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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Huang, Xiaofei Chen, Wenting Liu, Jie Lu, Qisheng Luo, Xiaoling Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Electrical Engineering City University of Hong Kong Kowloon Hong Kong National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

ISBN: (纸本)157735897X

Medical report generation is crucial for clinical diagnosis and patient management, summarizing diagnoses and recommendations based on medical imaging. However, existing work often overlook the clinical pipeline involved in report writing, where physicians typically conduct an initial quick review followed by a detailed examination. Moreover, current alignment methods may lead to misaligned relationships. To address these issues, we propose DAMPER, a dual-stage framework for medical report generation that mimics the clinical pipeline of report writing in two stages. In the first stage, a MeSH-Guided Coarse-Grained Alignment (MCG) stage that aligns chest X-ray (CXR) image features with medical subject headings (MeSH) features to generate a rough keyphrase representation of the overall impression. In the second stage, a Hypergraph-Enhanced Fine-Grained Alignment (HFG) stage that constructs hypergraphs for image patches and report annotations, modeling high-order relationships within each modality and performing hypergraph matching to capture semantic correlations between image regions and textual phrases. Finally,the coarse-grained visual features, generated MeSH representations, and visual hypergraph features are fed into a report decoder to produce the final medical report. Extensive experiments on public datasets demonstrate the effectiveness of DAMPER in generating comprehensive and accurate medical reports, outperforming state-of-the-art methods across various evaluation metrics. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Diagnosis

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Optimizing Endotracheal Suctioning Classification: Leveraging Prompt engineering in Machine Learning for Feature Selection

Optimizing Endotracheal Suctioning Classification: Leveragin...

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Activity and Behavior Computing (ABC), International Conference on

作者： Mahera Roksana Islam Anik Mahmud Ferdous Shahera Hossain Md Atiqur Rahman Ahad Fady Alnajjar Dept. of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh Dept. of Computer Science and Digital Technologies University of East London London UK Dept. of Computer Science and Software Engineering College of Information Technology UAE University Al Ain UAE

ISBN: (数字)9798350375503

ISBN: (纸本)9798350375510

In a world with an overgrowing elderly population, there exists a critical need for a greater number of skilled individuals in the nursing industry. AI-based systems can be useful, compared to traditional ones with require in-person assistance, to accurately identify nursing activities and assess the nursing trainees to help them become proficient. This paper addresses classifying activities in one such procedure, endotracheal suctioning, using skeletal keypoint data of the subject performing the procedure. A multi-step structured prompt engineering method was established and utilized on several LLMs to select or calculate key features from the data. Then the features are passed onto several tuned machine learning models to obtain results. A tuned XGBoost prevailed across all models, achieving 90% accuracy on the validation set.

关键词： Training Accuracy Computational modeling Machine learning Medical services Predictive models Feature extraction

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Anomaly Detection of Industrial Control Systems Based on Transfer Learning

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Tsinghua Science and technology 2021年第6期26卷 821-832页

作者： Weiping Wang Zhaorong Wang Zhanfan Zhou Haixia Deng Weiliang Zhao Chunyang Wang Yongzhen Guo School of Computer and Communication Engineering the Beijing Key Laboratory of Knowledge Engineering for Materials Scienceand the Institute of Artificial IntelligenceUniversity of Science and Technology BeijingBeijing 100083China Shunde Graduate School University of Science and Technology BeijingGuangzhou 528399China School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing 100083China School of Mechanical Engineering University of Science and Technology BeijingBeijing 100083China Donlinks School of Economics and Management University of Science and Technology Beijing Beijing 100083China School of Automation Beijing Institute of TechnologyBeijing 100081 China Software Testing Center Beijing 100048China.

Industrial Control Systems(ICSs)are the lifeline of a ***,the anomaly detection of ICS traffic is an important *** paper proposes a model based on a deep residual Convolution Neural Network(CNN)to prevent gradient explosion or gradient disappearance and guarantee *** developed methodology addresses two limitations:most traditional machine learning methods can only detect known network attacks and deep learning algorithms require a long time to *** utilization of transfer learning under the modification of the existing residual CNN structure guarantees the detection of unknown ***-dimensional ICS flow data are converted into two-dimensional grayscale images to take full advantage of the features of *** show that the proposed method achieves a high score and solves the time problem associated with deep learning model *** model can give reliable predictions for unknown or differently distributed abnormal data through short-term ***,the proposed model ensures the safety of ICSs and verifies the feasibility of transfer learning for ICS anomaly detection.

