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Q-Learning enabled Ant Colony (Q-ANT) based Navigation Optimization Framework for UAVs

IEEE Transactions on Consumer Electronics 2025年

作者： Wei, Xing Li, Ning Yang, Hua Guilin University of Aerospace Technology School of Computer Science and Engineering Guilin China Guilin University of Electric Technology School of Software Engineering school of Computer Science and Information Security Guilin China Huanghuai University School of Computer and Artificial Intelligence Henan China

Flying ad-hoc networks (FANETs) composed of unmanned aerial vehicles (UAVs) which are serving in both military as well as civilian domains. The environmental changes and alterations in network topology present considerable challenges to communication among drone swarms. Consequently, there is an imperative need to investigate a high-performance routing scheme for UAV based FANETs. In this study, a routing scheme is introduced that employs the reinforcement Q-learning algorithm to enhance the ant colony optimization routing protocol (Q-ANT). The primary innovation of this scheme lies in the implementation of a neighborhood link selection mechanism that incorporates link residual time and link signal strength. This approach addresses the inadequacy of relying solely on a single indicator for assessing link residual survival time and expedites the convergence velocity of the ant colony routing algorithm. Q-ANT possesses the capability to optimize its routing strategy in accordance with the prevailing network environment by utilizing the Q-learning algorithm to attain the ideal option strategy in response to environmental fluctuations. The proposed scheme is implemented within the NS3 simulation environment and scenario-based simulation experiments have been conducted on three critical network metrics: network lifetime, end-to-end delay, and packet delivery rate. The experimental results exhibit the superiority of Q-ANT in terms of minimal energy consumption, minimal latency, and an enhanced packet delivery rate to the existing approaches such as FAnt-HocNet and Q-learning based QRF. © 1975-2011 IEEE.

关键词： Ant colony optimization

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A STUDY OF UNSUPERVISED EVALUATION METRICS FOR PRACTICAL AND AUTOMATIC DOMAIN ADAPTATION

arXiv

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arXiv 2023年

作者： Chen, Minghao Gao, Zepeng Zhao, Shuai Qiu, Qibo Wang, Wenxiao Lin, Binbin He, Xiaofei State Key Lab of CAD&CG College of Computer Science Zhejiang University China School of Software Technology Zhejiang University China ReLER Lab CCAI Zhejiang University China Zhejiang Lab China Baidu Inc. China

Unsupervised domain adaptation (UDA) methods facilitate the transfer of models to target domains without labels. However, these methods necessitate a labeled target validation set for hyper-parameter tuning and model selection. In this paper, we aim to find an evaluation metric capable of assessing the quality of a transferred model without access to target validation labels. We begin with the metric based on mutual information of the model prediction. Through empirical analysis, we identify three prevalent issues with this metric: 1) It does not account for the source structure. 2) It can be easily attacked. 3) It fails to detect negative transfer caused by the over-alignment of source and target features. To address the first two issues, we incorporate source accuracy into the metric and employ a new MLP classifier that is held out during training, significantly improving the result. To tackle the final issue, we integrate this enhanced metric with data augmentation, resulting in a novel unsupervised UDA metric called the Augmentation Consistency Metric (ACM). Additionally, we empirically demonstrate the shortcomings of previous experiment settings and conduct large-scale experiments to validate the effectiveness of our proposed metric. Furthermore, we leverage our metric to automatically search for the optimal set of hyper-parameters, achieving superior performance comparable to manually tuned sets across four common benchmarks. © 2023, CC BY.

