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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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Deep Learning-Based Depth Map Generation and YOLO-Integrated Distance Estimation for Radiata Pine Branch Detection Using Drone Stereo Vision

Deep Learning-Based Depth Map Generation and YOLO-Integrated...

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International Conference on Image and Vision Computing New Zealand, IVCNZ

作者： Yida Lin Bing Xue Mengjie Zhang Sam Schofield Richard Green Centre for Data Science and Artificial Intelligence Victoria University of Wellington Wellington New Zealand Department of Computer Science and Software Engineering Canterbury University Canterbury New Zealand

ISBN: (数字)9798331518776

ISBN: (纸本)9798331518783

This research focuses on the development of a deep learning based method to enable a drone equipped with a stereo vision camera to accurately detect and measure the spatial positions of tree branches. YOLO is employed for branch segmentation, while two depth estimation approaches, monocular and stereo, are investigated. In comparison to Semi-Global Block Matching(SGBM), deep learning techniques produce more refined and accurate depth maps. In the absence of ground-truth data, a fine-tuning process with deep neural networks is applied to generate the depth map that most closely approximates the ground-truth. This methodology achieves accurate branch detection and precise distance measurement, addressing key challenges in automating pruning operations. The results indicate substantial improvements in accuracy, though further optimization is required to enhance processing speed, demonstrating the potential of deep learning to advance automation in agricultural systems.

关键词： YOLO Accuracy Position measurement Neural radiance field Cameras Distance measurement Robustness Stereo vision Optimization Drones

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Training Artificial Neural Networks by Coordinate Search Algorithm

Training Artificial Neural Networks by Coordinate Search Alg...

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2023 IEEE Symposium Series on Computational intelligence, SSCI 2023

作者： Rokhsatyazdi, Ehsan Rahnamayan, Shahryar Miyandoab, Sevil Zanjani Bidgoli, Azam Asilian Tizhoosh, H.R. Lab Ontario Tech University Department of Electrical Computer and Software Engineering OshawaON Canada Brock University Department of Engineering St. CatharinesON Canada Wilfrid Laurier University Faculty of Science WaterlooON Canada Rhazes Lab Mayo Clinic Department of Artificial Intelligence and Informatics RochesterMN United States

ISBN: (纸本)9781665430654

Training Artificial Neural Networks (ANNs) poses a challenging and critical problem in machine learning. Despite the effectiveness of gradient-based learning methods, such as Stochastic Gradient Descent (SGD), in training neural networks, they do have several limitations. For instance, they require differentiable activation functions, and cannot optimize a model based on several independent non-differentiable loss functions simultaneously;for example, the F1-score, which is used during testing, can be used during training when a gradient-free optimization algorithm is utilized. Furthermore, the training (i.e., optimization of weights) in any DNN can be possible with a small size of the training dataset. To address these concerns, we propose an efficient version of the gradient-free Coordinate Search (CS) algorithm, an instance of General Pattern Search (GPS) methods, for training (i.e., optimizing) neural networks. The proposed algorithm can be used with non-differentiable activation functions and tailored to multi-objective/multi-loss problems. Finding the optimal values for weights of ANNs is a large-scale optimization problem. Therefore instead of finding the optimal value for each variable, which is the common technique in classical CS, we accelerate optimization and convergence by bundling the variables (i.e., weights). In fact, this strategy is a form of dimension reduction for optimization problems. Based on the experimental results, the proposed method is comparable with the SGD algorithm, and in some cases, it outperforms the gradient-based approach. Particularly, in situations with insufficient labeled training data, the proposed CS method performs better. The performance plots demonstrate a high convergence rate, highlighting the capability of our suggested method to find a reasonable solution with fewer function calls. As of now, the only practical and efficient way of training ANNs with hundreds of thousands of weights is gradient-based algorithms such

关键词： Artificial Neural Network (ANN) Coordinate Search Expensive Optimization Gradient-free Large-Scale Optimization Stochastic Gradient Descent (SGD)

