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SSRN 2023年

作者： Guo, Xiangxing Zhou, Wei Yang, Zhengyi Fu, Cheng Wen, Junhao School of Big Data and Software Engineering Chongqing University Daxuecheng South Rd. 55 Chognqing400044 China Key Laboratory of Dependable Service Computing in Cyber Physical Society MoE Chognqing400030 China

Social recommendation has emerged as a crucial technology for online service platforms, with a growing body of research in recent years. People's varying personalities influence their preferences for items and friends, and neglecting these personalities can result in impure representations that affect recommendation outcomes. In this paper, we propose a Personality-Aware Graph Convolutional Network (PGCN-SRec) model to capture users' preferences based on their personalities. The model first classifies users and their neighbors into personality subgraphs using their representations, and then graph convolution is performed within these subgraphs. User representations from social and interaction subgraphs are fused through a novel fusion module, and layer representations are generated by weighted summation. Experimental results demonstrate that our proposed method significantly improves recommendation performance on the Lastfm and Ciao datasets (by 1\% to 5\%). Furthermore, PGCN-SRec effectively alleviate the oversmoothing problem associated with stacked graph neural networks. Our PyTorch implementation is available at https://***/hsianghsing/PGCN-SRec. © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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Empowering Large Language Models in Wireless Communication: A Novel dataset and Fine-Tuning Framework

arXiv

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arXiv 2025年

作者： Lin, Yushen Zhang, Ruichen Huang, Wenqi Wang, Kaidi Ding, Zhiguo So, Daniel K.C. Niyato, Dusit School of Electrical and Electronic Engineering The University of Manchester M13 9PL United Kingdom Department of Electrical and Electronic Engineering University of Manchester Manchester United Kingdom Department of Computer Science Khalifa University Abu Dhabi United Arab Emirates College of Computing and Data Science Nanyang Technological University Singapore

In this work, we develop a specialized dataset aimed at enhancing the evaluation and fine-tuning of large language models (LLMs) specifically for wireless communication applications. The dataset includes a diverse set of multi-hop questions, including true/false and multiple-choice types, spanning varying difficulty levels from easy to hard. By utilizing advanced language models for entity extraction and question generation, rigorous data curation processes are employed to maintain high quality and relevance. Additionally, we introduce a Pointwise V-Information (PVI) based fine-tuning method, providing a detailed theoretical analysis and justification for its use in quantifying the information content of training data with 2.24% and 1.31% performance boost for different models compared to baselines, respectively. To demonstrate the effectiveness of the fine-tuned models with the proposed methodologies on practical tasks, we also consider different tasks, including summarizing optimization problems from technical papers and solving the mathematical problems related to non-orthogonal multiple access (NOMA), which are generated by using the proposed multi-agent framework. Simulation results show significant performance gain in summarization tasks with 20.9% in the ROUGE-L metrics. We also study the scaling laws of fine-tuning LLMs and the challenges LLMs face in the field of wireless communications, offering insights into their adaptation to wireless communication tasks. This dataset and fine-tuning methodology aim to enhance the training and evaluation of LLMs, contributing to advancements in LLMs for wireless communication research and applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Large datasets

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Modeling Method for Sampling Zeros of Sampled-data System with Time Delay in Generalized Sample Hold

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IFAC-PapersOnLine 2023年第2期56卷 9799-9805页

作者： Minghui Ou Shan Liang Zhiyong Yang Zhenjie Yan Zhiyong Li School of Big Data and Internet of Things Chongqing Vocational Institute of Engineering Chongqing 402260 PR China College of Automation Chongqing University Chongqing 400044 PR China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing 400044 PR China. School of Electrical Engineering Southwest Jiaotong University Chengdu 611756 PR China

Unstable zeros are widely known to dramatically curb the performance of controllers and the direct applications of several control algorithms. Time delay is a crucial research topic due to the prevalence of communication and computational delays in the control process. In particular, time delay exacerbates the possibility of continuous-time systems with stable zeros transforming into discrete-time systems with unstable zeros. For continuous-time systems with a relative degree greater than or equal to two, at least one zero of the corresponding discrete-time system converges to either marginally stable or unstable locations as the sampling time approaches zero. This study investigates the sampling zeros of discrete-time systems in the case of a generalized sample hold function (GSHF) with time delay. Moreover, it presents a novel modeling method and stability conditions of the limiting zeros for the discrete-time system. The results demonstrate that the stable properties of zeros for the time delay systems can be preserved in the discretization process when GSHF is applied for signal reconstruction when zero-order hold fails to do so. Finally, numerical examples are provided to illustrate the effectiveness of the proposed method in this study.