关键词： Quality control

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Correction: Harnessing AI for sustainable higher education: ethical considerations, operational efficiency, and future directions

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Discover Sustainability 2025年第1期6卷 1-2页

作者： Khan, Sunawar Mazhar, Tehseen Shahzad, Tariq Khan, Muhammad Amir Rehman, Ateeq Ur Saeed, Mamoon M. Hamam, Habib Department of Software Engineering Islamia University of Bahawalpur Bahawalnagar Pakistan School of Computer Science National College of Business Administration and Economics Lahore Pakistan Department of Computer Science School Education Department Government of Punjab Layyah Pakistan Department of Computer Engineering COMSATS University Islamabad Sahiwal Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Malaysia School of Computing Gachon University Seongnam Republic of Korea Department of Communications and Electronics Engineering Faculty of Engineering University of Modern Sciences (UMS) Sana’a Yemen Faculty of Engineering Uni de Moncton Moncton Canada School of Electrical Engineering University of Johannesburg Johannesburg South Africa International Institute of Technology and Management (IITG) Av. Grandes Ecoles Libreville BP 1989 Gabon Bridges for Academic Excellence-Spectrum Tunis Tunisia

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A New Class Correlation-Based Dynamic Sample Weighting Method for Medical Image Classification

SSRN

引用

SSRN 2025年

作者： Yi, Guanxiu Ma, Ling Liu, Xiabi Hai, Zhaoyang Li, Yunlong Han, Mengqiao Chao, Yang Niu, Lijuan Song, Yuehao Liu, Qianglin School of Computer Science Beijing Institute of Technology Beijing100081 China College of Software Nankai University Tianjin300350 China Department of Ultrasound National Cancer Center National Clinical Research Center for Cancer Cancer Hospital Chinese Academy of Medical Sciences Peking Union Medical College Beijing100021 China

Background and Objective: Deep neural networks (DNNs) have achieved remarkable success in medical image classification tasks, but the performance of DNN-based methods in medical scenarios remains limited. Sample weighting has shown substantial promise in addressing this issue. However, existing approaches focus on individual sample information in isolation, which limits the application potential of sample weighting in medical image classification. Methods: In this paper, we propose a new class correlation-based dynamic sample weighting (CC-DSW) framework to improve medical image classification. Our approach leverages intra-class and inter-class correlations to model sample relationships in feature and label spaces, capturing intra-class consistency and inter-class separability. We design a sample weighting network composed of a weight measurement sub-network and a weight aggregation sub-network, which dynamically computes sample weights from class correlations to guide the classifier toward enhanced performance. In addition, we alternately update the classifier and sample weighting network through meta-learning to facilitate mutual learning. Our framework promotes the consistency of similar samples and the distinction between dissimilar samples for robust classification. Results: We evaluate the effectiveness of our method on three medical image classification benchmarks: PatchCamelyon for lymph node histopathology classification, ISIC 2020 for skin lesion classification, and MTC for medullary thyroid carcinoma classification. Our approach over existing state-of-the-art sample weighting methods across all three datasets and significantly outperforms methods without sample ***: We introduce class correlation into the sample weighting method for medical image classification and dynamically assign weights to samples. The results demonstrate the superior performance of the proposed method, underscoring its considerable potential for clinical application

关键词： Deep neural networks

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Abstract Syntax Tree for Programming Language Understanding and Representation: How Far Are We?

arXiv

引用

arXiv 2023年

作者： Sun, Weisong Fang, Chunrong Miao, Yun You, Yudu Yuan, Mengzhe Chen, Yuchen Zhang, Quanjun Guo, An Chen, Xiang Liu, Yang Chen, Zhenyu State Key Laboratory for Novel Software Technology Nanjing University Jiangsu Nanjing210093 China School of Computer Science and Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore Nantong University Jiangsu Nantong226019 China

Programming language understanding and representation (a.k.a code representation learning) has always been a hot and challenging task in the field of software engineering. It aims to apply deep learning techniques to produce numerical representations of the source code features while preserving its semantics. These representations can be used for facilitating subsequent code-related tasks, e.g., code summarization. The abstract syntax tree (AST), a fundamental code feature, illustrates the syntactic information of the source code and has been widely used in code representation learning. It is commonly acknowledged that AST-based code representation is critical to solving code-related tasks. However, there is still a lack of systematic and quantitative evaluation of how well AST-based code representation facilitates subsequent code-related tasks. Additionally, learning an AST-based code representation is an extremely complex endeavor involving three intertwining stages, including, AST parsing, AST preprocessing, and AST encoding. The solutions available inMSC Codes 68-04, 68T30 Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Human Observation-Inspired Trajectory Prediction for Autonomous Driving in Mixed-Autonomy Traffic Environments