关键词： anomaly detection transfer learning deep learning Industrial Control System(ICS)

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Flowmind2Digital: The First Comprehensive Flowmind Recognition and Conversion Approach

arXiv

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arXiv 2024年

作者： Liu, Huanyu Cai, Jianfeng Zhang, Tingjia Li, Hongsheng Wang, Siyuan Zhu, Guangming Shah, Syed Afaq Ali Bennamoun, Mohammed Zhang, Liang School of Artificial Intelligence Xidian University China School of Computer Science and Technology Xidian University China School of Science Centre for AI and Machine Learning ECU School of Physics Maths and Computing Computer Science and Software Engineering UWA

Flowcharts and mind maps, collectively referred to as flowmind, play an important role in our daily, and many companies have developed dedicated tools. Hand-drawn flowminds offer significant advantages for real-time and collaborative communication. However, there is an increasing demand to convert them into a digital format for further processing. Automated conversion methods are crucial in addressing the challenges associated with manual conversion, such as the cost of time and learning. Previous works have proposed diverse sketch recognition methods. However, these methods face significant limitations in practical situations. Firstly, most methods are designed for specific fields, making it challenging to extend their usage to other fields. Additionally, none of these methods address the critical step of digital conversion after recognition, which is essential to users. Moreover, existing datasets exhibit significant biases relative to actual data, hindering the methods’ generalization ability. Our paper proposes the Flowmind2digital method and hdFlowmind dataset to address the aforementioned challenges. Flowmind2digital is the first comprehensive recognition and conversion method for flowminds, utilizing a neural network architecture and keypoint detection technology to enhance overall recognition accuracy. Our hdFlowmind dataset consists of 1,776 hand-drawn and manually annotated flowminds, covering 22 scenarios and surpassing existing datasets in size. Our experiments showcase the effectiveness of our method, with an accuracy rate of 87.3% on the hdFlowmind dataset, surpassing the previous state-of-the-art work by 11.9%. Additionally, our dataset demonstrates effectiveness, with a 2.9% increase in accuracy after pre-training and fine-tuning on Handwritten-diagram-dataset. We also highlight the importance of simple graphics for sketch recognition, which can improve accuracy by 9.3%. Copyright © 2024, The Authors. All rights reserved.

关键词： Image recognition

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Customer Churn Prediction Framework of Inclusive Finance Based on Blockchain Smart Contract

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computer Systems Science & engineering 2023年第10期47卷 1-17页

作者： Fang Yu Wenbin Bi Ning Cao Hongjun Li Russell Higgs College of Information Engineering Qingdao Binhai UniversityQingdao266555China School of Computer and Software Dalian Neusoft University of InformationDalian116023China College of Information Engineering Shandong Vocational and Technical University of International StudiesRizhao276826China School of Intergrated Circuit Wuxi Vocational College of Science and TechnologyWuxi214028China School of Mathematics and Statistics University College DublinDublinIreland

In view of the fact that the prediction effect of influential financial customer churn in the Internet of Things environment is difficult to achieve the expectation,at the smart contract level of the blockchain,a customer churn prediction framework based on situational awareness and integrating customer attributes,the impact of project hotspots on customer interests,and customer satisfaction with the project has been *** framework introduces the background factors in the financial customer environment,and further discusses the relationship between customers,the background of customers and the characteristics of pre-lost *** improved Singular Value Decomposition(SVD)algorithm and the time decay function are used to optimize the search and analysis of the characteristics of pre-lost customers,and the key index combination is screened to obtain the data of potential lost *** framework will change with time according to the customer’s interest,adding the time factor to the customer churn prediction,and improving the dimensionality reduction and prediction generalization ability in feature *** regression,naive Bayes and decision tree are used to establish a prediction model in the experiment,and it is compared with the financial customer churn prediction framework under situational *** prediction results of the framework are evaluated from four aspects:accuracy,accuracy,recall rate and *** experimental results show that the context-aware customer churn prediction framework can be effectively applied to predict customer churn trends,so as to obtain potential customer data with high churn probability,and then these data can be transmitted to the company’s customer service department in time,so as to improve customer churn rate and customer loyalty through accurate service.

