Zhejiang Prov Ctr Dis Control & Prevent Hangzhou 310057 Peoples R China Shanghai Minhang Ctr Dis Control & Prevent Shanghai 201101 Peoples R China Chinese Peoples Liberat Army Gen Hosp Senior Dept Neurosurg Med Ctr 1 Beijing 100071 Peoples R China

Detection and control of potential breeding sites (PBS) for Aedes albopictus are considered among the most effective methods for managing mosquito-borne diseases. Due to the high adaptability and rapid reproduction of mosquitoes, PBS are widely distributed. Traditional manual investigation methods are inefficient and would increase the risk of exposure. Autonomous Aerial Vehicle (AAV) remote sensing technology, with its speed and broad coverage, can capture large-scale images quickly. However, current PBS detection methods primarily focus on the detection of large, regular containers. To address the issue for detecting diverse PBS in complex environments, we propose a method called MultiDCCSP-YOLO. This approach integrates several modules to improve the PBS detection accuracy from AAV remote sensing images with challenging backgrounds. We introduce the Multi-DC Block and use it to build the CSPDCStage module. This module captures multi-scale features while reducing computational demands without compromising the detection accuracy. The FasterELAN module minimizes overfitting risks and enhances the detection of PBS targets with limited-sample classes. Additionally, we incorporate the Adaptive Threshold Focal Loss (ATFL) function to emphasize small, hard-to-detect targets, thereby improving multi-scale PBS detection performance. The MultiDCCSP-YOLO method enables efficient and accurate PBS detection in complex environments, facilitating large-scale monitoring efforts.

关键词： Remote sensing Autonomous aerial vehicles Convolution Feature extraction Adaptation models computational modeling Accuracy Neck Computer architecture Training Potential breeding sites Aedes albopictus autonomous aerial vehicle (AAV) target detection remote sensing images

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RCLT-Net: An Indoor Panoramic Room Layout Estimation Framework Based on Consecutive Dilated Convolutions and Transformer

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IEEE ACCESS 2025年 13卷 45923-45935页

作者： Sun, Qiucheng Deng, Zhengkai Zhu, Jinlong Changchun Normal Univ Inst Comp Sci & Technol Changchun 130032 Peoples R China

Current mainstream models for indoor panoramic room layout depth estimation primarily utilize deep residual networks and various Transformer-based modifications to reconstruct room boundaries from 2D images. However, these methods often encounter challenges such as limited receptive fields, insufficient multi-scale feature interaction and integration capabilities, and blurred boundary estimations in complex scenes. To overcome these challenges, we propose ResCDC-Net, a novel feature extraction model that expands the receptive field by incorporating continuous dilated convolutions within convolutional layers and applying varying dilation rates in residual blocks. This design enhances the perception of fine-grained multi-scale features. Additionally, we introduce the Local-Global Feature Interaction Shifting Window Transformer, which significantly improves the Transformer model's ability to extract features by fostering robust interactions between local and global features, enabling more flexible and efficient feature representation. Furthermore, we employ a triplet loss function based on positive and negative anchor points to ensure clear boundary distinctions in complex object boundary regions. Extensive experiments validate that our proposed RCLT-Net surpasses SoTA models in depth estimation accuracy and demonstrates exceptional performance across multiple challenging public datasets.

关键词： Layout Depth measurement Feature extraction Transformers Estimation Accuracy Solid modeling computational modeling Training Sun Depth estimation panoramic room layout continuous dilated convolution local-global feature interaction

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