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Machine Learning Approaches for Predicting Tree Growth Trends Based on Basal Area Increment 18th

Machine Learning Approaches for Predicting Tree Growth Tren...

Proceedings of the 18th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2023

作者： Casas-Gómez, Pablo Martínez-Álvarez, Francisco Troncoso, Alicia Linares, Juan Carlos Physical Chemical and Natural Systems Department Faculty of Experimental Sciences Universidad Pablo de Olavide Seville41013 Spain Data Science and Big Data Lab Pablo de Olavide University Seville41013 Spain

ISBN: (纸本)9783031425288

This paper explores the potential of machine learning in predicting Basal Area Increment (BAI) for the species Abies spectabilis, a commonly used metric for measuring tree growth. Machine learning algorithms are used to analyze environmental factors, biotic responses, growth, and their interactions to obtain accurate predictions of BAI under different climatic scenarios. The study aims to identify vulnerable tree at risk of dieback due to changes in climate or other environmental factors. The methodology includes data acquisition, preprocessing, feature selection, model development, and evaluation. The results reported in the study show M5’ performs better in predicting BAI than other machine learning models. The study’s findings can help in forest management and conservation decisions, such as selective harvesting, reforestation, carbon sequestration, and biodiversity conservation. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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High-Performance and Resource-Efficient Dynamic Memory Management in High-Level Synthesis 24

High-Performance and Resource-Efficient Dynamic Memory Manag...

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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Wang, Qinggang Zheng, Long An, Zhaozeng Huang, Haoqin Zhu, Haoran Huang, Yu Yao, Pengcheng Liao, Xiaofei Jin, Hai National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan China Zhejiang Lab Hangzhou China

ISBN: (纸本)9798400706011

With the merits of high productivity and ease of use, highlevel synthesis (HLS) tools bring hope to fast FPGA-based architecture development. However, their usability and popularity are still limited due to lack of support for dynamic memory management (DMM). Though HLS-compatible DMM solutions have been proposed recently, nevertheless, based on our investigation, none of them can hit high performance (i.e., minimal memory (de-)allocation latency) and resource efficiency (i.e., managing arbitrarily sized memory with minimal FPGA resource consumption) with one stone, seriously limiting their practicality. In response, we propose HeroDMM, a high-performance and resource-efficient dynamic memory manager for HLS. Specifically, HeroDMM organizes the managed memory area with a novel cartesian-like tree (CT) structure, a key to resolving the dilemma between (de-)allocation latency and resource efficiency standing in front of prior efforts. With the CT structure, HeroDMM further devises a delicate memory management algorithm and specializes the hardware implementation for achieving ever-higher performance while ensuring resource efficiency. Results show that HeroDMM outperforms state-of-the-art HLS-compatible DMM solutions by 61.69%∼99.99% in performance improvement and 23.79%∼97.22% in resource consumption savings. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Memory management

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Feature Selection Guided by CVOA Metaheuristic for Deep Neural Networks: Application to Multivariate Time Series Forecasting 18th

Feature Selection Guided by CVOA Metaheuristic for Deep Ne...

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Proceedings of the 18th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2023

作者： Jiménez-Navarro, Manuel Jesús Restrepo-Estrada, Camilo Melgar-García, Laura Gutierrez-Aviles, David Department of Computer Languages and Systems University of Seville Seville Spain Faculty of Economic Sciences University of Antioquia Medellín Colombia Data Science and Big Data Lab Pablo de Olavide University Seville Spain

ISBN: (纸本)9783031425288

In the workflow of machine/deep learning, a common question that data scientists ask before training a model is whether to feed the model with the entire set of input dataset features or just a subset of them. In this scenario, the precise selection of features from the input data has a significant impact on the efficiency of model training and the final performance, especially in datasets with many variables. This decision is typically made before model training and in a static way (i.e., the feature selection is applied, and then the model is trained). This paper presents a new methodology based on metaheuristics to dynamically select features. This approach has been applied to train a deep neural network to solve two problems: univariate and multivariate time series forecasting. The performance of the proposed method has been compared to the random forest model (a well-known machine learning model with a highly efficient internal feature selection method). The proposed method outperformed the random forest model in terms of mean squared error, demonstrating the validity of the approach. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep neural networks

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Intersecting-Boundary-Sensitive Fingerprinting for Tampering Detection of DNN Models 41

Intersecting-Boundary-Sensitive Fingerprinting for Tampering...