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Drone Stereo Vision for Radiata Pine Branch Detection and Distance Measurement: Integrating SGBM and Segmentation Models

arXiv

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arXiv 2024年

作者： Lin, Yida Xue, Bing Zhang, Mengjie Schofield, Sam Green, Richard Centre for Data Science and Artificial Intelligence Victoria University of Wellington Wellington New Zealand Department of Computer Science and Software Engineering Canterbury University Canterbury New Zealand

Manual pruning of radiata pine trees presents significant safety risks due to their substantial height and the challenging terrains in which they thrive. To address these risks, this research proposes the development of a drone-based pruning system equipped with specialized pruning tools and a stereo vision camera, enabling precise detection and trimming of branches. Deep learning algorithms, including YOLO and Mask R-CNN, are employed to ensure accurate branch detection, while the Semi-Global Matching algorithm is integrated to provide reliable distance estimation. The synergy between these techniques facilitates the precise identification of branch locations and enables efficient, targeted pruning. Experimental results demonstrate that the combined implementation of YOLO and SGBM enables the drone to accurately detect branches and measure their distances from the drone. This research not only improves the safety and efficiency of pruning operations but also makes a significant contribution to the advancement of drone technology in the automation of agricultural and forestry practices, laying a foundational framework for further innovations in environmental management. Copyright © 2024, The Authors. All rights reserved.

关键词： Drones

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Imitation learning from purified demonstrations 24

Imitation learning from purified demonstrations

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Proceedings of the 41st International Conference on Machine Learning

作者： Yunke Wang Minjing Dong Yukun Zhao Bo Du Chang Xu School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence and Wuhan Institute of Data Intelligence Wuhan University China Department of Computer Science City University of Hong Kong China School of Computer Science Faculty of Engineering The University of Sydney Australia

Imitation learning has emerged as a promising approach for addressing sequential decision-making problems, with the assumption that expert demonstrations are optimal. However, in real-world scenarios, most demonstrations are often imperfect, leading to challenges in the effectiveness of imitation learning. While existing research has focused on optimizing with imperfect demonstrations, the training typically requires a certain proportion of optimal demonstrations to guarantee performance. To tackle these problems, we propose to purify the potential noises in imperfect demonstrations first, and subsequently conduct imitation learning from these purified demonstrations. Motivated by the success of diffusion model, we introduce a two-step purification via diffusion process. In the first step, we apply a forward diffusion process to smooth potential noises in imperfect demonstrations by introducing additional noise. Subsequently, a reverse generative process is utilized to recover the optimal demonstration from the diffused ones. We provide theoretical evidence supporting our approach, demonstrating that the distance between the purified and optimal demonstration can be bounded. Empirical results on MuJoCo and RoboSuite demonstrate the effectiveness of our method from different aspects.

关键词：

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On the Effectiveness of Acoustic BPE in Decoder-Only TTS

arXiv

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arXiv 2024年

作者： Li, Bohan Shen, Feiyu Guo, Yiwei Wang, Shuai Chen, Xie Yu, Kai MoE Key Lab of Artificial Intelligence AI Institute X-LANCE Lab Department of Computer Science and Engineering Shanghai Jiao Tong University China Shenzhen Research Institute of Big Data CUHK-Shenzhen China

Discretizing speech into tokens and generating them by a decoder-only model have been a promising direction for text-to-speech (TTS) and spoken language modeling (SLM). To shorten the sequence length of speech tokens, acoustic byte-pair encoding (BPE) has emerged in SLM that treats speech tokens from self-supervised semantic representations as characters to further compress the token sequence. But the gain in TTS has not been fully investigated, and the proper choice of acoustic BPE remains unclear. In this work, we conduct a comprehensive study on various settings of acoustic BPE to explore its effectiveness in decoder-only TTS models with semantic speech tokens. Experiments on LibriTTS verify that acoustic BPE uniformly increases the intelligibility and diversity of synthesized speech, while showing different features across BPE settings. Hence, acoustic BPE is a favorable tool for decoder-only TTS. © 2024, CC BY.