arXiv

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arXiv 2024年

作者： Liao, Haicheng Liu, Shangqian Li, Yongkang Li, Zhenning Wang, Chengyue Li, Yunjian Li, Shengbo Eben Xu, Chengzhong State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau China Department of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China State Key Laboratory of Internet of Things for Smart City Department of Civil and Environmental Engineering and Computer and Information Science University of Macau China State Key Laboratory of Internet of Things for Smart City Department of Civil and Environmental Engineering University of Macau China Faculty of Innovation Engineering Macau University of Science and Technology China School of Vehicle and Mobility Tsinghua University Beijing China

In the burgeoning field of autonomous vehicles (AVs), trajectory prediction remains a formidable challenge, especially in mixed autonomy environments. Traditional approaches often rely on computational methods such as time-series analysis. Our research diverges significantly by adopting an interdisciplinary approach that integrates principles of human cognition and observational behavior into trajectory prediction models for AVs. We introduce a novel "adaptive visual sector" mechanism that mimics the dynamic allocation of attention human drivers exhibit based on factors like spatial orientation, proximity, and driving speed. Additionally, we develop a "dynamic traffic graph" using Convolutional Neural Networks (CNN) and Graph Attention Networks (GAT) to capture spatio-temporal dependencies among agents. Benchmark tests on the NGSIM, HighD, and MoCAD datasets reveal that our model (GAVA) outperforms state-of-the-art baselines by at least 15.2%, 19.4%, and 12.0%, respectively. Our findings underscore the potential of leveraging human cognition principles to enhance the proficiency and adaptability of trajectory prediction algorithms in AVs. The code for the proposed model is available at our Github. © 2024, CC BY-NC-SA.

关键词： Time series analysis

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Towards Fully Decoupled End-to-End Person Search

Towards Fully Decoupled End-to-End Person Search

引用

Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Pengcheng Zhang Xiao Bai Jin Zheng Xin Ning School of Computer Science and Engineering State Key Laboratory of Software Development Environment Jiangxi Research Institute Beihang University Beijing China Institute of Semiconductors Chinese Academy of Sciences Cognitive Computing Technology Joint Laboratory Wave Group Beijing China

End-to-end person search aims to jointly detect and re-identify a target person in raw scene images with a unified model. The detection task unifies all persons while the re-id task discriminates different identities, resulting in conflict optimal objectives. Existing works proposed to decouple end-to-end person search to alleviate such conflict. Yet these methods are still sub-optimal on one or two of the sub-tasks due to their partially decoupled models, which limits the overall person search performance. In this paper, we propose to fully decouple person search towards optimal person search. A task-incremental person search network is proposed to incrementally construct an end-to-end model for the detection and re-id sub-task, which decouples the model architecture for the two sub-tasks. The proposed task-incremental network allows task-incremental training for the two conflicting tasks. This enables independent learning for different objectives thus fully decoupled the model for personsearch. Comprehensive experimental evaluations demonstrate the effectiveness of the proposed fully decoupled models for end-to-end person search.

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Pretrained Model with CNN for Lung Cancer Identification and Classification

Pretrained Model with CNN for Lung Cancer Identification and...

引用

Pattern Recognition and Artificial Intelligence (PRAI), International Conference on

作者： Shaista Kanwal Sajid Ali Tahani Jaser Alahmadi Muhammad Assam Ghulam Jillani Ansari Uzair Aslam Bhatti Department of Information Sciences University of Education Lahore Pakistan Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia Department of Software Engineering University of Science and Technology Bannu KPK Pakistan School of information and communication engineering Hainan University

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

Lung cancer impacts both genders, and its early detection is key to lowering death rates. Current deep learning techniques for automated identification and classification of lung carcinoma face issues with interpretability, data variability handling, and broader applicability. In our research, we propose a novel method that merges a convolutional neural network (CNN) with a streamlined RESNET18 framework. This approach is designed for lung cancer detection and categorization from CT scans, involving image preprocessing like resizing and rescaling. It effectively differentiates between healthy and cancerous tissues, further classifying them as either Normal, Benign, or Malignant, including variants such as adenocarcinoma and squamous cell carcinoma. Our CNN+RESNET18 model boasts an impressive 99% accuracy, supporting radiologists in the intricate task of image analysis, reducing risks of incorrect or delayed diagnosis, and fostering prompt treatment for improved patient outcomes.