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41st International Conference on Machine Learning, ICML 2024

作者： Bai, Xiaofan He, Chaoxiang Ma, Xiaojing Zhu, Bin Benjamin Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China Microsoft United States School of Computer Science and Technology Huazhong University of Science and Technology China Cluster and Grid Computing Lab China

Cloud-based AI services offer numerous benefits but also introduce vulnerabilities, allowing for tampering with deployed DNN models, ranging from injecting malicious behaviors to reducing computing resources. Fingerprint samples are generated to query models to detect such tampering. In this paper, we present Intersecting-Boundary-Sensitive Fingerprinting (IBSF), a novel method for black-box integrity verification of DNN models using only top-1 labels. Recognizing that tampering with a model alters its decision boundary, IBSF crafts fingerprint samples from normal samples by maximizing the partial Shannon entropy of a selected subset of categories to position the fingerprint samples near decision boundaries where the categories in the subset intersect. These fingerprint samples are almost indistinguishable from their source samples. We theoretically establish and confirm experimentally that these fingerprint samples' expected sensitivity to tampering increases with the cardinality of the subset. Extensive evaluation demonstrates that IBSF surpasses existing state-of-the-art fingerprinting methods, particularly with larger subset cardinality, establishing its state-of-the-art performance in black-box tampering detection using only top-1 labels. The IBSF code is available at: https://***/CGCL-codes/IBSF. Copyright 2024 by the author(s)

关键词： HTTP

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CMBSR: Contrastive Multi-Behavior Social Recommendation via Graph Neural Networks

CMBSR: Contrastive Multi-Behavior Social Recommendation via ...

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2024 China Automation Congress, CAC 2024

作者： Fu, Jiaxu Zong, Yuxing Shen, Xiaorong Cai, Shimin Big Data Research Center University of Electronic Science and Technology of China Chengdu611731 China i-Large Model Innovation Lab of Ideological and Political Science University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798350368604

Most of the traditional recommendation algorithms are based on single-behavior interaction, ignoring the multiple-interaction behaviors displayed by users in reality. In the real world, there is far more than one type of interaction between users and items. For example, in e-commerce platforms, users may perform various operations such as collecting, adding a shopping cart, and purchasing, which reflect users’ different intentions and preferences for items. At the same time, multi-behavior recommendations still seriously suffer from the problem of limited supervision signals. This paper proposes a Contrastive Multi-Behavior Social Recommendation model named CMBSR to solve the above problems. Based on multi-behavior social recommendation algorithm, we add three comparison mechanisms: multi-behavior comparison, user social interaction comparison and item knowledge comparison, which make full use of multi-behavior information, social information and item knowledge to capture user complex preferences and behavior patterns. Experimental results show that CMBSR performs significantly better than other advanced algorithms on multiple datasets. Especially on the Tmall dataset, CMBSR achieves a 4.958% and 5.923% performance improvement on HR@10 and NDCG@10 compared with the sub-optimal result, respectively. © 2024 IEEE.

关键词： Contrastive Learning

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Multi-Scale Feature Fusion and Advanced Representation Learning for Multi label Image Classification

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Computers, Materials & Continua 2025年第3期82卷 5285-5306页

作者： Naikang Zhong Xiao Lin Wen Du Jin Shi Institute of Artificial Intelligence on Education Research College of InformationMechanical and Electrical EngineeringShanghai Normal UniversityShanghai200234China Lab for Educational Big Data and Policymaking Ministry of EducationShanghai Normal UniversityShanghai200234China Shanghai Intelligent Education Big Data Engineering Technology Research Center Shanghai Normal UniversityShanghai200234China Shanghai Online Education Research Base for Primary and Secondary Schools Shanghai200234China DS Information Technology Co. Ltd.Shanghai200032China Faculty of Innovation Engineering Macao university of Science and TechnologyMacao999078China

Multi-label image classification is a challenging task due to the diverse sizes and complex backgrounds of objects in *** class-specific precise representations at different scales is a key aspect of feature ***,existing methods often rely on the single-scale deep feature,neglecting shallow and deeper layer features,which poses challenges when predicting objects of varying scales within the same *** some studies have explored multi-scale features,they rarely address the flow of information between scales or efficiently obtain class-specific precise representations for features at different *** address these issues,we propose a two-stage,three-branch Transformer-based *** first stage incorporates multi-scale image feature extraction and hierarchical scale *** design enables the model to consider objects at various scales while enhancing the flow of information across different feature scales,improving the model’s generalization to diverse object *** second stage includes a global feature enhancement module and a region selection *** global feature enhancement module strengthens interconnections between different image regions,mitigating the issue of incomplete represen-tations,while the region selection module models the cross-modal relationships between image features and ***,these components enable the efficient acquisition of class-specific precise feature *** experiments on public datasets,including COCO2014,VOC2007,and VOC2012,demonstrate the effectiveness of our proposed *** approach achieves consistent performance gains of 0.3%,0.4%,and 0.2%over state-of-the-art methods on the three datasets,*** results validate the reliability and superiority of our approach for multi-label image classification.

关键词： Image classification multi-label multi scale attention mechanisms feature fusion

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HEM: An Improved Parametric Link Prediction Algorithm Based on Hybrid Network Evolution Mechanism 9th

HEM: An Improved Parametric Link Prediction Algorithm Based ...