关键词： Sampling zeros stability generalized sample hold function (GSHF) time delay. sampled-data system

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Pulmonary Nodule Classification of CT Images with Attribute Self-guided Graph Convolutional V-Shape Networks 18th

Pulmonary Nodule Classification of CT Images with Attribute ...

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18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021

作者： Zhang, Xiangbo Wang, Kun Zhang, Xiaohong Huang, Sheng Key Laboratory of Dependabel Service Computing in Cyber Physical Society Ministry of Education Chongqing University Chongqing400044 China The School of Big Data and Software Engineering Chongqing University Chongqing401331 China

ISBN: (纸本)9783030891879

Accurate identification and early diagnosis of malignant pulmonary nodules are critical to improving the survival rate of lung cancer patients. Recently, deep learning methods have been proved to be successful in computer-aided diagnosis tasks. However, most advanced research work does not fully utilized valuable attribute prior knowledge for semantic reasoning to guide the network. Therefore, it lacks interpretability and hence is difficult for clinical radiologists to understand and apply. To comprehensively tackle these challenges, we propose a novel Attribute Self-guided Graph Convolutional V-shape Networks (AS-GCVN) for pulmonary nodules classification with steps as follows. We first develop a sub-network for representation learning, which can effectively extract image-level features. Second, we construct a graph convolution V-shape network to model the semantic information of attributes to guide the classification of benign and malignant pulmonary nodules accurately. Moreover, an Attribute Self-guided Feature Enhancement (ASFE) module is proposed to improve the ability of graph semantic reasoning, which can map the image features extracted by the convolutional neural network to attribute features through adaptive learning. Finally, the two sub-networks effectively integrate attribute inference knowledge and representation learning to enable end-to-end training. This way can further improve the interpretability and robustness of pulmonary nodule classification. Extensive experimental results on the LIDC-IDRI dataset demonstrate that our approach obviously outperforms other existing state-of-the-art methods. © 2021, Springer Nature Switzerland AG.

关键词： Convolution

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Research on Security Assessment of Intelligent Electronic Devices in Smart Grids Considering Cyber-Attacks and Physical Faults

SSRN

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SSRN 2023年

作者： Chen, Yang-Rong Li, Jun-E Xia, Yu Zhang, Rui-Wen Li, Ling-Ling Li, Xiao-Yu Ge, Lin School of Intelligent Engineering Zhengzhou University of Aeronautics Zhengzhou450046 China Henan Data Security Engineering Technology Research Center Zhengzhou450046 China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan430072 China Zhengzhou University of Aeronautics China

The smart grid contains lots of intelligent electronic devices (IEDs). IEDs are considered high-priority targets of cyber-attacks. Assessing the security of IEDs effectively can help operators take timely measures to improve the ability of the smart grid (SG) to resist cyber-attacks. Therefore, it is a new challenge how to evaluate the security of IEDs to support the decision-making of grid control policies in the condition of cyber-attacks probably occurring. However, there is a lack of research on the security assessment of IEDs considering cyber-attacks. Hence, this paper takes cyber-attacks and physical faults into consideration, and presents security assessment indicators and assessment methods for IEDs. Firstly, the security monitoring technology of IEDs is presented from the perspective of data sources required for the security assessment of IEDs. The classification of IEDs is carried out, and the security monitoring index system considering the running states of IEDs, network traffic, and abnormal behaviours is presented. Then, the quantitative methods of various security monitoring indicators are expounded. Furthermore, a security assessment method (i.e., GRCW-hybrid method) for IEDs based on grey relational analysis (GRA) and combination weight method (CWM) is proposed. Finally, to verify the effectiveness of the proposed index system and assessment method, 15 indicator exception scenarios, which are established based on monitoring indicators and the actual situation of IEDs, are evaluated. © 2023, The Authors. All rights reserved.