关键词： Contextual awareness customer churn prediction framework dimensionality reduction generalization ability

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Robust Phishing URL Classification Using FastText Character Embeddings and Hybrid Deep Learning 3

Robust Phishing URL Classification Using FastText Character ...

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3rd IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Al-Sakib, Abdullah Limon, Zishad Hossain Sakib, Anamul Pranto, Md Nuruzzaman Islam, Md Ariful Sultana, Shayma Ahmed, Md. Redwan Rahman, Shafiur Westcliff University Dept. of Information Technology CA92614 United States Westcliff University Dept. of Computer Science CA92614 United States International American University Dept. of Business Administration CA90010 United States International American University Dept. of Management Information System CA90010 United States East West University Dept. of Computer Science and Engineering Dhaka1212 Bangladesh Daffodil International University Dept. of Software Engineering Dhaka1216 Bangladesh

ISBN: (纸本)9798331534400

Phishing attacks are a major cybersecurity threat that resulted in over 1.2 million incidents in the first half of 2020. These attacks caused substantial financial losses and posed risks to individuals and organizations. Being able to identify fraudulent websites is crucial in order to effectively address these potential risks. This study introduces a novel method for detecting phishing URLs by using word and character embeddings to capture complex URL patterns. We used a dataset of 80,000 URLs, including 50,000 legitimate ones and 30,000 phishing instances, and applied thorough preprocessing techniques. We utilized word embeddings in FastText to handle unseen words, with the added advantage of n-gram representations. Additionally, we captured character-level features through dense character embeddings. We trained several machine learning and deep learning models, and one model, the Convolutional Bidirectional LSTM (CBiLSTM), stood out with an accuracy of 99.01% and an F1-score of 99.08%. Furthermore, we made a thorough comparison with the most advanced techniques available, and our findings demonstrated clear superiority over previous research. This study presents an effective approach for classifying phishing URLs, providing a valuable tool to combat fraud and protect against identity theft, thereby helping to minimize the financial and emotional harm experienced by victims. © 2024 IEEE.

关键词： Phishing

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Design of data processing scheme of 5G terminal protocol conformance test system

Design of data processing scheme of 5G terminal protocol con...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Cao Jingyao Amit Yadav Asif Khan Abhishek Pratap Sah School of Computer and Software Chengdu Neusoft University Chengdu China College of Engineering IT and Environment Charles Darwin University NT Australia Department of Computer Application Integral University Lucknow India School of Management Science and Engineering University of Electronic Science and Technology of China Chengdu China

Considering the fact that the test case runs abnormally and the test case execution fails during the development and debugging of the 5G terminal protocol conformance test system. Here useful approach is implemented by embedding data copy and storage module in the system, which completes the encapsulation, copy and storage of massive data generated during the operation of the system, so that equipment developers can correctly locate the errors in the system according to the data, and repair the system loopholes to ensure the stability and accuracy of the system.

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Dynamic Resource Allocation for Metaverse Applications with Deep Reinforcement Learning

Dynamic Resource Allocation for Metaverse Applications with ...

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IEEE Conference on Wireless Communications and Networking

作者： Nam H. Chu Diep N. Nguyen Dinh Thai Hoang Khoa T. Phan Eryk Dutkiewicz Dusit Niyato Tao Shu School of Electrical and Data Engineering University of Technology Sydney Australia School of Engineering and Mathematical Sciences La Trobe University Melbourne Australia School of Computer Science and Engineering Nanyang Technological University Singapore Department of Computer Science and Software Engineering Auburn University USA

This work proposes a novel framework to dynamically and effectively manage and allocate different types of resources for Metaverse applications, which are forecasted to demand massive resources of various types that have never been seen before. Specifically, by studying functions of Metaverse applications, we first propose an effective solution to divide applications into groups, namely MetaInstances, where common functions can be shared among applications to enhance resource usage efficiency. Then, to capture the real-time, dynamic, and uncertain characteristics of request arrival and application departure processes, we develop a semi-Markov decision process-based framework and propose an intelligent algorithm that can gradually learn the optimal admission policy to maximize the revenue and resource usage efficiency for the Metaverse service provider and at the same time enhance the Quality-of-Service for Metaverse users. Extensive simulation results show that our proposed approach can achieve up to 120% greater revenue for the Metaverse service providers and up to 178.9% higher acceptance probability for Metaverse application requests than those of other baselines.