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19th International Conference on Advanced data Mining and Applications, ADMA 2023

作者： Ke, Dejing Pu, Jiansu University of Electronic Science and Technology of China Sichuan611731 China Big Data Visual Analysis Lab University of Electronic Science and Technology of China Sichuan610000 China

ISBN: (纸本)9783031466632

Link prediction plays an important role in the research of complex networks. Its task is to predict missing links or possible new links in the future via existing information in the network. In recent years, many powerful link prediction algorithms have emerged, which have good results in prediction accuracy and interpretability. However, the existing research still cannot clearly point out the relationship between the characteristics of the network and the mechanism of link generation, and the predictability of complex networks with different features remains to be further analyzed. In view of this, this article proposes the corresponding link prediction indices Reg, DFPA and LW on regular network, scale-free network and small-world network respectively, and studies their prediction properties on these three network models. At the same time, we propose a parametric hybrid index HEM and compare the prediction accuracy of HEM and many similarity-based indices on real-world networks. The experimental results show that HEM performs better than other indices. In addition, we study the factors that play a major role in the prediction of HEM and analyze their relationship with the characteristics of real-world networks. The results show that the predictive properties of factors are closely related to the features of networks. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： data mining

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Explainable machine learning for sleep apnea prediction 26

Explainable machine learning for sleep apnea prediction

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26th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2022

作者： Troncoso-García, A.R. Martínez-Ballesteros, M. Martínez-Álvarez, F. Troncoso, A. Data Science and Big Data Lab Pablo de Olavide University Seville41013 Spain Department of Computer Science University of Seville Seville41012 ES Spain

Machine and deep learning has become one of the most useful tools in the last years as a diagnosis-decision-support tool in the health area. However, it is widely known that artificial intelligence models are considered a black box and most experts experience difficulties explaining and interpreting the models and their results. In this context, explainable artificial intelligence is emerging with the aim of providing black-box models with sufficient interpretability so that models can be easily understood and further applied. Obstructive sleep apnea is a common chronic respiratory disease related to sleep. Its diagnosis nowadays is done by processing different data signals, such as electrocardiogram or respiratory rate. The waveform of the respiratory signal is of importance too. Machine learning models could be applied to the signal's analysis. data from a polysomnography study for automatic sleep apnea detection have been used to evaluate the use of the Local Interpretable Model-Agnostic (LIME) library for explaining the health data models. Results obtained help to understand how several features have been used in the model and their influence in the quality of sleep. © 2022 The Authors. Published by Elsevier B.V.

关键词： Lime

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GoPIM: GCN-Oriented Pipeline Optimization for PIM Accelerators 31

GoPIM: GCN-Oriented Pipeline Optimization for PIM Accelerato...

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31st IEEE International Symposium on High Performance Computer Architecture, HPCA 2025

作者： Yang, Siling He, Shuibing Wang, Wenjiong Yin, Yanlong Wu, Tong Chen, Weijian Zhang, Xuechen Sun, Xian-He Feng, Dan The State Key Laboratory of Blockchain and Data Security Zhejiang University China Zhejiang Lab China Institute of Blockchain and Data Security China Zhejiang Key Laboratory of Big Data Intelligent Computing China Washington State University Vancouver United States Illinois Institute of Technology United States Huazhong University of Science and Technology China Wuhan National Laboratory for Optoelectronics China

ISBN: (纸本)9798331506476

Graph convolutional networks (GCNs) are popular for a variety of graph learning tasks. ReRAM-based processing-in-memory (PIM) accelerators are promising to expedite GCN training owing to their in-situ computing capability. However, existing accelerators can be severely underutilized even with pipelines, due to the oversight of the skewed execution times of various GCN stages and the ignorance of skewed degrees of graph vertices. In this work, we propose GOPIM, a GCN-oriented pipeline optimization for PIM accelerators to expedite GCN training. First, GOPIM proposes an ML-based scheme that allocates crossbar resources to the most needed stages to streamline the overall pipeline. Second, GOPIM utilizes a selective vertex updating technique that evenly distributes vertices on crossbars by interleaved mapping. These techniques collectively reduce the overall execution time without losing much accuracy. We also provide a practical architecture design for GOPIM. Our experimental results show that, GoPIM achieves up to 191 × speedup and 16.1 × energy saving, compared to the state-of-the-art work. © 2025 IEEE.

关键词： Graph neural networks

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Unlearnable 3D Point Clouds: Class-wise Transformation Is All You Need 38

Unlearnable 3D Point Clouds: Class-wise Transformation Is Al...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wang, Xianlong Li, Minghui Liu, Wei Zhang, Hangtao Hu, Shengshan Zhang, Yechao Zhou, Ziqi Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China

Traditional unlearnable strategies have been proposed to prevent unauthorized users from training on the 2D image data. With more 3D point cloud data containing sensitivity information, unauthorized usage of this new type data has also become a serious concern. To address this, we propose the first integral unlearnable framework for 3D point clouds including two processes: (i) we propose an unlearnable data protection scheme, involving a class-wise setting established by a category-adaptive allocation strategy and multi-transformations assigned to samples;(ii) we propose a data restoration scheme that utilizes class-wise inverse matrix transformation, thus enabling authorized-only training for unlearnable data. This restoration process is a practical issue overlooked in most existing unlearnable literature, i.e., even authorized users struggle to gain knowledge from 3D unlearnable data. Both theoretical and empirical results (including 6 datasets, 16 models, and 2 tasks) demonstrate the effectiveness of our proposed unlearnable framework. Our code is available at https://***/CGCL-codes/UnlearnablePC. © 2024 Neural information processing systems foundation. All rights reserved.

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