关键词： Semantics

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AsT: An Asymmetric-Sensitive Transformer for Osteonecrosis of the Femoral Head Detection 37

AsT: An Asymmetric-Sensitive Transformer for Osteonecrosis o...

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37th AAAI Conference on Artificial intelligence, AAAI 2023

作者： Chen, Haoyang Liu, Shuai Lu, Feng Li, Wei Sheng, Bin Li, Mi Jin, Hai Zomaya, Albert Y. National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Centre for Distributed and High Performance Computing School of Computer Science The University of Sydney Australia Department of Computer Science and Engineering Shanghai Jiao Tong University China Tongji Hospital Tongji Medical College Huazhong University of Science and Technology China

ISBN: (纸本)9781577358800

Early diagnosis of osteonecrosis of the femoral head (ONFH) can inhibit the progression and improve femoral head preservation. The radiograph difference between early ONFH and healthy ones is not apparent to the naked eye. It is also hard to produce a large dataset to train the classification model. In this paper, we propose Asymmetric-Sensitive Transformer (AsT) to capture the uneven development of the bilateral femoral head to enable robust ONFH detection. Our ONFH detection is realized using the self-attention mechanism to femoral head regions while conferring sensitivity to the uneven development by the attention-shared transformer. The real-world experiment studies show that AsT achieves the best performance of AUC 0.9313 in the early diagnosis of ONFH and can find out misdiagnosis cases firmly. Copyright © 2023, Association for the Advancement of Artificial intelligence (***). All rights reserved.

关键词： Large dataset

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Generalized Undersampling of Center Points Based on Granular Ball 7

Generalized Undersampling of Center Points Based on Granular...

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7th International Conference on Cloud Computing and big data Analytics, ICCCBDA 2022

作者： Bai, Hao Wu, Yanmin Qi, Jinli Chen, Yanyi Chongqing University of Posts and Telecommunications Chongqing Key Laboratory of Computer Science and Technology ChongQing China ChongQing College of Electronic Engineering Department of Artificial Intelligence and Big Data ChongQing China ChongQing College of Electronic Engineering Department of General Education and International Studies ChongQing China Chongqing University of Posts and Telecommunications Department of Software Engineering ChongQing China

ISBN: (纸本)9781665487115

In this paper, we propose a new generic sampling classification method based on granular Ball sampling because of good robust noise immunity and possessing properties that can be classified as an undersampling method, called Granular Ball Center-based Common Under-sampling. The method uses adaptively generated Ball to cover the data space, and the sampled data are supported by the centroid properties of the Ball. Unlike the boundary sampling of granular ball sampling, this method mainly uses the center points to smooth out the noise, which not only can clearly represent the attributes, but also can reduce some computational effort, and in most of the time, it can obtain almost the same accuracy as the original model, or even can perform better. Compared with undersampling, it can be better adapted to the task of unbalanced classification as well as non-unbalanced classification. The above can show that the method is able to be generalized without the restriction of specific classifiers and also has a strong adaptability to the dataset. The method can also be effectively used as an undersampling method for unbalanced classification because it is supported by the centroid property, and the method has a speedup effect for most classifiers. These advantages form a strong advantage of the method in improving classifier performance. © 2022 IEEE.

关键词： Cloud computing Adaptation models Computational modeling big data Task analysis

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Burst-Sensitive Traffic Forecast via Multi-Property Personalized Fusion in Federated Learning

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IEEE Transactions on Mobile Computing 2025年第7期24卷 5598-5614页

作者： Xue, Jingjing Sun, Sheng Liu, Min Wang, Yuwei Meng, Xuying Wang, Jingyuan Zhang, JunBo Xu, Ke Chinese Academy of Sciences Institute of Computing Technology Beijing100045 China University of Chinese Academy of Sciences Beijing101408 China Zhongguancun Lab Beijing100190 China Beihang University School of Computer Science and Engineering Beijing100191 China Beihang University MIIT Key Laboratory of Data Intelligence and Management Beijing100191 China JD Technology JD iCity JD Intelligent Cities Research Beijing101111 China Tsinghua University Department of Computer Science and Technology Beijing100190 China Zhongguancun Laboratory Beijing100081 China