关键词： Accuracy Squamous cell carcinoma Computed tomography Scalability Lung cancer Mortality Streaming media Feature extraction Convolutional neural networks Artificial intelligence

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Character, Word, or Both? Revisiting the Segmentation Granularity for Chinese Pre-trained Language Models

arXiv

引用

arXiv 2023年

作者： Liang, Xinnian Zhou, Zefan Huang, Hui Wu, Shuangzhi Xiao, Tong Yang, Muyun Li, Zhoujun Bian, Chao State Key Lab of Software Development Environment Beihang University Beijing China School of Computer Science and Engineering Northeastern University Shenyang China Faculty of Computing Harbin Institute of Technology Harbin China Lark Platform Engineering-AI Beijing China

Pretrained language models (PLMs) have shown marvelous improvements across various NLP tasks. Most Chinese PLMs simply treat an input text as a sequence of characters, and completely ignore word information. Although Whole Word Masking can alleviate this, the semantics in words is still not well represented. In this paper, we revisit the segmentation granularity of Chinese PLMs. We propose a mixed-granularity Chinese BERT (MigBERT) by considering both characters and words. To achieve this, we design objective functions for learning both character and word-level representations. We conduct extensive experiments on various Chinese NLP tasks to evaluate existing PLMs as well as the proposed MigBERT. Experimental results show that MigBERT achieves new SOTA performance on all these tasks. Further analysis demonstrates that words are semantically richer than characters. More interestingly, we show that MigBERT also works with Japanese. Our code has been released here 1 and you can download our model here 2 © 2023, CC BY.

关键词： Semantics

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AdaVITS: Tiny VITS for Low Computing Resource Speaker Adaptation

AdaVITS: Tiny VITS for Low Computing Resource Speaker Adapta...

引用

International Symposium on Chinese Spoken Language Processing

作者： Kun Song Heyang Xue Xinsheng Wang Jian Cong Yongmao Zhang Lei Xie Bing Yang Xiong Zhang Dan Su School of Computer Science Northwestern Polytechnical University Xi’an China Cloud and Smart Industries Group Tencent Technology Co. Ltd. China School of Software Northwestern Polytechnical University Xi’an China

ISBN: (纸本)9798350397970

Speaker adaptation in text-to-speech synthesis (TTS) is to fine-tune a pre-trained TTS model to adapt to new target speakers with limited data. While much effort has been conducted towards this task, seldom work has been performed for low computational resource scenarios due to the challenges raised by the requirement of the lightweight model and less computational complexity. In this paper, a tiny VITS-based [1] TTS model, named AdaVITS, for low computing resource speaker adaptation is proposed. To effectively reduce the parameters and computational complexity of VITS, an inverse short-time Fourier transform (iSTFT)-based wave construction decoder is proposed to replace the upsampling-based decoder which is resource-consuming in the original VITS. Besides, NanoFlow is introduced to share the density estimate across flow blocks to reduce the parameters of the prior encoder. Furthermore, to reduce the computational complexity of the textual encoder, scaled-dot attention is replaced with linear attention. To deal with the instability caused by the simplified model, we use phonetic posteriorgram (PPG) as a frame-level linguistic feature for supervising the model process from phoneme to spectrum. Experiments show that AdaVITS can generate stable and natural speech in speaker adaptation with 8. 97M model parameters and 0.72 GFlops computational complexity. 1

关键词： Adaptation models Computational modeling Vocoders Phonetics Stability analysis Decoding Computational complexity

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