关键词： Smart power grids

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MedVLM-R1: Incentivizing Medical Reasoning Capability of Vision-Language Models (VLMs) via Reinforcement Learning

arXiv

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arXiv 2025年

作者： Pan, Jiazhen Liu, Che Wu, Junde Liu, Fenglin Zhu, Jiayuan Li, Hongwei Bran Chen, Chen Ouyang, Cheng Rueckert, Daniel TUM University Hospital Germany Department of Engineering Science University of Oxford United Kingdom Data Science Institute Imperial College London United Kingdom Massachusetts General Hospital Harvard Medical School United States School of Computer Science University of Sheffield United Kingdom Department of Computing Imperial College London United Kingdom

Reasoning is a critical frontier for advancing medical image analysis, where transparency and trustworthiness play a central role in both clinician trust and regulatory approval. Although Medical Visual Language Models (VLMs) show promise for radiological tasks, most existing VLMs merely produce final answers without revealing the underlying reasoning. To address this gap, we introduce MedVLM-R1, a medical VLM that explicitly generates natural language reasoning to enhance transparency and trustworthiness. Instead of relying on supervised fine-tuning (SFT), which often suffers from overfitting to training distributions and fails to foster genuine reasoning, MedVLM-R1 employs a reinforcement learning framework that incentivizes the model to discover human-interpretable reasoning paths without using any reasoning references. Despite limited training data (600 visual question answering samples) and model parameters (2B), MedVLM-R1 boosts accuracy from 55.11% to 78.22% across MRI, CT, and X-ray benchmarks, outperforming larger models trained on over a million samples. It also demonstrates robust domain generalization under out-of-distribution tasks. By unifying medical image analysis with explicit reasoning, MedVLM-R1 marks a pivotal step toward trustworthy and interpretable AI in clinical practice. Inference model is available at: https://***/JZPeterPan/ MedVLM-R1. © 2025, CC BY.

关键词： Reinforcement learning

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Type-Representation Enhanced Event Detection via Structure Encoder

Type-Representation Enhanced Event Detection via Structure E...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Yuming Feng Linmeng Huang Haozhe Liang Xiaolong Jin Saiping Guan Jiafeng Guo School of Computer Science and Technology University of Chinese Academy of Sciences Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Institute of Systems Engineering Academy of Military Science Beijing

Event detection, aiming to detect whether a sentence contains the type of events of interest, is one of the important tasks in information Extraction. There exist many methods for event detection, which usually treat event detection as a classification problem. These existing methods suffer from the imbalanced training data of different event types. To relieve this problem, this paper proposes a new Type-Representation-Enhanced Event Detection (TREED) method, which introduce hierarchical event type representations to enhance classification accuracy. Specifically, TREED first models the hierarchical relationship among event types in the dataset; Next, upon interactions between the representations of trigger words and event types, it obtains the new representations of trigger words integrating event type information. Finally, TREED classifies the events based on the obtained more accurate trigger word representations. Experimental results on a commonly used benchmark dataset, ACE-2005, demonstrate that the proposed model can improve both accuracy and recall of the event detection task.

关键词：

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Bayesian Inverse Transfer in Evolutionary Multiobjective Optimization

arXiv

引用

arXiv 2023年

作者： Liu, Jiao Gupta, Abhishek Ong, Yew-Soon College of Computing & Data Science Nanyang Technological University 50 Nanyang Ave Singapore Singapore School of Mechanical Sciences Indian Institute of Technology Goa Goa India Engineering College Campus Farmagudi Ponda India

Transfer optimization enables data-efficient optimization of a target task by leveraging experiential priors from related source tasks. This is especially useful in multiobjective optimization settings where a set of trade-off solutions is sought under tight evaluation budgets. In this paper, we introduce a novel concept of inverse transfer in multiobjective optimization. Inverse transfer stands out by employing Bayesian inverse Gaussian process models to map performance vectors in the objective space to population search distributions in task-specific decision space, facilitating knowledge transfer through objective space unification. Building upon this idea, we introduce the first Inverse Transfer Evolutionary Multiobjective Optimizer (invTrEMO). A key highlight of invTrEMO is its ability to harness the common objective functions prevalent in many application areas, even when decision spaces do not precisely align between tasks. This allows invTrEMO to uniquely and effectively utilize information from heterogeneous source tasks as well. Furthermore, invTrEMO yields high-precision inverse models as a significant byproduct, enabling the generation of tailored solutions on-demand based on user preferences. Empirical studies on multi- and many-objective benchmark problems, as well as a practical case study, showcase the faster convergence rate and modelling accuracy of the invTrEMO relative to state-of-the-art evolutionary and Bayesian optimization algorithms. The source code of the invTrEMO is made available at https://***/LiuJ-2023/invTrEMO. © 2023, CC BY.