For distributed network traffic prediction with data localization and privacy protection, Federated Learning (FL) enables collaborative training without raw data exchange across Base Stations (BSs). Nevertheless, traffic across BSs exhibit inherently heterogeneous trend burst and smooth fluctuation properties, but existing FL methods model single-scale series from only one view, which cannot simultaneously capture diverse trend and fluctuation properties, especially distinct burst distributions. In this paper, we propose Personalized Federated Forecasting with Multi-property Self-fusion (P2FMS), which can represent multi-scale traffic properties from different views. With precise multi-property representations, a fusion-level prediction decision is learned for each client in a personalized manner to promptly sense traffic bursts and improve forecasting performance in non-IID settings. Specifically, P2FMS decomposes the traffic series into distinct time scales, based on which, we effectively extract closeness, period, and trend properties from different views. The closeness and period are embedded through global-view representations with spatial correlations, while non-stationary trends are individually fitted from the client-side view. Furthermore, a personalized combiner is designed to accurately quantify the proportion of general fluctuation raws (i.e., closeness and period) and specific trend property in predictions, which enables multi-property self-fusion for each client to accommodate heterogeneous traffic patterns and enhance prediction accuracy. Besides, an alternant training mechanism is introduced to optimize property representation and fusion modules with the convergence guarantee. Extensive experiments on real-world datasets show that P2FMS outperforms status quo methods in both prediction performance and convergence time. © 2002-2012 IEEE.

关键词： Weather forecasting

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Enhancing malware detection performance: leveraging K-Nearest Neighbors with Firefly Optimization Algorithm

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Multimedia Tools and Applications 2025年第12期84卷 10071-10094页

作者： Al Saaidah, Adeeb Abualhaj, Mosleh M. Shambour, Qusai Y. Abu-Shareha, Ahmad Adel Abualigah, Laith Al-Khatib, Sumaya N. Alraba’nah, Yousef H Department of Networks and Cybersecurity Al-Ahliyya Amman University Amman Jordan Department of Software Engineering Al-Ahliyya Amman University Amman Jordan Department of Data Science and Artificial Intelligence Al-Ahliyya Amman University Amman Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Research Center University of Tabuk Tabuk71491 Saudi Arabia MEU Research Unit Middle East University Amman11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon College of Engineering Yuan Ze University Taoyuan32003 Taiwan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia

Malware detection plays a crucial role in ensuring robust cybersecurity amidst the ever-evolving cyber threats. This research paper delves into the realm of machine learning (ML) algorithms for malware detection, with a specific emphasis on the K-Nearest Neighbors (KNN) algorithm, utilizing tailored parameter settings and the Firefly Optimization Algorithm (FOA). The study leverages the MalMem-2022 dataset to assess the efficacy of KNN and KNN with FOA in malware detection. The impact of parameter tuning and feature selection on classification is elucidated by comparing the performance of both approaches. Encouragingly, the results reveal promising advancements in one of the multiclass classification scenarios when employing KNN with FOA, yielding noteworthy enhancements in Accuracy, Recall, Precision, Matthews Correlation Coefficient, TNR, and F1-score by 2.65%, 2.65%, 2.47%, 3.59%, 0.17%, and 2.61%, respectively. These findings accentuate the significance of optimizing KNN’s parameters and implementing FOA for feature selection, culminating in heightened accuracy in malware detection. The symbiotic fusion of KNN and FOA exhibits remarkable effectiveness in augmenting the classification model’s performance, thus offering valuable insights for bolstering cybersecurity measures. Adopting ML algorithms, particularly KNN with specific parameter settings and FOA, presents a promising avenue for enhancing malware detection capabilities. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Nearest neighbor search

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