关键词： Deep learning Metaverse Heuristic algorithms Simulation Reinforcement learning Quality of service Dynamic scheduling

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A Parallel Change Detection Method for Spatiotemporally Multi-Temporal SAR Image Based on Q-Learning and Wavelet 14

A Parallel Change Detection Method for Spatiotemporally Mult...

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14th International Conference on Digital Image Processing, ICDIP 2022

作者： Peng, Jinxi Su, Yuanqi Xue, Xiaorong Song, Donghong Xie, Zhengbo Xue, Xiaoyong Fan, Wenlong Department of Computer Science and Technology Xían JiaoTong University Shaanxi Xi'an710049 China Department of software Engineering South China institute of software Engineering Guangdong Guangzhou510990 China Liaonin Jinzhou121001 China The Middle Training School of Zhenan County Zhen'an Shaanxi 711500 China The Middle School of Luonan County Luonan Shaanxi 726100 China

ISBN: (纸本)9781510657564

In order to make full use of multi-source remote sensing image resources in the application of remote sensing image change detection, a parallel computing for coarse-grained computing data is a very important choice. For the large-scale explosive growth of remote sensing image change which is contained coherent speckles detection sensor data, it’s the amount of data calculation is large and time-consuming. With the development of modern remote sensing big data and Artificial Intelligence (AI), however, it is difficult for traditional methods to solve an efficient algorithm for change detection in synthetic aperture radar application. In connection with acquiring SAR (Polarimetric Synthetic Aperture Radar) Image to accurately obtain an effectively computing power. Looking for data processing and task computing as the basis for parallelization, we propose a parallel change detection method for multi-temporal SAR images based on wavelet transform, and comprehensively apply the Q-learning model of parallel computing with intelligent processing. Firstly, according to the statistical features of SAR images and the semantics of Convolutional Neural Network Pixel (CNN-Pixel) analysis for an efficient change detection method based on Q-Learning semantic analysis and wavelet transform is proposed. Secondly, on the basis of Pixel-Data is achieved the accuracy of change detection, probabilistic and statistical conjugate multi-distribution function features, which is pixels and tasks are pre-processed, pixel-space data calculation of Gaussian mixture model and conjugate gradient transform task model for parallel calculation. Thirdly, the change-sequence data with obstacles at the time series model of Q-learning in the changing target object data on which the surrounding path is an obstacle to achieving that image-pixel sequence data of conjugate gradient calculation data and threshold iteratively optimizes the feature extraction of the changing area, the multi-sample training se

关键词： Change detection

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Tip the Scales: Achieving Balance in Adversarial Examples Across Modalities

Tip the Scales: Achieving Balance in Adversarial Examples Ac...

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

作者： Zhenbo Shi Zhidong Yu Yuxuan Zhang Shuchang Wang Xiaoman Liu Wei Yang Liusheng Huang School of Computer Science and Technology University of Science and Technology of China Hefei China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Laboratory for Advanced Computing and Intelligence Engineering Wuxi China School of Software Engineering University of Science and Technology of China Suzhou China Hefei National Laboratory University of Science and Technology of China Hefei China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In the field of multimodal learning, controlling the training of unimodal encoders from different perspectives is a primary approach to addressing Training Imbalance. However, the inherent capacity limitations of the modality affect the model’s capability. Therefore, generating adversarial examples that can achieve balanced transferability remains a challenging and perplexing problem. In this paper, we propose the InterModality Balanced Attack (MOBA) to address this problem. MOBA leverages Aggregated Modality Perturbation (AMP), which exploits the unbalanced effects of text and image perturbations to maximize the impact on the victim model. AMP capitalizes on the intrinsic feature connections between modalities during the optimization process, adjusting perturbations through Cross-Modality Discrepancy Loss to enhance attack success rates. Additionally, we devise the Transferability-Enhanced Evolution (TEE) to overcome the issue of diminished attack transferability due to model capacity limitations. TEE employs Transfer-Driven Optimization Loss to alleviate overfitting in single models, thereby enhancing the generalization ability.

关键词： Training Adaptation models Perturbation methods Architecture Buildings Signal processing Acoustics Speech processing Optimization Overfitting

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