关键词： Budget control

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Cloud-Enhanced Machine Learning Models for Predictive Maintenance in Industrial IoT

Cloud-Enhanced Machine Learning Models for Predictive Mainte...

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Trends in Quantum computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： R. Usharani V. M. Sivagami K. Saravanan S. Pushparani K. Sashi Rekha Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Department of Information Technology Sri Venkateswara College of Engineering Sriperumbudur Chennai Tamil Nadu Department of Artificial Intelligence & Data Science CARE College of Engineering Trichy Tamil Nadu Department of Computer Science and Engineering Meenakshi College of Engineering Chennai Tamil Nadu Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

This study examines the implementation and impact of Cloud-Enhanced Machine Learning Models in the realm of Predictive Maintenance within Industrial Internet of Things (IIoT) settings. Emphasizing the integration of cloud computing with machine learning, the research focuses on how this synergy improves the accuracy and efficiency of maintenance predictions in industrial environments. Key metrics evaluated include prediction accuracy, operational efficiency, cost savings, and reduction in equipment downtime. The study demonstrates a significant increase in prediction accuracy, from 65% to 88%, attributed to the advanced data processing and analytical capabilities of cloud-enhanced machine learning. Operational efficiency saw marked improvements, with a 50% reduction in response times and a 25% increase in maintenance scheduling efficiency, leading to a 20% rise in asset utilization rates. Financially, the implementation led to a 37% reduction in annual maintenance costs and a 40% decrease in both the cost per downtime incident and long-term repair expenses. Most notably, the study recorded a 62.5% reduction in both the duration and frequency of downtime incidents. These results highlight the transformative potential of integrating cloud computing and machine learning in IIoT for predictive maintenance, showcasing significant advancements in predictive accuracy, operational efficiency, cost-effectiveness, and overall reduction in downtime. The study underscores the critical role of technological innovation in advancing industrial maintenance strategies, leading to more proactive, efficient, and economically viable operations.

关键词： Cloud computing Technological innovation Costs Biological system modeling Machine learning Predictive models Maintenance

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Semi-Asynchronous Federated Learning with Trajectory Prediction for Vehicular Edge computing

Semi-Asynchronous Federated Learning with Trajectory Predict...

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International Workshop on Quality of Service

作者： Yuxuan Deng Xiuhua Li Chuan Sun Qilin Fan Xiaofei Wang Victor C. M. Leung School of Big Data & Software Engineering Chongqing University Chongqing China School of Computer Science and Engineering Nanyang Technological University Singapore TKLAN College of Intelligence & Computing Tianjin University Tianjin China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen China College of Computer Science & Software Engineering Shenzhen University Shenzhen China Department of Electrical & Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9798350350128

ISBN: (纸本)9798350350135

Federated learning, as a distributed machine learning paradigm, offers promising solutions for vehicular edge computing (VEC) networks. However, federated learning in VEC with classification tasks still faces two key challenges: i) Delayed data labeling hampers supervised training; ii) Dynamic vehicle behavior complicates training scheduling and model uploads to edge servers. In this paper, we propose a semi-asynchronous federated learning algorithm for VEC. Specifically, it utilizes knowledge distillation to generate soft labels from raw data for supervised training, and estimates model training and uploading time through trajectory prediction. We further logically group vehicles based on the characteristics of their dynamic behavior. We then employ synchronous aggregation within groups and asynchronous aggregation between groups to optimize model performance while reducing latency. Finally, we conduct separate comparative experiments for all components, demonstrating that each component possesses unique advantages. Experiment results show that the proposed algorithm outperforms existing schemes in terms of accuracy and latency. The code is available at: https://***/dyxcode/Semi-Asynchronous-Federated-Learning.

关键词： Training Federated learning Heuristic algorithms Predictive models Prediction algorithms data models Trajectory

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